diff --git a/.github/workflows/branch-docs.yml b/.github/workflows/branch-docs.yml index 3f2b8d549d..ef5c437c57 100644 --- a/.github/workflows/branch-docs.yml +++ b/.github/workflows/branch-docs.yml @@ -21,39 +21,21 @@ jobs: with: fetch-depth: 0 # get all tags, lets setuptools_scm do its thing - - name: Setup Miniforge - uses: conda-incubator/setup-miniconda@v2 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - miniforge-version: latest - python-version: "3.10" - activate-environment: docbuild - auto-activate-base: false - auto-update-conda: false + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Set cache date for year and month - run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV - - - uses: actions/cache@v3 + - name: "Set up Python" + uses: actions/setup-python@v5 with: - path: /usr/share/miniconda3/envs/docbuild - key: linux-64-conda-${{ hashFiles('conda-environments/docbuild.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} - id: cache - - - name: Update environment - run: conda env update --verbose -n docbuild -f conda-environments/docbuild.yml - if: steps.cache.outputs.cache-hit != 'true' + python-version-file: ".python-version" - name: Install activitysim run: | - python -m pip install . - - - name: Conda checkup - run: | - conda info -a - conda list - echo REPOSITORY ${{ github.repository }} - echo REF ${{ github.ref }} - echo REF_NAME ${{ github.ref_name }} + uv sync --locked --dev - name: Build the docs run: | diff --git a/.github/workflows/core_tests.yml b/.github/workflows/core_tests.yml index 17b3cbc2ae..3e4fd52f91 100644 --- a/.github/workflows/core_tests.yml +++ b/.github/workflows/core_tests.yml @@ -17,9 +17,6 @@ env: jobs: foundation: - strategy: - matrix: - python-version: ["3.10"] defaults: run: shell: bash -l {0} @@ -28,67 +25,46 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Setup Miniforge - uses: conda-incubator/setup-miniconda@v3 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - auto-update-conda: true - miniforge-version: latest - mamba-version: "2.0.5" - conda-solver: classic - conda-remove-defaults: true - activate-environment: asim-test - python-version: ${{ matrix.python-version }} - - - name: Set cache date for year and month - run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV - - - uses: actions/cache@v4 - with: - path: /usr/share/miniconda3/envs/asim-test - key: linux-64-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} - id: cache + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Update environment - run: | - conda env update -n asim-test -f conda-environments/github-actions-tests.yml - if: steps.cache.outputs.cache-hit != 'true' + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" - name: Install activitysim - # installing without dependencies is faster, we trust that all needed dependencies - # are in the conda environment defined above. Also, this avoids pip getting - # confused and reinstalling tables (pytables). run: | - python -m pip install -e . --no-deps - - - name: Conda checkup - run: | - conda info -a - conda list + uv sync --locked - name: Lint with Black run: | # stop the build if there are problems - black --check --diff . + uv run black --check --diff . - name: Test activitysim.core run: | - python -m pytest --pyargs activitysim.core + uv run pytest --pyargs activitysim.core - name: Test activitysim.abm.models run: | - python -m pytest --pyargs activitysim.abm.models + uv run pytest --pyargs activitysim.abm.models - name: Test activitysim.abm.test run: | - python -m pytest --pyargs activitysim.abm.test + uv run pytest --pyargs activitysim.abm.test - name: Test activitysim.cli run: | - python -m pytest --pyargs activitysim.cli + uv run pytest --pyargs activitysim.cli - name: Test activitysim.examples.test run: | - python -m pytest --pyargs activitysim.examples.test + uv run pytest --pyargs activitysim.examples.test cross-platform: @@ -115,70 +91,52 @@ jobs: steps: - uses: actions/checkout@v4 - - name: Setup Miniforge - uses: conda-incubator/setup-miniconda@v3 - with: - auto-update-conda: true - miniforge-version: latest - mamba-version: "2.0.5" - conda-solver: classic - conda-remove-defaults: true - activate-environment: asim-test - python-version: ${{ matrix.python-version }} - - - name: Set cache date for year and month - run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV - - - uses: actions/cache@v4 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - path: ${{ env.CONDA }}/envs - key: ${{ matrix.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} - id: cache + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Update environment - run: | - conda env update -n asim-test -f conda-environments/github-actions-tests.yml - if: steps.cache.outputs.cache-hit != 'true' + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" - name: Install activitysim - # installing without dependencies is faster, we trust that all needed dependencies - # are in the conda environment defined above. Also, this avoids pip getting - # confused and reinstalling tables (pytables). run: | - python -m pip install -e . --no-deps - - - name: Conda checkup - run: | - conda info -a - conda list + uv sync --locked - name: Lint with Black run: | # stop the build if there are problems - black --check --diff . + uv run black --check --diff . - name: Test activitysim.core run: | - python -m pytest --pyargs activitysim.core + uv run pytest --pyargs activitysim.core - name: Test activitysim.abm.models run: | - python -m pytest --pyargs activitysim.abm.models + uv run pytest --pyargs activitysim.abm.models - name: Test activitysim.abm.test run: | - python -m pytest --pyargs activitysim.abm.test + uv run pytest --pyargs activitysim.abm.test - name: Test activitysim.cli run: | - python -m pytest --pyargs activitysim.cli + uv run pytest --pyargs activitysim.cli + - run: uv run pytest test/test_skim_name_conflicts.py + - run: uv run pytest test/random_seed/test_random_seed.py + - run: uv run pytest test/trace_id/test_trace_id.py builtin_regional_models: needs: foundation env: python-version: "3.10" - label: linux-64 + label: win-64 strategy: matrix: region: @@ -186,7 +144,6 @@ jobs: - placeholder_psrc - prototype_marin - prototype_mtc_extended - - placeholder_sandag - prototype_sandag_xborder - production_semcog - prototype_mwcog @@ -194,48 +151,27 @@ jobs: fail-fast: false defaults: run: - shell: bash -l {0} + shell: pwsh name: ${{ matrix.region }} - runs-on: ubuntu-latest + runs-on: windows-latest steps: - uses: actions/checkout@v4 - - name: Setup Miniforge - uses: conda-incubator/setup-miniconda@v3 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - auto-update-conda: true - miniforge-version: latest - mamba-version: "2.0.5" - conda-solver: classic - conda-remove-defaults: true - activate-environment: asim-test - python-version: ${{ env.python-version }} - - - name: Set cache date for year and month - run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV - - - uses: actions/cache@v4 - with: - path: ${{ env.CONDA }}/envs - key: ${{ env.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} - id: cache + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Update environment - run: | - conda env update -n asim-test -f conda-environments/github-actions-tests.yml - if: steps.cache.outputs.cache-hit != 'true' + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" - name: Install activitysim - # installing without dependencies is faster, we trust that all needed dependencies - # are in the conda environment defined above. Also, this avoids pip getting - # confused and reinstalling tables (pytables). - run: | - python -m pip install -e . --no-deps - - - name: Conda checkup run: | - conda info -a - conda list + uv sync --locked # TODO: Cache sharrow compiled flows? The contents of __pycache__ appear to # be ignored, so this is not working as expected right now @@ -262,13 +198,13 @@ jobs: - name: Test ${{ matrix.region }} run: | - python -m pytest activitysim/examples/${{ matrix.region }}/test --durations=0 + uv run pytest activitysim/examples/${{ matrix.region }}/test --durations=0 external_regional_models: needs: foundation env: python-version: "3.10" - label: linux-64 + label: win-64 strategy: matrix: include: @@ -279,55 +215,32 @@ jobs: - region: Standard 2-Zone Example (SANDAG) region-org: ActivitySim region-repo: sandag-abm3-example - region-branch: pandas2 + region-branch: main fail-fast: false defaults: run: - shell: bash -l {0} + shell: pwsh name: ${{ matrix.region }} - runs-on: ubuntu-latest + runs-on: windows-latest steps: - name: Checkout ActivitySim uses: actions/checkout@v4 - - name: Setup Miniforge - uses: conda-incubator/setup-miniconda@v3 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - auto-update-conda: true - miniforge-version: latest - mamba-version: "2.0.5" - conda-solver: classic - conda-remove-defaults: true - activate-environment: asim-test - python-version: ${{ env.python-version }} - - - name: Set cache date for year and month - run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV - - - uses: actions/cache@v4 - with: - path: | - ${{ env.CONDA }}/envs - ~/.cache/ActivitySim - key: ${{ env.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} - id: cache + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Update environment - run: | - conda env update -n asim-test -f conda-environments/github-actions-tests.yml - if: steps.cache.outputs.cache-hit != 'true' + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" - name: Install activitysim - # installing without dependencies is faster, we trust that all needed dependencies - # are in the conda environment defined above. Also, this avoids pip getting - # confused and reinstalling tables (pytables). - run: | - python -m pip install . --no-deps - - - name: Conda checkup run: | - conda info -a - conda list + uv sync --locked - name: Checkout Example uses: actions/checkout@v4 @@ -338,120 +251,185 @@ jobs: - name: Test ${{ matrix.region }} run: | - cd ${{ matrix.region-repo }}/test - python -m pytest . + cd ${{ matrix.region-repo }} + uv run --project .. --locked pytest ./test random_seed_generation: needs: foundation env: python-version: "3.10" - label: linux-64 + label: win-64 defaults: run: - shell: bash -l {0} + shell: pwsh name: random_seed_generation_test - runs-on: ubuntu-latest + runs-on: windows-latest steps: - uses: actions/checkout@v4 - - name: Setup Miniforge - uses: conda-incubator/setup-miniconda@v3 - with: - auto-update-conda: true - miniforge-version: latest - mamba-version: "2.0.5" - conda-solver: classic - conda-remove-defaults: true - activate-environment: asim-test - python-version: ${{ env.python-version }} - - - name: Set cache date for year and month - run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV - - - uses: actions/cache@v4 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - path: ${{ env.CONDA }}/envs - key: ${{ env.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} - id: cache + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Update environment - run: | - conda env update -n asim-test -f conda-environments/github-actions-tests.yml - if: steps.cache.outputs.cache-hit != 'true' + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" - name: Install activitysim - # installing without dependencies is faster, we trust that all needed dependencies - # are in the conda environment defined above. Also, this avoids pip getting - # confused and reinstalling tables (pytables). run: | - python -m pip install -e . --no-deps - - - name: Conda checkup - run: | - conda info -a - conda list + uv sync --locked - name: Test Random Seed Generation run: | - python -m pytest test/random_seed/test_random_seed.py --durations=0 + uv run pytest test/random_seed/test_random_seed.py --durations=0 estimation_mode: needs: foundation env: python-version: "3.10" - label: linux-64 + label: win-64 defaults: run: - shell: bash -l {0} + shell: pwsh name: Estimation Mode Unit Tests - runs-on: ubuntu-latest + runs-on: windows-latest + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" + + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" + + - name: Install activitysim + run: | + uv sync --locked + + - name: Test Estimation Mode + run: | + uv run pytest activitysim/estimation/test/test_larch_estimation.py --durations=0 + + estimation_notebooks: + needs: foundation + env: + python-version: "3.10" + label: win-64 + defaults: + run: + shell: pwsh + name: Estimation Notebooks Test + runs-on: windows-latest steps: - uses: actions/checkout@v4 - - name: Setup Miniforge - uses: conda-incubator/setup-miniconda@v3 + - name: "Set up Python" + uses: actions/setup-python@v5 with: - auto-update-conda: true - miniforge-version: latest - mamba-version: "2.0.5" - conda-solver: classic - conda-remove-defaults: true - activate-environment: asim-test - python-version: ${{ env.python-version }} - - - name: Set cache date for year and month - run: echo "DATE=$(date +'%Y%m')" >> $GITHUB_ENV - - - uses: actions/cache@v4 + python-version-file: ".python-version" + + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - path: ${{ env.CONDA }}/envs - key: ${{ env.label }}-conda-${{ hashFiles('conda-environments/github-actions-tests.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} - id: cache + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" - - name: Update environment + - name: setup graphviz + uses: ts-graphviz/setup-graphviz@v2 + + - name: Install activitysim run: | - conda env update -n asim-test -f conda-environments/github-actions-tests.yml - if: steps.cache.outputs.cache-hit != 'true' + uv sync --locked + + - name: Create Estimation Data + run: > + uv run python activitysim/examples/example_estimation/notebooks/est_mode_setup.py + --household_sample_size 5000 - - name: Install Larch - run: mamba install "larch>=5.7.1" + - name: Test Estimation Notebooks + run: > + uv run pytest activitysim/examples/example_estimation/notebooks + --nbmake-timeout=3000 + --ignore=activitysim/examples/example_estimation/notebooks/01_estimation_mode.ipynb + --ignore-glob=activitysim/examples/example_estimation/notebooks/test-estimation-data/** + + estimation_edb_creation: + needs: foundation + env: + python-version: "3.10" + label: win-64 + defaults: + run: + shell: pwsh + name: estimation_edb_creation_test + runs-on: windows-latest + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" + + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" - name: Install activitysim - # installing without dependencies is faster, we trust that all needed dependencies - # are in the conda environment defined above. Also, this avoids pip getting - # confused and reinstalling tables (pytables). run: | - python -m pip install -e . --no-deps + uv sync --locked - - name: Conda checkup + - name: Test Estimation EDB Creation run: | - conda info -a - conda list + uv run pytest activitysim/estimation/test/test_edb_creation/test_edb_formation.py --durations=0 - - name: Test Estimation Mode + expression-profiling: + needs: foundation + env: + python-version: "3.10" + label: win-64 + defaults: + run: + shell: pwsh + name: Expression Profiling Tests + runs-on: windows-latest + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" + + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" + + - name: Install activitysim + run: | + uv sync --locked + + - name: Test Expression Profiling run: | - python -m pytest activitysim/estimation/test/test_larch_estimation.py --durations=0 + uv run pytest test/test_expression_profiling.py develop-docbuild: needs: foundation @@ -467,30 +445,19 @@ jobs: - uses: actions/checkout@v4 with: fetch-depth: 0 # get all tags, lets setuptools_scm do its thing - - name: Set up Python 3.10 - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - python-version: "3.10" - - name: Install dependencies - uses: conda-incubator/setup-miniconda@v3 + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" + - name: "Set up Python" + uses: actions/setup-python@v5 with: - miniforge-version: latest - mamba-version: "2.0.5" - conda-remove-defaults: true - environment-file: conda-environments/docbuild.yml - python-version: "3.10" - activate-environment: docbuild - auto-activate-base: false - auto-update-conda: false + python-version-file: ".python-version" - name: Install activitysim run: | - python -m pip install . - - name: Conda checkup - run: | - conda info -a - conda list - echo ${{ github.repository }} - echo ${{ github.ref_name }} + uv sync --locked - name: localize version switcher run: | python .github/workflows/localize-base-urls.py docs/_static/switcher.json @@ -507,4 +474,4 @@ jobs: github_token: ${{ secrets.GITHUB_TOKEN }} # Token is created automatically by Github Actions, no other config needed publish_dir: ./docs/_build/html - destination_dir: develop + destination_dir: develop \ No newline at end of file diff --git a/.github/workflows/deployment.yml b/.github/workflows/deployment.yml index 10652bba4d..b3766f0a22 100644 --- a/.github/workflows/deployment.yml +++ b/.github/workflows/deployment.yml @@ -68,7 +68,7 @@ jobs: python -m activitysim --version - name: Publish package to TestPyPI if: github.event_name == 'push' - uses: pypa/gh-action-pypi-publish@v1.5.1 + uses: pypa/gh-action-pypi-publish@v1.13.0 with: user: __token__ password: ${{ secrets.TESTPYPI_TOKEN }} @@ -89,31 +89,19 @@ jobs: with: name: releases path: dist - - name: Set up Python 3.10 - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - python-version: "3.10" - - name: Install dependencies - uses: conda-incubator/setup-miniconda@v2 + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" + - name: "Set up Python" + uses: actions/setup-python@v5 with: - miniforge-variant: Mambaforge - miniforge-version: latest - use-mamba: true - environment-file: conda-environments/docbuild.yml - python-version: "3.10" - activate-environment: docbuild - auto-activate-base: false - auto-update-conda: false + python-version-file: ".python-version" - name: Install activitysim run: | - python -m pip install dist/activitysim-*.whl - - name: Conda checkup - run: | - conda info -a - conda list - echo REPOSITORY ${{ github.repository }} - echo REF ${{ github.ref }} - echo REF_NAME ${{ github.ref_name }} + uv sync --locked --dev - name: Build the docs run: | cd docs @@ -137,7 +125,7 @@ jobs: name: releases path: dist - name: Publish package to PyPI - uses: pypa/gh-action-pypi-publish@v1.5.1 + uses: pypa/gh-action-pypi-publish@v1.13.0 with: user: __token__ password: ${{ secrets.PYPI_TOKEN }} diff --git a/.github/workflows/rebuild-docs.yml b/.github/workflows/rebuild-docs.yml new file mode 100644 index 0000000000..0a0b6a78ab --- /dev/null +++ b/.github/workflows/rebuild-docs.yml @@ -0,0 +1,51 @@ +name: Rebuild Docs +on: + workflow_dispatch: + inputs: + commit: + description: 'Commit SHA or branch/tag to build docs from' + required: true + type: string + version: + description: 'Version number to publish docs as' + required: true + type: string + +jobs: + docbuild: + name: ubuntu-latest py3.9 + runs-on: ubuntu-latest + permissions: + contents: write + defaults: + run: + shell: bash -l {0} + steps: + - uses: actions/checkout@v4 + with: + ref: ${{ github.event.inputs.commit }} + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + version: "0.7.12" + enable-cache: true + cache-dependency-glob: "uv.lock" + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" + - name: Install activitysim + run: | + uv sync --locked --dev + - name: Build the docs + run: | + cd docs + make clean + make html + - name: Push to GitHub Pages + uses: peaceiris/actions-gh-pages@v3.8.0 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + # Token is created automatically by Github Actions, no other config needed + publish_dir: ./docs/_build/html + destination_dir: ${{ github.event.inputs.version }} diff --git a/.github/workflows/test-deploy.yml b/.github/workflows/test-deploy.yml index bbcd938b35..bf626b3e7c 100644 --- a/.github/workflows/test-deploy.yml +++ b/.github/workflows/test-deploy.yml @@ -72,7 +72,7 @@ jobs: python -m activitysim --version - name: Publish package to TestPyPI if: github.event_name == 'push' - uses: pypa/gh-action-pypi-publish@v1.5.1 + uses: pypa/gh-action-pypi-publish@v1.13.0 with: user: __token__ password: ${{ secrets.TESTPYPI_TOKEN }} diff --git a/.gitignore b/.gitignore index ff98b3c68d..4cda55136e 100644 --- a/.gitignore +++ b/.gitignore @@ -49,6 +49,9 @@ coverage.xml .project .pydevproject +# ides +.vscode/ + # Rope .ropeproject diff --git a/.python-version b/.python-version new file mode 100644 index 0000000000..c8cfe39591 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.10 diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000000..d234a51a5a --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,24 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +type: software +title: "ActivitySim" +abstract: "Advanced, open-source, activity-based travel behavior modeling software based on best software development practices for distribution at no charge to the public." +authors: + - name: "Zephyr Foundation" +repository-code: "https://github.com/ActivitySim/activitysim" +url: "https://activitysim.github.io/activitysim" +license: BSD-3-Clause +keywords: + - "activity-based modeling" + - "travel behavior" + - "transportation planning" + - "microsimulation" + - "travel demand modeling" +preferred-citation: + type: software + title: "ActivitySim" + authors: + - name: "Zephyr Foundation" + repository-code: "https://github.com/ActivitySim/activitysim" + url: "https://activitysim.github.io/activitysim" + license: BSD-3-Clause \ No newline at end of file diff --git a/HOW_TO_RELEASE.md b/HOW_TO_RELEASE.md index 786a71d131..b47586d9d0 100644 --- a/HOW_TO_RELEASE.md +++ b/HOW_TO_RELEASE.md @@ -1,47 +1,32 @@ # How to issue an ActivitySim release -00. Check that the main branch is passing tests, especially the "core tests" on +1. Check that the main branch is passing tests, especially the "core tests" on [GitHub Actions](https://github.com/ActivitySim/activitysim/actions/workflows/core_tests.yml). It is generally the policy that the main branch should always be passing tests, - becuase PRs must pass tests before they can be merged. However, it is + because PRs must pass tests before they can be merged. However, it is possible that tests may fail after a PR is merged, so it is important to double-check that the main branch is passing tests before issuing a release. -00. Start from a completely clean conda environment - and git repository. Assuming you have `conda` installed, you can do so +2. Start from a completely clean environment + and git repository. Assuming you have `uv` installed, you can do so by starting where ActivitySim is not yet cloned (e.g. in an empty directory) and running: ```sh - conda create -p ./TEMP-ASIM-DEV python=3.10 git gh -c conda-forge --override-channels - conda activate ./TEMP-ASIM-DEV gh auth login # <--- (only needed if gh is not logged in) gh repo clone ActivitySim/activitysim cd activitysim + uv sync ``` -00. Update your Conda environment for testing. We do not want to use an - existing environment on your machine, as it may be out-of-sync - and we want to make sure everything passes muster using the - dependencies as they are available today. The following command - will update the active environment (we made this to be `TEMP-ASIM-DEV` - if you followed the directions above). - ```sh - conda env update --file=conda-environments/activitysim-dev.yml - ``` - If you add to the ActivitySim dependencies, make sure to also update - the environments in `conda-environments`, which are used for testing - and development. If they are not updated, these environments will end - up with dependencies loaded from *pip* instead of *conda-forge*. - -00. Run black to ensure that the codebase passes all style checks. +3. Run `black` to ensure that the codebase passes all style checks. This check should only take a few seconds. These checks are also done on GitHub Actions and are platform independent, so they should not be necessary to replicate locally, but are listed here for completeness. ```sh - black --check --diff . + uv run black --check --diff . ``` -00. Run the regular test suite on Windows. Most GitHub Actions tests are done on +4. Run the regular test suite on Windows. Most GitHub Actions tests are done on Linux (it's faster to start up and run a new clean VM for testing) but most users are on Windows, and the test suite should also be run on Windows to ensure that it works on that platform as well. If you @@ -54,12 +39,12 @@ regular test suite takes some time to run, between about half an hour and two hours depending on the specs of your machine. ```sh - python activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py - python activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py - pytest . + uv run activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py + uv run activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py + uv run pytest . ``` -00. Test the full-scale regional examples. These examples are big, too +5. Test the full-scale regional examples. These examples are big, too large to run on GitHub Actions, and will take a lot of time (many hours) to download and run. ```sh @@ -77,11 +62,17 @@ python ../activitysim/examples/scan_examples_for_errors.py . ``` -00. Test the notebooks in `activitysim/examples/prototype_mtc/notebooks`. +6. Test the notebooks in `activitysim/examples/prototype_mtc/notebooks`. There are also demo notebooks for estimation, but their functionality is completely tested in the unit tests run previously. -00. Tag the release commit with the new version number. ActivitySim uses +6. (Optional) After ensuring the code on the branch is passing all tests, pull in the latest versions of all dependencies that still satisfy current constraints, and repeat all above tests again. + ```sh + uv lock --upgrade + ``` + Make any adjustments using `uv add ...` with version constraints as needed to pass tests. If a version constraint is created, there should be a github issue created that identities the need in the future to debug and remove this constraint. + +7. Tag the release commit with the new version number. ActivitySim uses dynamic versioning, so the version number is not stored in a file but is instead read from the most recent git tag, so it is important to tag the repository with the correct version. The following command will @@ -90,12 +81,12 @@ git -a v1.2.3 -m "Release v1.2.3" ``` -00. Push the tagged commit to GitHub. +8. Push the tagged commit to GitHub. ```sh git push --tags ``` -00. Create a "release" on GitHub. You can do this from the command line using +9. Create a "release" on GitHub. You can do this from the command line using the `gh` command line tool: ```sh gh release create v1.2.3 @@ -110,25 +101,13 @@ The process of creating and tagging a release will automatically trigger various GitHub Actions scripts to build, test, and publish the - new release to PyPI and conda forge, assuming there are no errors. - -00. If the dependencies of ActivitySim have changed, also be sure to update the - dependencies of the conda-forge [recipe](https://github.com/conda-forge/activitysim-feedstock/tree/main/recipe). - If the dependencies of ActivitySim have not changed, the conda-forge version - of ActivitySim should auto-update within a few hours of making the release on - Github. + new release to PyPI, assuming there are no errors. -00. Build the ActivitySim Standalone Windows Installer. This is done using +10. Build the ActivitySim Standalone Windows Installer. This is done using GitHub Actions, but it is not done automatically when a release is created, instead it requires a manual workflow dispatch trigger. You can do this by going to the [build_installer workflow page](https://github.com/ActivitySim/activitysim/actions/workflows/build_installer.yml) and clicking on the "Run workflow" button. You will need to provide the version number and choose to add the built installer to the release. -00. Clean up your workspace, including removing the Conda environment used for - testing (which will prevent you from accidentally using an old - environment when you should have a fresh up-to-date one next time). - ```sh - conda deactivate - conda env remove -p ./TEMP-ASIM-DEV - ``` +11. Clean up your environment as is good practice by deleting `.venv` inside your workspace. However, `uv` will do this for you. Prior to every `uv run` invocation, `uv` will verify that the lockfile is up-to-date with the `pyproject.toml`, and that the environment is up-to-date with the lockfile, keeping your project in-sync without the need for manual intervention. `uv run` guarantees that your command is run in a consistent, locked environment. (Be sure to use `uv add` and `uv remove` to adjust dependencies always. Manually editing `pyproject.toml` dependencies *can* result in some problems with the environment that require `uv sync` before `uv run`.) \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt index c091c014e5..7f725194d6 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,6 +1,6 @@ BSD 3-Clause License -Copyright (c) Association of Metropolitan Planning Organizations Research Foundation +Copyright (c) Zephyr Foundation All rights reserved. diff --git a/README.md b/README.md index 65809763ba..6f7dfae189 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,6 @@ ActivitySim =========== -[![Build Status](https://travis-ci.com/ActivitySim/activitysim.svg?branch=main)](https://travis-ci.org/github/ActivitySim/activitysim)[![Coverage Status](https://coveralls.io/repos/github/ActivitySim/activitysim/badge.svg?branch=main)](https://coveralls.io/github/ActivitySim/activitysim?branch=main) - The mission of the ActivitySim project is to create and maintain advanced, open-source, activity-based travel behavior modeling software based on best software development practices for distribution at no charge to the public. @@ -15,11 +13,11 @@ and benefit from contributions of other agency partners. :fire: The `main` branch of this repository contains the Consortium's latest in-development codebase. It is not necessarily what you'll get if you install released -code from conda-forge or by downloading one of the "release" versions here on GitHub, +code from PyPI or by downloading one of the "release" versions here on GitHub, but it is generally expected that code in the `main` branch should be usable. ## Helpful Links -- [AMPO Research Foundation](https://research.ampo.org/#research) +- [Zephyr Foundation](https://activitysim.github.io) - [Documentation](https://activitysim.github.io/activitysim) - [Releases](https://github.com/ActivitySim/activitysim/releases) diff --git a/activitysim/abm/models/__init__.py b/activitysim/abm/models/__init__.py index 1383668431..44534fa531 100644 --- a/activitysim/abm/models/__init__.py +++ b/activitysim/abm/models/__init__.py @@ -33,6 +33,7 @@ stop_frequency, summarize, telecommute_frequency, + telecommute_status, tour_mode_choice, tour_od_choice, tour_scheduling_probabilistic, diff --git a/activitysim/abm/models/atwork_subtour_destination.py b/activitysim/abm/models/atwork_subtour_destination.py index d0d7fd9916..1c313a0896 100644 --- a/activitysim/abm/models/atwork_subtour_destination.py +++ b/activitysim/abm/models/atwork_subtour_destination.py @@ -7,7 +7,7 @@ import pandas as pd from activitysim.abm.models.util import tour_destination -from activitysim.core import config, estimation, los, tracing, workflow +from activitysim.core import config, estimation, los, tracing, workflow, expressions from activitysim.core.configuration.logit import TourLocationComponentSettings from activitysim.core.util import assign_in_place @@ -120,3 +120,11 @@ def atwork_subtour_destination( state.tracing.trace_df( tours, label="atwork_subtour_destination", columns=["destination"] ) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/atwork_subtour_frequency.py b/activitysim/abm/models/atwork_subtour_frequency.py index 3483554432..ca7ae6d259 100644 --- a/activitysim/abm/models/atwork_subtour_frequency.py +++ b/activitysim/abm/models/atwork_subtour_frequency.py @@ -37,8 +37,8 @@ class AtworkSubtourFrequencySettings(LogitComponentSettings, extra="forbid"): Settings for the `atwork_subtour_frequency` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" + # no additional fields are required for this component + pass @workflow.step @@ -92,15 +92,15 @@ def atwork_subtour_frequency( nest_spec = config.get_logit_model_settings(model_settings) constants = config.get_model_constants(model_settings) - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - expressions.assign_columns( - state, - df=work_tours, - model_settings=preprocessor_settings, - trace_label=trace_label, - ) + # preprocess choosers + expressions.annotate_preprocessors( + state, + df=work_tours, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) if estimator: estimator.write_spec(model_settings) @@ -164,3 +164,11 @@ def atwork_subtour_frequency( if trace_hh_id: state.tracing.trace_df(tours, label="atwork_subtour_frequency.tours") + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/atwork_subtour_mode_choice.py b/activitysim/abm/models/atwork_subtour_mode_choice.py index 4e1949dc06..b9889aee72 100644 --- a/activitysim/abm/models/atwork_subtour_mode_choice.py +++ b/activitysim/abm/models/atwork_subtour_mode_choice.py @@ -195,17 +195,6 @@ def atwork_subtour_mode_choice( ) state.add_table("tours", tours) - # - annotate tours table - if model_settings.annotate_tours: - tours = state.get_dataframe("tours") - expressions.assign_columns( - state, - df=tours, - model_settings=model_settings.annotate_tours, - trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), - ) - state.add_table("tours", tours) - if trace_hh_id: state.tracing.trace_df( tours[tours.tour_category == "atwork"], @@ -213,3 +202,11 @@ def atwork_subtour_mode_choice( slicer="tour_id", index_label="tour_id", ) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/atwork_subtour_scheduling.py b/activitysim/abm/models/atwork_subtour_scheduling.py index fff94ef30f..2ad67ff22a 100644 --- a/activitysim/abm/models/atwork_subtour_scheduling.py +++ b/activitysim/abm/models/atwork_subtour_scheduling.py @@ -141,3 +141,11 @@ def atwork_subtour_scheduling( trace_label, "tour_map", ) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/auto_ownership.py b/activitysim/abm/models/auto_ownership.py index a66ce763a7..fe6a472abb 100644 --- a/activitysim/abm/models/auto_ownership.py +++ b/activitysim/abm/models/auto_ownership.py @@ -18,8 +18,6 @@ from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable from activitysim.core.configuration.logit import LogitComponentSettings -from .util import annotate - logger = logging.getLogger(__name__) @@ -28,8 +26,8 @@ class AutoOwnershipSettings(LogitComponentSettings, extra="forbid"): Settings for the `auto_ownership` component. """ - preprocessor: PreprocessorSettings | None = None - annotate_households: PreprocessorSettings | None = None + # no additional fields are required for this component + pass @workflow.step @@ -69,20 +67,14 @@ def auto_ownership_simulate( logger.info("Running %s with %d households", trace_label, len(choosers)) - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) if estimator: estimator.write_model_settings(model_settings, model_settings_file_name) @@ -120,8 +112,13 @@ def auto_ownership_simulate( "auto_ownership", households.auto_ownership, value_counts=True ) - if model_settings.annotate_households: - annotate.annotate_households(state, model_settings, trace_label) - if trace_hh_id: state.tracing.trace_df(households, label="auto_ownership", warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/cdap.py b/activitysim/abm/models/cdap.py index 6776c06c7f..d5e92e5227 100644 --- a/activitysim/abm/models/cdap.py +++ b/activitysim/abm/models/cdap.py @@ -36,12 +36,17 @@ class CdapSettings(PydanticReadable, extra="forbid"): JOINT_TOUR_COEFFICIENTS: str = "cdap_joint_tour_coefficients.csv" JOINT_TOUR_USEFUL_COLUMNS: list[str] | None = None """Columns to include from the persons table that will be need to calculate household joint tour utility.""" - annotate_persons: PreprocessorSettings | None = None - annotate_households: PreprocessorSettings | None = None COEFFICIENTS: Path CONSTANTS: dict[str, Any] = {} compute_settings: ComputeSettings | None = None + preprocessor: PreprocessorSettings | None = None + """Preprocess choosers tables before running the model.""" + annotate_persons: PreprocessorSettings | None = None + """Postprocess persons table after model completion.""" + annotate_households: PreprocessorSettings | None = None + """Postprocess households table after model completion.""" + @workflow.step def cdap_simulate( @@ -171,6 +176,16 @@ def cdap_simulate( index=True, ) + # preprocess choosers + expressions.annotate_preprocessors( + state, + df=persons_merged, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + if estimator: estimator.write_model_settings(model_settings, "cdap.yaml") estimator.write_spec(model_settings, tag="INDIV_AND_HHSIZE1_SPEC") @@ -180,7 +195,7 @@ def cdap_simulate( estimator.write_coefficients(coefficients_df, model_settings) estimator.write_table( cdap_interaction_coefficients, - "interaction_coefficients", + "cdap_interaction_coefficients", index=False, append=False, ) @@ -189,7 +204,7 @@ def cdap_simulate( spec = cdap.get_cached_spec(state, hhsize) estimator.write_table(spec, "spec_%s" % hhsize, append=False) if add_joint_tour_utility: - joint_spec = cdap.get_cached_joint_spec(hhsize) + joint_spec = cdap.get_cached_joint_spec(state, hhsize) estimator.write_table( joint_spec, "joint_spec_%s" % hhsize, append=False ) @@ -241,14 +256,6 @@ def cdap_simulate( cap_cat_type = pd.api.types.CategoricalDtype(["", "M", "N", "H"], ordered=False) choices = choices.astype(cap_cat_type) persons["cdap_activity"] = choices - - expressions.assign_columns( - state, - df=persons, - model_settings=model_settings.annotate_persons, - trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"), - ) - state.add_table("persons", persons) # - annotate households table @@ -256,12 +263,6 @@ def cdap_simulate( hh_joint = hh_joint.reindex(households.index) households["has_joint_tour"] = hh_joint - expressions.assign_columns( - state, - df=households, - model_settings=model_settings.annotate_households, - trace_label=tracing.extend_trace_label(trace_label, "annotate_households"), - ) state.add_table("households", households) tracing.print_summary("cdap_activity", persons.cdap_activity, value_counts=True) @@ -269,3 +270,11 @@ def cdap_simulate( "cdap crosstabs:\n%s" % pd.crosstab(persons.ptype, persons.cdap_activity, margins=True) ) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/disaggregate_accessibility.py b/activitysim/abm/models/disaggregate_accessibility.py index 588db53913..5fe3375b31 100644 --- a/activitysim/abm/models/disaggregate_accessibility.py +++ b/activitysim/abm/models/disaggregate_accessibility.py @@ -601,9 +601,11 @@ def expand_template_zones(self, tables): for col, fill in col_filler.items(): df_ids[col] = df_ids[col].str.zfill(fill) - ex_table["proto_person_id"] = df_ids[cols].apply("".join, axis=1).astype(int) + ex_table["proto_person_id"] = ( + df_ids[cols].apply("".join, axis=1).astype(np.int64) + ) ex_table["proto_household_id"] = ( - df_ids[cols[:-1]].apply("".join, axis=1).astype(int) + df_ids[cols[:-1]].apply("".join, axis=1).astype(np.int64) ) # Separate out into households, persons, tours @@ -626,9 +628,11 @@ def create_proto_pop(self): # Create ID columns, defaults to "%tablename%_id" hhid, perid, tourid = ( - self.params[x]["index_col"] - if len(self.params[x]["index_col"]) > 0 - else x + "_id" + ( + self.params[x]["index_col"] + if len(self.params[x]["index_col"]) > 0 + else x + "_id" + ) for x in klist ) @@ -768,11 +772,12 @@ def get_disaggregate_logsums( state.filesystem, model_name + ".yaml" ) model_settings.SAMPLE_SIZE = disagg_model_settings.DESTINATION_SAMPLE_SIZE - estimator = estimation.manager.begin_estimation(state, trace_label) - if estimator: - location_choice.write_estimation_specs( - state, estimator, model_settings, model_name + ".yaml" - ) + # estimator = estimation.manager.begin_estimation(state, trace_label) + # if estimator: + # location_choice.write_estimation_specs( + # state, estimator, model_settings, model_name + ".yaml" + # ) + estimator = None # Append table references in settings with "proto_" # This avoids having to make duplicate copies of config files for disagg accessibilities diff --git a/activitysim/abm/models/free_parking.py b/activitysim/abm/models/free_parking.py index 9aa2800a67..ebcecd248a 100644 --- a/activitysim/abm/models/free_parking.py +++ b/activitysim/abm/models/free_parking.py @@ -26,9 +26,6 @@ class FreeParkingSettings(LogitComponentSettings, extra="forbid"): Settings for the `free_parking` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - FREE_PARKING_ALT: int """The code for free parking.""" @@ -78,21 +75,6 @@ def free_parking( constants = model_settings.CONSTANTS or {} - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) - model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) coefficients_df = state.filesystem.read_model_coefficients(model_settings) model_spec = simulate.eval_coefficients( @@ -101,6 +83,15 @@ def free_parking( nest_spec = config.get_logit_model_settings(model_settings) + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + if estimator: estimator.write_model_settings(model_settings, model_settings_file_name) estimator.write_spec(file_name=model_settings.SPEC) @@ -144,3 +135,11 @@ def free_parking( if state.settings.trace_hh_id: state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/initialize_tours.py b/activitysim/abm/models/initialize_tours.py index da69e8d227..79b0263ced 100644 --- a/activitysim/abm/models/initialize_tours.py +++ b/activitysim/abm/models/initialize_tours.py @@ -10,6 +10,7 @@ from activitysim.core import expressions, tracing, workflow from activitysim.core.configuration import PydanticReadable from activitysim.core.configuration.base import PreprocessorSettings +from activitysim.core.exceptions import InputTableError from activitysim.core.input import read_input_table logger = logging.getLogger(__name__) @@ -140,7 +141,7 @@ def initialize_tours( f"{tours_without_persons.sum()} tours out of {len(persons)} without persons\n" f"{pd.Series({'person_id': tours_without_persons.index.values})}" ) - raise RuntimeError(f"{tours_without_persons.sum()} tours with bad person_id") + raise InputTableError(f"{tours_without_persons.sum()} tours with bad person_id") if trace_hh_id: state.tracing.trace_df(tours, label="initialize_tours", warn_if_empty=True) diff --git a/activitysim/abm/models/input_checker.py b/activitysim/abm/models/input_checker.py index 568da851e1..68274ba694 100644 --- a/activitysim/abm/models/input_checker.py +++ b/activitysim/abm/models/input_checker.py @@ -13,6 +13,7 @@ from activitysim.core import workflow from activitysim.core.input import read_input_table +from activitysim.core.exceptions import ModelConfigurationError logger = logging.getLogger(__name__) file_logger = logger.getChild("logfile") @@ -468,6 +469,6 @@ def input_checker(state: workflow.State): if input_check_failure: logger.error("Run is killed due to input checker failure!!") - raise RuntimeError( + raise ModelConfigurationError( "Encountered error in input checker, see input_checker.log for details" ) diff --git a/activitysim/abm/models/joint_tour_composition.py b/activitysim/abm/models/joint_tour_composition.py index ee4ac3a69c..1c620e0709 100644 --- a/activitysim/abm/models/joint_tour_composition.py +++ b/activitysim/abm/models/joint_tour_composition.py @@ -18,6 +18,7 @@ from activitysim.core.configuration.base import PreprocessorSettings from activitysim.core.configuration.logit import LogitComponentSettings + logger = logging.getLogger(__name__) @@ -36,8 +37,7 @@ class JointTourCompositionSettings(LogitComponentSettings, extra="forbid"): Settings for the `joint_tour_composition` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" + pass @workflow.step @@ -78,26 +78,6 @@ def joint_tour_composition( "Running joint_tour_composition with %d joint tours" % joint_tours.shape[0] ) - # - run preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_dict = { - "persons": persons, - "hh_time_window_overlap": lambda *x: hh_time_window_overlap(state, *x), - } - - expressions.assign_columns( - state, - df=households, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) - - joint_tours_merged = pd.merge( - joint_tours, households, left_on="household_id", right_index=True, how="left" - ) - # - simple_simulate model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) coefficients_df = state.filesystem.read_model_coefficients(model_settings) @@ -108,6 +88,25 @@ def joint_tour_composition( nest_spec = config.get_logit_model_settings(model_settings) constants = config.get_model_constants(model_settings) + locals_dict = { + "persons": persons, + "hh_time_window_overlap": lambda *x: hh_time_window_overlap(state, *x), + } + locals_dict.update(constants) + + expressions.annotate_preprocessors( + state, + df=households, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + + joint_tours_merged = pd.merge( + joint_tours, households, left_on="household_id", right_index=True, how="left" + ) + if estimator: estimator.write_spec(model_settings) estimator.write_model_settings(model_settings, model_settings_file_name) @@ -156,3 +155,11 @@ def joint_tour_composition( label="joint_tour_composition.joint_tours", slicer="household_id", ) + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/joint_tour_destination.py b/activitysim/abm/models/joint_tour_destination.py index cd6c2fed01..29b117821b 100644 --- a/activitysim/abm/models/joint_tour_destination.py +++ b/activitysim/abm/models/joint_tour_destination.py @@ -7,10 +7,11 @@ import pandas as pd from activitysim.abm.models.util import tour_destination -from activitysim.core import estimation, los, tracing, workflow +from activitysim.core import estimation, los, tracing, workflow, expressions from activitysim.core.configuration.logit import TourLocationComponentSettings from activitysim.core.util import assign_in_place + logger = logging.getLogger(__name__) @@ -113,3 +114,11 @@ def joint_tour_destination( if trace_hh_id: state.tracing.trace_df(joint_tours, label="joint_tour_destination.joint_tours") + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/joint_tour_frequency.py b/activitysim/abm/models/joint_tour_frequency.py index 1700c143b0..d2dc67bc81 100644 --- a/activitysim/abm/models/joint_tour_frequency.py +++ b/activitysim/abm/models/joint_tour_frequency.py @@ -25,11 +25,11 @@ class JointTourFrequencySettings(LogitComponentSettings, extra="forbid"): """ - Settings for the `free_parking` component. + Settings for the `joint_tour_frequency` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" + # no additional settings are required for this model + pass @workflow.step @@ -72,22 +72,6 @@ def joint_tour_frequency( % multi_person_households.shape[0] ) - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_dict = { - "persons": persons, - "hh_time_window_overlap": lambda *x: hh_time_window_overlap(state, *x), - } - - expressions.assign_columns( - state, - df=multi_person_households, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) - model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) coefficients_df = state.filesystem.read_model_coefficients(model_settings) model_spec = simulate.eval_coefficients( @@ -97,6 +81,22 @@ def joint_tour_frequency( nest_spec = config.get_logit_model_settings(model_settings) constants = config.get_model_constants(model_settings) + # - preprocess choosers table + locals_dict = { + "persons": persons, + "hh_time_window_overlap": lambda *x: hh_time_window_overlap(state, *x), + } + locals_dict.update(constants) + + expressions.annotate_preprocessors( + state, + df=multi_person_households, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + if estimator: estimator.write_spec(model_settings) estimator.write_model_settings(model_settings, model_settings_file_name) @@ -137,7 +137,15 @@ def joint_tour_frequency( # - but we don't know the tour participants yet # - so we arbitrarily choose the first person in the household # - to be point person for the purpose of generating an index and setting origin - temp_point_persons = persons.loc[persons.PNUM == 1] + if "PNUM" in persons.columns: + temp_point_persons = persons.loc[persons.PNUM == 1] + else: + # if PNUM is not available, we can still get the first person in the household + temp_point_persons = ( + persons.sort_index() # ensure stable ordering + .groupby("household_id", as_index=False) + .first() + ) temp_point_persons["person_id"] = temp_point_persons.index temp_point_persons = temp_point_persons.set_index("household_id") temp_point_persons = temp_point_persons[["person_id", "home_zone_id"]] @@ -192,8 +200,11 @@ def joint_tour_frequency( print(f"len(joint_tours) {len(joint_tours)}") different = False + # need to check households as well because the full survey sample may not be used + # (e.g. if we set household_sample_size in settings.yaml) survey_tours_not_in_tours = survey_tours[ ~survey_tours.index.isin(joint_tours.index) + & survey_tours.household_id.isin(households.index) ] if len(survey_tours_not_in_tours) > 0: print(f"survey_tours_not_in_tours\n{survey_tours_not_in_tours}") @@ -201,7 +212,15 @@ def joint_tour_frequency( tours_not_in_survey_tours = joint_tours[ ~joint_tours.index.isin(survey_tours.index) ] - if len(survey_tours_not_in_tours) > 0: + if len(tours_not_in_survey_tours) > 0: print(f"tours_not_in_survey_tours\n{tours_not_in_survey_tours}") different = True assert not different + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/joint_tour_frequency_composition.py b/activitysim/abm/models/joint_tour_frequency_composition.py index 553b280fe7..1fffd892ac 100644 --- a/activitysim/abm/models/joint_tour_frequency_composition.py +++ b/activitysim/abm/models/joint_tour_frequency_composition.py @@ -22,6 +22,7 @@ ) from activitysim.core.interaction_simulate import interaction_simulate + logger = logging.getLogger(__name__) @@ -58,37 +59,6 @@ def joint_tour_frequency_composition( logger.info("Running %s with %d households", trace_label, len(choosers)) - # alt preprocessor - alt_preprocessor_settings = model_settings.ALTS_PREPROCESSOR - if alt_preprocessor_settings: - locals_dict = {} - - alts = alts.copy() - - expressions.assign_columns( - state, - df=alts, - model_settings=alt_preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) - - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_dict = { - "persons": persons, - "hh_time_window_overlap": lambda *x: hh_time_window_overlap(state, *x), - } - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) - estimator = estimation.manager.begin_estimation( state, "joint_tour_frequency_composition" ) @@ -101,6 +71,32 @@ def joint_tour_frequency_composition( constants = config.get_model_constants(model_settings) + # preprocess choosers table + locals_dict = { + "persons": persons, + "hh_time_window_overlap": lambda *x: hh_time_window_overlap(state, *x), + } + locals_dict.update(constants) + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + + # preprocess alternatives table + expressions.annotate_preprocessors( + state, + df=alts, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="ALTS_PREPROCESSOR", + ) + if estimator: estimator.write_spec(model_settings) estimator.write_model_settings(model_settings, model_settings_file_name) @@ -150,8 +146,15 @@ def joint_tour_frequency_composition( # - but we don't know the tour participants yet # - so we arbitrarily choose the first person in the household # - to be point person for the purpose of generating an index and setting origin - # FIXME: not all models are guaranteed to have PNUM - temp_point_persons = persons.loc[persons.PNUM == 1] + if "PNUM" in persons.columns: + temp_point_persons = persons.loc[persons.PNUM == 1] + else: + # if PNUM is not available, we can still get the first person in the household + temp_point_persons = ( + persons.sort_index() # ensure stable ordering + .groupby("household_id", as_index=False) + .first() + ) temp_point_persons["person_id"] = temp_point_persons.index temp_point_persons = temp_point_persons.set_index("household_id") temp_point_persons = temp_point_persons[["person_id", "home_zone_id"]] @@ -221,3 +224,11 @@ def joint_tour_frequency_composition( label="joint_tour_frequency_composition.joint_tours", slicer="household_id", ) + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/joint_tour_participation.py b/activitysim/abm/models/joint_tour_participation.py index 3844a3f217..68a8a3b9b4 100644 --- a/activitysim/abm/models/joint_tour_participation.py +++ b/activitysim/abm/models/joint_tour_participation.py @@ -21,6 +21,7 @@ from activitysim.core.configuration.base import ComputeSettings, PreprocessorSettings from activitysim.core.configuration.logit import LogitComponentSettings from activitysim.core.util import assign_in_place, reindex +from activitysim.core.exceptions import InvalidTravelError logger = logging.getLogger(__name__) @@ -60,6 +61,9 @@ def joint_tour_participation_candidates(joint_tours, persons_merged): # if this happens, participant_id may not be unique # channel random seeds will overlap at MAX_PARTICIPANT_PNUM (probably not a big deal) # and estimation infer will fail + if "PNUM" not in candidates.columns: + # create a PNUM column that just numbers the candidates for assignment of participant_id + candidates["PNUM"] = candidates.groupby("household_id").cumcount() + 1 assert ( candidates.PNUM.max() < MAX_PARTICIPANT_PNUM ), f"max persons.PNUM ({candidates.PNUM.max()}) > MAX_PARTICIPANT_PNUM ({MAX_PARTICIPANT_PNUM})" @@ -238,11 +242,11 @@ def participants_chooser( probs_or_utils[non_choice_col] = 1 - probs_or_utils[choice_col] if iter > MAX_ITERATIONS + 1: - raise RuntimeError( + raise InvalidTravelError( f"{num_tours_remaining} tours could not be satisfied even with forcing participation" ) else: - raise RuntimeError( + raise InvalidTravelError( f"{num_tours_remaining} tours could not be satisfied after {iter} iterations" ) @@ -302,22 +306,6 @@ def participants_chooser( return choices, rands -def annotate_jtp( - state: workflow.State, - model_settings: JointTourParticipationSettings, - trace_label: str, -): - # - annotate persons - persons = state.get_dataframe("persons") - expressions.assign_columns( - state, - df=persons, - model_settings=model_settings.annotate_persons, - trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"), - ) - state.add_table("persons", persons) - - def add_null_results( state: workflow.State, model_settings: JointTourParticipationSettings, @@ -333,7 +321,13 @@ def add_null_results( state.add_table("joint_tour_participants", participants) # - run annotations - annotate_jtp(state, model_settings, trace_label) + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) class JointTourParticipationSettings(LogitComponentSettings, extra="forbid"): @@ -341,12 +335,6 @@ class JointTourParticipationSettings(LogitComponentSettings, extra="forbid"): Settings for the `joint_tour_participation` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - - annotate_persons: PreprocessorSettings | None = None - """Instructions for annotating the persons table.""" - participation_choice: str = "participate" max_participation_choice_iterations: int = 5000 @@ -390,25 +378,6 @@ def joint_tour_participation( "Running joint_tours_participation with %d potential participants (candidates)" % candidates.shape[0] ) - - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_dict = { - "person_time_window_overlap": lambda x: person_time_window_overlap( - state, x - ), - "persons": persons_merged, - } - - expressions.assign_columns( - state, - df=candidates, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) - # - simple_simulate estimator = estimation.manager.begin_estimation(state, "joint_tour_participation") @@ -422,6 +391,21 @@ def joint_tour_participation( nest_spec = config.get_logit_model_settings(model_settings) constants = config.get_model_constants(model_settings) + # preprocess choosers table + locals_dict = { + "persons": persons_merged, + "person_time_window_overlap": lambda x: person_time_window_overlap(state, x), + } + locals_dict.update(constants) + expressions.annotate_preprocessors( + state, + df=candidates, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + if estimator: estimator.write_model_settings(model_settings, model_settings_file_name) estimator.write_spec(model_settings) @@ -506,14 +490,24 @@ def joint_tour_participation( PARTICIPANT_COLS = ["tour_id", "household_id", "person_id"] participants = candidates[participate][PARTICIPANT_COLS].copy() - # assign participant_num - # FIXME do we want something smarter than the participant with the lowest person_id? - participants["participant_num"] = ( - participants.sort_values(by=["tour_id", "person_id"]) - .groupby("tour_id") - .cumcount() - + 1 - ) + if estimator: + # In estimation mode, use participant_num from survey data to preserve consistency + # with the original survey data. ActivitySim treats participant_num=1 as the tour + # leader, so the joint tour in the tour table will be associated with the tour + # leader's person_id. We merge participant_num from survey data using the + # participant_id as the join key to ensure the correct tour leader is identified. + participants["participant_num"] = survey_participants_df.reindex( + participants.index + )["participant_num"] + else: + # assign participant_num + # FIXME do we want something smarter than the participant with the lowest person_id? + participants["participant_num"] = ( + participants.sort_values(by=["tour_id", "person_id"]) + .groupby("tour_id") + .cumcount() + + 1 + ) state.add_table("joint_tour_participants", participants) @@ -536,9 +530,6 @@ def joint_tour_participation( state.add_table("tours", tours) - # - run annotations - annotate_jtp(state, model_settings, trace_label) - if trace_hh_id: state.tracing.trace_df( participants, label="joint_tour_participation.participants" @@ -547,3 +538,11 @@ def joint_tour_participation( state.tracing.trace_df( joint_tours, label="joint_tour_participation.joint_tours" ) + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/joint_tour_scheduling.py b/activitysim/abm/models/joint_tour_scheduling.py index 9bdcbe146f..791dd7aaf4 100644 --- a/activitysim/abm/models/joint_tour_scheduling.py +++ b/activitysim/abm/models/joint_tour_scheduling.py @@ -22,20 +22,8 @@ from activitysim.core.configuration.logit import LogitComponentSettings from activitysim.core.util import assign_in_place, reindex -logger = logging.getLogger(__name__) - -# class JointTourSchedulingSettings(LogitComponentSettings, extra="forbid"): -# """ -# Settings for the `joint_tour_scheduling` component. -# """ -# -# preprocessor: PreprocessorSettings | None = None -# """Setting for the preprocessor.""" -# -# sharrow_skip: bool = False -# """Setting to skip sharrow""" -# +logger = logging.getLogger(__name__) @workflow.step @@ -85,21 +73,18 @@ def joint_tour_scheduling( constants = config.get_model_constants(model_settings) # - run preprocessor to annotate choosers - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - state, - df=joint_tours, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) - timetable = state.get_injectable("timetable") + locals_d = {"timetable": timetable} + locals_d.update(constants) + + expressions.annotate_preprocessors( + state, + df=joint_tours, + locals_dict=locals_d, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) estimator = estimation.manager.begin_estimation(state, "joint_tour_scheduling") @@ -172,3 +157,11 @@ def joint_tour_scheduling( state.tracing.trace_df( joint_tours, label="joint_tour_scheduling", slicer="household_id" ) + + expressions.annotate_tables( + state, + locals_dict=locals_d, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/location_choice.py b/activitysim/abm/models/location_choice.py index 83e794b2be..7f032a8ae6 100644 --- a/activitysim/abm/models/location_choice.py +++ b/activitysim/abm/models/location_choice.py @@ -18,7 +18,7 @@ from activitysim.core.interaction_sample import interaction_sample from activitysim.core.interaction_sample_simulate import interaction_sample_simulate from activitysim.core.util import reindex - +from activitysim.core.exceptions import DuplicateWorkflowTableError """ The school/workplace location model predicts the zones in which various people will @@ -140,7 +140,7 @@ def _location_sample( sample_size = model_settings.SAMPLE_SIZE - if estimator: + if estimator and model_settings.ESTIMATION_SAMPLE_SIZE >= 0: sample_size = model_settings.ESTIMATION_SAMPLE_SIZE logger.info( f"Estimation mode for {trace_label} using sample size of {sample_size}" @@ -163,6 +163,27 @@ def _location_sample( } locals_d.update(model_settings.CONSTANTS or {}) + # preprocess choosers table + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_d, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) + + # preprocess alternatives table + expressions.annotate_preprocessors( + state, + df=alternatives, + locals_dict=locals_d, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_sample", + ) + spec = simulate.spec_for_segment( state, None, @@ -402,7 +423,7 @@ def location_presample( # choose a MAZ for each DEST_TAZ choice, choice probability based on MAZ size_term fraction of TAZ total maz_choices = tour_destination.choose_MAZ_for_TAZ( - state, taz_sample, MAZ_size_terms, trace_label + state, taz_sample, MAZ_size_terms, trace_label, model_settings ) assert DEST_MAZ in maz_choices @@ -491,38 +512,6 @@ def run_location_sample( trace_label=trace_label, ) - # adding observed choice to alt set when running in estimation mode - if estimator: - # grabbing survey values - survey_persons = estimation.manager.get_survey_table("persons") - if "school_location" in trace_label: - survey_choices = survey_persons["school_zone_id"].reset_index() - elif ("workplace_location" in trace_label) and ("external" not in trace_label): - survey_choices = survey_persons["workplace_zone_id"].reset_index() - else: - return choices - survey_choices.columns = ["person_id", "alt_dest"] - survey_choices = survey_choices[ - survey_choices["person_id"].isin(choices.index) - & (survey_choices.alt_dest > 0) - ] - # merging survey destination into table if not available - joined_data = survey_choices.merge( - choices, on=["person_id", "alt_dest"], how="left", indicator=True - ) - missing_rows = joined_data[joined_data["_merge"] == "left_only"] - missing_rows["pick_count"] = 1 - if len(missing_rows) > 0: - new_choices = missing_rows[ - ["person_id", "alt_dest", "prob", "pick_count"] - ].set_index("person_id") - choices = choices.append(new_choices, ignore_index=False).sort_index() - # making probability the mean of all other sampled destinations by person - # FIXME is there a better way to do this? Does this even matter for estimation? - choices["prob"] = choices["prob"].fillna( - choices.groupby("person_id")["prob"].transform("mean") - ) - return choices @@ -664,6 +653,27 @@ def run_location_simulate( } locals_d.update(model_settings.CONSTANTS or {}) + # preprocess choosers table + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_d, + skims=None, # skims included in locals_d + model_settings=model_settings, + trace_label=trace_label, + ) + + # preprocess alternatives table + expressions.annotate_preprocessors( + state, + df=alternatives, + locals_dict=locals_d, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_simulate", + ) + if estimator: # write choosers after annotation estimator.write_choosers(choosers) @@ -796,19 +806,23 @@ def run_location_choice( ) # - location_logsums - location_sample_df = run_location_logsums( - state, - segment_name, - choosers, - network_los, - location_sample_df, - model_settings, - chunk_size, - chunk_tag=f"{chunk_tag}.logsums", - trace_label=tracing.extend_trace_label( - trace_label, "logsums.%s" % segment_name - ), - ) + # skip logsum calculations if LOGSUM_SETTINGS is None + if model_settings.LOGSUM_SETTINGS: + location_sample_df = run_location_logsums( + state, + segment_name, + choosers, + network_los, + location_sample_df, + model_settings, + chunk_size, + chunk_tag=f"{chunk_tag}.logsums", + trace_label=tracing.extend_trace_label( + trace_label, "logsums.%s" % segment_name + ), + ) + else: + location_sample_df[ALT_LOGSUM] = 0.0 # - location_simulate choices_df = run_location_simulate( @@ -1112,38 +1126,23 @@ def iterate_location_choice( assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) # lest they try to put school and workplace samples into the same table if state.is_table(sample_table_name): - raise RuntimeError( + raise DuplicateWorkflowTableError( "dest choice sample table %s already exists" % sample_table_name ) state.extend_table(sample_table_name, save_sample_df) - # - annotate persons table - if model_settings.annotate_persons: - expressions.assign_columns( - state, - df=persons_df, - model_settings=model_settings.annotate_persons, - trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"), - ) - - state.add_table("persons", persons_df) + state.add_table("persons", persons_df) - if state.settings.trace_hh_id: - state.tracing.trace_df(persons_df, label=trace_label, warn_if_empty=True) + if state.settings.trace_hh_id: + state.tracing.trace_df(persons_df, label=trace_label, warn_if_empty=True) - # - annotate households table - if model_settings.annotate_households: - households_df = households - expressions.assign_columns( - state, - df=households_df, - model_settings=model_settings.annotate_households, - trace_label=tracing.extend_trace_label(trace_label, "annotate_households"), - ) - state.add_table("households", households_df) - - if state.settings.trace_hh_id: - state.tracing.trace_df(households_df, label=trace_label, warn_if_empty=True) + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) if dc_logsum_column_name: tracing.print_summary( diff --git a/activitysim/abm/models/mandatory_scheduling.py b/activitysim/abm/models/mandatory_scheduling.py index 64fc26215f..a8cb46ecca 100644 --- a/activitysim/abm/models/mandatory_scheduling.py +++ b/activitysim/abm/models/mandatory_scheduling.py @@ -8,8 +8,9 @@ from activitysim.abm.models.util.tour_scheduling import run_tour_scheduling from activitysim.core import timetable as tt -from activitysim.core import tracing, workflow +from activitysim.core import tracing, workflow, expressions from activitysim.core.util import assign_in_place, reindex +from activitysim.abm.models.util.vectorize_tour_scheduling import TourSchedulingSettings logger = logging.getLogger(__name__) @@ -30,6 +31,12 @@ def mandatory_tour_scheduling( model_name = "mandatory_tour_scheduling" trace_label = model_name + model_settings = TourSchedulingSettings.read_settings_file( + state.filesystem, + f"{model_name}.yaml", + mandatory=False, + ) + mandatory_tours = tours[tours.tour_category == "mandatory"] # - if no mandatory_tours @@ -55,11 +62,12 @@ def mandatory_tour_scheduling( choices = run_tour_scheduling( state, - model_name, + model_settings, mandatory_tours, persons_merged, tdd_alts, tour_segment_col, + trace_label, ) assign_in_place( @@ -86,3 +94,11 @@ def mandatory_tour_scheduling( columns=None, warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/mandatory_tour_frequency.py b/activitysim/abm/models/mandatory_tour_frequency.py index 8ab69710f1..2e2657d31f 100644 --- a/activitysim/abm/models/mandatory_tour_frequency.py +++ b/activitysim/abm/models/mandatory_tour_frequency.py @@ -30,7 +30,7 @@ def add_null_results(state, trace_label, mandatory_tour_frequency_settings): logger.info("Skipping %s: add_null_results", trace_label) persons = state.get_dataframe("persons") - persons["mandatory_tour_frequency"] = pd.categorical( + persons["mandatory_tour_frequency"] = pd.Categorical( "", categories=["", "work1", "work2", "school1", "school2", "work_and_school"], ordered=False, @@ -58,10 +58,8 @@ class MandatoryTourFrequencySettings(LogitComponentSettings, extra="forbid"): Settings for the `mandatory_tour_frequency` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - - annotate_persons: PreprocessorSettings | None = None + # no additional fields are required for this component + pass @workflow.step @@ -95,19 +93,6 @@ def mandatory_tour_frequency( add_null_results(state, trace_label, model_settings) return - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_dict = {} - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) - estimator = estimation.manager.begin_estimation(state, "mandatory_tour_frequency") model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) @@ -119,6 +104,16 @@ def mandatory_tour_frequency( nest_spec = config.get_logit_model_settings(model_settings) constants = config.get_model_constants(model_settings) + # - preprocessor + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + if estimator: estimator.write_spec(model_settings) estimator.write_model_settings(model_settings, model_settings_file_name) @@ -183,13 +178,6 @@ def mandatory_tour_frequency( # need to reindex as we only handled persons with cdap_activity == 'M' persons["mandatory_tour_frequency"] = choices.reindex(persons.index).fillna("") - expressions.assign_columns( - state, - df=persons, - model_settings=model_settings.annotate_persons, - trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"), - ) - state.add_table("persons", persons) tracing.print_summary( @@ -206,3 +194,11 @@ def mandatory_tour_frequency( state.tracing.trace_df( persons, label="mandatory_tour_frequency.persons", warn_if_empty=True ) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/non_mandatory_destination.py b/activitysim/abm/models/non_mandatory_destination.py index 496c734cdd..b0c6ea3fb1 100644 --- a/activitysim/abm/models/non_mandatory_destination.py +++ b/activitysim/abm/models/non_mandatory_destination.py @@ -6,8 +6,8 @@ import pandas as pd -from activitysim.abm.models.util import annotate, tour_destination -from activitysim.core import estimation, los, tracing, workflow +from activitysim.abm.models.util import tour_destination +from activitysim.core import estimation, los, tracing, workflow, expressions from activitysim.core.configuration.logit import TourLocationComponentSettings from activitysim.core.util import assign_in_place @@ -129,9 +129,6 @@ def non_mandatory_tour_destination( state.add_table("tours", tours) - if model_settings.annotate_tours: - annotate.annotate_tours(state, model_settings, trace_label) - if want_sample_table: assert len(save_sample_df.index.get_level_values(0).unique()) == len(choices_df) # save_sample_df.set_index(model_settings['ALT_DEST_COL_NAME'], append=True, inplace=True) @@ -146,3 +143,11 @@ def non_mandatory_tour_destination( columns=None, warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/non_mandatory_scheduling.py b/activitysim/abm/models/non_mandatory_scheduling.py index 4e444107bc..bbb09f7f57 100644 --- a/activitysim/abm/models/non_mandatory_scheduling.py +++ b/activitysim/abm/models/non_mandatory_scheduling.py @@ -8,8 +8,9 @@ from activitysim.abm.models.util.tour_scheduling import run_tour_scheduling from activitysim.core import timetable as tt -from activitysim.core import tracing, workflow +from activitysim.core import tracing, workflow, expressions from activitysim.core.util import assign_in_place +from activitysim.abm.models.util.vectorize_tour_scheduling import TourSchedulingSettings logger = logging.getLogger(__name__) DUMP = False @@ -29,6 +30,13 @@ def non_mandatory_tour_scheduling( model_name = "non_mandatory_tour_scheduling" trace_label = model_name trace_hh_id = state.settings.trace_hh_id + + model_settings = TourSchedulingSettings.read_settings_file( + state.filesystem, + f"{model_name}.yaml", + mandatory=False, + ) + non_mandatory_tours = tours[tours.tour_category == "non_mandatory"] # - if no mandatory_tours @@ -40,11 +48,12 @@ def non_mandatory_tour_scheduling( choices = run_tour_scheduling( state, - model_name, + model_settings, non_mandatory_tours, persons_merged, tdd_alts, tour_segment_col, + trace_label, ) assign_in_place( @@ -71,3 +80,11 @@ def non_mandatory_tour_scheduling( columns=None, warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/non_mandatory_tour_frequency.py b/activitysim/abm/models/non_mandatory_tour_frequency.py index 69b0524d42..8afe368d86 100644 --- a/activitysim/abm/models/non_mandatory_tour_frequency.py +++ b/activitysim/abm/models/non_mandatory_tour_frequency.py @@ -10,7 +10,6 @@ import numpy as np import pandas as pd -from activitysim.abm.models.util import annotate from activitysim.abm.models.util.overlap import ( person_available_periods, person_max_window, @@ -28,7 +27,7 @@ tracing, workflow, ) -from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable +from activitysim.core.configuration.base import PydanticReadable, PreprocessorSettings from activitysim.core.configuration.logit import LogitComponentSettings from activitysim.core.interaction_simulate import interaction_simulate @@ -166,27 +165,21 @@ class NonMandatoryTourFrequencySettings(LogitComponentSettings, extra="forbid"): Settings for the `non_mandatory_tour_frequency` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - SEGMENT_COL: str = "ptype" # not used anymore TODO remove if needed SPEC_SEGMENTS: list[NonMandatoryTourSpecSegment] = [] # check the above - annotate_persons: PreprocessorSettings | None = None - """Preprocessor settings to annotate persons""" - - annotate_tours: PreprocessorSettings | None = None - """Preprocessor settings to annotate tours""" - explicit_chunk: float = 0 """ If > 0, use this chunk size instead of adaptive chunking. If less than 1, use this fraction of the total number of rows. """ + alts_preprocessor: PreprocessorSettings | None = None + """Settings for the alternatives preprocessor.""" + @workflow.step def non_mandatory_tour_frequency( @@ -233,27 +226,36 @@ def non_mandatory_tour_frequency( choosers = persons_merged choosers = choosers[choosers.cdap_activity.isin(["M", "N"])] - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_dict = { - "person_max_window": lambda x: person_max_window(state, x), - "person_available_periods": lambda persons, start_bin, end_bin, continuous: person_available_periods( - state, persons, start_bin, end_bin, continuous - ), - } - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) - logger.info("Running non_mandatory_tour_frequency with %d persons", len(choosers)) - + # preprocessing choosers constants = config.get_model_constants(model_settings) + locals_dict = { + "person_max_window": lambda x: person_max_window(state, x), + "person_available_periods": lambda persons, start_bin, end_bin, continuous: person_available_periods( + state, persons, start_bin, end_bin, continuous + ), + } + locals_dict.update(constants) + + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=alternatives, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor", + ) model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) spec_segments = model_settings.SPEC_SEGMENTS @@ -287,14 +289,22 @@ def non_mandatory_tour_frequency( ) if estimator: - estimator.write_spec(model_settings, bundle_directory=True) + bundle_directory = True + # writing to separte subdirectory for each segment if multiprocessing + if state.settings.multiprocess: + bundle_directory = False + estimator.write_spec(model_settings, bundle_directory=bundle_directory) estimator.write_model_settings( - model_settings, model_settings_file_name, bundle_directory=True + model_settings, + model_settings_file_name, + bundle_directory=bundle_directory, ) # preserving coefficients file name makes bringing back updated coefficients more straightforward estimator.write_coefficients(coefficients_df, segment_settings) estimator.write_choosers(chooser_segment) - estimator.write_alternatives(alternatives, bundle_directory=True) + estimator.write_alternatives( + alternatives, bundle_directory=bundle_directory + ) # FIXME #interaction_simulate_estimation_requires_chooser_id_in_df_column # shuold we do it here or have interaction_simulate do it? @@ -433,8 +443,10 @@ def non_mandatory_tour_frequency( if estimator: # make sure they created the right tours survey_tours = estimation.manager.get_survey_table("tours").sort_index() + # need the household_id check below incase household_sample_size != 0 non_mandatory_survey_tours = survey_tours[ - survey_tours.tour_category == "non_mandatory" + (survey_tours.tour_category == "non_mandatory") + & survey_tours.household_id.isin(persons.household_id) ] # need to remove the pure-escort tours from the survey tours table for comparison below if state.is_table("school_escort_tours"): @@ -479,16 +491,6 @@ def non_mandatory_tour_frequency( # need to re-compute tour frequency statistics to account for school escort tours recompute_tour_count_statistics(state) - if model_settings.annotate_tours: - annotate.annotate_tours(state, model_settings, trace_label) - - expressions.assign_columns( - state, - df=persons, - model_settings=model_settings.annotate_persons, - trace_label=trace_label, - ) - state.add_table("persons", persons) tracing.print_summary( @@ -513,3 +515,11 @@ def non_mandatory_tour_frequency( label="non_mandatory_tour_frequency.annotated_persons", warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/parking_location_choice.py b/activitysim/abm/models/parking_location_choice.py index 674b950aaa..32f3aabee2 100644 --- a/activitysim/abm/models/parking_location_choice.py +++ b/activitysim/abm/models/parking_location_choice.py @@ -3,6 +3,7 @@ from __future__ import annotations import logging +import warnings from pathlib import Path import numpy as np @@ -22,6 +23,7 @@ from activitysim.core.interaction_sample_simulate import interaction_sample_simulate from activitysim.core.tracing import print_elapsed_time from activitysim.core.util import assign_in_place, drop_unused_columns +from activitysim.core.exceptions import DuplicateWorkflowTableError logger = logging.getLogger(__name__) @@ -202,8 +204,10 @@ def choose_parking_location( additional_columns=model_settings.compute_settings.protect_columns, ) + # Passing only the index of the trips table to the interaction_dataset + # See ActivitySim issue #633 destination_sample = logit.interaction_dataset( - state, trips, alternatives, alt_index_id=alt_dest_col_name + state, trips[[]], alternatives, alt_index_id=alt_dest_col_name ) destination_sample.index = np.repeat(trips.index.values, len(alternatives)) destination_sample.index.name = trips.index.name @@ -325,7 +329,14 @@ class ParkingLocationSettings(LogitComponentSettings, extra="forbid"): """The school escort model does not use this setting, see `SPECIFICATION`.""" PREPROCESSOR: PreprocessorSettings | None = None - """Setting for the preprocessor.""" + """Setting for the preprocessor. + Runs before the choosers are filtered by the CHOOSER_FILTER_COLUMN_NAME. + Deprecated name -- use `preprocessor` instead. + """ + + alts_preprocessor: PreprocessorSettings | None = None + """Setting for the alternatives (aka landuse zones) preprocessor. + Runs before the alternatives are filtered by the ALTERNATIVE_FILTER_COLUMN_NAME.""" ALT_DEST_COL_NAME: str = "parking_zone" """Parking destination column name.""" @@ -362,6 +373,19 @@ class ParkingLocationSettings(LogitComponentSettings, extra="forbid"): If less than 1, use this fraction of the total number of rows. """ + def __init__(self, **data): + # Handle deprecated ALTS_PREPROCESSOR + if "PREPROCESSOR" in data: + warnings.warn( + "The 'PREPROCESSOR' setting is deprecated. Please use 'preprocessor' (lowercase) instead.", + DeprecationWarning, + stacklevel=2, + ) + # If both are provided, prefer the lowercase version + if "preprocessor" not in data: + data["preprocessor"] = data["PREPROCESSOR"] + super().__init__(**data) + @workflow.step def parking_location( @@ -388,8 +412,6 @@ def parking_location( trace_hh_id = state.settings.trace_hh_id alt_destination_col_name = model_settings.ALT_DEST_COL_NAME - preprocessor_settings = model_settings.PREPROCESSOR - trips_df = trips trips_merged_df = trips_merged land_use_df = land_use @@ -416,14 +438,28 @@ def parking_location( if constants is not None: locals_dict.update(constants) - if preprocessor_settings: - expressions.assign_columns( - state, - df=trips_merged_df, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) + # putting preprocessor and alts preprocessor here so that they are run before + # the filter columns are applied so the user can use the preprocessor to add filter + # preprocessing choosers + expressions.annotate_preprocessors( + state, + df=trips_merged_df, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=land_use_df, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor", + ) parking_locations, save_sample_df = run_parking_destination( state, @@ -465,5 +501,15 @@ def parking_location( # lest they try to put tour samples into the same table if state.is_table(sample_table_name): - raise RuntimeError("sample table %s already exists" % sample_table_name) + raise DuplicateWorkflowTableError( + "sample table %s already exists" % sample_table_name + ) state.extend_table(sample_table_name, save_sample_df) + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/school_escorting.py b/activitysim/abm/models/school_escorting.py index a0cf6a3312..32e5058e77 100644 --- a/activitysim/abm/models/school_escorting.py +++ b/activitysim/abm/models/school_escorting.py @@ -319,9 +319,6 @@ class SchoolEscortSettings(BaseLogitComponentSettings, extra="forbid"): Settings for the `telecommute_frequency` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - ALTS: Any NUM_ESCORTEES: int = 3 @@ -353,6 +350,8 @@ class SchoolEscortSettings(BaseLogitComponentSettings, extra="forbid"): preprocessor_outbound: PreprocessorSettings | None = None preprocessor_inbound: PreprocessorSettings | None = None preprocessor_outbound_cond: PreprocessorSettings | None = None + alts_preprocessor: PreprocessorSettings | None = None + """Preprocessor settings for the school escorting model alternatives.""" no_escorting_alterative: int = 1 """The alternative number for no escorting. Used to set the choice for households with no escortees.""" @@ -428,6 +427,16 @@ def school_escorting( constants = config.get_model_constants(model_settings) locals_dict = {} locals_dict.update(constants) + # alternatives preprocessor + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor", + ) school_escorting_stages = ["outbound", "inbound", "outbound_cond"] escort_bundles = [] @@ -476,15 +485,16 @@ def school_escorting( logger.info("Running %s with %d households", stage_trace_label, len(choosers)) - preprocessor_settings = getattr(model_settings, "preprocessor_" + stage, None) - if preprocessor_settings: - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=stage_trace_label, - ) + preprocessor_setting_name = "preprocessor_" + stage + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name=preprocessor_setting_name, + ) if estimator: estimator.write_model_settings(model_settings, model_settings_file_name) @@ -493,7 +503,10 @@ def school_escorting( coefficients_df, file_name=stage.upper() + "_COEFFICIENTS" ) estimator.write_choosers(choosers) - estimator.write_alternatives(alts, bundle_directory=True) + if state.settings.multiprocess: + estimator.write_alternatives(alts, bundle_directory=False) + else: + estimator.write_alternatives(alts, bundle_directory=True) # FIXME #interaction_simulate_estimation_requires_chooser_id_in_df_column # shuold we do it here or have interaction_simulate do it? @@ -655,3 +668,11 @@ def school_escorting( ) timetable.replace_table(state) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/settings_checker.py b/activitysim/abm/models/settings_checker.py new file mode 100644 index 0000000000..243b2fb742 --- /dev/null +++ b/activitysim/abm/models/settings_checker.py @@ -0,0 +1,769 @@ +import logging +import os +from pandas import DataFrame +from pydantic import BaseModel as PydanticBase +from typing import Type, Optional + +from activitysim.core.configuration.base import PydanticReadable + +# import core settings +from activitysim.core.configuration.logit import ( + LogitNestSpec, + TourLocationComponentSettings, + TourModeComponentSettings, + TemplatedLogitComponentSettings, +) +from activitysim.core import config +from activitysim.core.configuration.network import NetworkSettings +from activitysim.core.workflow import State +from activitysim.core.simulate import ( + eval_coefficients, + eval_nest_coefficients, + read_model_coefficient_template, +) +from activitysim.core.exceptions import ModelConfigurationError + +# import model settings +from activitysim.abm.models.accessibility import AccessibilitySettings +from activitysim.abm.models.atwork_subtour_frequency import ( + AtworkSubtourFrequencySettings, +) +from activitysim.abm.models.auto_ownership import AutoOwnershipSettings +from activitysim.abm.models.cdap import CdapSettings +from activitysim.abm.models.disaggregate_accessibility import ( + DisaggregateAccessibilitySettings, + read_disaggregate_accessibility_yaml, +) +from activitysim.abm.models.free_parking import FreeParkingSettings +from activitysim.abm.models.initialize import InitializeTableSettings +from activitysim.abm.models.joint_tour_composition import JointTourCompositionSettings +from activitysim.abm.models.joint_tour_frequency_composition import ( + JointTourFreqCompSettings, +) +from activitysim.abm.models.joint_tour_frequency import JointTourFrequencySettings +from activitysim.abm.models.joint_tour_participation import ( + JointTourParticipationSettings, +) +from activitysim.abm.models.mandatory_tour_frequency import ( + MandatoryTourFrequencySettings, +) +from activitysim.abm.models.non_mandatory_tour_frequency import ( + NonMandatoryTourFrequencySettings, +) +from activitysim.abm.models.parking_location_choice import ParkingLocationSettings +from activitysim.abm.models.school_escorting import SchoolEscortSettings +from activitysim.abm.models.stop_frequency import StopFrequencySettings +from activitysim.abm.models.summarize import SummarizeSettings +from activitysim.abm.models.telecommute_frequency import TelecommuteFrequencySettings +from activitysim.abm.models.tour_scheduling_probabilistic import ( + TourSchedulingProbabilisticSettings, +) +from activitysim.abm.models.transit_pass_ownership import TransitPassOwnershipSettings +from activitysim.abm.models.transit_pass_subsidy import TransitPassSubsidySettings +from activitysim.abm.models.trip_departure_choice import TripDepartureChoiceSettings +from activitysim.abm.models.trip_destination import TripDestinationSettings +from activitysim.abm.models.trip_matrices import WriteTripMatricesSettings +from activitysim.abm.models.trip_mode_choice import TripModeChoiceSettings +from activitysim.abm.models.trip_purpose_and_destination import ( + TripPurposeAndDestinationSettings, +) +from activitysim.abm.models.trip_purpose import TripPurposeSettings +from activitysim.abm.models.vehicle_allocation import VehicleAllocationSettings +from activitysim.abm.models.vehicle_type_choice import VehicleTypeChoiceSettings +from activitysim.abm.models.work_from_home import WorkFromHomeSettings + +# import util settings +from activitysim.abm.models.util.vectorize_tour_scheduling import ( + TourSchedulingSettings, +) +from activitysim.abm.models.util.tour_od import TourODSettings + +# import table settings +from activitysim.abm.tables.shadow_pricing import ShadowPriceSettings + + +class SettingsCheckerError(Exception): + """Custom exception for settings checker errors.""" + + def __init__( + self, + model_name: str, + exception: Exception, + error_files: None = None, + additional_info: str = None, + ): + self.model_name = model_name + self.exception = exception + self.error_files = error_files + self.additional_info = additional_info + self.message = self._construct_message() + + super().__init__(self.message) + + def _construct_message(self) -> str: + message = f"Error checking settings for {self.model_name}" + if self.error_files is not None: + # cast all files from path to strings if required and wrap into list + if not isinstance(self.error_files, list): + message_files = [self.error_files] + else: + message_files = self.error_files + message += f" using files {', '.join([str(f) for f in message_files])}" + message += f": {str(self.exception)}" + if self.additional_info is not None: + message += f". {self.additional_info}" + return message + + +# setup logging +logger = logging.getLogger(__name__) +file_logger = logger.getChild("logfile") + +CHECKER_SETTINGS = { + "compute_accessibility": { + "settings_cls": AccessibilitySettings, + "settings_file": "accessibility.yaml", + }, + "atwork_subtour_destination": { + "settings_cls": TourLocationComponentSettings, + "settings_file": "atwork_subtour_destination.yaml", + }, + "atwork_subtour_frequency": { + "settings_cls": AtworkSubtourFrequencySettings, + "settings_file": "atwork_subtour_frequency.yaml", + }, + "atwork_subtour_mode_choice": { + "settings_cls": TourModeComponentSettings, + "settings_file": "tour_mode_choice.yaml", + }, + "atwork_subtour_scheduling": { + "settings_cls": TourSchedulingSettings, + "settings_file": "tour_scheduling_atwork.yaml", + }, + "auto_ownership_simulate": { + "settings_cls": AutoOwnershipSettings, + "settings_file": "auto_ownership.yaml", + }, + "cdap_simulate": {"settings_cls": CdapSettings, "settings_file": "cdap.yaml"}, + "compute_disaggregate_accessibility": { + "settings_cls": DisaggregateAccessibilitySettings, + "settings_file": "disaggregate_accessibility.yaml", + }, + "free_parking": { + "settings_cls": FreeParkingSettings, + "settings_file": "free_parking.yaml", + }, + "initialize_households": { + "settings_cls": InitializeTableSettings, + "settings_file": "initialize_households.yaml", + }, + "initialize_landuse": { + "settings_cls": InitializeTableSettings, + "settings_file": "initialize_landuse.yaml", + }, + "initialize_los": { + "settings_cls": NetworkSettings, + "settings_file": "network_los.yaml", + }, + "input_checker": { + "settings_cls": PydanticReadable, # input checker uses state.filesystem.read_model_settings directly + "settings_file": "input_checker.yaml", + }, + "joint_tour_composition": { + "settings_cls": JointTourCompositionSettings, + "settings_file": "joint_tour_composition.yaml", + }, + "joint_tour_destination": { + "settings_cls": TourLocationComponentSettings, + "settings_file": "joint_tour_destination.yaml", + }, + "joint_tour_frequency_composition": { + "settings_cls": JointTourFreqCompSettings, + "settings_file": "joint_tour_frequency_composition.yaml", + }, + "joint_tour_frequency": { + "settings_cls": JointTourFrequencySettings, + "settings_file": "joint_tour_frequency.yaml", + }, + "joint_tour_participation": { + "settings_cls": JointTourParticipationSettings, + "settings_file": "joint_tour_participation.yaml", + }, + "joint_tour_scheduling": { + "settings_cls": TourSchedulingSettings, + "settings_file": "joint_tour_scheduling.yaml", + }, + "mandatory_tour_frequency": { + "settings_cls": MandatoryTourFrequencySettings, + "settings_file": "mandatory_tour_frequency.yaml", + }, + "mandatory_tour_scheduling": { + "settings_cls": TourSchedulingSettings, + "settings_file": "mandatory_tour_scheduling.yaml", + }, + "non_mandatory_tour_destination": { + "settings_cls": TourLocationComponentSettings, + "settings_file": "non_mandatory_tour_destination.yaml", + }, + "non_mandatory_tour_frequency": { + "settings_cls": NonMandatoryTourFrequencySettings, + "settings_file": "non_mandatory_tour_frequency.yaml", + }, + "non_mandatory_tour_scheduling": { + "settings_cls": TourSchedulingSettings, + "settings_file": "non_mandatory_tour_scheduling.yaml", + }, + "parking_location": { + "settings_cls": ParkingLocationSettings, + "settings_file": "parking_location_choice.yaml", + }, + "school_escorting": { + "settings_cls": SchoolEscortSettings, + "settings_file": "school_escorting.yaml", + "spec_coefficient_keys": [ + {"spec": "OUTBOUND_SPEC", "coefs": "OUTBOUND_COEFFICIENTS"}, + {"spec": "INBOUND_SPEC", "coefs": "INBOUND_COEFFICIENTS"}, + {"spec": "OUTBOUND_COND_SPEC", "coefs": "OUTBOUND_COND_COEFFICIENTS"}, + ], + }, + "school_location": { + "settings_cls": TourLocationComponentSettings, + "settings_file": "school_location.yaml", + }, + "shadow_pricing": { + "settings_cls": ShadowPriceSettings, + "settings_file": "shadow_pricing.yaml", + }, + "stop_frequency": { + "settings_cls": StopFrequencySettings, + "settings_file": "stop_frequency.yaml", + }, + "summarize": {"settings_cls": SummarizeSettings, "settings_file": "summarize.yaml"}, + "telecommute_frequency": { + "settings_cls": TelecommuteFrequencySettings, + "settings_file": "telecommute_frequency.yaml", + }, + "tour_mode_choice_simulate": { + "settings_cls": TourModeComponentSettings, + "settings_file": "tour_mode_choice.yaml", + }, + "tour_od_choice": { + "settings_cls": TourODSettings, + "settings_file": "tour_od_choice.yaml", + }, + "tour_scheduling_probabilistic": { + "settings_cls": TourSchedulingProbabilisticSettings, + "settings_file": "tour_scheduling_probabilistic.yaml", + }, + "transit_pass_ownership": { + "settings_cls": TransitPassOwnershipSettings, + "settings_file": "transit_pass_ownership.yaml", + }, + "transit_pass_subsidy": { + "settings_cls": TransitPassSubsidySettings, + "settings_file": "transit_pass_subsidy.yaml", + }, + "trip_departure_choice": { + "settings_cls": TripDepartureChoiceSettings, + "settings_file": "trip_departure_choice.yaml", + }, + "trip_destination": { + "settings_cls": TripDestinationSettings, + "settings_file": "trip_destination.yaml", + }, + "trip_mode_choice": { + "settings_cls": TripModeChoiceSettings, + "settings_file": "trip_mode_choice.yaml", + }, + "trip_purpose": { + "settings_cls": TripPurposeSettings, + "settings_file": "trip_purpose.yaml", + }, + "trip_purpose_and_destination": { + "settings_cls": TripPurposeAndDestinationSettings, + "settings_file": "trip_purpose_and_destination.yaml", + }, + "vehicle_allocation": { + "settings_cls": VehicleAllocationSettings, + "settings_file": "vehicle_allocation.yaml", + }, + "vehicle_type_choice": { + "settings_cls": VehicleAllocationSettings, + "settings_file": "vehicle_allocation.yaml", + }, + "vehicle_type_choice": { + "settings_cls": VehicleTypeChoiceSettings, + "settings_file": "vehicle_type_choice.yaml", + }, + "work_from_home": { + "settings_cls": WorkFromHomeSettings, + "settings_file": "work_from_home.yaml", + }, + "workplace_location": { + "settings_cls": TourLocationComponentSettings, + "settings_file": "workplace_location.yaml", + }, + "write_data_dictionary": { + "settings_cls": PydanticReadable, # write data dictionary uses state.filesystem.read_model_settings directly + "settings_file": "write_data_dictionary.yaml", + "warn_only": True, + }, + "write_trip_matrices": { + "settings_cls": WriteTripMatricesSettings, + "settings_file": "write_trip_matrices.yaml", + }, +} + + +def try_load_model_settings( + model_name: str, + model_settings_class: Type[PydanticBase], + model_settings_file: str, + state: State, +) -> tuple[PydanticBase | None, Exception | None]: + + msg = f"Attempting to load model settings for {model_name} via {model_settings_class.__name__} and {model_settings_file}" + logger.debug(msg) + file_logger.info(msg) + + try: + if isinstance(model_settings_class, DisaggregateAccessibilitySettings): + model_settings = read_disaggregate_accessibility_yaml( + state, model_settings_file + ) + elif model_name == "input_checker": + # HACK: input checker does not define a pydantic data model, but reads directly to dictionary. Wrapping in BaseModel + # provides the required model_dump interface downstream without adding additional branching logic. + class InputCheckerSettings(PydanticBase): + input_check_settings: dict + + input_check_settings = state.filesystem.read_model_settings( + model_settings_file, mandatory=True + ) + model_settings = InputCheckerSettings( + input_check_settings=input_check_settings + ) + else: + model_settings = model_settings_class.read_settings_file( + state.filesystem, model_settings_file + ) + result = model_settings, None + msg = f"Successfully loaded model settings from {model_settings_file}" + logger.debug(msg) + except Exception as e: + result = None, e + return result + + +def try_load_spec( + model_name: str, model_settings: PydanticBase, spec_file: str, state: State +) -> tuple[DataFrame | None, Exception | None]: + msg = f"Attempting to load SPEC for {model_name} via {model_settings.__class__.__name__}" + logger.debug(msg) + file_logger.info(msg) + try: + result = state.filesystem.read_model_spec(spec_file), None + msg = f"Successfully loaded model SPEC from {spec_file}" + logger.debug(msg) + file_logger.info(msg) + except Exception as e: + # always return a dataframe + result = None, e + return result + + +def try_load_coefs( + model_name: str, model_settings: PydanticBase, coefs_file: str, state: State +) -> tuple[DataFrame, Optional[Exception]]: + msg = f"Attempting to load COEFFICIENTS for {model_name} via {model_settings.__class__.__name__}" + logger.debug(msg) + file_logger.info(msg) + + try: + result = state.filesystem.read_model_coefficients(file_name=coefs_file), None + msg = f"Successfully loaded model Coefficients from {coefs_file}" + logger.debug(msg) + file_logger.info(msg) + except Exception as e: + result = None, e + return result + + +def try_eval_spec_coefs( + model_name: str, + model_settings: PydanticBase, + spec: DataFrame | None, + coefs: DataFrame | None, + state: State, +) -> tuple[DataFrame | None, Exception | None]: + + if spec is None or coefs is None: + msg_prefix = ( + f"Skipping Evaluation Check for {model_settings.__class__.__name__}" + ) + spec_msg = "No SPEC available" if spec is None else "" + coefs_msg = "No COEFFICENTS available" if coefs is None else "" + msg = ". ".join([msg_prefix, spec_msg, coefs_msg]) + logger.debug(msg) + file_logger.debug(msg) + return None, None + + try: + # check whether coefficients should be evaluated as NESTS or not + if model_settings.model_dump().get("NESTS"): + if isinstance(model_settings.NESTS, LogitNestSpec): + nests = model_settings.NESTS + else: + nests = None + if nests is not None: + # Proper Trace label is probably unneeded here + result = ( + eval_nest_coefficients(model_settings.NESTS, coefs, trace_label=None), + None, + ) + else: + result = eval_coefficients(state, spec, coefs, estimator=None), None + msg = f"Successfully evaluated coefficients for {model_name}" + logger.debug(msg) + file_logger.info(msg) + except Exception as e: + result = None, e + return result + + +def try_check_spec_coefs_templated( + model_name: str, model_settings: TemplatedLogitComponentSettings, state: State +) -> list[Exception]: + """Alternative function for checking mode choice settings using a templated coefficients files""" + + errors = [] + inner_errors = [] + + try: + coefs_template = read_model_coefficient_template( + state.filesystem, model_settings + ) + coefs_segments = list(coefs_template.columns) + + for segment_name in coefs_segments: + try: + nest_spec = config.get_logit_model_settings(model_settings) + coefs = state.filesystem.get_segment_coefficients( + model_settings, segment_name + ) + # Proper trace label probably unneeded here + nest_spec = eval_nest_coefficients(nest_spec, coefs, trace_label=None) + except Exception as e: + additional_info = f"Could not evaluate templated coefficients for segment {segment_name}. Check that SPEC, Coefficients, and Template files exist and have compatible labels." + inner_errors.append( + SettingsCheckerError( + model_name, + e, + [ + model_settings.SPEC, + model_settings.COEFFICIENTS, + model_settings.COEFFICIENT_TEMPLATE, + ], + additional_info, + ) + ) + continue + except Exception as e: + msg = f"{model_name}: Could not evaluate templated coefficients. Check that SPEC, Coefficients, and Template files exist and have compatible labels." + logger.warning(msg) + file_logger.warning(msg) + + additional_info = "Could not evaluated templated coefficients. Check that SPEC, Coefficients, and Template files exist and have compatible labels." + + errors.append( + SettingsCheckerError( + model_name, + e, + [ + model_settings.SPEC, + model_settings.COEFFICIENTS, + model_settings.COEFFICIENT_TEMPLATE, + ], + additional_info, + ) + ) + + errors.extend(inner_errors) + + return errors + + +def try_check_spec_coefs_ptype_spec_segments( + model_name: str, model_settings: PydanticBase, state: State +) -> list[Exception]: + """Alternative function for checking settings that are segmented by PTYPE within the main model spec""" + errors = [] + + try: + spec_segments = model_settings.SPEC_SEGMENTS + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + + # pick the spec column for the segment + for segment_settings in spec_segments: + segment_name = segment_settings.NAME + segment_spec = model_spec[[segment_name]] + + coefficients_df = state.filesystem.read_model_coefficients(segment_settings) + segment_spec = eval_coefficients( + state, segment_spec, coefficients_df, estimator=None + ) + except Exception as e: + errors.append( + SettingsCheckerError( + model_name, e, [model_settings.SPEC, model_settings.COEFFICIENTS] + ) + ) + return errors + + +def try_load_and_check_spec_coefs( + model_name: str, + model_settings: Type[PydanticBase], + state: State, + spec_coefficient_keys: list[dict] = None, +) -> list[Exception]: + """Attempt to load and evaluate SPEC and COEFFICIENTS. + By default, will look for SPEC and COEFFICIENTS at the top level of the settings. + This can be overriden by providing an alternative set of spec/coefs keys + in the settings checker register. + """ + # collect all errors + errors = [] + + if spec_coefficient_keys is None: + spec_coefficient_keys = [{"spec": "SPEC", "coefs": "COEFFICIENTS"}] + + for key_pair in spec_coefficient_keys: + + # attempt to read SPEC file + if hasattr(model_settings, key_pair["spec"]): + spec_file = model_settings.model_dump().get(key_pair["spec"]) + + # HACK: some models may use older "SPECIFICATION" field name instead of "SPEC" + if spec_file is None and hasattr(model_settings, "SPECIFICATION"): + spec_file = model_settings.SPECIFICATION + + if spec_file is not None: + spec, spec_error = try_load_spec( + model_name=model_name, + model_settings=model_settings, + spec_file=spec_file, + state=state, + ) + else: + spec, spec_error = None, None + msg = f"{model_name}: Field {key_pair['spec']} is None in {model_settings.__class__.__name__}. Ensure that a filepath is defined YAML settings if required" + logger.warning(msg) + file_logger.warning(msg) + else: + spec, spec_error = None, None + # msg = f"No SPEC file is associated with {model_settings.__class__.__name__}" + # logger.info(msg) + # file_logger.info(msg) + + if spec_error is not None: + errors.append(spec_error) + + # then attempt to read coefficients + if hasattr(model_settings, key_pair["coefs"]): + coefs_file = model_settings.model_dump().get(key_pair["coefs"]) + if coefs_file is not None: + coefs, coefs_error = try_load_coefs( + model_name=model_name, + model_settings=model_settings, + coefs_file=coefs_file, + state=state, + ) + else: + coefs, coefs_error = None, None + msg = f"{model_name}: Field {key_pair['coefs']} is None in {model_settings.__class__.__name__}. Ensure that a filepath is defined YAML settings if required" + logger.warning(msg) + file_logger.warning(msg) + else: + coefs, coefs_error = None, None + # msg = f"No coefficients file is associated with {model_settings.__class__.__name__}" + # logger.info(msg) + # file_logger.info(msg) + + if coefs_error is not None: + errors.append(SettingsCheckerError(model_name, coefs_error, coefs_file)) + + # then attempt to evaluate coefficients against spec + eval_coefs, eval_coefs_error = try_eval_spec_coefs( + model_name=model_name, + model_settings=model_settings, + spec=spec, + coefs=coefs, + state=state, + ) + + if eval_coefs_error is not None: + errors.append( + SettingsCheckerError( + model_name, eval_coefs_error, [spec_file, coefs_file] + ) + ) + + # then, check any other subsettings that may have a SPEC + # this includes preprocessors and annotators, etc. + # for now, check is limited to check that the SPEC file is loadable + for _, setting in model_settings: + if ( + isinstance(setting, PydanticBase) + and setting.model_dump().get("SPEC") is not None + ): + addl_spec_file = setting.SPEC + addl_spec, addl_spec_error = try_load_spec( + model_name=model_name + f": {setting.__class__.__name__}", + model_settings=setting, + spec_file=addl_spec_file, + state=state, + ) + if addl_spec_error: + errors.append( + SettingsCheckerError(model_name, addl_spec_error, addl_spec_file) + ) + return errors + + +def check_model_settings( + state: State, + checker_settings: dict = CHECKER_SETTINGS, + extension_settings: dict = {}, + log_file: str = "settings_checker.log", +) -> None: + + # Collect all errors + all_errors = [] + + # additional logging set up + formatter = logging.Formatter( + "%(asctime)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S" + ) + out_log_file = state.get_log_file_path(log_file) + if os.path.exists(out_log_file): + os.remove(out_log_file) + module_handler = logging.FileHandler(out_log_file) + module_handler.setFormatter(formatter) + file_logger.addHandler(module_handler) + file_logger.propagate = False + + # add extension settings to checker settings + if extension_settings is not None: + checker_settings.update(extension_settings) + + # extract all model components + all_models = state.settings.models.copy() + + # add shadow pricing and initalize los (not in state.settings.models) + if state.settings.use_shadow_pricing == True: + all_models.append("shadow_pricing") + if "initialize_los" in state._RUNNABLE_STEPS: + all_models.append("initialize_los") + + for model_name in all_models: + + if not model_name in checker_settings: + msg = ( + f"Cannot pre-check settings for model component {model_name}: " + "mapping to a Pydantic data model is undefined in the checker." + ) + logger.info(msg) + file_logger.info(msg) + continue + + model_settings_class = checker_settings[model_name]["settings_cls"] + model_settings_file = checker_settings[model_name]["settings_file"] + spec_coefficient_keys = checker_settings[model_name].get( + "spec_coefficient_keys" + ) + # do not raise errors if YAML file cannot be loaded + # this is used for write_data_dictionary + warn_only = checker_settings[model_name].get("warn_only", False) + + # first, attempt to load settings + # continue if any error + model_settings, model_settings_error = try_load_model_settings( + model_name=model_name, + model_settings_class=model_settings_class, + model_settings_file=model_settings_file, + state=state, + ) + + if model_settings_error is not None: + if warn_only: + msg = f"{model_name} settings file {model_settings_file} could not be loaded. Ensure inclusion of this configuration file is optional." + logger.warning(msg) + file_logger.warning(msg) + continue + else: + all_errors.append( + SettingsCheckerError( + model_name, model_settings_error, model_settings_file + ) + ) + continue + + # then attempt to load and resolve spec/coef files + if isinstance(model_settings, TemplatedLogitComponentSettings): + errors = try_check_spec_coefs_templated( + model_name=model_name, model_settings=model_settings, state=state + ) + else: + errors = try_load_and_check_spec_coefs( + model_name=model_name, + model_settings=model_settings, + state=state, + spec_coefficient_keys=spec_coefficient_keys, + ) + all_errors.extend(errors) + + # if model has nested SPEC_SEGMENTS, check each of these. + # there are two ways of segmenting specs, which are handled differently: + # 1) Settings using define separate pairs of spec/coefficient files. + # 2) Others define segments within the main model spec file, keyed by PTYPE. + if model_settings.model_dump().get("SPEC_SEGMENTS"): + + spec_segments = model_settings.SPEC_SEGMENTS + + if isinstance(spec_segments, dict): + spec_segments = [ + segment for segment_name, segment in spec_segments.items() + ] + + # check the first segment to see if PTYPE should be defined + # this avoids needing to hardcode branching logic to determine evaluation method + if "PTYPE" in spec_segments[0].model_fields: + errors = try_check_spec_coefs_ptype_spec_segments( + model_name=model_name, + model_settings=model_settings, + state=state, + ) + all_errors.extend(errors) + else: + for segment_settings in spec_segments: + errors = try_load_and_check_spec_coefs( + model_name=model_name, + model_settings=segment_settings, + state=state, + ) + all_errors.extend(errors) + + if len(all_errors) > 0: + msg = "Settings Checker Failed with the following errors:" + logger.error(msg) + file_logger.error(msg) + for e in all_errors: + logger.error(f"\t{str(e)}") + file_logger.error(f"\t{str(e)}") + raise ModelConfigurationError( + f"Encountered one or more errors in settings checker. See f{log_file} for details." + ) + msg = f"Setting Checker Complete. No runtime errors were raised. Check f{log_file} for warnings. These *may* prevent model from successfully running." + logger.info(msg) + file_logger.info(msg) diff --git a/activitysim/abm/models/stop_frequency.py b/activitysim/abm/models/stop_frequency.py index 2f0253f219..634eb0cd12 100644 --- a/activitysim/abm/models/stop_frequency.py +++ b/activitysim/abm/models/stop_frequency.py @@ -134,21 +134,15 @@ def stop_frequency( simulate.set_skim_wrapper_targets(tours_merged, skims) # this should be pre-slice as some expressions may count tours by type - annotations = expressions.compute_columns( + expressions.annotate_preprocessors( state, df=tours_merged, - model_settings=preprocessor_settings, locals_dict=locals_dict, + skims=None, # skims are already set on tours_merged above + model_settings=model_settings, trace_label=trace_label, ) - assign_in_place( - tours_merged, - annotations, - state.settings.downcast_int, - state.settings.downcast_float, - ) - tracing.print_summary( "stop_frequency segments", tours_merged.primary_purpose, value_counts=True ) @@ -197,9 +191,15 @@ def stop_frequency( if estimator: estimator.write_spec(segment_settings, bundle_directory=False) - estimator.write_model_settings( - model_settings, model_settings_file_name, bundle_directory=True - ) + # writing to separte subdirectory for each segment if multiprocessing + if state.settings.multiprocess: + estimator.write_model_settings( + model_settings, model_settings_file_name, bundle_directory=False + ) + else: + estimator.write_model_settings( + model_settings, model_settings_file_name, bundle_directory=True + ) estimator.write_coefficients(coefficients_df, segment_settings) estimator.write_choosers(chooser_segment) @@ -271,7 +271,11 @@ def stop_frequency( survey_trips = estimation.manager.get_survey_table(table_name="trips") different = False - survey_trips_not_in_trips = survey_trips[~survey_trips.index.isin(trips.index)] + # need the check below on household_id incase household_sample_size != 0 + survey_trips_not_in_trips = survey_trips[ + ~survey_trips.index.isin(trips.index) + & survey_trips.household_id.isin(trips.household_id) + ] if len(survey_trips_not_in_trips) > 0: print(f"survey_trips_not_in_trips\n{survey_trips_not_in_trips}") different = True @@ -304,10 +308,6 @@ def stop_frequency( trips, label="stop_frequency.trips", slicer="person_id", columns=None ) - state.tracing.trace_df( - annotations, label="stop_frequency.annotations", columns=None - ) - state.tracing.trace_df( tours_merged, label="stop_frequency.tours_merged", @@ -317,3 +317,11 @@ def stop_frequency( if state.is_table("school_escort_trips"): school_escort_tours_trips.merge_school_escort_trips_into_pipeline(state) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/summarize.py b/activitysim/abm/models/summarize.py index e842393fda..fb29fe19d6 100644 --- a/activitysim/abm/models/summarize.py +++ b/activitysim/abm/models/summarize.py @@ -4,11 +4,12 @@ import logging import os +from pathlib import Path import numpy as np import pandas as pd -from activitysim.core import expressions, workflow +from activitysim.core import expressions, timing, workflow from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable from activitysim.core.los import Network_LOS @@ -234,7 +235,7 @@ def summarize( trace_label: str = "summarize", ) -> None: """ - A standard model that uses expression files to summarize pipeline tables for vizualization. + A standard model that uses expression files to summarize pipeline tables for visualization. Summaries are configured in `summarize.yaml`, including specification of the expression file (`summarize.csv` by default). @@ -242,7 +243,7 @@ def summarize( Columns in pipeline tables can also be sliced and aggregated prior to summarization. This preprocessing is configured in `summarize.yaml`. - Outputs a seperate csv summary file for each expression; + Outputs a separate csv summary file for each expression; outputs starting with '_' are saved as temporary local variables. """ @@ -353,6 +354,12 @@ def summarize( } ) + if state.settings.expression_profile: + perf_log_file = Path(trace_label + ".log") + else: + perf_log_file = None + performance_timer = timing.EvalTiming(perf_log_file) + for i, row in spec.iterrows(): out_file = row["Output"] expr = row["Expression"] @@ -361,15 +368,19 @@ def summarize( if out_file.startswith("_"): logger.debug(f"Temp Variable: {expr} -> {out_file}") - locals_d[out_file] = eval(expr, globals(), locals_d) + with performance_timer.time_expression(expr): + locals_d[out_file] = eval(expr, globals(), locals_d) continue logger.debug(f"Summary: {expr} -> {out_file}.csv") - resultset = eval(expr, globals(), locals_d) + with performance_timer.time_expression(expr): + resultset = eval(expr, globals(), locals_d) resultset.to_csv( state.get_output_file_path( os.path.join(output_location, f"{out_file}.csv") ), index=False, ) + + performance_timer.write_log(state) diff --git a/activitysim/abm/models/telecommute_frequency.py b/activitysim/abm/models/telecommute_frequency.py index f98791a2a2..195335258a 100755 --- a/activitysim/abm/models/telecommute_frequency.py +++ b/activitysim/abm/models/telecommute_frequency.py @@ -61,20 +61,15 @@ def telecommute_frequency( constants = config.get_model_constants(model_settings) - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) + # choosers preprocessor + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) coefficients_df = state.filesystem.read_model_coefficients(model_settings) @@ -127,3 +122,11 @@ def telecommute_frequency( if state.settings.trace_hh_id: state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/telecommute_status.py b/activitysim/abm/models/telecommute_status.py new file mode 100644 index 0000000000..7954052591 --- /dev/null +++ b/activitysim/abm/models/telecommute_status.py @@ -0,0 +1,125 @@ +# ActivitySim +# See full license in LICENSE.txt. +from __future__ import annotations + +import logging + +import numpy as np +import pandas as pd + +from activitysim.core import ( + config, + estimation, + expressions, + simulate, + tracing, + workflow, +) +from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable +from activitysim.core.configuration.logit import LogitComponentSettings + +logger = logging.getLogger("activitysim") + + +class TelecommuteStatusSettings(LogitComponentSettings, extra="forbid"): + """ + Settings for the `telecommute_status` component. + """ + + TELECOMMUTE_ALT: int + """Value that specifies if the worker is telecommuting on the simulation day.""" + + CHOOSER_FILTER_COLUMN_NAME: str = "is_worker" + """Column name in the dataframe to represent worker.""" + + +@workflow.step +def telecommute_status( + state: workflow.State, + persons_merged: pd.DataFrame, + persons: pd.DataFrame, + model_settings: TelecommuteStatusSettings | None = None, + model_settings_file_name: str = "telecommute_status.yaml", + trace_label: str = "telecommute_status", +) -> None: + """ + This model predicts whether a person (worker) telecommutes on the simulation day. + The output from this model is TRUE (if telecommutes) or FALSE (if does not telecommute). + """ + if model_settings is None: + model_settings = TelecommuteStatusSettings.read_settings_file( + state.filesystem, + model_settings_file_name, + ) + + choosers = persons_merged + chooser_filter_column_name = model_settings.CHOOSER_FILTER_COLUMN_NAME + choosers = choosers[(choosers[chooser_filter_column_name])] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + estimator = estimation.manager.begin_estimation(state, "telecommute_status") + + constants = config.get_model_constants(model_settings) + + # - preprocessor + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + + model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) + coefficients_df = state.filesystem.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients( + state, model_spec, coefficients_df, estimator + ) + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df, model_settings) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + state, + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + trace_label=trace_label, + trace_choice_name="is_telecommuting", + estimator=estimator, + compute_settings=model_settings.compute_settings, + ) + + telecommute_alt = model_settings.TELECOMMUTE_ALT + choices = choices == telecommute_alt + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, "persons", "is_telecommuting") + estimator.write_override_choices(choices) + estimator.end_estimation() + + persons["is_telecommuting"] = choices.reindex(persons.index).fillna(0).astype(bool) + + state.add_table("persons", persons) + + tracing.print_summary( + "telecommute_status", persons.is_telecommuting, value_counts=True + ) + + if state.settings.trace_hh_id: + state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/tour_mode_choice.py b/activitysim/abm/models/tour_mode_choice.py index befcb0d7a3..d2052f099a 100644 --- a/activitysim/abm/models/tour_mode_choice.py +++ b/activitysim/abm/models/tour_mode_choice.py @@ -7,9 +7,18 @@ import numpy as np import pandas as pd -from activitysim.abm.models.util import annotate, school_escort_tours_trips, trip +from activitysim.abm.models.util import school_escort_tours_trips, trip from activitysim.abm.models.util.mode import run_tour_mode_choice_simulate -from activitysim.core import config, estimation, logit, los, simulate, tracing, workflow +from activitysim.core import ( + config, + estimation, + logit, + los, + simulate, + tracing, + workflow, + expressions, +) from activitysim.core.configuration.logit import TourModeComponentSettings from activitysim.core.util import assign_in_place, reindex @@ -450,10 +459,6 @@ def tour_mode_choice_simulate( state.add_table("tours", all_tours) - # - annotate tours table - if model_settings.annotate_tours: - annotate.annotate_tours(state, model_settings, trace_label) - if state.settings.trace_hh_id: state.tracing.trace_df( primary_tours, @@ -462,3 +467,11 @@ def tour_mode_choice_simulate( index_label="tour_id", warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/tour_od_choice.py b/activitysim/abm/models/tour_od_choice.py index b518b36f97..ad9f527d75 100644 --- a/activitysim/abm/models/tour_od_choice.py +++ b/activitysim/abm/models/tour_od_choice.py @@ -7,7 +7,7 @@ import pandas as pd from activitysim.abm.models.util import tour_od -from activitysim.core import estimation, los, workflow +from activitysim.core import estimation, los, workflow, expressions logger = logging.getLogger(__name__) @@ -147,3 +147,11 @@ def tour_od_choice( columns=None, warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/tour_scheduling_probabilistic.py b/activitysim/abm/models/tour_scheduling_probabilistic.py index 324db45667..8dcb1bbff2 100644 --- a/activitysim/abm/models/tour_scheduling_probabilistic.py +++ b/activitysim/abm/models/tour_scheduling_probabilistic.py @@ -7,7 +7,7 @@ import pandas as pd from activitysim.abm.models.util import probabilistic_scheduling as ps -from activitysim.core import chunk, estimation, workflow +from activitysim.core import chunk, estimation, workflow, expressions from activitysim.core.configuration.base import PydanticReadable logger = logging.getLogger(__name__) @@ -175,3 +175,11 @@ def tour_scheduling_probabilistic( assert not tours_df["duration"].isnull().any() state.add_table("tours", tours_df) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/transit_pass_ownership.py b/activitysim/abm/models/transit_pass_ownership.py index 9a34b7b0b4..19ba883212 100644 --- a/activitysim/abm/models/transit_pass_ownership.py +++ b/activitysim/abm/models/transit_pass_ownership.py @@ -17,6 +17,7 @@ from activitysim.core.configuration.base import PreprocessorSettings from activitysim.core.configuration.logit import LogitComponentSettings + logger = logging.getLogger("activitysim") @@ -25,8 +26,8 @@ class TransitPassOwnershipSettings(LogitComponentSettings, extra="forbid"): Settings for the `transit_pass_ownership` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" + # no additional fields are required for this component + pass @workflow.step @@ -56,19 +57,14 @@ def transit_pass_ownership( constants = config.get_model_constants(model_settings) # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) coefficients_df = state.filesystem.read_model_coefficients(model_settings) @@ -114,3 +110,11 @@ def transit_pass_ownership( if state.settings.trace_hh_id: state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/transit_pass_subsidy.py b/activitysim/abm/models/transit_pass_subsidy.py index 0f71279cdf..328b499929 100644 --- a/activitysim/abm/models/transit_pass_subsidy.py +++ b/activitysim/abm/models/transit_pass_subsidy.py @@ -17,6 +17,7 @@ from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable from activitysim.core.configuration.logit import LogitComponentSettings + logger = logging.getLogger("activitysim") @@ -25,9 +26,6 @@ class TransitPassSubsidySettings(LogitComponentSettings, extra="forbid"): Settings for the `transit_pass_subsidy` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - CHOOSER_FILTER_COLUMN_NAME: str | None = None """Column name which selects choosers. If None, all persons are choosers.""" @@ -56,20 +54,15 @@ def transit_pass_subsidy( constants = config.get_model_constants(model_settings) - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) + # - preprocessor, running before choosers are filtered so column can be created + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) filter_col = model_settings.CHOOSER_FILTER_COLUMN_NAME if filter_col is not None: @@ -122,3 +115,11 @@ def transit_pass_subsidy( if state.settings.trace_hh_id: state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/trip_departure_choice.py b/activitysim/abm/models/trip_departure_choice.py index 65a4468a62..33a6bbf098 100644 --- a/activitysim/abm/models/trip_departure_choice.py +++ b/activitysim/abm/models/trip_departure_choice.py @@ -27,6 +27,7 @@ from activitysim.core.skim_dataset import SkimDataset from activitysim.core.skim_dictionary import SkimDict from activitysim.core.util import reindex +from activitysim.core.exceptions import SegmentedSpecificationError logger = logging.getLogger(__name__) @@ -191,9 +192,21 @@ def choose_tour_leg_pattern( trace_label="trace_label", *, chunk_sizer: chunk.ChunkSizer, - compute_settings: ComputeSettings | None = None, + model_settings: TripDepartureChoiceSettings, ): alternatives = generate_alternatives(trip_segment, STOP_TIME_DURATION).sort_index() + + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=alternatives, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor", + ) + have_trace_targets = state.tracing.has_trace_targets(trip_segment) if have_trace_targets: @@ -207,7 +220,7 @@ def choose_tour_leg_pattern( ) if len(spec.columns) > 1: - raise RuntimeError("spec must have only one column") + raise SegmentedSpecificationError("spec must have only one column") # - join choosers and alts # in vanilla interaction_simulate interaction_df is cross join of choosers and alternatives @@ -245,7 +258,7 @@ def choose_tour_leg_pattern( trace_label, trace_rows, estimator=None, - compute_settings=compute_settings, + compute_settings=model_settings.compute_settings, ) interaction_utilities = pd.concat( @@ -416,7 +429,7 @@ def apply_stage_two_model( trips, chunk_size, trace_label: str, - compute_settings: ComputeSettings | None = None, + model_settings: TripDepartureChoiceSettings, ): if not trips.index.is_monotonic_increasing: trips = trips.sort_index() @@ -487,7 +500,7 @@ def apply_stage_two_model( spec, trace_label=segment_trace_label, chunk_sizer=chunk_sizer, - compute_settings=compute_settings, + model_settings=model_settings, ) choices = pd.merge( @@ -523,6 +536,9 @@ class TripDepartureChoiceSettings(PydanticCompute, extra="forbid"): PREPROCESSOR: PreprocessorSettings | None = None """Setting for the preprocessor.""" + alts_preprocessor: PreprocessorSettings | None = None + """Setting for the alternatives preprocessor.""" + SPECIFICATION: str = "trip_departure_choice.csv" """Filename for the trip departure choice (.csv) file.""" @@ -594,7 +610,7 @@ def trip_departure_choice( trips_merged_df, state.settings.chunk_size, trace_label, - compute_settings=model_settings.compute_settings, + model_settings=model_settings, ) trips_df = trips @@ -604,3 +620,11 @@ def trip_departure_choice( assert trips_df[trips_df["depart"].isnull()].empty state.add_table("trips", trips_df) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/trip_destination.py b/activitysim/abm/models/trip_destination.py index 2b6b5a2ff2..853cfc35e9 100644 --- a/activitysim/abm/models/trip_destination.py +++ b/activitysim/abm/models/trip_destination.py @@ -35,6 +35,7 @@ from activitysim.core.skim_dictionary import DataFrameMatrix from activitysim.core.tracing import print_elapsed_time from activitysim.core.util import assign_in_place, reindex +from activitysim.core.exceptions import InvalidTravelError, DuplicateWorkflowTableError logger = logging.getLogger(__name__) @@ -59,9 +60,6 @@ class TripDestinationSettings(LocationComponentSettings, extra="forbid"): PRIMARY_ORIGIN: str = "origin" PRIMARY_DEST: str = "tour_leg_dest" # must be created in preprocessor REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: list[str] | None = None - preprocessor: PreprocessorSettings | None = None - alts_preprocessor_sample: PreprocessorSettings | None = None - alts_preprocessor_simulate: PreprocessorSettings | None = None CLEANUP: bool fail_some_trips_for_testing: bool = False """This setting is used by testing code to force failed trip_destination.""" @@ -173,15 +171,17 @@ def _destination_sample( ) sample_size = model_settings.SAMPLE_SIZE - if state.settings.disable_destination_sampling or ( - estimator and estimator.want_unsampled_alternatives - ): - # FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count + if estimator and model_settings.ESTIMATION_SAMPLE_SIZE >= 0: + sample_size = model_settings.ESTIMATION_SAMPLE_SIZE logger.info( - f"Estimation mode for {trace_label} using " - f"unsampled alternatives short_circuit_choices" + f"Estimation mode for {trace_label} using sample size of {sample_size}" ) + + if state.settings.disable_destination_sampling: sample_size = 0 + logger.info( + f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set" + ) locals_dict = state.get_global_constants().copy() locals_dict.update(model_settings.CONSTANTS) @@ -203,14 +203,16 @@ def _destination_sample( log_alt_losers = state.settings.log_alt_losers - if model_settings.alts_preprocessor_sample: - expressions.assign_columns( - state, - df=alternatives, - model_settings=model_settings.alts_preprocessor_sample, - locals_dict=locals_dict, - trace_label=tracing.extend_trace_label(trace_label, "alts"), - ) + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=alternatives, + locals_dict=locals_dict, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_sample", + ) choices = interaction_sample( state, @@ -291,6 +293,7 @@ def choose_MAZ_for_TAZ( network_los, alt_dest_col_name, trace_label, + model_settings, ): """ Convert taz_sample table with TAZ zone sample choices to a table with a MAZ zone chosen for each TAZ @@ -530,6 +533,57 @@ def choose_MAZ_for_TAZ( transpose=False, ) + if estimation.manager.enabled and ( + model_settings.ESTIMATION_SAMPLE_SIZE > 0 + or ( + model_settings.ESTIMATION_SAMPLE_SIZE < 0 and model_settings.SAMPLE_SIZE > 0 + ) + ): + # want to ensure the override choice is in the choice set + survey_choices = estimation.manager.get_survey_destination_choices( + state, chooser_df, trace_label + ) + if survey_choices is not None: + assert ( + chooser_df.index == survey_choices.index + ).all(), "survey_choices index should match chooser_df index" + survey_choices.name = DEST_MAZ + survey_choices = survey_choices.dropna().astype(taz_choices[DEST_MAZ].dtype) + # merge maz_sizes onto survey choices + maz_sizes["MAZ_prob"] = maz_sizes.groupby(DEST_TAZ)["size_term"].transform( + lambda x: x / x.sum() + ) + survey_choices = pd.merge( + survey_choices.reset_index(), + maz_sizes[[DEST_MAZ, DEST_TAZ, "MAZ_prob"]], + on=[DEST_MAZ], + how="left", + ) + # merge TAZ_prob from taz_choices onto survey choices + survey_choices = pd.merge( + survey_choices, + # dropping duplicates to avoid duplicate rows as the same TAZ can be chosen multiple times + taz_choices[[chooser_id_col, DEST_TAZ, "TAZ_prob"]].drop_duplicates( + subset=[chooser_id_col, DEST_TAZ] + ), + on=[chooser_id_col, DEST_TAZ], + how="left", + ) + survey_choices["prob"] = ( + survey_choices["TAZ_prob"] * survey_choices["MAZ_prob"] + ) + + # Don't care about getting dest_TAZ correct as it gets dropped later + survey_choices.fillna(0, inplace=True) + + # merge survey choices back into choices_df and sort by chooser + taz_choices = pd.concat( + [taz_choices, survey_choices[taz_choices.columns]], ignore_index=True + ) + taz_choices.sort_values( + by=[chooser_id_col, DEST_TAZ], inplace=True, ignore_index=True + ) + taz_choices = taz_choices.drop(columns=["TAZ_prob", "MAZ_prob"]) taz_choices = taz_choices.groupby([chooser_id_col, DEST_MAZ]).agg( prob=("prob", "max"), pick_count=("prob", "count") @@ -603,6 +657,7 @@ def destination_presample( network_los, alt_dest_col_name, trace_label, + model_settings, ) assert alt_dest_col_name in maz_sample @@ -766,7 +821,7 @@ def compute_logsums( adds od_logsum and dp_logsum columns to trips (in place) """ trace_label = tracing.extend_trace_label(trace_label, "compute_logsums") - logger.info("Running %s with %d samples", trace_label, destination_sample.shape[0]) + logger.debug("Running %s with %d samples", trace_label, destination_sample.shape[0]) # chunk usage is uniform so better to combine chunk_tag = "trip_destination.compute_logsums" @@ -923,7 +978,7 @@ def trip_destination_simulate( alt_dest_col_name = model_settings.ALT_DEST_COL_NAME - logger.info("Running trip_destination_simulate with %d trips", len(trips)) + logger.debug("Running trip_destination_simulate with %d trips", len(trips)) skims = skim_hotel.sample_skims(presample=False) @@ -951,14 +1006,16 @@ def trip_destination_simulate( ) locals_dict.update(skims) - if model_settings.alts_preprocessor_simulate: - expressions.assign_columns( - state, - df=destination_sample, - model_settings=model_settings.alts_preprocessor_simulate, - locals_dict=locals_dict, - trace_label=tracing.extend_trace_label(trace_label, "alts"), - ) + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=destination_sample, + locals_dict=locals_dict, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_simulate", + ) log_alt_losers = state.settings.log_alt_losers destinations = interaction_sample_simulate( @@ -1046,24 +1103,29 @@ def choose_trip_destination( ) trips = trips[~dropped_trips] - t0 = print_elapsed_time("%s.trip_destination_sample" % trace_label, t0) + t0 = print_elapsed_time("%s.trip_destination_sample" % trace_label, t0, debug=True) if trips.empty: return pd.Series(index=trips.index).to_frame("choice"), None # - compute logsums - destination_sample = compute_logsums( - state, - primary_purpose=primary_purpose, - trips=trips, - destination_sample=destination_sample, - tours_merged=tours_merged, - model_settings=model_settings, - skim_hotel=skim_hotel, - trace_label=trace_label, - ) + # If LOGSUM_SETTINGS is set to None, we don't want to compute logsums + if model_settings.LOGSUM_SETTINGS: + destination_sample = compute_logsums( + state, + primary_purpose=primary_purpose, + trips=trips, + destination_sample=destination_sample, + tours_merged=tours_merged, + model_settings=model_settings, + skim_hotel=skim_hotel, + trace_label=trace_label, + ) + else: + destination_sample["od_logsum"] = 0.0 + destination_sample["dp_logsum"] = 0.0 - t0 = print_elapsed_time("%s.compute_logsums" % trace_label, t0) + t0 = print_elapsed_time("%s.compute_logsums" % trace_label, t0, debug=True) destinations = trip_destination_simulate( state, @@ -1094,7 +1156,9 @@ def choose_trip_destination( else: destination_sample = None - t0 = print_elapsed_time("%s.trip_destination_simulate" % trace_label, t0) + t0 = print_elapsed_time( + "%s.trip_destination_simulate" % trace_label, t0, debug=True + ) return destinations, destination_sample @@ -1271,9 +1335,14 @@ def run_trip_destination( state.filesystem, model_settings_file_name ) preprocessor_settings = model_settings.preprocessor - logsum_settings = state.filesystem.read_model_settings( - model_settings.LOGSUM_SETTINGS - ) + + # read in logsum settings if they exist, otherwise logsum calculations are skipped + if model_settings.LOGSUM_SETTINGS: + logsum_settings = state.filesystem.read_model_settings( + model_settings.LOGSUM_SETTINGS + ) + else: + logsum_settings = None logsum_column_name = model_settings.DEST_CHOICE_LOGSUM_COLUMN_NAME want_logsums = logsum_column_name is not None @@ -1312,6 +1381,8 @@ def run_trip_destination( # expect all the same trips survey_trips = estimator.get_survey_table("trips").sort_index() + # need to check household_id incase household_sample_size != 0 + survey_trips = survey_trips[survey_trips.household_id.isin(trips.household_id)] assert survey_trips.index.equals(trips.index) first = survey_trips.trip_num == 1 @@ -1341,7 +1412,9 @@ def run_trip_destination( # - filter tours_merged (AFTER copying destination and origin columns to trips) # tours_merged is used for logsums, we filter it here upfront to save space and time - tours_merged_cols = logsum_settings["TOURS_MERGED_CHOOSER_COLUMNS"] + tours_merged_cols = ( + logsum_settings["TOURS_MERGED_CHOOSER_COLUMNS"] if logsum_settings else [] + ) redundant_cols = model_settings.REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS or [] if redundant_cols: tours_merged_cols = [c for c in tours_merged_cols if c not in redundant_cols] @@ -1383,15 +1456,15 @@ def run_trip_destination( } locals_dict.update(model_settings.CONSTANTS) - # - annotate nth_trips - if preprocessor_settings: - expressions.assign_columns( - state, - df=nth_trips, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=nth_trace_label, - ) + # preprocessing choosers + expressions.annotate_preprocessors( + state, + df=nth_trips, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) if isinstance( nth_trips["trip_period"].dtype, pd.api.types.CategoricalDtype @@ -1414,30 +1487,51 @@ def run_trip_destination( else: None - logger.info("Running %s with %d trips", nth_trace_label, nth_trips.shape[0]) + logger.debug( + "Running %s with %d trips", nth_trace_label, nth_trips.shape[0] + ) # - choose destination for nth_trips, segmented by primary_purpose choices_list = [] for primary_purpose, trips_segment in nth_trips.groupby( "primary_purpose", observed=True ): - choices, destination_sample = choose_trip_destination( - state, - primary_purpose, - trips_segment, - alternatives, - tours_merged, - model_settings, - want_logsums, - want_sample_table, - size_term_matrix, - skim_hotel, - estimator, - chunk_size, - trace_label=tracing.extend_trace_label( - nth_trace_label, primary_purpose - ), - ) + try: + choices, destination_sample = choose_trip_destination( + state, + primary_purpose, + trips_segment, + alternatives, + tours_merged, + model_settings, + want_logsums, + want_sample_table, + size_term_matrix, + skim_hotel, + estimator, + chunk_size, + trace_label=tracing.extend_trace_label( + nth_trace_label, primary_purpose + ), + ) + except KeyError as err: + if err.args[0] == "purpose_index_num": + logger.error( + """ + + When using the trip destination model with sharrow, it is necessary + to set a value for `purpose_index_num` in the trip destination + annotate trips preprocessor. This allows for an optimized compiled + lookup of the size term from the array of size terms. The value of + `purpose_index_num` should be the integer column position in the size + matrix, with usual zero-based numpy indexing semantics (i.e. the first + column is zero). The preprocessor expression most likely needs to be + "size_terms.get_cols(df.purpose)" unless some unusual transform of + size terms has been employed. + + """ + ) + raise choices_list.append(choices) if want_sample_table: @@ -1579,7 +1673,7 @@ def trip_destination( estimator.write_table(state.get_dataframe("land_use"), "landuse", append=False) estimator.write_model_settings(model_settings, model_settings_file_name) - logger.info("Running %s with %d trips", trace_label, trips_df.shape[0]) + logger.debug("Running %s with %d trips", trace_label, trips_df.shape[0]) trips_df, save_sample_df = run_trip_destination( state, @@ -1594,7 +1688,7 @@ def trip_destination( # testing feature t0 make sure at least one trip fails so trip_purpose_and_destination model is run if state.settings.testing_fail_trip_destination and not trips_df.failed.any(): if (trips_df.trip_num < trips_df.trip_count).sum() == 0: - raise RuntimeError( + raise InvalidTravelError( "can't honor 'testing_fail_trip_destination' setting because no intermediate trips" ) @@ -1675,5 +1769,15 @@ def trip_destination( # lest they try to put tour samples into the same table if state.is_table(sample_table_name): - raise RuntimeError("sample table %s already exists" % sample_table_name) + raise DuplicateWorkflowTableError( + "sample table %s already exists" % sample_table_name + ) state.extend_table(sample_table_name, save_sample_df) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/trip_mode_choice.py b/activitysim/abm/models/trip_mode_choice.py index b3dd0e7f4f..a942b7af84 100644 --- a/activitysim/abm/models/trip_mode_choice.py +++ b/activitysim/abm/models/trip_mode_choice.py @@ -8,7 +8,7 @@ import numpy as np import pandas as pd -from activitysim.abm.models.util import annotate, school_escort_tours_trips +from activitysim.abm.models.util import school_escort_tours_trips from activitysim.abm.models.util.mode import mode_choice_simulate from activitysim.core import ( chunk, @@ -32,9 +32,6 @@ class TripModeChoiceSettings(TemplatedLogitComponentSettings, extra="forbid"): Settings for the `trip_mode_choice` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - MODE_CHOICE_LOGSUM_COLUMN_NAME: str = "mode_choice_logsum" """Column name of the mode choice logsum""" @@ -49,8 +46,11 @@ class TripModeChoiceSettings(TemplatedLogitComponentSettings, extra="forbid"): use_TVPB_constants: bool = True FORCE_ESCORTEE_CHAUFFEUR_MODE_MATCH: bool = True - - annotate_trips: PreprocessorSettings | None = None + """ + If True, overwrite the trip mode of escortee trips to match the mode selected + by the chauffeur. This is useful for school escort tours where the escortee trip + mode (e.g., "transit") should match the chauffeur trip mode. + """ LEGACY_COEFFICIENTS: str | None = None @@ -370,15 +370,6 @@ def trip_mode_choice( state.add_table("trips", trips_df) - if model_settings.annotate_trips: - # need to update locals_dict to access skims that are the same .shape as trips table - locals_dict = {} - locals_dict.update(constants) - simulate.set_skim_wrapper_targets(trips_merged, skims) - locals_dict.update(skims) - locals_dict["timeframe"] = "trip" - annotate.annotate_trips(state, model_settings, trace_label, locals_dict) - if state.settings.trace_hh_id: state.tracing.trace_df( trips_df, @@ -387,3 +378,17 @@ def trip_mode_choice( index_label="trip_id", warn_if_empty=True, ) + + # need to update locals_dict to access skims that are the same .shape as trips table + locals_dict = {} + locals_dict.update(constants) + simulate.set_skim_wrapper_targets(trips_merged, skims) + locals_dict.update(skims) + locals_dict["timeframe"] = "trip" + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/trip_purpose.py b/activitysim/abm/models/trip_purpose.py index 5f208f514d..73ccaaf8c0 100644 --- a/activitysim/abm/models/trip_purpose.py +++ b/activitysim/abm/models/trip_purpose.py @@ -22,6 +22,7 @@ ) from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable from activitysim.core.util import reindex +from activitysim.core.exceptions import InvalidTravelError logger = logging.getLogger(__name__) @@ -134,7 +135,7 @@ def choose_intermediate_trip_purpose( state.tracing.write_csv( unmatched_choosers, file_name=file_name, transpose=False ) - raise RuntimeError( + raise InvalidTravelError( "Some trips could not be matched to probs based on join columns %s." % probs_join_cols ) @@ -253,16 +254,14 @@ def run_trip_purpose( trips_df = trips_df[~last_trip] logger.info("assign purpose to %s intermediate trips", trips_df.shape[0]) - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_dict = config.get_model_constants(model_settings) - expressions.assign_columns( - state, - df=trips_df, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) + expressions.annotate_preprocessors( + state, + df=trips_df, + locals_dict=config.get_model_constants(model_settings), + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) use_depart_time = model_settings.use_depart_time @@ -303,6 +302,10 @@ def trip_purpose(state: workflow.State, trips: pd.DataFrame) -> None: """ trace_label = "trip_purpose" + model_settings = TripPurposeSettings.read_settings_file( + state.filesystem, "trip_purpose.yaml" + ) + trips_df = trips if state.is_table("school_escort_trips"): @@ -326,6 +329,7 @@ def trip_purpose(state: workflow.State, trips: pd.DataFrame) -> None: state, trips_df, estimator, + model_settings, trace_label=trace_label, ) @@ -359,3 +363,11 @@ def trip_purpose(state: workflow.State, trips: pd.DataFrame) -> None: index_label="trip_id", warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/trip_purpose_and_destination.py b/activitysim/abm/models/trip_purpose_and_destination.py index 66443e0892..02b8a6cbfd 100644 --- a/activitysim/abm/models/trip_purpose_and_destination.py +++ b/activitysim/abm/models/trip_purpose_and_destination.py @@ -12,7 +12,7 @@ cleanup_failed_trips, flag_failed_trip_leg_mates, ) -from activitysim.core import estimation, tracing, workflow +from activitysim.core import estimation, tracing, workflow, expressions from activitysim.core.configuration.base import PydanticReadable from activitysim.core.util import assign_in_place @@ -261,3 +261,11 @@ def trip_purpose_and_destination( index_label="trip_id", warn_if_empty=True, ) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/trip_scheduling.py b/activitysim/abm/models/trip_scheduling.py index fa69567ec9..0ce4f2ffff 100644 --- a/activitysim/abm/models/trip_scheduling.py +++ b/activitysim/abm/models/trip_scheduling.py @@ -18,6 +18,7 @@ from activitysim.core import chunk, config, estimation, expressions, tracing, workflow from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable from activitysim.core.util import reindex +from activitysim.core.exceptions import InvalidTravelError, PipelineError logger = logging.getLogger(__name__) @@ -224,7 +225,6 @@ def schedule_trips_in_leg( failfix = model_settings.FAILFIX depart_alt_base = model_settings.DEPART_ALT_BASE scheduling_mode = model_settings.scheduling_mode - preprocessor_settings = model_settings.preprocessor probs_join_cols = model_settings.probs_join_cols if probs_join_cols is None: @@ -286,14 +286,14 @@ def schedule_trips_in_leg( nth_trace_label = tracing.extend_trace_label(trace_label, "num_%s" % i) # - annotate trips - if preprocessor_settings: - expressions.assign_columns( - state, - df=trips, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=nth_trace_label, - ) + expressions.annotate_preprocessors( + state, + df=trips, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) if ( outbound @@ -585,9 +585,11 @@ def trip_scheduling( i = 0 while (i < max_iterations) and not trips_chunk.empty: # only chunk log first iteration since memory use declines with each iteration - with chunk.chunk_log( - state, trace_label - ) if i == 0 else chunk.chunk_log_skip(): + with ( + chunk.chunk_log(state, trace_label) + if i == 0 + else chunk.chunk_log_skip() + ): i += 1 is_last_iteration = i == max_iterations @@ -616,7 +618,9 @@ def trip_scheduling( logger.info("%s %s failed", trace_label_i, failed.sum()) if (failed.sum() > 0) & (model_settings.scheduling_mode == "relative"): - raise RuntimeError("failed trips with relative scheduling mode") + raise InvalidTravelError( + "failed trips with relative scheduling mode" + ) if not is_last_iteration: # boolean series of trips whose leg scheduling failed @@ -654,7 +658,7 @@ def trip_scheduling( ) if failfix != FAILFIX_DROP_AND_CLEANUP: - raise RuntimeError( + raise PipelineError( "%s setting '%s' not enabled in settings" % (FAILFIX, FAILFIX_DROP_AND_CLEANUP) ) @@ -682,3 +686,11 @@ def trip_scheduling( assert not trips_df.depart.isnull().any() state.add_table("trips", trips_df) + + expressions.annotate_tables( + state, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/trip_scheduling_choice.py b/activitysim/abm/models/trip_scheduling_choice.py index 5f58e68ee4..81d908ef1b 100644 --- a/activitysim/abm/models/trip_scheduling_choice.py +++ b/activitysim/abm/models/trip_scheduling_choice.py @@ -18,6 +18,7 @@ PreprocessorSettings, PydanticReadable, ) +from activitysim.core.configuration.logit import LogitComponentSettings from activitysim.core.interaction_sample_simulate import _interaction_sample_simulate from activitysim.core.skim_dataset import SkimDataset from activitysim.core.skim_dictionary import SkimDict @@ -81,6 +82,8 @@ def generate_schedule_alternatives(tours): schedules = pd.concat([no_stops, one_way, two_way], sort=True) schedules[SCHEDULE_ID] = np.arange(1, schedules.shape[0] + 1) + # this sort is necessary to keep single process and multiprocess results the same! + schedules.sort_values(by=["tour_id", SCHEDULE_ID], inplace=True) return schedules @@ -207,9 +210,7 @@ def get_spec_for_segment( :return: array of utility equations """ - omnibus_spec = state.filesystem.read_model_spec( - file_name=model_settings.SPECIFICATION - ) + omnibus_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) spec = omnibus_spec[[segment]] @@ -280,6 +281,17 @@ def run_trip_scheduling_choice( choosers = choosers.sort_index() schedules = generate_schedule_alternatives(choosers).sort_index() + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=schedules, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor", + ) + # Assuming we did the max_alt_size calculation correctly, # we should get the same sizes here. assert choosers[NUM_ALTERNATIVES].sum() == schedules.shape[0] @@ -333,19 +345,12 @@ def run_trip_scheduling_choice( return tours -class TripSchedulingChoiceSettings(PydanticReadable, extra="forbid"): +class TripSchedulingChoiceSettings(LogitComponentSettings, extra="forbid"): """ Settings for the `trip_scheduling_choice` component. """ - PREPROCESSOR: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - - SPECIFICATION: str - """file name of specification file""" - - compute_settings: ComputeSettings = ComputeSettings() - """Compute settings for this component.""" + pass @workflow.step @@ -396,34 +401,32 @@ def trip_scheduling_choice( .reindex(tours.index) ) - preprocessor_settings = model_settings.PREPROCESSOR - # hack: preprocessor adds origin column in place if it does not exist already od_skim_stack_wrapper = skim_dict.wrap("origin", "destination") do_skim_stack_wrapper = skim_dict.wrap("destination", "origin") obib_skim_stack_wrapper = skim_dict.wrap(LAST_OB_STOP, FIRST_IB_STOP) - skims = [od_skim_stack_wrapper, do_skim_stack_wrapper, obib_skim_stack_wrapper] - - locals_dict = { + skims = { "od_skims": od_skim_stack_wrapper, "do_skims": do_skim_stack_wrapper, "obib_skims": obib_skim_stack_wrapper, + } + locals_dict = { "orig_col_name": "origin", "dest_col_name": "destination", "timeframe": "timeless_directional", } + locals_dict.update(skims) - if preprocessor_settings: - simulate.set_skim_wrapper_targets(tours_df, skims) - - expressions.assign_columns( - state, - df=tours_df, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) + # preprocess choosers + expressions.annotate_preprocessors( + state, + df=tours_df, + locals_dict=locals_dict, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) tours_df = run_trip_scheduling_choice( state, @@ -436,3 +439,11 @@ def trip_scheduling_choice( ) state.add_table("tours", tours_df) + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/util/annotate.py b/activitysim/abm/models/util/annotate.py deleted file mode 100644 index 5c0f628219..0000000000 --- a/activitysim/abm/models/util/annotate.py +++ /dev/null @@ -1,136 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. -from __future__ import annotations - -import logging - -import pandas as pd - -from activitysim.core import expressions, tracing, workflow -from activitysim.core.configuration import PydanticBase - -""" -Code for annotating tables -""" - -logger = logging.getLogger(__name__) - - -def annotate_households( - state: workflow.State, - model_settings: dict | PydanticBase, - trace_label: str, - locals_dict: dict | None = None, -): - """ - Add columns to the households table in the pipeline according to spec. - - Parameters - ---------- - model_settings : dict - trace_label : str - """ - if isinstance(model_settings, PydanticBase): - model_settings = model_settings.dict() - if locals_dict is None: - locals_dict = {} - households = state.get_dataframe("households") - expressions.assign_columns( - state, - df=households, - model_settings=model_settings.get("annotate_households"), - locals_dict=locals_dict, - trace_label=tracing.extend_trace_label(trace_label, "annotate_households"), - ) - state.add_table("households", households) - - -def annotate_persons( - state: workflow.State, - model_settings: dict | PydanticBase, - trace_label: str, - locals_dict: dict | None = None, -): - """ - Add columns to the persons table in the pipeline according to spec. - - Parameters - ---------- - model_settings : dict - trace_label : str - """ - if isinstance(model_settings, PydanticBase): - model_settings = model_settings.dict() - if locals_dict is None: - locals_dict = {} - persons = state.get_dataframe("persons") - expressions.assign_columns( - state, - df=persons, - model_settings=model_settings.get("annotate_persons"), - locals_dict=locals_dict, - trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"), - ) - state.add_table("persons", persons) - - -def annotate_tours( - state: workflow.State, - model_settings: dict | PydanticBase, - trace_label: str, - locals_dict: dict | None = None, -): - """ - Add columns to the tours table in the pipeline according to spec. - - Parameters - ---------- - state : workflow.State - model_settings : dict or PydanticBase - trace_label : str - locals_dict : dict, optional - """ - if isinstance(model_settings, PydanticBase): - model_settings = model_settings.dict() - if locals_dict is None: - locals_dict = {} - tours = state.get_dataframe("tours") - expressions.assign_columns( - state, - df=tours, - model_settings=model_settings.get("annotate_tours"), - locals_dict=locals_dict, - trace_label=tracing.extend_trace_label(trace_label, "annotate_tours"), - ) - state.add_table("tours", tours) - - -def annotate_trips( - state: workflow.State, - model_settings: dict | PydanticBase, - trace_label: str, - locals_dict=None, -): - """ - Add columns to the trips table in the pipeline according to spec. - - Parameters - ---------- - state : workflow.State - model_settings : dict or PydanticBase - trace_label : str - locals_dict : dict, optional - """ - if isinstance(model_settings, PydanticBase): - model_settings = model_settings.dict() - if locals_dict is None: - locals_dict = {} - trips = state.get_dataframe("trips") - expressions.assign_columns( - state, - df=trips, - model_settings=model_settings.get("annotate_trips"), - locals_dict=locals_dict, - trace_label=tracing.extend_trace_label(trace_label, "annotate_trips"), - ) - state.add_table("trips", trips) diff --git a/activitysim/abm/models/util/canonical_ids.py b/activitysim/abm/models/util/canonical_ids.py index ab26239167..e3246a6233 100644 --- a/activitysim/abm/models/util/canonical_ids.py +++ b/activitysim/abm/models/util/canonical_ids.py @@ -255,7 +255,7 @@ def canonical_tours(state: workflow.State): ) non_mandatory_channels = enumerate_tour_types(non_mandatory_tour_flavors) - logger.info(f"Non-Mandatory tour flavors used are {non_mandatory_tour_flavors}") + logger.debug(f"Non-Mandatory tour flavors used are {non_mandatory_tour_flavors}") # ---- mandatory_channels mtf_model_settings_file_name = "mandatory_tour_frequency.yaml" @@ -271,7 +271,7 @@ def canonical_tours(state: workflow.State): ) mandatory_channels = enumerate_tour_types(mandatory_tour_flavors) - logger.info(f"Mandatory tour flavors used are {mandatory_tour_flavors}") + logger.debug(f"Mandatory tour flavors used are {mandatory_tour_flavors}") # ---- atwork_subtour_channels atwork_model_settings_file_name = "atwork_subtour_frequency.yaml" @@ -288,7 +288,7 @@ def canonical_tours(state: workflow.State): ) atwork_subtour_channels = enumerate_tour_types(atwork_subtour_flavors) - logger.info(f"Atwork subtour flavors used are {atwork_subtour_flavors}") + logger.debug(f"Atwork subtour flavors used are {atwork_subtour_flavors}") # we need to distinguish between subtours of different work tours # (e.g. eat1_1 is eat subtour for parent work tour 1 and eat1_2 is for work tour 2) @@ -317,7 +317,7 @@ def canonical_tours(state: workflow.State): joint_tour_flavors = determine_flavors_from_alts_file( jtf_alts, provided_joint_flavors, default_joint_flavors ) - logger.info(f"Joint tour flavors used are {joint_tour_flavors}") + logger.debug(f"Joint tour flavors used are {joint_tour_flavors}") joint_tour_channels = enumerate_tour_types(joint_tour_flavors) joint_tour_channels = ["j_%s" % c for c in joint_tour_channels] @@ -343,7 +343,7 @@ def canonical_tours(state: workflow.State): school_escort_flavors = {"escort": 2 * num_escortees} school_escort_channels = enumerate_tour_types(school_escort_flavors) school_escort_channels = ["se_%s" % c for c in school_escort_channels] - logger.info(f"School escort tour flavors used are {school_escort_flavors}") + logger.debug(f"School escort tour flavors used are {school_escort_flavors}") sub_channels = sub_channels + school_escort_channels diff --git a/activitysim/abm/models/util/cdap.py b/activitysim/abm/models/util/cdap.py index 71aca621de..48ec7a31a1 100644 --- a/activitysim/abm/models/util/cdap.py +++ b/activitysim/abm/models/util/cdap.py @@ -10,6 +10,7 @@ from activitysim.core import chunk, logit, simulate, tracing, workflow from activitysim.core.configuration.base import ComputeSettings +from activitysim.core.exceptions import ModelConfigurationError logger = logging.getLogger(__name__) @@ -48,7 +49,7 @@ def add_pn(col, pnum): elif isinstance(col, (list, tuple)): return [c if c == _hh_id_ else "%s_p%s" % (c, pnum) for c in col] else: - raise RuntimeError("add_pn col not list or str") + raise TypeError("add_pn col not list or str") def assign_cdap_rank( @@ -270,7 +271,7 @@ def preprocess_interaction_coefficients(interaction_coefficients): "Error in cdap_interaction_coefficients at row %s. Expect only M, N, or H!" % coefficients[~coefficients["activity"].isin(["M", "N", "H"])].index.values ) - raise RuntimeError(msg) + raise ModelConfigurationError(msg) coefficients["cardinality"] = ( coefficients["interaction_ptypes"].astype(str).str.len() @@ -470,8 +471,9 @@ def build_cdap_spec( continue if not (0 <= row.cardinality <= MAX_INTERACTION_CARDINALITY): - raise RuntimeError( - "Bad row cardinality %d for %s" % (row.cardinality, row.slug) + raise ModelConfigurationError( + "Bad row cardinality %d for %s. Try checking that all interaction terms include 3 or fewer person types." + % (row.cardinality, row.slug) ) # for all other interaction rules, we need to generate a row in the spec for each @@ -965,6 +967,7 @@ def household_activity_choices( spec, choosers, trace_label=trace_label, + have_trace_targets=(trace_hh_id in choosers.index), chunk_sizer=chunk_sizer, compute_settings=compute_settings, ) @@ -980,14 +983,15 @@ def household_activity_choices( interaction_coefficients, hhsize, trace_spec=(trace_hh_id in choosers.index), - trace_label=trace_label, + trace_label=tracing.extend_trace_label(trace_label, "joint"), ) joint_tour_utils = simulate.eval_utilities( state, joint_tour_spec, choosers, - trace_label=trace_label, + trace_label=tracing.extend_trace_label(trace_label, "joint"), + have_trace_targets=(trace_hh_id in choosers.index), chunk_sizer=chunk_sizer, compute_settings=compute_settings, ) diff --git a/activitysim/abm/models/util/mode.py b/activitysim/abm/models/util/mode.py index 49776684a3..d82dd09ea8 100644 --- a/activitysim/abm/models/util/mode.py +++ b/activitysim/abm/models/util/mode.py @@ -88,7 +88,8 @@ def mode_choice_simulate( choices[mode_column_name] = choices[mode_column_name].map( dict(list(zip(list(range(len(alts))), alts))) ) - cat_type = pd.api.types.CategoricalDtype([""] + alts.tolist(), ordered=True) + # note: do we want to order the mode categories? + cat_type = pd.api.types.CategoricalDtype([""] + alts.tolist()) choices[mode_column_name] = choices[mode_column_name].astype(cat_type) return choices diff --git a/activitysim/abm/models/util/probabilistic_scheduling.py b/activitysim/abm/models/util/probabilistic_scheduling.py index cdaf64da5a..193a1b703d 100644 --- a/activitysim/abm/models/util/probabilistic_scheduling.py +++ b/activitysim/abm/models/util/probabilistic_scheduling.py @@ -8,6 +8,7 @@ import pandas as pd from activitysim.core import chunk, logit, tracing, workflow +from activitysim.core.exceptions import InvalidTravelError logger = logging.getLogger(__name__) @@ -210,7 +211,7 @@ def _postprocess_scheduling_choices( if scheduling_mode == "relative": if failed.any(): - RuntimeError( + InvalidTravelError( f"Failed trips in realtive mode for {failed.sum()} trips: {choosers[failed]}" ) diff --git a/activitysim/abm/models/util/school_escort_tours_trips.py b/activitysim/abm/models/util/school_escort_tours_trips.py index 4a9f2ef170..665844023f 100644 --- a/activitysim/abm/models/util/school_escort_tours_trips.py +++ b/activitysim/abm/models/util/school_escort_tours_trips.py @@ -767,7 +767,9 @@ def merge_school_escort_trips_into_pipeline(state: workflow.State): if isinstance(school_escort_trips[c].dtype, pd.api.types.CategoricalDtype): from pandas.api.types import union_categoricals - uc = union_categoricals([trips[c], school_escort_trips[c]]) + uc = union_categoricals( + [trips[c], school_escort_trips[c]], sort_categories=True + ) trips[c] = pd.Categorical(trips[c], categories=uc.categories) school_escort_trips[c] = pd.Categorical( school_escort_trips[c], categories=uc.categories diff --git a/activitysim/abm/models/util/tour_destination.py b/activitysim/abm/models/util/tour_destination.py index 0891b8d216..d99803bd7d 100644 --- a/activitysim/abm/models/util/tour_destination.py +++ b/activitysim/abm/models/util/tour_destination.py @@ -9,7 +9,15 @@ from activitysim.abm.models.util import logsums as logsum from activitysim.abm.tables.size_terms import tour_destination_size_terms -from activitysim.core import config, los, simulate, tracing, workflow +from activitysim.core import ( + config, + estimation, + los, + simulate, + tracing, + workflow, + expressions, +) from activitysim.core.configuration.logit import TourLocationComponentSettings from activitysim.core.interaction_sample import interaction_sample from activitysim.core.interaction_sample_simulate import interaction_sample_simulate @@ -84,18 +92,20 @@ def _destination_sample( coefficients_file_name=model_settings.COEFFICIENTS, ) - logger.info("running %s with %d tours", trace_label, len(choosers)) + logger.debug("running %s with %d tours", trace_label, len(choosers)) sample_size = model_settings.SAMPLE_SIZE - if state.settings.disable_destination_sampling or ( - estimator and estimator.want_unsampled_alternatives - ): - # FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count - logger.info( - "Estimation mode for %s using unsampled alternatives short_circuit_choices" - % (trace_label,) + if estimator and model_settings.ESTIMATION_SAMPLE_SIZE >= 0: + sample_size = model_settings.ESTIMATION_SAMPLE_SIZE + logger.debug( + f"Estimation mode for {trace_label} using sample size of {sample_size}" ) + + if state.settings.disable_destination_sampling: sample_size = 0 + logger.debug( + f"SAMPLE_SIZE set to 0 for {trace_label} because disable_destination_sampling is set" + ) locals_d = { "skims": skims, @@ -109,6 +119,27 @@ def _destination_sample( log_alt_losers = state.settings.log_alt_losers + # preprocess choosers table + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_d, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) + + # preprocess alternatives table + expressions.annotate_preprocessors( + state, + df=destination_size_terms, + locals_dict=locals_d, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_sample", + ) + choices = interaction_sample( state, choosers, @@ -232,7 +263,9 @@ def aggregate_size_terms(dest_size_terms, network_los): return MAZ_size_terms, TAZ_size_terms -def choose_MAZ_for_TAZ(state: workflow.State, taz_sample, MAZ_size_terms, trace_label): +def choose_MAZ_for_TAZ( + state: workflow.State, taz_sample, MAZ_size_terms, trace_label, model_settings +): """ Convert taz_sample table with TAZ zone sample choices to a table with a MAZ zone chosen for each TAZ choose MAZ probabilistically (proportionally by size_term) from set of MAZ zones in parent TAZ @@ -308,7 +341,7 @@ def choose_MAZ_for_TAZ(state: workflow.State, taz_sample, MAZ_size_terms, trace_ # taz_choices index values should be contiguous assert ( - taz_choices[chooser_id_col] == np.repeat(chooser_df.index, taz_sample_size) + (taz_choices[chooser_id_col] == np.repeat(chooser_df.index, taz_sample_size)) ).all() # we need to choose a MAZ for each DEST_TAZ choice @@ -453,6 +486,59 @@ def choose_MAZ_for_TAZ(state: workflow.State, taz_sample, MAZ_size_terms, trace_ transpose=False, ) + if estimation.manager.enabled and ( + model_settings.ESTIMATION_SAMPLE_SIZE > 0 + or ( + model_settings.ESTIMATION_SAMPLE_SIZE < 0 and model_settings.SAMPLE_SIZE > 0 + ) + ): + # want to ensure the override choice is in the choice set + survey_choices = estimation.manager.get_survey_destination_choices( + state, chooser_df, trace_label + ) + + if survey_choices is not None: + assert ( + chooser_df.index == survey_choices.index + ).all(), "survey_choices index should match chooser_df index" + survey_choices.name = DEST_MAZ + survey_choices = survey_choices.dropna().astype(taz_choices[DEST_MAZ].dtype) + # merge maz_sizes onto survey choices + MAZ_size_terms["MAZ_prob"] = MAZ_size_terms.groupby("dest_TAZ")[ + "size_term" + ].transform(lambda x: x / x.sum()) + survey_choices = pd.merge( + survey_choices.reset_index(), + MAZ_size_terms.rename(columns={"zone_id": DEST_MAZ}), + on=[DEST_MAZ], + how="left", + ) + # merge TAZ_prob from taz_choices onto survey choices + survey_choices = pd.merge( + survey_choices, + # dropping duplicates to avoid duplicate rows as the same TAZ can be chosen multiple times + taz_choices[[chooser_id_col, "dest_TAZ", "TAZ_prob"]].drop_duplicates( + subset=[chooser_id_col, "dest_TAZ"] + ), + on=[chooser_id_col, "dest_TAZ"], + how="left", + ) + + survey_choices["prob"] = ( + survey_choices["TAZ_prob"] * survey_choices["MAZ_prob"] + ) + + # Don't care about getting dest_TAZ correct as it gets dropped later + survey_choices.fillna(0, inplace=True) + + # merge survey choices back into choices_df and sort by chooser + taz_choices = pd.concat( + [taz_choices, survey_choices[taz_choices.columns]], ignore_index=True + ) + taz_choices.sort_values( + by=[chooser_id_col, "dest_TAZ"], inplace=True, ignore_index=True + ) + taz_choices = taz_choices.drop(columns=["TAZ_prob", "MAZ_prob"]) taz_choices = taz_choices.groupby([chooser_id_col, DEST_MAZ]).agg( prob=("prob", "max"), pick_count=("prob", "count") @@ -476,7 +562,7 @@ def destination_presample( trace_label = tracing.extend_trace_label(trace_label, "presample") chunk_tag = "tour_destination.presample" - logger.info(f"{trace_label} location_presample") + logger.debug(f"{trace_label} location_presample") alt_dest_col_name = model_settings.ALT_DEST_COL_NAME assert DEST_TAZ != alt_dest_col_name @@ -511,7 +597,9 @@ def destination_presample( ) # choose a MAZ for each DEST_TAZ choice, choice probability based on MAZ size_term fraction of TAZ total - maz_choices = choose_MAZ_for_TAZ(state, taz_sample, MAZ_size_terms, trace_label) + maz_choices = choose_MAZ_for_TAZ( + state, taz_sample, MAZ_size_terms, trace_label, model_settings + ) assert DEST_MAZ in maz_choices maz_choices = maz_choices.rename(columns={DEST_MAZ: alt_dest_col_name}) @@ -564,7 +652,7 @@ def run_destination_sample( ) if pre_sample_taz: - logger.info( + logger.debug( "Running %s destination_presample with %d tours" % (trace_label, len(tours)) ) @@ -652,7 +740,7 @@ def run_destination_logsums( how="left", ) - logger.info("Running %s with %s rows", trace_label, len(choosers)) + logger.debug("Running %s with %s rows", trace_label, len(choosers)) state.tracing.dump_df(DUMP, persons_merged, trace_label, "persons_merged") state.tracing.dump_df(DUMP, choosers, trace_label, "choosers") @@ -744,7 +832,7 @@ def run_destination_simulate( constants = model_settings.CONSTANTS - logger.info("Running tour_destination_simulate with %d persons", len(choosers)) + logger.debug("Running tour_destination_simulate with %d persons", len(choosers)) # create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers # and a zone_id in the alternatives which get merged during interaction @@ -761,6 +849,27 @@ def run_destination_simulate( if constants is not None: locals_d.update(constants) + # preprocess choosers table + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_d, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) + + # preprocess alternatives table + expressions.annotate_preprocessors( + state, + df=destination_sample, + locals_dict=locals_d, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_simulate", + ) + state.tracing.dump_df(DUMP, choosers, trace_label, "choosers") log_alt_losers = state.settings.log_alt_losers @@ -852,17 +961,21 @@ def run_tour_destination( ) # - destination_logsums - tour_purpose = segment_name # tour_purpose is segment_name - location_sample_df = run_destination_logsums( - state, - tour_purpose, - persons_merged, - location_sample_df, - model_settings, - network_los, - chunk_size=state.settings.chunk_size, - trace_label=tracing.extend_trace_label(segment_trace_label, "logsums"), - ) + # if LOGSUM_SETTINGS is set to 'None', we skip this step + if model_settings.LOGSUM_SETTINGS: + tour_purpose = segment_name # tour_purpose is segment_name + location_sample_df = run_destination_logsums( + state, + tour_purpose, + persons_merged, + location_sample_df, + model_settings, + network_los, + chunk_size=state.settings.chunk_size, + trace_label=tracing.extend_trace_label(segment_trace_label, "logsums"), + ) + else: + location_sample_df["mode_choice_logsum"] = 0 # - destination_simulate spec_segment_name = segment_name # spec_segment_name is segment_name diff --git a/activitysim/abm/models/util/tour_frequency.py b/activitysim/abm/models/util/tour_frequency.py index 93c624b135..3c64f4bcc2 100644 --- a/activitysim/abm/models/util/tour_frequency.py +++ b/activitysim/abm/models/util/tour_frequency.py @@ -643,8 +643,7 @@ class JointTourFreqCompSettings(LogitComponentSettings, extra="forbid"): """ ALTS_TABLE_STRUCTURE: JointTourFreqCompAlts = JointTourFreqCompAlts() - preprocessor: PreprocessorSettings | None = None - ALTS_PREPROCESSOR: PreprocessorSettings | None = None + ALTS_PREPROCESSOR: PreprocessorSettings | list[PreprocessorSettings] | None = None def create_joint_tours( diff --git a/activitysim/abm/models/util/tour_od.py b/activitysim/abm/models/util/tour_od.py index 7c615142f4..5ec9dd493f 100644 --- a/activitysim/abm/models/util/tour_od.py +++ b/activitysim/abm/models/util/tour_od.py @@ -47,7 +47,6 @@ class TourODSettings(TourLocationComponentSettings): ORIGIN_ATTR_COLS_TO_USE: list[str] = [] ORIG_COL_NAME: str ORIG_FILTER: str | None = None - preprocessor: PreprocessorSettings | None = None def get_od_id_col(origin_col, destination_col): @@ -156,7 +155,7 @@ def _od_sample( else: alt_col_name = alt_od_col_name - logger.info("running %s with %d tours", trace_label, len(choosers)) + logger.debug("running %s with %d tours", trace_label, len(choosers)) sample_size = model_settings.SAMPLE_SIZE if state.settings.disable_destination_sampling or ( @@ -164,7 +163,7 @@ def _od_sample( ): # FIXME interaction_sample will return unsampled complete alternatives # with probs and pick_count - logger.info( + logger.debug( ( "Estimation mode for %s using unsampled alternatives " "short_circuit_choices" @@ -203,6 +202,17 @@ def _od_sample( elif skims.orig_key not in od_alts_df: logger.error("Alts df is missing origin skim key column.") + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=od_alts_df, + locals_dict=locals_d, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_sample", + ) + choices = interaction_sample( state, choosers, @@ -597,7 +607,7 @@ def od_presample( trace_label = tracing.extend_trace_label(trace_label, "presample") chunk_tag = "tour_od.presample" - logger.info(f"{trace_label} od_presample") + logger.debug(f"{trace_label} od_presample") alt_od_col_name = get_od_id_col(ORIG_MAZ, DEST_TAZ) @@ -658,54 +668,6 @@ def od_presample( return maz_choices -# class SizeTermCalculatorOD: # class SizeTermCalculator -# """ -# convenience object to provide size_terms for a selector (e.g. -# non_mandatory) for various segments (e.g. tour_type or purpose) -# returns size terms for specified segment in df or series form. -# """ -# -# def __init__(self, size_term_selector): -# # do this once so they can request size_terms for various segments (tour_type or purpose) -# land_use = state.checkpoint.load_dataframe("land_use") -# self.land_use = land_use -# size_terms = state.get_injectable("size_terms") -# self.destination_size_terms = tour_destination_size_terms( -# self.land_use, size_terms, size_term_selector -# ) -# -# assert not self.destination_size_terms.isna().any(axis=None) -# -# def omnibus_size_terms_df(self): -# return self.destination_size_terms -# -# def dest_size_terms_df(self, segment_name, trace_label): -# # return size terms as df with one column named 'size_term' -# # convenient if creating or merging with alts -# -# size_terms = self.destination_size_terms[[segment_name]].copy() -# size_terms.columns = ["size_term"] -# -# # FIXME - no point in considering impossible alternatives (where dest size term is zero) -# logger.debug( -# f"SizeTermCalculator dropping {(~(size_terms.size_term > 0)).sum()} " -# f"of {len(size_terms)} rows where size_term is zero for {segment_name}" -# ) -# size_terms = size_terms[size_terms.size_term > 0] -# -# if len(size_terms) == 0: -# logger.warning( -# f"SizeTermCalculator: no zones with non-zero size terms for {segment_name} in {trace_label}" -# ) -# -# return size_terms -# -# def dest_size_terms_series(self, segment_name): -# # return size terms as as series -# # convenient (and no copy overhead) if reindexing and assigning into alts column -# return self.destination_size_terms[segment_name] - - def run_od_sample( state, spec_segment_name, @@ -749,7 +711,7 @@ def run_od_sample( ) if pre_sample_taz: - logger.info( + logger.debug( "Running %s destination_presample with %d tours" % (trace_label, len(tours)) ) @@ -818,7 +780,7 @@ def run_od_logsums( choosers[origin_id_col].astype(str) + "_" + choosers[dest_id_col].astype(str) ) - logger.info("Running %s with %s rows", trace_label, len(choosers)) + logger.debug("Running %s with %s rows", trace_label, len(choosers)) state.tracing.dump_df(DUMP, choosers, trace_label, "choosers") @@ -1027,7 +989,7 @@ def run_od_simulate( constants = model_settings.CONSTANTS - logger.info("Running tour_destination_simulate with %d persons", len(choosers)) + logger.debug("Running tour_destination_simulate with %d persons", len(choosers)) # create wrapper with keys for this lookup - in this case there is an origin ID # column and a destination ID columns in the alternatives table. @@ -1044,6 +1006,17 @@ def run_od_simulate( if constants is not None: locals_d.update(constants) + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=od_sample, + locals_dict=locals_d, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor_simulate", + ) + state.tracing.dump_df(DUMP, choosers, trace_label, "choosers") choices = interaction_sample_simulate( state, @@ -1085,7 +1058,6 @@ def run_tour_od( trace_label, ): size_term_calculator = SizeTermCalculator(state, model_settings.SIZE_TERM_SELECTOR) - preprocessor_settings = model_settings.preprocessor origin_col_name = model_settings.ORIG_COL_NAME chooser_segment_column = model_settings.CHOOSER_SEGMENT_COLUMN_NAME @@ -1108,15 +1080,15 @@ def run_tour_od( right_index=True, ) - # - annotate choosers - if preprocessor_settings: - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - trace_label=trace_label, - ) - + # preprocessing choosers + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict={}, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) # size_term segment is segment_name segment_destination_size_terms = size_term_calculator.dest_size_terms_df( segment_name, trace_label @@ -1160,20 +1132,24 @@ def run_tour_od( ) # - destination_logsums - od_sample_df = run_od_logsums( - state, - spec_segment_name, - choosers, - od_sample_df, - model_settings, - network_los, - estimator, - chunk_size=chunk_size, - trace_hh_id=trace_hh_id, - trace_label=tracing.extend_trace_label( - trace_label, f"logsums.{segment_name}" - ), - ) + # Skip logsum calculation step if LOGSUM_SETTINGS is None + if model_settings.LOGSUM_SETTINGS: + od_sample_df = run_od_logsums( + state, + spec_segment_name, + choosers, + od_sample_df, + model_settings, + network_los, + estimator, + chunk_size=chunk_size, + trace_hh_id=trace_hh_id, + trace_label=tracing.extend_trace_label( + trace_label, f"logsums.{segment_name}" + ), + ) + else: + od_sample_df["tour_mode_choice_logsum"] = 0.0 # - od_simulate choices = run_od_simulate( diff --git a/activitysim/abm/models/util/tour_scheduling.py b/activitysim/abm/models/util/tour_scheduling.py index db003786f4..80474db596 100644 --- a/activitysim/abm/models/util/tour_scheduling.py +++ b/activitysim/abm/models/util/tour_scheduling.py @@ -16,20 +16,13 @@ def run_tour_scheduling( state: workflow.State, - model_name: str, + model_settings: TourSchedulingSettings, chooser_tours: pd.DataFrame, persons_merged: pd.DataFrame, tdd_alts: pd.DataFrame, tour_segment_col: str, + trace_label: str, ): - trace_label = model_name - model_settings_file_name = f"{model_name}.yaml" - - model_settings = TourSchedulingSettings.read_settings_file( - state.filesystem, - model_settings_file_name, - mandatory=False, - ) if model_settings.LOGSUM_SETTINGS: logsum_settings = TourModeComponentSettings.read_settings_file( @@ -52,18 +45,19 @@ def run_tour_scheduling( timetable = state.get_injectable("timetable") # - run preprocessor to annotate choosers - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {"tt": timetable.attach_state(state)} - locals_d.update(config.get_model_constants(model_settings)) - - expressions.assign_columns( - state, - df=chooser_tours, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) + locals_d = {"tt": timetable.attach_state(state)} + locals_d.update(config.get_model_constants(model_settings)) + + # preprocess choosers + expressions.annotate_preprocessors( + state, + df=chooser_tours, + locals_dict=locals_d, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) + # alts preprocessed in vectorize_tour_scheduling estimators = {} if model_settings.TOUR_SPEC_SEGMENTS: @@ -72,7 +66,7 @@ def run_tour_scheduling( specs = {} compute_settings = {} for spec_segment_name, spec_settings in spec_segment_settings.items(): - bundle_name = f"{model_name}_{spec_segment_name}" + bundle_name = f"{trace_label}_{spec_segment_name}" # estimator for this tour_segment estimator = estimation.manager.begin_estimation( @@ -91,7 +85,7 @@ def run_tour_scheduling( if estimator: estimators[spec_segment_name] = estimator # add to local list - estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_model_settings(model_settings, f"{trace_label}.yaml") estimator.write_spec(spec_settings) estimator.write_coefficients(coefficients_df, spec_settings) @@ -121,7 +115,7 @@ def run_tour_scheduling( assert not model_settings.TOUR_SPEC_SEGMENTS assert tour_segment_col is None - estimator = estimation.manager.begin_estimation(state, model_name) + estimator = estimation.manager.begin_estimation(state, trace_label) spec_file_name = model_settings.SPEC model_spec = state.filesystem.read_model_spec(file_name=spec_file_name) @@ -132,7 +126,7 @@ def run_tour_scheduling( if estimator: estimators[None] = estimator # add to local list - estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_model_settings(model_settings, f"{trace_label}.yaml") estimator.write_spec(model_settings) estimator.write_coefficients(coefficients_df, model_settings) @@ -146,7 +140,7 @@ def run_tour_scheduling( if estimators: timetable.begin_transaction(list(estimators.values())) - logger.info(f"Running {model_name} with %d tours", len(chooser_tours)) + logger.debug(f"Running {trace_label} with %d tours", len(chooser_tours)) choices = vts.vectorize_tour_scheduling( state, chooser_tours, diff --git a/activitysim/abm/models/util/vectorize_tour_scheduling.py b/activitysim/abm/models/util/vectorize_tour_scheduling.py index dfab8171de..c199ef40da 100644 --- a/activitysim/abm/models/util/vectorize_tour_scheduling.py +++ b/activitysim/abm/models/util/vectorize_tour_scheduling.py @@ -43,8 +43,6 @@ class TourSchedulingSettings(LogitComponentSettings, extra="forbid"): give the segements. """ SIMULATE_CHOOSER_COLUMNS: list[str] | None = None - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" SPEC_SEGMENTS: dict[str, LogitComponentSettings] = {} @@ -84,7 +82,7 @@ def skims_for_logsums( elif isinstance(destination_for_tour_purpose, dict): dest_col_name = destination_for_tour_purpose.get(tour_purpose) else: - raise RuntimeError( + raise TypeError( f"expected string or dict DESTINATION_FOR_TOUR_PURPOSE model_setting for {tour_purpose}" ) @@ -163,7 +161,7 @@ def _compute_logsums( mandatory=False, ) choosers = alt_tdd.join(tours_merged, how="left", rsuffix="_chooser") - logger.info( + logger.debug( f"{trace_label} compute_logsums for {choosers.shape[0]} choosers {alt_tdd.shape[0]} alts" ) @@ -196,7 +194,7 @@ def _compute_logsums( if preprocessor_settings: simulate.set_skim_wrapper_targets(choosers, skims) - logger.info( + logger.debug( f"{trace_label} start preprocessing prior to compute_logsums for {choosers.shape[0]} choosers {alt_tdd.shape[0]} alts" ) expressions.assign_columns( @@ -206,7 +204,7 @@ def _compute_logsums( locals_dict=locals_dict, trace_label=trace_label, ) - logger.info( + logger.debug( f"{trace_label} end preprocessing prior to compute_logsums for {choosers.shape[0]} choosers {alt_tdd.shape[0]} alts" ) @@ -428,7 +426,7 @@ def compute_tour_scheduling_logsums( ) chunk_sizer.log_df(trace_label, "deduped_alt_tdds", deduped_alt_tdds) - logger.info( + logger.debug( f"{trace_label} compute_logsums " f"deduped_alt_tdds reduced number of rows by " f"{round(100 * (len(alt_tdd) - len(deduped_alt_tdds)) / len(alt_tdd), 2)}% " @@ -760,7 +758,7 @@ def _schedule_tours( """ - logger.info( + logger.debug( "%s schedule_tours running %d tour choices" % (tour_trace_label, len(tours)) ) @@ -910,7 +908,7 @@ def schedule_tours( logger.info("schedule_tours %s tours not monotonic_increasing - sorting df") tours = tours.sort_index() - logger.info( + logger.debug( "%s schedule_tours running %d tour choices" % (tour_trace_label, len(tours)) ) diff --git a/activitysim/abm/models/vehicle_allocation.py b/activitysim/abm/models/vehicle_allocation.py index 8dfb35bfd4..a3f04037c0 100644 --- a/activitysim/abm/models/vehicle_allocation.py +++ b/activitysim/abm/models/vehicle_allocation.py @@ -87,13 +87,10 @@ def get_skim_dict(network_los: los.Network_LOS, choosers: pd.DataFrame): class VehicleAllocationSettings(LogitComponentSettings, extra="forbid"): """ - Settings for the `joint_tour_scheduling` component. + Settings for the `vehicle_allocation` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - - OCCUPANCY_LEVELS: list = [1] # TODO Check this + OCCUPANCY_LEVELS: list = [1] # TODO check this """Occupancy level It will create columns in the tour table selecting a vehicle for each of the @@ -101,9 +98,6 @@ class VehicleAllocationSettings(LogitComponentSettings, extra="forbid"): if not supplied, will default to only one occupancy level of 1 """ - annotate_tours: PreprocessorSettings | None = None - """Preprocessor settings to annotate tours""" - @workflow.step def vehicle_allocation( @@ -212,15 +206,14 @@ def vehicle_allocation( locals_dict.update(skims) # ------ preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_dict, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) logger.info("Running %s with %d tours", trace_label, len(choosers)) @@ -286,9 +279,13 @@ def vehicle_allocation( "vehicle_allocation", tours[tours_veh_occup_cols], value_counts=True ) - annotate_settings = model_settings.annotate_tours - if annotate_settings: - annotate_vehicle_allocation(state, model_settings, trace_label) - if state.settings.trace_hh_id: state.tracing.trace_df(tours, label="vehicle_allocation", warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=skims, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/vehicle_type_choice.py b/activitysim/abm/models/vehicle_type_choice.py index 93caae0381..5347b5bb79 100644 --- a/activitysim/abm/models/vehicle_type_choice.py +++ b/activitysim/abm/models/vehicle_type_choice.py @@ -407,15 +407,16 @@ def iterate_vehicle_type_choice( ) # alts preprocessor - alts_preprocessor_settings = model_settings.alts_preprocessor - if alts_preprocessor_settings: - expressions.assign_columns( - state, - df=alts_wide, - model_settings=alts_preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) + # preprocessing alternatives + expressions.annotate_preprocessors( + state, + df=alts_wide, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + preprocessor_setting_name="alts_preprocessor", + ) # - preparing choosers for iterating vehicles_merged["already_owned_veh"] = "" @@ -434,15 +435,15 @@ def iterate_vehicle_type_choice( # running preprocessor on entire vehicle table to enumerate vehicle types # already owned by the household choosers = vehicles_merged - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_dict, - trace_label=trace_label, - ) + # preprocessing choosers + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) # only make choices for vehicles that have not been selected yet choosers = choosers[choosers["vehicle_num"] == veh_num] @@ -564,7 +565,6 @@ class VehicleTypeChoiceSettings(LogitComponentSettings, extra="forbid"): VEHICLE_TYPE_DATA_FILE: str | None = None PROBS_SPEC: str | None = None combinatorial_alts: dict | None = None - preprocessor: PreprocessorSettings | None = None alts_preprocessor: PreprocessorSettings | None = None SIMULATION_TYPE: Literal[ "simple_simulate", "interaction_simulate" @@ -576,10 +576,6 @@ class VehicleTypeChoiceSettings(LogitComponentSettings, extra="forbid"): COLS_TO_INCLUDE_IN_ALTS_TABLE: list[str] = [] """Columns to include in the alternatives table for use in utility calculations.""" - annotate_households: PreprocessorSettings | None = None - annotate_persons: PreprocessorSettings | None = None - annotate_vehicles: PreprocessorSettings | None = None - REQUIRE_DATA_FOR_ALL_ALTS: bool = False WRITE_OUT_ALTS_FILE: bool = False @@ -708,14 +704,6 @@ def vehicle_type_choice( vehicles = pd.concat([vehicles, choices], axis=1) state.add_table("vehicles", vehicles) - # - annotate tables - if model_settings.annotate_households: - annotate_vehicle_type_choice_households(state, model_settings, trace_label) - if model_settings.annotate_persons: - annotate_vehicle_type_choice_persons(state, model_settings, trace_label) - if model_settings.annotate_vehicles: - annotate_vehicle_type_choice_vehicles(state, model_settings, trace_label) - tracing.print_summary( "vehicle_type_choice", vehicles.vehicle_type, value_counts=True ) @@ -724,3 +712,11 @@ def vehicle_type_choice( state.tracing.trace_df( vehicles, label="vehicle_type_choice", warn_if_empty=True ) + + expressions.annotate_tables( + state, + locals_dict=locals_dict, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/models/work_from_home.py b/activitysim/abm/models/work_from_home.py index 8b96dafa13..6b8f8d7815 100755 --- a/activitysim/abm/models/work_from_home.py +++ b/activitysim/abm/models/work_from_home.py @@ -18,6 +18,7 @@ from activitysim.core.configuration.base import PreprocessorSettings, PydanticReadable from activitysim.core.configuration.logit import LogitComponentSettings + logger = logging.getLogger("activitysim") @@ -26,9 +27,6 @@ class WorkFromHomeSettings(LogitComponentSettings, extra="forbid"): Settings for the `work_from_home` component. """ - preprocessor: PreprocessorSettings | None = None - """Setting for the preprocessor.""" - WORK_FROM_HOME_ALT: int """Value that specify if the person is working from home""" # TODO @@ -88,20 +86,14 @@ def work_from_home( constants = config.get_model_constants(model_settings) work_from_home_alt = model_settings.WORK_FROM_HOME_ALT - # - preprocessor - preprocessor_settings = model_settings.preprocessor - if preprocessor_settings: - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - state, - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label, - ) + expressions.annotate_preprocessors( + state, + df=choosers, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) model_spec = state.filesystem.read_model_spec(file_name=model_settings.SPEC) coefficients_df = state.filesystem.read_model_coefficients(model_settings) @@ -221,3 +213,11 @@ def work_from_home( if state.settings.trace_hh_id: state.tracing.trace_df(persons, label=trace_label, warn_if_empty=True) + + expressions.annotate_tables( + state, + locals_dict=constants, + skims=None, + model_settings=model_settings, + trace_label=trace_label, + ) diff --git a/activitysim/abm/tables/households.py b/activitysim/abm/tables/households.py index 9f121e7082..c0c33dcbcf 100644 --- a/activitysim/abm/tables/households.py +++ b/activitysim/abm/tables/households.py @@ -11,6 +11,7 @@ from activitysim.abm.tables.util import simple_table_join from activitysim.core import tracing, workflow from activitysim.core.input import read_input_table +from activitysim.core.exceptions import MissingInputTableDefinition logger = logging.getLogger(__name__) @@ -45,7 +46,7 @@ def households(state: workflow.State) -> pd.DataFrame: ) if df.shape[0] == 0: - raise RuntimeError("No override households found in store") + raise MissingInputTableDefinition("No override households found in store") # if we are tracing hh exclusively elif _trace_hh_id and households_sample_size == 1: diff --git a/activitysim/abm/tables/persons.py b/activitysim/abm/tables/persons.py index d5ab67fb57..2c7716ddcc 100644 --- a/activitysim/abm/tables/persons.py +++ b/activitysim/abm/tables/persons.py @@ -9,6 +9,7 @@ from activitysim.abm.tables.util import simple_table_join from activitysim.core import workflow +from activitysim.core.exceptions import InputTableError from activitysim.core.input import read_input_table logger = logging.getLogger(__name__) @@ -55,7 +56,7 @@ def persons(state: workflow.State) -> pd.DataFrame: f"{persons_without_households.sum()} persons out of {len(df)} without households\n" f"{pd.Series({'person_id': persons_without_households.index.values})}" ) - raise RuntimeError( + raise InputTableError( f"{persons_without_households.sum()} persons with bad household_id" ) @@ -67,7 +68,7 @@ def persons(state: workflow.State) -> pd.DataFrame: f"{households_without_persons.sum()} households out of {len(households.index)} without persons\n" f"{pd.Series({'household_id': households_without_persons.index.values})}" ) - raise RuntimeError( + raise InputTableError( f"{households_without_persons.sum()} households with no persons" ) @@ -107,5 +108,5 @@ def persons_merged( left_on="person_id", ) if n_persons != len(persons): - raise RuntimeError("number of persons changed") + raise InputTableError("number of persons changed") return persons diff --git a/activitysim/abm/tables/shadow_pricing.py b/activitysim/abm/tables/shadow_pricing.py index 586924efc9..fa2832181c 100644 --- a/activitysim/abm/tables/shadow_pricing.py +++ b/activitysim/abm/tables/shadow_pricing.py @@ -18,6 +18,7 @@ from activitysim.core.configuration import PydanticReadable from activitysim.core.configuration.logit import TourLocationComponentSettings from activitysim.core.input import read_input_table +from activitysim.core.exceptions import SystemConfigurationError, MissingNameError logger = logging.getLogger(__name__) @@ -181,7 +182,7 @@ def __init__( logger.warning( "deprecated combination of multiprocessing and not fail_fast" ) - raise RuntimeError( + raise SystemConfigurationError( "Shadow pricing requires fail_fast setting in multiprocessing mode" ) @@ -904,7 +905,10 @@ def update_shadow_prices(self, state): self.sampled_persons = sampled_persons else: - raise RuntimeError("unknown SHADOW_PRICE_METHOD %s" % shadow_price_method) + raise SystemConfigurationError( + "unknown SHADOW_PRICE_METHOD %s, method must be one of 'ctramp', 'daysim', or 'simulation'" + % shadow_price_method + ) def dest_size_terms(self, segment): assert segment in self.segment_ids @@ -922,8 +926,9 @@ def dest_size_terms(self, segment): elif shadow_price_method == "simulation": utility_adjustment = self.shadow_prices[segment] else: - raise RuntimeError( - "unknown SHADOW_PRICE_METHOD %s" % shadow_price_method + raise SystemConfigurationError( + "unknown SHADOW_PRICE_METHOD %s, method must be one of 'ctramp', 'daysim', or 'simulation'" + % shadow_price_method ) size_terms = pd.DataFrame( @@ -1036,9 +1041,7 @@ def buffers_for_shadow_pricing(shadow_pricing_info): if np.issubdtype(dtype, np.int64): typecode = ctypes.c_int64 else: - raise RuntimeError( - "buffer_for_shadow_pricing unrecognized dtype %s" % dtype - ) + raise TypeError("buffer_for_shadow_pricing unrecognized dtype %s" % dtype) shared_data_buffer = multiprocessing.Array(typecode, buffer_size) @@ -1085,9 +1088,7 @@ def buffers_for_shadow_pricing_choice(state, shadow_pricing_choice_info): if np.issubdtype(dtype, np.int64): typecode = ctypes.c_int64 else: - raise RuntimeError( - "buffer_for_shadow_pricing unrecognized dtype %s" % dtype - ) + raise TypeError("buffer_for_shadow_pricing unrecognized dtype %s" % dtype) shared_data_buffer = multiprocessing.Array(typecode, buffer_size) @@ -1145,12 +1146,12 @@ def shadow_price_data_from_buffers_choice( block_shapes = shadow_pricing_info["block_shapes"] if model_selector not in block_shapes: - raise RuntimeError( + raise MissingNameError( "Model selector %s not in shadow_pricing_info" % model_selector ) if block_name(model_selector + "_choice") not in data_buffers: - raise RuntimeError( + raise MissingNameError( "Block %s not in data_buffers" % block_name(model_selector + "_choice") ) @@ -1195,12 +1196,14 @@ def shadow_price_data_from_buffers(data_buffers, shadow_pricing_info, model_sele block_shapes = shadow_pricing_info["block_shapes"] if model_selector not in block_shapes: - raise RuntimeError( + raise MissingNameError( "Model selector %s not in shadow_pricing_info" % model_selector ) if block_name(model_selector) not in data_buffers: - raise RuntimeError("Block %s not in data_buffers" % block_name(model_selector)) + raise MissingNameError( + "Block %s not in data_buffers" % block_name(model_selector) + ) shape = block_shapes[model_selector] data = data_buffers[block_name(model_selector)] diff --git a/activitysim/abm/tables/vehicles.py b/activitysim/abm/tables/vehicles.py index c998c9abe3..36f13b0ccd 100644 --- a/activitysim/abm/tables/vehicles.py +++ b/activitysim/abm/tables/vehicles.py @@ -29,6 +29,12 @@ def vehicles(state: workflow.State, households: pd.DataFrame): """ # initialize vehicles table + if "auto_ownership" not in households.columns: + # grab the proto_households table instead + # this is called when running disaggregate accessibilities and the vehicles table is used in the logsum calculation + households = state.get_table("proto_households") + households.index.name = "household_id" + vehicles = households.loc[households.index.repeat(households["auto_ownership"])] vehicles = vehicles.reset_index()[["household_id"]] diff --git a/activitysim/abm/test/test_misc/configs_test_misc/network_los.yaml b/activitysim/abm/test/test_misc/configs_test_misc/network_los.yaml new file mode 100644 index 0000000000..391125a38e --- /dev/null +++ b/activitysim/abm/test/test_misc/configs_test_misc/network_los.yaml @@ -0,0 +1,14 @@ +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +zone_system: 1 + +taz_skims: skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59 + labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV'] \ No newline at end of file diff --git a/activitysim/abm/test/test_misc/configs_test_misc/settings_60_min.yaml b/activitysim/abm/test/test_misc/configs_test_misc/settings_60_min.yaml new file mode 100644 index 0000000000..a9ca5a0816 --- /dev/null +++ b/activitysim/abm/test/test_misc/configs_test_misc/settings_60_min.yaml @@ -0,0 +1,9 @@ +zone_system: 1 + +taz_skims: z1_taz_skims.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/abm/test/test_misc/data/z1_taz_skims.omx b/activitysim/abm/test/test_misc/data/z1_taz_skims.omx new file mode 100644 index 0000000000..18c6dc4408 Binary files /dev/null and b/activitysim/abm/test/test_misc/data/z1_taz_skims.omx differ diff --git a/activitysim/abm/test/test_misc/setup_utils.py b/activitysim/abm/test/test_misc/setup_utils.py index a4fe7e9c06..7169404432 100644 --- a/activitysim/abm/test/test_misc/setup_utils.py +++ b/activitysim/abm/test/test_misc/setup_utils.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. from __future__ import annotations +import importlib.resources import logging import os @@ -10,7 +11,6 @@ import openmatrix as omx import pandas as pd import pandas.testing as pdt -import pkg_resources import pytest import yaml @@ -32,7 +32,7 @@ def example_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def setup_dirs(ancillary_configs_dir=None, data_dir=None): diff --git a/activitysim/abm/test/test_misc/test_load_cached_accessibility.py b/activitysim/abm/test/test_misc/test_load_cached_accessibility.py index 60288d67c8..402b18783a 100644 --- a/activitysim/abm/test/test_misc/test_load_cached_accessibility.py +++ b/activitysim/abm/test/test_misc/test_load_cached_accessibility.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. from __future__ import annotations +import importlib.resources import logging import os @@ -10,7 +11,6 @@ import openmatrix as omx import pandas as pd import pandas.testing as pdt -import pkg_resources import pytest import yaml @@ -34,7 +34,7 @@ def example_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def close_handlers(): diff --git a/activitysim/abm/test/test_misc/test_trip_departure_choice.py b/activitysim/abm/test/test_misc/test_trip_departure_choice.py index 6d462c0bd0..94d47f57ac 100644 --- a/activitysim/abm/test/test_misc/test_trip_departure_choice.py +++ b/activitysim/abm/test/test_misc/test_trip_departure_choice.py @@ -1,152 +1,189 @@ -# import numpy as np -# import pandas as pd -# import pytest -# -# import activitysim.abm.models.trip_departure_choice as tdc -# from activitysim.abm.models.util.trip import get_time_windows -# from activitysim.core import los -# -# from .setup_utils import setup_dirs -# -# -# @pytest.fixture(scope="module") -# def trips(): -# outbound_array = [True, True, False, False, False, True, True, False, False, True] -# -# trips = pd.DataFrame( -# data={ -# "tour_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], -# "trip_duration": [2, 2, 7, 7, 7, 12, 12, 4, 4, 5], -# "inbound_duration": [0, 0, 7, 7, 7, 0, 0, 4, 4, 5], -# "main_leg_duration": [4, 4, 2, 2, 2, 2, 2, 1, 1, 2], -# "outbound_duration": [2, 2, 0, 0, 0, 12, 12, 0, 0, 5], -# "trip_count": [2, 2, 3, 3, 3, 2, 2, 2, 2, 1], -# "trip_num": [1, 2, 1, 2, 3, 1, 2, 1, 2, 1], -# "outbound": outbound_array, -# "chunk_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], -# "is_work": [ -# True, -# True, -# False, -# False, -# False, -# False, -# False, -# False, -# False, -# True, -# ], -# "is_school": [ -# False, -# False, -# False, -# False, -# False, -# False, -# False, -# True, -# True, -# False, -# ], -# "is_eatout": [ -# False, -# False, -# True, -# True, -# True, -# True, -# True, -# False, -# False, -# False, -# ], -# "start": [8, 8, 18, 18, 18, 18, 18, 24, 24, 19], -# "end": [14, 14, 39, 39, 39, 39, 39, 29, 29, 26], -# "origin": [3, 5, 15, 12, 24, 8, 17, 8, 9, 6], -# "destination": [5, 9, 12, 24, 20, 17, 18, 9, 11, 14], -# }, -# index=range(10), -# ) -# -# trips.index.name = "trip_id" -# return trips -# -# -# @pytest.fixture(scope="module") -# def settings(): -# return { -# "skims_file": "skims.omx", -# "skim_time_periods": {"labels": ["EA", "AM", "MD", "PM", "NT"]}, -# } -# -# -# @pytest.fixture(scope="module") -# def model_spec(): -# index = [ -# "@(df['stop_time_duration'] * df['is_work'].astype(int)).astype(int)", -# "@(df['stop_time_duration'] * df['is_school'].astype(int)).astype(int)", -# "@(df['stop_time_duration'] * df['is_eatout'].astype(int)).astype(int)", -# ] -# -# values = { -# "inbound": [0.933020, 0.370260, 0.994840], -# "outbound": [0.933020, 0.370260, 0.994840], -# } -# -# return pd.DataFrame(index=index, data=values) -# -# -# def test_build_patterns(trips): -# time_windows = get_time_windows(48, 3) -# patterns = tdc.build_patterns(trips, time_windows) -# patterns = patterns.sort_values(["tour_id", "outbound", "trip_num"]) -# -# assert patterns.shape[0] == 34 -# assert patterns.shape[1] == 6 -# assert patterns.index.name == tdc.TOUR_LEG_ID -# -# output_columns = [ -# tdc.TOUR_ID, -# tdc.PATTERN_ID, -# tdc.TRIP_NUM, -# tdc.STOP_TIME_DURATION, -# tdc.TOUR_ID, -# tdc.OUTBOUND, -# ] -# -# assert set(output_columns).issubset(patterns.columns) -# -# -# def test_get_tour_legs(trips): -# tour_legs = tdc.get_tour_legs(trips) -# assert tour_legs.index.name == tdc.TOUR_LEG_ID -# assert ( -# np.unique(tour_legs[tdc.TOUR_ID].values).shape[0] -# == np.unique(trips[tdc.TOUR_ID].values).shape[0] -# ) -# -# -# def test_generate_alternative(trips): -# alts = tdc.generate_alternatives(trips, tdc.STOP_TIME_DURATION) -# assert alts.shape[0] == 67 -# assert alts.shape[1] == 1 -# -# assert alts.index.name == tdc.TRIP_ID -# assert alts.columns[0] == tdc.STOP_TIME_DURATION -# -# pd.testing.assert_series_equal( -# trips.groupby(trips.index)["trip_duration"].max(), -# alts.groupby(alts.index)[tdc.STOP_TIME_DURATION].max(), -# check_names=False, -# ) -# -# -# def test_apply_stage_two_model(state, model_spec, trips): -# setup_dirs() -# departures = tdc.apply_stage_two_model( -# state, model_spec, trips, 0, "TEST Trip Departure" -# ) -# assert len(departures) == len(trips) -# pd.testing.assert_index_equal(departures.index, trips.index) -# -# departures = pd.concat([trips, departures], axis=1) +import numpy as np +import pandas as pd +import pytest +import os + +import activitysim.abm.models.trip_departure_choice as tdc +from activitysim.abm.models.util.trip import get_time_windows +from activitysim.core import workflow + +from .setup_utils import setup_dirs + + +@pytest.fixture(scope="module") +def trips(): + trips = pd.DataFrame( + data={ + "tour_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], + "trip_duration": [2, 2, 7, 7, 7, 12, 12, 4, 4, 5], + "inbound_duration": [0, 0, 7, 7, 7, 0, 0, 4, 4, 5], + "main_leg_duration": [4, 4, 2, 2, 2, 2, 2, 1, 1, 2], + "outbound_duration": [2, 2, 0, 0, 0, 12, 12, 0, 0, 5], + "trip_count": [2, 2, 3, 3, 3, 2, 2, 2, 2, 1], + "trip_num": [1, 2, 1, 2, 3, 1, 2, 1, 2, 1], + "outbound": [ + True, + True, + False, + False, + False, + True, + True, + False, + False, + True, + ], + "chunk_id": [1, 1, 2, 2, 2, 2, 2, 3, 3, 4], + "is_work": [ + True, + True, + False, + False, + False, + False, + False, + False, + False, + True, + ], + "is_school": [ + False, + False, + False, + False, + False, + False, + False, + True, + True, + False, + ], + "is_eatout": [ + False, + False, + True, + True, + True, + True, + True, + False, + False, + False, + ], + "start": [8, 8, 18, 18, 18, 18, 18, 24, 24, 19], + "end": [14, 14, 39, 39, 39, 39, 39, 29, 29, 26], + "origin": [3, 5, 15, 12, 24, 8, 17, 8, 9, 6], + "destination": [5, 9, 12, 24, 20, 17, 18, 9, 11, 14], + }, + index=range(10), + ) + + trips.index.name = "trip_id" + return trips + + +@pytest.fixture(scope="module") +def settings(): + return { + "skims_file": "skims.omx", + "skim_time_periods": {"labels": ["EA", "AM", "MD", "PM", "NT"]}, + } + + +def add_canonical_dirs(configs_dir_name): + state = workflow.State() + configs_dir = os.path.join(os.path.dirname(__file__), f"{configs_dir_name}") + data_dir = os.path.join(os.path.dirname(__file__), f"data") + output_dir = os.path.join(os.path.dirname(__file__), "output") + state.initialize_filesystem( + working_dir=os.path.dirname(__file__), + configs_dir=(configs_dir,), + output_dir=output_dir, + data_dir=(data_dir,), + ) + return state + + +@pytest.fixture(scope="module") +def model_spec(): + index = [ + "@(df['stop_time_duration'] * df['is_work'].astype(int)).astype(int)", + "@(df['stop_time_duration'] * df['is_school'].astype(int)).astype(int)", + "@(df['stop_time_duration'] * df['is_eatout'].astype(int)).astype(int)", + ] + + values = { + "inbound": [0.933020, 0.370260, 0.994840], + "outbound": [0.933020, 0.370260, 0.994840], + } + + return pd.DataFrame(index=index, data=values) + + +def test_build_patterns(trips): + time_windows = get_time_windows(48, 3) + patterns = tdc.build_patterns(trips, time_windows) + patterns = patterns.sort_values(["tour_id", "outbound", "trip_num"]) + + assert patterns.shape[0] == 34 + assert patterns.shape[1] == 6 + assert patterns.index.name == tdc.TOUR_LEG_ID + + output_columns = [ + tdc.TOUR_ID, + tdc.PATTERN_ID, + tdc.TRIP_NUM, + tdc.STOP_TIME_DURATION, + tdc.TOUR_ID, + tdc.OUTBOUND, + ] + + assert set(output_columns).issubset(patterns.columns) + + +def test_get_tour_legs(trips): + tour_legs = tdc.get_tour_legs(trips) + assert tour_legs.index.name == tdc.TOUR_LEG_ID + assert ( + np.unique(tour_legs[tdc.TOUR_ID].values).shape[0] + == np.unique(trips[tdc.TOUR_ID].values).shape[0] + ) + + +def test_generate_alternative(trips): + alts = tdc.generate_alternatives(trips, tdc.STOP_TIME_DURATION) + assert alts.shape[0] == 67 + assert alts.shape[1] == 1 + + assert alts.index.name == tdc.TRIP_ID + assert alts.columns[0] == tdc.STOP_TIME_DURATION + + pd.testing.assert_series_equal( + trips.groupby(trips.index)["trip_duration"].max(), + alts.groupby(alts.index)[tdc.STOP_TIME_DURATION].max(), + check_names=False, + ) + + +def test_apply_stage_two_model(model_spec, trips): + setup_dirs() + state = add_canonical_dirs("configs_test_misc").default_settings() + + # A settings object is needed to pass to the model application function, + # but for testing we can just use the default settings. + # In non-testing use cases, the SPEC would actually be read from the yaml file + # instead of being passed directly as a dataframe. + model_settings = tdc.TripDepartureChoiceSettings() + + departures = tdc.apply_stage_two_model( + state, + model_spec, + trips, + 0, + "TEST Trip Departure", + model_settings=model_settings, + ) + assert len(departures) == len(trips) + pd.testing.assert_index_equal(departures.index, trips.index) + + departures = pd.concat([trips, departures], axis=1) diff --git a/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py b/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py index 24fdebde3e..6823a5b123 100644 --- a/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py +++ b/activitysim/abm/test/test_misc/test_trip_scheduling_choice.py @@ -1,199 +1,287 @@ -# import numpy as np -# import pandas as pd -# import pytest -# -# from activitysim.abm.models import trip_scheduling_choice as tsc -# from activitysim.abm.tables.skims import skim_dict -# from activitysim.core import los, workflow -# -# from .setup_utils import setup_dirs -# -# -# @pytest.fixture(scope="module") -# def tours(): -# tours = pd.DataFrame( -# data={ -# "duration": [2, 44, 32, 12, 11, 16], -# "num_outbound_stops": [2, 4, 0, 0, 1, 3], -# "num_inbound_stops": [1, 0, 0, 2, 1, 2], -# "tour_type": ["othdisc"] * 2 + ["eatout"] * 4, -# "origin": [3, 10, 15, 23, 5, 8], -# "destination": [5, 9, 12, 24, 20, 17], -# tsc.LAST_OB_STOP: [1, 3, 0, 0, 12, 14], -# tsc.FIRST_IB_STOP: [2, 0, 0, 4, 6, 20], -# }, -# index=range(6), -# ) -# -# tours.index.name = "tour_id" -# -# tours[tsc.HAS_OB_STOPS] = tours[tsc.NUM_OB_STOPS] >= 1 -# tours[tsc.HAS_IB_STOPS] = tours[tsc.NUM_IB_STOPS] >= 1 -# -# return tours -# -# -# @pytest.fixture(scope="module") -# def settings(): -# return {"skims_file": "skims.omx", "skim_time_periods": {"labels": ["MD"]}} -# -# -# @pytest.fixture(scope="module") -# def model_spec(): -# index = [ -# "@(df['main_leg_duration']>df['duration']).astype(int)", -# "@(df['main_leg_duration'] == 0)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 1)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 2)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 3)&(df['tour_type']=='othdiscr')", -# "@(df['main_leg_duration'] == 4)&(df['tour_type']=='othdiscr')", -# "@df['tour_type']=='othdiscr'", -# "@df['tour_type']=='eatout'", -# "@df['tour_type']=='eatout'", -# ] -# -# values = [ -# -999, -# -6.5884, -# -5.0326, -# -2.0526, -# -1.0313, -# -0.46489, -# 0.060382, -# -0.7508, -# 0.53247, -# ] -# -# return pd.DataFrame(index=index, data=values, columns=["stage_one"]) -# -# -# @pytest.fixture(scope="module") -# def skims(settings): -# setup_dirs() -# nw_los = los.Network_LOS() -# nw_los.load_data() -# skim_d = skim_dict(nw_los) -# -# od_skim_stack_wrapper = skim_d.wrap("origin", "destination") -# do_skim_stack_wrapper = skim_d.wrap("destination", "origin") -# obib_skim_stack_wrapper = skim_d.wrap(tsc.LAST_OB_STOP, tsc.FIRST_IB_STOP) -# -# skims = [od_skim_stack_wrapper, do_skim_stack_wrapper, obib_skim_stack_wrapper] -# -# return skims -# -# -# @pytest.fixture(scope="module") -# def locals_dict(skims): -# return {"od_skims": skims[0], "do_skims": skims[1], "obib_skims": skims[2]} -# -# -# def test_generate_schedule_alternatives(tours): -# windows = tsc.generate_schedule_alternatives(tours) -# assert windows.shape[0] == 296 -# assert windows.shape[1] == 4 -# -# output_columns = [ -# tsc.SCHEDULE_ID, -# tsc.MAIN_LEG_DURATION, -# tsc.OB_DURATION, -# tsc.IB_DURATION, -# ] -# -# assert set(output_columns).issubset(windows.columns) -# -# -# def test_no_stops_patterns(tours): -# no_stops = tours[ -# (tours["num_outbound_stops"] == 0) & (tours["num_inbound_stops"] == 0) -# ].copy() -# windows = tsc.no_stops_patterns(no_stops) -# -# assert windows.shape[0] == 1 -# assert windows.shape[1] == 3 -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(windows.columns) -# -# pd.testing.assert_series_equal( -# windows[tsc.MAIN_LEG_DURATION], -# no_stops["duration"], -# check_names=False, -# check_dtype=False, -# ) -# assert windows[windows[tsc.IB_DURATION] > 0].empty -# assert windows[windows[tsc.OB_DURATION] > 0].empty -# -# -# def test_one_way_stop_patterns(tours): -# one_way_stops = tours[ -# ( -# (tours["num_outbound_stops"] > 0).astype(int) -# + (tours["num_inbound_stops"] > 0).astype(int) -# ) -# == 1 -# ].copy() -# windows = tsc.stop_one_way_only_patterns(one_way_stops) -# -# assert windows.shape[0] == 58 -# assert windows.shape[1] == 3 -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(windows.columns) -# -# inbound_options = windows[(windows[tsc.IB_DURATION] > 0)] -# outbound_options = windows[windows[tsc.OB_DURATION] > 0] -# assert np.unique(inbound_options.index).shape[0] == 1 -# assert np.unique(outbound_options.index).shape[0] == 1 -# -# -# def test_two_way_stop_patterns(tours): -# two_way_stops = tours[ -# ( -# (tours["num_outbound_stops"] > 0).astype(int) -# + (tours["num_inbound_stops"] > 0).astype(int) -# ) -# == 2 -# ].copy() -# windows = tsc.stop_two_way_only_patterns(two_way_stops) -# -# assert windows.shape[0] == 237 -# assert windows.shape[1] == 3 -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(windows.columns) -# -# -# def test_run_trip_scheduling_choice( -# state: workflow.State, model_spec, tours, skims, locals_dict -# ): -# """ -# Test run the model. -# """ -# -# out_tours = tsc.run_trip_scheduling_choice( -# state, -# model_spec, -# tours, -# skims, -# locals_dict, -# trace_label="PyTest Trip Scheduling", -# ) -# -# assert len(tours) == len(out_tours) -# pd.testing.assert_index_equal( -# tours.sort_index().index, out_tours.sort_index().index -# ) -# -# output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] -# -# assert set(output_columns).issubset(out_tours.columns) -# -# assert len( -# out_tours[ -# out_tours[output_columns].sum(axis=1) == out_tours[tsc.TOUR_DURATION_COLUMN] -# ] -# ) == len(tours) +import numpy as np +import pandas as pd +import pytest +import os +from pathlib import Path + + +from activitysim.abm.models import trip_scheduling_choice as tsc +from activitysim.abm.tables.skims import skim_dict +from activitysim.core import los, workflow + + +@pytest.fixture(scope="module") +def tours(): + tours = pd.DataFrame( + data={ + "duration": [2, 44, 32, 12, 11, 16], + "num_outbound_stops": [2, 4, 0, 0, 1, 3], + "num_inbound_stops": [1, 0, 0, 2, 1, 2], + "tour_type": ["othdisc"] * 2 + ["eatout"] * 4, + "origin": [3, 10, 15, 23, 5, 8], + "destination": [5, 9, 12, 24, 20, 17], + tsc.LAST_OB_STOP: [1, 3, 0, 0, 12, 14], + tsc.FIRST_IB_STOP: [2, 0, 0, 4, 6, 20], + }, + index=range(6), + ) + + tours.index.name = "tour_id" + + tours[tsc.HAS_OB_STOPS] = tours[tsc.NUM_OB_STOPS] >= 1 + tours[tsc.HAS_IB_STOPS] = tours[tsc.NUM_IB_STOPS] >= 1 + + return tours + + +@pytest.fixture(scope="module") +def model_spec(): + index = [ + "@(df['main_leg_duration']>df['duration']).astype(int)", + "@(df['main_leg_duration'] == 0)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 1)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 2)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 3)&(df['tour_type']=='othdiscr')", + "@(df['main_leg_duration'] == 4)&(df['tour_type']=='othdiscr')", + "@df['tour_type']=='othdiscr'", + "@df['tour_type']=='eatout'", + "@df['tour_type']=='eatout'", + ] + + values = [ + -999, + -6.5884, + -5.0326, + -2.0526, + -1.0313, + -0.46489, + 0.060382, + -0.7508, + 0.53247, + ] + + return pd.DataFrame(index=index, data=values, columns=["stage_one"]).rename_axis( + "Expression" + ) + + +def add_canonical_dirs(configs_dir_name): + state = workflow.State() + configs_dir = os.path.join(os.path.dirname(__file__), f"{configs_dir_name}") + data_dir = os.path.join(os.path.dirname(__file__), "data") + output_dir = os.path.join(os.path.dirname(__file__), "output") + state.initialize_filesystem( + working_dir=os.path.dirname(__file__), + configs_dir=(configs_dir,), + output_dir=output_dir, + data_dir=(data_dir,), + ) + return state + + +@pytest.fixture(scope="module") +def skims(): + state = add_canonical_dirs("configs_test_misc").default_settings() + nw_los = los.Network_LOS(state, los_settings_file_name="settings_60_min.yaml") + nw_los.load_data() + skim_d = skim_dict(state, nw_los) + + od_skim_stack_wrapper = skim_d.wrap("origin", "destination") + do_skim_stack_wrapper = skim_d.wrap("destination", "origin") + obib_skim_stack_wrapper = skim_d.wrap(tsc.LAST_OB_STOP, tsc.FIRST_IB_STOP) + + skims = [od_skim_stack_wrapper, do_skim_stack_wrapper, obib_skim_stack_wrapper] + + return skims + + +@pytest.fixture(scope="module") +def locals_dict(skims): + return {"od_skims": skims[0], "do_skims": skims[1], "obib_skims": skims[2]} + + +@pytest.fixture(scope="module") +def base_dir() -> Path: + """ + A pytest fixture that returns the data folder location. + :return: folder location for any necessary data to initialize the tests + """ + return Path(__file__).parent + + +@pytest.fixture(scope="module") +def module() -> str: + """ + A pytest fixture that returns the module name string used in test setup. + :return: module name string ("summarize") + """ + return "summarize" + + +# Used by conftest.py initialize_pipeline method +@pytest.fixture(scope="module") +def tables() -> dict[str, str]: + """ + A pytest fixture that returns the "mock" tables to build pipeline dataframes. The + key-value pair is the name of the table and the index column. + :return: dict + """ + return { + "land_use": "zone_id", + "tours": "tour_id", + "trips": "trip_id", + "persons": "person_id", + "households": "household_id", + } + + +# Used by conftest.py initialize_pipeline method +# Set to true if you need to read skims into the pipeline +@pytest.fixture(scope="module") +def initialize_network_los() -> bool: + """ + A pytest boolean fixture indicating whether network skims should be read from the + fixtures test data folder. + :return: bool + """ + return True + + +def test_generate_schedule_alternatives(tours): + windows = tsc.generate_schedule_alternatives(tours) + assert windows.shape[0] == 296 + assert windows.shape[1] == 4 + + output_columns = [ + tsc.SCHEDULE_ID, + tsc.MAIN_LEG_DURATION, + tsc.OB_DURATION, + tsc.IB_DURATION, + ] + + assert set(output_columns).issubset(windows.columns) + + +def test_no_stops_patterns(tours): + no_stops = tours[ + (tours["num_outbound_stops"] == 0) & (tours["num_inbound_stops"] == 0) + ].copy() + windows = tsc.no_stops_patterns(no_stops) + + assert windows.shape[0] == 1 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + pd.testing.assert_series_equal( + windows[tsc.MAIN_LEG_DURATION], + no_stops["duration"], + check_names=False, + check_dtype=False, + ) + assert windows[windows[tsc.IB_DURATION] > 0].empty + assert windows[windows[tsc.OB_DURATION] > 0].empty + + +def test_one_way_stop_patterns(tours): + one_way_stops = tours[ + ( + (tours["num_outbound_stops"] > 0).astype(int) + + (tours["num_inbound_stops"] > 0).astype(int) + ) + == 1 + ].copy() + windows = tsc.stop_one_way_only_patterns(one_way_stops) + + assert windows.shape[0] == 58 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + inbound_options = windows[(windows[tsc.IB_DURATION] > 0)] + outbound_options = windows[windows[tsc.OB_DURATION] > 0] + assert np.unique(inbound_options.index).shape[0] == 1 + assert np.unique(outbound_options.index).shape[0] == 1 + + +def test_two_way_stop_patterns(tours): + two_way_stops = tours[ + ( + (tours["num_outbound_stops"] > 0).astype(int) + + (tours["num_inbound_stops"] > 0).astype(int) + ) + == 2 + ].copy() + windows = tsc.stop_two_way_only_patterns(two_way_stops) + + assert windows.shape[0] == 237 + assert windows.shape[1] == 3 + + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + + assert set(output_columns).issubset(windows.columns) + + +def test_run_trip_scheduling_choice(model_spec, tours, skims, locals_dict): + # create a temporary workflow state with no content + state = workflow.State.make_temp() + + # Define model settings for this test. + # The settings for this model requires a filename for the spec, but in this test we + # are passing the spec dataframe directly, so the filename is just a placeholder. + # In non-testing use cases, the SPEC would actually be read from the yaml file + # instead of being passed directly as a dataframe. + model_settings = tsc.TripSchedulingChoiceSettings( + **{ + "SPEC": "placeholder.csv", + "compute_settings": { + "protect_columns": ["origin", "destination", "schedule_id"] + }, + } + ) + + # As is common in ActivitySim the component will modify the input dataframe in-place. + # For testing we make a copy of the input tours to compare against after running the model. + in_tours = tours.copy(deep=True) + + # run the trip scheduling choice model + out_tours = tsc.run_trip_scheduling_choice( + state, + model_spec, + tours, + skims, + locals_dict, + trace_label="PyTest Trip Scheduling", + model_settings=model_settings, + ) + + # check that the number of tours is unchanged + assert len(in_tours) == len(out_tours) + pd.testing.assert_index_equal( + in_tours.sort_index().index, out_tours.sort_index().index + ) + + # check that the expected output columns are not present in input tours + output_columns = [tsc.MAIN_LEG_DURATION, tsc.OB_DURATION, tsc.IB_DURATION] + for col in output_columns: + assert col not in in_tours.columns + + # check that the expected output columns *are* present in output tours + assert set(output_columns).issubset(out_tours.columns) + + # check that the sum of the output durations equals the tour duration + assert len( + out_tours[ + out_tours[output_columns].sum(axis=1) == out_tours[tsc.TOUR_DURATION_COLUMN] + ] + ) == len(in_tours) + + # check that tours with no outbound stops have zero outbound duration + assert out_tours[tsc.OB_DURATION].mask(in_tours[tsc.HAS_OB_STOPS], 0).sum() == 0 + + # check that tours with no inbound stops have zero inbound duration + assert out_tours[tsc.IB_DURATION].mask(in_tours[tsc.HAS_IB_STOPS], 0).sum() == 0 diff --git a/activitysim/abm/test/test_pipeline/test_pipeline.py b/activitysim/abm/test/test_pipeline/test_pipeline.py index 70bc26f4b1..2d837258a5 100644 --- a/activitysim/abm/test/test_pipeline/test_pipeline.py +++ b/activitysim/abm/test/test_pipeline/test_pipeline.py @@ -2,6 +2,7 @@ # See full license in LICENSE.txt. from __future__ import annotations +import importlib.resources import logging import os @@ -9,10 +10,13 @@ import openmatrix as omx import pandas as pd import pandas.testing as pdt -import pkg_resources import pytest from activitysim.core import random, tracing, workflow +from activitysim.core.exceptions import ( + CheckpointNameNotFoundError, + DuplicateWorkflowNameError, +) # set the max households for all tests (this is to limit memory use on travis) HOUSEHOLDS_SAMPLE_SIZE = 50 @@ -30,7 +34,7 @@ def example_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def setup_dirs(ancillary_configs_dir=None, data_dir=None): @@ -190,12 +194,12 @@ def test_mini_pipeline_run(): regress_mini_location_choice_logsums(state) # try to get a non-existant table - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(CheckpointNameNotFoundError) as excinfo: state.checkpoint.load_dataframe("bogus") assert "never checkpointed" in str(excinfo.value) # try to get an existing table from a non-existant checkpoint - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(CheckpointNameNotFoundError) as excinfo: state.checkpoint.load_dataframe("households", checkpoint_name="bogus") assert "not in checkpoints" in str(excinfo.value) @@ -235,7 +239,7 @@ def test_mini_pipeline_run2(): regress_mini_auto(state) # try to run a model already in pipeline - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(DuplicateWorkflowNameError) as excinfo: state.run.by_name("auto_ownership_simulate") assert "run model 'auto_ownership_simulate' more than once" in str(excinfo.value) diff --git a/activitysim/cli/create.py b/activitysim/cli/create.py index 4275ad3a1d..ec2e917bd8 100644 --- a/activitysim/cli/create.py +++ b/activitysim/cli/create.py @@ -11,7 +11,6 @@ import zipfile from pathlib import Path -import pkg_resources import requests import yaml diff --git a/activitysim/cli/run.py b/activitysim/cli/run.py index 03258be4b5..a1b23048c9 100644 --- a/activitysim/cli/run.py +++ b/activitysim/cli/run.py @@ -8,11 +8,17 @@ import os import sys import warnings +from datetime import datetime +import struct +import time import numpy as np -from activitysim.core import chunk, config, mem, tracing, workflow +from activitysim.core import chunk, config, mem, timing, tracing, workflow from activitysim.core.configuration import FileSystem, Settings +from activitysim.core.run_id import RunId + +from activitysim.abm.models.settings_checker import check_model_settings logger = logging.getLogger(__name__) @@ -25,6 +31,8 @@ "cache_dir", "settings_file_name", "imported_extensions", + "run_timestamp", + "run_id", ] @@ -157,6 +165,8 @@ def inject_arg(name, value): # 'configs', 'data', and 'output' folders by default os.chdir(args.working_dir) + inject_arg("run_id", state.tracing.run_id) + if args.ext: for e in args.ext: basepath, extpath = os.path.split(e) @@ -264,6 +274,7 @@ def run(args): """ state = workflow.State() + _init_run_id = state.tracing.run_id # register abm steps and other abm-specific injectables # by default, assume we are running activitysim.abm @@ -279,11 +290,19 @@ def run(args): if state.settings.rotate_logs: state.logging.rotate_log_directory() + # set a run timestamp + timestamp = state.get("run_timestamp", None) + if timestamp is None: + # if no run timestamp, use current time, and store it so + # it can be used later in the same run + timestamp = datetime.now().strftime("%Y%m%d-%H%M%S") + state.set("run_timestamp", timestamp) + if state.settings.memory_profile and not state.settings.multiprocess: # Memory sidecar is only useful for single process runs # multiprocess runs log memory usage without blocking in the controlling process. mem_prof_log = state.get_log_file_path("memory_profile.csv") - from ..core.memory_sidecar import MemorySidecar + from activitysim.core.memory_sidecar import MemorySidecar memory_sidecar_process = MemorySidecar(mem_prof_log) else: @@ -328,6 +347,9 @@ def run(args): config.filter_warnings(state) logging.captureWarnings(capture=True) + activitysim_version = importlib.metadata.version("activitysim") + logger.info(f"ActivitySim Version: {activitysim_version}") + # directories for k in ["configs_dir", "settings_file_name", "data_dir", "output_dir"]: logger.info("SETTING %s: %s" % (k, getattr(state.filesystem, k, None))) @@ -362,7 +384,10 @@ def run(args): ] for cfg_key in np_info_keys: - info = np.__config__.get_info(cfg_key) + try: + info = np.__config__.get_info(cfg_key) + except AttributeError: + info = np.show_config("dicts").get(cfg_key, "MISSING") if info: for info_key in ["libraries"]: if info_key in info: @@ -370,6 +395,28 @@ def run(args): t0 = tracing.print_elapsed_time() + if state.settings.check_model_settings == True: + logger.info( + "Settings checker will check core settings files. See settings_checker.log for details." + ) + # get any additional settings definitions from extensions + extension_checker_settings = {} + extension_names = state.get_injectable("imported_extensions") + if extension_names: + for ext in extension_names: + try: + settings_checker_ext = importlib.import_module( + ext + ".settings_checker" + ) + extension_checker_settings.update( + settings_checker_ext.EXTENSION_CHECKER_SETTINGS + ) + except ImportError: + logger.warning( + f"Extension {ext} does not have a settings_checker module or it cannot be imported." + ) + check_model_settings(state, extension_settings=extension_checker_settings) + try: if state.settings.multiprocess: logger.info("run multiprocess simulation") @@ -412,7 +459,13 @@ def run(args): raise chunk.consolidate_logs(state) - mem.consolidate_logs(state) + try: + mem.consolidate_logs(state) + except Exception as e: + logger.warning( + f"Memory log consolidation failed with error: {e}. " + "This does not affect model results, but memory usage logs will not be consolidated." + ) from activitysim.core.flow import TimeLogger @@ -423,6 +476,13 @@ def run(args): if memory_sidecar_process: memory_sidecar_process.stop() + if state.settings.expression_profile: + # generate a summary of slower expression evaluation times + # across all models and write to a file + analyze = timing.AnalyzeEvalTiming(state) + analyze.component_report(style=state.settings.expression_profile_style) + analyze.subcomponent_report(style=state.settings.expression_profile_style) + return 0 diff --git a/activitysim/core/assign.py b/activitysim/core/assign.py index c5f69d1d12..fe39bfc443 100644 --- a/activitysim/core/assign.py +++ b/activitysim/core/assign.py @@ -5,11 +5,12 @@ import logging from builtins import object, zip from collections import OrderedDict +from pathlib import Path import numpy as np import pandas as pd -from activitysim.core import chunk, util, workflow +from activitysim.core import chunk, timing, util, workflow logger = logging.getLogger(__name__) @@ -275,6 +276,12 @@ def to_series(x): assert assignment_expressions.shape[0] > 0 + if state.settings.expression_profile: + perf_log_file = Path(trace_label + ".log") + else: + perf_log_file = None + performance_timer = timing.EvalTiming(perf_log_file) + trace_assigned_locals = trace_results = None if trace_rows is not None: # convert to numpy array so we can slice ndarrays as well as series @@ -311,24 +318,25 @@ def to_series(x): n_randoms += 1 assignment_expressions.loc[expression_idx, "expression"] = expression if n_randoms: - try: - random_draws = state.get_rn_generator().normal_for_df( - df, broadcast=True, size=n_randoms - ) - except RuntimeError: - pass - else: - _locals_dict["random_draws"] = random_draws + with performance_timer.time_expression(""): + try: + random_draws = state.get_rn_generator().normal_for_df( + df, broadcast=True, size=n_randoms + ) + except RuntimeError: + pass + else: + _locals_dict["random_draws"] = random_draws - def rng_lognormal(random_draws, mu, sigma, broadcast=True, scale=False): - if scale: - x = 1 + ((sigma * sigma) / (mu * mu)) - mu = np.log(mu / (np.sqrt(x))) - sigma = np.sqrt(np.log(x)) - assert broadcast - return np.exp(random_draws * sigma + mu) + def rng_lognormal(random_draws, mu, sigma, broadcast=True, scale=False): + if scale: + x = 1 + ((sigma * sigma) / (mu * mu)) + mu = np.log(mu / (np.sqrt(x))) + sigma = np.sqrt(np.log(x)) + assert broadcast + return np.exp(random_draws * sigma + mu) - _locals_dict["rng_lognormal"] = rng_lognormal + _locals_dict["rng_lognormal"] = rng_lognormal sharrow_enabled = state.settings.sharrow @@ -352,11 +360,12 @@ def rng_lognormal(random_draws, mu, sigma, broadcast=True, scale=False): ) if trace_label: - logger.info(f"{trace_label}.assign_variables {target} = {expression}") + logger.debug(f"{trace_label}.assign_variables {target} = {expression}") if is_temp_singular(target) or is_throwaway(target): try: - x = eval(expression, globals(), _locals_dict) + with performance_timer.time_expression(expression): + x = eval(expression, globals(), _locals_dict) except Exception as err: logger.error( "assign_variables error: %s: %s", type(err).__name__, str(err) @@ -384,7 +393,8 @@ def rng_lognormal(random_draws, mu, sigma, broadcast=True, scale=False): # FIXME should whitelist globals for security? globals_dict = {} - expr_values = to_series(eval(expression, globals_dict, _locals_dict)) + with performance_timer.time_expression(expression): + expr_values = to_series(eval(expression, globals_dict, _locals_dict)) if sharrow_enabled: if isinstance(expr_values.dtype, pd.api.types.CategoricalDtype): @@ -459,4 +469,5 @@ def rng_lognormal(random_draws, mu, sigma, broadcast=True, scale=False): inplace=True, ) + performance_timer.write_log(state) return variables, trace_results, trace_assigned_locals diff --git a/activitysim/core/chunk.py b/activitysim/core/chunk.py index 7f09187f1f..f0074683d8 100644 --- a/activitysim/core/chunk.py +++ b/activitysim/core/chunk.py @@ -1078,7 +1078,7 @@ def ledger(self): if mem_monitor is not None: if not mem_monitor.is_alive(): logger.error(f"mem_monitor for {self.trace_label} died!") - raise RuntimeError("bug") + raise RuntimeError("mem_monitor for {self.trace_label} died!") if stop_snooping is not None: stop_snooping.set() @@ -1224,7 +1224,7 @@ def adaptive_chunked_choosers( # The adaptive chunking logic is expensive and sometimes results # in needless data copying. So we short circuit it entirely # when chunking is disabled. - logger.info(f"Running chunkless with {len(choosers)} choosers") + logger.debug(f"Running chunkless with {len(choosers)} choosers") yield 0, choosers, trace_label, ChunkSizer( state, "chunkless", trace_label, 0, 0, state.settings.chunk_training_mode ) @@ -1234,18 +1234,23 @@ def adaptive_chunked_choosers( num_choosers = len(choosers.index) + if state.settings.multiprocess: + num_processes = state.get_injectable("num_processes", 1) + else: + num_processes = 1 + if state.settings.chunk_training_mode == MODE_EXPLICIT: if explicit_chunk_size < 1: chunk_size = math.ceil(num_choosers * explicit_chunk_size) else: - chunk_size = int(explicit_chunk_size) + chunk_size = math.ceil(explicit_chunk_size / num_processes) elif chunk_size is None: chunk_size = state.settings.chunk_size assert num_choosers > 0 assert chunk_size >= 0 - logger.info( + logger.debug( f"{trace_label} Running adaptive_chunked_choosers with {num_choosers} choosers" ) @@ -1270,7 +1275,7 @@ def adaptive_chunked_choosers( # grab the next chunk based on current rows_per_chunk chooser_chunk = choosers[offset : offset + rows_per_chunk] - logger.info( + logger.debug( f"Running chunk {i} of {estimated_number_of_chunks or '?'} " f"with {len(chooser_chunk)} of {num_choosers} choosers" ) @@ -1337,7 +1342,7 @@ def adaptive_chunked_choosers_and_alts( # The adaptive chunking logic is expensive and sometimes results # in needless data copying. So we short circuit it entirely # when chunking is disabled. - logger.info(f"Running chunkless with {len(choosers)} choosers") + logger.debug(f"Running chunkless with {len(choosers)} choosers") chunk_sizer = ChunkSizer( state, "chunkless", trace_label, 0, 0, state.settings.chunk_training_mode ) @@ -1367,16 +1372,21 @@ def adaptive_chunked_choosers_and_alts( alternatives.index[last_repeat] ) - logger.info( + logger.debug( f"{trace_label} Running adaptive_chunked_choosers_and_alts " f"with {num_choosers} choosers and {num_alternatives} alternatives" ) + if state.settings.multiprocess: + num_processes = state.get_injectable("num_processes", 1) + else: + num_processes = 1 + if state.settings.chunk_training_mode == MODE_EXPLICIT: if explicit_chunk_size < 1: chunk_size = math.ceil(num_choosers * explicit_chunk_size) else: - chunk_size = int(explicit_chunk_size) + chunk_size = int(explicit_chunk_size / num_processes) elif chunk_size is None: chunk_size = state.settings.chunk_size @@ -1389,7 +1399,14 @@ def adaptive_chunked_choosers_and_alts( chunk_training_mode=state.settings.chunk_training_mode, ) rows_per_chunk, estimated_number_of_chunks = chunk_sizer.initial_rows_per_chunk() - assert (rows_per_chunk > 0) and (rows_per_chunk <= num_choosers) + assert (rows_per_chunk > 0) and ( + (rows_per_chunk <= num_choosers) + or ( + (rows_per_chunk >= num_choosers) + and (estimated_number_of_chunks == 1) + and (state.settings.chunk_training_mode == MODE_EXPLICIT) + ) + ) # alt chunks boundaries are where index changes alt_ids = alternatives.index.values @@ -1425,7 +1442,7 @@ def adaptive_chunked_choosers_and_alts( chooser_chunk.index == np.unique(alternative_chunk.index.values) ).all() - logger.info( + logger.debug( f"Running chunk {i} of {estimated_number_of_chunks or '?'} " f"with {len(chooser_chunk)} of {num_choosers} choosers" ) @@ -1464,7 +1481,7 @@ def adaptive_chunked_choosers_by_chunk_id( # The adaptive chunking logic is expensive and sometimes results # in needless data copying. So we short circuit it entirely # when chunking is disabled. - logger.info(f"Running chunkless with {len(choosers)} choosers") + logger.debug(f"Running chunkless with {len(choosers)} choosers") chunk_sizer = ChunkSizer( state, "chunkless", trace_label, 0, 0, state.settings.chunk_training_mode ) @@ -1502,7 +1519,7 @@ def adaptive_chunked_choosers_by_chunk_id( choosers["chunk_id"].between(offset, offset + rows_per_chunk - 1) ] - logger.info( + logger.debug( f"{trace_label} Running chunk {i} of {estimated_number_of_chunks or '?'} " f"with {rows_per_chunk} of {num_choosers} choosers" ) diff --git a/activitysim/core/config.py b/activitysim/core/config.py index 730c383157..e395769160 100644 --- a/activitysim/core/config.py +++ b/activitysim/core/config.py @@ -7,6 +7,7 @@ from activitysim.core import workflow from activitysim.core.configuration.base import PydanticBase from activitysim.core.configuration.logit import LogitComponentSettings +from activitysim.core.exceptions import ModelConfigurationError # ActivitySim # See full license in LICENSE.txt. @@ -100,7 +101,7 @@ def get_model_constants(model_settings): def get_logit_model_settings( - model_settings: LogitComponentSettings | dict[str, Any] | None + model_settings: LogitComponentSettings | dict[str, Any] | None, ): """ Read nest spec (for nested logit) from model settings file @@ -123,13 +124,18 @@ def get_logit_model_settings( if logit_type not in ["NL", "MNL"]: logger.error("Unrecognized logit type '%s'" % logit_type) - raise RuntimeError("Unrecognized logit type '%s'" % logit_type) + raise ModelConfigurationError( + "Unrecognized logit type '%s'. Logit type must be 'NL' for nested logit or 'MNL' for multinomial logit" + % logit_type + ) if logit_type == "NL": nests = model_settings.get("NESTS", None) if nests is None: logger.error("No NEST found in model spec for NL model type") - raise RuntimeError("No NEST found in model spec for NL model type") + raise ModelConfigurationError( + "No NEST found in model spec for NL model type" + ) return nests diff --git a/activitysim/core/configuration/base.py b/activitysim/core/configuration/base.py index 320278bf1a..aad266ff12 100644 --- a/activitysim/core/configuration/base.py +++ b/activitysim/core/configuration/base.py @@ -245,6 +245,28 @@ def pandas_option_context(self): else: yield + performance_log: Path | bool | None = None + """Log runtime performance to this file. + + The runtime performance log shows the time taken to evaluate each + expression in the specification files. It is useful for debugging + performance issues with complex expressions. + + Giving a filename here will override the global performance logging + setting, and will log performance to the specified file, unless the + global performance logging setting is set to `False`, in which case + no performance logging will be done for any component. + + If set to `True`, performance logging will be activated, and the filename + will be chosen based on the trace label for this component. + + If this path is set to None (the default), performance logging will be + activated based on the global performance logging setting, and the filename + will be chosen based on the trace label for this component. This is strongly + recommended for most use cases, unless the trace label causes the filename + to be too long or otherwise unsuitable for the filesystem. + """ + def subcomponent_settings(self, subcomponent: str) -> ComputeSettings: """Get the sharrow settings for a particular subcomponent.""" return ComputeSettings( diff --git a/activitysim/core/configuration/filesystem.py b/activitysim/core/configuration/filesystem.py index 704e179a14..75bd761365 100644 --- a/activitysim/core/configuration/filesystem.py +++ b/activitysim/core/configuration/filesystem.py @@ -16,7 +16,10 @@ from activitysim.core.configuration.base import PydanticBase from activitysim.core.configuration.logit import LogitComponentSettings -from activitysim.core.exceptions import SettingsFileNotFoundError +from activitysim.core.exceptions import ( + SettingsFileNotFoundError, + SystemConfigurationError, +) from activitysim.core.util import parse_suffix_args, suffix_tables_in_settings logger = logging.getLogger(__name__) @@ -169,7 +172,8 @@ def get_output_dir(self, subdir=None) -> Path: if subdir is not None: out = out.joinpath(subdir) if not out.exists(): - out.mkdir(parents=True) + out.mkdir(parents=True, exist_ok=True) + # we set exist_ok=True so we avoid multiprocess race conditions return out def get_output_file_path(self, file_name) -> Path: @@ -767,7 +771,7 @@ def backfill_settings(settings, backfill): logger.error( f"Unexpected additional settings: {additional_settings}" ) - raise RuntimeError( + raise SystemConfigurationError( "'include_settings' must appear alone in settings file." ) diff --git a/activitysim/core/configuration/logit.py b/activitysim/core/configuration/logit.py index d03bcab778..a97143f2dd 100644 --- a/activitysim/core/configuration/logit.py +++ b/activitysim/core/configuration/logit.py @@ -77,6 +77,31 @@ class BaseLogitComponentSettings(PydanticCompute): CONSTANTS: dict[str, Any] = {} """Named constants usable in the utility expressions.""" + preprocessor: PreprocessorSettings | list[PreprocessorSettings] | None = None + """Chooser preprocessor settings. + + This is a set of expressions to be evaluated on the choosers + before the logit model is run. It is used to prepare the choosers + for the logit model by adding columns that are used in the + utility expressions. + """ + + annotate_households: PreprocessorSettings | None = None + """Annotate households output tables with additional columns. + + These settings are used to add additional columns to the output tables + after the logit model is run. They are typically used to add + additional attributes that are derived from the model results. + """ + annotate_persons: PreprocessorSettings | None = None + """Annotate persons output tables with additional columns.""" + annotate_tours: PreprocessorSettings | None = None + """Annotate tours output tables with additional columns.""" + annotate_trips: PreprocessorSettings | None = None + """Annotate trips output tables with additional columns.""" + annotate_vehicles: PreprocessorSettings | None = None + """Annotate vehicles output tables with additional columns.""" + # sharrow_skip is deprecated in factor of compute_settings.sharrow_skip @model_validator(mode="before") @classmethod @@ -187,8 +212,19 @@ class LocationComponentSettings(BaseLogitComponentSettings): SAMPLE_SIZE: int """This many candidate alternatives will be sampled for each choice.""" - LOGSUM_SETTINGS: Path - """Settings for the logsum computation.""" + ESTIMATION_SAMPLE_SIZE: int = -1 + """ + The number of alternatives to sample for estimation mode. + If zero, then all alternatives are used. If negative, then the regular + `SAMPLE_SIZE` is used. + Truth alternative will be included in the sample. + """ + + LOGSUM_SETTINGS: Path | None = None + """ + Settings for the logsum computation. + If None, no logsum is computed and logsum field is populated with zeros. + """ explicit_chunk: float = 0 """ @@ -196,6 +232,11 @@ class LocationComponentSettings(BaseLogitComponentSettings): If less than 1, use this fraction of the total number of rows. """ + alts_preprocessor_sample: PreprocessorSettings | None = None + """Alternatives preprocessor settings to use when sampling alternatives.""" + alts_preprocessor_simulate: PreprocessorSettings | None = None + """Alternatives preprocessor settings to use when simulating choices.""" + class TourLocationComponentSettings(LocationComponentSettings, extra="forbid"): # Logsum-related settings @@ -207,7 +248,6 @@ class TourLocationComponentSettings(LocationComponentSettings, extra="forbid"): SEGMENTS: list[str] | None = None SIZE_TERM_SELECTOR: str | None = None - annotate_tours: PreprocessorSettings | None = None CHOOSER_FILTER_COLUMN_NAME: str | None = None DEST_CHOICE_COLUMN_NAME: str | None = None @@ -221,8 +261,6 @@ class TourLocationComponentSettings(LocationComponentSettings, extra="forbid"): SEGMENT_IDS: dict[str, int] | dict[str, str] | dict[str, bool] | None = None SHADOW_PRICE_TABLE: str | None = None MODELED_SIZE_TABLE: str | None = None - annotate_persons: PreprocessorSettings | None = None - annotate_households: PreprocessorSettings | None = None SIMULATE_CHOOSER_COLUMNS: list[str] | None = None ALT_DEST_COL_NAME: str LOGSUM_TOUR_PURPOSE: str | dict[str, str] | None = None @@ -233,15 +271,6 @@ class TourLocationComponentSettings(LocationComponentSettings, extra="forbid"): ORIG_ZONE_ID: str | None = None """This setting appears to do nothing...""" - ESTIMATION_SAMPLE_SIZE: int = 0 - """ - The number of alternatives to sample for estimation mode. - If zero, then all alternatives are used. - Truth alternative will be included in the sample. - Larch does not yet support sampling alternatives for estimation, - but this setting is still helpful for estimation mode runtime. - """ - class TourModeComponentSettings(TemplatedLogitComponentSettings, extra="forbid"): MODE_CHOICE_LOGSUM_COLUMN_NAME: str | None = None @@ -249,8 +278,6 @@ class TourModeComponentSettings(TemplatedLogitComponentSettings, extra="forbid") COMPUTE_TRIP_MODE_CHOICE_LOGSUMS: bool = False tvpb_mode_path_types: dict[str, Any] | None = None FORCE_ESCORTEE_CHAUFFEUR_MODE_MATCH: bool = True - annotate_tours: PreprocessorSettings | None = None - preprocessor: PreprocessorSettings | list[PreprocessorSettings] | None = None nontour_preprocessor: PreprocessorSettings | list[ PreprocessorSettings ] | None = None diff --git a/activitysim/core/configuration/top.py b/activitysim/core/configuration/top.py index 486c5b78f4..58eaea3b2c 100644 --- a/activitysim/core/configuration/top.py +++ b/activitysim/core/configuration/top.py @@ -2,6 +2,8 @@ from pathlib import Path from typing import Any, Literal +import struct +import time from pydantic import model_validator, validator @@ -537,6 +539,60 @@ def _check_store_skims_in_shm(self): own that pollutes the collected data. """ + expression_profile: bool | None = None + """ + Track the runtime of each individual expression in each spec file. + + .. versionadded:: 1.4 + + The default value of this setting is `None`, which sets no global control + of expression profiling, and allows this feature to be turned on or off + for individual components. If set to `True`, all components will have + expression profiling enabled, and the outputs will be written to files named + based on the trace label unless explicitly set in the `compute_settings` for + individual components. If set to `False`, all components will have expression + profiling disabled. + + This is generally a developer-only feature and not needed for regular usage. + It will add some overhead to the model run, which is only valuable if you + expect the review the expression runtimes with an eye towards improving them. + Production model users should typically have this set explicitly to `False`. + """ + + expression_profile_style: Literal["simple", "grid"] = "grid" + """ + The style of the expression profiling report. + + .. versionadded:: 1.4 + + This setting controls the style of the expression profiling report that is + generated at the end of a model run when :py:attr:`expression_profile` is + `True`. The report summarizes the runtime of each expression in each spec + file, and can be used to identify slow or inefficient expressions. + + The "simple" style generates a simple HTML table with the expression names, + runtimes, and other relevant information. This may be easier to import into + other tools (e.g. Excel) for further analysis if desired. The "grid" style + generates a JavaScript data grid that allows for sorting and filtering of the + expression runtimes, making it easier to analyze large sets of expressions + directly in a web browser with no other outside analysis tools. + """ + + expression_profile_cutoff: float = 0.1 + """ + Minimum runtime for an expression to be included in the expression profile. + + .. versionadded:: 1.4 + + Expressions that take less than this amount of time to evaluate will not be + included in the summary report of expression profiling generated at the end + of a model run. For large scale models, this value can be increased to make + the report file smaller, as only the largest values will typically be of + interest. + + This setting has no effect if :py:attr:`expression_profile` is not `True`. + """ + benchmarking: bool = False """ Flag this model run as a benchmarking run. @@ -721,6 +777,13 @@ def _check_store_skims_in_shm(self): .. versionadded:: 1.3 """ + check_model_settings: bool = True + """ + run checks to validate that YAML settings files are loadable and spec and coefficent csv can be resolved. + + should catch many common errors early, including missing required configurations or specified coefficient labels without defined values. + """ + use_explicit_error_terms: bool = False """ Make choice from random utility model by drawing from distribution of unobserved diff --git a/activitysim/core/estimation.py b/activitysim/core/estimation.py index 6e02aca76a..bbbe376ee4 100644 --- a/activitysim/core/estimation.py +++ b/activitysim/core/estimation.py @@ -5,35 +5,209 @@ import logging import os import shutil +from pathlib import Path +from typing import Literal import pandas as pd -import yaml +from pydantic import model_validator from activitysim.core import simulate, workflow +from activitysim.core.configuration import PydanticReadable from activitysim.core.configuration.base import PydanticBase from activitysim.core.util import reindex from activitysim.core.yaml_tools import safe_dump +from activitysim.core.exceptions import ( + DuplicateWorkflowTableError, + DuplicateLoadableObjectError, + EstimationDataError, +) logger = logging.getLogger("estimation") ESTIMATION_SETTINGS_FILE_NAME = "estimation.yaml" +ESTIMATION_TABLE_RECIPES = { + "interaction_sample_simulate": { + "omnibus_tables": { + "choosers_combined": ["choices", "override_choices", "choosers"], + "alternatives_combined": [ + "interaction_sample_alternatives", + "interaction_expression_values", + ], + }, + "omnibus_tables_append_columns": ["choosers_combined", "alternatives_combined"], + }, + "interaction_simulate": { + "omnibus_tables": { + "choosers_combined": ["choices", "override_choices", "choosers"], + "alternatives_combined": ["interaction_expression_values"], + }, + "omnibus_tables_append_columns": ["choosers_combined", "alternatives_combined"], + }, + "simple_simulate": { + "omnibus_tables": { + "values_combined": [ + "choices", + "override_choices", + "expression_values", + "choosers", + ] + }, + "omnibus_tables_append_columns": ["values_combined"], + }, + "cdap_simulate": { + "omnibus_tables": { + "values_combined": ["choices", "override_choices", "choosers"] + }, + "omnibus_tables_append_columns": ["values_combined"], + }, + "simple_probabilistic": { + "omnibus_tables": { + "values_combined": ["choices", "override_choices", "choosers", "probs"] + }, + "omnibus_tables_append_columns": ["values_combined"], + }, +} + + +def unlink_files(directory_path, file_types=("csv", "yaml", "parquet", "pkl")): + """ + Deletes existing files in directory_path with file_types extensions. + """ + if not os.path.exists(directory_path): + return -def unlink_files(directory_path, file_types=("csv", "yaml")): for file_name in os.listdir(directory_path): if file_name.endswith(file_types): file_path = os.path.join(directory_path, file_name) try: if os.path.isfile(file_path): os.unlink(file_path) - print(f"deleted {file_path}") + logger.debug(f"deleted {file_path}") except Exception as e: - print(e) + logger.error(e) + + +def estimation_enabled(state): + """ + Returns True if estimation.yaml exists in the configs directory. + """ + settings = state.filesystem.read_model_settings( + ESTIMATION_SETTINGS_FILE_NAME, mandatory=False + ) + return settings is not None + + +class SurveyTableConfig(PydanticBase): + file_name: str + index_col: str + + # The dataframe is stored in the loaded config dynamically but not given + # directly in the config file, as it's not a simple serializable object that + # can be written in a YAML file. + class Config: + arbitrary_types_allowed = True + + df: pd.DataFrame | None = None + + +class EstimationTableRecipeConfig(PydanticBase): + omnibus_tables: dict[str, list[str]] + omnibus_tables_append_columns: list[str] + + +class EstimationConfig(PydanticReadable): + SKIP_BUNDLE_WRITE_FOR: list[str] = [] + """List of bundle names to skip writing to disk. + + This is useful for saving disk space and decreasing runtime + if you do not care about the estimation output for all models. + """ + + EDB_FILETYPE: Literal["csv", "parquet", "pkl"] = "parquet" + """File type for dataframes written to the estimation data bundles. + + Options are 'csv', 'parquet', or 'pkl'. When set to 'parquet', if file + writing fails for any reason, it will fall back to 'pkl'. This typically + will happen when the data types in the dataframe are not compatible with + parquet (e.g. Python 'object' dtype with mixed format content). + + Legacy ActivitySim used 'csv' to maximize compatibility with other software. + As of version 1.4, the default changed to 'parquet', which is more efficient + and better preserves data types. + """ + + DELETE_MP_SUBDIRS: bool = True + """Flag to delete the multiprocessing subdirectories after coalescing the results. + + Typically only used for debugging purposes. + """ + + enable: bool = False + """Flag to enable estimation.""" + + bundles: list[str] = [] + """List of component names to create EDBs for.""" + + estimation_table_types: dict[str, str] = {} + """Mapping of component names to estimation table types. + + The keys of this mapping are the model component names, and the values are the + names of the estimation table recipes that should be used to generate the + estimation tables for the model component. The recipes are generally related + to the generic model types, such as 'simple_simulate', 'interaction_simulate', + 'interaction_sample_simulate', etc. + """ + + estimation_table_recipes: dict[str, EstimationTableRecipeConfig] = None + """This option has been removed from the user-facing configuration file. + + Mapping of estimation table recipe names to their configurations. + + The keys of this mapping are the names of the estimation table recipes. + The recipes are generally related to the generic model types, such as + 'simple_simulate', 'interaction_simulate', 'interaction_sample_simulate', + etc. The values are the configurations for the estimation table recipes. + """ + + @model_validator(mode="before") + def check_estimation_table_recipes(cls, values): + if ( + "estimation_table_recipes" in values + and values["estimation_table_recipes"] is not None + ): + raise ValueError( + "estimation_table_recipes is no longer an accepted input. Please delete it from your estimation.yaml file." + ) + return values + + survey_tables: dict[str, SurveyTableConfig] = {} + """Mapping of survey table names to their configurations. + + Each survey table should have a file name and an index column. These files + are where the survey data is read from while running in estimation mode.""" + + # pydantic class validator to ensure that the estimation_table_types + # dictionary is a valid dictionary with string keys and string values, and + # that all the values are in the estimation_table_recipes dictionary + @model_validator(mode="after") + def validate_estimation_table_types(self): + for key, value in self.estimation_table_types.items(): + if value not in ESTIMATION_TABLE_RECIPES: + raise ValueError( + f"estimation_table_types value '{value}' not in estimation_table_recipes" + ) + return self class Estimator: def __init__( - self, state: workflow.State, bundle_name, model_name, estimation_table_recipes + self, + state: workflow.State, + bundle_name: str, + model_name: str, + estimation_table_recipe: EstimationTableRecipeConfig, + settings: EstimationConfig, ): logger.info("Initialize Estimator for'%s'" % (model_name,)) @@ -41,8 +215,9 @@ def __init__( self.bundle_name = bundle_name self.model_name = model_name self.settings_name = model_name - self.estimation_table_recipes = estimation_table_recipes + self.estimation_table_recipe = estimation_table_recipe self.estimating = True + self.settings = settings # ensure the output data directory exists output_dir = self.output_directory() @@ -50,22 +225,21 @@ def __init__( os.makedirs(output_dir) # make directory if needed # delete estimation files - unlink_files(self.output_directory(), file_types=("csv", "yaml")) + unlink_files(self.output_directory()) if self.bundle_name != self.model_name: # kind of inelegant to always delete these, but ok as they are redundantly recreated for each sub model unlink_files( self.output_directory(bundle_directory=True), - file_types=("csv", "yaml"), ) # FIXME - not required? # assert 'override_choices' in self.model_settings, \ # "override_choices not found for %s in %s." % (model_name, ESTIMATION_SETTINGS_FILE_NAME) - self.omnibus_tables = self.estimation_table_recipes["omnibus_tables"] - self.omnibus_tables_append_columns = self.estimation_table_recipes[ - "omnibus_tables_append_columns" - ] + self.omnibus_tables = self.estimation_table_recipe.omnibus_tables + self.omnibus_tables_append_columns = ( + self.estimation_table_recipe.omnibus_tables_append_columns + ) self.tables = {} self.tables_to_cache = [ table_name @@ -139,6 +313,9 @@ def output_directory(self, bundle_directory=False): if self.bundle_name != self.model_name and not bundle_directory: dir = os.path.join(dir, self.model_name) + if self.state.settings.multiprocess: + dir = os.path.join(dir, self.state.get_injectable("pipeline_file_prefix")) + return dir def output_file_path(self, table_name, file_type=None, bundle_directory=False): @@ -150,7 +327,9 @@ def output_file_path(self, table_name, file_type=None, bundle_directory=False): if bundle_directory: file_name = f"{self.bundle_name}_{table_name}" else: - if self.model_name == self.bundle_name: + if "_coefficients" in table_name: + file_name = f"{table_name}" + elif self.model_name == self.bundle_name: file_name = f"{self.model_name}_{table_name}" else: file_name = f"{self.bundle_name}_{table_name}" @@ -160,8 +339,87 @@ def output_file_path(self, table_name, file_type=None, bundle_directory=False): return os.path.join(output_dir, file_name) + def write_parquet(self, df, file_path, index, append=False): + """Convert DF to be parquet compliant and write to disk""" + # Ensure column names are strings for parquet + df.columns = df.columns.astype(str) + + assert (not os.path.isfile(file_path)) or ( + append == True + ), f"file already exists: {file_path}" + + # Explicitly set the data types of the columns + for col_name, col_data in df.items(): + if "int" in str(col_data.dtype): + pass + elif ( + col_data.dtype == "float16" + ): # Handle halffloat type not allowed in parquet + df[col_name] = col_data.astype("float32") + elif "float" in str(col_data.dtype): + pass + elif col_data.dtype == "bool": + pass + elif col_data.dtype == "object": + # first try converting to numeric, if that fails, convert to string + try: + df[col_name] = pd.to_numeric(col_data, errors="raise") + except ValueError: + df[col_name] = col_data.astype(str) + else: + # Convert any other unsupported types to string + df[col_name] = col_data.astype(str) + + self.debug(f"writing table: {file_path}") + # want parquet file to be exactly the same as df read from csv + # therefore we are resetting the index into a column if we want to keep it + # if we don't want to keep it, we are dropping it on write with index=False + if index: + if df.index.name in df.columns: + # replace old index with new one + df.drop(columns=[df.index.name], inplace=True) + df = df.reset_index(drop=False) + + if append and os.path.isfile(file_path): + df.to_parquet(file_path, engine="fastparquet", append=True, index=False) + else: + df.to_parquet(file_path, index=False) + + def write_pickle(self, df, file_path, index, append=False): + """Write DF to disk as pickle""" + file_path = file_path.replace(".csv", ".pkl").replace(".parquet", ".pkl") + assert file_path.endswith(".pkl") + + # want pickle file to be exactly the same as df read from csv + # therefore we are resetting the index into a column if we want to keep it + # if we don't want to keep it, we are dropping it on write with index=False + if index: + if df.index.name in df.columns: + # replace old index with new one + df.drop(columns=[df.index.name], inplace=True) + df = df.reset_index(drop=False) + else: + df = df.reset_index(drop=True) + + assert (not os.path.isfile(file_path)) or ( + append == True + ), f"file already exists: {file_path}" + + if append and os.path.isfile(file_path): + # read the previous df and concat + prev_df = pd.read_pickle(file_path) + df = pd.concat([prev_df, df]) + + df.to_pickle(file_path) + def write_table( - self, df, table_name, index=True, append=True, bundle_directory=False + self, + df, + table_name, + index=True, + append=True, + bundle_directory=False, + filetype="csv", ): """ @@ -173,12 +431,14 @@ def write_table( index: booelan append: boolean bundle_directory: boolean + filetype: str + csv or parquet or pkl """ def cache_table(df, table_name, append): if table_name in self.tables and not append: - raise RuntimeError( + raise DuplicateWorkflowTableError( "cache_table %s append=False and table exists" % (table_name,) ) if table_name in self.tables: @@ -186,21 +446,38 @@ def cache_table(df, table_name, append): else: self.tables[table_name] = df.copy() - def write_table(df, table_name, index, append, bundle_directory): - if table_name.endswith(".csv"): - # pass through filename without adding model or bundle name prefix - file_path = os.path.join( - self.output_directory(bundle_directory), table_name - ) - else: - file_path = self.output_file_path(table_name, "csv", bundle_directory) + def write_table(df, table_name, index, append, bundle_directory, filetype): + # remove file extension if present + table_name = Path(table_name).stem + # set new full file path with desired file type + file_path = self.output_file_path(table_name, filetype, bundle_directory) + + # check if file exists file_exists = os.path.isfile(file_path) if file_exists and not append: - raise RuntimeError( + raise DuplicateLoadableObjectError( "write_table %s append=False and file exists: %s" % (table_name, file_path) ) - df.to_csv(file_path, mode="a", index=index, header=(not file_exists)) + if filetype == "csv": + # check if index is in columns and drop it if so + if index and (df.index.name in df.columns): + df.drop(columns=df.index.name, inplace=True) + df.to_csv(file_path, mode="a", index=index, header=(not file_exists)) + elif filetype == "parquet": + try: + self.write_parquet(df, file_path, index, append) + except Exception as e: + logger.error( + f"Error writing parquet: {file_path} because {e}, falling back to pickle" + ) + self.write_pickle(df, file_path, index, append) + elif filetype == "pkl": + self.write_pickle(df, file_path, index, append) + else: + raise IOError( + f"Unsupported filetype: {filetype}, allowed options are csv, parquet, pkl" + ) assert self.estimating @@ -213,18 +490,14 @@ def write_table(df, table_name, index, append, bundle_directory): self.debug("write_table cache: %s" % table_name) if write: - write_table(df, table_name, index, append, bundle_directory) + write_table(df, table_name, index, append, bundle_directory, filetype) self.debug("write_table write: %s" % table_name) def write_omnibus_table(self): if len(self.omnibus_tables) == 0: return - settings = self.state.filesystem.read_model_settings( - ESTIMATION_SETTINGS_FILE_NAME, mandatory=False - ) - - edbs_to_skip = settings.get("SKIP_BUNDLE_WRITE_FOR", []) + edbs_to_skip = self.settings.SKIP_BUNDLE_WRITE_FOR if self.bundle_name in edbs_to_skip: self.debug(f"Skipping write to disk for {self.bundle_name}") return @@ -252,16 +525,53 @@ def write_omnibus_table(self): else: df = pd.concat([self.tables[t] for t in table_names], axis=concat_axis) + # remove duplicated columns, keeping the first instance + df = df.loc[:, ~df.columns.duplicated()] + + # set index if not already set according to lowest level heirarchy + # df missing index is typically coming from interaction_simulate expression values + # important for sorting and thus for multiprocessing to be consistent with single + if df.index.name is None: + if "trip_id" in df.columns: + df.set_index("trip_id", inplace=True) + elif "tour_id" in df.columns: + df.set_index("tour_id", inplace=True) + elif "person_id" in df.columns: + df.set_index("person_id", inplace=True) + elif "household_id" in df.columns: + df.set_index("household_id", inplace=True) + else: + EstimationDataError( + f"No index column found in omnibus table {omnibus_table}: {df}" + ) + self.debug(f"sorting tables: {table_names}") df.sort_index(ascending=True, inplace=True, kind="mergesort") - file_path = self.output_file_path(omnibus_table, "csv") - assert not os.path.isfile(file_path) + filetype = self.settings.EDB_FILETYPE + + if filetype == "csv": + file_path = self.output_file_path(omnibus_table, "csv") + assert not os.path.isfile(file_path) + + self.debug(f"writing table: {file_path}") + # check if index is in columns and drop it if so + if df.index.name in df.columns: + df.drop(columns=df.index.name, inplace=True) + df.to_csv(file_path, mode="a", index=True, header=True) - self.debug(f"writing table: {file_path}") - df.to_csv(file_path, mode="a", index=True, header=True) + elif filetype == "parquet": + file_path = self.output_file_path(omnibus_table, "parquet") + self.write_parquet(df, file_path, index=True, append=False) - self.debug("write_omnibus_choosers: %s" % file_path) + elif filetype == "pkl": + file_path = self.output_file_path(omnibus_table, "pkl") + self.write_pickle(df, file_path, index=True, append=False) + + else: + raise IOError(f"Unsupported filetype: {filetype}") + + self.debug("wrote_omnibus_choosers: %s" % file_path) def write_dict(self, d, dict_name, bundle_directory): assert self.estimating @@ -304,33 +614,48 @@ def write_coefficients( base_file_name = os.path.basename(file_name) assert self.estimating - self.write_table(coefficients_df, base_file_name, append=False) + self.write_table(coefficients_df, base_file_name, append=False, filetype="csv") def write_coefficients_template(self, model_settings): assert self.estimating if isinstance(model_settings, PydanticBase): - model_settings = model_settings.dict() + model_settings = model_settings.model_dump() coefficients_df = simulate.read_model_coefficient_template( self.state.filesystem, model_settings ) tag = "coefficients_template" - self.write_table(coefficients_df, tag, append=False) + self.write_table(coefficients_df, tag, append=False, filetype="csv") def write_choosers(self, choosers_df): - self.write_table(choosers_df, "choosers", append=True) + self.write_table( + choosers_df, + "choosers", + append=True, + filetype=self.settings.EDB_FILETYPE, + ) def write_choices(self, choices): if isinstance(choices, pd.Series): choices = choices.to_frame(name="model_choice") assert list(choices.columns) == ["model_choice"] - self.write_table(choices, "choices", append=True) + self.write_table( + choices, + "choices", + append=True, + filetype=self.settings.EDB_FILETYPE, + ) def write_override_choices(self, choices): if isinstance(choices, pd.Series): choices = choices.to_frame(name="override_choice") assert list(choices.columns) == ["override_choice"] - self.write_table(choices, "override_choices", append=True) + self.write_table( + choices, + "override_choices", + append=True, + filetype=self.settings.EDB_FILETYPE, + ) def write_constants(self, constants): self.write_dict(self, constants, "model_constants") @@ -368,7 +693,7 @@ def write_model_settings( ) assert not os.path.isfile(file_path) with open(file_path, "w") as f: - safe_dump(model_settings.dict(), f) + safe_dump(model_settings.model_dump(), f) else: if "include_settings" in model_settings: file_path = self.output_file_path( @@ -386,68 +711,21 @@ def write_model_settings( model_settings, "inherited_model_settings", bundle_directory ) - def melt_alternatives(self, df): - alt_id_name = self.alt_id_column_name - - assert alt_id_name is not None, ( - "alt_id not set. Did you forget to call set_alt_id()? (%s)" - % self.model_name - ) - assert ( - alt_id_name in df - ), "alt_id_column_name '%s' not in alternatives table (%s)" % ( - alt_id_name, - self.model_name, - ) - - variable_column = "variable" - - # alt_dest util_dist_0_1 util_dist_1_2 ... - # person_id ... - # 31153 1 1.0 0.75 ... - # 31153 2 1.0 0.46 ... - # 31153 3 1.0 0.28 ... - - if df.index.name is not None: - chooser_name = df.index.name - assert self.chooser_id_column_name in (chooser_name, None) - df = df.reset_index() - else: - assert self.chooser_id_column_name is not None - chooser_name = self.chooser_id_column_name - assert chooser_name in df - - # mergesort is the only stable sort, and we want the expressions to appear in original df column order - melt_df = ( - pd.melt(df, id_vars=[chooser_name, alt_id_name]) - .sort_values(by=chooser_name, kind="mergesort") - .rename(columns={"variable": variable_column}) - ) - - # person_id,alt_dest,expression,value - # 31153,1,util_dist_0_1,1.0 - # 31153,2,util_dist_0_1,1.0 - # 31153,3,util_dist_0_1,1.0 - - melt_df = melt_df.set_index( - [chooser_name, variable_column, alt_id_name] - ).unstack(2) - melt_df.columns = melt_df.columns.droplevel(0) - melt_df = melt_df.reset_index(1) - - # person_id,expression,1,2,3,4,5,... - # 31153,util_dist_0_1,0.75,0.46,0.27,0.63,0.48,... - # 31153,util_dist_1_2,0.0,0.0,0.0,0.0,0.0,... - # 31153,util_dist_2_3,0.0,0.0,0.0,0.0,0.0,... - - return melt_df - def write_interaction_expression_values(self, df): - df = self.melt_alternatives(df) - self.write_table(df, "interaction_expression_values", append=True) + self.write_table( + df, + "interaction_expression_values", + append=True, + filetype=self.settings.EDB_FILETYPE, + ) def write_expression_values(self, df): - self.write_table(df, "expression_values", append=True) + self.write_table( + df, + "expression_values", + append=True, + filetype=self.settings.EDB_FILETYPE, + ) def write_alternatives(self, alternatives_df, bundle_directory=False): self.write_table( @@ -458,15 +736,19 @@ def write_alternatives(self, alternatives_df, bundle_directory=False): ) def write_interaction_sample_alternatives(self, alternatives_df): - alternatives_df = self.melt_alternatives(alternatives_df) self.write_table( - alternatives_df, "interaction_sample_alternatives", append=True + alternatives_df, + "interaction_sample_alternatives", + append=True, + filetype=self.settings.EDB_FILETYPE, ) def write_interaction_simulate_alternatives(self, interaction_df): - interaction_df = self.melt_alternatives(interaction_df) self.write_table( - interaction_df, "interaction_simulate_alternatives", append=True + interaction_df, + "interaction_simulate_alternatives", + append=True, + filetype=self.settings.EDB_FILETYPE, ) def get_survey_values(self, model_values, table_name, column_names): @@ -498,9 +780,11 @@ class EstimationManager(object): def __init__(self): self.settings_initialized = False self.bundles = [] - self.estimation_table_recipes = {} - self.model_estimation_table_types = {} + self.estimation_table_recipes: dict[str, EstimationTableRecipeConfig] = {} + self.estimation_table_types: dict[str, str] = {} self.estimating = {} + self.settings = None + self.enabled = False def initialize_settings(self, state): # FIXME - can't we just initialize in init and handle no-presence of settings file as not enabled @@ -508,46 +792,53 @@ def initialize_settings(self, state): return assert not self.settings_initialized - settings = state.filesystem.read_model_settings( - ESTIMATION_SETTINGS_FILE_NAME, mandatory=False + self.settings = EstimationConfig.read_settings_file( + state.filesystem, ESTIMATION_SETTINGS_FILE_NAME, mandatory=False ) - if not settings: - # if the model settings file is not found, we are not in estimation mode. + if not self.settings: + # if the model self.settings file is not found, we are not in estimation mode. self.enabled = False else: - self.enabled = settings.get("enable", "True") - self.bundles = settings.get("bundles", []) + self.enabled = self.settings.enable + self.bundles = self.settings.bundles - self.model_estimation_table_types = settings.get( - "model_estimation_table_types", {} - ) - self.estimation_table_recipes = settings.get("estimation_table_recipes", {}) + self.estimation_table_types = self.settings.estimation_table_types + self.estimation_table_recipes = ESTIMATION_TABLE_RECIPES if self.enabled: - self.survey_tables = settings.get("survey_tables", {}) + self.survey_tables = self.settings.survey_tables for table_name, table_info in self.survey_tables.items(): assert ( - "file_name" in table_info - ), "No file name specified for survey_table '%s' in %s" % ( - table_name, - ESTIMATION_SETTINGS_FILE_NAME, - ) + table_info.file_name + ), f"No file name specified for survey_table '{table_name}' in {ESTIMATION_SETTINGS_FILE_NAME}" file_path = state.filesystem.get_data_file_path( - table_info["file_name"], mandatory=True + table_info.file_name, mandatory=True ) assert os.path.exists( file_path ), "File for survey table '%s' not found: %s" % (table_name, file_path) df = pd.read_csv(file_path) - index_col = table_info.get("index_col") + index_col = table_info.index_col if index_col is not None: assert ( index_col in df.columns ), "Index col '%s' not in survey_table '%s' in file: %s % (index_col, table_name, file_path)" df.set_index(index_col, inplace=True) + # if multiprocessing then only return the households that are in the pipeline + if state.settings.multiprocess: + pipeline_hh_ids = state.get_table("households").index + if table_name == "households": + df = df.reindex(pipeline_hh_ids) + assert pipeline_hh_ids.equals( + df.index + ), "household_ids not equal between survey and pipeline" + else: + assert "household_id" in df.columns + df = df[df.household_id.isin(pipeline_hh_ids)] + # add the table df to survey_tables - table_info["df"] = df + table_info.df = df self.settings_initialized = True @@ -589,13 +880,13 @@ def begin_estimation( ), "Cant begin estimating %s - already estimating that model." % (model_name,) assert ( - bundle_name in self.model_estimation_table_types + bundle_name in self.estimation_table_types ), "No estimation_table_type for %s in %s." % ( bundle_name, ESTIMATION_SETTINGS_FILE_NAME, ) - model_estimation_table_type = self.model_estimation_table_types[bundle_name] + model_estimation_table_type = self.estimation_table_types[bundle_name] assert ( model_estimation_table_type in self.estimation_table_recipes @@ -609,9 +900,10 @@ def begin_estimation( state, bundle_name, model_name, - estimation_table_recipes=self.estimation_table_recipes[ - model_estimation_table_type - ], + estimation_table_recipe=EstimationTableRecipeConfig( + **self.estimation_table_recipes[model_estimation_table_type] + ), + settings=self.settings, ) return self.estimating[model_name] @@ -626,7 +918,7 @@ def get_survey_table(self, table_name): "EstimationManager. get_survey_table: survey table '%s' not in survey_tables" % table_name ) - df = self.survey_tables[table_name].get("df") + df = self.survey_tables[table_name].df return df def get_survey_values(self, model_values, table_name, column_names): @@ -658,7 +950,7 @@ def get_survey_values(self, model_values, table_name, column_names): % (missing_columns, table_name) ) print("survey table columns: %s" % (survey_df.columns,)) - raise RuntimeError( + raise EstimationDataError( "missing columns (%s) in survey table %s" % (missing_columns, table_name) ) @@ -711,7 +1003,7 @@ def get_survey_values(self, model_values, table_name, column_names): logger.error( "couldn't get_survey_values for %s in %s\n" % (c, table_name) ) - raise RuntimeError( + raise EstimationDataError( "couldn't get_survey_values for %s in %s\n" % (c, table_name) ) @@ -736,5 +1028,81 @@ def get_survey_values(self, model_values, table_name, column_names): return values[column_name] if column_name else values + def get_survey_destination_choices(self, state, choosers, trace_label): + """ + Returning the survey choices for the destination choice model. + This gets called from inside interaction_sample and is used to + ensure the choices include the override choices when sampling alternatives. + + Parameters + ---------- + state : workflow.State + trace_label : str + The model name. + + Returns + ------- + pd.Series : The survey choices for the destination choice model. + """ + if "accessibilities" in trace_label: + # accessibilities models to not have survey values + return None + + model = trace_label.split(".")[0] + if model == "school_location": + survey_choices = manager.get_survey_values( + choosers.index, "persons", "school_zone_id" + ) + elif model == "workplace_location": + survey_choices = manager.get_survey_values( + choosers.index, "persons", "workplace_zone_id" + ) + elif model in [ + "joint_tour_destination", + "atwork_subtour_destination", + "non_mandatory_tour_destination", + ]: + survey_choices = manager.get_survey_values( + choosers.index, "tours", "destination" + ) + elif model == "trip_destination": + survey_choices = manager.get_survey_values( + choosers.index, "trips", "destination" + ) + elif model == "parking_location": + # need to grab parking location column name from its settings + from activitysim.abm.models.parking_location_choice import ( + ParkingLocationSettings, + ) + + model_settings = ParkingLocationSettings.read_settings_file( + state.filesystem, + "parking_location_choice.yaml", + ) + survey_choices = manager.get_survey_values( + choosers.index, "trips", model_settings.ALT_DEST_COL_NAME + ) + else: + # since this fucntion is called from inside interaction_sample, + # we don't want to return anything for other models that aren't destination choice + # not implemented models include scheduling models and tour_od_choice + logger.debug(f"Not grabbing survey choices for {model}.") + return None + + if "presample.interaction_sample" in trace_label: + # presampling happens for destination choice of two-zone systems. + # They are pre-sampling TAZs but the survey value destination is MAZs. + land_use = state.get_table("land_use") + TAZ_col = "TAZ" if "TAZ" in land_use.columns else "taz" + assert ( + TAZ_col in land_use.columns + ), "Cannot find TAZ column in land_use table." + maz_to_taz_map = land_use[TAZ_col].to_dict() + # allow for unmapped TAZs + maz_to_taz_map[-1] = -1 + survey_choices = survey_choices.map(maz_to_taz_map) + + return survey_choices + manager = EstimationManager() diff --git a/activitysim/core/exceptions.py b/activitysim/core/exceptions.py index 29d8f03a1a..878ed6cfc9 100644 --- a/activitysim/core/exceptions.py +++ b/activitysim/core/exceptions.py @@ -56,3 +56,39 @@ class ReadOnlyError(IOError): class MissingInputTableDefinition(RuntimeError): """An input table definition was expected but not found.""" + + +class SystemConfigurationError(RuntimeError): + """An error in the system configuration (possibly in settings.yaml) was found.""" + + +class ModelConfigurationError(RuntimeError): + """An error in the model configuration was found.""" + + +class InvalidTravelError(RuntimeError): + """Travel behavior could not be completed in a valid way.""" + + +class TableSlicingError(RuntimeError): + """An error occurred trying to slice a table.""" + + +class InputTableError(RuntimeError): + """An issue with the input population was found.""" + + +class SubprocessError(RuntimeError): + """An error occurred in a subprocess.""" + + +class SegmentedSpecificationError(RuntimeError): + """An error was caused by creating an invalid spec table for a segmented model component.""" + + +class TableIndexError(RuntimeError): + """An error related to the index of a table in the pipeline.""" + + +class EstimationDataError(RuntimeError): + """An error related to estimation data.""" diff --git a/activitysim/core/expressions.py b/activitysim/core/expressions.py index 413636d3f4..f8a255fb12 100644 --- a/activitysim/core/expressions.py +++ b/activitysim/core/expressions.py @@ -6,7 +6,7 @@ import pandas as pd -from activitysim.core import assign, simulate, tracing, workflow +from activitysim.core import config, assign, simulate, tracing, workflow from activitysim.core.configuration.base import PreprocessorSettings, PydanticBase from activitysim.core.util import ( assign_in_place, @@ -139,6 +139,7 @@ def compute_columns( df, _locals_dict, trace_rows=state.tracing.trace_targets(df), + trace_label=trace_label, ) if trace_results is not None: @@ -184,26 +185,65 @@ def assign_columns( def annotate_preprocessors( state: workflow.State, df: pd.DataFrame, - locals_dict, - skims, + locals_dict: dict, + skims: dict | None, model_settings: PydanticBase | dict, trace_label: str, + preprocessor_setting_name: str = "preprocessor", ): - locals_d = {} - locals_d.update(locals_dict) - locals_d.update(skims) + """ + Look through the preprocessor settings and apply the calculations to the dataframe. + This is generally called before the main model calculations to prepare the data. + + Parameters + ---------- + state : workflow.State + The current state of the workflow. + df : pd.DataFrame + DataFrame to which the preprocessor settings will be applied. + locals_dict : dict + Dictionary of local variables to be used in the expressions. + skims : dict | None + Dictionary of skims to be used in the expressions. + model_settings : PydanticBase | dict + Model settings containing the preprocessor settings. + trace_label : str + Label for tracing the operations. + preprocessor_setting_name : str + Name of the preprocessor settings key in the model settings. + + Returns + ------- + None -- dataframe is modified in place + + """ + if isinstance(model_settings, PydanticBase): + preprocessor_settings = getattr(model_settings, preprocessor_setting_name, []) + elif isinstance(model_settings, dict): + preprocessor_settings = model_settings.get(preprocessor_setting_name, []) + else: + raise ValueError( + f"Expected model_settings to be PydanticBase or dict, got {type(model_settings)}" + ) + + if not preprocessor_settings or preprocessor_settings == []: + return - try: - preprocessor_settings = model_settings.preprocessor - except AttributeError: - preprocessor_settings = model_settings.get("preprocessor", []) - if preprocessor_settings is None: - preprocessor_settings = [] if not isinstance(preprocessor_settings, list): assert isinstance(preprocessor_settings, dict | PreprocessorSettings) preprocessor_settings = [preprocessor_settings] - simulate.set_skim_wrapper_targets(df, skims) + locals_d = {} + locals_d.update(locals_dict) + if skims: + try: + simulate.set_skim_wrapper_targets(df, skims) + locals_d.update(skims) + except AssertionError as e: + logger.warning( + "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.", + e, + ) for preproc_settings in preprocessor_settings: results = compute_columns( @@ -211,7 +251,9 @@ def annotate_preprocessors( df=df, model_settings=preproc_settings, locals_dict=locals_d, - trace_label=trace_label, + trace_label=tracing.extend_trace_label( + trace_label, preprocessor_setting_name + ), ) assign_in_place( @@ -219,6 +261,85 @@ def annotate_preprocessors( ) +def annotate_tables( + state: workflow.State, + model_settings: PydanticBase | dict, + trace_label: str, + skims: dict | None = None, + locals_dict: dict | None = None, +): + """ + Look through the annotate settings and apply the calculations to the tables. + This is generally called after the main model calculations to add data to output tables. + + Parameters + ---------- + state : workflow.State + The current state of the workflow. + model_settings : PydanticBase | dict + Model settings containing the annotation settings for various tables. + trace_label : str + Label for tracing the operations. + skims : dict | None + Dictionary of skims to be used in the expressions, if applicable. + locals_dict : dict | None + Dictionary of local variables to be used in the expressions, if applicable. + + Returns + ------- + None -- tables are modified in place + """ + + # process tables in least to most aggregated order + tables = ["trips", "tours", "vehicles", "persons", "households"] + + for table_name in tables: + if isinstance(model_settings, PydanticBase): + annotate_settings = getattr(model_settings, f"annotate_{table_name}", None) + elif isinstance(model_settings, dict): + annotate_settings = model_settings.get(f"annotate_{table_name}", None) + else: + raise ValueError( + f"Expected model_settings to be PydanticBase or dict, got {type(model_settings)}" + ) + + if annotate_settings is None: + continue + assert isinstance( + annotate_settings, (dict, PreprocessorSettings) + ), f"Expected annotate_{table_name} to be dict or PreprocessorSettings, got {type(annotate_settings)}" + + df = state.get_dataframe(table_name) + + locals_d = {} + if skims: + try: + simulate.set_skim_wrapper_targets(df, skims) + locals_d.update(skims) + except AssertionError as e: + logger.warning( + "Failed to set skim wrapper targets: %s. Skims wrappers may not be used in expressions.", + e, + ) + if locals_dict: + locals_d.update(locals_dict) + + results = compute_columns( + state, + df=df, + model_settings=annotate_settings, + locals_dict=locals_d, + trace_label=tracing.extend_trace_label(trace_label, "annotate_persons"), + ) + + assign_in_place( + df, results, state.settings.downcast_int, state.settings.downcast_float + ) + + # write table with new columns back to state + state.add_table(table_name, df) + + def filter_chooser_columns(choosers, chooser_columns): missing_columns = [c for c in chooser_columns if c not in choosers] if missing_columns: diff --git a/activitysim/core/flow.py b/activitysim/core/flow.py index d3e83e72bc..e6771b6f43 100644 --- a/activitysim/core/flow.py +++ b/activitysim/core/flow.py @@ -51,7 +51,7 @@ @contextlib.contextmanager def logtime(tag, tag2=""): - logger.info(f"begin {tag} {tag2}") + logger.debug(f"begin {tag} {tag2}") t0 = time.time() try: yield @@ -59,7 +59,7 @@ def logtime(tag, tag2=""): logger.error(f"error in {tag} after {timedelta(seconds=time.time()-t0)} {tag2}") raise else: - logger.info(f"completed {tag} in {timedelta(seconds=time.time()-t0)} {tag2}") + logger.debug(f"completed {tag} in {timedelta(seconds=time.time()-t0)} {tag2}") class TimeLogger: @@ -77,7 +77,7 @@ def mark(self, tag, ping=True, logger=None, suffix=""): self._time_log.append((tag, timedelta(seconds=elapsed))) self._time_point = now if logger is not None: - logger.info( + logger.debug( "elapsed time {0} {1} {2}".format( tag, timedelta(seconds=elapsed), @@ -94,7 +94,7 @@ def mark(self, tag, ping=True, logger=None, suffix=""): else: self.aggregate_timing[tag] += elapsed - def summary(self, logger, tag, level=20, suffix=None): + def summary(self, logger, tag, level=10, suffix=None): gross_elaspsed = time.time() - self._time_start if suffix: msg = f"{tag} in {timedelta(seconds=gross_elaspsed)}: ({suffix})\n" @@ -271,11 +271,11 @@ def skims_mapping( primary_origin_col_name=None, predigitized_time_periods=False, ): - logger.info("loading skims_mapping") - logger.info(f"- orig_col_name: {orig_col_name}") - logger.info(f"- dest_col_name: {dest_col_name}") - logger.info(f"- stop_col_name: {stop_col_name}") - logger.info(f"- primary_origin_col_name: {primary_origin_col_name}") + logger.debug("loading skims_mapping") + logger.debug(f"- orig_col_name: {orig_col_name}") + logger.debug(f"- dest_col_name: {dest_col_name}") + logger.debug(f"- stop_col_name: {stop_col_name}") + logger.debug(f"- primary_origin_col_name: {primary_origin_col_name}") skim_dataset = state.get_injectable("skim_dataset") if zone_layer == "maz" or zone_layer is None: odim = "omaz" if "omaz" in skim_dataset.dims else "otaz" @@ -297,7 +297,7 @@ def skims_mapping( else: raise ValueError(f"unknown zone layer {zone_layer!r}") if zone_layer: - logger.info(f"- zone_layer: {zone_layer}") + logger.debug(f"- zone_layer: {zone_layer}") if ( orig_col_name is not None and dest_col_name is not None @@ -574,7 +574,7 @@ def _apply_filter(_dataset, renames: list): if choosers is None: logger.info(f"empty flow on {trace_label}") else: - logger.info(f"{len(choosers)} chooser rows on {trace_label}") + logger.debug(f"{len(choosers)} chooser rows on {trace_label}") flow_tree = sh.DataTree(df=[] if choosers is None else choosers) idx_name = choosers.index.name or "index" rename_dataset_cols = [ @@ -598,7 +598,7 @@ def _apply_filter(_dataset, renames: list): ) flow_tree.root_dataset = flow_tree.root_dataset # apply the filter else: - logger.info( + logger.debug( f"{len(choosers)} chooser rows and {len(interacts)} interact rows on {trace_label}" ) top = sh.dataset.from_named_objects( @@ -697,7 +697,7 @@ def _apply_filter(_dataset, renames: list): for i, v in extra_vars.items(): readme += f"\n - {i}: {v}" - logger.info(f"setting up sharrow flow {trace_label}") + logger.debug(f"setting up sharrow flow {trace_label}") extra_hash_data = () if zone_layer: extra_hash_data += (zone_layer,) diff --git a/activitysim/core/input.py b/activitysim/core/input.py index 51730fcf02..ecac76b07b 100644 --- a/activitysim/core/input.py +++ b/activitysim/core/input.py @@ -5,11 +5,15 @@ import logging import os +import numpy as np import pandas as pd from activitysim.core import util, workflow from activitysim.core.configuration import InputTable -from activitysim.core.exceptions import MissingInputTableDefinition +from activitysim.core.exceptions import ( + MissingInputTableDefinition, + ModelConfigurationError, +) logger = logging.getLogger(__name__) @@ -194,7 +198,7 @@ def read_from_table_info(table_info: InputTable, state): assert ( df[index_col] == df[index_col].astype(int) ).all(), f"Index col '{index_col}' has non-integer values" - df[index_col] = df[index_col].astype(int) + df[index_col] = df[index_col].astype(np.int64) df.set_index(index_col, inplace=True) else: # FIXME not sure we want to do this. More likely they omitted index col than that they want to name it? @@ -203,7 +207,9 @@ def read_from_table_info(table_info: InputTable, state): f"index_col '{index_col}' specified in configs but not in {tablename} table!" ) logger.error(f"{tablename} columns are: {list(df.columns)}") - raise RuntimeError(f"index_col '{index_col}' not in {tablename} table!") + raise ModelConfigurationError( + f"index_col '{index_col}' not in {tablename} table!" + ) if keep_columns: logger.debug("keeping columns: %s" % keep_columns) @@ -213,7 +219,9 @@ def read_from_table_info(table_info: InputTable, state): f"{list(set(keep_columns).difference(set(df.columns)))}" ) logger.error(f"{tablename} table has columns: {list(df.columns)}") - raise RuntimeError(f"Required columns missing from {tablename} table") + raise ModelConfigurationError( + f"Required columns missing from {tablename} table" + ) df = df[keep_columns] diff --git a/activitysim/core/interaction_sample.py b/activitysim/core/interaction_sample.py index 47548e442d..6f61c009db 100644 --- a/activitysim/core/interaction_sample.py +++ b/activitysim/core/interaction_sample.py @@ -9,6 +9,7 @@ from activitysim.core import ( chunk, + estimation, interaction_simulate, logit, simulate, @@ -17,8 +18,10 @@ workflow, ) from activitysim.core.configuration.base import ComputeSettings +from activitysim.core.exceptions import SegmentedSpecificationError from activitysim.core.skim_dataset import DatasetWrapper from activitysim.core.skim_dictionary import SkimWrapper +from activitysim.core.workflow import State logger = logging.getLogger(__name__) @@ -108,13 +111,12 @@ def make_sample_choices_utility_based( chunk_sizer.log_df(trace_label, "chooser_idx", None) del chosen_destinations chunk_sizer.log_df(trace_label, "chosen_destinations", None) - del probs - chunk_sizer.log_df(trace_label, "probs", None) # handing this off to caller + chunk_sizer.log_df(trace_label, "probs", None) chunk_sizer.log_df(trace_label, "choices_df", None) - return choices_df + return choices_df, probs def make_sample_choices( @@ -310,7 +312,7 @@ def _interaction_sample( ) if len(spec.columns) > 1: - raise RuntimeError("spec must have only one column") + raise SegmentedSpecificationError("spec must have only one column") # if using skims, copy index into the dataframe, so it will be # available as the "destination" for set_skim_wrapper_targets @@ -578,7 +580,7 @@ def _interaction_sample( trace_choosers=choosers, ) - choices_df = make_sample_choices_utility_based( + choices_df, probs = make_sample_choices_utility_based( state, choosers, utilities, @@ -590,8 +592,25 @@ def _interaction_sample( trace_label=trace_label, chunk_sizer=chunk_sizer, ) + del utilities chunk_sizer.log_df(trace_label, "utilities", None) + + if estimation.manager.enabled and sample_size > 0: + + choices_df = _ensure_chosen_alts_in_sample( + alt_col_name, + alternatives, + choices_df, + choosers, + probs, + state, + trace_label, + ) + + del probs + chunk_sizer.log_df(trace_label, "probs", None) + else: # convert to probabilities (utilities exponentiated and normalized to probs) # probs is same shape as utilities, one row per chooser and one column for alternative @@ -628,6 +647,19 @@ def _interaction_sample( chunk_sizer=chunk_sizer, ) + chunk_sizer.log_df(trace_label, "choices_df", choices_df) + + if estimation.manager.enabled and sample_size > 0: + choices_df = _ensure_chosen_alts_in_sample( + alt_col_name, + alternatives, + choices_df, + choosers, + probs, + state, + trace_label, + ) + del probs chunk_sizer.log_df(trace_label, "probs", None) @@ -677,6 +709,49 @@ def _interaction_sample( return choices_df +def _ensure_chosen_alts_in_sample( + alt_col_name, + alternatives: pd.DataFrame, + choices_df: pd.DataFrame, + choosers: pd.DataFrame, + probs: pd.DataFrame, + state: State, + trace_label, +) -> pd.DataFrame: + # we need to ensure chosen alternative is included in the sample + survey_choices = estimation.manager.get_survey_destination_choices( + state, choosers, trace_label + ) + if survey_choices is not None: + assert ( + survey_choices.index == choosers.index + ).all(), "survey_choices and choosers must have the same index" + survey_choices.name = alt_col_name + survey_choices = survey_choices.dropna().astype(choices_df[alt_col_name].dtype) + + # merge all survey choices onto choices_df + probs_df = probs.reset_index().melt( + id_vars=[choosers.index.name], + var_name=alt_col_name, + value_name="prob", + ) + # probs are numbered 0..n-1 so we need to map back to alt ids + zone_map = pd.Series(alternatives.index).to_dict() + probs_df[alt_col_name] = probs_df[alt_col_name].map(zone_map) + + survey_choices = pd.merge( + survey_choices, + probs_df, + on=[choosers.index.name, alt_col_name], + how="left", + ) + survey_choices["rand"] = 0 + survey_choices["prob"].fillna(0, inplace=True) + choices_df = pd.concat([choices_df, survey_choices], ignore_index=True) + choices_df.sort_values(by=[choosers.index.name], inplace=True) + return choices_df + + def interaction_sample( state: workflow.State, choosers: pd.DataFrame, diff --git a/activitysim/core/interaction_sample_simulate.py b/activitysim/core/interaction_sample_simulate.py index 0914334276..e73f64f4fb 100644 --- a/activitysim/core/interaction_sample_simulate.py +++ b/activitysim/core/interaction_sample_simulate.py @@ -10,6 +10,7 @@ from activitysim.core import chunk, interaction_simulate, logit, tracing, util, workflow from activitysim.core.configuration.base import ComputeSettings from activitysim.core.simulate import set_skim_wrapper_targets +from activitysim.core.exceptions import SegmentedSpecificationError logger = logging.getLogger(__name__) @@ -115,7 +116,7 @@ def _interaction_sample_simulate( ) if len(spec.columns) > 1: - raise RuntimeError("spec must have only one column") + raise SegmentedSpecificationError("spec must have only one column") # if using skims, copy index into the dataframe, so it will be # available as the "destination" for the skims dereference below @@ -133,7 +134,7 @@ def _interaction_sample_simulate( # assert alternatives.index.name == choosers.index.name # asserting the index names are the same tells us nothing about the underlying data so why? - logger.info( + logger.debug( f"{trace_label} start merging choosers and alternatives to create interaction_df" ) @@ -169,7 +170,7 @@ def _interaction_sample_simulate( ) interaction_df = alternatives.join(choosers, how="left", rsuffix="_chooser") - logger.info( + logger.debug( f"{trace_label} end merging choosers and alternatives to create interaction_df" ) diff --git a/activitysim/core/interaction_simulate.py b/activitysim/core/interaction_simulate.py index 4aa3166d87..13abb9f77f 100644 --- a/activitysim/core/interaction_simulate.py +++ b/activitysim/core/interaction_simulate.py @@ -7,14 +7,16 @@ from builtins import zip from collections import OrderedDict from datetime import timedelta +from pathlib import Path from typing import Mapping import numpy as np import pandas as pd -from activitysim.core import chunk, logit, simulate, tracing, util, workflow +from activitysim.core import chunk, logit, simulate, timing, tracing, util, workflow from activitysim.core.configuration.base import ComputeSettings from activitysim.core.fast_eval import fast_eval +from activitysim.core.exceptions import SegmentedSpecificationError logger = logging.getLogger(__name__) @@ -82,7 +84,7 @@ def eval_interaction_utilities( start_time = time.time() trace_label = tracing.extend_trace_label(trace_label, "eval_interaction_utils") - logger.info("Running eval_interaction_utilities on %s rows" % df.shape[0]) + logger.debug("Running eval_interaction_utilities on %s rows" % df.shape[0]) sharrow_enabled = state.settings.sharrow if compute_settings is None: @@ -90,7 +92,7 @@ def eval_interaction_utilities( if compute_settings.sharrow_skip: sharrow_enabled = False - logger.info(f"{trace_label} sharrow_enabled is {sharrow_enabled}") + logger.debug(f"{trace_label} sharrow_enabled is {sharrow_enabled}") trace_eval_results = None @@ -263,6 +265,23 @@ def to_series(x): exprs = spec.index labels = spec.index + # init a performance timer if needed + if ( + state.settings.expression_profile + and compute_settings.performance_log is None + ): + perf_log_file = Path(trace_label + ".log") + elif ( + state.settings.expression_profile is False + or compute_settings.performance_log is False + ): + perf_log_file = None + elif compute_settings.performance_log is True: + perf_log_file = Path(trace_label + ".log") + else: + perf_log_file = compute_settings.performance_log + performance_timer = timing.EvalTiming(perf_log_file) + with compute_settings.pandas_option_context(): for expr, label, coefficient in zip(exprs, labels, spec.iloc[:, 0]): try: @@ -270,7 +289,8 @@ def to_series(x): if expr.startswith("_"): target = expr[: expr.index("@")] rhs = expr[expr.index("@") + 1 :] - v = to_series(eval(rhs, globals(), locals_d)) + with performance_timer.time_expression(expr): + v = to_series(eval(rhs, globals(), locals_d)) # update locals to allows us to ref previously assigned targets locals_d[target] = v @@ -285,10 +305,11 @@ def to_series(x): # they have a non-zero dummy coefficient to avoid being removed from spec as NOPs continue - if expr.startswith("@"): - v = to_series(eval(expr[1:], globals(), locals_d)) - else: - v = fast_eval(df, expr, resolvers=[locals_d]) + with performance_timer.time_expression(expr): + if expr.startswith("@"): + v = to_series(eval(expr[1:], globals(), locals_d)) + else: + v = fast_eval(df, expr, resolvers=[locals_d]) if check_for_variability and v.std() == 0: logger.info( @@ -403,6 +424,7 @@ def to_series(x): trace_label, "eval.trace_eval_results", trace_eval_results ) + performance_timer.write_log(state) chunk_sizer.log_df(trace_label, "v", None) chunk_sizer.log_df( trace_label, "eval.utilities", None @@ -612,11 +634,11 @@ def to_series(x): raise # enter debugger now to see what's up timelogger.mark("sharrow interact test", True, logger, trace_label) - logger.info(f"utilities.dtypes {trace_label}\n{utilities.dtypes}") + logger.debug(f"utilities.dtypes {trace_label}\n{utilities.dtypes}") end_time = time.time() timelogger.summary(logger, "TIMING interact_simulate.eval_utils") - logger.info( + logger.debug( f"interact_simulate.eval_utils runtime: {timedelta(seconds=end_time - start_time)} {trace_label}" ) @@ -701,7 +723,7 @@ def _interaction_simulate( ) if len(spec.columns) > 1: - raise RuntimeError("spec must have only one column") + raise SegmentedSpecificationError("spec must have only one column") sample_size = sample_size or len(alternatives) diff --git a/activitysim/core/logit.py b/activitysim/core/logit.py index 0162f0b8c8..0030168bb2 100644 --- a/activitysim/core/logit.py +++ b/activitysim/core/logit.py @@ -11,6 +11,11 @@ from activitysim.core import tracing, workflow from activitysim.core.choosing import choice_maker from activitysim.core.configuration.logit import LogitNestSpec +from activitysim.core.exceptions import ( + InvalidTravelError, + ModelConfigurationError, + TableIndexError, +) logger = logging.getLogger(__name__) @@ -88,7 +93,7 @@ def report_bad_choices( logger.warning(row_msg) if raise_error: - raise RuntimeError(msg_with_count) + raise InvalidTravelError(msg_with_count) def utils_to_logsums(utils, exponentiated=False, allow_zero_probs=False): @@ -524,11 +529,11 @@ def interaction_dataset( """ if not choosers.index.is_unique: - raise RuntimeError( + raise TableIndexError( "ERROR: choosers index is not unique, " "sample will not work correctly" ) if not alternatives.index.is_unique: - raise RuntimeError( + raise TableIndexError( "ERROR: alternatives index is not unique, " "sample will not work correctly" ) @@ -633,7 +638,7 @@ def validate_nest_spec(nest_spec: dict | LogitNestSpec, trace_label: str): # nest.print() if duplicates: - raise RuntimeError( + raise ModelConfigurationError( f"validate_nest_spec:duplicate nest key/s '{duplicates}' in nest spec - {trace_label}" ) @@ -733,7 +738,9 @@ def each_nest(nest_spec: dict | LogitNestSpec, type=None, post_order=False): Nest object with info about the current node (nest or leaf) """ if type is not None and type not in Nest.nest_types(): - raise RuntimeError("Unknown nest type '%s' in call to each_nest" % type) + raise ModelConfigurationError( + "Unknown nest type '%s' in call to each_nest" % type + ) if isinstance(nest_spec, dict): nest_spec = LogitNestSpec.model_validate(nest_spec) diff --git a/activitysim/core/mp_tasks.py b/activitysim/core/mp_tasks.py index 7d31134bc0..f54e2f8d2f 100644 --- a/activitysim/core/mp_tasks.py +++ b/activitysim/core/mp_tasks.py @@ -19,6 +19,7 @@ from activitysim.core import config, mem, tracing, util, workflow from activitysim.core.configuration import FileSystem, Settings +from activitysim.core.run_id import RunId from activitysim.core.workflow.checkpoint import ( CHECKPOINT_NAME, CHECKPOINT_TABLE_NAME, @@ -26,6 +27,7 @@ NON_TABLE_COLUMNS, ParquetStore, ) +from activitysim.core.exceptions import * logger = logging.getLogger(__name__) @@ -440,7 +442,9 @@ def build_slice_rules(state: workflow.State, slice_info, pipeline_tables): tables[table_name] = pipeline_tables[table_name] if primary_slicer not in tables: - raise RuntimeError("primary slice table '%s' not in pipeline" % primary_slicer) + raise SystemConfigurationError( + "primary slice table '%s' not in pipeline" % primary_slicer + ) # allow wildcard 'True' to avoid slicing (or coalescing) any tables no explicitly listed in slice_info.tables # populationsim uses slice.except wildcards to avoid listing control tables (etc) that should not be sliced, @@ -532,7 +536,7 @@ def apportion_pipeline(state: workflow.State, sub_proc_names, step_info): """ slice_info = step_info.get("slice", None) if slice_info is None: - raise RuntimeError("missing slice_info.slice") + raise SystemConfigurationError("missing slice_info.slice") multiprocess_step_name = step_info.get("name", None) pipeline_file_name = state.get_injectable("pipeline_file_name") @@ -542,14 +546,16 @@ def apportion_pipeline(state: workflow.State, sub_proc_names, step_info): "last_checkpoint_in_previous_multiprocess_step", None ) if last_checkpoint_in_previous_multiprocess_step is None: - raise RuntimeError("missing last_checkpoint_in_previous_multiprocess_step") + raise CheckpointNameNotFoundError( + "missing last_checkpoint_in_previous_multiprocess_step" + ) state.checkpoint.restore(resume_after=last_checkpoint_in_previous_multiprocess_step) # ensure all tables are in the pipeline checkpointed_tables = state.checkpoint.list_tables() for table_name in slice_info["tables"]: if table_name not in checkpointed_tables: - raise RuntimeError(f"slicer table {table_name} not found in pipeline") + raise StateAccessError(f"slicer table {table_name} not found in pipeline") checkpoints_df = state.checkpoint.get_inventory() @@ -601,7 +607,7 @@ def apportion_pipeline(state: workflow.State, sub_proc_names, step_info): if rule["slice_by"] is not None and num_sub_procs > len(df): # almost certainly a configuration error - raise RuntimeError( + raise SystemConfigurationError( f"apportion_pipeline: multiprocess step {multiprocess_step_name} " f"slice table {table_name} has fewer rows {df.shape} " f"than num_processes ({num_sub_procs})." @@ -634,7 +640,7 @@ def apportion_pipeline(state: workflow.State, sub_proc_names, step_info): # don't slice mirrored tables sliced_tables[table_name] = df else: - raise RuntimeError( + raise TableSlicingError( "Unrecognized slice rule '%s' for table %s" % (rule["slice_by"], table_name) ) @@ -678,7 +684,7 @@ def apportion_pipeline(state: workflow.State, sub_proc_names, step_info): if rule["slice_by"] is not None and num_sub_procs > len(df): # almost certainly a configuration error - raise RuntimeError( + raise SystemConfigurationError( f"apportion_pipeline: multiprocess step {multiprocess_step_name} " f"slice table {table_name} has fewer rows {df.shape} " f"than num_processes ({num_sub_procs})." @@ -711,7 +717,7 @@ def apportion_pipeline(state: workflow.State, sub_proc_names, step_info): # don't slice mirrored tables sliced_tables[table_name] = df else: - raise RuntimeError( + raise TableSlicingError( "Unrecognized slice rule '%s' for table %s" % (rule["slice_by"], table_name) ) @@ -885,6 +891,10 @@ def setup_injectables_and_logging(injectables, locutor: bool = True) -> workflow injects injectables """ state = workflow.State() + _run_id = injectables.get("run_id", None) + if _run_id: + state.tracing.run_id = RunId(_run_id) + state = state.initialize_filesystem(**injectables) state.settings = injectables.get("settings", Settings()) state.filesystem.parse_settings(state.settings) @@ -970,7 +980,7 @@ def adjust_chunk_size_for_shared_memory(chunk_size, data_buffers, num_processes) ) if adjusted_chunk_size <= 0: - raise RuntimeError( + raise SystemConfigurationError( f"adjust_chunk_size_for_shared_memory: chunk_size too small for shared memory. " f"adjusted_chunk_size: {adjusted_chunk_size}" ) @@ -1366,7 +1376,7 @@ def check_proc_status(state: workflow.State): state, f"error terminating process {op.name}: {e}", ) - raise RuntimeError("Process %s failed" % (p.name,)) + raise SubprocessError("Process %s failed" % (p.name,)) step_name = step_info["name"] @@ -1531,7 +1541,7 @@ def run_sub_task(state: workflow.State, p): if p.exitcode: error(state, f"Process {p.name} returned exitcode {p.exitcode}") - raise RuntimeError("Process %s returned exitcode %s" % (p.name, p.exitcode)) + raise SubprocessError("Process %s returned exitcode %s" % (p.name, p.exitcode)) def drop_breadcrumb(state: workflow.State, step_name, crumb, value=True): @@ -1596,7 +1606,7 @@ def run_multiprocess(state: workflow.State, injectables): run_list = get_run_list(state) if not run_list["multiprocess"]: - raise RuntimeError( + raise SubprocessError( "run_multiprocess called but multiprocess flag is %s" % run_list["multiprocess"] ) @@ -1719,7 +1729,7 @@ def find_breadcrumb(crumb, default=None): ) if len(completed) != num_processes: - raise RuntimeError( + raise SubprocessError( "%s processes failed in step %s" % (num_processes - len(completed), step_name) ) @@ -1787,7 +1797,9 @@ def get_breadcrumbs(state: workflow.State, run_list): # - can't resume multiprocess without breadcrumbs file if not breadcrumbs: error(state, f"empty breadcrumbs for resume_after '{resume_after}'") - raise RuntimeError("empty breadcrumbs for resume_after '%s'" % resume_after) + raise CheckpointNameNotFoundError( + "empty breadcrumbs for resume_after '%s'" % resume_after + ) # if resume_after is specified by name if resume_after != LAST_CHECKPOINT: @@ -1808,7 +1820,7 @@ def get_breadcrumbs(state: workflow.State, run_list): if resume_step_name not in previous_steps: error(state, f"resume_after model '{resume_after}' not in breadcrumbs") - raise RuntimeError( + raise CheckpointNameNotFoundError( "resume_after model '%s' not in breadcrumbs" % resume_after ) @@ -1826,7 +1838,7 @@ def get_breadcrumbs(state: workflow.State, run_list): multiprocess_step_names = [step["name"] for step in run_list["multiprocess_steps"]] if list(breadcrumbs.keys()) != multiprocess_step_names[: len(breadcrumbs)]: - raise RuntimeError( + raise CheckpointNameNotFoundError( "last run steps don't match run list: %s" % list(breadcrumbs.keys()) ) @@ -1911,15 +1923,15 @@ def get_run_list(state: workflow.State): } if not models or not isinstance(models, list): - raise RuntimeError("No models list in settings file") + raise SystemConfigurationError("No models list in settings file") if resume_after == models[-1]: - raise RuntimeError( + raise SystemConfigurationError( "resume_after '%s' is last model in models list" % resume_after ) if multiprocess: if not multiprocess_steps: - raise RuntimeError( + raise SystemConfigurationError( "multiprocess setting is %s but no multiprocess_steps setting" % multiprocess ) @@ -1935,15 +1947,15 @@ def get_run_list(state: workflow.State): # - validate step name name = step.get("name", None) if not name: - raise RuntimeError( + raise SystemConfigurationError( "missing name for step %s" " in multiprocess_steps" % istep ) if name in step_names: - raise RuntimeError( + raise SystemConfigurationError( "duplicate step name %s" " in multiprocess_steps" % name ) if name in models: - raise RuntimeError( + raise SystemConfigurationError( f"multiprocess_steps step name '{name}' cannot also be a model name" ) @@ -1953,7 +1965,7 @@ def get_run_list(state: workflow.State): num_processes = step.get("num_processes", 0) or 0 if not isinstance(num_processes, int) or num_processes < 0: - raise RuntimeError( + raise SystemConfigurationError( "bad value (%s) for num_processes for step %s" " in multiprocess_steps" % (num_processes, name) ) @@ -1975,7 +1987,7 @@ def get_run_list(state: workflow.State): if num_processes == 0: num_processes = 1 if num_processes > 1: - raise RuntimeError( + raise SystemConfigurationError( "num_processes > 1 but no slice info for step %s" " in multiprocess_steps" % name ) @@ -2004,19 +2016,19 @@ def get_run_list(state: workflow.State): slice = step.get("slice", None) if slice: if "tables" not in slice: - raise RuntimeError( + raise SystemConfigurationError( "missing tables list for step %s" " in multiprocess_steps" % istep ) start = step.get(start_tag, None) if not name: - raise RuntimeError( + raise SystemConfigurationError( "missing %s tag for step '%s' (%s)" " in multiprocess_steps" % (start_tag, name, istep) ) if start not in models: - raise RuntimeError( + raise SystemConfigurationError( "%s tag '%s' for step '%s' (%s) not in models list" % (start_tag, start, name, istep) ) @@ -2024,14 +2036,14 @@ def get_run_list(state: workflow.State): starts[istep] = models.index(start) if istep == 0 and starts[istep] != 0: - raise RuntimeError( + raise SystemConfigurationError( "%s tag '%s' for first step '%s' (%s)" " is not first model in models list" % (start_tag, start, name, istep) ) if istep > 0 and starts[istep] <= starts[istep - 1]: - raise RuntimeError( + raise SystemConfigurationError( "%s tag '%s' for step '%s' (%s)" " falls before that of prior step in models list" % (start_tag, start, name, istep) @@ -2048,7 +2060,7 @@ def get_run_list(state: workflow.State): step_models = models[starts[istep] : starts[istep + 1]] if step_models[-1][0] == LAST_CHECKPOINT: - raise RuntimeError( + raise CheckpointNameNotFoundError( "Final model '%s' in step %s models list not checkpointed" % (step_models[-1], name) ) diff --git a/activitysim/core/pathbuilder_cache.py b/activitysim/core/pathbuilder_cache.py index a59ca17ec4..6ed3a7061e 100644 --- a/activitysim/core/pathbuilder_cache.py +++ b/activitysim/core/pathbuilder_cache.py @@ -14,6 +14,7 @@ import psutil from activitysim.core import config, los, util +from activitysim.core.exceptions import StateAccessError, TableTypeError logger = logging.getLogger(__name__) @@ -177,7 +178,7 @@ def open(self): f"TVPBCache.open {self.cache_tag} read fully_populated data array from mmap file" ) else: - raise RuntimeError( + raise StateAccessError( f"Pathbuilder cache not found. Did you forget to run initialize tvpb?" f"Expected cache file: {self.cache_path}" ) @@ -253,7 +254,7 @@ def allocate_data_buffer(self, shared=False): elif dtype_name == "float32": typecode = "f" else: - raise RuntimeError( + raise TableTypeError( "allocate_data_buffer unrecognized dtype %s" % dtype_name ) diff --git a/activitysim/core/random.py b/activitysim/core/random.py index dcef272ff6..5541fcd41d 100644 --- a/activitysim/core/random.py +++ b/activitysim/core/random.py @@ -10,6 +10,7 @@ import pandas as pd from activitysim.core.util import reindex +from activitysim.core.exceptions import DuplicateLoadableObjectError, TableIndexError from .tracing import print_elapsed_time @@ -441,7 +442,7 @@ def get_channel_for_df(self, df): channel_name = self.index_to_channel.get(df.index.name, None) if channel_name is None: - raise RuntimeError("No channel with index name '%s'" % df.index.name) + raise TableIndexError("No channel with index name '%s'" % df.index.name) return self.channels[channel_name] # step handling @@ -574,7 +575,9 @@ def set_base_seed(self, seed=None): """ if self.step_name is not None or self.channels: - raise RuntimeError("Can only call set_base_seed before the first step.") + raise DuplicateLoadableObjectError( + "Can only call set_base_seed before the first step." + ) assert len(list(self.channels.keys())) == 0 diff --git a/activitysim/core/run_id.py b/activitysim/core/run_id.py new file mode 100644 index 0000000000..a5d4ea1c87 --- /dev/null +++ b/activitysim/core/run_id.py @@ -0,0 +1,11 @@ +import struct +import time + + +class RunId(str): + def __new__(cls, x=None): + if x is None: + return cls( + hex(struct.unpack(" 0: for wrn in w: @@ -686,6 +707,7 @@ def eval_utilities( expression_values[i] = expression_value i += 1 + performance_timer.write_log(state) chunk_sizer.log_df(trace_label, "expression_values", expression_values) if estimator: @@ -840,14 +862,16 @@ def eval_utilities( chunk_sizer.log_df(trace_label, "utilities", None) end_time = time.time() - logger.info( + logger.debug( f"simulate.eval_utils runtime: {timedelta(seconds=end_time - start_time)} {trace_label}" ) timelogger.summary(logger, "simulate.eval_utils timing") return utilities -def eval_variables(state: workflow.State, exprs, df, locals_d=None): +def eval_variables( + state: workflow.State, exprs, df, locals_d=None, trace_label: str | None = None +): """ Evaluate a set of variable expressions from a spec in the context of a given data table. @@ -874,6 +898,9 @@ def eval_variables(state: workflow.State, exprs, df, locals_d=None): locals_d : Dict This is a dictionary of local variables that will be the environment for an evaluation of an expression that begins with @ + trace_label : str + The trace label to use for performance logging. If None, performance + logging is not activated. Returns ------- @@ -908,13 +935,20 @@ def to_array(x): return a + if state.settings.expression_profile and trace_label: + perf_log_file = Path(trace_label + ".log") + else: + perf_log_file = None + performance_timer = timing.EvalTiming(perf_log_file) + values = OrderedDict() for expr in exprs: try: - if expr.startswith("@"): - expr_values = to_array(eval(expr[1:], globals_dict, locals_dict)) - else: - expr_values = to_array(fast_eval(df, expr)) + with performance_timer.time_expression(expr): + if expr.startswith("@"): + expr_values = to_array(eval(expr[1:], globals_dict, locals_dict)) + else: + expr_values = to_array(fast_eval(df, expr)) # read model spec should ensure uniqueness, otherwise we should uniquify assert expr not in values values[expr] = expr_values @@ -955,7 +989,7 @@ def to_array(x): # return utilities -def set_skim_wrapper_targets(df, skims): +def set_skim_wrapper_targets(df, skims, allow_partial_success: bool = True): """ Add the dataframe to the SkimWrapper object so that it can be dereferenced using the parameters of the skims object. @@ -973,6 +1007,11 @@ def set_skim_wrapper_targets(df, skims): dataframe that comes back from interacting choosers with alternatives. See the skims module for more documentation on how the skims object is intended to be used. + allow_partial_success : bool, optional + If True (default), failures to set skim targets for some skim objects + (for example due to missing required columns in `df`) will be collected + and logged as warnings but will not raise an exception. If False, any + such failure will be raised immediately, preventing partial success. """ skims = ( @@ -982,13 +1021,31 @@ def set_skim_wrapper_targets(df, skims): if isinstance(skims, dict) else [skims] ) + problems = [] # assume any object in skims can be treated as a skim for skim in skims: try: skim.set_df(df) except AttributeError: + # sometimes when passed as a dict, the skims have a few keys given as + # settings or constants, which are not actually "skim" objects and have + # no `set_df` attribute. This is fine and we just let them pass. pass + except AssertionError as e: + # An assertion error will get triggered if the columns of `df` are + # missing one of the required keys needed to look up values in the + # skims. This may not be a problem, if this particular set of skims + # is not actually used in this model component. So we'll warn about + # it but usually not raise a showstopping error. + problems.append(e) + if not allow_partial_success: + raise + + if problems: + # if problems were discovered, log them as warnings + for problem in problems: + logger.warning(str(problem)) # @@ -1716,11 +1773,13 @@ def list_of_skims(skims): return ( skims if isinstance(skims, list) - else skims.values() - if isinstance(skims, dict) - else [skims] - if skims is not None - else [] + else ( + skims.values() + if isinstance(skims, dict) + else [skims] + if skims is not None + else [] + ) ) return [ diff --git a/activitysim/core/skim_dataset.py b/activitysim/core/skim_dataset.py index 1ed871ec0f..14c7ed862b 100644 --- a/activitysim/core/skim_dataset.py +++ b/activitysim/core/skim_dataset.py @@ -3,6 +3,7 @@ import glob import logging import os +import re import time from functools import partial from pathlib import Path @@ -252,7 +253,7 @@ def set_df(self, df): and np.issubdtype(df[self.time_key].dtype, np.integer) and df[self.time_key].max() < self.dataset.dims["time_period"] ): - logger.info(f"natural use for time_period={self.time_key}") + logger.debug(f"natural use for time_period={self.time_key}") positions["time_period"] = df[self.time_key] elif ( df[self.time_key].dtype == "category" @@ -260,7 +261,7 @@ def set_df(self, df): ): positions["time_period"] = df[self.time_key].cat.codes else: - logger.info(f"vectorize lookup for time_period={self.time_key}") + logger.debug(f"vectorize lookup for time_period={self.time_key}") positions["time_period"] = pd.Series( np.vectorize(self.time_map.get, "I")(df[self.time_key], 0), index=df.index, @@ -499,8 +500,6 @@ def _apply_digital_encoding(dataset, digital_encodings): As modified """ if digital_encodings: - import re - # apply once, before saving to zarr, will stick around in cache for encoding in digital_encodings: logger.info(f"applying zarr digital-encoding: {encoding}") @@ -753,6 +752,68 @@ def load_skim_dataset_to_shared_memory(state, skim_tag="taz") -> xr.Dataset: d = None # skims are not stored in shared memory, so we need to load them do_not_save_zarr = False + potential_conflicts = network_los_preload.skims_info.get(skim_tag).skim_conflicts + if potential_conflicts: + # There are some conflicts in the skims, where both time-dependent + # and time-agnostic skim with the same name are present. We need + # to check we have sufficient ignore rules in place to correct this + # condition. + + def _should_ignore(ignore, x): + if isinstance(ignore, str): + ignore = [ignore] + if ignore is not None: + for i in ignore: + if re.match(i, x): + return True + return False + + ignore = state.settings.omx_ignore_patterns + problems = [] + for time_agnostic, time_dependent in potential_conflicts.items(): + # option 1, ignore all the time-dependent skims + # if this is fulfilled, we are ok and can proceed + if all((_should_ignore(ignore, i)) for i in time_dependent): + continue + # option 2, ignore the time-agnostic skim + # if this is fulfilled, we are ok and can proceed + if _should_ignore(ignore, time_agnostic): + continue + # otherwise, we have a problem. collect all the problems + # and raise an error at the end listing all of them + problems.append(time_agnostic) + if problems: + solution_1 = "__.+'\n - '^".join(problems) + solution_2 = "$'\n - '^".join(problems) + # we have a problem, raise an error + error_message = ( + f"skims {problems} are present in both time-dependent and time-agnostic formats.\n" + "Please add ignore rules to the omx_ignore_patterns setting to resolve this issue.\n" + "To ignore the time dependent skims, add the following to your settings file:\n" + "\n" + "omx_ignore_patterns:\n" + f" - '^{solution_1}__.+'\n" + "\n" + "To ignore the time agnostic skims, add the following to your settings file:\n" + "\n" + "omx_ignore_patterns:\n" + f" - '^{solution_2}$'\n" + "\n" + "You can also do some variation or combination of the two, as long as you resolve\n" + "the conflict(s). In addition, note that minor edits to model spec files may be\n" + "needed to accommodate these changes in how skim data is represented (e.g. changing\n" + "`odt_skims` to `od_skims`, or similar modifications wherever the offending variable\n" + "names are used). Alternatively, you can modify the skim data in the source files to\n" + "remove the naming conflicts, which is typically done upstream of ActivitySim in\n" + "whatever tool you are using to create the skims in the first place.\n" + "\n" + "See [https://activitysim.github.io/?q=skims] for more information.\n" + ) + # write the error message to the log + logger.error(error_message) + # raise an error to stop the run, put the entire error message there also + raise ValueError(error_message) + if d is None: time_periods = _dedupe_time_periods(network_los_preload) if zarr_file: diff --git a/activitysim/core/skim_dict_factory.py b/activitysim/core/skim_dict_factory.py index 78c7053583..2ca90c4c66 100644 --- a/activitysim/core/skim_dict_factory.py +++ b/activitysim/core/skim_dict_factory.py @@ -8,11 +8,13 @@ import os import warnings from abc import ABC +from collections import defaultdict import numpy as np import openmatrix as omx from activitysim.core import skim_dictionary, util +from activitysim.core.exceptions import TableTypeError logger = logging.getLogger(__name__) @@ -86,6 +88,7 @@ def __init__(self, state, skim_tag, network_los): self.offset_map_name = None self.offset_map = None self.omx_keys = None + self.skim_conflicts = None self.base_keys = None self.block_offsets = None @@ -117,7 +120,12 @@ def load_skim_info(self, state, skim_tag): logger.debug(f"load_skim_info {skim_tag} reading {omx_file_path}") with omx.open_file(omx_file_path, mode="r") as omx_file: - # fixme call to omx_file.shape() failing in windows p3.5 + + # Check the shape of the skims. All skim files loaded within this + # loop need to have the same shape. For the first file, the + # shape is set to the omx_shape attribute, so we know what shape + # to expect. For subsequent files, we check that the shape is the + # same as the first file. If not, we raise an error. if self.omx_shape is None: self.omx_shape = tuple( int(i) for i in omx_file.shape() @@ -127,6 +135,12 @@ def load_skim_info(self, state, skim_tag): int(i) for i in omx_file.shape() ), f"Mismatch shape {self.omx_shape} != {omx_file.shape()}" + # Check that all the matrix names are unique across all the + # omx files. This check is only looking at the name as stored in + # the file, and is not processing any time period transformations. + # If duplicate names are found, a warning is issued. This is not + # a fatal error, but it is inefficient and may be symptom of a + # deeper problem. for skim_name in omx_file.listMatrices(): if skim_name in self.omx_manifest: warnings.warn( @@ -134,6 +148,11 @@ def load_skim_info(self, state, skim_tag): ) self.omx_manifest[skim_name] = omx_file_path + # We load the offset map if it exists. This is expected to be + # a 1D array of integers that that gives ID values for each TAZ + # in the skims. ActivitySim expects there to be only one such + # mapping, although it can appear multiple times (e.g. once in + # each file). for m in omx_file.listMappings(): if self.offset_map is None: self.offset_map_name = m @@ -146,21 +165,54 @@ def load_skim_info(self, state, skim_tag): f"Multiple mappings in omx file: {self.offset_map_name} != {m}" ) - # - omx_keys dict maps skim key to omx_key - # DISTWALK: DISTWALK - # ('DRV_COM_WLK_BOARDS', 'AM'): DRV_COM_WLK_BOARDS__AM, ... + # Create the `omx_keys` mapping, which connects skim key to omx_key. + # The skim key is either a single string that names a skim that is not + # time-dependent, or a 2-tuple of strings which names a skim and a time + # period. The omx_key is the original name of the skim in the omx file. + # For non-time-dependent skims, the omx_key is the same as the skim key, + # e.g. DISTWALK: DISTWALK. For time-dependent skims, the omx_key is the + # skim key with the time period appended, + # e.g. ('DRV_COM_WLK_BOARDS', 'AM'): DRV_COM_WLK_BOARDS__AM. self.omx_keys = dict() for skim_name in self.omx_manifest.keys(): key1, sep, key2 = skim_name.partition("__") - # - ignore composite tags not in dim3_tags_to_load if dim3_tags_to_load and sep and key2 not in dim3_tags_to_load: + # If a skim is found that has a time period that is not one of + # the known named time periods, a warning is issued, and that + # skim is ignored. This is not a fatal error, but it may be a + # symptom of a deeper problem. + warnings.warn(f"skim '{key1}' has unknown time period '{key2}'") continue - skim_key = (key1, key2) if sep else key1 - self.omx_keys[skim_key] = skim_name + # Create a skim_conflicts set, which identifies any skims that have both + # time-dependent and time-agnostic versions. This condition in and of + # itself is not a fatal error, as it is possible to have both types of skims + # in the same data when using the legacy codebase. When using skim_dataset + # instead of skim_dictionary (the former is required when using sharrow) this + # condition is no longer allowed, although we can potentially recover if + # the user has specified instructions that certain skim variables are not + # to be loaded. The recovery option is checked later, in the skim_dataset + # module, as that is where the skim variables are actually loaded. + time_dependent_skims = defaultdict(set) + time_agnostic_skims = set() + for k, v in self.omx_keys.items(): + if isinstance(k, tuple): + time_dependent_skims[k[0]].add(v) + else: + time_agnostic_skims.add(k) + self.skim_conflicts = { + k: v for k, v in time_dependent_skims.items() if k in time_agnostic_skims + } + if self.skim_conflicts: + msg = "some skims have both time-dependent and time-agnostic versions:" + for k in self.skim_conflicts: + msg += f"\n- {k}" + warnings.warn(msg) + + # Count the number of skims in the omx file self.num_skims = len(self.omx_keys) # - key1_subkeys dict maps key1 to dict of subkeys with that key1 @@ -411,7 +463,7 @@ def allocate_skim_buffer(self, skim_info, shared=False): elif dtype_name == "float32": typecode = "f" else: - raise RuntimeError( + raise TableTypeError( "allocate_skim_buffer unrecognized dtype %s" % dtype_name ) diff --git a/activitysim/core/skim_dictionary.py b/activitysim/core/skim_dictionary.py index 020002e252..59692e3d37 100644 --- a/activitysim/core/skim_dictionary.py +++ b/activitysim/core/skim_dictionary.py @@ -10,7 +10,7 @@ import pandas as pd from activitysim.core import workflow -from activitysim.core.exceptions import StateAccessError +from activitysim.core.exceptions import StateAccessError, TableIndexError logger = logging.getLogger(__name__) @@ -400,7 +400,6 @@ def wrap_3d(self, orig_key, dest_key, dim3_key): class SkimWrapper(object): - """ A SkimWrapper object is an access wrapper around a SkimDict of multiple skim objects, where each object is identified by a key. @@ -907,7 +906,7 @@ def get(self, row_ids, col_ids): not_in_skim = not_in_skim.values logger.warning(f"row_ids: {row_ids[not_in_skim]}") logger.warning(f"col_ids: {col_ids[not_in_skim]}") - raise RuntimeError( + raise TableIndexError( f"DataFrameMatrix: {not_in_skim.sum()} row_ids of {len(row_ids)} not in skim." ) diff --git a/activitysim/core/steps/output.py b/activitysim/core/steps/output.py index b9d7cc13d6..ea543c8aaf 100644 --- a/activitysim/core/steps/output.py +++ b/activitysim/core/steps/output.py @@ -4,6 +4,9 @@ import logging import sys +import os +import shutil +from pathlib import Path import numpy as np import pandas as pd @@ -13,6 +16,7 @@ from activitysim.core import configuration, workflow from activitysim.core.workflow.checkpoint import CHECKPOINT_NAME +from activitysim.core.estimation import estimation_enabled, EstimationConfig logger = logging.getLogger(__name__) @@ -224,6 +228,144 @@ def write_data_dictionary(state: workflow.State) -> None: print(f"{info}\n", file=output_file) +def find_lowest_level_directories(starting_directory): + lowest_dirs = list() + + for root, dirs, files in os.walk(starting_directory): + if not dirs: + lowest_dirs.append(root) + + return lowest_dirs + + +def concat_and_write_edb(df_concat_dict, write_dir): + # concatenate the dataframes and output final file + for table_name, df_array in df_concat_dict.items(): + df = pd.concat(df_array) + + # sort the dataframe by index + if df.index.name is not None: + df = df.sort_index() + else: + df = df.sort_values(by=df.columns[0], kind="mergesort", ignore_index=True) + + if table_name.endswith(".csv"): + df.to_csv(os.path.join(write_dir, table_name), index=False) + elif table_name.endswith(".parquet"): + df.to_parquet(os.path.join(write_dir, table_name), index=True) + elif table_name.endswith(".pkl"): + df.to_pickle(os.path.join(write_dir, table_name)) + else: + raise ValueError(f"Unknown file type {table_name}") + + +def _coalesce_estimation_data_bundles(state): + """ + In estimation mode, estimation data bundles are written to separate subdirectories for each subprocess. + This model will go through each subdirectory and move the files to the parent directory. + This will only occur if the lowest level directory contains the multiprocess step names. + Only multiprocess step names are used because that's how EDBs are written in estimation mode. + """ + + logger.info("Coalescing Estimation Data Bundles") + + edb_dir = state.filesystem.get_output_dir("estimation_data_bundle") + estimation_settings = EstimationConfig.read_settings_file( + state.filesystem, "estimation.yaml", mandatory=True + ) + + lowest_dirs = find_lowest_level_directories(edb_dir) + + multiprocessing_step_names = [ + step.name for step in state.settings.multiprocess_steps + ] + lowest_dirs = [ + dir + for dir in lowest_dirs + if any(step in dir for step in multiprocessing_step_names) + ] + + if len(lowest_dirs) == 0: + logger.info("No estimation data bundles to coalesce") + return + + prev_edb = None + df_concat_dict = {} + + # loop through each lowest level directory + for dir in lowest_dirs: + logger.debug(f"Coalescing {dir}") + # get the parent directory + cur_edb = Path(dir).parent.absolute() + if prev_edb is None: + prev_edb = cur_edb + + # check if we have moved onto a new EDB + is_same_edb = cur_edb == prev_edb + + # if we have moved onto a new EDB, concatenate the dataframes and write the final files + if ( + (not is_same_edb) + and (len(df_concat_dict) > 0) + # and (len(df_concat_dict[list(df_concat_dict.keys())[0]]) > 1) + ): + concat_and_write_edb(df_concat_dict, prev_edb) + + # reset edb dir and dictionary + prev_edb = cur_edb + df_concat_dict = {} + + for i, file in enumerate(os.listdir(dir)): + + # get the file path + file_path = os.path.join(dir, file) + + # look for files that are duplicated across subprocesses + is_coefs_file = file.endswith(".csv") and "coef" in file + is_settings_file = file.endswith(".yaml") + is_spec_file = file.endswith(".csv") and ("spec" in file.lower()) + is_landuse_file = file.endswith("_landuse.csv") + is_size_terms_file = file.endswith("_size_terms.csv") + is_duplicate_file = ( + is_coefs_file + or is_spec_file + or is_settings_file + or is_landuse_file + or is_size_terms_file + ) + + if is_duplicate_file and not os.path.exists(os.path.join(cur_edb, file)): + # copy the file to the parent directory + shutil.copy(file_path, os.path.join(cur_edb, file)) + + if not is_duplicate_file: + # read file and store in dictionary + if file.endswith(".csv"): + df = pd.read_csv(file_path, low_memory=False) + elif file.endswith(".parquet"): + df = pd.read_parquet(file_path) + elif file.endswith(".pkl"): + df = pd.read_pickle(file_path) + else: + raise ValueError( + f"Unknown file type found {file}, expect csv, parquet, or pkl" + ) + + if file in df_concat_dict.keys(): + df_concat_dict[file].append(df) + else: + df_concat_dict[file] = [df] + + # delete the directory now that we have gone through all the files + if estimation_settings.DELETE_MP_SUBDIRS: + shutil.rmtree(dir) + + # need to concatenate the last set of dataframes + concat_and_write_edb(df_concat_dict, cur_edb) + + return + + @workflow.step def write_tables(state: workflow.State) -> None: """ @@ -434,3 +576,22 @@ def map_func(x): parquet.write_table(dt, file_path) else: raise ValueError(f"unknown file_type {file_type}") + + +@workflow.step +def coalesce_estimation_data_bundles(state: workflow.State) -> None: + """ + In estimation mode, estimation data bundles are written to separate subdirectories for each subprocess. + This model will go through each subdirectory and concat / copy the files to the parent directory. + This will only occur if the lowest level directory contains the multiprocess step names. + Only multiprocess step names are used because that's how EDBs are written in estimation mode. + + """ + is_estimation = estimation_enabled(state) + if state.settings.multiprocess and is_estimation: + _coalesce_estimation_data_bundles(state) + else: + logger.info( + "Not in estimation mode or not using multiprocess. Nothing to coalesce." + ) + return diff --git a/activitysim/core/test/_tools.py b/activitysim/core/test/_tools.py index 618b467bb9..3169b771c2 100644 --- a/activitysim/core/test/_tools.py +++ b/activitysim/core/test/_tools.py @@ -7,6 +7,8 @@ import pandas as pd +from activitysim.core.exceptions import PipelineError + def run_if_exists(filename): import pytest @@ -176,6 +178,6 @@ def progressive_checkpoint_test( # generate the reference pipeline if it did not exist if not ref_target.exists(): state.checkpoint.store.make_zip_archive(ref_target) - raise RuntimeError( + raise PipelineError( f"Reference pipeline {ref_target} did not exist, so it was created." ) diff --git a/activitysim/core/test/configs/preprocessor.csv b/activitysim/core/test/configs/preprocessor.csv new file mode 100644 index 0000000000..e2f5c29b97 --- /dev/null +++ b/activitysim/core/test/configs/preprocessor.csv @@ -0,0 +1,8 @@ +Description,Target,Expression +income from households table,_hh_income,"reindex(households.income, df.household_id)" +income test,is_high_income,_hh_income > 50000 +count persons test,num_persons,persons.groupby('household_id').size().reindex(df.household_id) +skim dict test,od_distance,"skim_dict.lookup(df.origin, df.destination, 'DIST')" +skim wrapper test,od_distance_wrapper,skims2d['DIST'] +sov time,od_sov_time,skims3d['SOV_TIME'] +testing constant from locals_dict,constant_test,test_constant / 2 \ No newline at end of file diff --git a/activitysim/core/test/test_pipeline.py b/activitysim/core/test/test_pipeline.py index dfa6d770a8..12f31dbc66 100644 --- a/activitysim/core/test/test_pipeline.py +++ b/activitysim/core/test/test_pipeline.py @@ -9,6 +9,7 @@ import tables from activitysim.core import workflow +from activitysim.core.exceptions import CheckpointNameNotFoundError from activitysim.core.test.extensions import steps # set the max households for all tests (this is to limit memory use on travis) @@ -70,17 +71,17 @@ def test_pipeline_run(state): state.checkpoint.load_dataframe("table1", checkpoint_name="step3") # try to get a table from a step before it was checkpointed - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(CheckpointNameNotFoundError) as excinfo: state.checkpoint.load_dataframe("table2", checkpoint_name="step1") assert "not in checkpoint 'step1'" in str(excinfo.value) # try to get a non-existant table - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(CheckpointNameNotFoundError) as excinfo: state.checkpoint.load_dataframe("bogus") assert "never checkpointed" in str(excinfo.value) # try to get an existing table from a non-existant checkpoint - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(CheckpointNameNotFoundError) as excinfo: state.checkpoint.load_dataframe("table1", checkpoint_name="bogus") assert "not in checkpoints" in str(excinfo.value) @@ -111,12 +112,12 @@ def test_pipeline_checkpoint_drop(state): state.checkpoint.load_dataframe("table1") - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(CheckpointNameNotFoundError) as excinfo: state.checkpoint.load_dataframe("table2") # assert "never checkpointed" in str(excinfo.value) # can't get a dropped table from current checkpoint - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(CheckpointNameNotFoundError) as excinfo: state.checkpoint.load_dataframe("table3") # assert "was dropped" in str(excinfo.value) diff --git a/activitysim/core/test/test_preprocessing.py b/activitysim/core/test/test_preprocessing.py new file mode 100644 index 0000000000..06c7785603 --- /dev/null +++ b/activitysim/core/test/test_preprocessing.py @@ -0,0 +1,197 @@ +# ActivitySim +# See full license in LICENSE.txt. +from __future__ import annotations + +import logging +import logging.config +import os.path + +import numpy as np +import pandas as pd +import pytest + +from activitysim.core import workflow, expressions, los +from activitysim.core.configuration.base import PreprocessorSettings + + +def add_canonical_dirs(configs_dir_name): + state = workflow.State() + los_configs_dir = os.path.join(os.path.dirname(__file__), f"los/{configs_dir_name}") + configs_dir = os.path.join(os.path.dirname(__file__), "configs") + data_dir = os.path.join(os.path.dirname(__file__), f"los/data") + output_dir = os.path.join(os.path.dirname(__file__), "output") + state.initialize_filesystem( + working_dir=os.path.dirname(__file__), + configs_dir=(los_configs_dir, configs_dir), + output_dir=output_dir, + data_dir=(data_dir,), + ) + return state + + +@pytest.fixture +def state() -> workflow.State: + state = add_canonical_dirs("configs_1z").load_settings() + network_los = los.Network_LOS(state) + network_los.load_data() + state.set("skim_dict", network_los.get_default_skim_dict()) + return state + + +@pytest.fixture(scope="module") +def households(): + return pd.DataFrame( + { + "household_id": [1, 2, 3], + "home_zone_id": [1, 2, 3], + "income": [50000, 60000, 70000], + } + ).set_index("household_id") + + +@pytest.fixture(scope="module") +def persons(): + return pd.DataFrame( + { + "person_id": [1, 2, 3, 4, 5], + "household_id": [1, 1, 2, 2, 3], + "age": [25, 30, 22, 28, 35], + } + ).set_index("person_id") + + +@pytest.fixture(scope="module") +def tours(): + return pd.DataFrame( + { + "tour_id": [1, 2, 3], + "household_id": [1, 2, 3], + "person_id": [1, 2, 3], + "tour_type": ["work", "shopping", "othmaint"], + "origin": [1, 2, 3], + "destination": [2, 3, 1], + "period": ["AM", "PM", "AM"], + } + ).set_index("tour_id") + + +def check_outputs(tours): + """ + Check that the tours DataFrame has the expected new columns and values + according to the preprocessor / annotator expressions. + """ + new_cols = [ + "is_high_income", + "num_persons", + "od_distance", + "od_distance_wrapper", + "od_sov_time", + "constant_test", + ] + + # check all new columns are added + assert all( + col in tours.columns for col in new_cols + ), f"Missing columns: {set(new_cols) - set(tours.columns)}" + + # column with _ shouldn't be in the columns + assert ( + "_hh_income" not in tours.columns + ), f"Unexpected column found: _hh_income in {tours.columns}" + + # check the values in the new columns + exppected_output = pd.DataFrame( + { + "tour_id": [1, 2, 3], + "is_high_income": [False, True, True], + "num_persons": [2, 2, 1], + "od_distance": [0.24, 0.28, 0.57], + "od_distance_wrapper": [0.24, 0.28, 0.57], + "od_sov_time": [0.78, 0.89, 1.76], + "constant_test": [21, 21, 21], + } + ).set_index("tour_id") + pd.testing.assert_frame_equal(tours[new_cols], exppected_output, check_dtype=False) + + +def setup_skims(state: workflow.State): + """Creates a set of skim wrappers to test in expressions.""" + skim_dict = state.get("skim_dict") + skims3d = skim_dict.wrap_3d( + orig_key="origin", dest_key="destination", dim3_key="period" + ) + skims2d = skim_dict.wrap("origin", "destination") + return {"skims3d": skims3d, "skims2d": skims2d} + + +def test_preprocessor(state: workflow.State, households, persons, tours): + # adding dataframes to state so they can be accessed in preprocessor + state.add_table("households", households) + state.add_table("persons", persons) + original_tours = tours.copy() + state.add_table("tours", original_tours) + + # defining preprocessor + preprocessor_settings = PreprocessorSettings( + SPEC="preprocessor.csv", + DF="tours", + TABLES=["persons", "households"], + ) + model_settings = {"preprocessor": preprocessor_settings} + + # annotating preprocessors + expressions.annotate_preprocessors( + state, + df=tours, + locals_dict={"test_constant": 42}, + skims=setup_skims(state), + model_settings=model_settings, + trace_label="ci_test_preprocessor", + ) + + check_outputs(tours) + + state_tours = state.get_table("tours") + # check that the state table is not modified + pd.testing.assert_frame_equal(state_tours, original_tours) + + +def test_annotator(state, households, persons, tours): + # adding dataframes to state so they can be accessed in annotator + state.add_table("households", households) + state.add_table("persons", persons) + original_tours = tours.copy() + state.add_table("tours", original_tours) + + # defining annotator + annotator_settings = PreprocessorSettings( + SPEC="preprocessor.csv", + DF="tours", + TABLES=["persons", "households"], + ) + model_settings = {"annotate_tours": annotator_settings} + + # annotating preprocessors + expressions.annotate_tables( + state, + model_settings=model_settings, + trace_label="ci_test_annotator", + skims=setup_skims(state), + locals_dict={"test_constant": 42}, + ) + + # outputs now put directly into the state object + check_outputs(state.get_table("tours")) + + # test what happens if we try to annotate a table that does not exist + model_settings = {"annotate_trips": annotator_settings} + + with pytest.raises(ValueError) as excinfo: + # this should raise an error because "trips" table does not exist in state + expressions.annotate_tables( + state, + model_settings=model_settings, + trace_label="ci_test_annotator", + skims=None, + locals_dict={"test_constant": 42}, + ) diff --git a/activitysim/core/test/test_random.py b/activitysim/core/test/test_random.py index 63809278c1..bcbc602685 100644 --- a/activitysim/core/test/test_random.py +++ b/activitysim/core/test/test_random.py @@ -8,6 +8,7 @@ import pytest from activitysim.core import random +from activitysim.core.exceptions import DuplicateLoadableObjectError def test_basic(): @@ -27,7 +28,7 @@ def test_basic(): assert "Arrays are not almost equal" in str(excinfo.value) # second call should return something different - with pytest.raises(RuntimeError) as excinfo: + with pytest.raises(DuplicateLoadableObjectError) as excinfo: rng.set_base_seed(1) assert "call set_base_seed before the first step" in str(excinfo.value) diff --git a/activitysim/core/test/test_util.py b/activitysim/core/test/test_util.py index 940c9a081d..ffb9e1ad04 100644 --- a/activitysim/core/test/test_util.py +++ b/activitysim/core/test/test_util.py @@ -67,7 +67,9 @@ def test_df_from_dict(): df = pd.DataFrame({"attrib": [1, 2, 2, 3, 1]}, index=index) # scramble index order for one expression and not the other - sorted = df.eval("attrib.sort_values()") + # use mergesort to ensure stable sort, if not specified the default is to + # use quicksort which is unstable and will cause this test to fail intermittently + sorted = df.eval("attrib.sort_values(kind='mergesort')") not_sorted = df.eval("attrib * 1") # check above expressions diff --git a/activitysim/core/timetable.py b/activitysim/core/timetable.py index 5743aeef0a..cedb3d2673 100644 --- a/activitysim/core/timetable.py +++ b/activitysim/core/timetable.py @@ -10,6 +10,7 @@ import pandas as pd from activitysim.core import chunk, configuration, workflow +from activitysim.core.exceptions import DuplicateWorkflowTableError logger = logging.getLogger(__name__) @@ -463,7 +464,9 @@ def replace_table(self, state: workflow.State): % self.windows_table_name, level=logging.ERROR, ) - raise RuntimeError("Attempt to replace_table while in transaction") + raise DuplicateWorkflowTableError( + "Attempt to replace_table while in transaction" + ) # get windows_df from bottleneck function in case updates to self.person_window # do not write through to pandas dataframe diff --git a/activitysim/core/timing.py b/activitysim/core/timing.py new file mode 100644 index 0000000000..618d02b741 --- /dev/null +++ b/activitysim/core/timing.py @@ -0,0 +1,787 @@ +from __future__ import annotations + +from contextlib import contextmanager +from pathlib import Path +from time import time_ns +from typing import TYPE_CHECKING, Literal + +import pandas as pd + +from .util import si_units + +if TYPE_CHECKING: + from .workflow import State + + +class NoTiming: + """Class that does no timing, serves as the default. + + This class is kept as simple as possible to avoid unnecessary overhead when + no timing is requested. + """ + + @contextmanager + def time_expression(self, expression: str): + """Context manager to time an expression.""" + yield + + def write_log(self, state: State) -> None: + """Write the log to a file.""" + pass + + +class EvalTiming(NoTiming): + def __new__(cls, log: Path | None = None, **kwargs): + if log is None: + return NoTiming() + else: + return super().__new__(cls) + + def __init__(self, log: Path | None = None, *, overwrite: bool = False): + """ + Timing class to log the time taken to evaluate expressions. + + Parameters + ---------- + log : Path | None, default None + Path to the log file. If None, no logging is done. If this is an + absolute path, the log file is created there. If this is just a + simple filename or a relative path, the log file is created relative + in or relative to the usual logging directory. + overwrite : bool, default False + If True, overwrite the log file if it already exists. If False, + create a new log file with a unique name. + """ + self.log_file = log + self.overwrite = overwrite + self.elapsed_times = {} + + @contextmanager + def time_expression(self, expression: str): + """Context manager to time an expression. + + Parameters + ---------- + expression : str + The expression to be timed. This is used as the key in the log file. + """ + + # when performance logging is not enabled, do nothing + if self.log_file is None: + yield + return + + # when performance logging is enabled, we track the time it takes to evaluate + # the expression and store it + start_time = time_ns() + yield + end_time = time_ns() + elapsed_time = end_time - start_time + if expression in self.elapsed_times: + self.elapsed_times[expression] += elapsed_time + else: + self.elapsed_times[expression] = elapsed_time + + def write_log(self, state: State) -> None: + """Write the log to a file. + + Parameters + ---------- + state : State + The state object containing configuration information. This is used + to determine the path for the log file, when the log file is not + given as an absolute path. + """ + if self.log_file is None: + return + + if self.log_file.is_absolute(): + filename = self.log_file + else: + filename = state.get_expr_performance_log_file_path(str(self.log_file)) + + # if the log file already exists and overwrite is false, create a new file + proposed_filename = filename + n = 0 + while not self.overwrite and proposed_filename.exists(): + n += 1 + proposed_filename = filename.with_stem(filename.stem + f"-{n}") + filename = proposed_filename + + # ensure the parent directory exists + filename.parent.mkdir(parents=True, exist_ok=True) + + # Determine the scale for the elapsed times. We want to use an appropriate + # timescale for the elapsed times, which provides useful information without + # reporting excessive precision. + # If the smallest elapsed time is greater than 1 second, use seconds. + # If the smallest elapsed time is greater than 1 millisecond, use milliseconds. + # Otherwise, use microseconds, no one should care about nanoseconds. + min_t = 1_000_000_000 + for t in self.elapsed_times.values(): + if t < min_t: + min_t = t + if min_t > 1_000_000_000: + scale = 1_000_000_000 + label = "Time (sec)" + elif min_t > 1_000_000: + scale = 1_000_000 + label = "Time (msec)" + else: + scale = 1_000 + label = "Time (µsec)" + + # The timing log is written in a tab-separated format, with times in the + # first column so they are easy to scan through for anomalies. + try: + with open(filename, "w", encoding="utf-8") as f: + f.write(f"{label:11}\tExpression\n") + for expression, elapsed_time in self.elapsed_times.items(): + t = int(elapsed_time / scale) + f.write(f"{t: 11d}\t{expression}\n") + except FileNotFoundError as err: + if not filename.parent.exists(): + raise FileNotFoundError( + f"Could not write log file {filename!r}, parent directory does not exist." + ) from err + else: + raise FileNotFoundError( + f"Could not write log file {filename!r}\n check permissions " + f"or path length ({len(str(filename))} characters in relative path, " + f"{len(str(filename.absolute()))} in absolute path)." + ) from err + + +def write_sortable_table(df: pd.DataFrame, filename: str | Path) -> None: + html_table = df.to_html(classes="sortable", index=False) + html_content = f""" + + + + + + + {html_table} + + + + """ + with open(filename, "w") as f: + f.write(html_content) + + +class AnalyzeEvalTiming: + """ + Class to analyze the timing of expressions. + """ + + def _read_log(self, log_file: Path) -> pd.DataFrame: + """Read the log file and return a DataFrame.""" + df = pd.read_csv(log_file, sep="\t") + if "(msec)" in df.columns[0]: + df.columns = ["Time (µsec)"] + df.columns[1:].tolist() + df.iloc[:, 0] = df.iloc[:, 0].astype(int) * 1_000 + elif "(sec)" in df.columns[0]: + df.columns = ["Time (µsec)"] + df.columns[1:].tolist() + df.iloc[:, 0] = df.iloc[:, 0].astype(int) * 1_000_000 + else: + df.iloc[:, 0] = df.iloc[:, 0].astype(int) + return df + + def __init__(self, state: State, collect_mp: bool = True) -> None: + self.log_dir = state.get_expr_performance_log_file_path(".") + self.default_cutoff = state.settings.expression_profile_cutoff + raw_data = {} + for f in self.log_dir.glob("*.log"): + raw_data[str(f.stem)] = self._read_log(f) + + if raw_data: + d = pd.concat(raw_data, names=["Component"]).reset_index() + d["Proc"] = "main" + else: + d = None + + if collect_mp: + raw_data = {} + mp_log_dirs = state.get_expr_performance_log_file_path(".").glob( + "*-expr-performance" + ) + for mp_log_dir in mp_log_dirs: + subproc_name = "-".join(mp_log_dir.stem.split("-")[:-2]) + for f in mp_log_dir.glob("*.log"): + raw_data[subproc_name, str(f.stem)] = self._read_log(f) + if raw_data: + d_mp = pd.concat(raw_data, names=["Proc", "Component"]).reset_index() + if d is None: + d = d_mp + else: + d = pd.concat([d, d_mp]) + + # break trace labels into components and subcomponents + try: + d["Subcomponent"] = d["Component"].str.split(".", n=1).str[1] + d["Component"] = d["Component"].str.split(".", n=1).str[0] + except TypeError: + # if the component is not a string, we cannot split it + d["Subcomponent"] = "" + d["Component"] = d["Component"].astype(str) + self.data = d[ + ["Time (µsec)", "Proc", "Component", "Subcomponent", "Expression"] + ] + self.data = self.data.sort_values(by=["Time (µsec)"], ascending=[False]) + + def subcomponent_report( + self, + filename: str | Path = "expression-timing-subcomponents.html", + cutoff_secs: float | None = None, + style: Literal["grid", "simple"] = "grid", + ) -> None: + """Write the data to an HTML file. + + Parameters + ---------- + filename : str | Path + The name of the file to write the HTML to. If a relative path is given, + it will be written in the log directory. + cutoff_secs : float, optional + The cutoff time in seconds. Only expressions with a runtime greater than + this will be included in the HTML file. This is used to avoid writing a + huge report full of expressions that run plenty fast. If not provided, + the default cutoff time from the settings is used. + style : "simple" | "grid", default "simple" + The style of the report. Either "simple" or "grid". "simple" is a + simple HTML table, "grid" is a JavaScript data grid. + """ + if cutoff_secs is None: + cutoff_secs = self.default_cutoff + + # include only expressions that took longer than cutoff_secs + df = self.data[self.data["Time (µsec)"] >= cutoff_secs * 1e6].copy() + + # convert the time to seconds + df["Time (µsec)"] /= 1e6 + df = df.rename(columns={"Time (µsec)": "Time (sec)"}) + + if style == "simple": + # format and write the report to HTML + df = ( + df.style.format( + { + "Time (sec)": lambda x: f"{x:.3f}", + } + ) + .background_gradient( + axis=0, gmap=df["Time (sec)"], cmap="YlOrRd", subset=["Time (sec)"] + ) + .hide(axis="index") + .set_table_styles( + [{"selector": "th", "props": [("text-align", "left")]}] + ) + .set_properties(**{"padding": "0 5px"}, subset=["Time (sec)"]) + ) + dat = df.to_html(index=False) + dat = dat.replace(" + + + + + Expression Timing Report + + + + {dat} + + """ + self.log_dir.joinpath(filename).parent.mkdir(parents=True, exist_ok=True) + with open(self.log_dir.joinpath(filename), "w") as f: + f.write(template) + elif style == "grid": + template = """ + + + + + + +
+ + + + """ + self.log_dir.joinpath(filename).parent.mkdir(parents=True, exist_ok=True) + with open(self.log_dir.joinpath(filename), "w") as f: + f.write(template.replace("<>", df.to_json(orient="records"))) + + def component_report_data(self, cutoff_secs: float | None = None): + """ + Return the data for the component report. + + Parameters + ---------- + cutoff_secs : float, optional + The cutoff time in seconds. Only expressions with a runtime greater than + this will be included in the report. This is used to avoid writing a + huge report full of expressions that run plenty fast. If not provided, + the default cutoff time from the settings is used. + """ + if cutoff_secs is None: + cutoff_secs = self.default_cutoff + + df = ( + self.data.groupby(["Component", "Expression"]) + .agg({"Time (µsec)": "sum"}) + .reset_index()[["Time (µsec)", "Component", "Expression"]] + .sort_values(by=["Time (µsec)"], ascending=[False]) + ) + + # include only expressions that took longer than cutoff_secs + df = df[df["Time (µsec)"] >= cutoff_secs * 1e6] + + # convert the time to seconds + df["Time (µsec)"] /= 1e6 + df.rename(columns={"Time (µsec)": "Time (sec)"}, inplace=True) + return df + + def component_report( + self, + filename: str | Path = "expression-timing-components.html", + cutoff_secs: float | None = None, + style: Literal["grid", "simple"] = "grid", + ) -> None: + """Write component-level aggregations to an HTML file. + + This will aggregate the expression timings by component, which may better + reveal expressions that are more problematic because they are evaluated + multiple times. + + Parameters + ---------- + filename : str | Path + The name of the file to write the HTML to. If a relative path is given, + it will be written in the log directory. + cutoff_secs : float + The cutoff time in seconds. Only expressions with a runtime greater than + this will be included in the HTML file. This is used to avoid writing a + huge report full of expressions that run plenty fast. + style : "simple" | "grid", default "simple" + The style of the report. Either "simple" or "grid". "simple" is a + simple HTML table, "grid" is a JavaScript data grid. + """ + + df = self.component_report_data(cutoff_secs=cutoff_secs) + + if style == "simple": + # format and write the report to HTML in a simple table + df = ( + df.style.format( + { + "Time (sec)": lambda x: f"{x:.3f}", + } + ) + .background_gradient( + axis=0, gmap=df["Time (sec)"], cmap="YlOrRd", subset=["Time (sec)"] + ) + .hide(axis="index") + .set_table_styles( + [{"selector": "th", "props": [("text-align", "left")]}] + ) + .set_properties(**{"padding": "0 5px"}, subset=["Time (sec)"]) + ) + dat = df.to_html(index=False) + dat = dat.replace("
+ + + + + Expression Timing Report + + + + {dat} + + """ + self.log_dir.joinpath(filename).parent.mkdir(parents=True, exist_ok=True) + with open(self.log_dir.joinpath(filename), "w") as f: + f.write(template) + elif style == "grid": + template = """ + + + + + + +
+ + + + """ + self.log_dir.joinpath(filename).parent.mkdir(parents=True, exist_ok=True) + with open(self.log_dir.joinpath(filename), "w") as f: + f.write(template.replace("<>", df.to_json(orient="records"))) + else: + raise ValueError(f"Unknown style {style}. Must be 'simple' or 'grid'.") + + +# Code below is from https://github.com/HubSpot/sortable +# Copyright (C) 2013 Adam Schwartz, http://adamschwartz.co +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +# THE SOFTWARE. + + +SORTABLE_JS = """ +(function() { + var SELECTOR, addEventListener, clickEvents, numberRegExp, sortable, touchDevice, trimRegExp; + + SELECTOR = 'table[data-sortable]'; + + numberRegExp = /^-?[£$¤]?[\d,.]+%?$/; + + trimRegExp = /^\s+|\s+$/g; + + clickEvents = ['click']; + + touchDevice = 'ontouchstart' in document.documentElement; + + if (touchDevice) { + clickEvents.push('touchstart'); + } + + addEventListener = function(el, event, handler) { + if (el.addEventListener != null) { + return el.addEventListener(event, handler, false); + } else { + return el.attachEvent("on" + event, handler); + } + }; + + sortable = { + init: function(options) { + var table, tables, _i, _len, _results; + if (options == null) { + options = {}; + } + if (options.selector == null) { + options.selector = SELECTOR; + } + tables = document.querySelectorAll(options.selector); + _results = []; + for (_i = 0, _len = tables.length; _i < _len; _i++) { + table = tables[_i]; + _results.push(sortable.initTable(table)); + } + return _results; + }, + initTable: function(table) { + var i, th, ths, _i, _len, _ref; + if (((_ref = table.tHead) != null ? _ref.rows.length : void 0) !== 1) { + return; + } + if (table.getAttribute('data-sortable-initialized') === 'true') { + return; + } + table.setAttribute('data-sortable-initialized', 'true'); + ths = table.querySelectorAll('th'); + for (i = _i = 0, _len = ths.length; _i < _len; i = ++_i) { + th = ths[i]; + if (th.getAttribute('data-sortable') !== 'false') { + sortable.setupClickableTH(table, th, i); + } + } + return table; + }, + setupClickableTH: function(table, th, i) { + var eventName, onClick, type, _i, _len, _results; + type = sortable.getColumnType(table, i); + onClick = function(e) { + var compare, item, newSortedDirection, position, row, rowArray, sorted, sortedDirection, tBody, ths, value, _compare, _i, _j, _k, _l, _len, _len1, _len2, _len3, _len4, _m, _ref, _ref1; + if (e.handled !== true) { + e.handled = true; + } else { + return false; + } + sorted = this.getAttribute('data-sorted') === 'true'; + sortedDirection = this.getAttribute('data-sorted-direction'); + if (sorted) { + newSortedDirection = sortedDirection === 'ascending' ? 'descending' : 'ascending'; + } else { + newSortedDirection = type.defaultSortDirection; + } + ths = this.parentNode.querySelectorAll('th'); + for (_i = 0, _len = ths.length; _i < _len; _i++) { + th = ths[_i]; + th.setAttribute('data-sorted', 'false'); + th.removeAttribute('data-sorted-direction'); + } + this.setAttribute('data-sorted', 'true'); + this.setAttribute('data-sorted-direction', newSortedDirection); + tBody = table.tBodies[0]; + rowArray = []; + if (!sorted) { + if (type.compare != null) { + _compare = type.compare; + } else { + _compare = function(a, b) { + return b - a; + }; + } + compare = function(a, b) { + if (a[0] === b[0]) { + return a[2] - b[2]; + } + if (type.reverse) { + return _compare(b[0], a[0]); + } else { + return _compare(a[0], b[0]); + } + }; + _ref = tBody.rows; + for (position = _j = 0, _len1 = _ref.length; _j < _len1; position = ++_j) { + row = _ref[position]; + value = sortable.getNodeValue(row.cells[i]); + if (type.comparator != null) { + value = type.comparator(value); + } + rowArray.push([value, row, position]); + } + rowArray.sort(compare); + for (_k = 0, _len2 = rowArray.length; _k < _len2; _k++) { + row = rowArray[_k]; + tBody.appendChild(row[1]); + } + } else { + _ref1 = tBody.rows; + for (_l = 0, _len3 = _ref1.length; _l < _len3; _l++) { + item = _ref1[_l]; + rowArray.push(item); + } + rowArray.reverse(); + for (_m = 0, _len4 = rowArray.length; _m < _len4; _m++) { + row = rowArray[_m]; + tBody.appendChild(row); + } + } + if (typeof window['CustomEvent'] === 'function') { + return typeof table.dispatchEvent === "function" ? table.dispatchEvent(new CustomEvent('Sortable.sorted', { + bubbles: true + })) : void 0; + } + }; + _results = []; + for (_i = 0, _len = clickEvents.length; _i < _len; _i++) { + eventName = clickEvents[_i]; + _results.push(addEventListener(th, eventName, onClick)); + } + return _results; + }, + getColumnType: function(table, i) { + var row, specified, text, type, _i, _j, _len, _len1, _ref, _ref1, _ref2; + specified = (_ref = table.querySelectorAll('th')[i]) != null ? _ref.getAttribute('data-sortable-type') : void 0; + if (specified != null) { + return sortable.typesObject[specified]; + } + _ref1 = table.tBodies[0].rows; + for (_i = 0, _len = _ref1.length; _i < _len; _i++) { + row = _ref1[_i]; + text = sortable.getNodeValue(row.cells[i]); + _ref2 = sortable.types; + for (_j = 0, _len1 = _ref2.length; _j < _len1; _j++) { + type = _ref2[_j]; + if (type.match(text)) { + return type; + } + } + } + return sortable.typesObject.alpha; + }, + getNodeValue: function(node) { + var dataValue; + if (!node) { + return ''; + } + dataValue = node.getAttribute('data-value'); + if (dataValue !== null) { + return dataValue; + } + if (typeof node.innerText !== 'undefined') { + return node.innerText.replace(trimRegExp, ''); + } + return node.textContent.replace(trimRegExp, ''); + }, + setupTypes: function(types) { + var type, _i, _len, _results; + sortable.types = types; + sortable.typesObject = {}; + _results = []; + for (_i = 0, _len = types.length; _i < _len; _i++) { + type = types[_i]; + _results.push(sortable.typesObject[type.name] = type); + } + return _results; + } + }; + + sortable.setupTypes([ + { + name: 'numeric', + defaultSortDirection: 'descending', + match: function(a) { + return a.match(numberRegExp); + }, + comparator: function(a) { + return parseFloat(a.replace(/[^0-9.-]/g, ''), 10) || 0; + } + }, { + name: 'date', + defaultSortDirection: 'ascending', + reverse: true, + match: function(a) { + return !isNaN(Date.parse(a)); + }, + comparator: function(a) { + return Date.parse(a) || 0; + } + }, { + name: 'alpha', + defaultSortDirection: 'ascending', + match: function() { + return true; + }, + compare: function(a, b) { + return a.localeCompare(b); + } + } + ]); + + setTimeout(sortable.init, 0); + + if (typeof define === 'function' && define.amd) { + define(function() { + return sortable; + }); + } else if (typeof exports !== 'undefined') { + module.exports = sortable; + } else { + window.Sortable = sortable; + } + +}).call(this); +""" + +SORTABLE_CSS = """ +/* line 2, ../sass/_sortable.sass */ +table[data-sortable] { + border-collapse: collapse; + border-spacing: 0; +} +/* line 6, ../sass/_sortable.sass */ +table[data-sortable] th { + vertical-align: bottom; + font-weight: bold; +} +/* line 10, ../sass/_sortable.sass */ +table[data-sortable] th, table[data-sortable] td { + text-align: left; + padding: 10px; +} +/* line 14, ../sass/_sortable.sass */ +table[data-sortable] th:not([data-sortable="false"]) { + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + -o-user-select: none; + user-select: none; + -webkit-tap-highlight-color: rgba(0, 0, 0, 0); + -webkit-touch-callout: none; + cursor: pointer; +} +/* line 26, ../sass/_sortable.sass */ +table[data-sortable] th:after { + content: ""; + visibility: hidden; + display: inline-block; + vertical-align: inherit; + height: 0; + width: 0; + border-width: 5px; + border-style: solid; + border-color: transparent; + margin-right: 1px; + margin-left: 10px; + float: right; +} +/* line 40, ../sass/_sortable.sass */ +table[data-sortable] th[data-sorted="true"]:after { + visibility: visible; +} +/* line 43, ../sass/_sortable.sass */ +table[data-sortable] th[data-sorted-direction="descending"]:after { + border-top-color: inherit; + margin-top: 8px; +} +/* line 47, ../sass/_sortable.sass */ +table[data-sortable] th[data-sorted-direction="ascending"]:after { + border-bottom-color: inherit; + margin-top: 3px; +}""" diff --git a/activitysim/core/tracing.py b/activitysim/core/tracing.py index 88bf0fc167..ecb0c363cd 100644 --- a/activitysim/core/tracing.py +++ b/activitysim/core/tracing.py @@ -11,6 +11,8 @@ import numpy as np import pandas as pd +from activitysim.core.exceptions import TableSlicingError + # Configurations ASIM_LOGGER = "activitysim" CSV_FILE_TYPE = "csv" @@ -147,12 +149,12 @@ def print_summary(label, df, describe=False, value_counts=False): if value_counts: n = 10 - logger.info( + logger.debug( "%s top %s value counts:\n%s" % (label, n, df.value_counts().nlargest(n)) ) if describe: - logger.info("%s summary:\n%s" % (label, df.describe())) + logger.debug("%s summary:\n%s" % (label, df.describe())) def write_df_csv( @@ -247,7 +249,9 @@ def slice_ids(df, ids, column=None): except KeyError: # this happens if specified slicer column is not in df # df = df[0:0] - raise RuntimeError("slice_ids slicer column '%s' not in dataframe" % column) + raise TableSlicingError( + "slice_ids slicer column '%s' not in dataframe" % column + ) return df diff --git a/activitysim/core/util.py b/activitysim/core/util.py index 940796e4bd..20f79c7609 100644 --- a/activitysim/core/util.py +++ b/activitysim/core/util.py @@ -74,7 +74,9 @@ def delete_files(file_list, trace_label): logger.debug(f"{trace_label} deleting {file_path}") os.unlink(file_path) except Exception as e: - logger.warning(f"{trace_label} exception (e) trying to delete {file_path}") + logger.warning( + f"{trace_label} exception ({e}) trying to delete {file_path}" + ) def df_size(df): @@ -296,7 +298,7 @@ def quick_loc_series(loc_list, target_series): elif isinstance(loc_list, np.ndarray) or isinstance(loc_list, list): left_df = pd.DataFrame({left_on: loc_list}) else: - raise RuntimeError( + raise TypeError( "quick_loc_series loc_list of unexpected type %s" % type(loc_list) ) @@ -340,6 +342,24 @@ def assign_in_place(df, df2, downcast_int=False, downcast_float=False): common_columns = df2.columns.intersection(df.columns) if len(common_columns) > 0: old_dtypes = [df[c].dtype for c in common_columns] + # in pandas 2.x, update a categorical column with any new categories will cause TypeError + # so we need to add the new categories first + # this is a workaround for pandas 2.x, see discussion in + # https://github.com/ActivitySim/activitysim/discussions/946 + for c in common_columns: + if isinstance(df[c].dtype, pd.CategoricalDtype): + if not isinstance(df2[c].dtype, pd.CategoricalDtype): + # if df column is categorical, but df2 column is not + # convert df2 column to categorical then union categories + df2[c] = df2[c].astype("category") + + # when df and df2 column are both categorical, union categories + from pandas.api.types import union_categoricals + + uc = union_categoricals([df[c], df2[c]], sort_categories=True) + df[c] = pd.Categorical(df[c], categories=uc.categories) + df2[c] = pd.Categorical(df2[c], categories=uc.categories) + df.update(df2) # avoid needlessly changing int columns to float @@ -426,7 +446,7 @@ def auto_opt_pd_dtypes( else: df[col] = pd.to_numeric(df[col], downcast="float") # Skip if the column is already categorical - if pd.api.types.is_categorical_dtype(dtype): + if isinstance(dtype, pd.CategoricalDtype): continue # Handle integer types if pd.api.types.is_integer_dtype(dtype): @@ -702,9 +722,9 @@ def drop_unused_columns( custom_chooser_lines = inspect.getsource(custom_chooser) unique_variables_in_spec.update(re.findall(pattern, custom_chooser_lines)) - logger.info("Dropping unused variables in chooser table") + logger.debug("Dropping unused variables in chooser table") - logger.info( + logger.debug( "before dropping, the choosers table has {} columns: {}".format( len(choosers.columns), choosers.columns ) @@ -713,7 +733,7 @@ def drop_unused_columns( # keep only variables needed for spec choosers = choosers[[c for c in choosers.columns if c in unique_variables_in_spec]] - logger.info( + logger.debug( "after dropping, the choosers table has {} columns: {}".format( len(choosers.columns), choosers.columns ) diff --git a/activitysim/core/workflow/checkpoint.py b/activitysim/core/workflow/checkpoint.py index d5a7286359..7391e1c9b9 100644 --- a/activitysim/core/workflow/checkpoint.py +++ b/activitysim/core/workflow/checkpoint.py @@ -18,6 +18,7 @@ TableNameNotFound, ) from activitysim.core.workflow.accessor import FromState, StateAccessor +from activitysim.core.exceptions import CheckpointNameNotFoundError, PipelineError logger = logging.getLogger(__name__) @@ -565,7 +566,7 @@ def open_store( """ if self._checkpoint_store is not None: - raise RuntimeError("Pipeline store is already open!") + raise PipelineError("Pipeline store is already open!") if pipeline_file_name is None: pipeline_file_path = self.default_pipeline_file_path() @@ -775,7 +776,7 @@ def load(self, checkpoint_name: str, store=None): msg = f"Couldn't find checkpoint '{checkpoint_name}' in checkpoints" print(checkpoints[CHECKPOINT_NAME]) logger.error(msg) - raise RuntimeError(msg) from None + raise CheckpointNameNotFoundError(msg) from None # convert pandas dataframe back to array of checkpoint dicts checkpoints = checkpoints.to_dict(orient="records") @@ -1201,7 +1202,7 @@ def load_dataframe(self, table_name, checkpoint_name=None): if table_name not in self.last_checkpoint and self._obj.is_table(table_name): if checkpoint_name is not None: - raise RuntimeError( + raise CheckpointNameNotFoundError( f"checkpoint.dataframe: checkpoint_name ({checkpoint_name!r}) not " f"supported for non-checkpointed table {table_name!r}" ) @@ -1211,10 +1212,14 @@ def load_dataframe(self, table_name, checkpoint_name=None): # if there is no checkpoint name given, do not attempt to read from store if checkpoint_name is None: if table_name not in self.last_checkpoint: - raise RuntimeError("table '%s' never checkpointed." % table_name) + raise CheckpointNameNotFoundError( + "table '%s' never checkpointed." % table_name + ) if not self.last_checkpoint[table_name]: - raise RuntimeError("table '%s' was dropped." % table_name) + raise CheckpointNameNotFoundError( + "table '%s' was dropped." % table_name + ) return self._obj.get_dataframe(table_name) @@ -1224,13 +1229,15 @@ def load_dataframe(self, table_name, checkpoint_name=None): None, ) if checkpoint is None: - raise RuntimeError("checkpoint '%s' not in checkpoints." % checkpoint_name) + raise CheckpointNameNotFoundError( + "checkpoint '%s' not in checkpoints." % checkpoint_name + ) # find the checkpoint that table was written to store last_checkpoint_name = checkpoint.get(table_name, None) if not last_checkpoint_name: - raise RuntimeError( + raise CheckpointNameNotFoundError( "table '%s' not in checkpoint '%s'." % (table_name, checkpoint_name) ) diff --git a/activitysim/core/workflow/runner.py b/activitysim/core/workflow/runner.py index 8eba53cbef..79ecd0ed4f 100644 --- a/activitysim/core/workflow/runner.py +++ b/activitysim/core/workflow/runner.py @@ -265,7 +265,7 @@ def _pre_run_step(self, model_name: str) -> bool | None: if model_name in checkpointed_models: if self._obj.settings.duplicate_step_execution == "error": checkpointed_model_bullets = "\n - ".join(checkpointed_models) - raise RuntimeError( + raise DuplicateWorkflowNameError( f"Checkpointed Models:\n - {checkpointed_model_bullets}\n" f"Cannot run model '{model_name}' more than once" ) diff --git a/activitysim/core/workflow/state.py b/activitysim/core/workflow/state.py index 45aa7a1f74..9f7dcd4d6f 100644 --- a/activitysim/core/workflow/state.py +++ b/activitysim/core/workflow/state.py @@ -8,6 +8,7 @@ import textwrap import warnings from collections.abc import Iterable +from datetime import datetime from pathlib import Path from typing import Any, Optional @@ -19,7 +20,7 @@ import activitysim.core.random from activitysim.core.configuration import FileSystem, NetworkSettings, Settings -from activitysim.core.exceptions import StateAccessError +from activitysim.core.exceptions import StateAccessError, CheckpointNameNotFoundError from activitysim.core.workflow.checkpoint import LAST_CHECKPOINT, Checkpoints from activitysim.core.workflow.chunking import Chunking from activitysim.core.workflow.dataset import Datasets @@ -1016,7 +1017,7 @@ def get_table(self, table_name, checkpoint_name=None): table_name ): if checkpoint_name is not None: - raise RuntimeError( + raise CheckpointNameNotFoundError( f"get_table: checkpoint_name ({checkpoint_name!r}) not " f"supported for non-checkpointed table {table_name!r}" ) @@ -1026,10 +1027,14 @@ def get_table(self, table_name, checkpoint_name=None): # if they want current version of table, no need to read from pipeline store if checkpoint_name is None: if table_name not in self.checkpoint.last_checkpoint: - raise RuntimeError("table '%s' never checkpointed." % table_name) + raise CheckpointNameNotFoundError( + "table '%s' never checkpointed." % table_name + ) if not self.checkpoint.last_checkpoint[table_name]: - raise RuntimeError("table '%s' was dropped." % table_name) + raise CheckpointNameNotFoundError( + "table '%s' was dropped." % table_name + ) return self._context.get(table_name) @@ -1043,13 +1048,15 @@ def get_table(self, table_name, checkpoint_name=None): None, ) if checkpoint is None: - raise RuntimeError("checkpoint '%s' not in checkpoints." % checkpoint_name) + raise CheckpointNameNotFoundError( + "checkpoint '%s' not in checkpoints." % checkpoint_name + ) # find the checkpoint that table was written to store last_checkpoint_name = checkpoint.get(table_name, None) if not last_checkpoint_name: - raise RuntimeError( + raise CheckpointNameNotFoundError( "table '%s' not in checkpoint '%s'." % (table_name, checkpoint_name) ) @@ -1099,7 +1106,9 @@ def extend_table(self, table_name, df, axis=0): if isinstance(df[c].dtype, pd.api.types.CategoricalDtype): from pandas.api.types import union_categoricals - uc = union_categoricals([table_df[c], df[c]]) + uc = union_categoricals( + [table_df[c], df[c]], sort_categories=True + ) table_df[c] = pd.Categorical( table_df[c], categories=uc.categories ) @@ -1169,6 +1178,49 @@ def get_log_file_path(self, file_name: str, prefix: bool = True) -> Path: file_name = f"{prefix}-{file_name}" return self.filesystem.get_log_file_path(file_name) + def get_expr_performance_log_file_path( + self, file_name: str, prefix: bool = True + ) -> Path: + """ + Get the log file path for this process. + + This method is not purely a pass-through to this state's `filesystem`, + as it also potentially adds a prefix to the filename based on the state. + + Parameters + ---------- + file_name : str + The name of the desired log file. + prefix : bool, default True + Whether to add a prefix to the desired log file name. This is + simply a boolean flag for whether to add the prefix, the actual + value of the prefix id drawn from the "log_file_prefix" key within + this state. If that key is not set, no prefix is added regardless + of the value of this argument. + timestamped : bool, default False + Whether to add a timestamp to the log file name. If True, a + timestamp is added as a directory prefix to the log file name, + and the directory is created if it does not already exist. + + Returns + ------- + Path + """ + prefix = prefix and self.get_injectable("log_file_prefix", None) + if prefix: + file_name = f"{prefix}-{file_name}" + + timestamp = self.get("run_timestamp", None) + if timestamp is None: + # if no run timestamp, use current time, and store it so + # it can be used later in the same run + timestamp = datetime.now().strftime("%Y%m%d-%H%M%S") + self.set("run_timestamp", timestamp) + perf_dir = os.path.join("expr-performance", timestamp, file_name) + result = self.filesystem.get_log_file_path(perf_dir) + result.parent.mkdir(parents=True, exist_ok=True) + return result + def set_step_args(self, args=None): assert isinstance(args, dict) or args is None self.add_injectable("step_args", args) diff --git a/activitysim/core/workflow/tracing.py b/activitysim/core/workflow/tracing.py index 580c6fad9b..b89a8bd26b 100644 --- a/activitysim/core/workflow/tracing.py +++ b/activitysim/core/workflow/tracing.py @@ -5,11 +5,9 @@ import logging import logging.config import os -import struct import sys import tarfile import tempfile -import time from collections.abc import Mapping, MutableMapping, Sequence from pathlib import Path from typing import Any, Optional @@ -21,6 +19,8 @@ from activitysim.core import tracing from activitysim.core.test import assert_equal, assert_frame_substantively_equal from activitysim.core.workflow.accessor import FromState, StateAccessor +from activitysim.core.exceptions import TableSlicingError +from activitysim.core.run_id import RunId logger = logging.getLogger(__name__) @@ -36,15 +36,6 @@ ] -class RunId(str): - def __new__(cls, x=None): - if x is None: - return cls( - hex(struct.unpack("= 0] - - m = Model() - # One of the alternatives is coded as 0, so - # we need to explicitly initialize the MNL nesting graph - # and set to root_id to a value other than zero. - m.initialize_graph(alternative_codes=altcodes, root_id=99) - - m.utility_co = dict_of_linear_utility_from_spec( - spec, - "Label", - dict(zip(altnames, altcodes)), - ) - - apply_coefficients(coefficients, m) - - d = DataFrames( - co=chooser_data, - av=True, - alt_codes=altcodes, - alt_names=altnames, - ) - - m.dataservice = d - m.choice_co_code = "override_choice" - - if return_data: - return ( - m, - Dict( - edb_directory=data.edb_directory, - chooser_data=chooser_data, - coefficients=coefficients, - spec=spec, - altnames=altnames, - altcodes=altcodes, - ), - ) - - return m diff --git a/activitysim/estimation/larch/cdap.py b/activitysim/estimation/larch/cdap.py index 60fcce6fed..ba63ec3c3c 100644 --- a/activitysim/estimation/larch/cdap.py +++ b/activitysim/estimation/larch/cdap.py @@ -9,24 +9,23 @@ import numpy as np import pandas as pd +import xarray as xr import yaml from ...abm.models.util import cdap from .general import apply_coefficients, explicit_value_parameters try: - import larch + import larch as lx except ImportError: - larch = None + lx = None logger_name = "larch" else: - from larch import DataFrames, Model, P, X - from larch.log import logger_name - from larch.model.model_group import ModelGroup + from larch import P, X from larch.util import Dict -_logger = logging.getLogger(logger_name) +_logger = logging.getLogger("larch") MAX_HHSIZE = 5 @@ -112,11 +111,17 @@ def cdap_base_utility_by_person( if n_persons == 1: for i in spec.index: if not pd.isna(spec.loc[i, "M"]): - model.utility_co[1] += X(spec.Expression[i]) * P(spec.loc[i, "M"]) + model.utility_co[1] += X(spec.Expression[i].lstrip("@")) * P( + spec.loc[i, "M"] + ) if not pd.isna(spec.loc[i, "N"]): - model.utility_co[2] += X(spec.Expression[i]) * P(spec.loc[i, "N"]) + model.utility_co[2] += X(spec.Expression[i].lstrip("@")) * P( + spec.loc[i, "N"] + ) if not pd.isna(spec.loc[i, "H"]): - model.utility_co[3] += X(spec.Expression[i]) * P(spec.loc[i, "H"]) + model.utility_co[3] += X(spec.Expression[i].lstrip("@")) * P( + spec.loc[i, "H"] + ) else: if alts is None: alts = generate_alternatives(n_persons, add_joint) @@ -129,7 +134,9 @@ def cdap_base_utility_by_person( x = apply_replacements( spec.Expression[i], f"p{pnum}", value_tokens ) - model.utility_co[anum] += X(x) * P(spec.loc[i, aname[z]]) + model.utility_co[anum] += X(x.lstrip("@")) * P( + spec.loc[i, aname[z]] + ) def interact_pattern(n_persons, select_persons, tag): @@ -281,7 +288,7 @@ def cdap_joint_tour_utility(model, n_persons, alts, joint_coef, values): expression_value = "&".join(expression_list) # FIXME only apply to alternative if dependency satisfied - bug + raise NotImplementedError("bug") model.utility_co[anum] += X(expression_value) * P(coefficient) elif "_px" in expression: @@ -289,7 +296,7 @@ def cdap_joint_tour_utility(model, n_persons, alts, joint_coef, values): dependency_name = row.dependency.replace("x", str(pnum)) expression = row.Expression.replace("x", str(pnum)) # FIXME only apply to alternative if dependency satisfied - bug + raise NotImplementedError("bug") model.utility_co[anum] += X(expression) * P(coefficient) else: @@ -335,48 +342,25 @@ def cdap_split_data(households, values, add_joint): return cdap_data -def cdap_dataframes(households, values, add_joint): +def cdap_dataframes(households, values, add_joint) -> dict[int, lx.Dataset]: data = cdap_split_data(households, values, add_joint) dfs = {} for hhsize in data.keys(): alts = generate_alternatives(hhsize, add_joint) - dfs[hhsize] = DataFrames( - co=data[hhsize], - alt_names=alts.keys(), - alt_codes=alts.values(), - av=1, - ch=data[hhsize].override_choice.map(alts), + dfs[hhsize] = lx.Dataset.construct.from_idco( + data[hhsize], + alts=dict(zip(alts.values(), alts.keys())), + ) + # convert override_choice to alternative code from alternative name + dfs[hhsize]["override_choice"] = xr.DataArray( + np.vectorize(alts.get)(dfs[hhsize].override_choice.data), + coords=dfs[hhsize].override_choice.coords, + dims=dfs[hhsize].override_choice.dims, + name="override_choice", ) return dfs -# def _cdap_model(households, values, spec1, interaction_coef, coefficients): -# cdap_data = cdap_dataframes(households, values) -# m = {} -# _logger.info(f"building for model 1") -# m[1] = Model(dataservice=cdap_data[1]) -# cdap_base_utility_by_person(m[1], n_persons=1, spec=spec1) -# m[1].choice_any = True -# m[1].availability_any = True -# -# # Add cardinality into interaction_coef if not present -# if 'cardinality' not in interaction_coef: -# interaction_coef['cardinality'] = interaction_coef['interaction_ptypes'].str.len() -# for s in [2, 3, 4, 5]: -# _logger.info(f"building for model {s}") -# m[s] = Model(dataservice=cdap_data[s]) -# alts = generate_alternatives(s) -# cdap_base_utility_by_person(m[s], s, spec1, alts, values.columns) -# cdap_interaction_utility(m[s], s, alts, interaction_coef, coefficients) -# m[s].choice_any = True -# m[s].availability_any = True -# -# result = ModelGroup(m.values()) -# explicit_value_parameters(result) -# apply_coefficients(coefficients, result) -# return result - - def cdap_data( name="cdap", edb_directory="output/estimation_data_bundle/{name}/", @@ -394,11 +378,22 @@ def cdap_data( raise FileNotFoundError(edb_directory) def read_csv(filename, **kwargs): - filename = filename.format(name=name) - return pd.read_csv(os.path.join(edb_directory, filename), **kwargs) + filename = Path(edb_directory).joinpath(filename.format(name=name)).resolve() + if filename.with_suffix(".parquet").exists(): + if "comment" in kwargs: + del kwargs["comment"] + print(f"Reading {filename.with_suffix('.parquet')}") + df = pd.read_parquet(filename.with_suffix(".parquet"), **kwargs) + if df.index.name is not None: + # want the data to be read in the same as csv format -- without the index + df = df.reset_index(drop=False) + return df + print(f"Reading {filename}") + return pd.read_csv(filename, **kwargs) def read_yaml(filename, **kwargs): filename = filename.format(name=name) + print(f"Reading {os.path.join(edb_directory, filename)}") with open(os.path.join(edb_directory, filename), "rt") as f: return yaml.load(f, Loader=yaml.SafeLoader, **kwargs) @@ -442,7 +437,7 @@ def read_yaml(filename, **kwargs): except FileNotFoundError: joint_coef = None add_joint = False - print("Including joint tour utiltiy?:", add_joint) + print("Including joint tour utility?:", add_joint) spec1 = read_csv(spec1_file, comment="#") values = read_csv(chooser_data_file, comment="#") @@ -499,13 +494,17 @@ def cdap_model( interaction_coef = d.interaction_coef coefficients = d.coefficients add_joint = d.add_joint + extra_vars = d.settings.get("CONSTANTS", {}) cdap_dfs = cdap_dataframes(households, values, add_joint) m = {} _logger.info(f"building for model 1") - m[1] = Model(dataservice=cdap_dfs[1]) + m[1] = lx.Model( + datatree=cdap_dfs[1].dc.as_tree("df", extra_vars=extra_vars), + compute_engine="numba", + ) cdap_base_utility_by_person(m[1], n_persons=1, spec=spec1) - m[1].choice_any = True + m[1].choice_co_code = "override_choice" m[1].availability_any = True # Add cardinality into interaction_coef if not present @@ -516,16 +515,16 @@ def cdap_model( for s in range(2, MAX_HHSIZE + 1): # for s in [2, 3, 4, 5]: _logger.info(f"building for model {s}") - m[s] = Model(dataservice=cdap_dfs[s]) + m[s] = lx.Model(datatree=cdap_dfs[s].dc.as_tree("df", extra_vars=extra_vars)) alts = generate_alternatives(s, add_joint) cdap_base_utility_by_person(m[s], s, spec1, alts, values.columns) cdap_interaction_utility(m[s], s, alts, interaction_coef, coefficients) # if add_joint: # cdap_joint_tour_utility(m[s], s, alts, d.joint_coef, values) - m[s].choice_any = True + m[s].choice_co_code = "override_choice" m[s].availability_any = True - model = ModelGroup(m.values()) + model = lx.ModelGroup(m.values()) explicit_value_parameters(model) apply_coefficients(coefficients, model) if return_data: diff --git a/activitysim/estimation/larch/general.py b/activitysim/estimation/larch/general.py index 14f8bd3f66..cfb81c756b 100644 --- a/activitysim/estimation/larch/general.py +++ b/activitysim/estimation/larch/general.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import logging import os from pathlib import Path @@ -7,18 +9,17 @@ import pandas as pd try: - import larch + # Larch is an optional dependency, and we don't want to fail when importing + # this module simply because larch is not installed. + import larch as lx except ImportError: - larch = None - logger_name = "larch" + lx = None else: - from larch import DataFrames, Model, P, X # noqa: F401 - from larch.log import logger_name - from larch.model.abstract_model import AbstractChoiceModel + from larch import Model, P, X # noqa: F401 from larch.model.tree import NestingTree from larch.util import Dict # noqa: F401 -_logger = logging.getLogger(logger_name) +_logger = logging.getLogger("larch") def cv_to_ca(alt_values, dtype="float64", required_labels=None): @@ -74,7 +75,12 @@ def cv_to_ca(alt_values, dtype="float64", required_labels=None): x_ca_tall = x_ca_tall.astype(dtype) # Unstack the variables dimension - x_ca = x_ca_tall.unstack(1) + x_ca = ( + x_ca_tall.reset_index() + .drop_duplicates() + .set_index(x_ca_tall.index.names) + .unstack(1) + ) # Code above added a dummy top level to columns, remove it here. x_ca.columns = x_ca.columns.droplevel(0) @@ -111,7 +117,9 @@ def str_repr(x): return x -def linear_utility_from_spec(spec, x_col, p_col, ignore_x=(), segment_id=None): +def linear_utility_from_spec( + spec, x_col, p_col, ignore_x=(), segment_id=None, x_validator=None, expr_col=None +): """ Create a linear function from a spec DataFrame. @@ -136,6 +144,13 @@ def linear_utility_from_spec(spec, x_col, p_col, ignore_x=(), segment_id=None): The CHOOSER_SEGMENT_COLUMN_NAME identified for ActivitySim. This value is ignored if `p_col` is a string, and required if `p_col` is a dict. + x_validator : Container, optional + A container of valid values for the x_col. If given, the + x_col values will be used if they are `in` the x_validator, + otherwise the value from `expr_col` will be used. + expr_col : str, optional + The name of the column to use when the x_col value is not + in the x_validator. Returns ------- @@ -151,6 +166,8 @@ def linear_utility_from_spec(spec, x_col, p_col, ignore_x=(), segment_id=None): x_col, seg_p_col, ignore_x, + x_validator=x_validator, + expr_col=expr_col, ) * X(f"{segment_id}=={str_repr(segval)}") return sum(partial_utility.values()) parts = [] @@ -163,6 +180,16 @@ def linear_utility_from_spec(spec, x_col, p_col, ignore_x=(), segment_id=None): _x = None else: raise + + # when a validator is given, use the expression column if the original + # x value is not in the validator + if _x is not None and _x not in ignore_x: + if x_validator is not None and _x not in x_validator: + _x = spec.loc[i, expr_col] + if _x.startswith("@"): + _x = _x[1:] + + # handle the parameter... _p = spec.loc[i, p_col] if _x is not None and (_x not in ignore_x) and not pd.isna(_p): @@ -201,7 +228,9 @@ def linear_utility_from_spec(spec, x_col, p_col, ignore_x=(), segment_id=None): return sum(parts) -def dict_of_linear_utility_from_spec(spec, x_col, p_col, ignore_x=()): +def dict_of_linear_utility_from_spec( + spec, x_col, p_col, ignore_x=(), x_validator=None, expr_col=None +): """ Create a linear function from a spec DataFrame. @@ -223,6 +252,13 @@ def dict_of_linear_utility_from_spec(spec, x_col, p_col, ignore_x=()): The CHOOSER_SEGMENT_COLUMN_NAME identified for ActivitySim. This value is ignored if `p_col` is a string, and required if `p_col` is a dict. + x_validator : Container, optional + A container of valid values for the x_col. If given, the + x_col values will be used if they are `in` the x_validator, + otherwise the value from `expr_col` will be used. + expr_col : str, optional + The name of the column to use when the x_col value is not + in the x_validator. Returns ------- @@ -231,7 +267,12 @@ def dict_of_linear_utility_from_spec(spec, x_col, p_col, ignore_x=()): utils = {} for altname, altcode in p_col.items(): utils[altcode] = linear_utility_from_spec( - spec, x_col, altname, ignore_x=ignore_x + spec, + x_col, + altname, + ignore_x=ignore_x, + x_validator=x_validator, + expr_col=expr_col, ) return utils @@ -295,15 +336,7 @@ def explicit_value_parameters(model): except Exception: pass else: - model.set_value( - i, - value=j, - initvalue=j, - nullvalue=j, - minimum=j, - maximum=j, - holdfast=True, - ) + model.lock_value(i, value=j) def apply_coefficients(coefficients, model, minimum=None, maximum=None): @@ -333,25 +366,22 @@ def apply_coefficients(coefficients, model, minimum=None, maximum=None): ) coefficients["constrain"] = "F" assert coefficients.index.name == "coefficient_name" - assert isinstance(model, AbstractChoiceModel) + # assert isinstance(model, AbstractChoiceModel) explicit_value_parameters(model) for i in coefficients.itertuples(): - if i.Index in model: + if i.Index in model.pnames: holdfast = i.constrain == "T" if holdfast: - minimum_ = i.value - maximum_ = i.value + model.lock_value(i.Index, value=i.value) else: - minimum_ = minimum - maximum_ = maximum - model.set_value( - i.Index, - value=i.value, - initvalue=i.value, - holdfast=holdfast, - minimum=minimum_, - maximum=maximum_, - ) + model.set_value( + i.Index, + value=i.value, + initvalue=i.value, + holdfast=holdfast, + minimum=minimum, + maximum=maximum, + ) def apply_coef_template(linear_utility, template_col, condition=None): @@ -426,7 +456,8 @@ def make_nest(cfg, parent_code=0): else: make_nest(a, parent_code=nest_names_to_codes[cfg["name"]]) - make_nest(nesting_settings) + if nesting_settings: + make_nest(nesting_settings) return tree diff --git a/activitysim/estimation/larch/location_choice.py b/activitysim/estimation/larch/location_choice.py index 75814ff6f7..5c50f6b736 100644 --- a/activitysim/estimation/larch/location_choice.py +++ b/activitysim/estimation/larch/location_choice.py @@ -1,7 +1,9 @@ from __future__ import annotations +import collections import os import pickle +import warnings from datetime import datetime from pathlib import Path from typing import Collection @@ -21,11 +23,12 @@ ) try: - import larch + # Larch is an optional dependency, and we don't want to fail when importing + # this module simply because larch is not installed. + import larch as lx except ImportError: - larch = None + lx = None else: - from larch import DataFrames, Model, P, X from larch.util import Dict @@ -43,6 +46,23 @@ def size_coefficients_from_spec(size_spec): return size_coef +LocationChoiceData = collections.namedtuple( + "LocationChoiceData", + field_names=[ + "edb_directory", + "alt_values", + "chooser_data", + "coefficients", + "landuse", + "spec", + "size_spec", + "master_size_spec", + "model_selector", + "settings", + ], +) + + def location_choice_model( name="workplace_location", edb_directory="output/estimation_data_bundle/{name}/", @@ -56,7 +76,60 @@ def location_choice_model( return_data=False, alt_values_to_feather=False, chunking_size=None, -): + *, + alts_in_cv_format=False, + availability_expression=None, +) -> lx.Model | tuple[lx.Model, LocationChoiceData]: + """ + Construct a location choice model from the estimation data bundle. + + Parameters + ---------- + name : str, optional + The name of the location choice model. The default is "workplace_location". + edb_directory : str, optional + The directory containing the estimation data bundle. The default is + "output/estimation_data_bundle/{name}/", where "{name}" is the name of + the model (see above). + coefficients_file : str, optional + The name of the coefficients file. The default is "{name}_coefficients.csv", + where "{name}" is the name of the model (see above). + spec_file : str, optional + The name of the spec file. The default is "{name}_SPEC.csv", where "{name}" + is the name of the model (see above). + size_spec_file : str, optional + The name of the size spec file. The default is "{name}_size_terms.csv", where + "{name}" is the name of the model (see above). + alt_values_file : str, optional + The name of the alternative values file. The default is + "{name}_alternatives_combined.csv", where "{name}" is the name of the model + (see above). + chooser_file : str, optional + The name of the chooser file. The default is "{name}_choosers_combined.csv", + where "{name}" is the name of the model (see above). + settings_file : str, optional + The name of the settings file. The default is "{name}_model_settings.yaml", + where "{name}" is the name of the model (see above). + landuse_file : str, optional + The name of the land use file. The default is "{name}_landuse.csv", where + "{name}" is the name of the model (see above). + return_data : bool, optional + If True, return a tuple containing the model and the location choice data. + The default is False, which returns only the model. + alt_values_to_feather : bool, default False + If True, convert the alternative values to a feather file. + chunking_size : int, optional + The number of rows per chunk for processing the alternative values. The default + is None, which processes all rows at once. + alts_in_cv_format : bool, default False + If True, the alternatives are in CV format. The default is False. + availability_expression : str, optional + The name of the availability expression. This is the "Label" from the + spec file that identifies an expression that evaluates truthy (non-zero) + if the alternative is available, and falsey otherwise. If not provided, + the code will attempt to infer the availability expression from the + expressions, but this is not reliable. The default is None. + """ model_selector = name.replace("_location", "") model_selector = model_selector.replace("_destination", "") model_selector = model_selector.replace("_subtour", "") @@ -66,8 +139,12 @@ def location_choice_model( edb_directory = edb_directory.format(name=name) def _read_csv(filename, **kwargs): - filename = filename.format(name=name) - return pd.read_csv(os.path.join(edb_directory, filename), **kwargs) + filename = Path(edb_directory).joinpath(filename.format(name=name)) + if filename.with_suffix(".parquet").exists(): + print("loading from", filename.with_suffix(".parquet")) + return pd.read_parquet(filename.with_suffix(".parquet"), **kwargs) + print("loading from", filename) + return pd.read_csv(filename, **kwargs) def _read_feather(filename, **kwargs): filename = filename.format(name=name) @@ -185,7 +262,10 @@ def _file_exists(filename): if label_column_name == "Expression": spec.insert(0, "Label", spec["Expression"].map(expression_labels)) - alt_values["variable"] = alt_values["variable"].map(expression_labels) + if alts_in_cv_format: + alt_values["variable"] = alt_values["variable"].map(expression_labels) + else: + alt_values = alt_values.rename(columns=expression_labels) label_column_name = "Label" if name == "trip_destination": @@ -200,44 +280,58 @@ def split(a, n): k, m = divmod(len(a), n) return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)) - # process x_ca with cv_to_ca with or without chunking - x_ca_pickle_file = "{name}_x_ca.pkl" - if chunking_size == None: - x_ca = cv_to_ca( - alt_values.set_index([chooser_index_name, alt_values.columns[1]]) - ) - elif _file_exists(x_ca_pickle_file): - # if pickle file from previous x_ca processing exist, load it to save time - time_start = datetime.now() - x_ca = _read_pickle(x_ca_pickle_file) - print( - f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}" - ) - else: - time_start = datetime.now() - # calculate num_chunks based on chunking_size (or max number of rows per chunk) - num_chunks = int(len(alt_values) / chunking_size) - id_col_name = alt_values.columns[0] - all_ids = list(alt_values[id_col_name].unique()) - split_ids = list(split(all_ids, num_chunks)) - x_ca_list = [] - i = 0 - for chunk_ids in split_ids: - alt_values_i = alt_values[alt_values[id_col_name].isin(chunk_ids)] - x_ca_i = cv_to_ca( - alt_values_i.set_index([chooser_index_name, alt_values_i.columns[1]]) + if alts_in_cv_format: + # if alternatives are in CV format, convert them to CA format. + # The CV format has the chooser index as the first column and the variable name + # as the second column, with values for each alternative in the remaining columns. + # This format is inefficient and deprecated as of ActivitySim version 1.4. + + # process x_ca with cv_to_ca with or without chunking + x_ca_pickle_file = "{name}_x_ca.pkl" + if chunking_size == None: + x_ca = cv_to_ca( + alt_values.set_index([chooser_index_name, alt_values.columns[1]]) ) - x_ca_list.append(x_ca_i) + elif _file_exists(x_ca_pickle_file): + # if pickle file from previous x_ca processing exist, load it to save time + time_start = datetime.now() + x_ca = _read_pickle(x_ca_pickle_file) print( - f"\rx_ca_i compute done for chunk {i}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}" + f"x_ca data loaded from {name}_x_ca.fea - time elapsed {(datetime.now() - time_start).total_seconds()}" ) - i = i + 1 - x_ca = pd.concat(x_ca_list, axis=0) - # save final x_ca result as pickle file to save time for future data loading - _to_pickle(df=x_ca, filename=x_ca_pickle_file) - print( - f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}" - ) + else: + time_start = datetime.now() + # calculate num_chunks based on chunking_size (or max number of rows per chunk) + num_chunks = int(len(alt_values) / chunking_size) + id_col_name = alt_values.columns[0] + all_ids = list(alt_values[id_col_name].unique()) + split_ids = list(split(all_ids, num_chunks)) + x_ca_list = [] + i = 0 + for chunk_ids in split_ids: + alt_values_i = alt_values[alt_values[id_col_name].isin(chunk_ids)] + x_ca_i = cv_to_ca( + alt_values_i.set_index( + [chooser_index_name, alt_values_i.columns[1]] + ) + ) + x_ca_list.append(x_ca_i) + print( + f"\rx_ca_i compute done for chunk {i}/{num_chunks} - time elapsed {(datetime.now() - time_start).total_seconds()}" + ) + i = i + 1 + x_ca = pd.concat(x_ca_list, axis=0) + # save final x_ca result as pickle file to save time for future data loading + _to_pickle(df=x_ca, filename=x_ca_pickle_file) + print( + f"x_ca compute done - time elapsed {(datetime.now() - time_start).total_seconds()}" + ) + else: + # otherwise, we assume that the alternatives are already in the correct IDCA format with + # the cases and alternatives as the first two columns, and the variables as the + # remaining columns. This is a much more efficient format for the data. + assert alt_values.columns[0] == chooser_index_name + x_ca = alt_values.set_index([chooser_index_name, alt_values.columns[1]]) if CHOOSER_SEGMENT_COLUMN_NAME is not None: # label segments with names @@ -256,6 +350,10 @@ def split(a, n): total_size_segment += ( landuse[land_use_field] * size_spec.loc[segment, land_use_field] ) + if -1 in x_co["override_choice"].values: + print("Warning: override_choice contains -1, adding 0 to total_size") + print("You should probably remove data containing -1 from your data") + total_size_segment.loc[-1] = 0 x_co["total_size_" + segment] = total_size_segment.loc[ x_co["override_choice"] ].to_numpy() @@ -269,46 +367,91 @@ def split(a, n): # Remove choosers with invalid observed choice (appropriate total size value = 0) valid_observed_zone = x_co["total_size_segment"] > 0 + prior_n_cases = len(x_co) x_co = x_co[valid_observed_zone] x_ca = x_ca[x_ca.index.get_level_values(chooser_index_name).isin(x_co.index)] + after_n_cases = len(x_co) + if prior_n_cases != after_n_cases: + warnings.warn( + f"Removed {prior_n_cases - after_n_cases} choosers with invalid (zero-sized) observed choice", + stacklevel=2, + ) # Merge land use characteristics into CA data - try: - x_ca_1 = pd.merge(x_ca, landuse, on="zone_id", how="left") - except KeyError: - # Missing the zone_id variable? - # Use the alternative id's instead, which assumes no sampling of alternatives - x_ca_1 = pd.merge( - x_ca, - landuse, - left_on=x_ca.index.get_level_values(1), - right_index=True, - how="left", - ) - x_ca_1.index = x_ca.index + x_ca_1 = pd.merge( + x_ca, landuse, left_on=x_ca.index.get_level_values(1), right_index=True + ) + x_ca_1 = x_ca_1.sort_index() + + # relabel zones to reduce memory usage. + # We will core the original zone ids in a new column _original_zone_id, + # and create a new index with a dummy zone id. This way, if we have sampled + # only a subset of 30 zones, then we only need 30 unique alternatives in the + # data structure. + original_zone_ids = x_ca_1.index.get_level_values(1) + + dummy_zone_ids_index = pd.MultiIndex.from_arrays( + [ + x_ca_1.index.get_level_values(0), + x_ca_1.groupby(level=0).cumcount() + 1, + ], + names=[x_ca_1.index.names[0], "dummy_zone_id"], + ) + x_ca_1.index = dummy_zone_ids_index + x_ca_1["_original_zone_id"] = original_zone_ids + choice_def = {"choice_ca_var": "override_choice == _original_zone_id"} # Availability of choice zones - if "util_no_attractions" in x_ca_1: + if availability_expression is not None and availability_expression in x_ca_1: + av = ( + x_ca_1[availability_expression] + .apply(lambda x: False if x == 1 else True) + .astype(np.int8) + .to_xarray() + ) + elif "util_no_attractions" in x_ca_1: av = ( x_ca_1["util_no_attractions"] .apply(lambda x: False if x == 1 else True) .astype(np.int8) + .to_xarray() ) elif "@df['size_term']==0" in x_ca_1: av = ( x_ca_1["@df['size_term']==0"] .apply(lambda x: False if x == 1 else True) .astype(np.int8) + .to_xarray() + ) + elif expression_labels is not None and "@df['size_term']==0" in expression_labels: + av = ( + x_ca_1[expression_labels["@df['size_term']==0"]] + .apply(lambda x: False if x == 1 else True) + .astype(np.int8) + .to_xarray() ) else: - av = 1 + av = None assert len(x_co) > 0, "Empty chooser dataframe" assert len(x_ca_1) > 0, "Empty alternatives dataframe" - d = DataFrames(co=x_co, ca=x_ca_1, av=av) + d_ca = lx.Dataset.construct.from_idca(x_ca_1) + d_co = lx.Dataset.construct.from_idco(x_co) + d = d_ca.merge(d_co) + if av is not None: + d["_avail_"] = av + + m = lx.Model(datatree=d, compute_engine="numba") + + # One of the alternatives might be coded as 0, so + # we need to explicitly initialize the MNL nesting graph + # and set to root_id to a value other than zero. + root_id = 0 + if root_id in d.dc.altids(): + root_id = -1 + m.initialize_graph(alternative_codes=d.dc.altids(), root_id=root_id) - m = Model(dataservice=d) if len(spec.columns) == 4 and all( spec.columns == ["Label", "Description", "Expression", "coefficient"] ): @@ -317,6 +460,8 @@ def split(a, n): x_col="Label", p_col=spec.columns[-1], ignore_x=("local_dist",), + x_validator=d, + expr_col="Expression", ) elif ( len(spec.columns) == 4 @@ -329,6 +474,8 @@ def split(a, n): x_col="Label", p_col=spec.columns[-1], ignore_x=("local_dist",), + x_validator=d, + expr_col="Expression", ) else: m.utility_ca = linear_utility_from_spec( @@ -337,21 +484,23 @@ def split(a, n): p_col=SEGMENT_IDS, ignore_x=("local_dist",), segment_id=CHOOSER_SEGMENT_COLUMN_NAME, + x_validator=d, + expr_col="Expression", ) if CHOOSER_SEGMENT_COLUMN_NAME is None: assert len(size_spec) == 1 m.quantity_ca = sum( - P(f"{i}_{q}") * X(q) + lx.P(f"{i}_{q}") * lx.X(q) for i in size_spec.index for q in size_spec.columns if size_spec.loc[i, q] != 0 ) else: m.quantity_ca = sum( - P(f"{i}_{q}") - * X(q) - * X(f"{CHOOSER_SEGMENT_COLUMN_NAME}=={str_repr(SEGMENT_IDS[i])}") + lx.P(f"{i}_{q}") + * lx.X(q) + * lx.X(f"{CHOOSER_SEGMENT_COLUMN_NAME}=={str_repr(SEGMENT_IDS[i])}") for i in size_spec.index for q in size_spec.columns if size_spec.loc[i, q] != 0 @@ -360,12 +509,16 @@ def split(a, n): apply_coefficients(coefficients, m, minimum=-25, maximum=25) apply_coefficients(size_coef, m, minimum=-6, maximum=6) - m.choice_co_code = "override_choice" + m.choice_def(choice_def) + if av is not None: + m.availability_ca_var = "_avail_" + else: + m.availability_any = True if return_data: return ( m, - Dict( + LocationChoiceData( edb_directory=Path(edb_directory), alt_values=alt_values, chooser_data=chooser_data, diff --git a/activitysim/estimation/larch/mode_choice.py b/activitysim/estimation/larch/mode_choice.py index 74aa4d1be6..f3d933d3f1 100644 --- a/activitysim/estimation/larch/mode_choice.py +++ b/activitysim/estimation/larch/mode_choice.py @@ -6,6 +6,7 @@ import numpy as np import pandas as pd +import xarray as xr import yaml from .general import ( @@ -19,11 +20,12 @@ from .simple_simulate import construct_availability, simple_simulate_data try: - import larch + # Larch is an optional dependency, and we don't want to fail when importing + # this module simply because larch is not installed. + import larch as lx except ImportError: - larch = None + lx = None else: - from larch import DataFrames, Model, P, X from larch.util import Dict @@ -62,7 +64,10 @@ def mode_choice_model( purposes.remove("atwork") # Setup purpose specific models - m = {purpose: Model(graph=tree, title=purpose) for purpose in purposes} + m = { + purpose: lx.Model(graph=tree, title=purpose, compute_engine="numba") + for purpose in purposes + } for alt_code, alt_name in tree.elemental_names().items(): # Read in base utility function for this alt_name u = linear_utility_from_spec( @@ -70,42 +75,43 @@ def mode_choice_model( x_col="Label", p_col=alt_name, ignore_x=("#",), + x_validator=chooser_data.columns, + expr_col="Expression", ) for purpose in purposes: # Modify utility function based on template for purpose u_purp = sum( - (P(coef_template[purpose].get(i.param, i.param)) * i.data * i.scale) + (lx.P(coef_template[purpose].get(i.param, i.param)) * i.data * i.scale) for i in u ) m[purpose].utility_co[alt_code] = u_purp for model in m.values(): explicit_value_parameters(model) + model.availability_ca_var = "_avail_" apply_coefficients(coefficients, m) avail = construct_availability( m[purposes[0]], chooser_data, data.alt_codes_to_names ) - d = DataFrames( - co=chooser_data, - av=avail, - alt_codes=data.alt_codes, - alt_names=data.alt_names, + d = lx.Dataset.construct.from_idco( + chooser_data, alts=dict(zip(data.alt_codes, data.alt_names)) ) + d["_avail_"] = xr.DataArray(avail, dims=(d.dc.CASEID, d.dc.ALTID)) if "atwork" not in name: for purpose, model in m.items(): - model.dataservice = d.selector_co(f"tour_type=='{purpose}'") + model.datatree = d.dc.query_cases(f"tour_type=='{purpose}'") model.choice_co_code = "override_choice_code" else: for purpose, model in m.items(): - model.dataservice = d + model.datatree = d model.choice_co_code = "override_choice_code" - from larch.model.model_group import ModelGroup - - mg = ModelGroup(m.values()) + mg = lx.ModelGroup(m.values()) + explicit_value_parameters(mg) + apply_coefficients(coefficients, mg) if return_data: return ( diff --git a/activitysim/estimation/larch/nonmand_tour_freq.py b/activitysim/estimation/larch/nonmand_tour_freq.py index 9fbcbb1ed5..c35d44453a 100644 --- a/activitysim/estimation/larch/nonmand_tour_freq.py +++ b/activitysim/estimation/larch/nonmand_tour_freq.py @@ -1,5 +1,6 @@ from __future__ import annotations +import glob import logging import os import pickle @@ -17,17 +18,16 @@ ) try: - import larch + # Larch is an optional dependency, and we don't want to fail when importing + # this module simply because larch is not installed. + import larch as lx except ImportError: - larch = None - logger_name = "larch" + lx = None else: - from larch import DataFrames, Model - from larch.log import logger_name from larch.util import Dict -_logger = logging.getLogger(logger_name) +_logger = logging.getLogger("larch") def interaction_simulate_data( @@ -38,17 +38,48 @@ def interaction_simulate_data( alt_def_file="{name}_alternatives.csv", coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv", chooser_data_files="{segment_name}/{name}_choosers_combined.csv", - alt_values_files="{segment_name}/{name}_interaction_expression_values.csv", - segment_subset=[], + alt_values_files="{segment_name}/{name}_alternatives_combined.csv", + segment_subset=(), ): edb_directory = edb_directory.format(name=name) def _read_csv(filename, **kwargs): - filename = filename.format(name=name) - return pd.read_csv(os.path.join(edb_directory, filename), **kwargs) + filename = Path(edb_directory).joinpath(filename.format(name=name)) + if filename.with_suffix(".parquet").exists(): + print("loading from", filename.with_suffix(".parquet")) + if "comment" in kwargs: + kwargs.pop("comment") + return pd.read_parquet(filename.with_suffix(".parquet"), **kwargs) + if filename.exists(): + print("loading from", filename) + return pd.read_csv(filename, **kwargs) + # if the file does not exist, try to load parquet from a subdirectory + search_glob = os.path.join( + edb_directory, "*", filename.with_suffix(".parquet").name + ) + files = glob.glob(search_glob) + if files: + print("loading from", files[0]) + return pd.read_parquet(files[0], **kwargs) + # otherwise try to load csv from a subdirectory + search_glob = os.path.join(edb_directory, "*", filename.name) + files = glob.glob(search_glob) + if files: + print("loading from", files[0]) + return pd.read_csv(files[0], **kwargs) + raise FileNotFoundError(f"File {filename} not found") settings_file = settings_file.format(name=name) - with open(os.path.join(edb_directory, settings_file), "r") as yf: + settings_file_resolved = os.path.join(edb_directory, settings_file) + if not os.path.exists(settings_file_resolved): + search_glob = os.path.join(edb_directory, "*", settings_file) + settings_files = glob.glob(search_glob) + if not settings_files: + raise FileNotFoundError(f"Settings file {settings_file} not found") + else: + settings_file_resolved = settings_files[0] + + with open(settings_file_resolved) as yf: settings = yaml.load( yf, Loader=yaml.SafeLoader, @@ -123,7 +154,7 @@ def link_same_value_coefficients(segment_names, coefficients, spec): def unavail_parameters(model): - return model.pf.index[(model.pf.value < -900) & (model.pf.holdfast != 0)] + return model.pnames[(model.pvals < -900) & (model.pholdfast != 0)] def unavail_data_cols(model): @@ -133,10 +164,15 @@ def unavail_data_cols(model): def unavail(model, x_ca): lock_data = unavail_data_cols(model) + # intialize unavail to False array of same length as x_ca + unav = pd.Series(False, index=x_ca.index) if len(lock_data): unav = x_ca[lock_data[0]] > 0 for j in lock_data[1:]: unav |= x_ca[j] > 0 + else: + # no unavailability parameters are included + return pd.DataFrame(0, index=x_ca.index, columns=["_avail_"]) return unav @@ -212,8 +248,10 @@ def nonmand_tour_freq_model( edb_directory="output/estimation_data_bundle/{name}/", return_data=False, condense_parameters=False, - segment_subset=[], + segment_subset=(), num_chunks=1, + *, + alts_in_cv_format=False, ): """ Prepare nonmandatory tour frequency models for estimation. @@ -253,10 +291,13 @@ def nonmand_tour_freq_model( alt_values = data.alt_values alt_def = data.alt_def + # deduplicate rows in alt_def + alt_def = alt_def[~alt_def.index.duplicated(keep="first")] + m = {} for segment_name in segment_names: print(f"Creating larch model for {segment_name}") - segment_model = m[segment_name] = Model() + segment_model = m[segment_name] = lx.Model(compute_engine="numba") # One of the alternatives is coded as 0, so # we need to explicitly initialize the MNL nesting graph # and set to root_id to a value other than zero. @@ -267,6 +308,9 @@ def nonmand_tour_freq_model( spec, x_col="Label", p_col=segment_name, + x_validator=set(chooser_data[segment_name].columns) + | set(alt_values[segment_name].columns), + expr_col="Expression", ) apply_coefficients(coefficients[segment_name], segment_model) segment_model.choice_co_code = "override_choice" @@ -277,21 +321,29 @@ def nonmand_tour_freq_model( .set_index("person_id") .rename(columns={"TAZ": "HOMETAZ"}) ) - print("\t performing cv to ca step") - # x_ca = cv_to_ca(alt_values[segment_name].set_index(["person_id", "variable"])) - x_ca = get_x_ca_df( - alt_values=alt_values[segment_name].set_index(["person_id", "variable"]), - name=segment_name, - edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"), - num_chunks=num_chunks, - ) + if alts_in_cv_format: + print("\t performing cv to ca step") + x_ca = get_x_ca_df( + alt_values=alt_values[segment_name].set_index( + ["person_id", "variable"] + ), + name=segment_name, + edb_directory=edb_directory.format(name="non_mandatory_tour_frequency"), + num_chunks=num_chunks, + ) + else: + x_ca = alt_values[segment_name].set_index(["person_id", "alt_id"]) - d = DataFrames( - co=x_co, - ca=x_ca, - av=~unavail(segment_model, x_ca), + d_co = lx.Dataset.construct.from_idco( + x_co, + alts=alt_def.index.rename("alt_id"), ) - m[segment_name].dataservice = d + x_ca["_avail_"] = ~unavail(segment_model, x_ca) + # we set crack to False here so that we do not dissolve zero variance IDCAs + d_ca = lx.Dataset.construct.from_idca(x_ca, crack=False) + d = d_ca.merge(d_co) + m[segment_name].datatree = d + m[segment_name].availability_ca_var = "_avail_" if return_data: return m, data diff --git a/activitysim/estimation/larch/scheduling.py b/activitysim/estimation/larch/scheduling.py index 7d9461b5ca..e0adb34e56 100644 --- a/activitysim/estimation/larch/scheduling.py +++ b/activitysim/estimation/larch/scheduling.py @@ -21,11 +21,13 @@ ) try: - import larch + # Larch is an optional dependency, and we don't want to fail when importing + # this module simply because larch is not installed. + import larch as lx except ImportError: - larch = None + lx = None else: - from larch import DataFrames, Model, P, X + from larch import P, X from larch.util import Dict @@ -38,6 +40,8 @@ def schedule_choice_model( chooser_file="{name}_choosers_combined.csv", settings_file="{name}_model_settings.yaml", return_data=False, + *, + alts_in_cv_format=False, ): model_selector = name.replace("_location", "") model_selector = model_selector.replace("_destination", "") @@ -46,14 +50,16 @@ def schedule_choice_model( edb_directory = edb_directory.format(name=name) def _read_csv(filename, optional=False, **kwargs): - filename = filename.format(name=name) - try: - return pd.read_csv(os.path.join(edb_directory, filename), **kwargs) - except FileNotFoundError: - if optional: - return None - else: - raise + filename = Path(edb_directory).joinpath(filename.format(name=name)) + if filename.with_suffix(".parquet").exists(): + print("loading from", filename.with_suffix(".parquet")) + return pd.read_parquet(filename.with_suffix(".parquet"), **kwargs) + if filename.exists(): + print("loading from", filename) + return pd.read_csv(filename, **kwargs) + if optional: + return None + raise FileNotFoundError(filename) settings_file = settings_file.format(name=name) with open(os.path.join(edb_directory, settings_file), "r") as yf: @@ -106,7 +112,7 @@ def _read_csv(filename, optional=False, **kwargs): else: raise ValueError("cannot find Label or Expression in spec file") - m = Model() + m = lx.Model(compute_engine="numba") if len(spec.columns) == 4 and ( [c.lower() for c in spec.columns] == ["label", "description", "expression", "coefficient"] @@ -141,12 +147,16 @@ def _read_csv(filename, optional=False, **kwargs): apply_coefficients(coefficients, m, minimum=-25, maximum=25) chooser_index_name = chooser_data.columns[0] - x_co = chooser_data.set_index(chooser_index_name) + x_co = chooser_data.set_index(chooser_index_name).dropna(axis=1, how="all") alt_values.fillna(0, inplace=True) - x_ca = cv_to_ca( - alt_values.set_index([chooser_index_name, alt_values.columns[1]]), - required_labels=spec[label_column_name], - ) + if alts_in_cv_format: + x_ca = cv_to_ca( + alt_values.set_index([chooser_index_name, alt_values.columns[1]]), + required_labels=spec[label_column_name], + ) + else: + # the alternative code is "tdd" + x_ca = alt_values.set_index([chooser_index_name, "tdd"]) # if CHOOSER_SEGMENT_COLUMN_NAME is not None: # # label segments with names @@ -158,22 +168,36 @@ def _read_csv(filename, optional=False, **kwargs): # x_co["_segment_label"] = size_spec.index[0] alt_codes = np.arange(len(x_ca.index.levels[1])) + 1 - x_ca.index = x_ca.index.set_levels(alt_codes, 1) + x_ca.index = x_ca.index.set_levels(alt_codes, level=1) x_co["override_choice_plus1"] = x_co["override_choice"] + 1 x_co["model_choice_plus1"] = x_co["model_choice"] + 1 unavail_coefs = coefficients.query("(constrain == 'T') & (value < -900)").index unavail_data = [i.data for i in m.utility_ca if i.param in unavail_coefs] - if len(unavail_data): + + if "mode_choice_logsum" in x_ca and not len(unavail_data): + joint_avail = "~(mode_choice_logsum_missing)" + elif len(unavail_data): joint_unavail = "|".join(f"({i}>0)" for i in unavail_data) joint_avail = f"~({joint_unavail})" else: - joint_avail = 1 - - d = DataFrames(co=x_co, ca=x_ca, av=joint_avail) - m.dataservice = d + joint_avail = None + + # d = DataFrames(co=x_co, ca=x_ca, av=joint_avail) # larch 5.7 + d_ca = lx.Dataset.construct.from_idca(x_ca) + if joint_avail == "~(mode_choice_logsum_missing)": + tmp = np.isnan(d_ca["mode_choice_logsum"]) + tmp = tmp.drop_vars(tmp.coords) + d_ca = d_ca.assign(mode_choice_logsum_missing=tmp) + d_co = lx.Dataset.construct.from_idco(x_co) + d = d_ca.merge(d_co) + # if joint_avail is not None: + # d["_avail_"] = joint_avail + + m.datatree = d m.choice_co_code = "override_choice_plus1" - # m.choice_co_code = "model_choice_plus1" + if joint_avail is not None: + m.availability_ca_var = joint_avail if return_data: return ( @@ -226,38 +250,64 @@ def construct_availability_ca(model, chooser_data, alt_codes_to_names): return avail -def mandatory_tour_scheduling_work_model(return_data=False): +def mandatory_tour_scheduling_work_model( + edb_directory="output/estimation_data_bundle/{name}/", return_data=False +): return schedule_choice_model( name="mandatory_tour_scheduling_work", + edb_directory=edb_directory, return_data=return_data, coefficients_file="tour_scheduling_work_coefficients.csv", ) -def mandatory_tour_scheduling_school_model(return_data=False): +def mandatory_tour_scheduling_school_model( + edb_directory="output/estimation_data_bundle/{name}/", return_data=False +): return schedule_choice_model( name="mandatory_tour_scheduling_school", + edb_directory=edb_directory, return_data=return_data, coefficients_file="tour_scheduling_school_coefficients.csv", ) -def non_mandatory_tour_scheduling_model(return_data=False): +def mandatory_tour_scheduling_univ_model( + edb_directory="output/estimation_data_bundle/{name}/", return_data=False +): + return schedule_choice_model( + name="mandatory_tour_scheduling_univ", + edb_directory=edb_directory, + return_data=return_data, + coefficients_file="tour_scheduling_univ_coefficients.csv", + ) + + +def non_mandatory_tour_scheduling_model( + edb_directory="output/estimation_data_bundle/{name}/", return_data=False +): return schedule_choice_model( name="non_mandatory_tour_scheduling", + edb_directory=edb_directory, return_data=return_data, ) -def joint_tour_scheduling_model(return_data=False): +def joint_tour_scheduling_model( + edb_directory="output/estimation_data_bundle/{name}/", return_data=False +): return schedule_choice_model( name="joint_tour_scheduling", + edb_directory=edb_directory, return_data=return_data, ) -def atwork_subtour_scheduling_model(return_data=False): +def atwork_subtour_scheduling_model( + edb_directory="output/estimation_data_bundle/{name}/", return_data=False +): return schedule_choice_model( name="atwork_subtour_scheduling", + edb_directory=edb_directory, return_data=return_data, ) diff --git a/activitysim/estimation/larch/simple_simulate.py b/activitysim/estimation/larch/simple_simulate.py index e608a50084..2ec8a574f3 100644 --- a/activitysim/estimation/larch/simple_simulate.py +++ b/activitysim/estimation/larch/simple_simulate.py @@ -1,5 +1,6 @@ from __future__ import annotations +import collections import os from pathlib import Path @@ -17,11 +18,12 @@ ) try: - import larch + # Larch is an optional dependency, and we don't want to fail when importing + # this module simply because larch is not installed. + import larch as lx except ImportError: - larch = None + lx = None else: - from larch import DataFrames, Model from larch.util import Dict @@ -40,7 +42,7 @@ def construct_availability(model, chooser_data, alt_codes_to_names): pandas.DataFrame """ avail = {} - for acode, aname in alt_codes_to_names.items(): + for acode, _aname in alt_codes_to_names.items(): unavail_cols = list( ( chooser_data[i.data] @@ -59,6 +61,37 @@ def construct_availability(model, chooser_data, alt_codes_to_names): return avail +SimpleSimulateData = collections.namedtuple( + "SimpleSimulateData", + field_names=[ + "edb_directory", + "settings", + "chooser_data", + "coefficients", + "coef_template", + "spec", + "alt_names", + "alt_codes", + "alt_names_to_codes", + "alt_codes_to_names", + ], +) + + +def read_spec(filename: str | os.PathLike) -> pd.DataFrame: + """Read a simple simulate spec file""" + print("loading spec from", filename) + spec = pd.read_csv(filename, comment="#") + spec = remove_apostrophes(spec, ["Label"]) + + # remove temp rows from spec, ASim uses them to calculate the other values written + # to the EDB, but they are not actually part of the utility function themselves. + spec = spec.loc[~spec.Expression.isna()] + spec = spec.loc[~spec.Expression.str.startswith("_")].copy() + + return spec + + def simple_simulate_data( name="tour_mode_choice", edb_directory="output/estimation_data_bundle/{name}/", @@ -68,15 +101,20 @@ def simple_simulate_data( settings_file="{name}_model_settings.yaml", chooser_data_file="{name}_values_combined.csv", values_index_col="tour_id", -): - edb_directory = edb_directory.format(name=name) +) -> SimpleSimulateData: + edb_directory = str(edb_directory).format(name=name) def _read_csv(filename, **kwargs): - filename = filename.format(name=name) - return pd.read_csv(os.path.join(edb_directory, filename), **kwargs) + filename = Path(edb_directory).joinpath(filename.format(name=name)) + if filename.with_suffix(".parquet").exists(): + print("loading from", filename.with_suffix(".parquet")) + return pd.read_parquet(filename.with_suffix(".parquet"), **kwargs) + if filename.exists(): + print("loading from", filename) + return pd.read_csv(filename, **kwargs) settings_file = settings_file.format(name=name) - with open(os.path.join(edb_directory, settings_file), "r") as yf: + with open(os.path.join(edb_directory, settings_file)) as yf: settings = yaml.load( yf, Loader=yaml.SafeLoader, @@ -96,23 +134,16 @@ def _read_csv(filename, **kwargs): except FileNotFoundError: coef_template = None - spec = _read_csv(spec_file, comment="#") - spec = remove_apostrophes(spec, ["Label"]) - - # remove temp rows from spec, ASim uses them to calculate the other values written - # to the EDB, but they are not actually part of the utility function themselves. - spec = spec.loc[~spec.Expression.isna()] - spec = spec.loc[~spec.Expression.str.startswith("_")].copy() + spec = read_spec(Path(edb_directory).joinpath(spec_file.format(name=name))) alt_names = list(spec.columns[3:]) alt_codes = np.arange(1, len(alt_names) + 1) - alt_names_to_codes = dict(zip(alt_names, alt_codes)) - alt_codes_to_names = dict(zip(alt_codes, alt_names)) + alt_names_to_codes = dict(zip(alt_names, alt_codes, strict=False)) + alt_codes_to_names = dict(zip(alt_codes, alt_names, strict=False)) chooser_data = _read_csv( chooser_data_file, - index_col=values_index_col, - ) + ).set_index(values_index_col) except Exception: # when an error happens in reading anything other than settings, print settings @@ -121,7 +152,7 @@ def _read_csv(filename, **kwargs): pprint(settings) raise - return Dict( + return SimpleSimulateData( edb_directory=Path(edb_directory), settings=settings, chooser_data=chooser_data, @@ -167,33 +198,40 @@ def simple_simulate_model( if settings.get("LOGIT_TYPE") == "NL": tree = construct_nesting_tree(data.alt_names, settings["NESTS"]) - m = Model(graph=tree) else: - m = Model(alts=data.alt_codes_to_names) + tree = construct_nesting_tree(data.alt_names_to_codes, {}) + m = lx.Model(compute_engine="numba") m.utility_co = dict_of_linear_utility_from_spec( spec, "Label", - dict(zip(alt_names, alt_codes)), + dict(zip(alt_names, alt_codes, strict=False)), + x_validator=chooser_data, + expr_col="Expression", ) apply_coefficients(coefficients, m) if construct_avail: avail = construct_availability(m, chooser_data, data.alt_codes_to_names) + d = lx.Dataset.construct.from_idco( + pd.concat([chooser_data, avail], axis=1), + alts=dict(zip(alt_codes, alt_names, strict=False)), + ) else: avail = True + d = lx.Dataset.construct.from_idco( + chooser_data, alts=dict(zip(alt_codes, alt_names, strict=False)) + ) - d = DataFrames( - co=chooser_data, - av=avail, - alt_codes=alt_codes, - alt_names=alt_names, - ) - - m.dataservice = d + m.datatree = d.dc.as_tree("df") + m.graph = tree m.choice_co_code = "override_choice_code" + # set bounds on unbounded coefficients, so that they don't get big + # and cause numerical errors which some optimizers can't handle + m.set_cap(50) + if return_data: return ( m, @@ -257,6 +295,22 @@ def work_from_home_model( ) +def telecommute_status_model( + name="telecommute_status", + edb_directory="output/estimation_data_bundle/{name}/", + return_data=False, +): + return simple_simulate_model( + name=name, + edb_directory=edb_directory, + return_data=return_data, + choices={ + True: 1, + False: 2, + }, # True is telecommute, false is does not telecommute, names match spec positions + ) + + def mandatory_tour_frequency_model( name="mandatory_tour_frequency", edb_directory="output/estimation_data_bundle/{name}/", diff --git a/activitysim/estimation/larch/stop_frequency.py b/activitysim/estimation/larch/stop_frequency.py index cf36096dd7..d3344ad60c 100644 --- a/activitysim/estimation/larch/stop_frequency.py +++ b/activitysim/estimation/larch/stop_frequency.py @@ -15,11 +15,12 @@ ) try: - import larch + # Larch is an optional dependency, and we don't want to fail when importing + # this module simply because larch is not installed. + import larch as lx except ImportError: - larch = None + lx = None else: - from larch import DataFrames, Model from larch.util import Dict @@ -33,11 +34,29 @@ def stop_frequency_data( edb_directory = edb_directory.format(name=name) settings_file = settings_file.format(name=name) - with open(os.path.join(edb_directory, settings_file), "r") as yf: - settings = yaml.load( - yf, - Loader=yaml.SafeLoader, - ) + try: + with open(os.path.join(edb_directory, settings_file), "r") as yf: + settings = yaml.load( + yf, + Loader=yaml.SafeLoader, + ) + except FileNotFoundError: + # search in all first level subdirectories for the settings file + for subdir in os.listdir(edb_directory): + if os.path.isdir(os.path.join(edb_directory, subdir)): + try: + with open( + os.path.join(edb_directory, subdir, settings_file), "r" + ) as yf: + settings = yaml.load( + yf, + Loader=yaml.SafeLoader, + ) + break + except FileNotFoundError: + pass + else: + raise segments = [i["primary_purpose"] for i in settings["SPEC_SEGMENTS"]] @@ -117,10 +136,30 @@ def stop_frequency_data( seg_alt_names_to_codes.append(alt_names_to_codes) seg_alt_codes_to_names.append(alt_codes_to_names) - chooser_data = pd.read_csv( - seg_subdir / chooser_data_file.format(name=name), - index_col=values_index_col, - ) + # load parquet if available, otherwise pickle, or if all else fails csv + if ( + (seg_subdir / chooser_data_file.format(name=name)) + .with_suffix(".parquet") + .exists() + ): + chooser_data = pd.read_parquet( + (seg_subdir / chooser_data_file.format(name=name)).with_suffix( + ".parquet" + ), + ).set_index(values_index_col) + elif ( + (seg_subdir / chooser_data_file.format(name=name)) + .with_suffix(".pkl") + .exists() + ): + chooser_data = pd.read_pickle( + (seg_subdir / chooser_data_file.format(name=name)).with_suffix(".pkl"), + ).set_index(values_index_col) + else: + chooser_data = pd.read_csv( + seg_subdir / chooser_data_file.format(name=name), + index_col=values_index_col, + ) seg_chooser_data.append(chooser_data) return Dict( @@ -170,9 +209,9 @@ def stop_frequency_model( if settings.get("LOGIT_TYPE") == "NL": tree = construct_nesting_tree(data.alt_names[n], settings["NESTS"]) - m = Model(graph=tree) + m = lx.Model(graph=tree, compute_engine="numba") else: - m = Model() + m = lx.Model(compute_engine="numba") m.utility_co = dict_of_linear_utility_from_spec( spec, @@ -184,15 +223,13 @@ def stop_frequency_model( avail = True - d = DataFrames( - co=chooser_data, - av=avail, - alt_codes=alt_codes, - alt_names=alt_names, + d = lx.Dataset.construct.from_idco( + chooser_data, alts=dict(zip(alt_codes, alt_names)) ) - m.dataservice = d + m.datatree = d m.choice_co_code = "override_choice_code" + m.availability_any = True models.append(m) from larch.model.model_group import ModelGroup diff --git a/activitysim/estimation/test/test_edb_creation/configs_estimation/.gitignore b/activitysim/estimation/test/test_edb_creation/configs_estimation/.gitignore new file mode 100644 index 0000000000..e767d25cef --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/configs_estimation/.gitignore @@ -0,0 +1,2 @@ +settings.yaml +estimation.yaml \ No newline at end of file diff --git a/activitysim/estimation/test/test_edb_creation/configs_estimation/estimation_template.yaml b/activitysim/estimation/test/test_edb_creation/configs_estimation/estimation_template.yaml new file mode 100644 index 0000000000..6cab9136ad --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/configs_estimation/estimation_template.yaml @@ -0,0 +1,81 @@ +EDB_FILETYPE: csv # options: csv, parquet, pkl + +enable: True + +bundles: + - school_location + - workplace_location + - auto_ownership + - vehicle_type_choice + - free_parking + - cdap + - mandatory_tour_frequency + - mandatory_tour_scheduling_work + - mandatory_tour_scheduling_school + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_scheduling + - trip_mode_choice + +# - atwork_subtour_mode_choice subtours.tour_mode + +survey_tables: + households: + file_name: override_households.csv + index_col: household_id + persons: + file_name: override_persons.csv + index_col: person_id + tours: + file_name: override_tours.csv + index_col: tour_id + joint_tour_participants: + file_name: override_joint_tour_participants.csv + index_col: participant_id + trips: + file_name: override_trips.csv + index_col: trip_id + + +estimation_table_types: + school_location: interaction_sample_simulate + workplace_location: interaction_sample_simulate + auto_ownership: simple_simulate + vehicle_type_choice: interaction_simulate + free_parking: simple_simulate + cdap: cdap_simulate + mandatory_tour_frequency: simple_simulate + mandatory_tour_scheduling_work: interaction_sample_simulate + mandatory_tour_scheduling_school: interaction_sample_simulate + joint_tour_frequency: simple_simulate + joint_tour_composition: simple_simulate + joint_tour_participation: simple_simulate + joint_tour_destination: interaction_sample_simulate + joint_tour_scheduling: interaction_sample_simulate + non_mandatory_tour_frequency: interaction_simulate + non_mandatory_tour_destination: interaction_sample_simulate + non_mandatory_tour_scheduling: interaction_sample_simulate + tour_mode_choice: simple_simulate + atwork_subtour_frequency: simple_simulate + atwork_subtour_destination: interaction_sample_simulate + atwork_subtour_scheduling: interaction_sample_simulate + atwork_subtour_mode_choice: simple_simulate + stop_frequency: simple_simulate + trip_purpose: simple_probabilistic + trip_destination: interaction_sample_simulate + trip_scheduling: simple_probabilistic + trip_mode_choice: simple_simulate diff --git a/activitysim/estimation/test/test_edb_creation/configs_estimation/logging.yaml b/activitysim/estimation/test/test_edb_creation/configs_estimation/logging.yaml new file mode 100644 index 0000000000..f4902943d6 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/configs_estimation/logging.yaml @@ -0,0 +1,67 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile, elogfile] + + loggers: + + estimation: + level: DEBUG + handlers: [console, elogfile] + propagate: false + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + elogfile: + class: logging.FileHandler + filename: + get_log_file_path: 'estimation.log' + mode: w + formatter: fileFormatter + level: NOTSET + + logfile: + class: logging.FileHandler + filename: + get_log_file_path: 'activitysim.log' + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/estimation/test/test_edb_creation/configs_estimation/settings_template.yaml b/activitysim/estimation/test/test_edb_creation/configs_estimation/settings_template.yaml new file mode 100644 index 0000000000..75b9720c75 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/configs_estimation/settings_template.yaml @@ -0,0 +1,165 @@ + +inherit_settings: True + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# input tables +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: override_households.csv + index_col: household_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + # + # persons (table index 'person_id') + # + - tablename: persons + filename: override_persons.csv + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: land_use.csv + rename_columns: + # accept either TAZ or ZONE (but not both) + TAZ: zone_id + ZONE: zone_id + COUNTY: county_id + keep_columns: + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL + +write_raw_tables: False +rng_base_seed: 0 + +fail_fast: True + +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# number of households to simulate +households_sample_size: 0 + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: initialize_households + +trace_hh_id: + +multiprocess: false +num_processes: 2 + + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +resume_after: + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination +# - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice +# - write_data_dictionary +# - track_skim_usage +# - write_trip_matrices + - write_tables + - coalesce_estimation_data_bundles + + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + slice: + tables: + - accessibility + # don't slice any tables not explicitly listed above in slice.tables + exclude: True + - name: mp_households + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_tables \ No newline at end of file diff --git a/activitysim/estimation/test/test_edb_creation/outputs/.gitignore b/activitysim/estimation/test/test_edb_creation/outputs/.gitignore new file mode 100644 index 0000000000..08194de49b --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/outputs/.gitignore @@ -0,0 +1,2 @@ +/output*/ +*.csv \ No newline at end of file diff --git a/activitysim/estimation/test/test_edb_creation/outputs/infer_output/.gitignore b/activitysim/estimation/test/test_edb_creation/outputs/infer_output/.gitignore new file mode 100644 index 0000000000..16f2dc5fa9 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/outputs/infer_output/.gitignore @@ -0,0 +1 @@ +*.csv \ No newline at end of file diff --git a/activitysim/estimation/test/test_edb_creation/survey_data/final_households.csv b/activitysim/estimation/test/test_edb_creation/survey_data/final_households.csv new file mode 100644 index 0000000000..c91fceec9d --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/survey_data/final_households.csv @@ -0,0 +1,51 @@ +"home_zone_id","income","hhsize","HHT","auto_ownership","num_workers","sample_rate","income_in_thousands","income_segment","median_value_of_time","hh_value_of_time","num_non_workers","num_drivers","num_adults","num_children","num_young_children","num_children_5_to_15","num_children_16_to_17","num_college_age","num_young_adults","non_family","family","home_is_urban","home_is_rural","hh_work_auto_savings_ratio","num_under16_not_at_school","num_travel_active","num_travel_active_adults","num_travel_active_preschoolers","num_travel_active_children","num_travel_active_non_preschoolers","participates_in_jtf_model","joint_tour_frequency","num_hh_joint_tours","household_id" +16,30900,2,5,1,2,0.01,30.9,2,8.81,6.021274002645185,0,2,2,0,0,0,0,0,2,true,false,true,false,0.39972082,0,2,2,0,0,2,true,"0_tours",0,982875 +16,99700,9,2,1,4,0.01,99.7,3,10.44,3.705326363656352,5,8,8,1,1,0,0,3,3,false,true,true,false,0.71195495,0,7,6,1,1,6,true,"0_tours",0,1810015 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+16,12000,2,7,0,2,0.01,12,1,6.01,8.417809979092251,0,2,2,0,0,0,0,2,0,true,false,true,false,0.3178517,0,2,2,0,0,2,true,"0_tours",0,257531 +9,92700,2,3,0,0,0.01,92.7,3,10.44,11.518178930487295,2,1,1,1,0,1,0,0,0,false,true,true,false,0,1,2,1,0,1,2,true,"0_tours",0,1645132 +17,4200,1,4,0,0,0.01,4.2,1,6.01,4.768890669889258,1,1,1,0,0,0,0,0,0,true,false,true,false,0,0,1,1,0,0,1,false,"0_tours",0,29625 +25,37200,4,1,1,4,0.01,37.2,2,8.81,10.32100270756728,0,4,4,0,0,0,0,2,0,false,true,true,false,0.56286997,0,4,4,0,0,4,true,"1_Disc",1,1402945 +12,30000,1,4,0,1,0.01,30,1,6.01,4.360890352038947,0,1,1,0,0,0,0,0,0,true,false,true,false,0.06425,0,1,1,0,0,1,false,"0_tours",0,823501 +16,76000,3,1,0,2,0.01,76,3,10.44,15.5812014089547,1,2,2,1,0,1,0,0,0,false,true,true,false,0.3402317,0,3,2,0,1,3,true,"0_tours",0,1747467 +17,68000,1,6,0,1,0.01,68,3,10.44,7.595766438703255,0,1,1,0,0,0,0,0,0,true,false,true,false,0.31015667,0,1,1,0,0,1,false,"0_tours",0,1445222 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+8,88600,2,1,0,1,0.01,88.6,3,10.44,4.2960940332239455,1,2,2,0,0,0,0,0,0,false,true,true,false,0.1417767,0,2,2,0,0,2,true,"0_tours",0,1511234 +7,195000,1,4,0,1,0.01,195,4,12.86,8.219354788406312,0,1,1,0,0,0,0,0,1,true,false,true,false,0.15494083,0,1,1,0,0,1,false,"0_tours",0,2048382 +10,27800,2,7,0,2,0.01,27.8,1,6.01,1.5416062020885206,0,2,2,0,0,0,0,2,0,true,false,true,false,0.29182667,0,1,1,0,0,1,false,"0_tours",0,256660 +25,17210,2,1,1,0,0.01,17.21,1,6.01,1.8372202530691708,2,2,2,0,0,0,0,0,0,false,true,true,false,0,0,2,2,0,0,2,true,"0_tours",0,703381 +9,4000,2,1,0,1,0.01,4,1,6.01,2.5758755480394475,1,2,2,0,0,0,0,0,2,false,true,true,false,0.23177332,0,2,2,0,0,2,true,"0_tours",0,226869 +11,50000,1,4,1,1,0.01,50,2,8.81,12.952762239760721,0,1,1,0,0,0,0,0,1,true,false,true,false,0.17517333,0,0,0,0,0,0,false,"0_tours",0,823426 diff --git a/activitysim/estimation/test/test_edb_creation/survey_data/final_joint_tour_participants.csv b/activitysim/estimation/test/test_edb_creation/survey_data/final_joint_tour_participants.csv new file mode 100644 index 0000000000..7e7029f731 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/survey_data/final_joint_tour_participants.csv @@ -0,0 +1,8 @@ +"tour_id","household_id","person_id","participant_num","participant_id" +220958279,2223759,5389226,1,22095827901 +220958279,2223759,5389227,2,22095827902 +100798519,1173905,2458502,1,10079851903 +100798519,1173905,2458503,2,10079851904 +130727777,1402945,3188483,1,13072777702 +130727777,1402945,3188484,2,13072777703 +130727777,1402945,3188485,3,13072777704 diff --git a/activitysim/estimation/test/test_edb_creation/survey_data/final_persons.csv b/activitysim/estimation/test/test_edb_creation/survey_data/final_persons.csv new file mode 100644 index 0000000000..edfe675874 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/survey_data/final_persons.csv @@ -0,0 +1,91 @@ +"household_id","age","PNUM","sex","pemploy","pstudent","ptype","age_16_to_19","age_16_p","adult","male","female","has_non_worker","has_retiree","has_preschool_kid","has_driving_kid","has_school_kid","has_full_time","has_part_time","has_university","student_is_employed","nonstudent_to_school","is_student","is_gradeschool","is_highschool","is_university","school_segment","is_worker","home_zone_id","value_of_time","school_zone_id","school_location_logsum","distance_to_school","roundtrip_auto_time_to_school","workplace_zone_id","workplace_location_logsum","distance_to_work","workplace_in_cbd","work_zone_area_type","roundtrip_auto_time_to_work","work_auto_savings","work_auto_savings_ratio","free_parking_at_work","cdap_activity","travel_active","under16_not_at_school","has_preschool_kid_at_home","has_school_kid_at_home","mandatory_tour_frequency","work_and_school_and_worker","work_and_school_and_student","num_mand","num_work_tours","num_joint_tours","non_mandatory_tour_frequency","num_non_mand","num_escort_tours","num_eatout_tours","num_shop_tours","num_maint_tours","num_discr_tours","num_social_tours","num_non_escort_tours","person_id" 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b/activitysim/estimation/test/test_edb_creation/survey_data/override_households.csv new file mode 100644 index 0000000000..b0910c7860 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/survey_data/override_households.csv @@ -0,0 +1,51 @@ +household_id,home_zone_id,income,hhsize,HHT,auto_ownership,num_workers,joint_tour_frequency +982875,16,30900,2,5,1,2,0_tours +1810015,16,99700,9,2,1,4,0_tours +1099626,20,58160,3,1,1,1,0_tours +763879,6,59220,1,4,1,0,0_tours +824207,18,51000,1,4,0,1,0_tours +2822230,8,0,1,0,0,1,0_tours +2821179,8,0,1,0,0,1,0_tours +1196298,25,31360,5,1,0,1,0_tours +1363467,24,58300,2,2,1,1,0_tours +386761,16,21000,3,1,1,2,0_tours +2223027,9,133000,2,1,0,2,0_tours +2832182,7,0,1,0,0,0,0_tours +2727273,20,118800,2,1,1,2,0_tours +1444715,14,60000,1,4,0,1,0_tours +112064,16,18000,1,6,1,1,0_tours +1952792,14,61900,1,4,1,1,0_tours +2223759,16,144100,2,1,0,2,1_Eat +2820538,8,0,1,0,0,1,0_tours +27726,10,24000,1,4,0,0,0_tours +570454,21,1500,1,4,0,0,0_tours 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a/activitysim/estimation/test/test_edb_creation/survey_data/override_joint_tour_participants.csv b/activitysim/estimation/test/test_edb_creation/survey_data/override_joint_tour_participants.csv new file mode 100644 index 0000000000..a19d8616a2 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/survey_data/override_joint_tour_participants.csv @@ -0,0 +1,8 @@ +survey_participant_id,survey_tour_id,household_id,person_id,participant_num,tour_id,participant_id +220958279010,2209582790,2223759,5389226,1,220958279,22095827901 +220958279020,2209582790,2223759,5389227,2,220958279,22095827902 +100798519030,1007985190,1173905,2458502,1,100798519,10079851903 +100798519040,1007985190,1173905,2458503,2,100798519,10079851904 +130727777020,1307277770,1402945,3188483,1,130727777,13072777702 +130727777030,1307277770,1402945,3188484,2,130727777,13072777703 +130727777040,1307277770,1402945,3188485,3,130727777,13072777704 diff --git a/activitysim/estimation/test/test_edb_creation/survey_data/override_persons.csv b/activitysim/estimation/test/test_edb_creation/survey_data/override_persons.csv new file mode 100644 index 0000000000..030bcafa71 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/survey_data/override_persons.csv @@ -0,0 +1,91 @@ +person_id,household_id,age,PNUM,sex,pemploy,pstudent,ptype,school_zone_id,workplace_zone_id,free_parking_at_work,cdap_activity,mandatory_tour_frequency,_escort,_shopping,_othmaint,_othdiscr,_eatout,_social,non_mandatory_tour_frequency +26686,26686,39,1,1,3,3,4,-1,-1,False,N,,0,0,1,0,1,0,12 +26844,26844,51,1,1,3,3,4,-1,-1,False,N,,0,0,0,0,0,1,2 +27726,27726,52,1,1,3,3,4,-1,-1,False,N,,0,0,0,1,0,0,1 +29625,29625,61,1,1,3,3,4,-1,-1,False,N,,0,1,0,0,0,0,16 +110675,110675,30,1,1,2,3,2,-1,19,False,H,,0,0,0,0,0,0,0 +112064,112064,48,1,2,2,3,2,-1,21,False,N,,0,1,0,1,0,0,17 +264107,226869,28,1,1,2,3,2,-1,24,False,M,work1,0,0,0,0,0,0,0 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+143309067,1433090670,3495343,1594621,eat,atwork,16,9,14,14,WALK,1433091020.0,143309102.0,,135,,3out_0in +171036547,1710365470,4171623,1810015,eat,atwork,7,21,10,10,WALK,1710365820.0,171036582.0,,85,,0out_1in +220958270,2209582700,5389226,2223759,eat,atwork,2,1,13,13,WALK,2209583050.0,220958305.0,,124,,0out_0in +307996473,3079964730,7512109,2820774,eat,atwork,8,2,10,10,WALK,3079965080.0,307996508.0,,85,,0out_1in diff --git a/activitysim/estimation/test/test_edb_creation/survey_data/override_trips.csv b/activitysim/estimation/test/test_edb_creation/survey_data/override_trips.csv new file mode 100644 index 0000000000..e2f36dab74 --- /dev/null +++ b/activitysim/estimation/test/test_edb_creation/survey_data/override_trips.csv @@ -0,0 +1,278 @@ +trip_id,survey_trip_id,person_id,household_id,survey_tour_id,outbound,purpose,destination,origin,depart,trip_mode,tour_id,trip_num +8753057,87530570,26686,26686,10941320,True,eatout,5,8,19,WALK,1094132,1 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b/activitysim/estimation/test/test_edb_creation/test_edb_formation.py @@ -0,0 +1,276 @@ +import os +import pandas as pd +import numpy as np +import pandas.testing as pdt +import yaml + + +configs_estimation_dir = ( + "activitysim/estimation/test/test_edb_creation/configs_estimation" +) +configs_dir = "activitysim/examples/prototype_mtc/configs" +survey_data_dir = "activitysim/estimation/test/test_edb_creation/survey_data" +data_dir = "activitysim/examples/prototype_mtc/data" +base_output_dir = "activitysim/estimation/test/test_edb_creation/outputs" + + +def launch_est_example(output_dir, multiprocess, fileformat): + + # setting multiprocess setting + settings_template_file = os.path.join( + configs_estimation_dir, "settings_template.yaml" + ) + settings_file = os.path.join(configs_estimation_dir, "settings.yaml") + settings = yaml.safe_load(open(settings_template_file)) + assert multiprocess in [True, False] + settings["multiprocess"] = multiprocess + yaml.dump(settings, open(settings_file, "w")) + + # setting fileformat setting + settings_template_file = os.path.join( + configs_estimation_dir, "estimation_template.yaml" + ) + est_settings_file = os.path.join(configs_estimation_dir, "estimation.yaml") + est_settings = yaml.safe_load(open(settings_template_file)) + assert fileformat in ["csv", "parquet", "pkl"] + est_settings["EDB_FILETYPE"] = fileformat + yaml.dump(est_settings, open(est_settings_file, "w")) + + run_cmd = f"activitysim run -c {configs_estimation_dir} -c {configs_dir} -d {survey_data_dir} -d {data_dir} -o {base_output_dir}/{output_dir}" + print( + f"launching with options output_dir={output_dir} multiprocess={multiprocess} fileformat={fileformat}" + ) + print("launching from ", os.getcwd()) + result = os.system(run_cmd) + assert result == 0, "ActivitySim run failed" + + +def read_table(file_name): + if file_name.endswith(".csv"): + return pd.read_csv(file_name, low_memory=False) + elif file_name.endswith(".parquet"): + df = pd.read_parquet(file_name).reset_index(drop=True) + df.columns.name = None + return df + elif file_name.endswith(".pkl"): + df = pd.read_pickle(file_name).reset_index(drop=True) + df.columns = df.columns.astype(str) + df.columns.name = None + return df + else: + raise ValueError(f"Unsupported file format: {file_name}") + + +def process_table(df): + if "variable" == df.columns[1]: + # need to sort variable column + df = df.sort_values(by=[df.columns[0], "variable"]).reset_index(drop=True) + df.columns.name = None + + if "chunk_id" in df.columns: + # remove chunk_id column + df = df.drop(columns=["chunk_id"]) + + return df + + +def find_lowest_level_directories(starting_directory): + lowest_dirs = list() + + for root, dirs, files in os.walk(starting_directory): + if not dirs: + lowest_dirs.append(root) + + return lowest_dirs + + +def try_compare_with_same_dtypes(ser1, ser2, rtol, atol): + try: + concatenated = pd.concat([ser1, ser2]) + common_type = concatenated.dtype + pdt.assert_series_equal( + ser1.astype(common_type), + ser2.astype(common_type), + check_dtype=False, + rtol=rtol, + atol=atol, + ) + except (ValueError, AssertionError) as e: + return False + return True + + +def try_compare_with_numeric(ser1, ser2, rtol, atol): + try: + ser1_num = pd.to_numeric(ser1, errors="coerce") + ser2_num = pd.to_numeric(ser2, errors="coerce") + pdt.assert_series_equal( + ser1_num, ser2_num, check_dtype=False, rtol=rtol, atol=atol + ) + except AssertionError as e: + return False + return True + + +def try_compare_with_strings(ser1, ser2): + try: + pdt.assert_series_equal(ser1.astype(str), ser2.astype(str), check_dtype=False) + except AssertionError as e: + return False + return True + + +def try_compare_with_combination(ser1, ser2, rtol=1e-5, atol=1e-8): + """ + This is necessary because we have columns like this: + [index]: [0, 1, 2, 3, 4, 5, ...] + [left]: [False, False, 1, 6, AM, -1.3037984712857993, EA, ...] + [right]: [False, False, 1, 6, AM, -1.3037984712857994, EA, ...] + (notice the machine precision difference in the float) + """ + # replace annoying string values of bools + ser1 = ser1.replace({"True": True, "False": False}) + ser2 = ser2.replace({"True": True, "False": False}) + # Separate numerical and non-numerical values + ser1_num = pd.to_numeric(ser1, errors="coerce") + ser2_num = pd.to_numeric(ser2, errors="coerce") + ser1_non_num = ser1[ser1_num.isna()] + ser2_non_num = ser2[ser2_num.isna()] + ser1_num = ser1_num.dropna() + ser2_num = ser2_num.dropna() + try: + pdt.assert_series_equal( + ser1_num, ser2_num, check_dtype=False, rtol=rtol, atol=atol + ) + pdt.assert_series_equal(ser1_non_num, ser2_non_num, check_dtype=False) + except AssertionError as e: + return False + return True + + +def compare_dataframes_with_tolerance(df1, df2, rtol=1e-3, atol=1e-3): + dfs_are_same = True + e_msg = "" + try: + pdt.assert_frame_equal(df1, df2, check_dtype=False, rtol=rtol, atol=atol) + return dfs_are_same, e_msg + except AssertionError as e: + print(e) + print("trying to compare columns individually") + for col in df1.columns: + try: + if try_compare_with_same_dtypes(df1[col], df2[col], rtol, atol): + continue + elif try_compare_with_numeric(df1[col], df2[col], rtol, atol): + continue + elif try_compare_with_strings(df1[col], df2[col]): + continue + elif try_compare_with_combination( + df1[col], df2[col], rtol=rtol, atol=atol + ): + continue + else: + dfs_are_same = False + e_msg += f"Column '{col}' failed: {df1[col]} vs {df2[col]}\n" + print(f"Column '{col}' failed:\n {df1[col]} vs {df2[col]}\n") + except Exception as e: + dfs_are_same = False + e_msg = e + + return dfs_are_same, e_msg + + +def regress_EDBs(regress_folder, output_folder, fileformat="csv"): + edb_file = os.path.join(base_output_dir, regress_folder, "estimation_data_bundle") + + edb_dirs = find_lowest_level_directories(edb_file) + + for dir in edb_dirs: + dir_basename = dir.split("estimation_data_bundle")[1][1:] + output_dir = os.path.join( + base_output_dir, output_folder, "estimation_data_bundle", dir_basename + ) + + for file in os.listdir(dir): + if file.endswith(".yaml"): + continue + print(f"Regressing {file}") + + regress_path = os.path.join(dir, file) + output_path = os.path.join(output_dir, file) + + # regress against csv for parquet, but regress against parquet for pkl (faster) + if not os.path.exists(output_path) and (fileformat == "parquet"): + output_path = output_path.replace(".csv", ".parquet") + if not os.path.exists(output_path) and (fileformat == "pkl"): + output_path = output_path.replace(".parquet", ".pkl") + + try: + regress_df = read_table(regress_path) + output_df = read_table(output_path) + except FileNotFoundError as e: + assert False, f"File not found: {e}" + + regress_df = process_table(regress_df) + output_df = process_table(output_df) + + dfs_are_same, e = compare_dataframes_with_tolerance(regress_df, output_df) + if not dfs_are_same: + assert False, f"Regression test failed for {file} with error: {e}" + return + + +def test_infer(): + infer_file = r"activitysim/examples/example_estimation/scripts/infer.py" + data_dir = r"activitysim/estimation/test/test_edb_creation" + configs_dir = r"activitysim/examples/prototype_mtc/configs" + output_dir = r"activitysim/estimation/test/test_edb_creation/outputs/infer_output" + run_cmd = f"python {infer_file} {data_dir} {configs_dir} {output_dir}" + print(f"launching {run_cmd} from {os.getcwd()}") + result = os.system(run_cmd) + assert result == 0, "Infer.py run failed" + + for file in os.listdir(output_dir): + if not file.endswith(".csv"): + continue + print("Regressing ", file) + regress_df = pd.read_csv(os.path.join(data_dir, "survey_data", file)) + output_df = pd.read_csv(os.path.join(output_dir, file)) + + pdt.assert_frame_equal(regress_df, output_df) + + +def test_generating_sp_csv(): + # first generate original tables + output_dir = "output_single_csv" + launch_est_example(output_dir, False, "csv") + + +def test_sp_parquet(): + # multiprocess = False, fileformat = "parquet" + output_dir = "output_single_parquet" + launch_est_example(output_dir, False, "parquet") + regress_EDBs("output_single_csv", output_dir, "parquet") + + +def test_sp_pkl(): + # multiprocess = False, fileformat = "pkl" + output_dir = "output_single_pkl" + launch_est_example(output_dir, False, "pkl") + regress_EDBs("output_single_parquet", output_dir, "pkl") + + +def test_mp_parquet(): + # multiprocess = True, fileformat = "parquet" + output_dir = "output_multiprocess_parquet" + launch_est_example(output_dir, True, "parquet") + regress_EDBs("output_single_parquet", output_dir, "parquet") + + +if __name__ == "__main__": + + test_infer() + test_generating_sp_csv() + test_sp_pkl() + test_sp_parquet() + test_mp_parquet() diff --git a/activitysim/estimation/test/test_larch_estimation.py b/activitysim/estimation/test/test_larch_estimation.py index 09355f8469..e688b1098a 100644 --- a/activitysim/estimation/test/test_larch_estimation.py +++ b/activitysim/estimation/test/test_larch_estimation.py @@ -39,12 +39,12 @@ def _regression_check(dataframe_regression, df, basename=None, rtol=None): rtol = 0.1 dataframe_regression.check( df.select_dtypes("number") - .drop(columns=["holdfast"], errors="ignore") + .drop(columns=["holdfast", "minimum", "maximum"], errors="ignore") .clip(-9e9, 9e9), # pandas 1.3 handles int8 dtypes as actual numbers, so holdfast needs to be dropped manually # we're dropping it not adding to the regression check so older pandas will also work. basename=basename, - default_tolerance=dict(atol=1e-6, rtol=rtol) + default_tolerance=dict(atol=1e-6, rtol=rtol), # can set a little loose, as there is sometimes a little variance in these # results when switching backend implementations. We're checking all # the parameters and the log likelihood, so modest variance in individual @@ -73,7 +73,7 @@ def test_simple_simulate(est_data, num_regression, dataframe_regression, name, m m.load_data() m.doctor(repair_ch_av="-") loglike_prior = m.loglike() - r = m.maximize_loglike(method=method, options={"maxiter": 1000}) + r = m.maximize_loglike(method=method, options={"maxiter": 1000, "ftol": 1e-9}) num_regression.check( {"loglike_prior": loglike_prior, "loglike_converge": r.loglike}, basename=f"test_simple_simulate_{name}_{method}_loglike", @@ -87,9 +87,11 @@ def test_simple_simulate(est_data, num_regression, dataframe_regression, name, m [ ("workplace_location", "SLSQP", None), ("school_location", "SLSQP", None), + ("school_location", "BHHH", None), ("non_mandatory_tour_destination", "SLSQP", None), ("atwork_subtour_destination", "BHHH", None), - ("trip_destination", "SLSQP", 0.12), + ("trip_destination", "BHHH", None), + ("trip_destination", "SLSQP", None), # trip_destination model has unusual parameter variance on a couple # parameters when switching platforms, possibly related to default data # types and high standard errors. Most parameters and the overall @@ -103,12 +105,19 @@ def test_location_model( from activitysim.estimation.larch import component_model, update_size_spec m, data = component_model(name, return_data=True) - m.load_data() + + if name == "trip_destination": + # this model is overspecified in the example, so we need to lock a + # parameter to make it identifiable. + m.lock_value("coef_prox_dest_outbound_work", 0.0) + m.set_cap(25.0) + + m.doctor(repair_av_zq="-", repair_nan_utility=True) loglike_prior = m.loglike() - r = m.maximize_loglike(method=method, options={"maxiter": 1000}) + r = m.maximize_loglike(method=method, options={"maxiter": 1000, "ftol": 1.0e-8}) num_regression.check( {"loglike_prior": loglike_prior, "loglike_converge": r.loglike}, - basename=f"test_loc_{name}_loglike", + basename=f"test_loc_{name}_{method}_loglike", ) _regression_check(dataframe_regression, m.pf, rtol=rtol) size_spec = update_size_spec( @@ -119,8 +128,8 @@ def test_location_model( ) dataframe_regression.check( size_spec, - basename=f"test_loc_{name}_size_spec", - default_tolerance=dict(atol=1e-6, rtol=5e-2) + basename=f"test_loc_{name}_{method}_size_spec", + default_tolerance=dict(atol=1e-6, rtol=5e-2), # set a little loose, as there is sometimes a little variance in these # results when switching backend implementations. ) @@ -143,7 +152,7 @@ def test_scheduling_model(est_data, num_regression, dataframe_regression, name, m.load_data() m.doctor(repair_ch_av="-") loglike_prior = m.loglike() - r = m.maximize_loglike(method=method) + r = m.maximize_loglike(method=method, options={"maxiter": 1000, "ftol": 1.0e-9}) num_regression.check( {"loglike_prior": loglike_prior, "loglike_converge": r.loglike}, basename=f"test_{name}_loglike", @@ -158,7 +167,7 @@ def test_stop_freq_model(est_data, num_regression, dataframe_regression): m, data = component_model(name, return_data=True) m.load_data() loglike_prior = m.loglike() - r = m.maximize_loglike() + r = m.maximize_loglike(method="SLSQP", options={"maxiter": 1000, "ftol": 1.0e-9}) num_regression.check( {"loglike_prior": loglike_prior, "loglike_converge": r.loglike}, basename=f"test_{name}_loglike", @@ -222,7 +231,7 @@ def test_cdap_model(est_data, num_regression, dataframe_regression): m = cdap_model() m.load_data() loglike_prior = m.loglike() - r = m.maximize_loglike(method="SLSQP", options={"maxiter": 1000}) + r = m.maximize_loglike(method="SLSQP", options={"maxiter": 1000, "ftol": 1.0e-7}) num_regression.check( {"loglike_prior": loglike_prior, "loglike_converge": r.loglike}, basename="test_cdap_model_loglike", @@ -260,7 +269,7 @@ def test_tour_and_subtour_mode_choice(est_data, num_regression, dataframe_regres m.load_data() m.doctor(repair_ch_av="-") loglike_prior = m.loglike() - r = m.maximize_loglike(method="SLSQP", options={"maxiter": 1000}) + r = m.maximize_loglike(method="SLSQP", options={"maxiter": 1000, "ftol": 1.0e-9}) num_regression.check( {"loglike_prior": loglike_prior, "loglike_converge": r.loglike}, basename="test_tour_mode_choice_loglike", diff --git a/activitysim/estimation/test/test_larch_estimation/test_cdap_model.csv b/activitysim/estimation/test/test_larch_estimation/test_cdap_model.csv index a9e237b235..400eb2fb9c 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_cdap_model.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_cdap_model.csv @@ -1,162 +1,162 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_UNAVAILABLE,-999,-999,0,-999,-999,-999 -coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N,0.87159546592932291,0.08233,0,,,0.87159546592932291 -coef_driving_age_child_who_is_in_school_asc_M,0.74815228797618816,2.3309186849999999,0,,,0.74815228797618816 -coef_driving_age_child_who_is_in_school_asc_N,-9.0640177490324412,-0.59911911200000001,0,,,-9.0640177490324412 -coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H,-0.90601686275009163,-0.50309999999999999,0,,,-0.90601686275009163 -coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H,0.42727506400961712,0.64749999999999996,0,,,0.42727506400961712 -coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H,-1.7272824609972235,-2.0459999999999998,0,,,-1.7272824609972235 -coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H,0.45396678614268526,1.3069999999999999,0,,,0.45396678614268526 -coef_full_time_worker_asc_M,0.98435866822120488,1.3787345790000001,0,,,0.98435866822120488 -coef_full_time_worker_asc_N,0.73324510331920412,0.62266239099999998,0,,,0.73324510331920412 -coef_full_time_worker_interaction_with_age_less_than_40_M,0.44443952843908197,0.20910000000000001,0,,,0.44443952843908197 -coef_full_time_worker_interaction_with_female_gender_M,0.038410812572387439,-0.12590000000000001,0,,,0.038410812572387439 -coef_full_time_worker_interaction_with_fewer_cars_than_workers_H,0.41538570763618882,0.50390000000000001,0,,,0.41538570763618882 -coef_full_time_worker_interaction_with_income_less_than_20k_H,0.44991051165021323,0.53129999999999999,0,,,0.44991051165021323 -coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,0.15132046128679377,0.1212,0,,,0.15132046128679377 -coef_non_working_adult_asc_N,0.61315184259117572,0.59464538600000005,0,,,0.61315184259117572 -coef_non_working_adult_interaction_with_female_gender_M,-0.74299999999999999,-0.74299999999999999,0,,,-0.74299999999999999 -coef_non_working_adult_interaction_with_fewer_cars_than_workers_H,0.80008083982324651,0.89649999999999996,0,,,0.80008083982324651 -coef_non_working_adult_interaction_with_income_between_50k_and_100k_H,-1.135877229516298,-0.56020000000000003,0,,,-1.135877229516298 -coef_non_working_adult_interaction_with_income_more_than_100k_H,-0.53695636446511075,-0.71879999999999999,0,,,-0.53695636446511075 -coef_non_working_adult_interaction_with_more_cars_than_workers_M,0.65149999999999997,0.65149999999999997,0,,,0.65149999999999997 -coef_non_working_adult_interaction_with_more_cars_than_workers_N,1.4756696612117433,0.81679999999999997,0,,,1.4756696612117433 -coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,0.23139999999999999,0.23139999999999999,0,,,0.23139999999999999 -coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N,0.05369614335005668,0.072069999999999995,0,,,0.05369614335005668 -coef_part_time_worker_asc_M,4.6632375293425374,-0.71882373799999999,0,,,4.6632375293425374 -coef_part_time_worker_asc_N,0.63327669200378511,0.63603246700000005,0,,,0.63327669200378511 -coef_part_time_worker_interaction_with_income_between_50k_and_100k_H,0.084764438455744495,-0.4032,0,,,0.084764438455744495 -coef_part_time_worker_interaction_with_income_less_than_20k_H,0.38471102017852971,0.32319999999999999,0,,,0.38471102017852971 -coef_part_time_worker_interaction_with_income_more_than_100k_H,-1.3396802216835464,-0.35339999999999999,0,,,-1.3396802216835464 -coef_part_time_worker_interaction_with_income_more_than_100k_N,0.57320315896108665,0.42070000000000002,0,,,0.57320315896108665 -coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,-0.22927275954114743,0.20039999999999999,0,,,-0.22927275954114743 -coef_pre_driving_age_child_who_is_in_school_asc_M,3.9697740814213565,3.295863529,0,,,3.9697740814213565 -coef_pre_driving_age_child_who_is_in_school_asc_N,-6.9669814176755258,0.57142433999999998,0,,,-6.9669814176755258 -coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,-1.5862536567965162,-0.71409999999999996,0,,,-1.5862536567965162 -coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N,-1.1444471156694567,-0.67200000000000004,0,,,-1.1444471156694567 -coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M,-0.71694326641444928,-0.29430000000000001,0,,,-0.71694326641444928 -coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H,1.0962667615224149,0.58620000000000005,0,,,1.0962667615224149 -coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,0.92486435043901694,1.052531189,0,,,0.92486435043901694 -coef_pre_driving_age_child_who_is_too_young_for_school_asc_N,-8.6130259294995177,-0.83756777599999999,0,,,-8.6130259294995177 -coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,-0.94468281293415513,-0.45150000000000001,0,,,-0.94468281293415513 -coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,0.23268035055770159,0.61070000000000002,0,,,0.23268035055770159 -coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H,-0.056007934757943807,0.50609999999999999,0,,,-0.056007934757943807 -coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H,-0.71502868941234388,-0.57079999999999997,0,,,-0.71502868941234388 -coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H,-0.7760438496909059,-0.61860000000000004,0,,,-0.7760438496909059 -coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N,0.77447196962877585,0.29909999999999998,0,,,0.77447196962877585 -coef_retired_asc_N,1.1425900456454805,0.408202071,0,,,1.1425900456454805 -coef_retired_interaction_with_age_more_than_80_H,1.1363693439466673,0.76659999999999995,0,,,1.1363693439466673 -coef_retired_interaction_with_female_M,0.47689999999999999,0.47689999999999999,0,,,0.47689999999999999 -coef_retired_interaction_with_fewer_cars_than_workers_H,1.1295952477348739,0.54959999999999998,0,,,1.1295952477348739 -coef_retired_interaction_with_income_less_than_20k_H,0.65398902348188781,0.53300000000000003,0,,,0.65398902348188781 -coef_retired_interaction_with_more_cars_than_workers_M,2.992,2.992,0,,,2.992 -coef_retired_interaction_with_more_cars_than_workers_N,0.76262176538305015,1.056,0,,,0.76262176538305015 -coef_retired_interaction_with_peak_accessibility_to_all_employment_M,0.2792,0.2792,0,,,0.2792 -coef_university_student_asc_M,2.1206612172447477,2.3535951759999998,0,,,2.1206612172447477 -coef_university_student_asc_N,0.47323909115586493,0.609709846,0,,,0.47323909115586493 --999.0,-999,-999,-999,-999,-999,-999 -coef_H_11,1.5446242163709172,1.6259999999999999,0,,,1.5446242163709172 -coef_H_12,0.15833568776463425,0.74070000000000003,0,,,0.15833568776463425 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+coef_N_68,1.2729999999999999,1.2729999999999999,1.2729999999999999,0 +coef_N_77,2.3038032526813228,2.3038032526813228,1.5529999999999999,0 +coef_N_78,-0.050192112434025701,-0.050192112434025701,0.61839999999999995,0 +coef_N_88,-0.12160277921446122,-0.12160277921446122,0.87709999999999999,0 +coef_N_xxxxx,-1.0454032140665002,-1.0454032140665002,-3.4529999999999998,0 +coef_UNAVAILABLE,-999,-999,-999,0 +coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N,0.31817765512188612,0.31817765512188612,0.08233,0 +coef_driving_age_child_who_is_in_school_asc_M,0.76528459302162444,0.76528459302162444,2.3309186849999999,0 +coef_driving_age_child_who_is_in_school_asc_N,-3.5523769241342968,-3.5523769241342968,-0.59911911200000001,0 +coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H,-0.94575683546498501,-0.94575683546498501,-0.50309999999999999,0 +coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H,0.45457650403543642,0.45457650403543642,0.64749999999999996,0 +coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H,-1.7469955117343503,-1.7469955117343503,-2.0459999999999998,0 +coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H,0.45063485271679943,0.45063485271679943,1.3069999999999999,0 +coef_full_time_worker_asc_M,0.74125913107999808,0.74125913107999808,1.3787345790000001,0 +coef_full_time_worker_asc_N,0.73554763956044567,0.73554763956044567,0.62266239099999998,0 +coef_full_time_worker_interaction_with_age_less_than_40_M,0.44384674710175359,0.44384674710175359,0.20910000000000001,0 +coef_full_time_worker_interaction_with_female_gender_M,0.038779513853859825,0.038779513853859825,-0.12590000000000001,0 +coef_full_time_worker_interaction_with_fewer_cars_than_workers_H,0.41683751563975829,0.41683751563975829,0.50390000000000001,0 +coef_full_time_worker_interaction_with_income_less_than_20k_H,0.4546699336623663,0.4546699336623663,0.53129999999999999,0 +coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,0.17061134225870994,0.17061134225870994,0.1212,0 +coef_non_working_adult_asc_N,0.5032527423546157,0.5032527423546157,0.59464538600000005,0 +coef_non_working_adult_interaction_with_female_gender_M,-0.74299999999999999,-0.74299999999999999,-0.74299999999999999,0 +coef_non_working_adult_interaction_with_fewer_cars_than_workers_H,0.79550405235037291,0.79550405235037291,0.89649999999999996,0 +coef_non_working_adult_interaction_with_income_between_50k_and_100k_H,-1.1335473989478433,-1.1335473989478433,-0.56020000000000003,0 +coef_non_working_adult_interaction_with_income_more_than_100k_H,-0.52876095625819419,-0.52876095625819419,-0.71879999999999999,0 +coef_non_working_adult_interaction_with_more_cars_than_workers_M,0.65149999999999997,0.65149999999999997,0.65149999999999997,0 +coef_non_working_adult_interaction_with_more_cars_than_workers_N,1.4789652448709654,1.4789652448709654,0.81679999999999997,0 +coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,0.23139999999999999,0.23139999999999999,0.23139999999999999,0 +coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N,0.064939130822218152,0.064939130822218152,0.072069999999999995,0 +coef_part_time_worker_asc_M,6.3592603940417298,6.3592603940417298,-0.71882373799999999,0 +coef_part_time_worker_asc_N,0.62900096289821572,0.62900096289821572,0.63603246700000005,0 +coef_part_time_worker_interaction_with_income_between_50k_and_100k_H,0.083715416192307907,0.083715416192307907,-0.4032,0 +coef_part_time_worker_interaction_with_income_less_than_20k_H,0.37591205189659627,0.37591205189659627,0.32319999999999999,0 +coef_part_time_worker_interaction_with_income_more_than_100k_H,-1.344577739113451,-1.344577739113451,-0.35339999999999999,0 +coef_part_time_worker_interaction_with_income_more_than_100k_N,0.57926282447447563,0.57926282447447563,0.42070000000000002,0 +coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,-0.36291295851748201,-0.36291295851748201,0.20039999999999999,0 +coef_pre_driving_age_child_who_is_in_school_asc_M,3.9364917022114958,3.9364917022114958,3.295863529,0 +coef_pre_driving_age_child_who_is_in_school_asc_N,-1.4901825824862984,-1.4901825824862984,0.57142433999999998,0 +coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,-1.560760489357776,-1.560760489357776,-0.71409999999999996,0 +coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N,-1.1582880792288159,-1.1582880792288159,-0.67200000000000004,0 +coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M,-0.69702865509680834,-0.69702865509680834,-0.29430000000000001,0 +coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H,1.0807804461266468,1.0807804461266468,0.58620000000000005,0 +coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,0.93791299554872232,0.93791299554872232,1.052531189,0 +coef_pre_driving_age_child_who_is_too_young_for_school_asc_N,-3.1168217219705499,-3.1168217219705499,-0.83756777599999999,0 +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,-0.97840886625486712,-0.97840886625486712,-0.45150000000000001,0 +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,0.22680433121070148,0.22680433121070148,0.61070000000000002,0 +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H,-0.064351049344718039,-0.064351049344718039,0.50609999999999999,0 +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H,-0.70378572103967652,-0.70378572103967652,-0.57079999999999997,0 +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H,-0.77304583320350251,-0.77304583320350251,-0.61860000000000004,0 +coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N,0.69633632402948109,0.69633632402948109,0.29909999999999998,0 +coef_retired_asc_N,1.0264990433635548,1.0264990433635548,0.408202071,0 +coef_retired_interaction_with_age_more_than_80_H,1.1355402656793447,1.1355402656793447,0.76659999999999995,0 +coef_retired_interaction_with_female_M,0.47689999999999999,0.47689999999999999,0.47689999999999999,0 +coef_retired_interaction_with_fewer_cars_than_workers_H,1.1284456224788169,1.1284456224788169,0.54959999999999998,0 +coef_retired_interaction_with_income_less_than_20k_H,0.65094671152574535,0.65094671152574535,0.53300000000000003,0 +coef_retired_interaction_with_more_cars_than_workers_M,2.992,2.992,2.992,0 +coef_retired_interaction_with_more_cars_than_workers_N,0.76654574573265821,0.76654574573265821,1.056,0 +coef_retired_interaction_with_peak_accessibility_to_all_employment_M,0.2792,0.2792,0.2792,0 +coef_university_student_asc_M,2.1131909971292351,2.1131909971292351,2.3535951759999998,0 +coef_university_student_asc_N,0.35532700566947006,0.35532700566947006,0.609709846,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_cdap_model_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_cdap_model_loglike.csv index dcd755572e..cf7fc482c4 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_cdap_model_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_cdap_model_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-2460.72797581254872057,-2335.77494762967762654 +0,-2460.7280651590818,-2336.352120296393 diff --git a/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv index a8e60cc389..c5a6e449eb 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_joint_tour_scheduling_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-1295.3015770898169,-175.64109886722656 +0,-185.1805538740314,-163.38981780168791 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_BHHH_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_BHHH_loglike.csv new file mode 100644 index 0000000000..90f36e10ea --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_BHHH_loglike.csv @@ -0,0 +1,2 @@ +,loglike_prior,loglike_converge +0,-1604.7051182769926,-1393.2868770044743 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_BHHH_size_spec.csv similarity index 97% rename from activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_size_spec.csv rename to activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_BHHH_size_spec.csv index 55915d90aa..1472d05ed2 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_size_spec.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_BHHH_size_spec.csv @@ -12,7 +12,7 @@ 10,othmaint,non_mandatory,0,0.48199999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 11,social,non_mandatory,0,0.52200000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 12,othdiscr,non_mandatory,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 -13,atwork,atwork,0,0.80359528369795752,0,0.19640471630204245,0,0,0,0,0,0,0 +13,atwork,atwork,0,0.9565182592635646,0,0.043481740736435397,0,0,0,0,0,0,0 14,work,trip,0,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0,0,0,0 15,escort,trip,0.001,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46400000000000002,0.16600000000000001,0,0 16,shopping,trip,0.001,0.999,0,0,0,0,0,0,0,0,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_loglike.csv deleted file mode 100644 index eb025e263b..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_destination_loglike.csv +++ /dev/null @@ -1,2 +0,0 @@ -,loglike_prior,loglike_converge -0,-2334.2258751965092,-2329.8700459477177 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_scheduling_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_scheduling_loglike.csv deleted file mode 100644 index 65489ba050..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_atwork_subtour_scheduling_loglike.csv +++ /dev/null @@ -1,2 +0,0 @@ -,loglike_prior,loglike_converge -0,-6685.5153616099333,-1437.800794640111 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_SLSQP_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_SLSQP_loglike.csv new file mode 100644 index 0000000000..77c3090874 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_SLSQP_loglike.csv @@ -0,0 +1,2 @@ +,loglike_prior,loglike_converge +0,-8511.5032542114423,-7720.5833427253137 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_SLSQP_size_spec.csv similarity index 51% rename from activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_size_spec.csv rename to activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_SLSQP_size_spec.csv index af3285fe26..9aee2eecc1 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_size_spec.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_SLSQP_size_spec.csv @@ -1,23 +1,23 @@ ,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE -0,work_low,workplace,0,0.12912912912912916,0.19319319319319325,0.38338338338338335,0.12012012012012015,0.010010010010010012,0.1641641641641641,0,0,0,0 -1,work_med,workplace,0,0.12012012012012013,0.19719719719719711,0.32532532532532538,0.13913913913913917,0.0080080080080080097,0.21021021021021022,0,0,0,0 +0,work_low,workplace,0,0.12912912912912911,0.19319319319319317,0.38338338338338335,0.12012012012012011,0.01001001001001001,0.16416416416416416,0,0,0,0 +1,work_med,workplace,0,0.12012012012012012,0.19719719719719719,0.32532532532532532,0.13913913913913914,0.0080080080080080079,0.21021021021021019,0,0,0,0 2,work_high,workplace,0,0.11,0.20699999999999999,0.28399999999999997,0.154,0.0060000000000000001,0.23899999999999999,0,0,0,0 3,work_veryhigh,workplace,0,0.092999999999999999,0.27000000000000002,0.24099999999999999,0.14599999999999999,0.0040000000000000001,0.246,0,0,0,0 -4,university,school,0,0,0,0,0,0,0,0,0,0.59200000000000008,0.40799999999999997 +4,university,school,0,0,0,0,0,0,0,0,0,0.59199999999999997,0.40799999999999997 5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 -7,escort,non_mandatory,0,0.34195879720614686,0,0.13084293500721081,0,0,0,0.47127943957016599,0.055918828216476292,0,0 +7,escort,non_mandatory,0,0.46217099216294472,0,0.076417211673566368,0,0,0,0.45632020794852418,0.0050915882149647294,0,0 8,shopping,non_mandatory,0,1,0,0,0,0,0,0,0,0,0 -9,eatout,non_mandatory,0,0.65324866493964562,0,0.34675133506035422,0,0,0,0,0,0,0 -10,othmaint,non_mandatory,0,0.4233662207110353,0,0.57663377928896464,0,0,0,0,0,0,0 -11,social,non_mandatory,0,0.41656965799658413,0,0.58343034200341592,0,0,0,0,0,0,0 -12,othdiscr,non_mandatory,0.29737723981663911,0.27581414711676105,0,0.23370128672645701,0.12262191040591303,0,0,0,0.070485415934229814,0,0 +9,eatout,non_mandatory,0,0.90706301951236668,0,0.092936980487633311,0,0,0,0,0,0,0 +10,othmaint,non_mandatory,0,0.93116571483751687,0,0.068834285162483244,0,0,0,0,0,0,0 +11,social,non_mandatory,0,0.92391195421170202,0,0.076088045788297989,0,0,0,0,0,0,0 +12,othdiscr,non_mandatory,0.52511273726308272,0.27498108262234988,0,0.023448766399240456,0.15609458779050345,0,0,0,0.020362825924823438,0,0 13,atwork,atwork,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 14,work,trip,0,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0,0,0,0 -15,escort,trip,0.0010000000000000002,0.22500000000000003,0,0.14400000000000002,0,0,0,0.46399999999999997,0.16600000000000004,0,0 +15,escort,trip,0.001,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46400000000000002,0.16600000000000001,0,0 16,shopping,trip,0.001,0.999,0,0,0,0,0,0,0,0,0 17,eatout,trip,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 18,othmaint,trip,0.001,0.48099999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 19,social,trip,0.001,0.52100000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 20,othdiscr,trip,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 -21,univ,trip,0.000999000999000999,0,0,0,0,0,0,0,0,0.59140859140859148,0.4075924075924075 +21,univ,trip,0.00099900099900099922,0,0,0,0,0,0,0,0,0.59140859140859148,0.40759240759240761 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_loglike.csv deleted file mode 100644 index c5df93cabd..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_destination_loglike.csv +++ /dev/null @@ -1,2 +0,0 @@ -,loglike_prior,loglike_converge -0,-12819.670044726949,-12802.203974292237 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_scheduling_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_scheduling_loglike.csv deleted file mode 100644 index d774c94c68..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_non_mandatory_tour_scheduling_loglike.csv +++ /dev/null @@ -1,2 +0,0 @@ -,loglike_prior,loglike_converge -0,-12689.271731526222,-9843.6723572271876 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_BHHH_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_BHHH_loglike.csv new file mode 100644 index 0000000000..61faa0892d --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_BHHH_loglike.csv @@ -0,0 +1,2 @@ +,loglike_prior,loglike_converge +0,-3150.9027927232128,-2472.1256126886806 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_BHHH_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_BHHH_size_spec.csv new file mode 100644 index 0000000000..b5a4700325 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_BHHH_size_spec.csv @@ -0,0 +1,23 @@ +,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE +0,work_low,workplace,0,0.12912912912912911,0.19319319319319317,0.38338338338338335,0.12012012012012011,0.01001001001001001,0.16416416416416416,0,0,0,0 +1,work_med,workplace,0,0.12012012012012012,0.19719719719719719,0.32532532532532532,0.13913913913913914,0.0080080080080080079,0.21021021021021019,0,0,0,0 +2,work_high,workplace,0,0.11,0.20699999999999999,0.28399999999999997,0.154,0.0060000000000000001,0.23899999999999999,0,0,0,0 +3,work_veryhigh,workplace,0,0.092999999999999999,0.27000000000000002,0.24099999999999999,0.14599999999999999,0.0040000000000000001,0.246,0,0,0,0 +4,university,school,0,0,0,0,0,0,0,0,0,1,0 +5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 +6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 +7,escort,non_mandatory,0,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46500000000000002,0.16600000000000001,0,0 +8,shopping,non_mandatory,0,1,0,0,0,0,0,0,0,0,0 +9,eatout,non_mandatory,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +10,othmaint,non_mandatory,0,0.48199999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 +11,social,non_mandatory,0,0.52200000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 +12,othdiscr,non_mandatory,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 +13,atwork,atwork,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +14,work,trip,0,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0,0,0,0 +15,escort,trip,0.001,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46400000000000002,0.16600000000000001,0,0 +16,shopping,trip,0.001,0.999,0,0,0,0,0,0,0,0,0 +17,eatout,trip,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +18,othmaint,trip,0.001,0.48099999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 +19,social,trip,0.001,0.52100000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 +20,othdiscr,trip,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 +21,univ,trip,0.00099900099900099922,0,0,0,0,0,0,0,0,0.59140859140859148,0.40759240759240761 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_SLSQP_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_SLSQP_loglike.csv new file mode 100644 index 0000000000..11f796f1c2 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_SLSQP_loglike.csv @@ -0,0 +1,2 @@ +,loglike_prior,loglike_converge +0,-3150.9027927232128,-2472.5944704127669 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_SLSQP_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_SLSQP_size_spec.csv new file mode 100644 index 0000000000..569194ef5e --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_SLSQP_size_spec.csv @@ -0,0 +1,23 @@ +,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE +0,work_low,workplace,0,0.12912912912912911,0.19319319319319317,0.38338338338338335,0.12012012012012011,0.01001001001001001,0.16416416416416416,0,0,0,0 +1,work_med,workplace,0,0.12012012012012012,0.19719719719719719,0.32532532532532532,0.13913913913913914,0.0080080080080080079,0.21021021021021019,0,0,0,0 +2,work_high,workplace,0,0.11,0.20699999999999999,0.28399999999999997,0.154,0.0060000000000000001,0.23899999999999999,0,0,0,0 +3,work_veryhigh,workplace,0,0.092999999999999999,0.27000000000000002,0.24099999999999999,0.14599999999999999,0.0040000000000000001,0.246,0,0,0,0 +4,university,school,0,0,0,0,0,0,0,0,0,0.99583037710619249,0.004169622893807531 +5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 +6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 +7,escort,non_mandatory,0,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46500000000000002,0.16600000000000001,0,0 +8,shopping,non_mandatory,0,1,0,0,0,0,0,0,0,0,0 +9,eatout,non_mandatory,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +10,othmaint,non_mandatory,0,0.48199999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 +11,social,non_mandatory,0,0.52200000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 +12,othdiscr,non_mandatory,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 +13,atwork,atwork,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +14,work,trip,0,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0,0,0,0 +15,escort,trip,0.001,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46400000000000002,0.16600000000000001,0,0 +16,shopping,trip,0.001,0.999,0,0,0,0,0,0,0,0,0 +17,eatout,trip,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +18,othmaint,trip,0.001,0.48099999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 +19,social,trip,0.001,0.52100000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 +20,othdiscr,trip,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 +21,univ,trip,0.00099900099900099922,0,0,0,0,0,0,0,0,0.59140859140859148,0.40759240759240761 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_loglike.csv index 645ce49776..2e9a9a9bb2 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-4070.349038545839,-4058.2217927321499 +0,-3150.9027927232128,-2472.12561268868057596 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_size_spec.csv index 06a6f09b3b..569194ef5e 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_size_spec.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_school_location_size_spec.csv @@ -1,9 +1,9 @@ ,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE -0,work_low,workplace,0,0.12912912912912916,0.19319319319319325,0.38338338338338335,0.12012012012012015,0.010010010010010012,0.1641641641641641,0,0,0,0 -1,work_med,workplace,0,0.12012012012012013,0.19719719719719711,0.32532532532532538,0.13913913913913917,0.0080080080080080097,0.21021021021021022,0,0,0,0 +0,work_low,workplace,0,0.12912912912912911,0.19319319319319317,0.38338338338338335,0.12012012012012011,0.01001001001001001,0.16416416416416416,0,0,0,0 +1,work_med,workplace,0,0.12012012012012012,0.19719719719719719,0.32532532532532532,0.13913913913913914,0.0080080080080080079,0.21021021021021019,0,0,0,0 2,work_high,workplace,0,0.11,0.20699999999999999,0.28399999999999997,0.154,0.0060000000000000001,0.23899999999999999,0,0,0,0 3,work_veryhigh,workplace,0,0.092999999999999999,0.27000000000000002,0.24099999999999999,0.14599999999999999,0.0040000000000000001,0.246,0,0,0,0 -4,university,school,0,0,0,0,0,0,0,0,0,0.66488586546020301,0.33511413453979705 +4,university,school,0,0,0,0,0,0,0,0,0,0.99583037710619249,0.004169622893807531 5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 7,escort,non_mandatory,0,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46500000000000002,0.16600000000000001,0,0 @@ -14,10 +14,10 @@ 12,othdiscr,non_mandatory,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 13,atwork,atwork,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 14,work,trip,0,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0,0,0,0 -15,escort,trip,0.0010000000000000002,0.22500000000000003,0,0.14400000000000002,0,0,0,0.46399999999999997,0.16600000000000004,0,0 +15,escort,trip,0.001,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46400000000000002,0.16600000000000001,0,0 16,shopping,trip,0.001,0.999,0,0,0,0,0,0,0,0,0 17,eatout,trip,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 18,othmaint,trip,0.001,0.48099999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 19,social,trip,0.001,0.52100000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 20,othdiscr,trip,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 -21,univ,trip,0.000999000999000999,0,0,0,0,0,0,0,0,0.59140859140859148,0.4075924075924075 +21,univ,trip,0.00099900099900099922,0,0,0,0,0,0,0,0,0.59140859140859148,0.40759240759240761 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_BHHH_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_BHHH_loglike.csv new file mode 100644 index 0000000000..83144fe122 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_BHHH_loglike.csv @@ -0,0 +1,2 @@ +,loglike_prior,loglike_converge +0,-21855.73573427651,-8695.4742752868151 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_BHHH_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_BHHH_size_spec.csv new file mode 100644 index 0000000000..ebfe3d5b78 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_BHHH_size_spec.csv @@ -0,0 +1,23 @@ +,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE +0,work_low,workplace,0,0.12912912912912911,0.19319319319319317,0.38338338338338335,0.12012012012012011,0.01001001001001001,0.16416416416416416,0,0,0,0 +1,work_med,workplace,0,0.12012012012012012,0.19719719719719719,0.32532532532532532,0.13913913913913914,0.0080080080080080079,0.21021021021021019,0,0,0,0 +2,work_high,workplace,0,0.11,0.20699999999999999,0.28399999999999997,0.154,0.0060000000000000001,0.23899999999999999,0,0,0,0 +3,work_veryhigh,workplace,0,0.092999999999999999,0.27000000000000002,0.24099999999999999,0.14599999999999999,0.0040000000000000001,0.246,0,0,0,0 +4,university,school,0,0,0,0,0,0,0,0,0,0.59199999999999997,0.40799999999999997 +5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 +6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 +7,escort,non_mandatory,0,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46500000000000002,0.16600000000000001,0,0 +8,shopping,non_mandatory,0,1,0,0,0,0,0,0,0,0,0 +9,eatout,non_mandatory,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +10,othmaint,non_mandatory,0,0.48199999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 +11,social,non_mandatory,0,0.52200000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 +12,othdiscr,non_mandatory,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 +13,atwork,atwork,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 +14,work,trip,0,0.50988404412746136,2.8830314727604896e-146,0,1.2350245131417982e-12,0.48708399966352267,0.0030319562077810091,0,0,0,0 +15,escort,trip,0.99999999999752021,4.6306836616861319e-13,0,1.8807680387063182e-13,0,0,0,1.4480163162588933e-12,3.8063890886682389e-13,0,0 +16,shopping,trip,0.99999999865706501,1.3429349752304603e-09,0,0,0,0,0,0,0,0,0 +17,eatout,trip,0,1,0,0,0,0,0,0,0,0,0 +18,othmaint,trip,0.99999999998646205,9.0646768310272159e-12,0,4.4735228850332918e-12,0,0,0,0,0,0,0 +19,social,trip,0.99999999999998024,1.5851156966866637e-14,0,3.8443449490946144e-15,0,0,0,0,0,0,0 +20,othdiscr,trip,0.96881871015685461,0.031181191433995798,0,0,0,0,0,0,9.8409149668972833e-08,0,0 +21,univ,trip,1,0,0,0,0,0,0,0,0,4.8326974397473536e-21,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_SLSQP_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_SLSQP_loglike.csv new file mode 100644 index 0000000000..a2e43ac4ff --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_SLSQP_loglike.csv @@ -0,0 +1,2 @@ +,loglike_prior,loglike_converge +0,-21855.73573427651,-8952.9587499637983 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_SLSQP_size_spec.csv similarity index 63% rename from activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_size_spec.csv rename to activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_SLSQP_size_spec.csv index 04abb0cc9c..e3f4e0538d 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_size_spec.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_SLSQP_size_spec.csv @@ -13,11 +13,11 @@ 11,social,non_mandatory,0,0.52200000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 12,othdiscr,non_mandatory,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 13,atwork,atwork,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 -14,work,trip,0,0.24014195523694865,0.00059525239425258316,0.0025343890988937991,0.0013168081587181715,0.75481634271693376,0.00059525239425293412,0,0,0,0 -15,escort,trip,0.0025746521059848222,0.31583599496207332,0,0.0063819245118687607,0,0,0,0.56721029531390887,0.10799713310616434,0,0 -16,shopping,trip,0.28745939613255439,0.71254060386744567,0,0,0,0,0,0,0,0,0 -17,eatout,trip,0,0.98157984232483642,0,0.018420157675163609,0,0,0,0,0,0,0 -18,othmaint,trip,0.066897386356299673,0.76728057159935903,0,0.16582204204434134,0,0,0,0,0,0,0 -19,social,trip,0.028735588735319624,0.90003600813919549,0,0.071228403125484938,0,0,0,0,0,0,0 -20,othdiscr,trip,0.27326163859230651,0.71190812297572337,0,0.0026878884184924783,0.0094544615949851765,0,0,0,0.0026878884184924428,0,0 -21,univ,trip,0.16785552148845903,0,0,0,0,0,0,0,0,0.41607223925575049,0.41607223925579045 +14,work,trip,0,0.74787786056587513,0.0018538038747582422,0.001853803876100025,0.0018538038747592663,0.24470692393187074,0.0018538038766366914,0,0,0,0 +15,escort,trip,4.4398622176416287e-05,0.0018145828132440462,0,0.00025862750482263144,0,0,0,0.97914326797093931,0.018739123088817596,0,0 +16,shopping,trip,0.28745937648450037,0.71254062351549963,0,0,0,0,0,0,0,0,0 +17,eatout,trip,0,0.99667048634136579,0,0.0033295136586341872,0,0,0,0,0,0,0 +18,othmaint,trip,0.16785550808846833,0.41607224595773862,0,0.41607224595379305,0,0,0,0,0,0,0 +19,social,trip,0.00044482261527767418,0.98033336959213913,0,0.019221807792583361,0,0,0,0,0,0,0 +20,othdiscr,trip,0.9406951398181691,0.022347626204726488,0,0.0092529765597407446,0.009252976559785164,0,0,0,0.018451280857578346,0,0 +21,univ,trip,0.16785550808973149,0,0,0,0,0,0,0,0,0.41607224595513576,0.41607224595513281 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_loglike.csv deleted file mode 100644 index 7bed7a2ee0..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_trip_destination_loglike.csv +++ /dev/null @@ -1,2 +0,0 @@ -,loglike_prior,loglike_converge -0,-27955.701284580879,-12569.486100216422 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_SLSQP_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_SLSQP_loglike.csv new file mode 100644 index 0000000000..4068e18c58 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_SLSQP_loglike.csv @@ -0,0 +1,2 @@ +,loglike_prior,loglike_converge +0,-8694.1663931267794,-7268.5499545875718 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_SLSQP_size_spec.csv similarity index 72% rename from activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_size_spec.csv rename to activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_SLSQP_size_spec.csv index b568bafc7b..2ef299a31a 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_size_spec.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_SLSQP_size_spec.csv @@ -1,8 +1,8 @@ ,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE -0,work_low,workplace,0,0.04331096924066484,0.17606247084331519,0.37453642466033488,0.16935644669165648,0.000968861288590913,0.23576482727543777,0,0,0,0 -1,work_med,workplace,0,0.11999183383359351,0.14366373395357587,0.25125860903337882,0.079402445282216183,0.0024785834941433989,0.40320479440309209,0,0,0,0 -2,work_high,workplace,0,0.15790294154771911,0.19045576988955706,0.29641460514585227,0.1682066889662423,0.0063903077215986892,0.18062968672903063,0,0,0,0 -3,work_veryhigh,workplace,0,0.062641019242734242,0.11667678718376452,0.13342796655219047,0.056368476597385232,0.51025642529636983,0.12062932512755588,0,0,0,0 +0,work_low,workplace,0,0.81009695995589714,0.015566120986122242,0.038205865646079792,0.059310593251792124,0.061254339173986733,0.015566120986122242,0,0,0,0 +1,work_med,workplace,0,0.8281013406144393,0.017105484027381986,0.031082288899413148,0.052366292140338909,0.054239110291044855,0.017105484027382,0,0,0,0 +2,work_high,workplace,0,0.83264652845133025,0.018762949054789986,0.021130105260407248,0.064579726133974238,0.044117742044708114,0.018762949054789986,0,0,0,0 +3,work_veryhigh,workplace,0,0.85877445882802128,0.022889127413081023,0.032513950668635973,0.027093642194477335,0.035839693482703265,0.022889127413081023,0,0,0,0 4,university,school,0,0,0,0,0,0,0,0,0,0.59199999999999997,0.40799999999999997 5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_loglike.csv deleted file mode 100644 index 71e9b13b18..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_loc_workplace_location_loglike.csv +++ /dev/null @@ -1,2 +0,0 @@ -,loglike_prior,loglike_converge -0,-13535.154991016063,-13520.930381371694 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_None_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_None_.csv index f40a770d1b..69549a8874 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_None_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_location_model_atwork_subtour_destination_BHHH_None_.csv @@ -1,11 +1,11 @@ -,value,initvalue,nullvalue,minimum,maximum,best -atwork_HEREMPN,-1.7073243906685389,-1.3547956940605197,0,-6,6,-1.7073243906685389 -atwork_RETEMPN,-0.29840603581475661,-0.29840603581475661,0,-0.29840603581475661,-0.29840603581475661,-0.29840603581475661 -coef_distance_piecewise_linear_for_15_plus_miles,-0.20450000000000218,-0.20449999999999999,0,-25,25,-0.20450000000000218 -coef_distance_piecewise_linear_from_0_to_1_miles,-1.3641542578406347,-0.79259999999999997,0,-25,25,-1.3641542578406347 -coef_distance_piecewise_linear_from_1_to_2_miles,-0.73953227083074224,-0.79259999999999997,0,-25,25,-0.73953227083074224 -coef_distance_piecewise_linear_from_2_to_5_miles,-0.50452550250971084,-0.51970000000000005,0,-25,25,-0.50452550250971084 -coef_distance_piecewise_linear_from_5_to_15_miles,-0.073402957716087874,-0.20449999999999999,0,-25,25,-0.073402957716087874 -coef_mode_choice_logsum,0.50327067752307442,0.51359999999999995,0,-25,25,0.50327067752307442 -coef_no_attractions_atwork_size_variable_is_0,-999,-999,0,-999,-999,-999 -coef_sample_of_alternatives_correction_factor,1,1,0,1,1,1 +param_name,value,best,initvalue,nullvalue +atwork_HEREMPN,-3.389364816281855,-3.389364816281855,-1.3547956943511963,0 +atwork_RETEMPN,-0.29840603470802307,-0.29840603470802307,-0.29840603470802307,0 +coef_distance_piecewise_linear_for_15_plus_miles,-0.20450000464916254,-0.20450000464916254,-0.20450000464916229,0 +coef_distance_piecewise_linear_from_0_to_1_miles,-0.40672843393342367,-0.40672843393342367,-0.79259997606277466,0 +coef_distance_piecewise_linear_from_1_to_2_miles,0.46709869805144771,0.46709869805144771,-0.79259997606277466,0 +coef_distance_piecewise_linear_from_2_to_5_miles,0.11788881847828234,0.11788881847828234,-0.51969999074935913,0 +coef_distance_piecewise_linear_from_5_to_15_miles,0.25609050297480096,0.25609050297480096,-0.20450000464916229,0 +coef_mode_choice_logsum,0.73082605315228077,0.73082605315228077,0.51359999179840088,0 +coef_no_attractions_atwork_size_variable_is_0,-999,-999,-999,0 +coef_sample_of_alternatives_correction_factor,1,1,1,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_None_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_None_.csv index e771c163a3..20b6225820 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_None_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_location_model_non_mandatory_tour_destination_SLSQP_None_.csv @@ -1,36 +1,36 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -0,0,0,0,0,0,0 -1,1,1,1,1,1,1 -coef_eatout_dist_0_2,-0.84467402735490316,-0.56089999999999995,0,-25,25,-0.84467402735490316 -coef_eatout_dist_2_5,-0.19454520781373841,-0.31919999999999998,0,-25,25,-0.19454520781373841 -coef_eatout_dist_5_plus,-0.24629159495698957,-0.12379999999999999,0,-25,25,-0.24629159495698957 -coef_escort_dist_0_2,0.2228884413970586,-0.14990000000000001,0,-25,25,0.2228884413970586 -coef_escort_dist_2_5,-0.81521338400578147,-0.86709999999999998,0,-25,25,-0.81521338400578147 -coef_escort_dist_5_plus,-0.2563656478267789,-0.2137,0,-25,25,-0.2563656478267789 -coef_mode_logsum,0.65403821794149997,0.67549999999999999,0,-25,25,0.65403821794149997 -coef_othdiscr_dist_0_2,-0.18332164122229277,-0.16769999999999999,0,-25,25,-0.18332164122229277 -coef_othdiscr_dist_2_5,-0.57243431566525749,-0.4955,0,-25,25,-0.57243431566525749 -coef_othdiscr_dist_5_plus,0.022109773636770347,-0.1193,0,-25,25,0.022109773636770347 -coef_othmaint_dist_2_5,-0.56157171580357979,-0.60550000000000004,0,-25,25,-0.56157171580357979 -coef_othmaint_dist_5_plus,-0.22835432716850948,-0.10929999999999999,0,-25,25,-0.22835432716850948 -coef_shopping_dist_2_5,-0.62326413831589389,-0.5655,0,-25,25,-0.62326413831589389 -coef_shopping_dist_5_plus,-0.15909882569148343,-0.1832,0,-25,25,-0.15909882569148343 -coef_social_dist_2_5,-0.22738363386509225,-0.34849999999999998,0,-25,25,-0.22738363386509225 -coef_social_dist_5_plus,-0.17253037782714531,-0.13059999999999999,0,-25,25,-0.17253037782714531 -eatout_HEREMPN,-0.93175594508470749,-1.3547956940605197,0,-6,6,-0.93175594508470749 -eatout_RETEMPN,-0.29840603581475661,-0.29840603581475661,0,-0.29840603581475661,-0.29840603581475661,-0.29840603581475661 -escort_AGE0519,-1.1709029084521012,-0.7657178733947807,0,-6,6,-1.1709029084521012 -escort_HEREMPN,-2.4523610869729757,-1.9379419794061366,0,-6,6,-2.4523610869729757 -escort_HSENROLL,-3.3024337875479479,-1.7957674906255938,0,-6,6,-3.3024337875479479 -escort_RETEMPN,-1.4916548767777169,-1.4916548767777169,0,-1.4916548767777169,-1.4916548767777169,-1.4916548767777169 -othdiscr_HEREMPN,-1.6192946896579288,-1.3019532126861397,0,-6,6,-1.6192946896579288 -othdiscr_HSENROLL,-2.8175790455412613,-2.322787800311565,0,-6,6,-2.8175790455412613 -othdiscr_OTHEMPN,-2.264214574744007,-1.8018098050815563,0,-6,6,-2.264214574744007 -othdiscr_RETEMPN,-1.4536083464363203,-1.5511690043101247,0,-6,6,-1.4536083464363203 -othdiscr_TOTHH,-1.3783261914707137,-1.3783261914707137,0,-1.3783261914707137,-1.3783261914707137,-1.3783261914707137 -othmaint_HEREMPN,-0.42083070691744962,-0.65778003672265395,0,-6,6,-0.42083070691744962 -othmaint_RETEMPN,-0.72981116493153675,-0.72981116493153675,0,-0.72981116493153675,-0.72981116493153675,-0.72981116493153675 -shopping_RETEMPN,0,0,0,0,0,0 -social_HEREMPN,-0.31321849637618082,-0.73814454649068106,0,-6,6,-0.31321849637618082 -social_RETEMPN,-0.65008769109949827,-0.65008769109949827,0,-0.65008769109949827,-0.65008769109949827,-0.65008769109949827 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +0,0,0,0,0 +1,1,1,1,0 +coef_eatout_dist_0_2,-0.36241753757283562,-0.36241753757283562,-0.56089997291564941,0 +coef_eatout_dist_2_5,0.1455948784794848,0.1455948784794848,-0.31920000910758972,0 +coef_eatout_dist_5_plus,-0.10347949759758775,-0.10347949759758775,-0.12380000203847885,0 +coef_escort_dist_0_2,0.37311835505986723,0.37311835505986723,-0.14990000426769257,0 +coef_escort_dist_2_5,0.021780514441237811,0.021780514441237811,-0.86710000038146973,0 +coef_escort_dist_5_plus,-0.11719861112519546,-0.11719861112519546,-0.21369999647140503,0 +coef_mode_logsum,0.58058639629827868,0.58058639629827868,0.67549997568130493,0 +coef_othdiscr_dist_0_2,-0.034089264034826726,-0.034089264034826726,-0.16769999265670776,0 +coef_othdiscr_dist_2_5,-0.064582241662395776,-0.064582241662395776,-0.49549999833106995,0 +coef_othdiscr_dist_5_plus,0.12360016041678727,0.12360016041678727,-0.1193000003695488,0 +coef_othmaint_dist_2_5,0.047194485653217981,0.047194485653217981,-0.6054999828338623,0 +coef_othmaint_dist_5_plus,-0.070139812280223396,-0.070139812280223396,-0.10930000245571136,0 +coef_shopping_dist_2_5,-0.084141591614817768,-0.084141591614817768,-0.56550002098083496,0 +coef_shopping_dist_5_plus,0.023925641427879701,0.023925641427879701,-0.18320000171661377,0 +coef_social_dist_2_5,0.15872711881616419,0.15872711881616419,-0.34850001335144043,0 +coef_social_dist_5_plus,-0.056983193388194325,-0.056983193388194325,-0.13060000538825989,0 +eatout_HEREMPN,-2.5766963293863645,-2.5766963293863645,-1.3547956943511963,0 +eatout_RETEMPN,-0.29840603470802307,-0.29840603470802307,-0.29840603470802307,0 +escort_AGE0519,-1.504395034914503,-1.504395034914503,-0.76571786403656006,0 +escort_HEREMPN,-3.2913818539226063,-3.2913818539226063,-1.9379420280456543,0 +escort_HSENROLL,-6,-6,-1.7957675457000732,0 +escort_RETEMPN,-1.4916548728942871,-1.4916548728942871,-1.4916548728942871,0 +othdiscr_HEREMPN,-4.4871212668473426,-4.4871212668473426,-1.3019531965255737,0 +othdiscr_HSENROLL,-4.6282281748496779,-4.6282281748496779,-2.3227877616882324,0 +othdiscr_OTHEMPN,-2.5914769992848368,-2.5914769992848368,-1.8018097877502441,0 +othdiscr_RETEMPN,-2.0252368499435924,-2.0252368499435924,-1.5511690378189087,0 +othdiscr_TOTHH,-1.3783261775970459,-1.3783261775970459,-1.3783261775970459,0 +othmaint_HEREMPN,-3.3345464979357597,-3.3345464979357597,-0.65778005123138428,0 +othmaint_RETEMPN,-0.72981119155883789,-0.72981119155883789,-0.72981119155883789,0 +shopping_RETEMPN,0,0,0,0 +social_HEREMPN,-3.1468133266863965,-3.1468133266863965,-0.73814451694488525,0 +social_RETEMPN,-0.65008771419525146,-0.65008771419525146,-0.65008771419525146,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_BHHH_None_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_BHHH_None_.csv new file mode 100644 index 0000000000..33b657c2c3 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_BHHH_None_.csv @@ -0,0 +1,21 @@ +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +1,1,1,1,0 +coef_grade_dist_0_1,0.080031235198373782,0.080031235198373782,-1.6418999433517456,0 +coef_grade_dist_15_up,-0.046010368210409304,-0.046010368210409304,-0.046000000089406967,0 +coef_grade_dist_5_15,0.011795679963699891,0.011795679963699891,-0.20309999585151672,0 +coef_high_dist_0_1,-0.48267138976026713,-0.48267138976026713,-0.95230001211166382,0 +coef_high_dist_15_up,-0.18818654818448871,-0.18818654818448871,-0.18819999694824219,0 +coef_high_dist_5_15,-0.12203943917498229,-0.12203943917498229,-0.19300000369548798,0 +coef_high_grade_dist_1_2,-0.2740077638163641,-0.2740077638163641,-0.56999999284744263,0 +coef_high_grade_dist_2_5,-0.12717991662132286,-0.12717991662132286,-0.56999999284744263,0 +coef_mode_logsum,0.28675535602711366,0.28675535602711366,0.53579998016357422,0 +coef_univ_dist_0_1,-0.20246864835878667,-0.20246864835878667,-3.2451000213623047,0 +coef_univ_dist_15_up,-0.072999830047635036,-0.072999830047635036,-0.072999998927116394,0 +coef_univ_dist_1_2,-0.50654663083945994,-0.50654663083945994,-2.7011001110076904,0 +coef_univ_dist_2_5,0.095856729554638809,0.095856729554638809,-0.57069998979568481,0 +coef_univ_dist_5_15,-0.1412133832985831,-0.1412133832985831,-0.50019997358322144,0 +gradeschool_AGE0519,0,0,0,0 +highschool_HSENROLL,0,0,0,0 +university_COLLFTE,-0.52424865961074829,-0.52424865961074829,-0.52424865961074829,0 +university_COLLPTE,-1275009.279056468,-1275009.279056468,-0.89648813009262085,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_None_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_None_.csv index a8d3375aba..1f4a5bf905 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_None_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_location_model_school_location_SLSQP_None_.csv @@ -1,21 +1,21 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -1,1,1,1,1,1,1 -coef_grade_dist_0_1,-1.8247870973053,-1.6418999999999999,0,-25,25,-1.8247870973053 -coef_grade_dist_15_up,-0.045999999999999999,-0.045999999999999999,0,-25,25,-0.045999999999999999 -coef_grade_dist_5_15,-0.14366307042924978,-0.2031,0,-25,25,-0.14366307042924978 -coef_high_dist_0_1,-2.1639997502226276,-0.95230000000000004,0,-25,25,-2.1639997502226276 -coef_high_dist_15_up,-0.18820000000000001,-0.18820000000000001,0,-25,25,-0.18820000000000001 -coef_high_dist_5_15,-0.10033338505701977,-0.193,0,-25,25,-0.10033338505701977 -coef_high_grade_dist_1_2,-0.74768688779537662,-0.56999999999999995,0,-25,25,-0.74768688779537662 -coef_high_grade_dist_2_5,-0.66638573965332526,-0.56999999999999995,0,-25,25,-0.66638573965332526 -coef_mode_logsum,0.37504471661044059,0.53580000000000005,0,-25,25,0.37504471661044059 -coef_univ_dist_0_1,-3.3138919948169607,-3.2450999999999999,0,-25,25,-3.3138919948169607 -coef_univ_dist_15_up,-0.072999999999999995,-0.072999999999999995,0,-25,25,-0.072999999999999995 -coef_univ_dist_1_2,-2.8752075620640434,-2.7010999999999998,0,-25,25,-2.8752075620640434 -coef_univ_dist_2_5,-0.45544418705038636,-0.57069999999999999,0,-25,25,-0.45544418705038636 -coef_univ_dist_5_15,-0.57189713873239967,-0.50019999999999998,0,-25,25,-0.57189713873239967 -gradeschool_AGE0519,0,0,0,0,0,0 -highschool_HSENROLL,0,0,0,0,0,0 -university_COLLFTE,-0.52424864409813143,-0.52424864409813143,0,-0.52424864409813143,-0.52424864409813143,-0.52424864409813143 -university_COLLPTE,-1.2091557399547264,-0.89648810457797545,0,-6,6,-1.2091557399547264 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +1,1,1,1,0 +coef_grade_dist_0_1,0.097116326549615037,0.097116329571923371,-1.6418999433517456,0 +coef_grade_dist_15_up,-0.046000000089406967,-0.046000000089406967,-0.046000000089406967,0 +coef_grade_dist_5_15,0.012514539653739084,0.012514539708327558,-0.20309999585151672,0 +coef_high_dist_0_1,-0.4391430660668546,-0.43914306516185797,-0.95230001211166382,0 +coef_high_dist_15_up,-0.18819999694824219,-0.18819999694824219,-0.18819999694824219,0 +coef_high_dist_5_15,-0.12223669864939674,-0.12223669850105187,-0.19300000369548798,0 +coef_high_grade_dist_1_2,-0.27721668286846185,-0.27721668361053109,-0.56999999284744263,0 +coef_high_grade_dist_2_5,-0.12625067319470287,-0.12625067314690649,-0.56999999284744263,0 +coef_mode_logsum,0.28931317404209661,0.28931317414746177,0.53579998016357422,0 +coef_univ_dist_0_1,-0.34044181122123379,-0.34044180684505398,-3.2451000213623047,0 +coef_univ_dist_15_up,-0.072999998927116394,-0.072999998927116394,-0.072999998927116394,0 +coef_univ_dist_1_2,-0.42252956674554115,-0.42252956729334301,-2.7011001110076904,0 +coef_univ_dist_2_5,0.092094672169464362,0.092094672140662429,-0.57069998979568481,0 +coef_univ_dist_5_15,-0.14137251312653148,-0.14137251329149328,-0.50019997358322144,0 +gradeschool_AGE0519,0,0,0,0 +highschool_HSENROLL,0,0,0,0 +university_COLLFTE,-0.52424865961074829,-0.52424865961074829,-0.52424865961074829,0 +university_COLLPTE,-6,-6,-0.89648813009262085,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_BHHH_None_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_BHHH_None_.csv new file mode 100644 index 0000000000..6c9c6beb27 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_BHHH_None_.csv @@ -0,0 +1,48 @@ +param_name,value,best,initvalue,nullvalue +coef_UNAVAILABLE,-999,-999,-999,0 +coef_distance_joint,-0.30113542071627925,-0.30113542071627925,-0.12380000203847885,0 +coef_mode_choice_logsum,0.082682969035499751,0.082682969035499751,1.8209999799728394,0 +coef_one,1,1,1,0 +coef_prox_dest_outbound_work,0,0,0,0 +coef_prox_home_inbound_work,-0.42511545535361761,-0.42511545535361761,-0.15000000596046448,0 +coef_prox_home_outbound_work,-0.015586316153796702,-0.015586316153796702,-0.37999999523162842,0 +coef_util_distance_atwork,-0.12233412535963523,-0.12233412535963523,-0.12233459949493408,0 +coef_util_distance_eatout,-0.31527860777732059,-0.31527860777732059,-0.10289999842643738,0 +coef_util_distance_escort,-0.20896432333181236,-0.20896432333181236,-0.14910000562667847,0 +coef_util_distance_othdiscr,-0.32948686834710317,-0.32948686834710317,-0.12617222964763641,0 +coef_util_distance_othmaint,-0.189836518023934,-0.189836518023934,-0.096199996769428253,0 +coef_util_distance_school,-0.10560055139570937,-0.10560055139570937,-0.10559999942779541,0 +coef_util_distance_shopping,-0.31439120387739444,-0.31439120387739444,-0.11919999867677689,0 +coef_util_distance_social,-0.21252031607377456,-0.21252031607377456,-0.13289999961853027,0 +coef_util_distance_univ,-0.16366566463671706,-0.16366566463671706,-0.061299998313188553,0 +coef_util_distance_work_inbound,-0.43284610617518143,-0.43284610617518143,0.14781327545642853,0 +coef_util_distance_work_outbound,-0.15694162700147077,-0.15694162700147077,-0.049725916236639023,0 +eatout_HEREMPN,-735.35391763180496,-735.35391763180496,-1.3547956943511963,0 +eatout_RETEMPN,-0.29840603470802307,-0.29840603470802307,-0.29840603470802307,0 +escort_AGE0519,-34.146330319047635,-34.146330319047635,-0.76787072420120239,0 +escort_HEREMPN,-36.185934740550735,-36.185934740550735,-1.9379420280456543,0 +escort_HSENROLL,-35.482441440764148,-35.482441440764148,-1.7957675457000732,0 +escort_RETEMPN,-35.28779133946658,-35.28779133946658,-1.4916548728942871,0 +escort_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +othdiscr_HEREMPN,-1407.1767066933987,-1407.1767066933987,-1.3019531965255737,0 +othdiscr_HSENROLL,-16.410343601026089,-16.410343601026089,-2.3227877616882324,0 +othdiscr_OTHEMPN,-18880.507669912509,-18880.507669912509,-1.8018097877502441,0 +othdiscr_RETEMPN,-4.8145914198400339,-4.8145914198400339,-1.5511690378189087,0 +othdiscr_TOTHH,-1.3783261775970459,-1.3783261775970459,-1.3783261775970459,0 +othmaint_HEREMPN,-33.024962428999153,-33.024962428999153,-0.65778005123138428,0 +othmaint_RETEMPN,-32.319889481391705,-32.319889481391705,-0.73188799619674683,0 +othmaint_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +shopping_RETEMPN,-23.892728970934463,-23.892728970934463,-0.0010005002841353416,0 +shopping_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +social_HEREMPN,-40.084022821895907,-40.084022821895907,-0.73814451694488525,0 +social_RETEMPN,-38.668703835173424,-38.668703835173424,-0.65200525522232056,0 +social_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +univ_COLLFTE,-52.916360377104091,-52.916360377104091,-0.52424865961074829,0 +univ_COLLPTE,-19661.749288729949,-19661.749288729949,-0.89648813009262085,0 +univ_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +work_AGREMPN,-0.045747060225834933,-0.045747060225834933,0,0 +work_FPSEMPN,-485.71888319359556,-485.71888319359556,0,0 +work_HEREMPN,-138.10600027993303,-138.10600027993303,0,0 +work_MWTEMPN,-5.1249743665982441,-5.1249743665982441,0,0 +work_OTHEMPN,-24.742530479368806,-24.742530479368806,0,0 +work_RETEMPN,0,0,0,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_0_12_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_0_12_.csv deleted file mode 100644 index abe98d47df..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_0_12_.csv +++ /dev/null @@ -1,48 +0,0 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_UNAVAILABLE,-999,-999,0,-999,-999,-999 -coef_distance_joint,-0.37640163363394014,-0.12379999999999999,0,-25,25,-0.37640163363394014 -coef_mode_choice_logsum,0.16302637352162752,1.821,0,-25,25,0.16302637352162752 -coef_one,1,1,0,1,1,1 -coef_prox_dest_outbound_work,-0.59976096803081336,-0.26000000000000001,0,-25,25,-0.59976096803081336 -coef_prox_home_inbound_work,-0.17411187178992643,-0.14999999999999999,0,-25,25,-0.17411187178992643 -coef_prox_home_outbound_work,-0.76295690223363544,-0.38,0,-25,25,-0.76295690223363544 -coef_util_distance_atwork,-0.122334597,-0.122334597,0,-25,25,-0.122334597 -coef_util_distance_eatout,-0.36619050394180647,-0.10290000000000001,0,-25,25,-0.36619050394180647 -coef_util_distance_escort,-0.33778430073643445,-0.14910000000000001,0,-25,25,-0.33778430073643445 -coef_util_distance_othdiscr,-0.40879077774005879,-0.126172224,0,-25,25,-0.40879077774005879 -coef_util_distance_othmaint,-0.2579687786027548,-0.096199999999999994,0,-25,25,-0.2579687786027548 -coef_util_distance_school,-0.1056,-0.1056,0,-25,25,-0.1056 -coef_util_distance_shopping,-0.39048426507116651,-0.1192,0,-25,25,-0.39048426507116651 -coef_util_distance_social,-0.33876758749031255,-0.13289999999999999,0,-25,25,-0.33876758749031255 -coef_util_distance_univ,-0.18638287666609779,-0.0613,0,-25,25,-0.18638287666609779 -coef_util_distance_work_inbound,0.14549074690265271,0.147813278663948,0,-25,25,0.14549074690265271 -coef_util_distance_work_outbound,-0.30050379136271688,-0.049725915742290003,0,-25,25,-0.30050379136271688 -eatout_HEREMPN,-4.2741238026831576,-1.3547956940605197,0,-6,6,-4.2741238026831576 -eatout_RETEMPN,-0.29840603581475661,-0.29840603581475661,0,-0.29840603581475661,-0.29840603581475661,-0.29840603581475661 -escort_AGE0519,-1.5127395733276923,-0.76787072675588175,0,-6,6,-1.5127395733276923 -escort_HEREMPN,-5.9999999999999591,-1.9379419794061366,0,-6,6,-5.9999999999999591 -escort_HSENROLL,-3.1713650180398978,-1.7957674906255938,0,-6,6,-3.1713650180398978 -escort_RETEMPN,-2.0982466239249806,-1.4916548767777169,0,-6,6,-2.0982466239249806 -escort_TOTHH,-6.9077552789821368,-6.9077552789821368,0,-6.9077552789821368,-6.9077552789821368,-6.9077552789821368 -othdiscr_HEREMPN,-5.9999999999999867,-1.3019532126861397,0,-6,6,-5.9999999999999867 -othdiscr_HSENROLL,-6,-2.322787800311565,0,-6,6,-6 -othdiscr_OTHEMPN,-4.7422691542793025,-1.8018098050815563,0,-6,6,-4.7422691542793025 -othdiscr_RETEMPN,-0.42080704850071809,-1.5511690043101247,0,-6,6,-0.42080704850071809 -othdiscr_TOTHH,-1.3783261914707137,-1.3783261914707137,0,-1.3783261914707137,-1.3783261914707137,-1.3783261914707137 -othmaint_HEREMPN,-5.9999999999977494,-0.65778003672265395,0,-6,6,-5.9999999999977494 -othmaint_RETEMPN,-4.4680626390475311,-0.73188800887637595,0,-6,6,-4.4680626390475311 -othmaint_TOTHH,-6.9077552789821368,-6.9077552789821368,0,-6.9077552789821368,-6.9077552789821368,-6.9077552789821368 -shopping_RETEMPN,-5.9999999999990461,-0.0010005003335835344,0,-6,6,-5.9999999999990461 -shopping_TOTHH,-6.9077552789821368,-6.9077552789821368,0,-6.9077552789821368,-6.9077552789821368,-6.9077552789821368 -social_HEREMPN,-5.9999999999996891,-0.73814454649068106,0,-6,6,-5.9999999999996891 -social_RETEMPN,-3.4634568876043041,-0.65200523722877013,0,-6,6,-3.4634568876043041 -social_TOTHH,-6.9077552789821368,-6.9077552789821368,0,-6.9077552789821368,-6.9077552789821368,-6.9077552789821368 -univ_COLLFTE,-5.9999999999981588,-0.52424864409813143,0,-6,6,-5.9999999999981588 -univ_COLLPTE,-5.9999999999980629,-0.89648810457797545,0,-6,6,-5.9999999999980629 -univ_TOTHH,-6.9077552789821368,-6.9077552789821368,0,-6.9077552789821368,-6.9077552789821368,-6.9077552789821368 -work_AGREMPN,1.1452442363377451,0,0,-6,6,1.1452442363377451 -work_FPSEMPN,-5.9999999999998632,0,0,-6,6,-5.9999999999998632 -work_HEREMPN,-4.5512776072689656,0,0,-6,6,-4.5512776072689656 -work_MWTEMPN,-5.9999999999992735,0,0,-6,6,-5.9999999999992735 -work_OTHEMPN,-5.2060194818516452,0,0,-6,6,-5.2060194818516452 -work_RETEMPN,0,0,0,0,0,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_None_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_None_.csv new file mode 100644 index 0000000000..00234b5079 --- /dev/null +++ b/activitysim/estimation/test/test_larch_estimation/test_location_model_trip_destination_SLSQP_None_.csv @@ -0,0 +1,48 @@ +param_name,value,best,initvalue,nullvalue +coef_UNAVAILABLE,-999,-999,-999,0 +coef_distance_joint,-0.33134844410661268,-0.33135365019482871,-0.12380000203847885,0 +coef_mode_choice_logsum,0.10404782558776673,0.10404902119734737,1.8209999799728394,0 +coef_one,1,1,1,0 +coef_prox_dest_outbound_work,0,0,0,0 +coef_prox_home_inbound_work,-0.43355983407617132,-0.43356515858610667,-0.15000000596046448,0 +coef_prox_home_outbound_work,-0.013630490468606973,-0.013630962107906125,-0.37999999523162842,0 +coef_util_distance_atwork,-0.12233459949493408,-0.12233459949493408,-0.12233459949493408,0 +coef_util_distance_eatout,-0.30369611711207345,-0.30370280168537217,-0.10289999842643738,0 +coef_util_distance_escort,-0.20130525011921385,-0.20130417694904884,-0.14910000562667847,0 +coef_util_distance_othdiscr,-0.31715754635603827,-0.31715481907154325,-0.12617222964763641,0 +coef_util_distance_othmaint,-0.1812975225573846,-0.18128613104067043,-0.096199996769428253,0 +coef_util_distance_school,-0.10559999942779541,-0.10559999942779541,-0.10559999942779541,0 +coef_util_distance_shopping,-0.3307589512022982,-0.33076061020463604,-0.11919999867677689,0 +coef_util_distance_social,-0.2659252249486278,-0.265920931953054,-0.13289999961853027,0 +coef_util_distance_univ,-0.14321261083041822,-0.14321492496348073,-0.061299998313188553,0 +coef_util_distance_work_inbound,-0.42854398962663698,-0.42853407623707079,0.14781327545642853,0 +coef_util_distance_work_outbound,-0.14563469653404526,-0.14563242024181883,-0.049725916236639023,0 +eatout_HEREMPN,-5.9999999999987965,-5.9999999998720117,-1.3547956943511963,0 +eatout_RETEMPN,-0.29840603470802307,-0.29840603470802307,-0.29840603470802307,0 +escort_AGE0519,3.0934694401381719,3.0934669149506493,-0.76787072420120239,0 +escort_HEREMPN,-5.1455749906308634,-5.1455755332943429,-1.9379420280456543,0 +escort_HSENROLL,-0.86259505065782127,-0.86258990666041102,-1.7957675457000732,0 +escort_RETEMPN,-3.197352946486133,-3.1973553524377385,-1.4916548728942871,0 +escort_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +othdiscr_HEREMPN,-5.9999999999996385,-5.9999999999615348,-1.3019531965255737,0 +othdiscr_HSENROLL,-5.3098114987510021,-5.3098141692353842,-2.3227877616882324,0 +othdiscr_OTHEMPN,-5.9999999999948379,-5.9999999994512185,-1.8018097877502441,0 +othdiscr_RETEMPN,-5.1182251846633031,-5.1182380614929812,-1.5511690378189087,0 +othdiscr_TOTHH,-1.3783261775970459,-1.3783261775970459,-1.3783261775970459,0 +othmaint_HEREMPN,-5.999999999995695,-5.9999999995423137,-0.65778005123138428,0 +othmaint_RETEMPN,-5.9999999999862119,-5.9999999985341876,-0.73188799619674683,0 +othmaint_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +shopping_RETEMPN,-5.9999999999999991,-5.9999999999999076,-0.0010005002841353416,0 +shopping_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +social_HEREMPN,-3.1416302242642606,-3.1416126892727569,-0.73814451694488525,0 +social_RETEMPN,0.79021700560209307,0.79019473285367858,-0.65200525522232056,0 +social_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +univ_COLLFTE,-5.9999999999999929,-5.9999999999999938,-0.52424865961074829,0 +univ_COLLPTE,-6,-5.9999999999999902,-0.89648813009262085,0 +univ_TOTHH,-6.9077553749084473,-6.9077553749084473,-6.9077553749084473,0 +work_AGREMPN,-1.1171784109856115,-1.1171768623786988,0,0 +work_FPSEMPN,-6,-5.9999999999999938,0,0 +work_HEREMPN,-5.9999999992762003,-5.9999999993504787,0,0 +work_MWTEMPN,-5.9999999989867057,-5.9999999990906927,0,0 +work_OTHEMPN,-5.9999999999994476,-5.999999999941287,0,0 +work_RETEMPN,0,0,0,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_None_.csv b/activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_None_.csv index 4fbab98906..d4b8d728d6 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_None_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_location_model_workplace_location_SLSQP_None_.csv @@ -1,35 +1,35 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -1,1,1,1,1,1,1 -coef_dist_0_1,-1.0822922946274103,-0.84279999999999999,0,-25,25,-1.0822922946274103 -coef_dist_0_5_high,0.1207879915621165,0.14999999999999999,0,-25,25,0.1207879915621165 -coef_dist_15_up,-0.091700000000000004,-0.091700000000000004,0,-25,25,-0.091700000000000004 -coef_dist_1_2,-0.24766620079389307,-0.31040000000000001,0,-25,25,-0.24766620079389307 -coef_dist_2_5,-0.3685512775196465,-0.37830000000000003,0,-25,25,-0.3685512775196465 -coef_dist_5_15,-0.15169975841932976,-0.1285,0,-25,25,-0.15169975841932976 -coef_dist_5_up_high,-0.015068815366553389,0.02,0,-25,25,-0.015068815366553389 -coef_mode_logsum,0.1608428973358158,0.29999999999999999,0,-25,25,0.1608428973358158 -work_high_AGREMPN,-5.414473039484391,-5.1159958097540823,0,-6,6,-5.414473039484391 -work_high_FPSEMPN,-2.0198354749074623,-1.575036485716768,0,-6,6,-2.0198354749074623 -work_high_HEREMPN,-1.5774962961301324,-1.258781040820931,0,-6,6,-1.5774962961301324 -work_high_MWTEMPN,-2.0728064576402221,-1.4312917270506265,0,-6,6,-2.0728064576402221 -work_high_OTHEMPN,-2.1440619487784449,-1.870802676568508,0,-6,6,-2.1440619487784449 -work_high_RETEMPN,-2.2072749131897207,-2.2072749131897207,0,-2.2072749131897207,-2.2072749131897207,-2.2072749131897207 -work_low_AGREMPN,-5.8479826351063204,-4.6051701859880909,0,-6,6,-5.8479826351063204 -work_low_FPSEMPN,-0.64550992870675994,-1.6450650900772514,0,-6,6,-0.64550992870675994 -work_low_HEREMPN,0.10934025159883133,-0.95972028980149104,0,-6,6,0.10934025159883133 -work_low_MWTEMPN,-0.35351399513998311,-1.8078888511579385,0,-6,6,-0.35351399513998311 -work_low_OTHEMPN,-0.68434316293298214,-2.120263536200091,0,-6,6,-0.68434316293298214 -work_low_RETEMPN,-2.0479428746204649,-2.0479428746204649,0,-2.0479428746204649,-2.0479428746204649,-2.0479428746204649 -work_med_AGREMPN,-6,-4.8283137373023015,0,-6,6,-6 -work_med_FPSEMPN,-1.9402118373687092,-1.6245515502441485,0,-6,6,-1.9402118373687092 -work_med_HEREMPN,-1.3812045016840873,-1.1239300966523995,0,-6,6,-1.3812045016840873 -work_med_MWTEMPN,-0.90824261771266779,-1.5606477482646683,0,-6,6,-0.90824261771266779 -work_med_OTHEMPN,-2.533158060495702,-1.9732813458514451,0,-6,6,-2.533158060495702 -work_med_RETEMPN,-2.120263536200091,-2.120263536200091,0,-2.120263536200091,-2.120263536200091,-2.120263536200091 -work_veryhigh_AGREMPN,-0.27766271475654764,-5.521460917862246,0,-6,6,-0.27766271475654764 -work_veryhigh_FPSEMPN,-1.7531684994723506,-1.3093333199837622,0,-6,6,-1.7531684994723506 -work_veryhigh_HEREMPN,-1.6190143529862893,-1.422958345491482,0,-6,6,-1.6190143529862893 -work_veryhigh_MWTEMPN,-1.7198536938187046,-1.4024237430497744,0,-6,6,-1.7198536938187046 -work_veryhigh_OTHEMPN,-2.4806660321461727,-1.9241486572738007,0,-6,6,-2.4806660321461727 -work_veryhigh_RETEMPN,-2.375155785828881,-2.375155785828881,0,-2.375155785828881,-2.375155785828881,-2.375155785828881 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +1,1,1,1,0 +coef_dist_0_1,-0.29614981943016305,-0.29614981943016305,-0.84280002117156982,0 +coef_dist_0_5_high,-0.037934685148704593,-0.037934685148704593,0.15000000596046448,0 +coef_dist_15_up,-0.091700002551078796,-0.091700002551078796,-0.091700002551078796,0 +coef_dist_1_2,0.36470038809157851,0.36470038809157851,-0.31040000915527344,0 +coef_dist_2_5,0.074882919278809829,0.074882919278809829,-0.3783000111579895,0 +coef_dist_5_15,0.042000918183646346,0.042000918183646346,-0.12849999964237213,0 +coef_dist_5_up_high,-0.09087337502871097,-0.09087337502871097,0.019999999552965164,0 +coef_mode_logsum,0.30896144806031556,0.30896144806031556,0.30000001192092896,0 +work_high_AGREMPN,-5.1450221148210131,-5.1450221148210131,-5.1159958839416504,0 +work_high_FPSEMPN,-6,-6,-1.5750365257263184,0 +work_high_HEREMPN,-5.8811853171285584,-5.8811853171285584,-1.2587810754776001,0 +work_high_MWTEMPN,-6,-6,-1.4312916994094849,0 +work_high_OTHEMPN,-4.7639836057101634,-4.7639836057101634,-1.870802640914917,0 +work_high_RETEMPN,-2.2072749137878418,-2.2072749137878418,-2.2072749137878418,0 +work_low_AGREMPN,-4.6300621303951566,-4.6300621303951566,-4.6051702499389648,0 +work_low_FPSEMPN,-6,-6,-1.6450650691986084,0 +work_low_HEREMPN,-5.1021077661983387,-5.1021077661983387,-0.95972031354904175,0 +work_low_MWTEMPN,-6,-6,-1.8078888654708862,0 +work_low_OTHEMPN,-4.6623088926077152,-4.6623088926077152,-2.1202635765075684,0 +work_low_RETEMPN,-2.0479428768157959,-2.0479428768157959,-2.0479428768157959,0 +work_med_AGREMPN,-4.8459968751410516,-4.8459968751410516,-4.8283138275146484,0 +work_med_FPSEMPN,-6,-6,-1.62455153465271,0 +work_med_HEREMPN,-5.4027609472499369,-5.4027609472499369,-1.1239300966262817,0 +work_med_MWTEMPN,-5.9999999999999991,-5.9999999999999991,-1.56064772605896,0 +work_med_OTHEMPN,-4.8811360108806872,-4.8811360108806872,-1.9732813835144043,0 +work_med_RETEMPN,-2.1202635765075684,-2.1202635765075684,-2.1202635765075684,0 +work_veryhigh_AGREMPN,-5.5516059766686876,-5.5516059766686876,-5.521461009979248,0 +work_veryhigh_FPSEMPN,-6,-6,-1.309333324432373,0 +work_veryhigh_HEREMPN,-5.6489927637825792,-5.6489927637825792,-1.4229583740234375,0 +work_veryhigh_MWTEMPN,-6,-6,-1.4024237394332886,0 +work_veryhigh_OTHEMPN,-5.8313629173672439,-5.8313629173672439,-1.9241486787796021,0 +work_veryhigh_RETEMPN,-2.3751556873321533,-2.3751556873321533,-2.3751556873321533,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv index e76a45b3fb..13b40bf6a1 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_school_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-2475.6453326598094,-2344.7409183750583 +0,-2372.124053104777,-2329.0008675965573 diff --git a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv index 6970d3e666..66fdd2bfd0 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_mandatory_tour_scheduling_work_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-9138.134506637969,-8404.2487574734187 +0,-8343.7438769575056,-8319.0773809188413 diff --git a/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv index 3282b45e71..027ad8ce1f 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_non_mandatory_tour_scheduling_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-12772.911183835131,-9506.0541725800285 +0,-9330.0289068958136,-9291.4339963381062 diff --git a/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice.csv b/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice.csv index 5a8bf11db5..95558980d2 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice.csv @@ -1,36 +1,36 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -0,0,0,0,0,0,0 -1,1,1,1,1,1,1 -coef_eatout_dist_0_2,-0.76797916075917105,-0.56089999999999995,0,-25,25,-0.76797916075917105 -coef_eatout_dist_2_5,-0.22644636259204301,-0.31919999999999998,0,-25,25,-0.22644636259204301 -coef_eatout_dist_5_plus,-0.18814992028058361,-0.12379999999999999,0,-25,25,-0.18814992028058361 -coef_escort_dist_0_2,0.22669529391335932,-0.14990000000000001,0,-25,25,0.22669529391335932 -coef_escort_dist_2_5,-0.81349761658617425,-0.86709999999999998,0,-25,25,-0.81349761658617425 -coef_escort_dist_5_plus,-0.25505635884043121,-0.2137,0,-25,25,-0.25505635884043121 -coef_mode_logsum,0.66293335178956303,0.67549999999999999,0,-25,25,0.66293335178956303 -coef_othdiscr_dist_0_2,-0.20320115406190975,-0.16769999999999999,0,-25,25,-0.20320115406190975 -coef_othdiscr_dist_2_5,-0.55094376688046098,-0.4955,0,-25,25,-0.55094376688046098 -coef_othdiscr_dist_5_plus,-0.00014599013956733095,-0.1193,0,-25,25,-0.00014599013956733095 -coef_othmaint_dist_2_5,-0.57993229022084924,-0.60550000000000004,0,-25,25,-0.57993229022084924 -coef_othmaint_dist_5_plus,-0.22966135112433173,-0.10929999999999999,0,-25,25,-0.22966135112433173 -coef_shopping_dist_2_5,-0.62979386608506294,-0.5655,0,-25,25,-0.62979386608506294 -coef_shopping_dist_5_plus,-0.13065191570180024,-0.1832,0,-25,25,-0.13065191570180024 -coef_social_dist_2_5,-0.24518547406208724,-0.34849999999999998,0,-25,25,-0.24518547406208724 -coef_social_dist_5_plus,-0.19021662093061398,-0.13059999999999999,0,-25,25,-0.19021662093061398 -eatout_HEREMPN,-0.94688018419944264,-1.3547956940605197,0,-6,6,-0.94688018419944264 -eatout_RETEMPN,-0.29840603581475661,-0.29840603581475661,0,-0.29840603581475661,-0.29840603581475661,-0.29840603581475661 -escort_AGE0519,-1.1713218472625264,-0.7657178733947807,0,-6,6,-1.1713218472625264 -escort_HEREMPN,-2.4501503469929982,-1.9379419794061366,0,-6,6,-2.4501503469929982 -escort_HSENROLL,-3.3045642735486878,-1.7957674906255938,0,-6,6,-3.3045642735486878 -escort_RETEMPN,-1.4916548767777169,-1.4916548767777169,0,-1.4916548767777169,-1.4916548767777169,-1.4916548767777169 -othdiscr_HEREMPN,-1.5849006741800804,-1.3019532126861397,0,-6,6,-1.5849006741800804 -othdiscr_HSENROLL,-2.4667805407825685,-2.322787800311565,0,-6,6,-2.4667805407825685 -othdiscr_OTHEMPN,-2.3703426620470296,-1.8018098050815563,0,-6,6,-2.3703426620470296 -othdiscr_RETEMPN,-1.2385802852918961,-1.5511690043101247,0,-6,6,-1.2385802852918961 -othdiscr_TOTHH,-1.3783261914707137,-1.3783261914707137,0,-1.3783261914707137,-1.3783261914707137,-1.3783261914707137 -othmaint_HEREMPN,-0.66895307929197778,-0.65778003672265395,0,-6,6,-0.66895307929197778 -othmaint_RETEMPN,-0.72981116493153675,-0.72981116493153675,0,-0.72981116493153675,-0.72981116493153675,-0.72981116493153675 -shopping_RETEMPN,0,0,0,0,0,0 -social_HEREMPN,-0.3028654470570214,-0.73814454649068106,0,-6,6,-0.3028654470570214 -social_RETEMPN,-0.65008769109949827,-0.65008769109949827,0,-0.65008769109949827,-0.65008769109949827,-0.65008769109949827 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +0,0,0,0,0 +1,1,1,1,0 +coef_eatout_dist_0_2,-0.27121792391961891,-0.27121792391961891,-0.56089997291564941,0 +coef_eatout_dist_2_5,0.10608796923916697,0.10608796923916697,-0.31920000910758972,0 +coef_eatout_dist_5_plus,-0.030055040831786589,-0.030055040831786589,-0.12380000203847885,0 +coef_escort_dist_0_2,0.36566723960275005,0.36566723960275005,-0.14990000426769257,0 +coef_escort_dist_2_5,0.028378662901016108,0.028378662901016108,-0.86710000038146973,0 +coef_escort_dist_5_plus,-0.11708701992134836,-0.11708701992134836,-0.21369999647140503,0 +coef_mode_logsum,0.59887430720061385,0.59887430720061385,0.67549997568130493,0 +coef_othdiscr_dist_0_2,-0.034497995271121927,-0.034497995271121927,-0.16769999265670776,0 +coef_othdiscr_dist_2_5,-0.050531906736139574,-0.050531906736139574,-0.49549999833106995,0 +coef_othdiscr_dist_5_plus,0.10722115306679328,0.10722115306679328,-0.1193000003695488,0 +coef_othmaint_dist_2_5,0.021990226822960424,0.021990226822960424,-0.6054999828338623,0 +coef_othmaint_dist_5_plus,-0.069040145806240003,-0.069040145806240003,-0.10930000245571136,0 +coef_shopping_dist_2_5,-0.088053277034711469,-0.088053277034711469,-0.56550002098083496,0 +coef_shopping_dist_5_plus,0.053178032652148891,0.053178032652148891,-0.18320000171661377,0 +coef_social_dist_2_5,0.13875731050537282,0.13875731050537282,-0.34850001335144043,0 +coef_social_dist_5_plus,-0.093851542275907265,-0.093851542275907265,-0.13060000538825989,0 +eatout_HEREMPN,-2.6485746777114287,-2.6485746777114287,-1.3547956943511963,0 +eatout_RETEMPN,-0.29840603470802307,-0.29840603470802307,-0.29840603470802307,0 +escort_AGE0519,-1.5700822201838556,-1.5700822201838556,-0.76571786403656006,0 +escort_HEREMPN,-3.381566795567077,-3.381566795567077,-1.9379420280456543,0 +escort_HSENROLL,-5.5115296809721706,-5.5115296809721706,-1.7957675457000732,0 +escort_RETEMPN,-1.4916548728942871,-1.4916548728942871,-1.4916548728942871,0 +othdiscr_HEREMPN,-4.3767539149782362,-4.3767539149782362,-1.3019531965255737,0 +othdiscr_HSENROLL,-4.0323913073099753,-4.0323913073099753,-2.3227877616882324,0 +othdiscr_OTHEMPN,-2.6010090126387664,-2.6010090126387664,-1.8018097877502441,0 +othdiscr_RETEMPN,-1.8907979924961573,-1.8907979924961573,-1.5511690378189087,0 +othdiscr_TOTHH,-1.3783261775970459,-1.3783261775970459,-1.3783261775970459,0 +othmaint_HEREMPN,-3.5067524801333883,-3.5067524801333883,-0.65778005123138428,0 +othmaint_RETEMPN,-0.72981119155883789,-0.72981119155883789,-0.72981119155883789,0 +shopping_RETEMPN,0,0,0,0 +social_HEREMPN,-3.1930226314841379,-3.1930226314841379,-0.73814451694488525,0 +social_RETEMPN,-0.65008771419525146,-0.65008771419525146,-0.65008771419525146,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice_loglike.csv index 3d4d12de95..60449226e2 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_nonmand_and_joint_tour_dest_choice_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-13284.574594749327,-13268.709190928732 +0,-8815.2207980563562,-8000.0090578510162 diff --git a/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq.csv b/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq.csv index c69badeb95..a5798dcdaa 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq.csv @@ -1,426 +1,426 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_0_auto_household_and_escorting_tour,-2,-2,0,-2,-2,-2 -coef_1_escort_tour_constant,0.3190372575850004,0.0298,0,,,0.3190372575850004 -coef_1_plus_eating_out_tours_constant,-1.0128556061868639,0.0097000000000000003,0,,,-1.0128556061868639 -coef_1_plus_maintenance_tours_constant,-2.8426429307763517,0.1202,0,,,-2.8426429307763517 -coef_1_plus_other_discretionary_tours_constant,10.543978753178383,0.74119999999999997,0,,,10.543978753178383 -coef_1_plus_shopping_tours_constant,7.1839137908242963,0.47739999999999999,0,,,7.1839137908242963 -coef_1_plus_visting_tours_constant,-0.3208195851541456,0.052200000000000003,0,,,-0.3208195851541456 -coef_2_plus_escort_tours_constant,0.60671030252342817,0.74019999999999997,0,,,0.60671030252342817 -coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.92647632492670284,-0.27460000000000001,0,,,-0.92647632492670284 -coef_at_home_pre_school_kid_and_discretionary_tour,-0.65566150836617298,-0.4703,0,,,-0.65566150836617298 -coef_at_home_pre_school_kid_and_escorting_tour,-0.79299240501544976,-1.5674999999999999,0,,,-0.79299240501544976 -coef_auto_access_to_retail_and_discretionary,-1.0508623054596398,0.084400000000000003,0,,,-1.0508623054596398 -coef_auto_access_to_retail_and_maintenance,0.27881164131795588,0.074899999999999994,0,,,0.27881164131795588 -coef_auto_access_to_retail_and_shopping,-0.6308268427373509,0.1067,0,,,-0.6308268427373509 -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.16837878665395392,0.13039999999999999,0,,,0.16837878665395392 -coef_female_and_escorting_tour,0.60135724689113756,0.18240000000000001,0,,,0.60135724689113756 -coef_female_and_tour_frequency_is_1,-0.31583892945263869,-0.076600000000000001,0,,,-0.31583892945263869 -coef_female_and_tour_frequency_is_2,-0.5828100966419405,-0.1062,0,,,-0.5828100966419405 -coef_female_and_tour_frequency_is_5,0.022734817568325703,-0.32740000000000002,0,,,0.022734817568325703 -coef_high_income_group_and_discretionary_tour,-0.060443076774255075,0.25650000000000001,0,,,-0.060443076774255075 -coef_high_income_group_and_eating_out_tour,0.72620905564966565,0.55810000000000004,0,,,0.72620905564966565 -coef_high_income_group_and_tour_frequency_is_1,1.1372979170796778,0.51890000000000003,0,,,1.1372979170796778 -coef_high_income_group_and_tour_frequency_is_2,2.2159303224114666,1.1335999999999999,0,,,2.2159303224114666 -coef_high_income_group_and_tour_frequency_is_5_plus,0.10501223044514314,1.3898999999999999,0,,,0.10501223044514314 -coef_high_income_group_and_visiting_tour,-0.86265476671748431,-0.24229999999999999,0,,,-0.86265476671748431 -coef_logged_maximum_residual_window_tour_frequency_is_1,1.3354533697510027,1.2562,0,,,1.3354533697510027 -coef_logged_maximum_residual_window_tour_frequency_is_2,1.4260046446625716,1.2867999999999999,0,,,1.4260046446625716 -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,0.64024624278385667,1.3993,0,,,0.64024624278385667 -coef_mediumhigh_income_group_and_tour_frequency_is_1,1.0937153306192198,0.49809999999999999,0,,,1.0937153306192198 -coef_mediumhigh_income_group_and_tour_frequency_is_2,1.8148765360700809,0.83450000000000002,0,,,1.8148765360700809 -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0390643994599666,1.0213000000000001,0,,,1.0390643994599666 -coef_number_of_joint_eating_out_tours,-9.9809215555613431,-0.58660000000000001,0,,,-9.9809215555613431 -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.4334081077188456,-0.88870000000000005,0,,,-1.4334081077188456 -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-1.6366589797047477,-2.3342999999999998,0,,,-1.6366589797047477 -coef_presence_of_driving_school_kid_and_discretionary_tour,0.38403906373496743,-0.39629999999999999,0,,,0.38403906373496743 -coef_presence_of_driving_school_kid_and_escorting_tour,-0.21108592167092743,0.36009999999999998,0,,,-0.21108592167092743 -coef_presence_of_full_time_worker_and_discretionary_tour,-0.78342819804391672,-0.66700000000000004,0,,,-0.78342819804391672 -coef_presence_of_full_time_worker_and_eating_out_tour,-0.74762089644693475,-0.35709999999999997,0,,,-0.74762089644693475 -coef_presence_of_full_time_worker_and_maintenance_tour,-0.34233564267666977,-0.16850000000000001,0,,,-0.34233564267666977 -coef_presence_of_full_time_worker_and_shopping_tour,-0.63058521173008519,-0.30590000000000001,0,,,-0.63058521173008519 -coef_presence_of_non_worker_and_discretionary_tour,-1.2669707808842081,-0.42809999999999998,0,,,-1.2669707808842081 -coef_presence_of_non_worker_and_eating_out_tour,-0.44503023956321403,-0.2014,0,,,-0.44503023956321403 -coef_presence_of_non_worker_and_escorting_tour,-0.85427331034949039,-0.48149999999999998,0,,,-0.85427331034949039 -coef_presence_of_non_worker_and_maintenance_tour,-0.20935037072535292,-0.32369999999999999,0,,,-0.20935037072535292 -coef_presence_of_non_worker_and_shopping_tour,-0.84441949424610041,-0.41599999999999998,0,,,-0.84441949424610041 -coef_presence_of_part_time_worker_and_discretionary_tour,-0.041239276355498081,-0.2102,0,,,-0.041239276355498081 -coef_presence_of_part_time_worker_and_maintenance_tour,-0.18336221092296712,-0.15840000000000001,0,,,-0.18336221092296712 -coef_presence_of_part_time_worker_and_shopping_tour,-0.14852190396109033,-0.15409999999999999,0,,,-0.14852190396109033 -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,-0.12585844615812486,-0.39589999999999997,0,,,-0.12585844615812486 -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.4021102250286928,1.3974,0,,,1.4021102250286928 -coef_presence_of_pre_school_kid_and_discretionary_tour,-0.017076036823995389,-0.5081,0,,,-0.017076036823995389 -coef_presence_of_pre_school_kid_and_eating_out_tour,-0.84547304246409216,-0.42249999999999999,0,,,-0.84547304246409216 -coef_presence_of_pre_school_kid_and_escorting_tour,0.74822720714906121,0.68420000000000003,0,,,0.74822720714906121 -coef_presence_of_pre_school_kid_and_shopping_tour,-0.006143364129761947,-0.20799999999999999,0,,,-0.006143364129761947 -coef_presence_of_retiree_and_discretionary_tour,-0.5968298706214118,-0.91039999999999999,0,,,-0.5968298706214118 -coef_presence_of_retiree_and_eating_out_tour,-1.2874101904899586,-0.57079999999999997,0,,,-1.2874101904899586 -coef_presence_of_retiree_and_escorting_tour,-12.931599172963331,-0.80800000000000005,0,,,-12.931599172963331 -coef_presence_of_university_student_and_discretionary_tour,-0.40727682870124049,-0.85509999999999997,0,,,-0.40727682870124049 -coef_total_number_of_tours_is_1,-7.5238510045347198,-7.3571999999999997,0,,,-7.5238510045347198 -coef_total_number_of_tours_is_2,-10.052745008623061,-10.647,0,,,-10.052745008623061 -coef_total_number_of_tours_is_3,-11.089117141589441,-13.500500000000001,0,,,-11.089117141589441 -coef_total_number_of_tours_is_4,-12.79747158559379,-16.3965,0,,,-12.79747158559379 -coef_total_number_of_tours_is_5,-21.780097785311945,-19.6843,0,,,-21.780097785311945 -coef_total_number_of_tours_is_6_plus,-999,-999,0,-999,-999,-999 -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.07445921318512129,0.022599999999999999,0,,,0.07445921318512129 -coef_urban_and_discretionary_tour,0,0,0,0,0,0 -coef_urban_and_escorting_tour,-0.40934213736815739,-0.43159999999999998,0,,,-0.40934213736815739 -coef_walk_access_to_retail_and_discretionary,0.16052040097513201,0.0567,0,,,0.16052040097513201 -coef_walk_access_to_retail_and_eating_out,0.21128946397868445,0.14499999999999999,0,,,0.21128946397868445 -coef_walk_access_to_retail_and_escorting,-0.10528530451785624,0.045100000000000001,0,,,-0.10528530451785624 -coef_walk_access_to_retail_and_shopping,0.030181871306816859,0.033000000000000002,0,,,0.030181871306816859 -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.22749157163698558,-0.34860000000000002,0,,,-0.22749157163698558 -coef_0_auto_household_and_escorting_tour,-2,-2,0,-2,-2,-2 -coef_1_escort_tour_constant,0.77792661515451589,0.5272,0,,,0.77792661515451589 -coef_1_plus_eating_out_tours_constant,1.6650752768925599,0.69140000000000001,0,,,1.6650752768925599 -coef_1_plus_maintenance_tours_constant,1.2147335336677649,0.55330000000000001,0,,,1.2147335336677649 -coef_1_plus_other_discretionary_tours_constant,1.7838713459166913,0.79890000000000005,0,,,1.7838713459166913 -coef_1_plus_shopping_tours_constant,1.5216128214649225,0.75690000000000002,0,,,1.5216128214649225 -coef_1_plus_visting_tours_constant,1.0454819231998753,0.14050000000000001,0,,,1.0454819231998753 -coef_2_plus_escort_tours_constant,1.8826614596684659,1.5987,0,,,1.8826614596684659 -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0.049231503501832231,-0.54979999999999996,0,,,0.049231503501832231 -coef_female_and_discretionary_tour,0.4392516750827225,0.30719999999999997,0,,,0.4392516750827225 -coef_female_and_shopping_tour,0.75313279306505232,0.45240000000000002,0,,,0.75313279306505232 -coef_high_income_group_and_discretionary_tour,0.23650597334594267,0.29599999999999999,0,,,0.23650597334594267 -coef_high_income_group_and_maintenance_tour,1.3851001428210838,0.67630000000000001,0,,,1.3851001428210838 -coef_high_income_group_and_shopping_tour,0.8194174808417779,0.70660000000000001,0,,,0.8194174808417779 -coef_high_income_group_and_tour_frequency_is_1,1.1457147075553702,0.86819999999999997,0,,,1.1457147075553702 -coef_high_income_group_and_tour_frequency_is_2,1.3475187007328813,1.5362,0,,,1.3475187007328813 -coef_high_income_group_and_tour_frequency_is_5_plus,1.4989029758459638,1.9331,0,,,1.4989029758459638 -coef_high_income_group_and_visiting_tour,-0.51731565603377461,-0.68679999999999997,0,,,-0.51731565603377461 -coef_logged_maximum_residual_window_tour_frequency_is_1,0.7685190519346432,1.5748,0,,,0.7685190519346432 -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.2439948002596291,2.0026000000000002,0,,,1.2439948002596291 -coef_mediumhigh_income_group_and_shopping_tour,0.6050472895465131,0.44209999999999999,0,,,0.6050472895465131 -coef_mediumlow_income_group_and_tour_frequency_is_1,0.78437721900423418,0.59809999999999997,0,,,0.78437721900423418 -coef_mediumlow_income_group_and_tour_frequency_is_2,0.77646590224476053,0.91779999999999995,0,,,0.77646590224476053 -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0.85089808758167285,1.7539,0,,,0.85089808758167285 -coef_number_of_joint_tours_and_tour_frequency_is_4,-1.3476574161190644,-1.1986000000000001,0,,,-1.3476574161190644 -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,0,-999,-999,-999 -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.98906870125269652,-0.23899999999999999,0,,,-0.98906870125269652 -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-2.8297942396940736,-1.8208,0,,,-2.8297942396940736 -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.7856768576922941,-2.5922999999999998,0,,,-2.7856768576922941 -coef_presence_of_driving_school_kid_and_escorting_tour,1.029446605173121,0.41639999999999999,0,,,1.029446605173121 -coef_presence_of_full_time_worker_and_maintenance_tour,-0.48499903259305888,-0.31309999999999999,0,,,-0.48499903259305888 -coef_presence_of_non_worker_and_discretionary_tour,-0.55680343236172403,-1.0370999999999999,0,,,-0.55680343236172403 -coef_presence_of_non_worker_and_eating_out_tour,-0.23160785989506891,-0.65449999999999997,0,,,-0.23160785989506891 -coef_presence_of_non_worker_and_escorting_tour,-0.61711333679139824,-0.52629999999999999,0,,,-0.61711333679139824 -coef_presence_of_part_time_worker_and_maintenance_tour,-1.0349086974724879,-0.56210000000000004,0,,,-1.0349086974724879 -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.8088903614971785,1.5794999999999999,0,,,1.8088903614971785 -coef_presence_of_pre_school_kid_and_escorting_tour,0.29170166111444701,0.54139999999999999,0,,,0.29170166111444701 -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0.3086114999500732,-0.15590000000000001,0,,,0.3086114999500732 -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.53068484571189922,-0.56810000000000005,0,,,-0.53068484571189922 -coef_presence_of_retiree_and_eating_out_tour,0.10920851120872498,-1.389,0,,,0.10920851120872498 -coef_presence_of_retiree_and_escorting_tour,-0.46115035352397654,-0.75160000000000005,0,,,-0.46115035352397654 -coef_presence_of_university_student_and_eating_out_tour,-1.7593233495192167,-1.4318,0,,,-1.7593233495192167 -coef_total_number_of_tours_is_1,-6.8126152336072634,-7.6391,0,,,-6.8126152336072634 -coef_total_number_of_tours_is_2,-9.848059513629396,-10.4557,0,,,-9.848059513629396 -coef_total_number_of_tours_is_3,-12.828277375142353,-14.0176,0,,,-12.828277375142353 -coef_total_number_of_tours_is_4,-17.097027294518412,-16.971699999999998,0,,,-17.097027294518412 -coef_urban_and_discretionary_tour,0,0,0,0,0,0 -coef_urban_and_escorting_tour,0.42574953449141995,-0.39290000000000003,0,,,0.42574953449141995 -coef_walk_access_to_retail_and_tour_frequency_is_1,0.080139740576734939,0.089899999999999994,0,,,0.080139740576734939 -coef_walk_access_to_retail_and_tour_frequency_is_2,0.10381957230648611,0.1447,0,,,0.10381957230648611 -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.050631059321334597,0.34789999999999999,0,,,0.050631059321334597 -coef_0_auto_household_and_escorting_tour,-2,-2,0,-2,-2,-2 -coef_1_escort_tour_constant,1.8515063856421023,1.7028000000000001,0,,,1.8515063856421023 -coef_1_plus_eating_out_tours_constant,-13.970900895148123,2.0722999999999998,0,,,-13.970900895148123 -coef_1_plus_maintenance_tours_constant,-0.33803118769084733,0.33479999999999999,0,,,-0.33803118769084733 -coef_1_plus_other_discretionary_tours_constant,-1.4739923019690557,1.3389,0,,,-1.4739923019690557 -coef_1_plus_shopping_tours_constant,2.0188727174813774,1.8403,0,,,2.0188727174813774 -coef_1_plus_visting_tours_constant,1.1323634825734481,1.2172000000000001,0,,,1.1323634825734481 -coef_2_plus_escort_tours_constant,2.7388802993288452,2.8378999999999999,0,,,2.7388802993288452 -coef_auto_access_to_retail_and_discretionary,0.6683734729533154,0.094,0,,,0.6683734729533154 -coef_auto_access_to_retail_and_eating_out,3.3138758891504487,0.1018,0,,,3.3138758891504487 -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-1.1118144810513726,-0.58099999999999996,0,,,-1.1118144810513726 -coef_female_and_discretionary_tour,-0.25441035312551963,-0.3266,0,,,-0.25441035312551963 -coef_female_and_eatingout_tour,-0.67597119919881721,-0.65680000000000005,0,,,-0.67597119919881721 -coef_female_and_tour_frequency_is_1,-0.50912757178913237,0.097299999999999998,0,,,-0.50912757178913237 -coef_female_and_tour_frequency_is_2,0.67536539039172305,0.2361,0,,,0.67536539039172305 -coef_female_and_tour_frequency_is_5,1.992028104740627,1.9001999999999999,0,,,1.992028104740627 -coef_high_income_group_and_eating_out_tour,-1.7911774479045783,-0.72070000000000001,0,,,-1.7911774479045783 -coef_high_income_group_and_shopping_tour,0.0036422160874866197,0.56930000000000003,0,,,0.0036422160874866197 -coef_high_income_group_and_tour_frequency_is_1,1.4404498701731279,0.39860000000000001,0,,,1.4404498701731279 -coef_high_income_group_and_tour_frequency_is_2,0.83745190593386265,0.80089999999999995,0,,,0.83745190593386265 -coef_high_income_group_and_tour_frequency_is_5_plus,-16.251792370439588,0.82540000000000002,0,,,-16.251792370439588 -coef_high_income_group_and_visiting_tour,-0.82404884406611401,-0.36940000000000001,0,,,-0.82404884406611401 -coef_logged_maximum_residual_window_tour_frequency_is_0,0.2804768002125545,1.1858,0,,,0.2804768002125545 -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,2.3895231997864537,1.4842,0,,,2.3895231997864537 -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.50210130383758622,0.1109,0,,,0.50210130383758622 -coef_mediumhigh_income_group_and_tour_frequency_is_2,2.1358136364876388,0.39140000000000003,0,,,2.1358136364876388 -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,3.8895054347112406,0.61370000000000002,0,,,3.8895054347112406 -coef_number_of_joint_discretionary_tours,-12.752183693752794,0.67130000000000001,0,,,-12.752183693752794 -coef_number_of_joint_shopping_tours,-26.373288193463711,-0.71299999999999997,0,,,-26.373288193463711 -coef_number_of_joint_tours_and_tour_frequency_is_2,-13.926155494658921,-0.31530000000000002,0,,,-13.926155494658921 -coef_number_of_joint_tours_and_tour_frequency_is_3,-1.3634750179314692,-0.73509999999999998,0,,,-1.3634750179314692 -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,0,-999,-999,-999 -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.80994304158323804,-0.1852,0,,,-0.80994304158323804 -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.9296858293686057,-0.87529999999999997,0,,,-1.9296858293686057 -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-3.1165358610084146,-1.6157999999999999,0,,,-3.1165358610084146 -coef_presence_of_full_time_worker_and_discretionary_tour,-1.0912632357636489,-0.48330000000000001,0,,,-1.0912632357636489 -coef_presence_of_full_time_worker_and_eating_out_tour,0.77587232422920438,-0.52510000000000001,0,,,0.77587232422920438 -coef_presence_of_full_time_worker_and_shopping_tour,-0.47914248744882598,-0.77280000000000004,0,,,-0.47914248744882598 -coef_presence_of_non_worker_and_discretionary_tour,-0.59191766592824591,0.97809999999999997,0,,,-0.59191766592824591 -coef_presence_of_non_worker_and_tour_frequency_is_1,0.20739098927232799,-0.85060000000000002,0,,,0.20739098927232799 -coef_presence_of_non_worker_and_tour_frequency_is_5,-0.10809095623412202,-1.1803999999999999,0,,,-0.10809095623412202 -coef_presence_of_part_time_worker_and_eating_out_tour,-3.2046073684551888,-1.9795,0,,,-3.2046073684551888 -coef_presence_of_part_time_worker_and_escorting_tour,-2.2247073118309659,-1.8212999999999999,0,,,-2.2247073118309659 -coef_presence_of_part_time_worker_and_shopping_tour,-0.72896673490600039,-0.51990000000000003,0,,,-0.72896673490600039 -coef_presence_of_pre_driving_school_kid_and_escorting_tour,-0.075902664875801412,0.94889999999999997,0,,,-0.075902664875801412 -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0.072697095095962505,0.38629999999999998,0,,,0.072697095095962505 -coef_presence_of_pre_school_kid_and_escorting_tour,1.7499151704017379,2.1465000000000001,0,,,1.7499151704017379 -coef_presence_of_pre_school_kid_and_maintenance_tour,-0.61302038898844102,0.96940000000000004,0,,,-0.61302038898844102 -coef_presence_of_pre_school_kid_and_shopping_tour,0.4218433052411511,1.3134999999999999,0,,,0.4218433052411511 -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-2.0284687546419584,-0.99609999999999999,0,,,-2.0284687546419584 -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.79737893556046791,-1.9096,0,,,-0.79737893556046791 -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.31913117480031128,-2.8469000000000002,0,,,-0.31913117480031128 -coef_presence_of_university_student_and_discretionary_tour,0.090801806893705261,-0.6542,0,,,0.090801806893705261 -coef_presence_of_university_student_and_eating_out_tour,-0.42835318314992721,-0.65290000000000004,0,,,-0.42835318314992721 -coef_total_number_of_tours_is_1,-8.5863403053428833,-6.2138,0,,,-8.5863403053428833 -coef_total_number_of_tours_is_2,-11.31949482279566,-8.9079999999999995,0,,,-11.31949482279566 -coef_total_number_of_tours_is_3,-14.109861113838994,-12.3261,0,,,-14.109861113838994 -coef_total_number_of_tours_is_4,-17.545826976965291,-15.811400000000001,0,,,-17.545826976965291 -coef_transit_access_to_retail_and_discretionary,0,0,0,0,0,0 -coef_transit_access_to_retail_and_maintenance,0.25755159606468364,0.031399999999999997,0,,,0.25755159606468364 -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.20705109302348529,0.066400000000000001,0,,,0.20705109302348529 -coef_urban_and_discretionary_tour,-1.8565923019691735,0.95630000000000004,0,,,-1.8565923019691735 -coef_urban_and_eatingout_tour,-15.36320089514812,0.68000000000000005,0,,,-15.36320089514812 -coef_urban_and_escorting_tour,0.80226698426219045,0.85160000000000002,0,,,0.80226698426219045 -coef_urban_and_maintenance_tour,0.35876881230913166,1.0316000000000001,0,,,0.35876881230913166 -coef_urban_and_shopping_tour,0.71157271748139417,0.53300000000000003,0,,,0.71157271748139417 -coef_urban_and_tour_frequency_is_1,-3.5373403053428834,-1.1648000000000001,0,,,-3.5373403053428834 -coef_urban_and_tour_frequency_is_2,-4.7291948227956535,-2.3176999999999999,0,,,-4.7291948227956535 -coef_urban_and_tour_frequency_is_5_plus,-6.0208880908118116,-2.5026999999999999,0,,,-6.0208880908118116 -coef_walk_access_to_retail_and_shopping,0.10237878294870741,0.097199999999999995,0,,,0.10237878294870741 -coef_0_auto_household_and_escorting_tour,-2,-2,0,-2,-2,-2 -coef_1_escort_tour_constant,-4.6370945761626077,-0.062899999999999998,0,,,-4.6370945761626077 -coef_1_plus_eating_out_tours_constant,-4.7784926750203027,-0.1429,0,,,-4.7784926750203027 -coef_1_plus_maintenance_tours_constant,-9.9288535518266823,-0.065299999999999997,0,,,-9.9288535518266823 -coef_1_plus_other_discretionary_tours_constant,-4.8073687191051899,0.33339999999999997,0,,,-4.8073687191051899 -coef_1_plus_shopping_tours_constant,-4.0132353551593232,0.46829999999999999,0,,,-4.0132353551593232 -coef_1_plus_visting_tours_constant,-4.8733834129994724,-0.12720000000000001,0,,,-4.8733834129994724 -coef_2_plus_escort_tours_constant,-8.967938948525461,0.92730000000000001,0,,,-8.967938948525461 -coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.85247075086999724,-1.1479999999999999,0,,,-0.85247075086999724 -coef_at_home_pre_school_kid_and_eating_out_tour,0.60985731009359834,-0.3926,0,,,0.60985731009359834 -coef_at_home_pre_school_kid_and_escorting_tour,-0.061265250325528015,-0.13730000000000001,0,,,-0.061265250325528015 -coef_auto_access_to_retail_and_maintenance,0.61777145381329102,0.095600000000000004,0,,,0.61777145381329102 -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.33803024620733235,-0.36230000000000001,0,,,-0.33803024620733235 -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-1.4819665998101139,-1.272,0,,,-1.4819665998101139 -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-2.5557207485506246,-1.9307000000000001,0,,,-2.5557207485506246 -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,3.121774566538436,0.77380000000000004,0,,,3.121774566538436 -coef_female_and_maintenance_tour,-0.24639047530045038,-0.24640000000000001,0,,,-0.24639047530045038 -coef_female_and_tour_frequency_is_1,0.74864834160230664,0.39019999999999999,0,,,0.74864834160230664 -coef_female_and_tour_frequency_is_2,0.67696554036131518,0.5323,0,,,0.67696554036131518 -coef_female_and_tour_frequency_is_3,1.281698729380099,0.74519999999999997,0,,,1.281698729380099 -coef_female_and_tour_frequency_is_5,2.3807456280650667,1.1294,0,,,2.3807456280650667 -coef_high_income_group_and_discretionary_tour,0.51835922730368678,0.88460000000000005,0,,,0.51835922730368678 -coef_high_income_group_and_eating_out_tour,1.5486575358906367,0.70860000000000001,0,,,1.5486575358906367 -coef_high_income_group_and_shopping_tour,0.72862383681691834,0.97760000000000002,0,,,0.72862383681691834 -coef_high_income_group_and_tour_frequency_is_2,19.097882832130001,1.0632999999999999,0,,,19.097882832130001 -coef_high_income_group_and_tour_frequency_is_3,19.408558066997916,1.7742,0,,,19.408558066997916 -coef_high_income_group_and_tour_frequency_is_5_plus,19.832444995943295,2.3940999999999999,0,,,19.832444995943295 -coef_high_income_group_and_visiting_tour,-0.54232747274109727,-0.94489999999999996,0,,,-0.54232747274109727 -coef_logged_maximum_residual_window_tour_frequency_is_1,6.5467446485012912,1.7637,0,,,6.5467446485012912 -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,7.3593803409865783,1.7927999999999999,0,,,7.3593803409865783 -coef_mediumhigh_income_group_and_discretionary_tour,0.18835358603238547,0.50090000000000001,0,,,0.18835358603238547 -coef_mediumhigh_income_group_and_eating_out_tour,-0.18198447365326542,0.46310000000000001,0,,,-0.18198447365326542 -coef_mediumhigh_income_group_and_shopping_tour,1.2220802803491335,0.89059999999999995,0,,,1.2220802803491335 -coef_mediumhigh_income_group_and_tour_frequency_is_1,19.971750133805212,0.74260000000000004,0,,,19.971750133805212 -coef_mediumhigh_income_group_and_tour_frequency_is_2,19.468344647994446,0.85460000000000003,0,,,19.468344647994446 -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,19.731739849414112,1.0791999999999999,0,,,19.731739849414112 -coef_mediumhigh_income_group_and_visiting_tour,0.027868052585523006,-0.26700000000000002,0,,,0.027868052585523006 -coef_mediumlow_income_group_and_discretionary_tour,-0.31620776069124212,0.17069999999999999,0,,,-0.31620776069124212 -coef_mediumlow_income_group_and_eating_out_tour,0.64577775646714697,0.27660000000000001,0,,,0.64577775646714697 -coef_mediumlow_income_group_and_shopping_tour,0.55581824053699758,0.77339999999999998,0,,,0.55581824053699758 -coef_mediumlow_income_group_and_tour_frequency_is_1,17.934259369140999,0.57089999999999996,0,,,17.934259369140999 -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,18.117542759840582,0.83150000000000002,0,,,18.117542759840582 -coef_number_of_joint_eating_out_tours,-13.182546391318507,-0.77270000000000005,0,,,-13.182546391318507 -coef_number_of_joint_shopping_tours,-16.839997040358107,-0.23910000000000001,0,,,-16.839997040358107 -coef_number_of_joint_tours_and_tour_frequency_is_1,-7.786577241296107,-0.1699,0,,,-7.786577241296107 -coef_number_of_joint_tours_and_tour_frequency_is_2,-9.7470048997804675,-0.42849999999999999,0,,,-9.7470048997804675 -coef_number_of_joint_tours_and_tour_frequency_is_3,-9.2917736869942509,-0.65510000000000002,0,,,-9.2917736869942509 -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-9.5554593661807772,-1.0410999999999999,0,,,-9.5554593661807772 -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.67659999999999998,-0.67659999999999998,0,,,-0.67659999999999998 -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.0518000000000001,-1.0518000000000001,0,,,-1.0518000000000001 -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,-999,0,-999,-999,-999 -coef_presence_of_full_time_worker_and_eating_out_tour,-1.3147958654758525,-0.4667,0,,,-1.3147958654758525 -coef_presence_of_full_time_worker_and_escorting_tour,0.35065195049774833,0.3947,0,,,0.35065195049774833 -coef_presence_of_non_worker_and_eating_out_tour,-0.40882607128740478,-0.49759999999999999,0,,,-0.40882607128740478 -coef_presence_of_non_worker_and_tour_frequency_is_1,1.8072738927257486,-0.37630000000000002,0,,,1.8072738927257486 -coef_presence_of_non_worker_and_tour_frequency_is_2,1.6043590091288431,-0.71899999999999997,0,,,1.6043590091288431 -coef_presence_of_non_worker_and_tour_frequency_is_5,2.2189804494458247,-1.0228999999999999,0,,,2.2189804494458247 -coef_presence_of_part_time_worker_and_discretionary_tour,-0.47082010921072343,-0.35449999999999998,0,,,-0.47082010921072343 -coef_presence_of_part_time_worker_and_escorting_tour,-0.93192205579588727,-0.58609999999999995,0,,,-0.93192205579588727 -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.6418290951424679,1.3773,0,,,1.6418290951424679 -coef_presence_of_pre_school_kid_and_escorting_tour,0.7399830171990065,0.71940000000000004,0,,,0.7399830171990065 -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-3.592889456087303,0.14860000000000001,0,,,-3.592889456087303 -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-3.4708451274125283,0.48399999999999999,0,,,-3.4708451274125283 -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-3.861870451391785,-0.71609999999999996,0,,,-3.861870451391785 -coef_presence_of_retiree_and_eating_out_tour,-1.019099347827936,-0.69110000000000005,0,,,-1.019099347827936 -coef_presence_of_retiree_and_tour_frequency_is_1,2.0287807170341305,-0.46400000000000002,0,,,2.0287807170341305 -coef_presence_of_retiree_and_tour_frequency_is_5,1.9825418671121779,-0.47949999999999998,0,,,1.9825418671121779 -coef_total_number_of_tours_is_1,-23.000695056294269,-8.9791000000000007,0,,,-23.000695056294269 -coef_total_number_of_tours_is_2,-21.149512681682054,-12.024800000000001,0,,,-21.149512681682054 -coef_total_number_of_tours_is_3,-20.410765355763306,-14.851599999999999,0,,,-20.410765355763306 -coef_total_number_of_tours_is_4,-18.774647425097385,-17.703700000000001,0,,,-18.774647425097385 -coef_urban_and_discretionary_tour,0,0,0,0,0,0 -coef_walk_access_to_retail_and_discretionary,0.21406620307694346,0.077200000000000005,0,,,0.21406620307694346 -coef_walk_access_to_retail_and_shopping,0.039842855774011393,0.059799999999999999,0,,,0.039842855774011393 -coef_walk_access_to_retail_and_tour_frequency_is_1,-0.50693837400775899,0.071300000000000002,0,,,-0.50693837400775899 -coef_walk_access_to_retail_and_tour_frequency_is_2,-0.40382295711954785,0.12559999999999999,0,,,-0.40382295711954785 -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,-0.26053331276767533,0.15079999999999999,0,,,-0.26053331276767533 -coef_0_auto_household_and_escorting_tour,-2,-2,0,-2,-2,-2 -coef_1_escort_tour_constant,-2.730596441707033,-0.3992,0,,,-2.730596441707033 -coef_1_plus_eating_out_tours_constant,-2.5833046285296968,0.024500000000000001,0,,,-2.5833046285296968 -coef_1_plus_maintenance_tours_constant,-2.2992187432888329,0.1046,0,,,-2.2992187432888329 -coef_1_plus_other_discretionary_tours_constant,-2.2431962112746411,0.42820000000000003,0,,,-2.2431962112746411 -coef_1_plus_shopping_tours_constant,-1.9166425444609607,0.59470000000000001,0,,,-1.9166425444609607 -coef_1_plus_visting_tours_constant,-2.3440181385910446,0.27889999999999998,0,,,-2.3440181385910446 -coef_2_plus_escort_tours_constant,-4.9359365117262612,0.51749999999999996,0,,,-4.9359365117262612 -coef_car_surplus_vs_workers_and_tour_frequency_is_1,3.0947542803847758,0.79649999999999999,0,,,3.0947542803847758 -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,3.4758535212559294,2.1301999999999999,0,,,3.4758535212559294 -coef_female_and_discretionary_tour,0.54152756838822758,0.49540000000000001,0,,,0.54152756838822758 -coef_female_and_maintenance_tour,0.70832787326060975,0.74239999999999995,0,,,0.70832787326060975 -coef_female_and_shopping_tour,0.60931588761842026,0.96879999999999999,0,,,0.60931588761842026 -coef_female_and_tour_frequency_is_1,-2.5191550523228474,-0.93479999999999996,0,,,-2.5191550523228474 -coef_female_and_tour_frequency_is_2,-2.7271688013300479,-1.3028,0,,,-2.7271688013300479 -coef_female_and_tour_frequency_is_5,-4.1602031889993674,-2.266,0,,,-4.1602031889993674 -coef_high_income_group_and_discretionary_tour,0.92588438224313008,1.0095000000000001,0,,,0.92588438224313008 -coef_high_income_group_and_eating_out_tour,1.6244927835124816,1.4842,0,,,1.6244927835124816 -coef_high_income_group_and_maintenance_tour,1.0061046178584812,1.3794999999999999,0,,,1.0061046178584812 -coef_high_income_group_and_shopping_tour,1.1051711361584451,1.0949,0,,,1.1051711361584451 -coef_high_income_group_and_visiting_tour,-0.65666362660607858,-0.51370000000000005,0,,,-0.65666362660607858 -coef_logged_maximum_residual_window_tour_frequency_is_1,2.9665568629163652,1.8357000000000001,0,,,2.9665568629163652 -coef_logged_maximum_residual_window_tour_frequency_is_2,5.581416234805145,2.2707000000000002,0,,,5.581416234805145 -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,31.909531343908711,4.4023000000000003,0,,,31.909531343908711 -coef_mediumhigh_income_group_and_eating_out_tour,0.96427913024112555,1.181,0,,,0.96427913024112555 -coef_mediumhigh_income_group_and_maintenance_tour,0.46535126543893468,0.76480000000000004,0,,,0.46535126543893468 -coef_mediumhigh_income_group_and_visiting_tour,-0.47816865355794147,-0.43680000000000002,0,,,-0.47816865355794147 -coef_mediumlow_income_group_and_eating_out_tour,0.87748591593357028,0.97689999999999999,0,,,0.87748591593357028 -coef_number_of_joint_shopping_tours,-0.29099820983920754,-0.80720000000000003,0,,,-0.29099820983920754 -coef_number_of_joint_tours_and_tour_frequency_is_2,-6.3271431915064547,-0.94999999999999996,0,,,-6.3271431915064547 -coef_number_of_joint_tours_and_tour_frequency_is_3,-85.627388567464422,-7.1429999999999998,0,,,-85.627388567464422 -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,0,-999,-999,-999 -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.0195999999999996,-5.0195999999999996,0,,,-5.0195999999999996 -coef_presence_of_full_time_worker_and_discretionary_tour,-0.33198445309738206,-0.48349999999999999,0,,,-0.33198445309738206 -coef_presence_of_full_time_worker_and_shopping_tour,-0.20732525834821677,-0.3609,0,,,-0.20732525834821677 -coef_presence_of_non_worker_and_discretionary_tour,-0.084103900991314984,-0.56030000000000002,0,,,-0.084103900991314984 -coef_presence_of_non_worker_and_eating_out_tour,-0.70153618577934562,-0.78800000000000003,0,,,-0.70153618577934562 -coef_presence_of_non_worker_and_tour_frequency_is_1,1.1906806536749803,0.224,0,,,1.1906806536749803 -coef_presence_of_non_worker_and_tour_frequency_is_2,1.3741954582206455,0.24360000000000001,0,,,1.3741954582206455 -coef_presence_of_non_worker_and_tour_frequency_is_3,2.1568221868185788,0.62,0,,,2.1568221868185788 -coef_presence_of_non_worker_and_tour_frequency_is_5,3.6943030762883984,3.3742000000000001,0,,,3.6943030762883984 -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.6828802924993791,1.4903,0,,,1.6828802924993791 -coef_presence_of_pre_school_kid_and_escorting_tour,0.64181265687243838,0.50270000000000004,0,,,0.64181265687243838 -coef_presence_of_retiree_and_eating_out_tour,-0.9446797599672041,-0.92820000000000003,0,,,-0.9446797599672041 -coef_presence_of_retiree_and_tour_frequency_is_1,0.32142426327796303,-0.44579999999999997,0,,,0.32142426327796303 -coef_presence_of_retiree_and_tour_frequency_is_5,-0.05124161320643273,-0.53149999999999997,0,,,-0.05124161320643273 -coef_total_number_of_tours_is_1,-14.532344602813701,-8.5684000000000005,0,,,-14.532344602813701 -coef_total_number_of_tours_is_2,-15.267479639550283,-12.7416,0,,,-15.267479639550283 -coef_total_number_of_tours_is_3,-15.029318290282511,-15.097799999999999,0,,,-15.029318290282511 -coef_total_number_of_tours_is_4,-14.905139738811323,-19.543900000000001,0,,,-14.905139738811323 -coef_total_number_of_tours_is_5,-27.549766404642561,-20.7897,0,,,-27.549766404642561 -coef_urban_and_discretionary_tour,0,0,0,0,0,0 -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.69108202810785446,0.061600000000000002,0,,,0.69108202810785446 -coef_0_auto_household_and_escorting_tour,-289.81943839376481,-2,0,,,-289.81943839376481 -coef_1_escort_tour_constant,-6434.9613170821285,-0.49340000000000001,0,,,-6434.9613170821285 -coef_1_plus_eating_out_tours_constant,-3885.4119129943556,-0.024199999999999999,0,,,-3885.4119129943556 -coef_1_plus_maintenance_tours_constant,-1195.8070135696348,-0.43440000000000001,0,,,-1195.8070135696348 -coef_1_plus_other_discretionary_tours_constant,-2406.8061209898142,-0.26019999999999999,0,,,-2406.8061209898142 -coef_1_plus_shopping_tours_constant,-2389.30594524151,0.53200000000000003,0,,,-2389.30594524151 -coef_1_plus_visting_tours_constant,-2389.3059957968449,0.23669999999999999,0,,,-2389.3059957968449 -coef_2_plus_escort_tours_constant,-5667.6854005226833,1.4155,0,,,-5667.6854005226833 -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,-44.188372748106481,0.1004,0,,,-44.188372748106481 -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-17232.832070588393,-0.63690000000000002,0,,,-17232.832070588393 -coef_car_surplus_vs_workers_and_tour_frequency_is_1,-4035.619541886927,0.29020000000000001,0,,,-4035.619541886927 -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,15225.992542144893,2.0352000000000001,0,,,15225.992542144893 -coef_high_income_group_and_discretionary_tour,4481.0843932231746,2.327,0,,,4481.0843932231746 -coef_high_income_group_and_eating_out_tour,-3157.2738344019517,0.49159999999999998,0,,,-3157.2738344019517 -coef_high_income_group_and_maintenance_tour,4461.4848620401744,0.3982,0,,,4461.4848620401744 -coef_high_income_group_and_shopping_tour,4461.3908032762156,0.24429999999999999,0,,,4461.3908032762156 -coef_high_income_group_and_visiting_tour,4461.3908519450242,0.2858,0,,,4461.3908519450242 -coef_logged_maximum_residual_window_tour_frequency_is_1,16.594329465647881,1.3298000000000001,0,,,16.594329465647881 -coef_logged_maximum_residual_window_tour_frequency_is_2,-19742.937757107196,1.3758999999999999,0,,,-19742.937757107196 -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,-11491.665678473473,3.2808000000000002,0,,,-11491.665678473473 -coef_mediumhigh_income_group_and_discretionary_tour,572.84750941634763,1.405,0,,,572.84750941634763 -coef_mediumlow_income_group_and_discretionary_tour,-4429.9422108889057,0.91690000000000005,0,,,-4429.9422108889057 -coef_number_of_joint_tours_and_tour_frequency_is_1,-4180.3539679243559,-0.2162,0,,,-4180.3539679243559 -coef_number_of_joint_tours_and_tour_frequency_is_2,-1606.5028797398816,-0.35870000000000002,0,,,-1606.5028797398816 -coef_number_of_joint_tours_and_tour_frequency_is_3,-109.81932871468001,-4.2701000000000002,0,,,-109.81932871468001 -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,0,-999,-999,-999 -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-16877.023969621659,-0.23400000000000001,0,,,-16877.023969621659 -coef_number_of_mandatory_tours_and_tour_frequency_is_2,12620.764595473987,-0.92310000000000003,0,,,12620.764595473987 -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-4861.9979116103832,-6.5834999999999999,0,,,-4861.9979116103832 -coef_presence_of_driving_school_kid_and_discretionary_tour,-578.87207263924711,-0.92020000000000002,0,,,-578.87207263924711 -coef_presence_of_driving_school_kid_and_eating_out_tour,6.3817283359748718,-0.63770000000000004,0,,,6.3817283359748718 -coef_presence_of_non_worker_and_tour_frequency_is_2,878.00882728962233,-0.65710000000000002,0,,,878.00882728962233 -coef_presence_of_non_worker_and_tour_frequency_is_5,-1944.9226435678067,-1.4044000000000001,0,,,-1944.9226435678067 -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,-164.90625497529834,-1.5698000000000001,0,,,-164.90625497529834 -coef_presence_of_pre_school_kid_and_eating_out_tour,313.47630877463621,-0.29870000000000002,0,,,313.47630877463621 -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,4033.5668708392936,-0.32190000000000002,0,,,4033.5668708392936 -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,10149.331976157329,-1.0873999999999999,0,,,10149.331976157329 -coef_presence_of_university_student_and_discretionary_tour,3906.3784034663036,-1.2834000000000001,0,,,3906.3784034663036 -coef_total_number_of_tours_is_1,15200.662336544106,-7.1505999999999998,0,,,15200.662336544106 -coef_total_number_of_tours_is_2,-1638.5561861275439,-11.1214,0,,,-1638.5561861275439 -coef_total_number_of_tours_is_3,-14010.60832366959,-13.175000000000001,0,,,-14010.60832366959 -coef_urban_and_discretionary_tour,0,0,0,0,0,0 -coef_urban_and_maintenance_tour,-1194.333213569586,1.0394000000000001,0,,,-1194.333213569586 -coef_0_auto_household_and_escorting_tour,-2,-2,0,-2,-2,-2 -coef_1_escort_tour_constant,-1.5720814237734582,-0.75509999999999999,0,,,-1.5720814237734582 -coef_1_plus_eating_out_tours_constant,-2.2222102013387453,1.1145,0,,,-2.2222102013387453 -coef_1_plus_maintenance_tours_constant,-1.4971354138568955,-0.50600000000000001,0,,,-1.4971354138568955 -coef_1_plus_other_discretionary_tours_constant,0.090817823541839565,0.46339999999999998,0,,,0.090817823541839565 -coef_1_plus_shopping_tours_constant,1.4746879181605808,0.4783,0,,,1.4746879181605808 -coef_1_plus_visting_tours_constant,-1.7926081421309932,-0.40060000000000001,0,,,-1.7926081421309932 -coef_2_plus_escort_tours_constant,-2.0596227390284212,-0.0086,0,,,-2.0596227390284212 -coef_auto_access_to_retail_and_escorting,0.55510802136078319,0.062899999999999998,0,,,0.55510802136078319 -coef_high_income_group_and_eating_out_tour,-1.3619797807034295,-0.70099999999999996,0,,,-1.3619797807034295 -coef_high_income_group_and_shopping_tour,-3.7366703425339884,-0.65059999999999996,0,,,-3.7366703425339884 -coef_high_income_group_and_tour_frequency_is_5_plus,16.017262145435474,2.0175000000000001,0,,,16.017262145435474 -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.5260250309714616,1.5603,0,,,1.5260250309714616 -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,15.096813647053857,1.5197000000000001,0,,,15.096813647053857 -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,14.227758877255997,1.0872999999999999,0,,,14.227758877255997 -coef_number_of_joint_maintenance_tours,-1.3475999999999999,-1.3475999999999999,0,,,-1.3475999999999999 -coef_number_of_joint_tours_and_tour_frequency_is_2,-1.1971153311644798,-0.6149,0,,,-1.1971153311644798 -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,0,-999,-999,-999 -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-9.8896793584290901,-1.0330999999999999,0,,,-9.8896793584290901 -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-10.675107092433787,-2.7444999999999999,0,,,-10.675107092433787 -coef_presence_of_full_time_worker_and_discretionary_tour,0.16721747201730811,0.75260000000000005,0,,,0.16721747201730811 -coef_presence_of_non_worker_and_eating_out_tour,-1.2956623157773164,-1.3073999999999999,0,,,-1.2956623157773164 -coef_presence_of_non_worker_and_shopping_tour,-1.353669617377284,-0.64500000000000002,0,,,-1.353669617377284 -coef_presence_of_non_worker_and_tour_frequency_is_5,-0.43468555779873808,0.2177,0,,,-0.43468555779873808 -coef_presence_of_part_time_worker_and_discretionary_tour,-0.34215511287271527,0.37209999999999999,0,,,-0.34215511287271527 -coef_presence_of_pre_driving_school_kid_and_shopping_tour,1.4153790711463878,0.9365,0,,,1.4153790711463878 -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0.29018085236023033,-0.22639999999999999,0,,,0.29018085236023033 -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0.32894398996272695,-0.44390000000000002,0,,,0.32894398996272695 -coef_total_number_of_tours_is_1,-11.291182385016718,-7.4863,0,,,-11.291182385016718 -coef_total_number_of_tours_is_2,-13.228339349370408,-10.718,0,,,-13.228339349370408 -coef_total_number_of_tours_is_3,-14.184904611232321,-13.788399999999999,0,,,-14.184904611232321 -coef_urban_and_discretionary_tour,0,0,0,0,0,0 -coef_urban_and_escorting_tour,-4.4838269018301071,0.43519999999999998,0,,,-4.4838269018301071 -coef_walk_access_to_retail_and_eating_out,0.40010315143817771,0.073800000000000004,0,,,0.40010315143817771 -coef_0_auto_household_and_escorting_tour,-2,-2,0,-2,-2,-2 -coef_1_escort_tour_constant,0.17965076785235903,0.36220000000000002,0,,,0.17965076785235903 -coef_1_plus_eating_out_tours_constant,0.20482664652109922,0.96120000000000005,0,,,0.20482664652109922 -coef_1_plus_maintenance_tours_constant,0.073169219104483346,0.67879999999999996,0,,,0.073169219104483346 -coef_1_plus_other_discretionary_tours_constant,1.1336617402336022,1.4935,0,,,1.1336617402336022 -coef_1_plus_shopping_tours_constant,0.78536748101077303,1.6919,0,,,0.78536748101077303 -coef_1_plus_visting_tours_constant,-0.01128610640731513,0.44240000000000002,0,,,-0.01128610640731513 -coef_2_plus_escort_tours_constant,1.7444288874445422,2.2219000000000002,0,,,1.7444288874445422 -coef_discretionary_tour,0.54316174023360153,0.90300000000000002,0,,,0.54316174023360153 -coef_escorting_tour,1.3535085427415612,2.4910000000000001,0,,,1.3535085427415612 -coef_maintenance_tour,0.41636921910448493,1.022,0,,,0.41636921910448493 -coef_presence_of_full_time_worker_and_escorting_tour,-0.61349175561144009,-0.89300000000000002,0,,,-0.61349175561144009 -coef_presence_of_non_worker_and_discretionary_tour,0.01387890311352221,0.79100000000000004,0,,,0.01387890311352221 -coef_presence_of_non_worker_and_eating_out_tour,1.1150524768977683,1.157,0,,,1.1150524768977683 -coef_presence_of_non_worker_and_escorting_tour,0.89099305834953102,0.89000000000000001,0,,,0.89099305834953102 -coef_presence_of_non_worker_and_shopping_tour,0.23105344171814296,0.80800000000000005,0,,,0.23105344171814296 -coef_presence_of_part_time_worker_and_eating_out_tour,1.3926752534763249,1.0369999999999999,0,,,1.3926752534763249 -coef_presence_of_part_time_worker_and_shopping_tour,1.0440437570455019,1.155,0,,,1.0440437570455019 -coef_total_number_of_tours_is_1,-4.4367465516985458,-5.7590000000000003,0,,,-4.4367465516985458 -coef_total_number_of_tours_is_2,-9.0395579310490177,-11.516999999999999,0,,,-9.0395579310490177 -coef_total_number_of_tours_is_3,-12.714428561518217,-17.276,0,,,-12.714428561518217 -coef_total_number_of_tours_is_4,-29.080351094611324,-23.035,0,,,-29.080351094611324 -coef_total_number_of_tours_is_6_plus,-999,-999,0,-999,-999,-999 -coef_urban_and_discretionary_tour,0,0,0,0,0,0 -coef_visiting_or_social_tour,0.31531389359268519,0.76900000000000002,0,,,0.31531389359268519 +param_name,value,best,initvalue,nullvalue +coef_0_auto_household_and_escorting_tour,-2,-2,-2,0 +coef_1_escort_tour_constant,0.4427992642013524,0.4427992642013524,0.029799999669194221,0 +coef_1_plus_eating_out_tours_constant,-0.55916615444423889,-0.55916615444423889,0.0097000002861022949,0 +coef_1_plus_maintenance_tours_constant,-0.048218934526752716,-0.048218934526752716,0.12020000070333481,0 +coef_1_plus_other_discretionary_tours_constant,1.1187985007824965,1.1187985007824965,0.74119997024536133,0 +coef_1_plus_shopping_tours_constant,0.80935601218553088,0.80935601218553088,0.47740000486373901,0 +coef_1_plus_visting_tours_constant,0.32772255127625538,0.32772255127625538,0.052200000733137131,0 +coef_2_plus_escort_tours_constant,0.86516558906506291,0.86516558906506291,0.7401999831199646,0 +coef_at_home_pre_driving_school_kid_and_escorting_tour,-1.1058628439891163,-1.1058628439891163,-0.27459999918937683,0 +coef_at_home_pre_school_kid_and_discretionary_tour,-0.70795142385539511,-0.70795142385539511,-0.47029998898506165,0 +coef_at_home_pre_school_kid_and_escorting_tour,-0.59160407292839623,-0.59160407292839623,-1.5674999952316284,0 +coef_auto_access_to_retail_and_discretionary,0.045130482105384076,0.045130482105384076,0.084399998188018799,0 +coef_auto_access_to_retail_and_maintenance,0.066126086285235641,0.066126086285235641,0.074900001287460327,0 +coef_auto_access_to_retail_and_shopping,0.12409575232027123,0.12409575232027123,0.10670000314712524,0 +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.17885689584708081,0.17885689584708081,0.13040000200271606,0 +coef_female_and_escorting_tour,0.56410426544507486,0.56410426544507486,0.18240000307559967,0 +coef_female_and_tour_frequency_is_1,-0.30708391029855547,-0.30708391029855547,-0.076600000262260437,0 +coef_female_and_tour_frequency_is_2,-0.55559756867326349,-0.55559756867326349,-0.10620000213384628,0 +coef_female_and_tour_frequency_is_5,0.018075964162005607,0.018075964162005607,-0.32739999890327454,0 +coef_high_income_group_and_discretionary_tour,-0.091486067503143295,-0.091486067503143295,0.2565000057220459,0 +coef_high_income_group_and_eating_out_tour,0.72625729628634217,0.72625729628634217,0.55809998512268066,0 +coef_high_income_group_and_tour_frequency_is_1,1.155230706967018,1.155230706967018,0.51889997720718384,0 +coef_high_income_group_and_tour_frequency_is_2,2.3389197436575069,2.3389197436575069,1.1335999965667725,0 +coef_high_income_group_and_tour_frequency_is_5_plus,0.47586062840136895,0.47586062840136895,1.3898999691009521,0 +coef_high_income_group_and_visiting_tour,-0.86542592778132377,-0.86542592778132377,-0.24230000376701355,0 +coef_logged_maximum_residual_window_tour_frequency_is_1,1.3596125256366514,1.3596125256366514,1.2561999559402466,0 +coef_logged_maximum_residual_window_tour_frequency_is_2,1.4369987512243636,1.4369987512243636,1.2868000268936157,0 +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,0.84347379326069005,0.84347379326069005,1.3992999792098999,0 +coef_mediumhigh_income_group_and_tour_frequency_is_1,1.1094796643673868,1.1094796643673868,0.49810001254081726,0 +coef_mediumhigh_income_group_and_tour_frequency_is_2,1.9181926670959439,1.9181926670959439,0.8345000147819519,0 +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.3393581345532311,1.3393581345532311,1.0212999582290649,0 +coef_number_of_joint_eating_out_tours,-1.7726850801344511,-1.7726850801344511,-0.58660000562667847,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.4026438175955793,-1.4026438175955793,-0.88870000839233398,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.025313408839498,-2.025313408839498,-2.3343000411987305,0 +coef_presence_of_driving_school_kid_and_discretionary_tour,0.33629833566221962,0.33629833566221962,-0.39629998803138733,0 +coef_presence_of_driving_school_kid_and_escorting_tour,-0.19071865882881728,-0.19071865882881728,0.36010000109672546,0 +coef_presence_of_full_time_worker_and_discretionary_tour,-0.75882600647076048,-0.75882600647076048,-0.66699999570846558,0 +coef_presence_of_full_time_worker_and_eating_out_tour,-0.73960069695319364,-0.73960069695319364,-0.35710000991821289,0 +coef_presence_of_full_time_worker_and_maintenance_tour,-0.38630750284054705,-0.38630750284054705,-0.16850000619888306,0 +coef_presence_of_full_time_worker_and_shopping_tour,-0.60182073500121425,-0.60182073500121425,-0.30590000748634338,0 +coef_presence_of_non_worker_and_discretionary_tour,-1.2394724190464534,-1.2394724190464534,-0.42809998989105225,0 +coef_presence_of_non_worker_and_eating_out_tour,-0.43749171051759073,-0.43749171051759073,-0.20139999687671661,0 +coef_presence_of_non_worker_and_escorting_tour,-0.92810302506396747,-0.92810302506396747,-0.48149999976158142,0 +coef_presence_of_non_worker_and_maintenance_tour,-0.25135759242376515,-0.25135759242376515,-0.32370001077651978,0 +coef_presence_of_non_worker_and_shopping_tour,-0.81193650011085927,-0.81193650011085927,-0.41600000858306885,0 +coef_presence_of_part_time_worker_and_discretionary_tour,-0.037914012004716779,-0.037914012004716779,-0.2101999968290329,0 +coef_presence_of_part_time_worker_and_maintenance_tour,-0.18845880952367774,-0.18845880952367774,-0.15839999914169312,0 +coef_presence_of_part_time_worker_and_shopping_tour,-0.13360094221187135,-0.13360094221187135,-0.15410000085830688,0 +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,-0.098195561407346782,-0.098195561407346782,-0.39590001106262207,0 +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.4005304562436685,1.4005304562436685,1.3974000215530396,0 +coef_presence_of_pre_school_kid_and_discretionary_tour,0.0018788117570303462,0.0018788117570303462,-0.50809997320175171,0 +coef_presence_of_pre_school_kid_and_eating_out_tour,-0.86481034258984602,-0.86481034258984602,-0.42250001430511475,0 +coef_presence_of_pre_school_kid_and_escorting_tour,0.74141629258937403,0.74141629258937403,0.6841999888420105,0 +coef_presence_of_pre_school_kid_and_shopping_tour,-0.018826868013972368,-0.018826868013972368,-0.20800000429153442,0 +coef_presence_of_retiree_and_discretionary_tour,-0.56414341094868059,-0.56414341094868059,-0.91039997339248657,0 +coef_presence_of_retiree_and_eating_out_tour,-1.2971397912010263,-1.2971397912010263,-0.57080000638961792,0 +coef_presence_of_retiree_and_escorting_tour,-3.9791405866488541,-3.9791405866488541,-0.80800002813339233,0 +coef_presence_of_university_student_and_discretionary_tour,-0.35370106427869641,-0.35370106427869641,-0.85509997606277466,0 +coef_total_number_of_tours_is_1,-8.0929871920905487,-8.0929871920905487,-7.3572001457214355,0 +coef_total_number_of_tours_is_2,-11.373665685061901,-11.373665685061901,-10.647000312805176,0 +coef_total_number_of_tours_is_3,-13.230959643793058,-13.230959643793058,-13.500499725341797,0 +coef_total_number_of_tours_is_4,-15.595277265944466,-15.595277265944466,-16.396499633789062,0 +coef_total_number_of_tours_is_5,-19.867033359049749,-19.867033359049749,-19.684299468994141,0 +coef_total_number_of_tours_is_6_plus,-999,-999,-999,0 +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.050586414602862534,0.050586414602862534,0.022600000724196434,0 +coef_urban_and_discretionary_tour,0,0,0,0 +coef_urban_and_escorting_tour,0.23133047198776119,0.23133047198776119,-0.43160000443458557,0 +coef_walk_access_to_retail_and_discretionary,0.035768845177126592,0.035768845177126592,0.056699998676776886,0 +coef_walk_access_to_retail_and_eating_out,0.23990162152188213,0.23990162152188213,0.14499999582767487,0 +coef_walk_access_to_retail_and_escorting,-0.12003056391798796,-0.12003056391798796,0.045099999755620956,0 +coef_walk_access_to_retail_and_shopping,-0.045608290266312995,-0.045608290266312995,0.032999999821186066,0 +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.2160690015503576,-0.2160690015503576,-0.34860000014305115,0 +coef_0_auto_household_and_escorting_tour,-2,-2,-2,0 +coef_1_escort_tour_constant,0.78022647864874106,0.78022647864874106,0.52719998359680176,0 +coef_1_plus_eating_out_tours_constant,1.6369882471832848,1.6369882471832848,0.69139999151229858,0 +coef_1_plus_maintenance_tours_constant,1.2611597485996247,1.2611597485996247,0.55330002307891846,0 +coef_1_plus_other_discretionary_tours_constant,1.8042028984875396,1.8042028984875396,0.79890000820159912,0 +coef_1_plus_shopping_tours_constant,1.4577278573241144,1.4577278573241144,0.75690001249313354,0 +coef_1_plus_visting_tours_constant,1.0730718283709404,1.0730718283709404,0.14049999415874481,0 +coef_2_plus_escort_tours_constant,1.8729239069881625,1.8729239069881625,1.5987000465393066,0 +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0.047550202162169208,0.047550202162169208,-0.54979997873306274,0 +coef_female_and_discretionary_tour,0.44246508408459873,0.44246508408459873,0.30720001459121704,0 +coef_female_and_shopping_tour,0.72280754095601429,0.72280754095601429,0.45239999890327454,0 +coef_high_income_group_and_discretionary_tour,0.17993929507162121,0.17993929507162121,0.29600000381469727,0 +coef_high_income_group_and_maintenance_tour,1.2972259045977341,1.2972259045977341,0.67629998922348022,0 +coef_high_income_group_and_shopping_tour,0.87248673602279736,0.87248673602279736,0.70660001039505005,0 +coef_high_income_group_and_tour_frequency_is_1,1.2363061506956392,1.2363061506956392,0.86820000410079956,0 +coef_high_income_group_and_tour_frequency_is_2,1.3997702009895265,1.3997702009895265,1.5362000465393066,0 +coef_high_income_group_and_tour_frequency_is_5_plus,1.5247592588483323,1.5247592588483323,1.9330999851226807,0 +coef_high_income_group_and_visiting_tour,-0.58407376090955276,-0.58407376090955276,-0.68680000305175781,0 +coef_logged_maximum_residual_window_tour_frequency_is_1,0.78207461828672042,0.78207461828672042,1.5748000144958496,0 +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.2295616602720314,1.2295616602720314,2.0025999546051025,0 +coef_mediumhigh_income_group_and_shopping_tour,0.64569736253979326,0.64569736253979326,0.44209998846054077,0 +coef_mediumlow_income_group_and_tour_frequency_is_1,0.88613463643879131,0.88613463643879131,0.59810000658035278,0 +coef_mediumlow_income_group_and_tour_frequency_is_2,0.84850399647408248,0.84850399647408248,0.91780000925064087,0 +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0.87831822891977274,0.87831822891977274,1.7539000511169434,0 +coef_number_of_joint_tours_and_tour_frequency_is_4,-1.2633997095537546,-1.2633997095537546,-1.1986000537872314,0 +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,-999,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.94282353171733657,-0.94282353171733657,-0.23899999260902405,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-2.7338328554607796,-2.7338328554607796,-1.8207999467849731,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.6682909569935234,-2.6682909569935234,-2.5922999382019043,0 +coef_presence_of_driving_school_kid_and_escorting_tour,1.0188615044012985,1.0188615044012985,0.41639998555183411,0 +coef_presence_of_full_time_worker_and_maintenance_tour,-0.48961457222493282,-0.48961457222493282,-0.31310001015663147,0 +coef_presence_of_non_worker_and_discretionary_tour,-0.57727792649553333,-0.57727792649553333,-1.0370999574661255,0 +coef_presence_of_non_worker_and_eating_out_tour,-0.21267685745459988,-0.21267685745459988,-0.65450000762939453,0 +coef_presence_of_non_worker_and_escorting_tour,-0.59185572336636527,-0.59185572336636527,-0.52630001306533813,0 +coef_presence_of_part_time_worker_and_maintenance_tour,-1.0186820306196382,-1.0186820306196382,-0.56209999322891235,0 +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.7856557294189983,1.7856557294189983,1.5794999599456787,0 +coef_presence_of_pre_school_kid_and_escorting_tour,0.27408822617776946,0.27408822617776946,0.54140001535415649,0 +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0.30438027742581492,0.30438027742581492,-0.15590000152587891,0 +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.52997357567752201,-0.52997357567752201,-0.5680999755859375,0 +coef_presence_of_retiree_and_eating_out_tour,0.057141478624328376,0.057141478624328376,-1.3890000581741333,0 +coef_presence_of_retiree_and_escorting_tour,-0.44904086319222258,-0.44904086319222258,-0.75160002708435059,0 +coef_presence_of_university_student_and_eating_out_tour,-1.819545601982254,-1.819545601982254,-1.4318000078201294,0 +coef_total_number_of_tours_is_1,-7.0159278521309831,-7.0159278521309831,-7.6391000747680664,0 +coef_total_number_of_tours_is_2,-10.006686059458314,-10.006686059458314,-10.455699920654297,0 +coef_total_number_of_tours_is_3,-12.780090082076166,-12.780090082076166,-14.017600059509277,0 +coef_total_number_of_tours_is_4,-17.006726946049447,-17.006726946049447,-16.971700668334961,0 +coef_urban_and_discretionary_tour,0,0,0,0 +coef_urban_and_escorting_tour,0.40857422586786191,0.40857422586786191,-0.39289999008178711,0 +coef_walk_access_to_retail_and_tour_frequency_is_1,0.091591528383907397,0.091591528383907397,0.089900001883506775,0 +coef_walk_access_to_retail_and_tour_frequency_is_2,0.12013944932753479,0.12013944932753479,0.14470000565052032,0 +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.043892915968164865,0.043892915968164865,0.34790000319480896,0 +coef_0_auto_household_and_escorting_tour,-2,-2,-2,0 +coef_1_escort_tour_constant,1.8320083268912393,1.8320081212504946,1.7028000354766846,0 +coef_1_plus_eating_out_tours_constant,-13.842117153486281,-13.842113426324161,2.0722999572753906,0 +coef_1_plus_maintenance_tours_constant,-0.36965786889297769,-0.36965847869322271,0.33480000495910645,0 +coef_1_plus_other_discretionary_tours_constant,-1.2004258495977875,-1.2004252032682035,1.3388999700546265,0 +coef_1_plus_shopping_tours_constant,1.9630014272782856,1.9630010920712406,1.8402999639511108,0 +coef_1_plus_visting_tours_constant,1.0077266905164448,1.0077264438102758,1.2172000408172607,0 +coef_2_plus_escort_tours_constant,2.7101792728099783,2.7101793241888337,2.837899923324585,0 +coef_auto_access_to_retail_and_discretionary,0.60576917052564894,0.60576901559373153,0.093999996781349182,0 +coef_auto_access_to_retail_and_eating_out,3.2803304602848562,3.2803296298812761,0.10180000215768814,0 +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-1.1286721006590705,-1.1286716773156127,-0.58099997043609619,0 +coef_female_and_discretionary_tour,-0.27866976927760068,-0.27866980957578169,-0.32659998536109924,0 +coef_female_and_eatingout_tour,-0.71346970397905807,-0.71346918819904392,-0.65679997205734253,0 +coef_female_and_tour_frequency_is_1,-0.48821855191385899,-0.48821751083893139,0.097300000488758087,0 +coef_female_and_tour_frequency_is_2,0.70828217386581849,0.70828183909772424,0.23610000312328339,0 +coef_female_and_tour_frequency_is_5,2.0698314021941049,2.0698320315070937,1.9002000093460083,0 +coef_high_income_group_and_eating_out_tour,-1.7871396439814193,-1.7871393359274383,-0.72070002555847168,0 +coef_high_income_group_and_shopping_tour,-0.0097579376258961487,-0.0097578081985406004,0.56929999589920044,0 +coef_high_income_group_and_tour_frequency_is_1,1.4527093121517425,1.4527099923825588,0.3986000120639801,0 +coef_high_income_group_and_tour_frequency_is_2,0.90039297576289479,0.90039284655601348,0.80089998245239258,0 +coef_high_income_group_and_tour_frequency_is_5_plus,-6.947254854872086,-6.9472539654420844,0.82539999485015869,0 +coef_high_income_group_and_visiting_tour,-0.77942127489278434,-0.77942163984758739,-0.36939999461174011,0 +coef_logged_maximum_residual_window_tour_frequency_is_0,0.26793790497643488,0.26793818025889998,1.1857999563217163,0 +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,2.4020620521081604,2.4020617768256951,1.4842000007629395,0 +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.4738896197844194,0.4738898434218578,0.11089999973773956,0 +coef_mediumhigh_income_group_and_tour_frequency_is_2,2.1475070128100544,2.1475072544785649,0.39140000939369202,0 +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,3.9137980844922171,3.9137982636389732,0.61369997262954712,0 +coef_number_of_joint_discretionary_tours,-6.8227040161278429,-6.8227030499165631,0.67129999399185181,0 +coef_number_of_joint_shopping_tours,-4.5845196404815836,-4.5845189614516721,-0.71299999952316284,0 +coef_number_of_joint_tours_and_tour_frequency_is_2,-9.1761287341051698,-9.1761275454648299,-0.31529998779296875,0 +coef_number_of_joint_tours_and_tour_frequency_is_3,-1.7257215207841496,-1.7257219586505792,-0.73509997129440308,0 +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,-999,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.64122576357731442,-0.64122588480735709,-0.18520000576972961,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.7823789287319811,-1.7823796487869576,-0.87529999017715454,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.9941269208431893,-2.9941272442555316,-1.6158000230789185,0 +coef_presence_of_full_time_worker_and_discretionary_tour,-1.1123947161390522,-1.1123952221405766,-0.48330000042915344,0 +coef_presence_of_full_time_worker_and_eating_out_tour,0.7644158078010701,0.76441605865788576,-0.5250999927520752,0 +coef_presence_of_full_time_worker_and_shopping_tour,-0.47330099399200232,-0.4733010233040793,-0.7728000283241272,0 +coef_presence_of_non_worker_and_discretionary_tour,-0.59088716119227658,-0.59088747572415357,0.9781000018119812,0 +coef_presence_of_non_worker_and_tour_frequency_is_1,0.1878408165883074,0.18784146495376636,-0.85060000419616699,0 +coef_presence_of_non_worker_and_tour_frequency_is_5,-0.17945867023496131,-0.17945929629633911,-1.180400013923645,0 +coef_presence_of_part_time_worker_and_eating_out_tour,-3.2345296325509101,-3.234529777482781,-1.9795000553131104,0 +coef_presence_of_part_time_worker_and_escorting_tour,-2.2546395276485063,-2.2546394925137023,-1.8213000297546387,0 +coef_presence_of_part_time_worker_and_shopping_tour,-0.7496461309021506,-0.74964534531823057,-0.51990002393722534,0 +coef_presence_of_pre_driving_school_kid_and_escorting_tour,-0.083989210795650721,-0.083989664675771999,0.94889998435974121,0 +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0.053444488601664397,0.053444497296591398,0.38629999756813049,0 +coef_presence_of_pre_school_kid_and_escorting_tour,1.725644687339964,1.7256441682495762,2.1465001106262207,0 +coef_presence_of_pre_school_kid_and_maintenance_tour,-0.5795847743089354,-0.57958494820750939,0.96939998865127563,0 +coef_presence_of_pre_school_kid_and_shopping_tour,0.45827102667224523,0.45827002311694837,1.3135000467300415,0 +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-2.0326667193288683,-2.0326652307764594,-0.99610000848770142,0 +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.71238012908852999,-0.71237818006976983,-1.9096000194549561,0 +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.20193836611977739,-0.201935994121831,-2.8468999862670898,0 +coef_presence_of_university_student_and_discretionary_tour,0.066507097009832913,0.066507630930055919,-0.65420001745223999,0 +coef_presence_of_university_student_and_eating_out_tour,-0.42018064953666173,-0.42018066679895533,-0.65289998054504395,0 +coef_total_number_of_tours_is_1,-8.6552470544456295,-8.6552472298372294,-6.2137999534606934,0 +coef_total_number_of_tours_is_2,-11.339670857033484,-11.33967023685339,-8.9079999923706055,0 +coef_total_number_of_tours_is_3,-14.129960972164627,-14.129960682683405,-12.32610034942627,0 +coef_total_number_of_tours_is_4,-17.475109164095372,-17.475108877724633,-15.811400413513184,0 +coef_transit_access_to_retail_and_discretionary,0,0,0,0 +coef_transit_access_to_retail_and_maintenance,0.25155766645491617,0.25155779901079267,0.031399998813867569,0 +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.20940431632076822,0.20940454322004187,0.066399998962879181,0 +coef_urban_and_discretionary_tour,-1.5830257994345194,-1.5830251531049353,0.95630002021789551,0 +coef_urban_and_eatingout_tour,-15.234417103609095,-15.234413376446975,0.68000000715255737,0 +coef_urban_and_escorting_tour,0.72536698170688063,0.72536687882384698,0.85159999132156372,0 +coef_urban_and_maintenance_tour,0.32714212462203368,0.32714151482178866,1.0315999984741211,0 +coef_urban_and_shopping_tour,0.65570145569778049,0.65570112049073548,0.53299999237060547,0 +coef_urban_and_tour_frequency_is_1,-3.606247148859373,-3.6062473242509725,-1.1648000478744507,0 +coef_urban_and_tour_frequency_is_2,-4.7493707738730677,-4.7493701536929747,-2.3176999092102051,0 +coef_urban_and_tour_frequency_is_5_plus,-5.9702694637288616,-5.9702688878768999,-2.5027000904083252,0 +coef_walk_access_to_retail_and_shopping,0.10528331492362221,0.1052833269153322,0.097199998795986176,0 +coef_0_auto_household_and_escorting_tour,-2,-2,-2,0 +coef_1_escort_tour_constant,-1.8969002612948864,-1.8972809252170171,-0.06289999932050705,0 +coef_1_plus_eating_out_tours_constant,-2.0443889188163173,-2.0448333669216079,-0.1429000049829483,0 +coef_1_plus_maintenance_tours_constant,-7.7254359495798148,-7.7270897339714733,-0.065300002694129944,0 +coef_1_plus_other_discretionary_tours_constant,-2.0647872487949561,-2.0651921993347462,0.33340001106262207,0 +coef_1_plus_shopping_tours_constant,-1.282411214400667,-1.2827907558560692,0.46830001473426819,0 +coef_1_plus_visting_tours_constant,-2.1187265722127613,-2.119130128830065,-0.12720000743865967,0 +coef_2_plus_escort_tours_constant,-3.4882178837837579,-3.4889221159472918,0.92729997634887695,0 +coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.83394458908199209,-0.83391904211683632,-1.1480000019073486,0 +coef_at_home_pre_school_kid_and_eating_out_tour,0.6315649245505468,0.63153544170594222,-0.39259999990463257,0 +coef_at_home_pre_school_kid_and_escorting_tour,-0.08455563088757391,-0.084558517582898715,-0.13729999959468842,0 +coef_auto_access_to_retail_and_maintenance,0.67210093938356752,0.67223833895969887,0.095600001513957977,0 +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0.22076749188228875,0.22093605520065349,-0.36230000853538513,0 +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.9349732023831957,-0.93482141743941694,-1.2719999551773071,0 +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-2.0213316139356832,-2.0212701718601216,-1.9306999444961548,0 +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,3.5090722695093683,3.5095724879606411,0.77380001544952393,0 +coef_female_and_maintenance_tour,-0.24827336118817248,-0.24851003473203095,-0.24639999866485596,0 +coef_female_and_tour_frequency_is_1,0.55537913022754337,0.55558571844411853,0.39019998908042908,0 +coef_female_and_tour_frequency_is_2,0.4700121436993433,0.47033652415332988,0.53229999542236328,0 +coef_female_and_tour_frequency_is_3,1.0935778897473212,1.0937278933542067,0.74519997835159302,0 +coef_female_and_tour_frequency_is_5,2.1058304843489379,2.1060351207036332,1.1294000148773193,0 +coef_high_income_group_and_discretionary_tour,0.53542200030194598,0.53532544473639576,0.88459998369216919,0 +coef_high_income_group_and_eating_out_tour,1.5430004952789953,1.5430357486801614,0.70859998464584351,0 +coef_high_income_group_and_shopping_tour,0.72248005627607348,0.72246863725799271,0.97759997844696045,0 +coef_high_income_group_and_tour_frequency_is_2,4.8659060743107148,4.8667865382550879,1.0633000135421753,0 +coef_high_income_group_and_tour_frequency_is_3,5.1569073742137981,5.1577005315354292,1.7741999626159668,0 +coef_high_income_group_and_tour_frequency_is_5_plus,5.5695995714955888,5.5703869156647645,2.3940999507904053,0 +coef_high_income_group_and_visiting_tour,-0.55652923789707232,-0.55654488685220238,-0.94489997625350952,0 +coef_logged_maximum_residual_window_tour_frequency_is_1,6.8143657923425485,6.815671298679093,1.763700008392334,0 +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,7.4778936905915021,7.4792750296860024,1.7927999496459961,0 +coef_mediumhigh_income_group_and_discretionary_tour,0.19484718211939026,0.1949087350001007,0.50089997053146362,0 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+coef_mediumlow_income_group_and_shopping_tour,0.5503864174305747,0.55034836543703713,0.77340000867843628,0 +coef_mediumlow_income_group_and_tour_frequency_is_1,3.6362917244942166,3.6370680682721321,0.57090002298355103,0 +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,3.8030230281375874,3.8037930038540892,0.83149999380111694,0 +coef_number_of_joint_eating_out_tours,-5.7578659480764109,-5.7590292220149779,-0.77270001173019409,0 +coef_number_of_joint_shopping_tours,-10.86032878033782,-10.862820539578552,-0.23909999430179596,0 +coef_number_of_joint_tours_and_tour_frequency_is_1,-3.5806784726726657,-3.5814001794995587,-0.16990000009536743,0 +coef_number_of_joint_tours_and_tour_frequency_is_2,-5.1193513695195279,-5.1202366413321725,-0.42849999666213989,0 +coef_number_of_joint_tours_and_tour_frequency_is_3,-4.6446726603410955,-4.6456045066231875,-0.65509998798370361,0 +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-4.8470304959707029,-4.8478742633711249,-1.041100025177002,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.67659997940063477,-0.67659997940063477,-0.67659997940063477,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.051800012588501,-1.051800012588501,-1.051800012588501,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,-999,-999,0 +coef_presence_of_full_time_worker_and_eating_out_tour,-1.3129272099813087,-1.3129546317286533,-0.4666999876499176,0 +coef_presence_of_full_time_worker_and_escorting_tour,0.34977167501271478,0.3497410147889421,0.39469999074935913,0 +coef_presence_of_non_worker_and_eating_out_tour,-0.40015678475147548,-0.40015646938307892,-0.49759998917579651,0 +coef_presence_of_non_worker_and_tour_frequency_is_1,1.2564012158056577,1.2568441721992365,-0.37630000710487366,0 +coef_presence_of_non_worker_and_tour_frequency_is_2,1.0428547824447958,1.0433986649176088,-0.71899998188018799,0 +coef_presence_of_non_worker_and_tour_frequency_is_5,1.6729377444156812,1.673362537920315,-1.0228999853134155,0 +coef_presence_of_part_time_worker_and_discretionary_tour,-0.46853620786950861,-0.46851175171192849,-0.35449999570846558,0 +coef_presence_of_part_time_worker_and_escorting_tour,-0.91620401760820491,-0.91626917282183395,-0.58609998226165771,0 +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.645226868283342,1.6452028413894657,1.3773000240325928,0 +coef_presence_of_pre_school_kid_and_escorting_tour,0.73413015866674558,0.73411877105653434,0.71939998865127563,0 +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-4.2717006949434637,-4.2727718042560454,0.14859999716281891,0 +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-4.1531060929831938,-4.1542847014169757,0.48399999737739563,0 +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-4.0092293410093136,-4.0100024764367959,-0.71609997749328613,0 +coef_presence_of_retiree_and_eating_out_tour,-0.98695379970331221,-0.98695373145616694,-0.69110000133514404,0 +coef_presence_of_retiree_and_tour_frequency_is_1,2.3328682638704601,2.3340717788338958,-0.46399998664855957,0 +coef_presence_of_retiree_and_tour_frequency_is_5,2.2895526967814139,2.2902926082161419,-0.47949999570846558,0 +coef_total_number_of_tours_is_1,-14.862387943118435,-14.863701190898123,-8.979100227355957,0 +coef_total_number_of_tours_is_2,-15.76114107396082,-15.762046562760142,-12.024800300598145,0 +coef_total_number_of_tours_is_3,-17.754112538361621,-17.754634820463089,-14.85159969329834,0 +coef_total_number_of_tours_is_4,-18.779593636700369,-18.779689721119343,-17.703699111938477,0 +coef_urban_and_discretionary_tour,0,0,0,0 +coef_walk_access_to_retail_and_discretionary,0.21083331596043739,0.21082887854988322,0.07720000296831131,0 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+coef_1_plus_shopping_tours_constant,-0.56851096405906976,-0.56851065297640646,0.59469997882843018,0 +coef_1_plus_visting_tours_constant,-0.99997515158264494,-0.99997480861181898,0.27889999747276306,0 +coef_2_plus_escort_tours_constant,-2.2472239741783695,-2.2472230312575778,0.51749998331069946,0 +coef_car_surplus_vs_workers_and_tour_frequency_is_1,2.6263187421217502,2.6263182643290257,0.79650002717971802,0 +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,3.0138266039361694,3.0138263991996164,2.1301999092102051,0 +coef_female_and_discretionary_tour,0.57421820949821378,0.57421776265903679,0.49540001153945923,0 +coef_female_and_maintenance_tour,0.7032371772890682,0.70323730968090981,0.74239999055862427,0 +coef_female_and_shopping_tour,0.60749249967525931,0.60749236436044163,0.96880000829696655,0 +coef_female_and_tour_frequency_is_1,-2.3057887932980217,-2.3057879644985477,-0.93480002880096436,0 +coef_female_and_tour_frequency_is_2,-2.5245735788315185,-2.5245730541075631,-1.3028000593185425,0 +coef_female_and_tour_frequency_is_5,-3.9777594453014777,-3.9777585979711256,-2.2660000324249268,0 +coef_high_income_group_and_discretionary_tour,0.96176887062837879,0.9617690305973885,1.0095000267028809,0 +coef_high_income_group_and_eating_out_tour,1.6107399863189256,1.6107399132659015,1.4842000007629395,0 +coef_high_income_group_and_maintenance_tour,0.99860462074351897,0.99860457400478175,1.3795000314712524,0 +coef_high_income_group_and_shopping_tour,1.0955552497273626,1.0955552433296207,1.0949000120162964,0 +coef_high_income_group_and_visiting_tour,-0.62823584789675457,-0.62823569014018499,-0.51370000839233398,0 +coef_logged_maximum_residual_window_tour_frequency_is_1,2.0411409487638097,2.0411408722900823,1.8357000350952148,0 +coef_logged_maximum_residual_window_tour_frequency_is_2,5.0543518983412508,5.0543512662707899,2.2706999778747559,0 +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,5.1028111260779792,5.1028109074006487,4.4022998809814453,0 +coef_mediumhigh_income_group_and_eating_out_tour,0.9601832770329618,0.96018319037111421,1.1809999942779541,0 +coef_mediumhigh_income_group_and_maintenance_tour,0.46026894334674823,0.46026867052213322,0.76480001211166382,0 +coef_mediumhigh_income_group_and_visiting_tour,-0.47448562479929113,-0.47448576085276917,-0.43680000305175781,0 +coef_mediumlow_income_group_and_eating_out_tour,0.8587186097762155,0.85871850010116635,0.97689998149871826,0 +coef_number_of_joint_shopping_tours,-0.34163048458137285,-0.34163086120064357,-0.80720001459121704,0 +coef_number_of_joint_tours_and_tour_frequency_is_2,-7.40575378545664,-7.4057519268091907,-0.94999998807907104,0 +coef_number_of_joint_tours_and_tour_frequency_is_3,-8.2528437399202961,-8.2528434839801079,-7.1430001258850098,0 +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,-999,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.0195999145507812,-5.0195999145507812,-5.0195999145507812,0 +coef_presence_of_full_time_worker_and_discretionary_tour,-0.32502706520068697,-0.3250272176315594,-0.48350000381469727,0 +coef_presence_of_full_time_worker_and_shopping_tour,-0.18718310533550805,-0.18718290004050162,-0.36090001463890076,0 +coef_presence_of_non_worker_and_discretionary_tour,-0.080381601681135048,-0.080381489052422414,-0.56029999256134033,0 +coef_presence_of_non_worker_and_eating_out_tour,-0.71343718792066768,-0.71343705083784981,-0.78799998760223389,0 +coef_presence_of_non_worker_and_tour_frequency_is_1,1.3664318707961971,1.3664313016982996,0.22400000691413879,0 +coef_presence_of_non_worker_and_tour_frequency_is_2,1.5541568438314686,1.5541565822514429,0.24359999597072601,0 +coef_presence_of_non_worker_and_tour_frequency_is_3,2.3378066705431362,2.3378063973066343,0.62000000476837158,0 +coef_presence_of_non_worker_and_tour_frequency_is_5,3.886799486754084,3.8867989469114872,3.3742001056671143,0 +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.6990307296529008,1.6990305339512453,1.4903000593185425,0 +coef_presence_of_pre_school_kid_and_escorting_tour,0.64317839301043844,0.64317845433272425,0.50269997119903564,0 +coef_presence_of_retiree_and_eating_out_tour,-0.93915814650169016,-0.93915820457557886,-0.92820000648498535,0 +coef_presence_of_retiree_and_tour_frequency_is_1,0.53309324048384155,0.53309327034571541,-0.44580000638961792,0 +coef_presence_of_retiree_and_tour_frequency_is_5,0.15143338882997009,0.15143298802740426,-0.53149998188018799,0 +coef_total_number_of_tours_is_1,-12.29669710898526,-12.296696353770589,-8.5684003829956055,0 +coef_total_number_of_tours_is_2,-14.366430853959836,-14.366430219226835,-12.741600036621094,0 +coef_total_number_of_tours_is_3,-15.470851663726473,-15.47085200579302,-15.097800254821777,0 +coef_total_number_of_tours_is_4,-16.697442099429654,-16.697442489781661,-19.543899536132812,0 +coef_total_number_of_tours_is_5,-23.975944547216859,-23.975943616807008,-20.789699554443359,0 +coef_urban_and_discretionary_tour,0,0,0,0 +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.50908621780365026,0.50908614236387195,0.061599999666213989,0 +coef_0_auto_household_and_escorting_tour,-2.4007662488833401,-2.4007662488833401,-2,0 +coef_1_escort_tour_constant,-11.100045881453859,-11.100045881453859,-0.49340000748634338,0 +coef_1_plus_eating_out_tours_constant,-12.674000548814972,-12.674000548814972,-0.024199999868869781,0 +coef_1_plus_maintenance_tours_constant,1.4021937042970116,1.4021937042970116,-0.43439999222755432,0 +coef_1_plus_other_discretionary_tours_constant,-2.4760858637499221,-2.4760858637499221,-0.26019999384880066,0 +coef_1_plus_shopping_tours_constant,5.1109144099794959,5.1109144099794959,0.53200000524520874,0 +coef_1_plus_visting_tours_constant,5.1132912897133904,5.1132912897133904,0.23669999837875366,0 +coef_2_plus_escort_tours_constant,-4.7231718945294894,-4.7231718945294894,1.4155000448226929,0 +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,-2.8928262121700787,-2.8928262121700787,0.10040000081062317,0 +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-23.462489242832998,-23.462489242832998,-0.63690000772476196,0 +coef_car_surplus_vs_workers_and_tour_frequency_is_1,-11.054245969090358,-11.054245969090358,0.29019999504089355,0 +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,26.810453753037919,26.810453753037919,2.0352001190185547,0 +coef_high_income_group_and_discretionary_tour,9.3746353630306327,9.3746353630306327,2.3269999027252197,0 +coef_high_income_group_and_eating_out_tour,-8.894067988889379,-8.894067988889379,0.49160000681877136,0 +coef_high_income_group_and_maintenance_tour,-0.37810556759048242,-0.37810556759048242,0.39820000529289246,0 +coef_high_income_group_and_shopping_tour,-0.46662150365308386,-0.46662150365308386,0.2442999929189682,0 +coef_high_income_group_and_visiting_tour,-0.46885622247201569,-0.46885622247201569,0.28580000996589661,0 +coef_logged_maximum_residual_window_tour_frequency_is_1,15.538672905979983,15.538672905979983,1.329800009727478,0 +coef_logged_maximum_residual_window_tour_frequency_is_2,-15.31294086052077,-15.31294086052077,1.3759000301361084,0 +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,-2.8783154540415228,-2.8783154540415228,3.2808001041412354,0 +coef_mediumhigh_income_group_and_discretionary_tour,2.8464215562810216,2.8464215562810216,1.4049999713897705,0 +coef_mediumlow_income_group_and_discretionary_tour,-7.8393223758039596,-7.8393223758039596,0.91689997911453247,0 +coef_number_of_joint_tours_and_tour_frequency_is_1,-6.5374366056093152,-6.5374366056093152,-0.21619999408721924,0 +coef_number_of_joint_tours_and_tour_frequency_is_2,-4.4265938354565844,-4.4265938354565844,-0.35870000720024109,0 +coef_number_of_joint_tours_and_tour_frequency_is_3,-4.2928228670338138,-4.2928228670338138,-4.2701001167297363,0 +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,-999,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-20.768569059727319,-20.768569059727319,-0.23399999737739563,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_2,13.166961657521094,13.166961657521094,-0.92309999465942383,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-9.1884865390771271,-9.1884865390771271,-6.5834999084472656,0 +coef_presence_of_driving_school_kid_and_discretionary_tour,-1.5287699271825661,-1.5287699271825661,-0.92019999027252197,0 +coef_presence_of_driving_school_kid_and_eating_out_tour,-0.67899286342910536,-0.67899286342910536,-0.63770002126693726,0 +coef_presence_of_non_worker_and_tour_frequency_is_2,7.8203518915786985,7.8203518915786985,-0.65710002183914185,0 +coef_presence_of_non_worker_and_tour_frequency_is_5,-2.3380879397360088,-2.3380879397360088,-1.4043999910354614,0 +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,-2.8548975360289837,-2.8548975360289837,-1.5698000192642212,0 +coef_presence_of_pre_school_kid_and_eating_out_tour,-1.1155840296979183,-1.1155840296979183,-0.2987000048160553,0 +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,7.9990670481874249,7.9990670481874249,-0.32190001010894775,0 +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,9.8647248326504506,9.8647248326504506,-1.087399959564209,0 +coef_presence_of_university_student_and_discretionary_tour,4.5866749933879518,4.5866749933879518,-1.2834000587463379,0 +coef_total_number_of_tours_is_1,5.7323576505278915,5.7323576505278915,-7.1505999565124512,0 +coef_total_number_of_tours_is_2,-13.716885058175075,-13.716885058175075,-11.121399879455566,0 +coef_total_number_of_tours_is_3,-24.558188200243801,-24.558188200243801,-13.175000190734863,0 +coef_urban_and_discretionary_tour,0,0,0,0 +coef_urban_and_maintenance_tour,2.8759936780232791,2.8759936780232791,1.0393999814987183,0 +coef_0_auto_household_and_escorting_tour,-2,-2,-2,0 +coef_1_escort_tour_constant,-0.80019821892406418,-0.80019821892406418,-0.75510001182556152,0 +coef_1_plus_eating_out_tours_constant,-2.2137830257191138,-2.2137830257191138,1.1145000457763672,0 +coef_1_plus_maintenance_tours_constant,-0.87384629825268301,-0.87384629825268301,-0.50599998235702515,0 +coef_1_plus_other_discretionary_tours_constant,0.6794818887801406,0.6794818887801406,0.46340000629425049,0 +coef_1_plus_shopping_tours_constant,1.9914236119221842,1.9914236119221842,0.47830000519752502,0 +coef_1_plus_visting_tours_constant,-1.1732951429240632,-1.1732951429240632,-0.40059998631477356,0 +coef_2_plus_escort_tours_constant,-0.52085404537857938,-0.52085404537857938,-0.0086000002920627594,0 +coef_auto_access_to_retail_and_escorting,0.14995921405552753,0.14995921405552753,0.06289999932050705,0 +coef_high_income_group_and_eating_out_tour,-1.2746622030953361,-1.2746622030953361,-0.70099997520446777,0 +coef_high_income_group_and_shopping_tour,-3.6565535610834519,-3.6565535610834519,-0.65060001611709595,0 +coef_high_income_group_and_tour_frequency_is_5_plus,8.6597646574930796,8.6597646574930796,2.0174999237060547,0 +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.56299641940097,1.56299641940097,1.5602999925613403,0 +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,7.735469931243947,7.735469931243947,1.5197000503540039,0 +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,6.8720130507569017,6.8720130507569017,1.0872999429702759,0 +coef_number_of_joint_maintenance_tours,-1.347599983215332,-1.347599983215332,-1.347599983215332,0 +coef_number_of_joint_tours_and_tour_frequency_is_2,-1.2555358714256173,-1.2555358714256173,-0.61489999294281006,0 +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,-999,-999,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-5.0803581001504661,-5.0803581001504661,-1.0331000089645386,0 +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.8935586600908669,-5.8935586600908669,-2.7444999217987061,0 +coef_presence_of_full_time_worker_and_discretionary_tour,0.19647967582561615,0.19647967582561615,0.75260001420974731,0 +coef_presence_of_non_worker_and_eating_out_tour,-1.3548917345809393,-1.3548917345809393,-1.3073999881744385,0 +coef_presence_of_non_worker_and_shopping_tour,-1.3398609967955151,-1.3398609967955151,-0.64499998092651367,0 +coef_presence_of_non_worker_and_tour_frequency_is_5,-0.42351455281362266,-0.42351455281362266,0.21770000457763672,0 +coef_presence_of_part_time_worker_and_discretionary_tour,-0.33314199741532502,-0.33314199741532502,0.37209999561309814,0 +coef_presence_of_pre_driving_school_kid_and_shopping_tour,1.4440737444047911,1.4440737444047911,0.93650001287460327,0 +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0.29958946114398161,0.29958946114398161,-0.22640000283718109,0 +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0.32067429398079011,0.32067429398079011,-0.44389998912811279,0 +coef_total_number_of_tours_is_1,-9.4531005722326533,-9.4531005722326533,-7.486299991607666,0 +coef_total_number_of_tours_is_2,-11.996770119024941,-11.996770119024941,-10.718000411987305,0 +coef_total_number_of_tours_is_3,-13.550028148803801,-13.550028148803801,-13.788399696350098,0 +coef_urban_and_discretionary_tour,0,0,0,0 +coef_urban_and_escorting_tour,-0.6344062915018166,-0.6344062915018166,0.43520000576972961,0 +coef_walk_access_to_retail_and_eating_out,0.48030056947070726,0.48030056947070726,0.073799997568130493,0 +coef_0_auto_household_and_escorting_tour,-2,-2,-2,0 +coef_1_escort_tour_constant,0.39617684877862708,0.39617684877862708,0.36219999194145203,0 +coef_1_plus_eating_out_tours_constant,1.4990449238097932,1.4990449238097932,0.96119999885559082,0 +coef_1_plus_maintenance_tours_constant,0.71287833893706021,0.71287833893706021,0.67879998683929443,0 +coef_1_plus_other_discretionary_tours_constant,1.7799792001679569,1.7799792001679569,1.4934999942779541,0 +coef_1_plus_shopping_tours_constant,2.0942830938898398,2.0942830938898398,1.6919000148773193,0 +coef_1_plus_visting_tours_constant,0.62786207413189388,0.62786207413189388,0.4424000084400177,0 +coef_2_plus_escort_tours_constant,2.1826221262636776,2.1826221262636776,2.2218999862670898,0 +coef_discretionary_tour,1.1894792030289798,1.1894792030289798,0.90299999713897705,0 +coef_escorting_tour,2.4464210738878447,2.4464210738878447,2.4909999370574951,0 +coef_maintenance_tour,1.0560783072750726,1.0560783072750726,1.0219999551773071,0 +coef_presence_of_full_time_worker_and_escorting_tour,-0.6300922367614431,-0.6300922367614431,-0.89300000667572021,0 +coef_presence_of_non_worker_and_discretionary_tour,-0.0080480967981517125,-0.0080480967981517125,0.79100000858306885,0 +coef_presence_of_non_worker_and_eating_out_tour,1.1025455434303448,1.1025455434303448,1.156999945640564,0 +coef_presence_of_non_worker_and_escorting_tour,0.86673078874996601,0.86673078874996601,0.88999998569488525,0 +coef_presence_of_non_worker_and_shopping_tour,0.20878201493509621,0.20878201493509621,0.80800002813339233,0 +coef_presence_of_part_time_worker_and_eating_out_tour,1.3767801830490909,1.3767801830490909,1.0369999408721924,0 +coef_presence_of_part_time_worker_and_shopping_tour,1.0143615015555587,1.0143615015555587,1.1549999713897705,0 +coef_total_number_of_tours_is_1,-5.7103540103201968,-5.7103540103201968,-5.7589998245239258,0 +coef_total_number_of_tours_is_2,-11.596952013557841,-11.596952013557841,-11.517000198364258,0 +coef_total_number_of_tours_is_3,-16.568257064319742,-16.568257064319742,-17.275999069213867,0 +coef_total_number_of_tours_is_4,-23.18757470591768,-23.18757470591768,-23.034999847412109,0 +coef_total_number_of_tours_is_6_plus,-999,-999,-999,0 +coef_urban_and_discretionary_tour,0,0,0,0 +coef_visiting_or_social_tour,0.95446205949299312,0.95446205949299312,0.76899999380111694,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq_loglike.csv index 7e2418fafd..9dea4a6b40 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_nonmand_tour_freq_loglike.csv @@ -1,9 +1,9 @@ ,loglike_prior,loglike_converge -0,-36.842915290359244,-13.427352349597697 -1,-1867.7130849406183,-1831.0744991065408 -2,-1076.3668030822264,-1038.9605638228181 -3,-877.44304962652359,-856.8595081575636 -4,-230.6429613110198,-225.00971520122775 -5,-806.81758251120061,-787.40004316431327 -6,-277.18561139007045,-256.78783280769073 -7,-358.3650717429976,-324.85841724064665 +0,-36.842915240821576,-13.63752199393485 +1,-1867.7130819595852,-1832.6445382071518 +2,-1076.3667995930466,-1039.2235240389089 +3,-877.44304860975865,-856.98760672658591 +4,-230.64296021604497,-225.07312798066818 +5,-806.81757896884028,-787.83786863094554 +6,-277.18561178415462,-256.90644089706086 +7,-358.36507163326337,-324.9104211327566 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_atwork_subtour_scheduling_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_atwork_subtour_scheduling_SLSQP_.csv deleted file mode 100644 index 1e5284852c..0000000000 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_atwork_subtour_scheduling_SLSQP_.csv +++ /dev/null @@ -1,50 +0,0 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_am_peak_end,-1.3731693426269276,-2.928312295,0,-25,25,-1.3731693426269276 -coef_am_peak_start_at_6,-20.592813760446127,-6.1567178269999996,0,-25,25,-20.592813760446127 -coef_am_peak_start_at_7,-2.8269839190766564,-4.0617081419999996,0,-25,25,-2.8269839190766564 -coef_am_peak_start_at_8,-1.1965282925054144,-2.330535201,0,-25,25,-1.1965282925054144 -coef_am_peak_start_at_9,-1.6198974929815253,-1.881593386,0,-25,25,-1.6198974929815253 -coef_dummy_for_business_related_purpose_and_duration_from_0_to_1,-0.66312907740821159,-1.5429999999999999,0,-25,25,-0.66312907740821159 -coef_dummy_for_eating_out_purpose_and_departure_at_11,1.1518273421311946,1.5109999999999999,0,-25,25,1.1518273421311946 -coef_dummy_for_eating_out_purpose_and_departure_at_12,2.6254363230451085,2.7210000000000001,0,-25,25,2.6254363230451085 -coef_dummy_for_eating_out_purpose_and_departure_at_13,2.36098076410833,2.1219999999999999,0,-25,25,2.36098076410833 -coef_dummy_for_eating_out_purpose_and_duration_of_1_hour,0.31040519075637613,0.39989999999999998,0,-25,25,0.31040519075637613 -coef_duration_of_0_hours,9.9599848609439601,-0.90668151200000002,0,-25,25,9.9599848609439601 -coef_duration_of_11_to_13_hours,-0.37405450634311865,0.29999999999999999,0,-25,25,-0.37405450634311865 -coef_duration_of_14_to_18_hours,0,0,0,0,0,0 -coef_duration_of_1_hour,0,0,0,0,0,0 -coef_duration_of_2_to_3_hours,9.1812833977279276,-1.3621758020000001,0,-25,25,9.1812833977279276 -coef_duration_of_4_to_5_hours,-0.48314094706644228,-0.81961761600000005,0,-25,25,-0.48314094706644228 -coef_duration_of_6_to_7_hours,10.535613300026082,1.088111072,0,-25,25,10.535613300026082 -coef_duration_of_8_to_10_hours,10.402182684572614,1.734038505,0,-25,25,10.402182684572614 -coef_duration_shift_for_business_related_,0.60004385998820309,0.2646,0,-25,25,0.60004385998820309 -coef_duration_shift_for_first_sub_tour_of_same_work_tour,0.31988469057309121,-0.3992,0,-25,25,0.31988469057309121 -coef_duration_shift_for_inbound_auto_travel_time_off_peak,-0.11857498443158188,0.0098099999999999993,0,-25,25,-0.11857498443158188 -coef_duration_shift_for_number_of_individual_nonmandatory_tours,0.0098944086043264642,-0.042200000000000001,0,-25,25,0.0098944086043264642 -coef_duration_shift_for_number_of_joint_tours,-0.57768141215671864,-0.24970000000000001,0,-25,25,-0.57768141215671864 -coef_duration_shift_for_number_of_mandatory_tours,-1.0587875726336362,-0.7702,0,-25,25,-1.0587875726336362 -coef_duration_shift_for_outbound_auto_travel_time_off_peak,0.1453238873904982,0.0098099999999999993,0,-25,25,0.1453238873904982 -coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour,-10.333603350935554,-0.18440000000000001,0,-25,25,-10.333603350935554 -coef_early_end_at_5_6,-25,-2.928312295,0,-25,25,-25 -coef_early_start_at_5,-2.2040675056867611,-7.7655484760000002,0,-25,25,-2.2040675056867611 -coef_evening_end_at_19_20_21,-4.5210371252389265,-2.3199822600000002,0,-25,25,-4.5210371252389265 -coef_evening_start_at_19_20_21,-0.32574110086394298,-1.015090023,0,-25,25,-0.32574110086394298 -coef_late_end_at_22_23,-11.516564630810274,-2.3199822600000002,0,-25,25,-11.516564630810274 -coef_late_start_at_22_23,-5.1429250271310103,-0.73757005399999997,0,-25,25,-5.1429250271310103 -coef_midday_end_at_10_11_12,-1.3051956922349568,-2.2972643740000001,0,-25,25,-1.3051956922349568 -coef_midday_end_at_13_14,0,0,0,0,0,0 -coef_midday_start_at_10_11_12,0,0,0,0,0,0 -coef_midday_start_at_13_14_15,-1.0515620376346457,-0.77502157999999999,0,-25,25,-1.0515620376346457 -coef_pm_peak_end_at_15,-1.4329808631274803,-0.57834445700000003,0,-25,25,-1.4329808631274803 -coef_pm_peak_end_at_16,-2.0551088799651303,-1.09408722,0,-25,25,-2.0551088799651303 -coef_pm_peak_end_at_17,-2.4042523970600072,-1.1658466000000001,0,-25,25,-2.4042523970600072 -coef_pm_peak_end_at_18,-3.5003721078254548,-1.4961310809999999,0,-25,25,-3.5003721078254548 -coef_pm_peak_start_at_16_17_18,-0.7985883513777744,-0.227528489,0,-25,25,-0.7985883513777744 -coef_start_shift_for_business_related_,-0.083357458155514152,-0.1113,0,-25,25,-0.083357458155514152 -coef_start_shift_for_first_sub_tour_of_same_work_tour,-0.16832429973206386,-0.54330000000000001,0,-25,25,-0.16832429973206386 -coef_start_shift_for_inbound_auto_travel_time_off_peak,-0.09901878555282706,0.00064999999999999997,0,-25,25,-0.09901878555282706 -coef_start_shift_for_number_of_individual_nonmandatory_tours,0.026776673811965086,-0.012800000000000001,0,-25,25,0.026776673811965086 -coef_start_shift_for_number_of_joint_tours,-0.019721699715671427,-0.0206,0,-25,25,-0.019721699715671427 -coef_start_shift_for_number_of_mandatory_tours,0.059018046122538706,-0.019300000000000001,0,-25,25,0.059018046122538706 -coef_start_shift_for_outbound_auto_travel_time_off_peak,0.10034873691181434,0.00064999999999999997,0,-25,25,0.10034873691181434 -coef_start_shift_for_subsequent_sub_tour_of_same_work_tour,-0.36020623998165718,-0.18440000000000001,0,-25,25,-0.36020623998165718 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv index 091e76401e..51f0e05222 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_joint_tour_scheduling_SLSQP_.csv @@ -1,60 +1,60 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.028774249414067657,-0.025700000000000001,0,-25,25,-0.028774249414067657 -coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,0.91901966813332936,-0.02734,0,-25,25,0.91901966813332936 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.03464166423412067,0.0084419999999999999,0,-25,25,0.03464166423412067 -coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,-18.311237242705495,-0.059299999999999999,0,-25,25,-18.311237242705495 -coef_adult_with_children_in_hh_arrive_19_21,0.04436348324103874,0.33600000000000002,0,-25,25,0.04436348324103874 -coef_arrival_constants_am_peak,4.7614100686980576,-8.7288800000000002,0,-25,25,4.7614100686980576 -coef_arrival_constants_early,24.924968513710187,-8.7288800000000002,0,-25,25,24.924968513710187 -coef_arrival_constants_evening,-4.3200714359409025,-2.7489400000000002,0,-25,25,-4.3200714359409025 -coef_arrival_constants_late,-9.6327188982749732,-4.2425300000000004,0,-25,25,-9.6327188982749732 -coef_arrival_constants_midday_1,0,0,0,0,0,0 -coef_arrival_constants_midday_2,2.6541662776744461,1.40804,0,-25,25,2.6541662776744461 -coef_arrival_constants_pm_peak_1,1.8388653479429438,1.0203599999999999,0,-25,25,1.8388653479429438 -coef_arrival_constants_pm_peak_2,1.4625561851708289,1.06863,0,-25,25,1.4625561851708289 -coef_arrival_constants_pm_peak_3,0,0,0,0,0,0 -coef_arrival_constants_pm_peak_4,-1.2333276504506303,-0.59626000000000001,0,-25,25,-1.2333276504506303 -coef_departure_constants_am_peak_1,-25,-11.595050000000001,0,-25,25,-25 -coef_departure_constants_am_peak_2,-4.2487039295223656,-9.0051900000000007,0,-25,25,-4.2487039295223656 -coef_departure_constants_am_peak_3,0.75112055157913948,-2.7331500000000002,0,-25,25,0.75112055157913948 -coef_departure_constants_am_peak_4,1.3921313310397618,0.26654,0,-25,25,1.3921313310397618 -coef_departure_constants_early,-24.394585604972157,-14.477080000000001,0,-25,25,-24.394585604972157 -coef_departure_constants_evening,-14.12448484303447,-18.987369999999999,0,-25,25,-14.12448484303447 -coef_departure_constants_late,-19.873640270611908,-20.27807,0,-25,25,-19.873640270611908 -coef_departure_constants_midday_1,0,0,0,0,0,0 -coef_departure_constants_midday_2,-4.3735430875047783,-1.6026,0,-25,25,-4.3735430875047783 -coef_departure_constants_pm_peak,-12.231456579430871,-17.695979999999999,0,-25,25,-12.231456579430871 -coef_destination_in_cbd_duration_shift_effects,0.56926682397665584,0.1067,0,-25,25,0.56926682397665584 -coef_discretionary_tour_duration_lt_2_hours,-0.96979691863694628,-0.69740000000000002,0,-25,25,-0.96979691863694628 -coef_duration_constants_0_to_1_hours,-2.7866437326016174,-2.2282600000000001,0,-25,25,-2.7866437326016174 -coef_duration_constants_11_to_13_hours,-5.6834374392880065,-0.81518999999999997,0,-25,25,-5.6834374392880065 -coef_duration_constants_14_to_18_hours,-2.7347130621577005,-2.7384400000000002,0,-25,25,-2.7347130621577005 -coef_duration_constants_2_to_3_hours,0,0,0,0,0,0 -coef_duration_constants_4_to_5_hours,0.35927958946513583,-0.56174000000000002,0,-25,25,0.35927958946513583 -coef_duration_constants_6_to_7_hours,0.25829995673545386,-0.65547,0,-25,25,0.25829995673545386 -coef_duration_constants_8_to_10_hours,-10.443272918037978,-0.74061999999999995,0,-25,25,-10.443272918037978 -coef_eat_out_tour_departure_shift_effects,-0.026030105628492072,0.075490000000000002,0,-25,25,-0.026030105628492072 -coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-1.6735672025262123,-0.2364,0,-25,25,-1.6735672025262123 -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.016957888892283637,0.0031949999999999999,0,-25,25,0.016957888892283637 -coef_maintenance_tour_depart_before_7,1.1216498838609328,-0.88260000000000005,0,-25,25,1.1216498838609328 -coef_maintenance_tour_departure_shift_effects,-0.16045036076756827,-0.1489,0,-25,25,-0.16045036076756827 -coef_maintenance_tour_duration_shift_effects,-0.15774061753063057,-0.083720000000000003,0,-25,25,-0.15774061753063057 -coef_number_of_joint_tours_departure_shift_effects,1.3426208902212988,0.052080000000000001,0,-25,25,1.3426208902212988 -coef_number_of_mandatory_tours_departure_shift_effects,0.073116531424716663,0.046730000000000001,0,-25,25,0.073116531424716663 -coef_school_child_age_16_plus_departure_shift_effects,0.072660000000000002,0.072660000000000002,0,-25,25,0.072660000000000002 -coef_school_child_age_16_plus_duration_shift_effects,0.20949999999999999,0.20949999999999999,0,-25,25,0.20949999999999999 -coef_school_child_age_under_16_departure_shift_effects,0.22437768613209164,0.04657,0,-25,25,0.22437768613209164 -coef_school_child_age_under_16_duration_shift_effects,-0.55690620785460743,0.32719999999999999,0,-25,25,-0.55690620785460743 -coef_school_child_under_16_arrive_after_22,-6.2179802555750401,-1.1799999999999999,0,-25,25,-6.2179802555750401 -coef_shopping_tour_arrive_after_22,-6.8841149173847738,-0.60270000000000001,0,-25,25,-6.8841149173847738 -coef_shopping_tour_depart_before_8,1.6329282274649815,-1.0369999999999999,0,-25,25,1.6329282274649815 -coef_shopping_tour_departure_shift_effects,-0.032901677188934476,-0.060150000000000002,0,-25,25,-0.032901677188934476 -coef_shopping_tour_duration_lt_2_hours,0.069480688387001235,0.51680000000000004,0,-25,25,0.069480688387001235 -coef_shopping_tour_duration_shift_effects,-0.41367749415560207,-0.1208,0,-25,25,-0.41367749415560207 -coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,0.46823426868427825,-0.3992,0,-25,25,0.46823426868427825 -coef_some_previously_scheduled_tour_ends_in_this_departure_hour,0.26003373330967577,-0.45619999999999999,0,-25,25,0.26003373330967577 -coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.015340339657372392,-0.1731,0,-25,25,-0.015340339657372392 -coef_unavailable,-999,-999,0,-999,-999,-999 -coef_university_student_arrive_after_22,-9.1734571378829219,0.54659999999999997,0,-25,25,-9.1734571378829219 -coef_visit_tour_departure_shift_effects,-0.11236540231411092,0.096879999999999994,0,-25,25,-0.11236540231411092 -coef_visit_tour_duration_shift_effects,-0.13492097024550309,0.1638,0,-25,25,-0.13492097024550309 +param_name,value,best,initvalue,nullvalue +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.025656219142801435,-0.025656219142801435,-0.025699999183416367,0 +coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,-0.025716469680719514,-0.025716469680719514,-0.027340000495314598,0 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.008442354676521463,0.008442354676521463,0.0084419995546340942,0 +coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,-0.05935757810299315,-0.05935757810299315,-0.059300001710653305,0 +coef_adult_with_children_in_hh_arrive_19_21,0.5355161653253353,0.5355161653253353,0.335999995470047,0 +coef_arrival_constants_am_peak,-2.4250190851365487,-2.4250190851365487,-8.7288799285888672,0 +coef_arrival_constants_early,2.2958899169886267,2.2958899169886267,-8.7288799285888672,0 +coef_arrival_constants_evening,-3.3154480276617018,-3.3154480276617018,-2.7489399909973145,0 +coef_arrival_constants_late,-8.3307149468221589,-8.3307149468221589,-4.2425298690795898,0 +coef_arrival_constants_midday_1,0,0,0,0 +coef_arrival_constants_midday_2,1.5294906566988582,1.5294906566988582,1.4080400466918945,0 +coef_arrival_constants_pm_peak_1,1.2175035036665733,1.2175035036665733,1.020359992980957,0 +coef_arrival_constants_pm_peak_2,1.2998241903588883,1.2998241903588883,1.0686299800872803,0 +coef_arrival_constants_pm_peak_3,0,0,0,0 +coef_arrival_constants_pm_peak_4,-0.84944529895620147,-0.84944529895620147,-0.59626001119613647,0 +coef_departure_constants_am_peak_1,-24.483912895120231,-24.483912895120231,-11.595049858093262,0 +coef_departure_constants_am_peak_2,-17.791981333117047,-17.791981333117047,-9.0051898956298828,0 +coef_departure_constants_am_peak_3,-4.1499437678248832,-4.1499437678248832,-2.7331500053405762,0 +coef_departure_constants_am_peak_4,-0.2038777988755586,-0.2038777988755586,0.26653999090194702,0 +coef_departure_constants_early,-24.999999999999986,-24.999999999999986,-14.477080345153809,0 +coef_departure_constants_evening,-24.057955302201297,-24.057955302201297,-18.987369537353516,0 +coef_departure_constants_late,-25,-25,-20.278070449829102,0 +coef_departure_constants_midday_1,0,0,0,0 +coef_departure_constants_midday_2,-2.6945721193468217,-2.6945721193468217,-1.6025999784469604,0 +coef_departure_constants_pm_peak,-22.802101526690983,-22.802101526690983,-17.695980072021484,0 +coef_destination_in_cbd_duration_shift_effects,0.57637590919194726,0.57637590919194726,0.10670000314712524,0 +coef_discretionary_tour_duration_lt_2_hours,-1.2587487442550029,-1.2587487442550029,-0.69739997386932373,0 +coef_duration_constants_0_to_1_hours,-2.7292315354064538,-2.7292315354064538,-2.2282600402832031,0 +coef_duration_constants_11_to_13_hours,-16.641361610345623,-16.641361610345623,-0.81519001722335815,0 +coef_duration_constants_14_to_18_hours,-4.2019174642931754,-4.2019174642931754,-2.7384400367736816,0 +coef_duration_constants_2_to_3_hours,0,0,0,0 +coef_duration_constants_4_to_5_hours,0.041336040196562979,0.041336040196562979,-0.56173998117446899,0 +coef_duration_constants_6_to_7_hours,-0.28010083544612574,-0.28010083544612574,-0.65547001361846924,0 +coef_duration_constants_8_to_10_hours,-11.834841336602297,-11.834841336602297,-0.74062001705169678,0 +coef_eat_out_tour_departure_shift_effects,-0.191471104246424,-0.191471104246424,0.075489997863769531,0 +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.65914361514389619,-0.65914361514389619,-0.23639999330043793,0 +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0103470192509738,0.0103470192509738,0.0031950001139193773,0 +coef_maintenance_tour_depart_before_7,-0.6995036880611385,-0.6995036880611385,-0.88260000944137573,0 +coef_maintenance_tour_departure_shift_effects,-0.51463329667967828,-0.51463329667967828,-0.14890000224113464,0 +coef_maintenance_tour_duration_shift_effects,-0.37483619241205374,-0.37483619241205374,-0.083719998598098755,0 +coef_number_of_joint_tours_departure_shift_effects,0.5518545554827089,0.5518545554827089,0.052080001682043076,0 +coef_number_of_mandatory_tours_departure_shift_effects,0.15108304860381977,0.15108304860381977,0.046730000525712967,0 +coef_school_child_age_16_plus_departure_shift_effects,0.072659999132156372,0.072659999132156372,0.072659999132156372,0 +coef_school_child_age_16_plus_duration_shift_effects,0.20949999988079071,0.20949999988079071,0.20949999988079071,0 +coef_school_child_age_under_16_departure_shift_effects,0.90140346155759687,0.90140346155759687,0.046569999307394028,0 +coef_school_child_age_under_16_duration_shift_effects,-0.36276804513883948,-0.36276804513883948,0.32719999551773071,0 +coef_school_child_under_16_arrive_after_22,-25,-25,-1.1799999475479126,0 +coef_shopping_tour_arrive_after_22,-16.816060937232734,-16.816060937232734,-0.60269999504089355,0 +coef_shopping_tour_depart_before_8,0.8484680406149383,0.8484680406149383,-1.0369999408721924,0 +coef_shopping_tour_departure_shift_effects,-0.35167841899554614,-0.35167841899554614,-0.06015000119805336,0 +coef_shopping_tour_duration_lt_2_hours,0.1873898983230104,0.1873898983230104,0.51679998636245728,0 +coef_shopping_tour_duration_shift_effects,-0.63534186935788817,-0.63534186935788817,-0.12080000340938568,0 +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.25327854790648219,-0.25327854790648219,-0.39919999241828918,0 +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.35052002043887132,-0.35052002043887132,-0.4562000036239624,0 +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.035834399723830755,-0.035834399723830755,-0.17309999465942383,0 +coef_unavailable,-999,-999,-999,0 +coef_university_student_arrive_after_22,-15.517205687701349,-15.517205687701349,0.54659998416900635,0 +coef_visit_tour_departure_shift_effects,-0.34370578081497799,-0.34370578081497799,0.096879996359348297,0 +coef_visit_tour_duration_shift_effects,-0.33210961592070104,-0.33210961592070104,0.16380000114440918,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv index 93c03c449c..047fcd6a31 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_school_SLSQP_.csv @@ -1,56 +1,56 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,11.716776044898809,-0.0030490000000000001,0,-25,25,11.716776044898809 -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.58735581754454202,-0.52710000000000001,0,-25,25,-0.58735581754454202 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,9.2169111822551333,0.089749999999999996,0,-25,25,9.2169111822551333 -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-15.186947397585952,-0.44,0,-25,25,-15.186947397585952 -coef_all_adults_ft_worker_duration,0.0083360428258746171,0.10929999999999999,0,-25,25,0.0083360428258746171 -coef_arrival_constants_am_peak,-2.2525597933199202,-2.4287183990000001,0,-25,25,-2.2525597933199202 -coef_arrival_constants_early,-1.0600927435131249,-2.4287183990000001,0,-25,25,-1.0600927435131249 -coef_arrival_constants_evening,-1.024968926440146,-0.87014690400000005,0,-25,25,-1.024968926440146 -coef_arrival_constants_late,-1.3622777646923283,-1.7520004899999999,0,-25,25,-1.3622777646923283 -coef_arrival_constants_midday_1,-1.3329442334964623,-1.237908768,0,-25,25,-1.3329442334964623 -coef_arrival_constants_midday_2,-0.56302790885535348,-0.53976893100000001,0,-25,25,-0.56302790885535348 -coef_arrival_constants_pm_peak_1,0,0,0,0,0,0 -coef_arrival_constants_pm_peak_2,-0.47120087260372584,-0.38916924800000002,0,-25,25,-0.47120087260372584 -coef_arrival_constants_pm_peak_3,-0.2889635829589125,-0.198120349,0,-25,25,-0.2889635829589125 -coef_arrival_constants_pm_peak_4,-0.42904089312882804,-0.25362468399999999,0,-25,25,-0.42904089312882804 -coef_departure_constants_am_peak_1,-1.8402276885639066,-1.6176440560000001,0,-25,25,-1.8402276885639066 -coef_departure_constants_am_peak_2,0,0,0,0,0,0 -coef_departure_constants_am_peak_3,-0.14762834941122654,-0.073826841000000004,0,-25,25,-0.14762834941122654 -coef_departure_constants_am_peak_4,-2.0052907227909373,-2.0805707689999999,0,-25,25,-2.0052907227909373 -coef_departure_constants_early,-3.6385102149182318,-3.8206624040000001,0,-25,25,-3.6385102149182318 -coef_departure_constants_evening,-5.1709311530899091,-5.2302878359999996,0,-25,25,-5.1709311530899091 -coef_departure_constants_late,-25,-11.88604728,0,-25,25,-25 -coef_departure_constants_midday_1,-3.235655391222839,-2.9857394570000002,0,-25,25,-3.235655391222839 -coef_departure_constants_midday_2,-3.7122959166670451,-3.6284346460000001,0,-25,25,-3.7122959166670451 -coef_departure_constants_pm_peak,-2.9909526474284074,-3.1025051499999998,0,-25,25,-2.9909526474284074 -coef_duration_constants_0_to_2_hours,-1.3817519840859342,-1.409955689,0,-25,25,-1.3817519840859342 -coef_duration_constants_10_hours,-0.85439568067710703,-0.90478898299999999,0,-25,25,-0.85439568067710703 -coef_duration_constants_11_hours,-1.0970644700531507,-1.5211626039999999,0,-25,25,-1.0970644700531507 -coef_duration_constants_12_to_13_hours,-2.6774099148644868,-2.4184889169999999,0,-25,25,-2.6774099148644868 -coef_duration_constants_14_to_18_hours,-2.6488356018470931,-2.5031372950000002,0,-25,25,-2.6488356018470931 -coef_duration_constants_3_to_4_hours,-0.56119226026187308,-0.74589325200000001,0,-25,25,-0.56119226026187308 -coef_duration_constants_5_to_6_hours,-0.52869720683509602,-0.56763662199999998,0,-25,25,-0.52869720683509602 -coef_duration_constants_7_to_8_hours,0,0,0,0,0,0 -coef_duration_constants_9_hours,-0.47578654406256893,-0.65080668399999997,0,-25,25,-0.47578654406256893 -coef_first_of_2plus_school_lt_6_hours,0.36465272850981928,1.4870000000000001,0,-25,25,0.36465272850981928 -coef_first_of_2plus_school_tours_departure,-0.28345842741237504,-0.30020000000000002,0,-25,25,-0.28345842741237504 -coef_first_of_2plus_school_tours_duration,-0.0072205064801072029,-0.1593,0,-25,25,-0.0072205064801072029 -coef_ft_worker_departure,0.39710000000000001,0.39710000000000001,0,-25,25,0.39710000000000001 -coef_ft_worker_duration,-0.1908,-0.1908,0,-25,25,-0.1908 -coef_hh_income_early_departure,-12.781415662231149,-0.88370000000000004,0,-25,25,-12.781415662231149 -coef_hh_income_late_arrival,0.13481375603358139,-0.3533,0,-25,25,0.13481375603358139 -coef_mode_choice_logsum,-0.15485890248282916,2.1269999999999998,0,-25,25,-0.15485890248282916 -coef_non_worker_departure,0.55389999999999995,0.55389999999999995,0,-25,25,0.55389999999999995 -coef_previous_tour_begins_this_arrival_hour,-5.5053579900953125,-1.1020000000000001,0,-25,25,-5.5053579900953125 -coef_previous_tour_ends_this_departure_hour,-15.038466619861389,-0.59950000000000003,0,-25,25,-15.038466619861389 -coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-24.999999999999979,-16.670000000000002,0,-25,25,-24.999999999999979 -coef_roundtrip_auto_time_to_work,0.0053019952333040361,0.0031949999999999999,0,-25,25,0.0053019952333040361 -coef_school_plus_work_tours_by_student_lt_6_hours,-1.1660913354672906,1.73,0,-25,25,-1.1660913354672906 -coef_school_plus_work_tours_by_worker_lt_6_hours,-0.75409133546729057,2.1419999999999999,0,-25,25,-0.75409133546729057 -coef_student_driver_duration,-0.0068046815074113429,0.034639999999999997,0,-25,25,-0.0068046815074113429 -coef_subsequent_2plus_school_tours_duration,-0.21896654709477803,-0.23380000000000001,0,-25,25,-0.21896654709477803 -coef_subsequent_of_2plus_school_lt_6_hours,24.999999999999982,2.1419999999999999,0,-25,25,24.999999999999982 -coef_subsequent_tour_must_start_after_previous_tour_ends,-100,-100,0,-100,-100,-100 -coef_univ_departure,0.22263900806749343,0.28000000000000003,0,-25,25,0.22263900806749343 -coef_univ_duration,-0.41672805296299825,-0.29070000000000001,0,-25,25,-0.41672805296299825 +param_name,value,best,initvalue,nullvalue +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.080775447776812753,0.080774252667694169,-0.0030489999335259199,0 +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.51836135663447769,-0.51836174434913773,-0.52710002660751343,0 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.73433095650246494,0.73432154552615458,0.089749999344348907,0 +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.99378159059164084,-0.99377515349106949,-0.43999999761581421,0 +coef_all_adults_ft_worker_duration,0.033395472694728347,0.033395444616199747,0.10930000245571136,0 +coef_arrival_constants_am_peak,-2.1992574993931737,-2.1992595446798857,-2.4287183284759521,0 +coef_arrival_constants_early,-1.0152816371137836,-1.0152823898389056,-2.4287183284759521,0 +coef_arrival_constants_evening,-1.089014071549075,-1.089012444258362,-0.87014693021774292,0 +coef_arrival_constants_late,-1.4094125261836428,-1.4094104317113427,-1.7520004510879517,0 +coef_arrival_constants_midday_1,-1.3102781509339738,-1.3102792663184144,-1.2379087209701538,0 +coef_arrival_constants_midday_2,-0.5570164575887101,-0.55701686342809786,-0.53976893424987793,0 +coef_arrival_constants_pm_peak_1,0,0,0,0 +coef_arrival_constants_pm_peak_2,-0.48446733503368733,-0.48446736247771904,-0.38916924595832825,0 +coef_arrival_constants_pm_peak_3,-0.31506510944596333,-0.31506390971297477,-0.1981203556060791,0 +coef_arrival_constants_pm_peak_4,-0.4718642141386703,-0.4718634888383082,-0.25362467765808105,0 +coef_departure_constants_am_peak_1,-1.8553572932845479,-1.8553571741469013,-1.6176440715789795,0 +coef_departure_constants_am_peak_2,0,0,0,0 +coef_departure_constants_am_peak_3,-0.14106707640383398,-0.14106757305590037,-0.07382684201002121,0 +coef_departure_constants_am_peak_4,-1.9882913840565575,-1.988292157571423,-2.0805706977844238,0 +coef_departure_constants_early,-3.6786226227417624,-3.6786220135061134,-3.8206624984741211,0 +coef_departure_constants_evening,-5.2925208449121817,-5.2925240767147788,-5.230288028717041,0 +coef_departure_constants_late,-11.916832030849566,-11.916831561227097,-11.88604736328125,0 +coef_departure_constants_midday_1,-3.2113751054704132,-3.2113771777110944,-2.9857394695281982,0 +coef_departure_constants_midday_2,-3.6981526513555871,-3.6981547479895061,-3.6284346580505371,0 +coef_departure_constants_pm_peak,-2.9973467155658162,-2.9973494814295885,-3.1025052070617676,0 +coef_duration_constants_0_to_2_hours,-1.3996475888297963,-1.3996458652500943,-1.4099557399749756,0 +coef_duration_constants_10_hours,-0.82164737941042931,-0.82164784328828933,-0.90478897094726562,0 +coef_duration_constants_11_hours,-1.0464064831927971,-1.0464075406530777,-1.5211626291275024,0 +coef_duration_constants_12_to_13_hours,-2.5901815246633957,-2.5901814921437252,-2.4184889793395996,0 +coef_duration_constants_14_to_18_hours,-2.5870585605577858,-2.5870607126569682,-2.5031373500823975,0 +coef_duration_constants_3_to_4_hours,-0.56514744374131198,-0.56514595486014574,-0.74589323997497559,0 +coef_duration_constants_5_to_6_hours,-0.53548913813929444,-0.53548808091419775,-0.56763660907745361,0 +coef_duration_constants_7_to_8_hours,0,0,0,0 +coef_duration_constants_9_hours,-0.45285392800018198,-0.4528543455236822,-0.65080666542053223,0 +coef_first_of_2plus_school_lt_6_hours,0.024871811138907744,0.024881803705537723,1.4869999885559082,0 +coef_first_of_2plus_school_tours_departure,-0.29460766022668305,-0.29460728950989984,-0.30019998550415039,0 +coef_first_of_2plus_school_tours_duration,-0.10138864589931282,-0.10138705511807192,-0.15929999947547913,0 +coef_ft_worker_departure,0.39710000157356262,0.39710000157356262,0.39710000157356262,0 +coef_ft_worker_duration,-0.19079999625682831,-0.19079999625682831,-0.19079999625682831,0 +coef_hh_income_early_departure,-2.4518333628592055,-2.4518122384333916,-0.88370001316070557,0 +coef_hh_income_late_arrival,0.0099574185770053714,0.0099600258474820229,-0.35330000519752502,0 +coef_mode_choice_logsum,0.10646038792012041,0.10645974892637354,2.127000093460083,0 +coef_non_worker_departure,0.55390000343322754,0.55390000343322754,0.55390000343322754,0 +coef_previous_tour_begins_this_arrival_hour,-1.1044099604419229,-1.1044099196469588,-1.1019999980926514,0 +coef_previous_tour_ends_this_departure_hour,-0.98618911184573188,-0.98618197798375551,-0.59950000047683716,0 +coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-16.801395249039167,-16.801393279303245,-16.670000076293945,0 +coef_roundtrip_auto_time_to_work,0.0052250922026635376,0.0052251100813601051,0.0031950001139193773,0 +coef_school_plus_work_tours_by_student_lt_6_hours,1.7724913916824447,1.7724908031221758,1.7300000190734863,0 +coef_school_plus_work_tours_by_worker_lt_6_hours,2.1844913325546376,2.1844907439943686,2.1419999599456787,0 +coef_student_driver_duration,-0.0049771487710083039,-0.0049771201652761641,0.034639999270439148,0 +coef_subsequent_2plus_school_tours_duration,-0.22458834432940281,-0.22458959182491992,-0.23379999399185181,0 +coef_subsequent_of_2plus_school_lt_6_hours,2.8438198153956726,2.8438099345673602,2.1419999599456787,0 +coef_subsequent_tour_must_start_after_previous_tour_ends,-100,-100,-100,0 +coef_univ_departure,0.23875445577073184,0.23875440862293787,0.2800000011920929,0 +coef_univ_duration,-0.40329629710371528,-0.40329638768202386,-0.29069998860359192,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv index 9465192ef9..70e5cc97c5 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_mandatory_tour_scheduling_work_SLSQP_.csv @@ -1,64 +1,64 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,25,0.36270000000000002,0,-25,25,25 -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,0.67172411345437255,-0.1012,0,-25,25,0.67172411345437255 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,-0.2705642011106818,0.17710000000000001,0,-25,25,-0.2705642011106818 -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,2.3126663890343506,-0.21229999999999999,0,-25,25,2.3126663890343506 -coef_arrival_constants_am_peak,-2.5608583720316691,-1.854520626,0,-25,25,-2.5608583720316691 -coef_arrival_constants_early,0,0,0,0,0,0 -coef_arrival_constants_evening,-0.11313543070951976,0.103566251,0,-25,25,-0.11313543070951976 -coef_arrival_constants_late,-1.2047708015331426,-0.96595733900000003,0,-25,25,-1.2047708015331426 -coef_arrival_constants_midday_1,-0.4666152062959858,-0.49597203699999998,0,-25,25,-0.4666152062959858 -coef_arrival_constants_midday_2,-0.37297675905912303,-0.37855408099999999,0,-25,25,-0.37297675905912303 -coef_arrival_constants_pm_peak_1,0,0,0,0,0,0 -coef_arrival_constants_pm_peak_2,0.20356469833981647,0.27608389999999999,0,-25,25,0.20356469833981647 -coef_arrival_constants_pm_peak_3,0.55940049285496218,0.699587132,0,-25,25,0.55940049285496218 -coef_arrival_constants_pm_peak_4,0.69955932869966053,0.79928937700000002,0,-25,25,0.69955932869966053 -coef_departure_constants_am_peak_1,-0.65012576984935755,-0.61618090599999997,0,-25,25,-0.65012576984935755 -coef_departure_constants_am_peak_2,0,0,0,0,0,0 -coef_departure_constants_am_peak_3,-0.32740035887947389,-0.254714726,0,-25,25,-0.32740035887947389 -coef_departure_constants_am_peak_4,-1.2190845361495699,-1.2513460240000001,0,-25,25,-1.2190845361495699 -coef_departure_constants_early,-0.92470832388900248,-0.95272526999999996,0,-25,25,-0.92470832388900248 -coef_departure_constants_evening,-1.8906229365299492,-1.610513243,0,-25,25,-1.8906229365299492 -coef_departure_constants_late,-2.9986176789858154,-2.8834152230000001,0,-25,25,-2.9986176789858154 -coef_departure_constants_midday_1,-1.8241536006077277,-1.7058689920000001,0,-25,25,-1.8241536006077277 -coef_departure_constants_midday_2,-1.8783523324805591,-1.6935705830000001,0,-25,25,-1.8783523324805591 -coef_departure_constants_pm_peak,-1.6396663560770837,-1.4399919619999999,0,-25,25,-1.6396663560770837 -coef_destination_in_cbd_departure_shift_effects,0.04109611455586798,0.047169999999999997,0,-25,25,0.04109611455586798 -coef_destination_in_cbd_duration_shift_effects,0.10130692865710192,0.086790000000000006,0,-25,25,0.10130692865710192 -coef_destination_in_cbd_early_departure_interaction,-0.38984353468069122,-0.45660000000000001,0,-25,25,-0.38984353468069122 -coef_destination_in_cbd_late_arrival_interaction,-0.27055298689359897,-0.2334,0,-25,25,-0.27055298689359897 -coef_duration_constants_0_to_2_hours,-2.1189652588431978,-2.5282663900000002,0,-25,25,-2.1189652588431978 -coef_duration_constants_10_hours,0,0,0,0,0,0 -coef_duration_constants_11_hours,-0.35443610468830039,-0.34779539100000001,0,-25,25,-0.35443610468830039 -coef_duration_constants_12_to_13_hours,-1.1153352032051054,-1.0082223459999999,0,-25,25,-1.1153352032051054 -coef_duration_constants_14_to_18_hours,-1.8418267064323726,-1.701858847,0,-25,25,-1.8418267064323726 -coef_duration_constants_3_to_4_hours,-0.63573505547017051,-0.91897445700000002,0,-25,25,-0.63573505547017051 -coef_duration_constants_5_to_6_hours,-0.35577473775309831,-0.71855028799999998,0,-25,25,-0.35577473775309831 -coef_duration_constants_7_to_8_hours,-0.033757586303302736,-0.139623566,0,-25,25,-0.033757586303302736 -coef_duration_constants_9_hours,0.12934760728519379,0.055706243000000003,0,-25,25,0.12934760728519379 -coef_first_of_2plus_work_tours_departure_shift_effects,-0.27106753260112321,-0.30330000000000001,0,-25,25,-0.27106753260112321 -coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.8948098471889567,1.98,0,-25,25,1.8948098471889567 -coef_first_of_2plus_work_tours_duration_shift_effects,-0.29362220747793244,-0.18609999999999999,0,-25,25,-0.29362220747793244 -coef_free_flow_round_trip_auto_time_shift_effects_departure,0.00054374628944530747,-0.00114,0,-25,25,0.00054374628944530747 -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0040398779948456849,0.0022100000000000002,0,-25,25,0.0040398779948456849 -coef_full_time_worker_10_to_12_departure_interaction,-0.66614758973349719,-0.51819999999999999,0,-25,25,-0.66614758973349719 -coef_full_time_worker_duration_lt_9_hours_interaction,-1.3694302807642016,-1.2569999999999999,0,-25,25,-1.3694302807642016 -coef_household_income_departure_shift_effects,0.00011200417166098945,0.00020799999999999999,0,-25,25,0.00011200417166098945 -coef_household_income_early_departure_interaction,-0.57489507784478577,-0.4854,0,-25,25,-0.57489507784478577 -coef_household_income_late_arrival_interaction,0.036426020508396037,-0.38390000000000002,0,-25,25,0.036426020508396037 -coef_mode_choice_logsum,0.20613495321472861,1.0269999999999999,0,-25,25,0.20613495321472861 -coef_non_working_adult_duration_shift_effects,-0.1207,-0.1207,0,-25,25,-0.1207 -coef_part_time_worker_13_to_15_arrival_interaction,0.33677938786979689,0.54330000000000001,0,-25,25,0.33677938786979689 -coef_part_time_worker_departure_shift_effects,0.089714285580281661,0.067360000000000003,0,-25,25,0.089714285580281661 -coef_previously_scheduled_tour_begins_in_this_arrival_hour,-6.6033938783133914,-1.3340000000000001,0,-25,25,-6.6033938783133914 -coef_previously_scheduled_tour_ends_in_this_departure_hour,2.4216789099176217,-0.89349999999999996,0,-25,25,2.4216789099176217 -coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-6.3408961734536069,-18.68,0,-25,25,-6.3408961734536069 -coef_rural_household_early_departure_interaction,0.40389999999999998,0.40389999999999998,0,-25,25,0.40389999999999998 -coef_rural_household_late_arrival_interaction,-0.34510000000000002,-0.34510000000000002,0,-25,25,-0.34510000000000002 -coef_subsequent_2plus_work_departure_tours_shift_effects,0.015346535529312815,-0.53810000000000002,0,-25,25,0.015346535529312815 -coef_subsequent_2plus_work_duration_tours_shift_effects,-0.12773109887960765,-0.31740000000000002,0,-25,25,-0.12773109887960765 -coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,3.5917959509926001,2.5819999999999999,0,-25,25,3.5917959509926001 -coef_subsequent_tour_must_start_after_previous_tour_ends,-100,-100,0,-100,-100,-100 -coef_tours_by_student_duration_lt_8_hrs,14.705420185896203,2.5819999999999999,0,-25,25,14.705420185896203 -coef_tours_by_worker_duration_lt_8_hrs,13.036020185896209,0.91259999999999997,0,-25,25,13.036020185896209 -coef_university_student_departure_shift_effects,0.052139288632078368,0.05747,0,-25,25,0.052139288632078368 +param_name,value,best,initvalue,nullvalue +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.61251353990327295,0.61251327841360381,0.36269998550415039,0 +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.48096392277398275,-0.48096399411879609,-0.10119999945163727,0 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,-0.2616069716319126,-0.26160684847759125,0.17710000276565552,0 +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.21226499679315533,-0.2122651617637292,-0.21230000257492065,0 +coef_arrival_constants_am_peak,-2.5606473297230243,-2.5606476523528108,-1.8545206785202026,0 +coef_arrival_constants_early,0,0,0,0 +coef_arrival_constants_evening,-0.052167725404331296,-0.052167735953000123,0.1035662516951561,0 +coef_arrival_constants_late,-1.1308494423719302,-1.1308494377605862,-0.96595734357833862,0 +coef_arrival_constants_midday_1,-0.49891346385413454,-0.49891340446037452,-0.49597203731536865,0 +coef_arrival_constants_midday_2,-0.41039411498088946,-0.41039409639542923,-0.3785540759563446,0 +coef_arrival_constants_pm_peak_1,0,0,0,0 +coef_arrival_constants_pm_peak_2,0.19372908430399155,0.19372906136658175,0.27608388662338257,0 +coef_arrival_constants_pm_peak_3,0.56028646263237181,0.5602864708684514,0.69958710670471191,0 +coef_arrival_constants_pm_peak_4,0.71571247641605751,0.71571248954657329,0.79928940534591675,0 +coef_departure_constants_am_peak_1,-0.64620733049904744,-0.6462073659079336,-0.6161808967590332,0 +coef_departure_constants_am_peak_2,0,0,0,0 +coef_departure_constants_am_peak_3,-0.32992168661687343,-0.32992174133643332,-0.2547147274017334,0 +coef_departure_constants_am_peak_4,-1.2230313612788632,-1.2230313737462095,-1.2513459920883179,0 +coef_departure_constants_early,-0.91653242223268816,-0.91653247537180627,-0.95272529125213623,0 +coef_departure_constants_evening,-1.5545124755865074,-1.5545123072441225,-1.6105132102966309,0 +coef_departure_constants_late,-3.4650976197335996,-3.4650970444155127,-2.8834152221679688,0 +coef_departure_constants_midday_1,-1.8459227529362676,-1.845922814199904,-1.7058689594268799,0 +coef_departure_constants_midday_2,-1.872714720755712,-1.8727145745963636,-1.693570613861084,0 +coef_departure_constants_pm_peak,-1.5304306861299761,-1.5304305915064496,-1.4399919509887695,0 +coef_destination_in_cbd_departure_shift_effects,0.046972898038599296,0.046972900170898703,0.047169998288154602,0 +coef_destination_in_cbd_duration_shift_effects,0.10546600473625205,0.10546600158406226,0.086790002882480621,0 +coef_destination_in_cbd_early_departure_interaction,-0.36454380057515523,-0.36454382109906797,-0.45660001039505005,0 +coef_destination_in_cbd_late_arrival_interaction,-0.34125580756261459,-0.34125584021066124,-0.23340000212192535,0 +coef_duration_constants_0_to_2_hours,-2.3611872557258775,-2.3611872136185283,-2.528266429901123,0 +coef_duration_constants_10_hours,0,0,0,0 +coef_duration_constants_11_hours,-0.36305106792718905,-0.36305106353810024,-0.34779539704322815,0 +coef_duration_constants_12_to_13_hours,-1.1336847773846157,-1.1336846557009517,-1.0082223415374756,0 +coef_duration_constants_14_to_18_hours,-1.846775754300668,-1.8467757187472769,-1.7018588781356812,0 +coef_duration_constants_3_to_4_hours,-0.69884369798611612,-0.69884383804256189,-0.91897445917129517,0 +coef_duration_constants_5_to_6_hours,-0.36088550481193393,-0.36088561328123253,-0.71855026483535767,0 +coef_duration_constants_7_to_8_hours,-0.034562406928364778,-0.03456241250333917,-0.13962356746196747,0 +coef_duration_constants_9_hours,0.12410673449583931,0.12410671848676948,0.055706243962049484,0 +coef_first_of_2plus_work_tours_departure_shift_effects,-0.27939395433691766,-0.27939398231314772,-0.30329999327659607,0 +coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.6819917313175938,1.681991727718217,1.9800000190734863,0 +coef_first_of_2plus_work_tours_duration_shift_effects,-0.26704509800964044,-0.26704506536823513,-0.18610000610351562,0 +coef_free_flow_round_trip_auto_time_shift_effects_departure,-4.6712935036587893e-05,-4.6713265092855231e-05,-0.0011399999493733048,0 +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0034709225619443279,0.0034709219570404487,0.0022100000642240047,0 +coef_full_time_worker_10_to_12_departure_interaction,-0.67084155893679287,-0.67084150978347679,-0.51819998025894165,0 +coef_full_time_worker_duration_lt_9_hours_interaction,-1.3707861942780288,-1.3707861874425684,-1.2569999694824219,0 +coef_household_income_departure_shift_effects,9.8289823547548591e-05,9.8289830800329784e-05,0.00020799999765586108,0 +coef_household_income_early_departure_interaction,-0.59011172274320045,-0.59011172534232015,-0.48539999127388,0 +coef_household_income_late_arrival_interaction,-0.018847335744915452,-0.018847292627549972,-0.383899986743927,0 +coef_mode_choice_logsum,0.41216509072053042,0.41216516341709108,1.0269999504089355,0 +coef_non_working_adult_duration_shift_effects,-0.12070000171661377,-0.12070000171661377,-0.12070000171661377,0 +coef_part_time_worker_13_to_15_arrival_interaction,0.32725200921090258,0.32725199183573034,0.54329997301101685,0 +coef_part_time_worker_departure_shift_effects,0.089866833957687201,0.089866835119917624,0.067359998822212219,0 +coef_previously_scheduled_tour_begins_in_this_arrival_hour,-1.3452365484106898,-1.3452365345169786,-1.3339999914169312,0 +coef_previously_scheduled_tour_ends_in_this_departure_hour,-0.62131382592128181,-0.62131390854231106,-0.89349997043609619,0 +coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-18.639061018727794,-18.639061062575436,-18.680000305175781,0 +coef_rural_household_early_departure_interaction,0.40389999747276306,0.40389999747276306,0.40389999747276306,0 +coef_rural_household_late_arrival_interaction,-0.34509998559951782,-0.34509998559951782,-0.34509998559951782,0 +coef_subsequent_2plus_work_departure_tours_shift_effects,-0.3844333665111534,-0.38443334417322472,-0.53810000419616699,0 +coef_subsequent_2plus_work_duration_tours_shift_effects,-0.38214881037696108,-0.38214876273993459,-0.3174000084400177,0 +coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,1.2383538709437807,1.2383542865170418,2.5820000171661377,0 +coef_subsequent_tour_must_start_after_previous_tour_ends,-100,-100,-100,0 +coef_tours_by_student_duration_lt_8_hrs,2.6806620027310135,2.6806618957074346,2.5820000171661377,0 +coef_tours_by_worker_duration_lt_8_hrs,1.0112619663960221,1.0112618593724432,0.91259998083114624,0 +coef_university_student_departure_shift_effects,0.068330955102533286,0.068330906364886979,0.05747000128030777,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv index 68cc071e89..3c8980345c 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_scheduling_model_non_mandatory_tour_scheduling_SLSQP_.csv @@ -1,90 +1,90 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.4470328861441506,-0.025700000000000001,0,-25,25,0.4470328861441506 -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,0.56540266502429914,-0.02734,0,-25,25,0.56540266502429914 -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,1.0259323894447316,0.0084419999999999999,0,-25,25,1.0259323894447316 -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,0.39614317482379957,-0.059299999999999999,0,-25,25,0.39614317482379957 -coef_adult_with_children_in_hh_arrive_19_21,0.31331407963369601,0.33600000000000002,0,-25,25,0.31331407963369601 -coef_arrival_constants_am_peak,0.29950075228772494,-1.814822602,0,-25,25,0.29950075228772494 -coef_arrival_constants_early,2.725267289110473,-0.051990748000000003,0,-25,25,2.725267289110473 -coef_arrival_constants_evening,0,0,0,0,0,0 -coef_arrival_constants_late,-1.1446323020070934,-0.86667131500000005,0,-25,25,-1.1446323020070934 -coef_arrival_constants_midday_1,1.3878018540712422,0.00037150099999999999,0,-25,25,1.3878018540712422 -coef_arrival_constants_midday_2,1.5044255895039795,0.53211603100000004,0,-25,25,1.5044255895039795 -coef_arrival_constants_pm_peak_1,1.3575212925702953,0.62848156700000002,0,-25,25,1.3575212925702953 -coef_arrival_constants_pm_peak_2,1.5579462028570845,0.65052141600000002,0,-25,25,1.5579462028570845 -coef_arrival_constants_pm_peak_3,1.0806181769202525,0.40289440599999998,0,-25,25,1.0806181769202525 -coef_arrival_constants_pm_peak_4,0.69735759210419612,0.154213293,0,-25,25,0.69735759210419612 -coef_departure_constants_am_peak_1,-0.52735012006812276,-0.65416357300000005,0,-25,25,-0.52735012006812276 -coef_departure_constants_am_peak_2,1.1919565496484956,0.554282571,0,-25,25,1.1919565496484956 -coef_departure_constants_am_peak_3,1.4522880996476126,1.0505610869999999,0,-25,25,1.4522880996476126 -coef_departure_constants_am_peak_4,1.5114204226663379,0.97156822799999998,0,-25,25,1.5114204226663379 -coef_departure_constants_early,-0.92104869293940328,-1.7401356610000001,0,-25,25,-0.92104869293940328 -coef_departure_constants_evening,-1.892558890126933,-1.856475096,0,-25,25,-1.892558890126933 -coef_departure_constants_late,-5.6265216713840269,-8.2288801409999994,0,-25,25,-5.6265216713840269 -coef_departure_constants_midday_1,1.3642337556324493,0.88199198599999995,0,-25,25,1.3642337556324493 -coef_departure_constants_midday_2,0.64556751877721019,0.411103634,0,-25,25,0.64556751877721019 -coef_departure_constants_pm_peak,0,0,0,0,0,0 -coef_destination_in_cbd_duration_shift_effects,0.11545735776937631,0.1067,0,-25,25,0.11545735776937631 -coef_discretionary_tour_duration_lt_2_hours,-0.28570443543294716,-0.69740000000000002,0,-25,25,-0.28570443543294716 -coef_duration_constants_0_to_1_hours,0,0,0,0,0,0 -coef_duration_constants_11_to_13_hours,-1.0074136237311473,-0.955635554,0,-25,25,-1.0074136237311473 -coef_duration_constants_14_to_18_hours,-2.9449650044604194,-1.042580879,0,-25,25,-2.9449650044604194 -coef_duration_constants_2_to_3_hours,0.3987726313758776,0.051385565000000001,0,-25,25,0.3987726313758776 -coef_duration_constants_4_to_5_hours,-0.27952507767146284,-0.59395132100000003,0,-25,25,-0.27952507767146284 -coef_duration_constants_6_to_7_hours,-0.65619904538586438,-0.95115532800000002,0,-25,25,-0.65619904538586438 -coef_duration_constants_8_to_10_hours,-0.62326492676484069,-0.828108399,0,-25,25,-0.62326492676484069 -coef_eat_out_tour_departure_shift_effects,0.10408922995281709,0.075490000000000002,0,-25,25,0.10408922995281709 -coef_escort_tour_arrival_constants_am_peak,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_early,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_evening,-0.8931637699400693,-0.53691872799999996,0,-25,25,-0.8931637699400693 -coef_escort_tour_arrival_constants_late,-1.1770797692787565,-1.008290213,0,-25,25,-1.1770797692787565 -coef_escort_tour_arrival_constants_midday_1,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_midday_2,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_pm_peak_1,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_pm_peak_2,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_pm_peak_3,0,0,0,0,0,0 -coef_escort_tour_arrival_constants_pm_peak_4,0,0,0,0,0,0 -coef_escort_tour_departure_constants_am_peak_1,0.48291466233476604,-1.1123577529999999,0,-25,25,0.48291466233476604 -coef_escort_tour_departure_constants_am_peak_2,2.0132225505169186,0.69878818499999995,0,-25,25,2.0132225505169186 -coef_escort_tour_departure_constants_am_peak_3,2.1327074544894873,1.1962688130000001,0,-25,25,2.1327074544894873 -coef_escort_tour_departure_constants_am_peak_4,0.29911257358855436,-0.22525822100000001,0,-25,25,0.29911257358855436 -coef_escort_tour_departure_constants_early,-0.5151665525123571,-1.7401356610000001,0,-25,25,-0.5151665525123571 -coef_escort_tour_departure_constants_evening,-4.7638859598327503,-3.9487328110000002,0,-25,25,-4.7638859598327503 -coef_escort_tour_departure_constants_late,-6.5766274559402813,-8.2288801409999994,0,-25,25,-6.5766274559402813 -coef_escort_tour_departure_constants_midday_1,0.64080123578544568,0.028662017000000001,0,-25,25,0.64080123578544568 -coef_escort_tour_departure_constants_midday_2,0,0,0,0,0,0 -coef_escort_tour_departure_constants_pm_peak,-1.78947739154685,-1.180140161,0,-25,25,-1.78947739154685 -coef_escort_tour_duration_constants_0_to_1_hours,0,0,0,0,0,0 -coef_escort_tour_duration_constants_11_to_13_hours,-6.4203324936004007,-2.9743649759999999,0,-25,25,-6.4203324936004007 -coef_escort_tour_duration_constants_14_to_18_hours,-9.2410499900666672,-2.5074471460000001,0,-25,25,-9.2410499900666672 -coef_escort_tour_duration_constants_2_to_3_hours,-2.600430326093385,-2.0420138969999999,0,-25,25,-2.600430326093385 -coef_escort_tour_duration_constants_4_to_5_hours,-3.5279045037998431,-2.880293896,0,-25,25,-3.5279045037998431 -coef_escort_tour_duration_constants_6_to_7_hours,-3.9930263301758195,-2.9735337309999998,0,-25,25,-3.9930263301758195 -coef_escort_tour_duration_constants_8_to_10_hours,-5.1822459061747068,-3.0202137580000001,0,-25,25,-5.1822459061747068 -coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.30486234400858059,-0.2364,0,-25,25,-0.30486234400858059 -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0050429319781780269,0.0047410000000000004,0,-25,25,0.0050429319781780269 -coef_maintenance_tour_depart_before_7,0.052350894248540496,-0.88260000000000005,0,-25,25,0.052350894248540496 -coef_maintenance_tour_departure_shift_effects,-0.041432645424667555,-0.1489,0,-25,25,-0.041432645424667555 -coef_maintenance_tour_duration_shift_effects,-0.11717847942290449,-0.083720000000000003,0,-25,25,-0.11717847942290449 -coef_number_of_escort_tours_departure_shift_effects,0.1003621581289256,0.020129999999999999,0,-25,25,0.1003621581289256 -coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.098215723941853808,0.038960000000000002,0,-25,25,0.098215723941853808 -coef_number_of_joint_tours_departure_shift_effects,0.12273335177942903,0.052080000000000001,0,-25,25,0.12273335177942903 -coef_number_of_mandatory_tours_departure_shift_effects,0.1726876041064854,0.046730000000000001,0,-25,25,0.1726876041064854 -coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,5.7502384461866223,-13.630000000000001,0,-25,25,5.7502384461866223 -coef_school_child_age_16_plus_departure_shift_effects,0.002334001767561128,0.072660000000000002,0,-25,25,0.002334001767561128 -coef_school_child_age_16_plus_duration_shift_effects,0.30512330091593426,0.20949999999999999,0,-25,25,0.30512330091593426 -coef_school_child_age_under_16_departure_shift_effects,0.1028104094868405,0.04657,0,-25,25,0.1028104094868405 -coef_school_child_age_under_16_duration_shift_effects,0.33667040040989843,0.32719999999999999,0,-25,25,0.33667040040989843 -coef_school_child_under_16_arrive_after_22,-0.29870062575080597,-1.1799999999999999,0,-25,25,-0.29870062575080597 -coef_shopping_tour_arrive_after_22,-2.4414941640980703,-0.60270000000000001,0,-25,25,-2.4414941640980703 -coef_shopping_tour_depart_before_8,-0.84790437236499061,-1.0369999999999999,0,-25,25,-0.84790437236499061 -coef_shopping_tour_departure_shift_effects,0.0090862416686240035,-0.060150000000000002,0,-25,25,0.0090862416686240035 -coef_shopping_tour_duration_lt_2_hours,0.912982413595951,0.51680000000000004,0,-25,25,0.912982413595951 -coef_shopping_tour_duration_shift_effects,-0.08012681553606732,-0.1208,0,-25,25,-0.08012681553606732 -coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.004765610611209883,-0.3992,0,-25,25,-0.004765610611209883 -coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.28646699211356075,-0.45619999999999999,0,-25,25,-0.28646699211356075 -coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.10477927540016083,-0.1731,0,-25,25,-0.10477927540016083 -coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,-999,-999,0,-999,-999,-999 -coef_university_student_arrive_after_22,-0.4840281170137713,0.54659999999999997,0,-25,25,-0.4840281170137713 -coef_visit_tour_departure_shift_effects,0.1674492151765343,0.096879999999999994,0,-25,25,0.1674492151765343 -coef_visit_tour_duration_shift_effects,0.15890840399796446,0.1638,0,-25,25,0.15890840399796446 +param_name,value,best,initvalue,nullvalue +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.37873530652415688,-0.37876029334525779,-0.025699999183416367,0 +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.12409933488783795,-0.12412525709082863,-0.027340000495314598,0 +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.16013173725389518,0.16011972294122639,0.0084419995546340942,0 +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.057675497288593967,-0.057678856669874018,-0.059300001710653305,0 +coef_adult_with_children_in_hh_arrive_19_21,0.26358073098680268,0.26357366595375664,0.335999995470047,0 +coef_arrival_constants_am_peak,-1.618479614096983,-1.6184779227729857,-1.8148225545883179,0 +coef_arrival_constants_early,0.012632463274022693,0.012638163573529756,-0.051990747451782227,0 +coef_arrival_constants_evening,0,0,0,0 +coef_arrival_constants_late,-0.77439458777380288,-0.77440113819912593,-0.86667132377624512,0 +coef_arrival_constants_midday_1,0.16302672327058698,0.16302166341062885,0.0003715009952429682,0 +coef_arrival_constants_midday_2,0.60562412779331609,0.60561823037837537,0.53211605548858643,0 +coef_arrival_constants_pm_peak_1,0.66305388881325122,0.66304628483786687,0.62848156690597534,0 +coef_arrival_constants_pm_peak_2,0.9403736312996136,0.94036844970191236,0.65052139759063721,0 +coef_arrival_constants_pm_peak_3,0.57943549534338168,0.57943058291341509,0.40289440751075745,0 +coef_arrival_constants_pm_peak_4,0.32882206659224378,0.32881862675984186,0.15421329438686371,0 +coef_departure_constants_am_peak_1,-0.8444053940276266,-0.8443867382251623,-0.65416359901428223,0 +coef_departure_constants_am_peak_2,0.61095352829384308,0.6109683536427154,0.554282546043396,0 +coef_departure_constants_am_peak_3,0.89783495233382704,0.89783925656317809,1.0505610704421997,0 +coef_departure_constants_am_peak_4,0.97678705420018241,0.97679068633385335,0.97156822681427002,0 +coef_departure_constants_early,-1.5655173152332842,-1.5655400591434045,-1.740135669708252,0 +coef_departure_constants_evening,-1.6745782734879573,-1.6745908894768251,-1.8564751148223877,0 +coef_departure_constants_late,-5.8677842794743897,-5.867810078393469,-8.2288799285888672,0 +coef_departure_constants_midday_1,0.90219691225188225,0.90220256652108655,0.88199198246002197,0 +coef_departure_constants_midday_2,0.33966490947802791,0.339668134288378,0.41110363602638245,0 +coef_departure_constants_pm_peak,0,0,0,0 +coef_destination_in_cbd_duration_shift_effects,0.12481964214980576,0.12482003975020485,0.10670000314712524,0 +coef_discretionary_tour_duration_lt_2_hours,-0.49111660197637685,-0.49110487979747403,-0.69739997386932373,0 +coef_duration_constants_0_to_1_hours,0,0,0,0 +coef_duration_constants_11_to_13_hours,-0.71826442870005769,-0.71826950153593283,-0.95563554763793945,0 +coef_duration_constants_14_to_18_hours,-1.3448264303153219,-1.3448138847159017,-1.0425808429718018,0 +coef_duration_constants_2_to_3_hours,0.18806168958819869,0.18807127149669869,0.051385566592216492,0 +coef_duration_constants_4_to_5_hours,-0.47110934268158594,-0.4711057056246632,-0.59395134449005127,0 +coef_duration_constants_6_to_7_hours,-0.82518931580282029,-0.82518905004791432,-0.95115530490875244,0 +coef_duration_constants_8_to_10_hours,-0.69268933767290863,-0.6927031726601327,-0.82810837030410767,0 +coef_eat_out_tour_departure_shift_effects,0.051316259249107425,0.051316222550347332,0.075489997863769531,0 +coef_escort_tour_arrival_constants_am_peak,0,0,0,0 +coef_escort_tour_arrival_constants_early,0,0,0,0 +coef_escort_tour_arrival_constants_evening,-0.71223140451440192,-0.71223691887938245,-0.53691869974136353,0 +coef_escort_tour_arrival_constants_late,-0.72737915035423661,-0.72736904512133638,-1.00829017162323,0 +coef_escort_tour_arrival_constants_midday_1,0,0,0,0 +coef_escort_tour_arrival_constants_midday_2,0,0,0,0 +coef_escort_tour_arrival_constants_pm_peak_1,0,0,0,0 +coef_escort_tour_arrival_constants_pm_peak_2,0,0,0,0 +coef_escort_tour_arrival_constants_pm_peak_3,0,0,0,0 +coef_escort_tour_arrival_constants_pm_peak_4,0,0,0,0 +coef_escort_tour_departure_constants_am_peak_1,-1.4235419252088168,-1.4235406297742828,-1.1123577356338501,0 +coef_escort_tour_departure_constants_am_peak_2,0.42531847435099135,0.42534779985548571,0.69878816604614258,0 +coef_escort_tour_departure_constants_am_peak_3,0.83882621454109263,0.83887850091215199,1.1962687969207764,0 +coef_escort_tour_departure_constants_am_peak_4,-0.83712685138635567,-0.83714012331863175,-0.22525821626186371,0 +coef_escort_tour_departure_constants_early,-2.860916885051735,-2.8608848408720089,-1.740135669708252,0 +coef_escort_tour_departure_constants_evening,-3.9409129509835932,-3.9409128427055573,-3.948732852935791,0 +coef_escort_tour_departure_constants_late,-5.7702770461708415,-5.77029206272538,-8.2288799285888672,0 +coef_escort_tour_departure_constants_midday_1,-0.1390533603155775,-0.1390075193706598,0.028662016615271568,0 +coef_escort_tour_departure_constants_midday_2,0,0,0,0 +coef_escort_tour_departure_constants_pm_peak,-1.4131855726269698,-1.4131494988499986,-1.1801401376724243,0 +coef_escort_tour_duration_constants_0_to_1_hours,0,0,0,0 +coef_escort_tour_duration_constants_11_to_13_hours,-3.9202328149945442,-3.9202404500970576,-2.9743649959564209,0 +coef_escort_tour_duration_constants_14_to_18_hours,-3.5604092863860122,-3.5604047727293211,-2.5074472427368164,0 +coef_escort_tour_duration_constants_2_to_3_hours,-2.421823365334085,-2.421833770793457,-2.0420138835906982,0 +coef_escort_tour_duration_constants_4_to_5_hours,-3.0455000043621441,-3.0455133085082999,-2.8802938461303711,0 +coef_escort_tour_duration_constants_6_to_7_hours,-3.1421992928711329,-3.1422099147681193,-2.9735336303710938,0 +coef_escort_tour_duration_constants_8_to_10_hours,-3.7497784769391589,-3.7497803018839528,-3.0202138423919678,0 +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.25834434616162955,-0.25834415314457576,-0.23639999330043793,0 +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.0065276813016913781,0.0065276968976601671,0.0047410000115633011,0 +coef_maintenance_tour_depart_before_7,-0.5056248112333217,-0.50561957768539378,-0.88260000944137573,0 +coef_maintenance_tour_departure_shift_effects,-0.14454038609055034,-0.14454021307852283,-0.14890000224113464,0 +coef_maintenance_tour_duration_shift_effects,-0.12686493168577978,-0.12686440360779777,-0.083719998598098755,0 +coef_number_of_escort_tours_departure_shift_effects,0.010507493295063721,0.010508213446682341,0.02012999914586544,0 +coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.044135687848751132,0.044135987290636135,0.03895999863743782,0 +coef_number_of_joint_tours_departure_shift_effects,0.084912280527453296,0.08491252930940224,0.052080001682043076,0 +coef_number_of_mandatory_tours_departure_shift_effects,0.049914049123035174,0.049914648254726204,0.046730000525712967,0 +coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,-14.166516751822661,-14.166519647615067,-13.630000114440918,0 +coef_school_child_age_16_plus_departure_shift_effects,-0.1010476102967722,-0.10104136957996175,0.072659999132156372,0 +coef_school_child_age_16_plus_duration_shift_effects,0.27141008329162575,0.27140733987575305,0.20949999988079071,0 +coef_school_child_age_under_16_departure_shift_effects,0.040466224371894295,0.040465357220666222,0.046569999307394028,0 +coef_school_child_age_under_16_duration_shift_effects,0.32700056448616932,0.32699981446388821,0.32719999551773071,0 +coef_school_child_under_16_arrive_after_22,-0.2464951245477969,-0.2464607983596627,-1.1799999475479126,0 +coef_shopping_tour_arrive_after_22,-2.0657698295000624,-2.065800932685089,-0.60269999504089355,0 +coef_shopping_tour_depart_before_8,-1.1138212368924993,-1.1138115434095399,-1.0369999408721924,0 +coef_shopping_tour_departure_shift_effects,-0.067931916027145156,-0.067931597698885862,-0.06015000119805336,0 +coef_shopping_tour_duration_lt_2_hours,0.67436877539980855,0.67440552001221821,0.51679998636245728,0 +coef_shopping_tour_duration_shift_effects,-0.10944476789632213,-0.10943885166753625,-0.12080000340938568,0 +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.30742282105349766,-0.30742665676699332,-0.39919999241828918,0 +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.42079038970222704,-0.42079362641039114,-0.4562000036239624,0 +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.15663240624251576,-0.15663237202517855,-0.17309999465942383,0 +coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,-999,-999,-999,0 +coef_university_student_arrive_after_22,-0.75698586492006881,-0.7570144484766741,0.54659998416900635,0 +coef_visit_tour_departure_shift_effects,0.093345651330126359,0.093345498661509735,0.096879996359348297,0 +coef_visit_tour_duration_shift_effects,0.15548956158347038,0.15549039376025442,0.16380000114440918,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_school_location.csv b/activitysim/estimation/test/test_larch_estimation/test_school_location.csv index 9149b65cb7..33b657c2c3 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_school_location.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_school_location.csv @@ -1,21 +1,21 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -1,1,1,1,1,1,1 -coef_grade_dist_0_1,-1.8247599716114487,-1.6418999999999999,0,-25,25,-1.8247599716114487 -coef_grade_dist_15_up,-0.045999999999986392,-0.045999999999999999,0,-25,25,-0.045999999999986392 -coef_grade_dist_5_15,-0.14366066473346295,-0.2031,0,-25,25,-0.14366066473346295 -coef_high_dist_0_1,-2.1639631787042557,-0.95230000000000004,0,-25,25,-2.1639631787042557 -coef_high_dist_15_up,-0.18819999999999787,-0.18820000000000001,0,-25,25,-0.18819999999999787 -coef_high_dist_5_15,-0.10033006353999419,-0.193,0,-25,25,-0.10033006353999419 -coef_high_grade_dist_1_2,-0.747678833666925,-0.56999999999999995,0,-25,25,-0.747678833666925 -coef_high_grade_dist_2_5,-0.66638388058588283,-0.56999999999999995,0,-25,25,-0.66638388058588283 -coef_mode_logsum,0.37505355574886973,0.53580000000000005,0,-25,25,0.37505355574886973 -coef_univ_dist_0_1,-3.3133821471324696,-3.2450999999999999,0,-25,25,-3.3133821471324696 -coef_univ_dist_15_up,-0.072999999999999995,-0.072999999999999995,0,-25,25,-0.072999999999999995 -coef_univ_dist_1_2,-2.8750960133089687,-2.7010999999999998,0,-25,25,-2.8750960133089687 -coef_univ_dist_2_5,-0.45559440871309681,-0.57069999999999999,0,-25,25,-0.45559440871309681 -coef_univ_dist_5_15,-0.5718644285076383,-0.50019999999999998,0,-25,25,-0.5718644285076383 -gradeschool_AGE0519,0,0,0,0,0,0 -highschool_HSENROLL,0,0,0,0,0,0 -university_COLLFTE,-0.52424864409813143,-0.52424864409813143,0,-0.52424864409813143,-0.52424864409813143,-0.52424864409813143 -university_COLLPTE,-1.2088722041237281,-0.89648810457797545,0,-6,6,-1.2088722041237281 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +1,1,1,1,0 +coef_grade_dist_0_1,0.080031235198373782,0.080031235198373782,-1.6418999433517456,0 +coef_grade_dist_15_up,-0.046010368210409304,-0.046010368210409304,-0.046000000089406967,0 +coef_grade_dist_5_15,0.011795679963699891,0.011795679963699891,-0.20309999585151672,0 +coef_high_dist_0_1,-0.48267138976026713,-0.48267138976026713,-0.95230001211166382,0 +coef_high_dist_15_up,-0.18818654818448871,-0.18818654818448871,-0.18819999694824219,0 +coef_high_dist_5_15,-0.12203943917498229,-0.12203943917498229,-0.19300000369548798,0 +coef_high_grade_dist_1_2,-0.2740077638163641,-0.2740077638163641,-0.56999999284744263,0 +coef_high_grade_dist_2_5,-0.12717991662132286,-0.12717991662132286,-0.56999999284744263,0 +coef_mode_logsum,0.28675535602711366,0.28675535602711366,0.53579998016357422,0 +coef_univ_dist_0_1,-0.20246864835878667,-0.20246864835878667,-3.2451000213623047,0 +coef_univ_dist_15_up,-0.072999830047635036,-0.072999830047635036,-0.072999998927116394,0 +coef_univ_dist_1_2,-0.50654663083945994,-0.50654663083945994,-2.7011001110076904,0 +coef_univ_dist_2_5,0.095856729554638809,0.095856729554638809,-0.57069998979568481,0 +coef_univ_dist_5_15,-0.1412133832985831,-0.1412133832985831,-0.50019997358322144,0 +gradeschool_AGE0519,0,0,0,0 +highschool_HSENROLL,0,0,0,0 +university_COLLFTE,-0.52424865961074829,-0.52424865961074829,-0.52424865961074829,0 +university_COLLPTE,-1275009.279056468,-1275009.279056468,-0.89648813009262085,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_school_location_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_school_location_loglike.csv index cf413cb0ad..61faa0892d 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_school_location_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_school_location_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-4070.349038545839,-4058.2217953154559 +0,-3150.9027927232128,-2472.1256126886806 diff --git a/activitysim/estimation/test/test_larch_estimation/test_school_location_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_school_location_size_spec.csv index 44852f23f4..b5a4700325 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_school_location_size_spec.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_school_location_size_spec.csv @@ -1,9 +1,9 @@ ,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE -0,work_low,workplace,0,0.12912912912912916,0.19319319319319325,0.38338338338338335,0.12012012012012015,0.010010010010010012,0.1641641641641641,0,0,0,0 -1,work_med,workplace,0,0.12012012012012013,0.19719719719719711,0.32532532532532538,0.13913913913913917,0.0080080080080080097,0.21021021021021022,0,0,0,0 +0,work_low,workplace,0,0.12912912912912911,0.19319319319319317,0.38338338338338335,0.12012012012012011,0.01001001001001001,0.16416416416416416,0,0,0,0 +1,work_med,workplace,0,0.12012012012012012,0.19719719719719719,0.32532532532532532,0.13913913913913914,0.0080080080080080079,0.21021021021021019,0,0,0,0 2,work_high,workplace,0,0.11,0.20699999999999999,0.28399999999999997,0.154,0.0060000000000000001,0.23899999999999999,0,0,0,0 3,work_veryhigh,workplace,0,0.092999999999999999,0.27000000000000002,0.24099999999999999,0.14599999999999999,0.0040000000000000001,0.246,0,0,0,0 -4,university,school,0,0,0,0,0,0,0,0,0,0.66475996651277125,0.33524003348722875 +4,university,school,0,0,0,0,0,0,0,0,0,1,0 5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 7,escort,non_mandatory,0,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46500000000000002,0.16600000000000001,0,0 @@ -14,10 +14,10 @@ 12,othdiscr,non_mandatory,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 13,atwork,atwork,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 14,work,trip,0,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0.16666666666666666,0,0,0,0 -15,escort,trip,0.0010000000000000002,0.22500000000000003,0,0.14400000000000002,0,0,0,0.46399999999999997,0.16600000000000004,0,0 +15,escort,trip,0.001,0.22500000000000001,0,0.14399999999999999,0,0,0,0.46400000000000002,0.16600000000000001,0,0 16,shopping,trip,0.001,0.999,0,0,0,0,0,0,0,0,0 17,eatout,trip,0,0.74199999999999999,0,0.25800000000000001,0,0,0,0,0,0,0 18,othmaint,trip,0.001,0.48099999999999998,0,0.51800000000000002,0,0,0,0,0,0,0 19,social,trip,0.001,0.52100000000000002,0,0.47799999999999998,0,0,0,0,0,0,0 20,othdiscr,trip,0.25225225225225223,0.2122122122122122,0,0.2722722722722723,0.16516516516516516,0,0,0,0.098098098098098108,0,0 -21,univ,trip,0.000999000999000999,0,0,0,0,0,0,0,0,0.59140859140859148,0.4075924075924075 +21,univ,trip,0.00099900099900099922,0,0,0,0,0,0,0,0,0.59140859140859148,0.40759240759240761 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_.csv index b05f6c9283..a34334c190 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_.csv @@ -1,111 +1,111 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coefficient_at_work_sub_tour_asc_business1,1.4214079856419304,-0.53720000000000001,0,,,1.4214079856419304 -coefficient_at_work_sub_tour_asc_business2,-0.13077793218906725,-2.1337000000000002,0,,,-0.13077793218906725 -coefficient_at_work_sub_tour_asc_eat,5.8090564007216319,0.85760000000000003,0,,,5.8090564007216319 -coefficient_at_work_sub_tour_asc_eat_business,-23.415365723862234,-0.97209999999999996,0,,,-23.415365723862234 -coefficient_at_work_sub_tour_asc_maint,12.910479269683176,-0.61980000000000002,0,,,12.910479269683176 -coefficient_auto_accessibility_to_retail_for_work_taz_business1,-0.23508337555533637,0.053400000000000003,0,,,-0.23508337555533637 -coefficient_auto_accessibility_to_retail_for_work_taz_business2,2.9511349220652563,0.1067,0,,,2.9511349220652563 -coefficient_auto_accessibility_to_retail_for_work_taz_eat,-0.7610481619548336,0.014999999999999999,0,,,-0.7610481619548336 -coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,1.5155478484531482,0.0683,0,,,1.5155478484531482 -coefficient_auto_accessibility_to_retail_for_work_taz_maint,-3.2006512330401198,0.026499999999999999,0,,,-3.2006512330401198 -coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,3.9438022928732326,0.99009999999999998,0,,,3.9438022928732326 -coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,-7.2851228526180369,1.9802,0,,,-7.2851228526180369 -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,3.2162405355786916,0.48039999999999999,0,,,3.2162405355786916 -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,2.3334830273468778,1.4704999999999999,0,,,2.3334830273468778 -coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,3.8657969968183186,1.153,0,,,3.8657969968183186 -coefficient_dummy_for_full_time_worker_business1,-11.365293683630657,-7.375,0,,,-11.365293683630657 -coefficient_dummy_for_full_time_worker_business2,-13.833915024299738,-14.279999999999999,0,,,-13.833915024299738 -coefficient_dummy_for_full_time_worker_eat,-9.545917028191262,-7.2800000000000002,0,,,-9.545917028191262 -coefficient_dummy_for_full_time_worker_eat_business,-11.192168369499433,-14.789999999999999,0,,,-11.192168369499433 -coefficient_dummy_for_full_time_worker_maint,-5.8807058943821362,-8.093,0,,,-5.8807058943821362 -coefficient_dummy_for_non_full_time_worker_business1,-2.3700983307274996,-8.3190000000000008,0,,,-2.3700983307274996 -coefficient_dummy_for_non_full_time_worker_business2,-12.723162907889261,-14.279999999999999,0,,,-12.723162907889261 -coefficient_dummy_for_non_full_time_worker_eat,-1.3866265710866461,-8.6039999999999992,0,,,-1.3866265710866461 -coefficient_dummy_for_non_full_time_worker_eat_business,-40.831097354363017,-14.789999999999999,0,,,-40.831097354363017 -coefficient_dummy_for_non_full_time_worker_maint,3.1039851640661054,-8.2140000000000004,0,,,3.1039851640661054 -coefficient_dummy_for_non_workers_business1,-5,-5,0,-5,-5,-5 -coefficient_dummy_for_non_workers_business2,-5,-5,0,-5,-5,-5 -coefficient_dummy_for_non_workers_eat,0,0,0,0,0,0 -coefficient_dummy_for_non_workers_eat_business,-5,-5,0,-5,-5,-5 -coefficient_dummy_for_non_workers_maint,-5,-5,0,-5,-5,-5 -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,0,0,0,0,0 -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,0,0,0,0,0 -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,0,0,0,0,0 -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,0,0,0,0,0 -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.44654348916818676,-0.35730000000000001,0,,,-0.44654348916818676 -coefficient_high_hh_income_dummy_business1,-5.6994858911518085,1.0660000000000001,0,,,-5.6994858911518085 -coefficient_high_hh_income_dummy_business2,18.177579828448355,2.1320000000000001,0,,,18.177579828448355 -coefficient_high_hh_income_dummy_eat,-5.3601399728515835,0.86929999999999996,0,,,-5.3601399728515835 -coefficient_high_hh_income_dummy_eat_business,5.9493499938492986,1.9353,0,,,5.9493499938492986 -coefficient_high_hh_income_dummy_maint,-6.8996039582959119,0.1651,0,,,-6.8996039582959119 -coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,-1.4238674423340238,0.70450000000000002,0,,,-1.4238674423340238 -coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,19.497249614698436,1.409,0,,,19.497249614698436 -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,-1.4579805637692655,0.2334,0,,,-1.4579805637692655 -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,-11.112941997302819,0.93789999999999996,0,,,-11.112941997302819 -coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,-1.7115596112922862,0.50609999999999999,0,,,-1.7115596112922862 -coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,1.7016751707114186,0.70450000000000002,0,,,1.7016751707114186 -coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.5313378193861862,1.409,0,,,1.5313378193861862 -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.98156496358878631,0.67759999999999998,0,,,0.98156496358878631 -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,0.21527427337992269,1.3821000000000001,0,,,0.21527427337992269 -coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.24944777293344775,0.50609999999999999,0,,,0.24944777293344775 -coefficient_individual_eating_out_tours_made_by_person_business1,0.42490805390828246,0.54339999999999999,0,,,0.42490805390828246 -coefficient_individual_eating_out_tours_made_by_person_business2,16.88422034232115,1.0868,0,,,16.88422034232115 -coefficient_individual_eating_out_tours_made_by_person_eat,-0.21103858651833801,0.54910000000000003,0,,,-0.21103858651833801 -coefficient_individual_eating_out_tours_made_by_person_eat_business,-13.757488989198457,1.0925,0,,,-13.757488989198457 -coefficient_individual_eating_out_tours_made_by_person_maint,0.84779917948707073,0.91659999999999997,0,,,0.84779917948707073 -coefficient_log_of_the_work_tour_duration_business1,10.797431417603184,1.1419999999999999,0,,,10.797431417603184 -coefficient_log_of_the_work_tour_duration_business2,-41.041133510722389,2.2839999999999998,0,,,-41.041133510722389 -coefficient_log_of_the_work_tour_duration_eat,9.912073454627409,1.55,0,,,9.912073454627409 -coefficient_log_of_the_work_tour_duration_eat_business,19.216017820975473,2.6920000000000002,0,,,19.216017820975473 -coefficient_log_of_the_work_tour_duration_maint,10.442610817511719,1.659,0,,,10.442610817511719 -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,5.2452009890599847,-0.1903,0,,,5.2452009890599847 -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-5.9574096875877984,-0.38059999999999999,0,,,-5.9574096875877984 -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,5.7343399360868084,0.051999999999999998,0,,,5.7343399360868084 -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-11.365551805409792,-0.24229999999999999,0,,,-11.365551805409792 -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,5.726820567850968,0.14460000000000001,0,,,5.726820567850968 -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-14.379203352289357,-0.1903,0,,,-14.379203352289357 -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,0.18243743982694705,-0.38059999999999999,0,,,0.18243743982694705 -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,6.6450879837969055,-0.30990000000000001,0,,,6.6450879837969055 -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.49226068812509693,-0.50019999999999998,0,,,-0.49226068812509693 -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,6.3906386167906994,-0.27229999999999999,0,,,6.3906386167906994 -coefficient_medium_hh_income_dummy_business1,-3.2862939594211058,0.55549999999999999,0,,,-3.2862939594211058 -coefficient_medium_hh_income_dummy_business2,2.0060702575802463,1.111,0,,,2.0060702575802463 -coefficient_medium_hh_income_dummy_eat,-2.5300981505732136,0.60999999999999999,0,,,-2.5300981505732136 -coefficient_medium_hh_income_dummy_eat_business,10.500987924287303,1.1655,0,,,10.500987924287303 -coefficient_medium_hh_income_dummy_maint,-3.0959660718740842,0.1527,0,,,-3.0959660718740842 -coefficient_participation_in_joint_discretionary_tours_business1,0.88783303805250202,-0.26369999999999999,0,,,0.88783303805250202 -coefficient_participation_in_joint_discretionary_tours_business2,-0.41998352565813962,-0.52739999999999998,0,,,-0.41998352565813962 -coefficient_participation_in_joint_discretionary_tours_eat,-0.24481723660555421,0.35880000000000001,0,,,-0.24481723660555421 -coefficient_participation_in_joint_discretionary_tours_eat_business,-2.7238766448704519,0.095100000000000004,0,,,-2.7238766448704519 -coefficient_participation_in_joint_discretionary_tours_maint,2.7458443690817487,0.58220000000000005,0,,,2.7458443690817487 -coefficient_participation_in_joint_shop_main_eat_tours_business1,3.5611554115337705,0.083000000000000004,0,,,3.5611554115337705 -coefficient_participation_in_joint_shop_main_eat_tours_business2,-0.28005028801856696,0.16600000000000001,0,,,-0.28005028801856696 -coefficient_participation_in_joint_shop_main_eat_tours_eat,1.988636928413654,0.24579999999999999,0,,,1.988636928413654 -coefficient_participation_in_joint_shop_main_eat_tours_eat_business,-6.5451744879809857,0.32879999999999998,0,,,-6.5451744879809857 -coefficient_participation_in_joint_shop_main_eat_tours_maint,2.1793324360516571,0.080299999999999996,0,,,2.1793324360516571 -coefficient_two_work_tours_by_person_business1,3.6940239753295181,0.37530000000000002,0,,,3.6940239753295181 -coefficient_two_work_tours_by_person_business2,-4.1046837844384205,0.75060000000000004,0,,,-4.1046837844384205 -coefficient_two_work_tours_by_person_eat,1.927236938048462,-0.98619999999999997,0,,,1.927236938048462 -coefficient_two_work_tours_by_person_eat_business,-5.4249750305237576,-0.6109,0,,,-5.4249750305237576 -coefficient_two_work_tours_by_person_maint,3.2059979015841251,-0.23119999999999999,0,,,3.2059979015841251 -coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,0,0,0,0,0 -coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,0,0,0,0,0 -coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.053293769108333916,0.059999999999999998,0,,,0.053293769108333916 -coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,-0.46901654329224335,0.059999999999999998,0,,,-0.46901654329224335 -coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.39304906249970034,0.040000000000000001,0,,,0.39304906249970034 -coefficient_workplace_suburban_area_dummy_business1,-0.11020000000000001,-0.11020000000000001,0,,,-0.11020000000000001 -coefficient_workplace_suburban_area_dummy_business2,-0.22040000000000001,-0.22040000000000001,0,,,-0.22040000000000001 -coefficient_workplace_suburban_area_dummy_eat,-0.29160000000000003,-0.29160000000000003,0,,,-0.29160000000000003 -coefficient_workplace_suburban_area_dummy_eat_business,-0.40179999999999999,-0.40179999999999999,0,,,-0.40179999999999999 -coefficient_workplace_suburban_area_dummy_maint,0,0,0,0,0,0 -coefficient_workplace_urban_area_dummy_business1,1.7351079856418732,-0.2235,0,,,1.7351079856418732 -coefficient_workplace_urban_area_dummy_business2,1.5559220678109973,-0.44700000000000001,0,,,1.5559220678109973 -coefficient_workplace_urban_area_dummy_eat,4.5332564007214371,-0.41820000000000002,0,,,4.5332564007214371 -coefficient_workplace_urban_area_dummy_eat_business,-23.084965723862449,-0.64170000000000005,0,,,-23.084965723862449 -coefficient_workplace_urban_area_dummy_maint,13.382379269683053,-0.1479,0,,,13.382379269683053 -coefficient_zero_cars_owned_by_hh_dummy_business1,-0.5268759138584419,-0.33910000000000001,0,,,-0.5268759138584419 -coefficient_zero_cars_owned_by_hh_dummy_business2,0,0,0,0,0,0 -coefficient_zero_cars_owned_by_hh_dummy_eat,0,0,0,0,0,0 -coefficient_zero_cars_owned_by_hh_dummy_eat_business,-19.11629098153702,-0.33910000000000001,0,,,-19.11629098153702 -coefficient_zero_cars_owned_by_hh_dummy_maint,0.55934969853310723,0.1762,0,,,0.55934969853310723 +param_name,value,best,initvalue,nullvalue +coefficient_at_work_sub_tour_asc_business1,-4.6364509649106624,-4.636450908579854,-0.53719997406005859,0 +coefficient_at_work_sub_tour_asc_business2,-1.8249400470488537,-1.8249400513348371,-2.133699893951416,0 +coefficient_at_work_sub_tour_asc_eat,-1.2025517519916142,-1.2025517383789965,0.85759997367858887,0 +coefficient_at_work_sub_tour_asc_eat_business,-2.4601386278749016,-2.460138609917645,-0.97210001945495605,0 +coefficient_at_work_sub_tour_asc_maint,6.7188815064577412,6.718881422843042,-0.61979997158050537,0 +coefficient_auto_accessibility_to_retail_for_work_taz_business1,1.5195960607537637,1.5195960534758026,0.053399998694658279,0 +coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.39623374265129618,0.3962337362698467,0.10670000314712524,0 +coefficient_auto_accessibility_to_retail_for_work_taz_eat,1.244843971401054,1.2448439700604323,0.014999999664723873,0 +coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,-1.4936433037222552,-1.4936433133522584,0.068300001323223114,0 +coefficient_auto_accessibility_to_retail_for_work_taz_maint,-1.3971304685665173,-1.397130443936482,0.026499999687075615,0 +coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,2.5403075484019295,2.5403075438954636,0.99010002613067627,0 +coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,-1.5471111587719255,-1.5471111364437549,1.9802000522613525,0 +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,1.8134290820782721,1.8134290762249321,0.48039999604225159,0 +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,0.81302147728988705,0.81302147967997473,1.4704999923706055,0 +coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,2.4545531149456474,2.4545531005871952,1.1529999971389771,0 +coefficient_dummy_for_full_time_worker_business1,-10.835277488785893,-10.83527745465712,-7.375,0 +coefficient_dummy_for_full_time_worker_business2,-13.651388366502351,-13.651388372174022,-14.279999732971191,0 +coefficient_dummy_for_full_time_worker_eat,-9.4982645189968196,-9.4982645055658654,-7.2800002098083496,0 +coefficient_dummy_for_full_time_worker_eat_business,-12.473124948668486,-12.473124950259384,-14.789999961853027,0 +coefficient_dummy_for_full_time_worker_maint,-5.3599449936662609,-5.3599450339634185,-8.0930004119873047,0 +coefficient_dummy_for_non_full_time_worker_business1,-8.9579737462053064,-8.9579737240032706,-8.319000244140625,0 +coefficient_dummy_for_non_full_time_worker_business2,-14.599851252537475,-14.599851251151787,-14.279999732971191,0 +coefficient_dummy_for_non_full_time_worker_eat,-8.4458875080344917,-8.445887507852829,-8.6040000915527344,0 +coefficient_dummy_for_non_full_time_worker_eat_business,-18.594913583457473,-18.594913563909319,-14.789999961853027,0 +coefficient_dummy_for_non_full_time_worker_maint,-3.6083736885127893,-3.6083737318303313,-8.2139997482299805,0 +coefficient_dummy_for_non_workers_business1,-5,-5,-5,0 +coefficient_dummy_for_non_workers_business2,-5,-5,-5,0 +coefficient_dummy_for_non_workers_eat,0,0,0,0 +coefficient_dummy_for_non_workers_eat_business,-5,-5,-5,0 +coefficient_dummy_for_non_workers_maint,-5,-5,-5,0 +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,0,0,0 +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,0,0,0 +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,0,0,0 +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,0,0,0 +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.41501623497381018,-0.4150162451792187,-0.35730001330375671,0 +coefficient_high_hh_income_dummy_business1,-0.33979337120056141,-0.33979335118765108,1.0659999847412109,0 +coefficient_high_hh_income_dummy_business2,4.2112639057799823,4.2112638870883581,2.1319999694824219,0 +coefficient_high_hh_income_dummy_eat,0.023039749877190804,0.023039775965731642,0.86930000782012939,0 +coefficient_high_hh_income_dummy_eat_business,3.7450872508803248,3.745087211197407,1.9352999925613403,0 +coefficient_high_hh_income_dummy_maint,-1.471897587383632,-1.471897575110541,0.16509999334812164,0 +coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,-0.258886010731674,-0.25888602469708244,0.70450001955032349,0 +coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,10.013216960858641,10.013216913800566,1.409000039100647,0 +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,-0.27143991243306015,-0.27143990533123996,0.23340000212192535,0 +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,-5.1450511716765561,-5.1450511394525638,0.93790000677108765,0 +coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,-0.54693979952226501,-0.54693977782459402,0.50609999895095825,0 +coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,1.5008073499859345,1.5008073606710928,0.70450001955032349,0 +coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.3725147779808102,1.3725147781405138,1.409000039100647,0 +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.8707190441243009,0.8707190632298788,0.67760002613067627,0 +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,0.75707630852833474,0.75707631165373623,1.382099986076355,0 +coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.17818258918958901,0.17818255611374761,0.50609999895095825,0 +coefficient_individual_eating_out_tours_made_by_person_business1,1.2291884533614996,1.2291884357040239,0.54339998960494995,0 +coefficient_individual_eating_out_tours_made_by_person_business2,5.3890915350859343,5.3890915063685627,1.0867999792098999,0 +coefficient_individual_eating_out_tours_made_by_person_eat,0.59241286979897301,0.59241286727354991,0.54909998178482056,0 +coefficient_individual_eating_out_tours_made_by_person_eat_business,-4.6396338093217437,-4.6396337749049197,1.0924999713897705,0 +coefficient_individual_eating_out_tours_made_by_person_maint,1.6173408620021299,1.6173408764855761,0.91659998893737793,0 +coefficient_log_of_the_work_tour_duration_business1,2.8745950964750677,2.8745950289586566,1.1419999599456787,0 +coefficient_log_of_the_work_tour_duration_business2,-9.4213725166497184,-9.4213723813479699,2.2839999198913574,0 +coefficient_log_of_the_work_tour_duration_eat,2.0174558496514647,2.0174558005400733,1.5499999523162842,0 +coefficient_log_of_the_work_tour_duration_eat_business,11.401343306668366,11.401343332337083,2.6919999122619629,0 +coefficient_log_of_the_work_tour_duration_maint,2.4549780473709193,2.4549780030282569,1.659000039100647,0 +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,1.1088398218159976,1.1088398127197108,-0.19030000269412994,0 +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.57516973946012429,-0.575169738593175,-0.38060000538825989,0 +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,1.5872866399810972,1.5872866248218596,0.052000001072883606,0 +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-4.2886646505292108,-4.2886646240189545,-0.24230000376701355,0 +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,1.5511079213734775,1.5511079182517966,0.14460000395774841,0 +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-6.3194536970520918,-6.3194536671381956,-0.19030000269412994,0 +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.45741583885508108,-0.45741583852963658,-0.38060000538825989,0 +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,3.0910693656661703,3.0910693473970023,-0.30989998579025269,0 +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.85983485391771264,-0.85983485226518608,-0.50019997358322144,0 +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,2.8923350517436197,2.8923350381209207,-0.27230000495910645,0 +coefficient_medium_hh_income_dummy_business1,-1.1331895926048559,-1.1331895937415595,0.55549997091293335,0 +coefficient_medium_hh_income_dummy_business2,0.81318352183524256,0.81318352315710596,1.1109999418258667,0 +coefficient_medium_hh_income_dummy_eat,-0.34741746210681906,-0.3474174464669022,0.61000001430511475,0 +coefficient_medium_hh_income_dummy_eat_business,5.121964519155811,5.1219645052482301,1.1655000448226929,0 +coefficient_medium_hh_income_dummy_maint,-0.85984100745095871,-0.8598410093684542,0.15270000696182251,0 +coefficient_participation_in_joint_discretionary_tours_business1,0.30193135342719479,0.30193135643871699,-0.26370000839233398,0 +coefficient_participation_in_joint_discretionary_tours_business2,-0.59379173063206181,-0.59379173033201538,-0.52740001678466797,0 +coefficient_participation_in_joint_discretionary_tours_eat,-0.97053293091235093,-0.9705329212314463,0.35879999399185181,0 +coefficient_participation_in_joint_discretionary_tours_eat_business,-0.5490816124017508,-0.54908160890359503,0.095100000500679016,0 +coefficient_participation_in_joint_discretionary_tours_maint,2.0564748805838153,2.056474864093186,0.58219999074935913,0 +coefficient_participation_in_joint_shop_main_eat_tours_business1,1.8768257580478833,1.8768257619258122,0.082999996840953827,0 +coefficient_participation_in_joint_shop_main_eat_tours_business2,0.055786350132296811,0.055786350662721744,0.16599999368190765,0 +coefficient_participation_in_joint_shop_main_eat_tours_eat,0.31370718460939551,0.31370718509133344,0.24580000340938568,0 +coefficient_participation_in_joint_shop_main_eat_tours_eat_business,-1.8724721811608103,-1.8724721667874074,0.32879999279975891,0 +coefficient_participation_in_joint_shop_main_eat_tours_maint,0.53005287839341431,0.53005285912971956,0.080300003290176392,0 +coefficient_two_work_tours_by_person_business1,0.78174817096251792,0.78174816097861666,0.37529999017715454,0 +coefficient_two_work_tours_by_person_business2,0.67460859753132585,0.67460859786572203,0.75059998035430908,0 +coefficient_two_work_tours_by_person_eat,-0.96831618429168598,-0.96831618402710151,-0.98619997501373291,0 +coefficient_two_work_tours_by_person_eat_business,-1.4448302925402916,-1.4448302854771486,-0.61089998483657837,0 +coefficient_two_work_tours_by_person_maint,0.25438972433601764,0.2543897266577953,-0.23119999468326569,0 +coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,0,0,0 +coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,0,0,0 +coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.024318701408312954,0.024318700872964293,0.059999998658895493,0 +coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,-0.098985033594679236,-0.098985043830151581,0.059999998658895493,0 +coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.37714309948187719,0.37714309907729421,0.039999999105930328,0 +coefficient_workplace_suburban_area_dummy_business1,-0.11020000278949738,-0.11020000278949738,-0.11020000278949738,0 +coefficient_workplace_suburban_area_dummy_business2,-0.22040000557899475,-0.22040000557899475,-0.22040000557899475,0 +coefficient_workplace_suburban_area_dummy_eat,-0.29159998893737793,-0.29159998893737793,-0.29159998893737793,0 +coefficient_workplace_suburban_area_dummy_eat_business,-0.4018000066280365,-0.4018000066280365,-0.4018000066280365,0 +coefficient_workplace_suburban_area_dummy_maint,0,0,0,0 +coefficient_workplace_urban_area_dummy_business1,-4.3227509893008715,-4.3227509329700631,-0.22349999845027924,0 +coefficient_workplace_urban_area_dummy_business2,-0.13824014999799764,-0.13824015428398095,-0.44699999690055847,0 +coefficient_workplace_urban_area_dummy_eat,-2.4783517118896161,-2.4783516982769984,-0.41819998621940613,0 +coefficient_workplace_urban_area_dummy_eat_business,-2.1297386377930989,-2.1297386198358423,-0.64170002937316895,0 +coefficient_workplace_urban_area_dummy_maint,7.1907814778236281,7.1907813942089289,-0.14790000021457672,0 +coefficient_zero_cars_owned_by_hh_dummy_business1,-0.48372662658348159,-0.48372661407098899,-0.33910000324249268,0 +coefficient_zero_cars_owned_by_hh_dummy_business2,0,0,0,0 +coefficient_zero_cars_owned_by_hh_dummy_eat,0,0,0,0 +coefficient_zero_cars_owned_by_hh_dummy_eat_business,-7.372632298534227,-7.3726322561599522,-0.33910000324249268,0 +coefficient_zero_cars_owned_by_hh_dummy_maint,0.53982915542758059,0.53982913595117754,0.17620000243186951,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_loglike.csv index 2f32cb32aa..b0b7c83312 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_atwork_subtour_frequency_SLSQP_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-339.26475293528824,-311.08163243830268 +0,-339.26475419242206,-311.72832904369682 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_auto_ownership_BHHH_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_auto_ownership_BHHH_.csv index 36f7acad3f..a45f944823 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_auto_ownership_BHHH_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_auto_ownership_BHHH_.csv @@ -1,4 +1,4 @@ -,value,initvalue,nullvalue,minimum,maximum,best +param_name,value,initvalue,nullvalue,minimum,maximum,best coef_cars1_asc,4.7447107380921025,1.1865000000000001,0,,,4.7447107380921025 coef_cars1_asc_county,-0.56599882256075029,-0.56599999999999995,0,,,-0.56599882256075029 coef_cars1_asc_marin,-0.24339921273778259,-0.24340000000000001,0,,,-0.24339921273778259 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_free_parking_BHHH_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_free_parking_BHHH_.csv index d721934bd3..ab1d9efd30 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_free_parking_BHHH_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_free_parking_BHHH_.csv @@ -1,4 +1,4 @@ -,value,initvalue,nullvalue,minimum,maximum,holdfast,best +param_name,value,initvalue,nullvalue,minimum,maximum,holdfast,best 0.0,0,0,0,0,0,1,0 coef_asc_alameda,-0.10920000000000001,-0.10920000000000001,0,,,0,-0.10920000000000001 coef_asc_san_francisco,-2.4563318326358723,-2.6402999999999999,0,,,0,-2.4563318326358723 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_.csv index 40c2da343d..f6db0bb7da 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_.csv @@ -1,32 +1,32 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_asc_children,121.62774629212394,5.3517000000000001,0,,,121.62774629212394 -coef_asc_mixed,-88.249612624384525,5.6289999999999996,0,,,-88.249612624384525 -coef_household_has_more_cars_than_workers_adults,-40.894264494991035,1.3859999999999999,0,,,-40.894264494991035 -coef_household_has_more_cars_than_workers_mixed,153.8152034125998,0.751,0,,,153.8152034125998 -coef_household_in_suburban_area_adults,0.51049999999999995,0.51049999999999995,0,,,0.51049999999999995 -coef_household_in_suburban_area_mixed,0.1283,0.1283,0,,,0.1283 -coef_household_in_urban_area,-21.823333667739977,0.57410000000000005,0,,,-21.823333667739977 -coef_log_max_overlap_of_adults_time_windows,-36.285976396423372,1.1919999999999999,0,,,-36.285976396423372 -coef_log_max_overlap_of_childrens_time_windows,283.69666707732551,1.841,0,,,283.69666707732551 -coef_log_max_overlap_of_time_windows,196.72857307970887,1.958,0,,,196.72857307970887 -coef_low_income_households_adults,-16.894953557817836,1.248,0,,,-16.894953557817836 -coef_low_income_households_mixed,-3.1435928555076917,0.57550000000000001,0,,,-3.1435928555076917 -coef_medium_income_households,-161.72738528562726,0.83689999999999998,0,,,-161.72738528562726 -coef_number_of_children_too_young_for_school_children,-110.04828004959086,0.73060000000000003,0,,,-110.04828004959086 -coef_number_of_children_too_young_for_school_mixed,9.5393263775476171,0.79059999999999997,0,,,9.5393263775476171 -coef_number_of_driving_age_children_children,-172.31193722793208,-0.26669999999999999,0,,,-172.31193722793208 -coef_number_of_driving_age_children_mixed,184.76140753321386,-0.93989999999999996,0,,,184.76140753321386 -coef_number_of_full_time_workers_adults,185.49031080785167,1.024,0,,,185.49031080785167 -coef_number_of_full_time_workers_mixed,50.943192997924122,0.3624,0,,,50.943192997924122 -coef_number_of_non_workers_adults,40.041010494333662,0.62629999999999997,0,,,40.041010494333662 -coef_number_of_non_workers_mixed,-96.438665924994751,-0.37240000000000001,0,,,-96.438665924994751 -coef_number_of_part_time_workers_adults,47.088158071231007,0.54120000000000001,0,,,47.088158071231007 -coef_number_of_part_time_workers_mixed,58.958712458292865,0.31640000000000001,0,,,58.958712458292865 -coef_number_of_pre_driving_age_children_children,12.678858152945852,0.73060000000000003,0,,,12.678858152945852 -coef_number_of_pre_driving_age_children_mixed,158.96026734481441,0.35320000000000001,0,,,158.96026734481441 -coef_number_of_university_students,-5.007830161412854,0.82450000000000001,0,,,-5.007830161412854 -coef_tour_purpose_is_discretionary_adults,0.76480000000000004,0.76480000000000004,0,,,0.76480000000000004 -coef_tour_purpose_is_discretionary_children,0.5101,0.5101,0,,,0.5101 -coef_tour_purpose_is_eating_out_children,-0.96779999999999999,-0.96779999999999999,0,,,-0.96779999999999999 -coef_tour_purpose_is_eating_out_mixed,-0.80269999999999997,-0.80269999999999997,0,,,-0.80269999999999997 -coef_unavailable,-999,-999,0,-999,-999,-999 +param_name,value,best,initvalue,nullvalue +coef_asc_children,31.580459657771478,31.580459657771478,5.3516998291015625,0 +coef_asc_mixed,-8.4815755302186577,-8.4815755302186577,5.629000186920166,0 +coef_household_has_more_cars_than_workers_adults,-14.008943838901908,-14.008943838901908,1.3860000371932983,0 +coef_household_has_more_cars_than_workers_mixed,25.695328937041246,25.695328937041246,0.75099998712539673,0 +coef_household_in_suburban_area_adults,0.51050001382827759,0.51050001382827759,0.51050001382827759,0 +coef_household_in_suburban_area_mixed,0.12829999625682831,0.12829999625682831,0.12829999625682831,0 +coef_household_in_urban_area,-11.544084093965571,-11.544084093965571,0.57410001754760742,0 +coef_log_max_overlap_of_adults_time_windows,-25.921679816393258,-25.921679816393258,1.1920000314712524,0 +coef_log_max_overlap_of_childrens_time_windows,50,50,1.840999960899353,0 +coef_log_max_overlap_of_time_windows,43.185336045455792,43.185336045455792,1.9579999446868896,0 +coef_low_income_households_adults,-4.7176489111037725,-4.7176489111037725,1.2480000257492065,0 +coef_low_income_households_mixed,-9.0408081421508903,-9.0408081421508903,0.5755000114440918,0 +coef_medium_income_households,-34.499486089811263,-34.499486089811263,0.83689999580383301,0 +coef_number_of_children_too_young_for_school_children,-13.855996312009184,-13.855996312009184,0.73059999942779541,0 +coef_number_of_children_too_young_for_school_mixed,4.4802268807421637,4.4802268807421637,0.7906000018119812,0 +coef_number_of_driving_age_children_children,-46.217713310558153,-46.217713310558153,-0.26669999957084656,0 +coef_number_of_driving_age_children_mixed,50,50,-0.9398999810218811,0 +coef_number_of_full_time_workers_adults,28.469430749747154,28.469430749747154,1.0240000486373901,0 +coef_number_of_full_time_workers_mixed,2.5181990493930466,2.5181990493930466,0.36239999532699585,0 +coef_number_of_non_workers_adults,6.8107526564830181,6.8107526564830181,0.62629997730255127,0 +coef_number_of_non_workers_mixed,-27.464491079595931,-27.464491079595931,-0.37239998579025269,0 +coef_number_of_part_time_workers_adults,12.100761555344075,12.100761555344075,0.54119998216629028,0 +coef_number_of_part_time_workers_mixed,-2.6898434127139574,-2.6898434127139574,0.31639999151229858,0 +coef_number_of_pre_driving_age_children_children,6.9278442677705971,6.9278442677705971,0.73059999942779541,0 +coef_number_of_pre_driving_age_children_mixed,34.880087194025478,34.880087194025478,0.35319998860359192,0 +coef_number_of_university_students,-3.4995325852033643,-3.4995325852033643,0.82450002431869507,0 +coef_tour_purpose_is_discretionary_adults,0.76480001211166382,0.76480001211166382,0.76480001211166382,0 +coef_tour_purpose_is_discretionary_children,0.51010000705718994,0.51010000705718994,0.51010000705718994,0 +coef_tour_purpose_is_eating_out_children,-0.96780002117156982,-0.96780002117156982,-0.96780002117156982,0 +coef_tour_purpose_is_eating_out_mixed,-0.8026999831199646,-0.8026999831199646,-0.8026999831199646,0 +coef_unavailable,-999,-999,-999,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_loglike.csv index 449c94c47f..1db96994d6 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_composition_SLSQP_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-9.9910306200781598,-9.7865975960368618e-07 +0,-9.9910303886936802,-0.01512359141580788 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_frequency_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_frequency_SLSQP_.csv index f6102d3c39..833d9f4425 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_frequency_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_frequency_SLSQP_.csv @@ -1,77 +1,77 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_asc_0_tours,0,0,0,0,0,0 -coef_asc_1_Disc,-5.8578427473231667,-5.4805999999999999,0,,,-5.8578427473231667 -coef_asc_1_Eat,-5.140469329663599,-6.3757000000000001,0,,,-5.140469329663599 -coef_asc_1_Main,-4.843654155024959,-5.7389000000000001,0,,,-4.843654155024959 -coef_asc_1_Shop,-7.2448952266310895,-6.0148999999999999,0,,,-7.2448952266310895 -coef_asc_1_Visit,-5.9621489630619173,-5.8818000000000001,0,,,-5.9621489630619173 -coef_asc_2_tours,-15.379573080742464,-14.457599999999999,0,,,-15.379573080742464 -coef_drivingAgeStuMandMaxThree_disc,0.26420610778627868,0.19320000000000001,0,,,0.26420610778627868 -coef_drivingAgeStuMandMaxThree_maint,-0.20497838559876363,-0.32369999999999999,0,,,-0.20497838559876363 -coef_fewerCarsThanDrivers_maint,-0.23616022935294756,0.46100000000000002,0,,,-0.23616022935294756 -coef_fewerCarsThanDrivers_shopping,0.61204166063349064,0.25230000000000002,0,,,0.61204166063349064 -coef_fullTimeHomeMaxThree_zero_tours,2.0014370751255011,1.175,0,,,2.0014370751255011 -coef_fullTimeMandMaxThree_maint,-0.46248818198458952,-0.3009,0,,,-0.46248818198458952 -coef_fullTimeMandMaxThree_shopping,0.14353937037596973,-0.2424,0,,,0.14353937037596973 -coef_fullTimeNonMandMaxThree_disc,0.21817599548271277,0.1275,0,,,0.21817599548271277 -coef_fullTimeNonMandMaxThree_eatout,0.32573071809647042,0.22750000000000001,0,,,0.32573071809647042 -coef_fullTimeNonMandMaxThree_maint,0.75962622449225758,0.31730000000000003,0,,,0.75962622449225758 -coef_fullTimeNonMandMaxThree_shopping,-0.88812895503399825,0.20519999999999999,0,,,-0.88812895503399825 -coef_fullTimeNonMandMaxThree_visiting,0.78859263531566071,0.64449999999999996,0,,,0.78859263531566071 -coef_incomeBetween50And100_disc,0.0083529208127054756,0.31669999999999998,0,,,0.0083529208127054756 -coef_incomeBetween50And100_eatout,0.1031885654385253,0.29770000000000002,0,,,0.1031885654385253 -coef_incomeGreaterThan100_disc,0.62493454802487425,0.48599999999999999,0,,,0.62493454802487425 -coef_incomeGreaterThan100_eatout,0.63535874552918015,0.44919999999999999,0,,,0.63535874552918015 -coef_incomeMissing_dummy_always_zero_disc,0.37230000000000002,0.37230000000000002,0,,,0.37230000000000002 -coef_incomeMissing_dummy_always_zero_eatout,0.27800000000000002,0.27800000000000002,0,,,0.27800000000000002 -coef_logTimeWindowOverlapAdultChild_disc,0.1502559618722121,0.22120000000000001,0,,,0.1502559618722121 -coef_logTimeWindowOverlapAdultChild_eatout,-0.32038060987279043,0.092100000000000001,0,,,-0.32038060987279043 -coef_logTimeWindowOverlapAdultChild_maint,0.26959218597469214,0.24429999999999999,0,,,0.26959218597469214 -coef_logTimeWindowOverlapAdultChild_shopping,-0.41532462974126461,0.1086,0,,,-0.41532462974126461 -coef_logTimeWindowOverlapAdult_disc,0.55965786525579331,0.34279999999999999,0,,,0.55965786525579331 -coef_logTimeWindowOverlapAdult_eatout,0.56061433677445105,0.48559999999999998,0,,,0.56061433677445105 -coef_logTimeWindowOverlapAdult_maint,0.4376387335084278,0.37140000000000001,0,,,0.4376387335084278 -coef_logTimeWindowOverlapAdult_shopping,0.86405876135539128,0.59450000000000003,0,,,0.86405876135539128 -coef_logTimeWindowOverlapChild_disc,-0.1693892803512011,0.1162,0,,,-0.1693892803512011 -coef_logTimeWindowOverlapChild_maint,-0.010378552711083917,0.17599999999999999,0,,,-0.010378552711083917 -coef_logTimeWindowOverlapChild_shopping,1.0366536984300598,0.1416,0,,,1.0366536984300598 -coef_moreCarsThanWorkers_eatout,0.079480888637893488,0.38250000000000001,0,,,0.079480888637893488 -coef_moreCarsThanWorkers_shopping,-0.62780216023973168,-0.30270000000000002,0,,,-0.62780216023973168 -coef_nonWorkerHomeMaxThree_zero_tours,-0.16941211649357898,1.514,0,,,-0.16941211649357898 -coef_nonWorkerNonMandMaxThree_disc,-0.11901239265508992,0.28710000000000002,0,,,-0.11901239265508992 -coef_nonWorkerNonMandMaxThree_eatout,-0.35447708036701164,0.182,0,,,-0.35447708036701164 -coef_nonWorkerNonMandMaxThree_maint,0.37255083379371662,0.46429999999999999,0,,,0.37255083379371662 -coef_nonWorkerNonMandMaxThree_shopping,0.52326322821604476,0.70779999999999998,0,,,0.52326322821604476 -coef_nonWorkerNonMandMaxThree_visiting,-0.35989963706226813,0.54749999999999999,0,,,-0.35989963706226813 -coef_partTimeHomeMaxThree_zero_tours,1.7000746681627035,1.4470000000000001,0,,,1.7000746681627035 -coef_partTimeNonMandMaxThree_disc,0.22644058039117623,0.49790000000000001,0,,,0.22644058039117623 -coef_partTimeNonMandMaxThree_eatout,-15.770618924654787,0.3765,0,,,-15.770618924654787 -coef_partTimeNonMandMaxThree_maint,0.12155923236230654,0.2452,0,,,0.12155923236230654 -coef_partTimeNonMandMaxThree_shopping,-0.19149733628062993,0.18659999999999999,0,,,-0.19149733628062993 -coef_partTimeNonMandMaxThree_visiting,0.34840797626260722,0.13320000000000001,0,,,0.34840797626260722 -coef_preDrivingAgeMandMaxThree_disc,0.58830436658291341,0.38619999999999999,0,,,0.58830436658291341 -coef_preDrivingAgeMandMaxThree_maint,0.05724992253344495,0.22989999999999999,0,,,0.05724992253344495 -coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,-0.26493959294530756,0.53000000000000003,0,,,-0.26493959294530756 -coef_preDrivingNonMandMaxThree_disc,0.46978892402747974,0.53310000000000002,0,,,0.46978892402747974 -coef_preDrivingNonMandMaxThree_eatout,-13.674346835309482,0.3851,0,,,-13.674346835309482 -coef_preDrivingNonMandMaxThree_maint,-0.4780031657291039,0.6482,0,,,-0.4780031657291039 -coef_preDrivingNonMandMaxThree_shopping,0.0054016798205857386,0.5474,0,,,0.0054016798205857386 -coef_preDrivingNonMandMaxThree_visiting,-1.7283257423524336,0.6008,0,,,-1.7283257423524336 -coef_retireeHomeMaxThree_zero_tours,0.3072054262466079,0.60529999999999995,0,,,0.3072054262466079 -coef_retireeNonMandMaxThree_disc,0.59539272893690953,0.61360000000000003,0,,,0.59539272893690953 -coef_retireeNonMandMaxThree_eatout,0.33706472938654847,0.4264,0,,,0.33706472938654847 -coef_retireeNonMandMaxThree_maint,0.93064231251598528,0.90500000000000003,0,,,0.93064231251598528 -coef_retireeNonMandMaxThree_shopping,0.95235573746714874,0.94099999999999995,0,,,0.95235573746714874 -coef_retireeNonMandMaxThree_visiting,0.17432279378364646,0.55789999999999995,0,,,0.17432279378364646 -coef_timeWindowOverlapAdultChild_visiting,0.14049394553264929,0.025600000000000001,0,,,0.14049394553264929 -coef_timeWindowOverlapAdult_visiting,0.11030183056319119,0.0596,0,,,0.11030183056319119 -coef_timeWindowOverlapChild_visiting,-0.060644638255823571,0.0091999999999999998,0,,,-0.060644638255823571 -coef_universityHomeMaxThree_univ_and_driving_zero_tours,-0.41040663722807053,0.56850000000000001,0,,,-0.41040663722807053 -coef_universityNonMandMaxThree_disc,-0.14472811802653573,0.75460000000000005,0,,,-0.14472811802653573 -coef_universityNonMandMaxThree_eatout,-14.080266896152475,0.40970000000000001,0,,,-14.080266896152475 -coef_universityNonMandMaxThree_maint,-0.4377287237690044,0.26429999999999998,0,,,-0.4377287237690044 -coef_universityNonMandMaxThree_shopping,1.1340537653332554,0.76480000000000004,0,,,1.1340537653332554 -coef_universityNonMandMaxThree_visiting,-18.048773540202976,0.28089999999999998,0,,,-18.048773540202976 -coef_walkRetailAccessibility_eatout,-0.027362834622908064,0.062,0,,,-0.027362834622908064 -coef_zeroAutomobiles_disc,-0.53993914183433878,-0.90900000000000003,0,,,-0.53993914183433878 -coef_zeroAutomobiles_visiting,-18.311268197408783,-0.97999999999999998,0,,,-18.311268197408783 +param_name,value,best,initvalue,nullvalue +coef_asc_0_tours,0,0,0,0 +coef_asc_1_Disc,-5.9160878944391584,-5.9160871584456292,-5.4805998802185059,0 +coef_asc_1_Eat,-5.05602837728296,-5.0560299542489355,-6.3756999969482422,0 +coef_asc_1_Main,-4.8254586155918471,-4.8254584245546122,-5.7389001846313477,0 +coef_asc_1_Shop,-7.4469694983528809,-7.4469670187782491,-6.0149002075195312,0 +coef_asc_1_Visit,-5.9551540139107804,-5.9551539232808333,-5.8818001747131348,0 +coef_asc_2_tours,-15.797075280937081,-15.797070989460588,-14.457599639892578,0 +coef_drivingAgeStuMandMaxThree_disc,0.27124464344272958,0.27124444259441294,0.1932000070810318,0 +coef_drivingAgeStuMandMaxThree_maint,-0.20737041043238222,-0.2073702201780428,-0.32370001077651978,0 +coef_fewerCarsThanDrivers_maint,-0.23816146636980665,-0.23816149718467414,0.460999995470047,0 +coef_fewerCarsThanDrivers_shopping,0.61694158860106285,0.61694159482964805,0.25229999423027039,0 +coef_fullTimeHomeMaxThree_zero_tours,1.996135901304358,1.9961359267238226,1.1749999523162842,0 +coef_fullTimeMandMaxThree_maint,-0.46241893261416922,-0.46241924052217082,-0.30090001225471497,0 +coef_fullTimeMandMaxThree_shopping,0.16560459271309827,0.16560432789309174,-0.24240000545978546,0 +coef_fullTimeNonMandMaxThree_disc,0.20480561409516004,0.20480581045554858,0.12749999761581421,0 +coef_fullTimeNonMandMaxThree_eatout,0.33389502103327529,0.33389509678618362,0.22750000655651093,0 +coef_fullTimeNonMandMaxThree_maint,0.76952765626273956,0.76952794806055913,0.31729999184608459,0 +coef_fullTimeNonMandMaxThree_shopping,-0.98031955850826391,-0.98031911603182831,0.20520000159740448,0 +coef_fullTimeNonMandMaxThree_visiting,0.76906716737028125,0.76906729748219271,0.6445000171661377,0 +coef_incomeBetween50And100_disc,0.0095094226633637528,0.0095095849856016851,0.31670001149177551,0 +coef_incomeBetween50And100_eatout,0.090560692608283727,0.09056083387775557,0.29769998788833618,0 +coef_incomeGreaterThan100_disc,0.62453536178251734,0.62453510934345091,0.48600000143051147,0 +coef_incomeGreaterThan100_eatout,0.63786709459088209,0.63786734556950364,0.44920000433921814,0 +coef_incomeMissing_dummy_always_zero_disc,0.37229999899864197,0.37229999899864197,0.37229999899864197,0 +coef_incomeMissing_dummy_always_zero_eatout,0.27799999713897705,0.27799999713897705,0.27799999713897705,0 +coef_logTimeWindowOverlapAdultChild_disc,0.15109942470522381,0.15109971903599334,0.22120000422000885,0 +coef_logTimeWindowOverlapAdultChild_eatout,-0.32364532221490494,-0.32364510943311381,0.092100001871585846,0 +coef_logTimeWindowOverlapAdultChild_maint,0.27843409869332575,0.27843422542528723,0.2442999929189682,0 +coef_logTimeWindowOverlapAdultChild_shopping,-0.42145916023520369,-0.42145897391197473,0.10859999805688858,0 +coef_logTimeWindowOverlapAdult_disc,0.5845950974994687,0.5845947676478761,0.34279999136924744,0 +coef_logTimeWindowOverlapAdult_eatout,0.54628409582637161,0.5462840489123495,0.48559999465942383,0 +coef_logTimeWindowOverlapAdult_maint,0.43015346279128713,0.43015341420863468,0.37139999866485596,0 +coef_logTimeWindowOverlapAdult_shopping,0.94448584150730952,0.94448492089225045,0.59450000524520874,0 +coef_logTimeWindowOverlapChild_disc,-0.18532337973096763,-0.18532355001439704,0.11620000004768372,0 +coef_logTimeWindowOverlapChild_maint,-0.010727634274141281,-0.010727832451482898,0.17599999904632568,0 +coef_logTimeWindowOverlapChild_shopping,1.0468422941706472,1.0468423276707255,0.14159999787807465,0 +coef_moreCarsThanWorkers_eatout,0.076914343292072182,0.076914576167953802,0.38249999284744263,0 +coef_moreCarsThanWorkers_shopping,-0.59564874997193351,-0.59564894067055596,-0.30270001292228699,0 +coef_nonWorkerHomeMaxThree_zero_tours,-0.17003939484477312,-0.17003948323362453,1.5140000581741333,0 +coef_nonWorkerNonMandMaxThree_disc,-0.13432804559300732,-0.13432811971898589,0.28709998726844788,0 +coef_nonWorkerNonMandMaxThree_eatout,-0.33401066087685138,-0.33401068347396745,0.18199999630451202,0 +coef_nonWorkerNonMandMaxThree_maint,0.37323111519137453,0.37323093350637826,0.4643000066280365,0 +coef_nonWorkerNonMandMaxThree_shopping,0.49264301488042911,0.49264292085137712,0.70779997110366821,0 +coef_nonWorkerNonMandMaxThree_visiting,-0.36437091579618403,-0.36437083429284089,0.54750001430511475,0 +coef_partTimeHomeMaxThree_zero_tours,1.7312454999054936,1.7312459096132369,1.4470000267028809,0 +coef_partTimeNonMandMaxThree_disc,0.23705977049445798,0.23705989380574119,0.49790000915527344,0 +coef_partTimeNonMandMaxThree_eatout,-5.6531002354715127,-5.6530850270781112,0.37650001049041748,0 +coef_partTimeNonMandMaxThree_maint,0.11494946476265139,0.11494961705361134,0.24519999325275421,0 +coef_partTimeNonMandMaxThree_shopping,-0.20746715181319589,-0.20746695386959577,0.18659999966621399,0 +coef_partTimeNonMandMaxThree_visiting,0.32137505891849261,0.32137524780407384,0.13320000469684601,0 +coef_preDrivingAgeMandMaxThree_disc,0.60675129362611258,0.60675111363025258,0.38620001077651978,0 +coef_preDrivingAgeMandMaxThree_maint,0.043178367483864805,0.043178519613054106,0.22990000247955322,0 +coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,-0.25725057670419188,-0.25725026315677951,0.52999997138977051,0 +coef_preDrivingNonMandMaxThree_disc,0.49100040690235336,0.49100032365079044,0.53310000896453857,0 +coef_preDrivingNonMandMaxThree_eatout,-3.5007131527538413,-3.5007016569353477,0.38510000705718994,0 +coef_preDrivingNonMandMaxThree_maint,-0.47732053847248312,-0.47732051937471986,0.64819997549057007,0 +coef_preDrivingNonMandMaxThree_shopping,-0.0012986266652305054,-0.0012987293702714738,0.54739999771118164,0 +coef_preDrivingNonMandMaxThree_visiting,-1.6293887060843797,-1.6293896015520914,0.60079997777938843,0 +coef_retireeHomeMaxThree_zero_tours,0.34966179334551972,0.34966146316807412,0.60530000925064087,0 +coef_retireeNonMandMaxThree_disc,0.59640088460538709,0.59640107482430083,0.61360001564025879,0 +coef_retireeNonMandMaxThree_eatout,0.3499795761045531,0.34997959789937416,0.42640000581741333,0 +coef_retireeNonMandMaxThree_maint,0.93716736904164089,0.93716740436084434,0.90499997138977051,0 +coef_retireeNonMandMaxThree_shopping,0.9325100820582326,0.93251039206065178,0.94099998474121094,0 +coef_retireeNonMandMaxThree_visiting,0.1668284024031404,0.16682870749583406,0.55790001153945923,0 +coef_timeWindowOverlapAdultChild_visiting,0.13881492846958488,0.13881496732242621,0.025599999353289604,0 +coef_timeWindowOverlapAdult_visiting,0.11182157577140597,0.11182154572794807,0.059599999338388443,0 +coef_timeWindowOverlapChild_visiting,-0.059018654499507453,-0.05901868676849447,0.0092000002041459084,0 +coef_universityHomeMaxThree_univ_and_driving_zero_tours,-0.3806558129102493,-0.38065581874970073,0.56849998235702515,0 +coef_universityNonMandMaxThree_disc,-0.15852221488464341,-0.15852211136829161,0.75459998846054077,0 +coef_universityNonMandMaxThree_eatout,-4.1215214477423272,-4.1215091850562358,0.40970000624656677,0 +coef_universityNonMandMaxThree_maint,-0.46680029014718827,-0.46680012462022286,0.26429998874664307,0 +coef_universityNonMandMaxThree_shopping,1.1067199892366495,1.1067201908411548,0.76480001211166382,0 +coef_universityNonMandMaxThree_visiting,-5.6416612820161021,-5.6416452449924739,0.28090000152587891,0 +coef_walkRetailAccessibility_eatout,-0.035796009413743196,-0.035795796225119095,0.061999998986721039,0 +coef_zeroAutomobiles_disc,-0.52859607918243423,-0.52859621558900038,-0.9089999794960022,0 +coef_zeroAutomobiles_visiting,-5.8405847342282176,-5.8405705977466207,-0.98000001907348633,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_participation_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_participation_SLSQP_.csv index c60173aa06..b9a0635d75 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_participation_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_joint_tour_participation_SLSQP_.csv @@ -1,60 +1,60 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_adult_log_of_max_window_overlap_with_a_child_mixed,4.5940415543572257,2.1890000000000001,0,,,4.5940415543572257 -coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,1.6916936883427531,0.84360000000000002,0,,,1.6916936883427531 -coef_adult_more_automobiles_than_workers_adult_only_party,0.31139003577268975,-0.21329999999999999,0,,,0.31139003577268975 -coef_adult_more_automobiles_than_workers_mixed_party,-1.8020541380547712,-0.60309999999999997,0,,,-1.8020541380547712 -coef_adult_number_of_joint_tours_adult_only,-2.1332672475172423,-0.32419999999999999,0,,,-2.1332672475172423 -coef_adult_number_of_joint_tours_mixed,-12.011984079880781,-0.3584,0,,,-12.011984079880781 -coef_adult_number_of_other_adults_in_the_household_adults_only_party,0,0,0,0,0,0 -coef_adult_number_of_other_adults_in_the_household_mixed_party,0,0,0,0,0,0 -coef_child_log_of_max_window_overlap_with_a_child_child,1.296,1.296,0,,,1.296 -coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.538,1.538,0,,,1.538 -coef_child_more_automobiles_than_workers_child_only_party,-0.4214,-0.4214,0,,,-0.4214 -coef_child_more_automobiles_than_workers_mixed_party,-1.5765541380547705,-0.37759999999999999,0,,,-1.5765541380547705 -coef_child_number_of_joint_tours_child_only,25.834459257330721,0.1047,0,,,25.834459257330721 -coef_child_number_of_joint_tours_mixed,89.315973889991952,-0.50890000000000002,0,,,89.315973889991952 -coef_child_number_of_other_children_in_the_household_child_only_party,0,0,0,0,0,0 -coef_child_number_of_other_children_in_the_household_mixed,0,0,0,0,0,0 -coef_child_too_young_for_school_children_only_party,-2.786,-2.786,0,,,-2.786 -coef_child_too_young_for_school_mixed_party,12.484734556961985,-1.893,0,,,12.484734556961985 -coef_child_too_young_for_school_specific_to_discretionary_joint_tours,0.12839999999999999,0.12839999999999999,0,,,0.12839999999999999 -coef_child_too_young_for_school_specific_to_eating_out_joint_tours,0.65890000000000004,0.65890000000000004,0,,,0.65890000000000004 -coef_driving_age_student_children_only_party,-1.8220000000000001,-1.8220000000000001,0,,,-1.8220000000000001 -coef_driving_age_student_mixed_party,-131.65706593958799,-1.353,0,,,-131.65706593958799 -coef_driving_age_student_specific_to_discretionary_joint_tours,-0.66749999999999998,-0.66749999999999998,0,,,-0.66749999999999998 -coef_driving_age_student_specific_to_eating_out_joint_tours,2.3439999999999999,2.3439999999999999,0,,,2.3439999999999999 -coef_dummy_for_high_income_for_adult_in_adult_party,0.1318998436337066,-0.16819999999999999,0,,,0.1318998436337066 -coef_dummy_for_high_income_for_adult_in_mixed_party,-0.38289361131114391,-0.02613,0,,,-0.38289361131114391 -coef_dummy_for_high_income_for_child_in_children_party,-0.56189999999999996,-0.56189999999999996,0,,,-0.56189999999999996 -coef_dummy_for_high_income_for_child_in_mixed_party,-0.50956361131120043,-0.15279999999999999,0,,,-0.50956361131120043 -coef_full_time_worker_mixed_party,-50.846659765191049,-3.5659999999999998,0,,,-50.846659765191049 -coef_full_time_worker_mixed_party_not,0.5,0.5,0,0.5,0.5,0.5 -coef_full_time_worker_specific_to_discretionary_joint_tours,0.43919999999999998,0.43919999999999998,0,,,0.43919999999999998 -coef_full_time_worker_specific_to_discretionary_joint_tours_not,0.5,0.5,0,0.5,0.5,0.5 -coef_full_time_worker_specific_to_eating_out_joint_tours,0.7157,0.7157,0,,,0.7157 -coef_full_time_worker_specific_to_eating_out_joint_tours_not,0.5,0.5,0,0.5,0.5,0.5 -coef_household_in_suburban_area_adult_adult_only_party,0,0,0,0,0,0 -coef_household_in_suburban_area_adult_mixed_party,-0.060069999999999998,-0.060069999999999998,0,,,-0.060069999999999998 -coef_household_in_suburban_area_child_child_only_party,0,0,0,0,0,0 -coef_household_in_suburban_area_child_mixed_party,0,0,0,0,0,0 -coef_household_in_urban_area_adult_adult_only_party,0,0,0,0,0,0 -coef_household_in_urban_area_adult_mixed_party,55.591278075644887,-0.13700000000000001,0,,,55.591278075644887 -coef_household_in_urban_area_child_child_only_party,26.939759257330742,1.21,0,,,26.939759257330742 -coef_household_in_urban_area_child_mixed_party,-101.1258709375923,0.62649999999999995,0,,,-101.1258709375923 -coef_non_worker_adults_only_party,-4.0792805161736174,-3.1640000000000001,0,,,-4.0792805161736174 -coef_non_worker_mixed_party,76.628151288066263,0.71519999999999995,0,,,76.628151288066263 -coef_non_worker_specific_to_discretionary_joint_tours,-0.1835,-0.1835,0,,,-0.1835 -coef_non_worker_specific_to_eating_out_joint_tours,0.16170000000000001,0.16170000000000001,0,,,0.16170000000000001 -coef_part_time_worker_adults_only_party,-3.2478258169504657,-3.5659999999999998,0,,,-3.2478258169504657 -coef_part_time_worker_adults_only_party_not,0.5,0.5,0,0.5,0.5,0.5 -coef_part_time_worker_mixed_party,70.07529311830892,-0.36549999999999999,0,,,70.07529311830892 -coef_part_time_worker_specific_to_discretionary_joint_tours,0.28499999999999998,0.28499999999999998,0,,,0.28499999999999998 -coef_part_time_worker_specific_to_eating_out_joint_tours,2.1880000000000002,2.1880000000000002,0,,,2.1880000000000002 -coef_pre_driving_age_student_children_only_party,25.008059257330711,-0.72170000000000001,0,,,25.008059257330711 -coef_pre_driving_age_student_mixed_party,12.421960445033344,-1.752,0,,,12.421960445033344 -coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.66259999999999997,0.66259999999999997,0,,,0.66259999999999997 -coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.391,1.391,0,,,1.391 -coef_unavailable,-999,-999,0,-999,-999,-999 -coef_university_student_mixed_party,-52.255368272022992,-3.0409999999999999,0,,,-52.255368272022992 -coef_university_student_specific_to_discretionary_joint_tours,0,0,0,0,0,0 -coef_university_student_specific_to_eating_out_joint_tours,-0.81999999999999995,-0.81999999999999995,0,,,-0.81999999999999995 +param_name,value,best,initvalue,nullvalue +coef_adult_log_of_max_window_overlap_with_a_child_mixed,4.5951782780582482,4.5951781033354839,2.1889998912811279,0 +coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,1.6633592801800163,1.6633593140698184,0.84359997510910034,0 +coef_adult_more_automobiles_than_workers_adult_only_party,0.31066873854811811,0.31066870271882263,-0.21330000460147858,0 +coef_adult_more_automobiles_than_workers_mixed_party,-1.8032448224599575,-1.803244785082267,-0.6031000018119812,0 +coef_adult_number_of_joint_tours_adult_only,-2.0764783287527235,-2.0764784513924197,-0.32420000433921814,0 +coef_adult_number_of_joint_tours_mixed,-4.6560544367999661,-4.6560542069402064,-0.35839998722076416,0 +coef_adult_number_of_other_adults_in_the_household_adults_only_party,0,0,0,0 +coef_adult_number_of_other_adults_in_the_household_mixed_party,0,0,0,0 +coef_child_log_of_max_window_overlap_with_a_child_child,1.2960000038146973,1.2960000038146973,1.2960000038146973,0 +coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.5379999876022339,1.5379999876022339,1.5379999876022339,0 +coef_child_more_automobiles_than_workers_child_only_party,-0.42140001058578491,-0.42140001058578491,-0.42140001058578491,0 +coef_child_more_automobiles_than_workers_mixed_party,-1.5777448348577667,-1.5777447974800762,-0.37760001420974731,0 +coef_child_number_of_joint_tours_child_only,13.579540492742831,13.579540463017146,0.1046999990940094,0 +coef_child_number_of_joint_tours_mixed,20.015257060305416,20.01525831304766,-0.508899986743927,0 +coef_child_number_of_other_children_in_the_household_child_only_party,0,0,0,0 +coef_child_number_of_other_children_in_the_household_mixed,0,0,0,0 +coef_child_too_young_for_school_children_only_party,-2.7860000133514404,-2.7860000133514404,-2.7860000133514404,0 +coef_child_too_young_for_school_mixed_party,8.3656227270333634,8.3656222958055366,-1.8930000066757202,0 +coef_child_too_young_for_school_specific_to_discretionary_joint_tours,0.12839999794960022,0.12839999794960022,0.12839999794960022,0 +coef_child_too_young_for_school_specific_to_eating_out_joint_tours,0.65890002250671387,0.65890002250671387,0.65890002250671387,0 +coef_driving_age_student_children_only_party,-1.8220000267028809,-1.8220000267028809,-1.8220000267028809,0 +coef_driving_age_student_mixed_party,-50,-50,-1.3530000448226929,0 +coef_driving_age_student_specific_to_discretionary_joint_tours,-0.66750001907348633,-0.66750001907348633,-0.66750001907348633,0 +coef_driving_age_student_specific_to_eating_out_joint_tours,2.3440001010894775,2.3440001010894775,2.3440001010894775,0 +coef_dummy_for_high_income_for_adult_in_adult_party,0.12621381061004386,0.12621385992786305,-0.16820000112056732,0 +coef_dummy_for_high_income_for_adult_in_mixed_party,-0.38299804175110763,-0.38299804898262102,-0.026130000129342079,0 +coef_dummy_for_high_income_for_child_in_children_party,-0.56190001964569092,-0.56190001964569092,-0.56190001964569092,0 +coef_dummy_for_high_income_for_child_in_mixed_party,-0.50966803537514371,-0.5096680426066571,-0.15279999375343323,0 +coef_full_time_worker_mixed_party,-18.56632640293509,-18.566326638673267,-3.5659999847412109,0 +coef_full_time_worker_mixed_party_not,0.5,0.5,0.5,0 +coef_full_time_worker_specific_to_discretionary_joint_tours,0.4392000138759613,0.4392000138759613,0.4392000138759613,0 +coef_full_time_worker_specific_to_discretionary_joint_tours_not,0.5,0.5,0.5,0 +coef_full_time_worker_specific_to_eating_out_joint_tours,0.71569997072219849,0.71569997072219849,0.71569997072219849,0 +coef_full_time_worker_specific_to_eating_out_joint_tours_not,0.5,0.5,0.5,0 +coef_household_in_suburban_area_adult_adult_only_party,0,0,0,0 +coef_household_in_suburban_area_adult_mixed_party,-0.06007000058889389,-0.06007000058889389,-0.06007000058889389,0 +coef_household_in_suburban_area_child_child_only_party,0,0,0,0 +coef_household_in_suburban_area_child_mixed_party,0,0,0,0 +coef_household_in_urban_area_adult_adult_only_party,0,0,0,0 +coef_household_in_urban_area_adult_mixed_party,15.95349033775349,15.953490704515609,-0.13699999451637268,0 +coef_household_in_urban_area_child_child_only_party,14.684840531795903,14.684840502070218,1.2100000381469727,0 +coef_household_in_urban_area_child_mixed_party,-27.706374555887564,-27.706375390127885,0.62650001049041748,0 +coef_non_worker_adults_only_party,-4.0512566140539823,-4.0512566665328293,-3.1640000343322754,0 +coef_non_worker_mixed_party,24.232596176450738,24.232596639515961,0.71520000696182251,0 +coef_non_worker_specific_to_discretionary_joint_tours,-0.1835000067949295,-0.1835000067949295,-0.1835000067949295,0 +coef_non_worker_specific_to_eating_out_joint_tours,0.16169999539852142,0.16169999539852142,0.16169999539852142,0 +coef_part_time_worker_adults_only_party,-3.2257432923239446,-3.2257432422956698,-3.5659999847412109,0 +coef_part_time_worker_adults_only_party_not,0.5,0.5,0.5,0 +coef_part_time_worker_mixed_party,22.126834905159047,22.126835266655284,-0.36550000309944153,0 +coef_part_time_worker_specific_to_discretionary_joint_tours,0.28499999642372131,0.28499999642372131,0.28499999642372131,0 +coef_part_time_worker_specific_to_eating_out_joint_tours,2.187999963760376,2.187999963760376,2.187999963760376,0 +coef_pre_driving_age_student_children_only_party,12.753140480965085,12.7531404512394,-0.72170001268386841,0 +coef_pre_driving_age_student_mixed_party,8.3035034193009718,8.3035030162498487,-1.7519999742507935,0 +coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.66259998083114624,0.66259998083114624,0.66259998083114624,0 +coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.3910000324249268,1.3910000324249268,1.3910000324249268,0 +coef_unavailable,-999,-999,-999,0 +coef_university_student_mixed_party,-19.976674136225505,-19.976674380042439,-3.0409998893737793,0 +coef_university_student_specific_to_discretionary_joint_tours,0,0,0,0 +coef_university_student_specific_to_eating_out_joint_tours,-0.81999999284744263,-0.81999999284744263,-0.81999999284744263,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_BHHH_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_BHHH_.csv index 3f5af851e4..2c5dc3ee14 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_BHHH_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_BHHH_.csv @@ -1,54 +1,54 @@ -,value,initvalue,nullvalue,minimum,maximum,best -0,0,0,0,0,0,0 -coef_can_walk_to_work_and_school,0.072221053570087068,0.1391,0,,,0.072221053570087068 -coef_can_walk_to_work_school2,1.368189229871656,0.71140000000000003,0,,,1.368189229871656 -coef_can_walk_to_work_work2,0.6511271300905388,0.52680000000000005,0,,,0.6511271300905388 -coef_driving_age_child_school2_asc,-10.76072459479807,-3.1360000000000001,0,,,-10.76072459479807 -coef_driving_age_child_work_and_school_asc,-58.117517308879499,-4.4362000000000004,0,,,-58.117517308879499 -coef_female_school1,0.2474431563125136,0.15920000000000001,0,,,0.2474431563125136 -coef_female_school2,-0.3012348896462545,0.114,0,,,-0.3012348896462545 -coef_female_work1,-2.2434862184806872,0.17369999999999999,0,,,-2.2434862184806872 -coef_female_work2,-0.3875204207258891,-0.22550000000000001,0,,,-0.3875204207258891 -coef_female_work_and_school,-3.2183973184973143,-0.34420000000000001,0,,,-3.2183973184973143 -coef_few_cars_than_drivers_school2,-0.86352025026207002,-0.57589999999999997,0,,,-0.86352025026207002 -coef_ft_worker_work2_asc,-15.631616769904962,-3.3780999999999999,0,,,-15.631616769904962 -coef_hh_income_gt_50k_school1,0.034700030521069632,0.034700000000000002,0,,,0.034700030521069632 -coef_hh_income_gt_50k_student_work_and_school,-0.92170661372181706,-0.0528,0,,,-0.92170661372181706 -coef_hh_income_gt_50k_work,-1.4190912531326889,-0.0528,0,,,-1.4190912531326889 -coef_hh_income_gt_50k_worker_work_and_school,0.034699971829526256,0.034700000000000002,0,,,0.034699971829526256 -coef_home_urban_school1,-0.13610000344523163,-0.1361,0,,,-0.13610000344523163 -coef_home_urban_school2,-11.830881608079386,0.317,0,,,-11.830881608079386 -coef_home_urban_work1,24.254352793957736,-0.28310000000000002,0,,,24.254352793957736 -coef_home_urban_work2,12.738503782102926,0.23080000000000001,0,,,12.738503782102926 -coef_home_urban_work_and_school,-2.9755482471380152,-0.35089999999999999,0,,,-2.9755482471380152 -coef_no_cars_in_hh_school2,-0.6201622889361833,-1.413,0,,,-0.6201622889361833 -coef_no_cars_in_hh_work2,-0.72588656952288921,-1.306,0,,,-0.72588656952288921 -coef_no_cars_in_hh_work_and_school,-0.7768760244151115,-1.302,0,,,-0.7768760244151115 -coef_non_family_hh_category1,-0.25000005548208493,-0.25,0,,,-0.25000005548208493 -coef_non_family_hh_category2,-18.224089926229215,-0.1792,0,,,-18.224089926229215 -coef_non_student_goes_to_school,3.883000012592801,3.883,0,,,3.883000012592801 -coef_num_non_workers_in_hh_school1,0.25739997881575821,0.25740000000000002,0,,,0.25739997881575821 -coef_num_preschool_in_hh_school1,-0.13350001004490689,-0.13350000000000001,0,,,-0.13350001004490689 -coef_num_preschool_in_hh_school2,0.35689505282246176,-0.55769999999999997,0,,,0.35689505282246176 -coef_num_preschool_in_hh_work1,-2.9320917687605954,0.21909999999999999,0,,,-2.9320917687605954 -coef_num_preschool_in_hh_work2,0.22500429804995042,-0.14779999999999999,0,,,0.22500429804995042 -coef_num_preschool_in_hh_work_and_school,-291.65982798845073,-0.12509999999999999,0,,,-291.65982798845073 -coef_num_under_16_not_at_school_school2,-0.63862137361032489,0.086599999999999996,0,,,-0.63862137361032489 -coef_num_under_16_not_at_school_work2,-0.11866867638023079,0.1804,0,,,-0.11866867638023079 -coef_num_under_16_not_at_school_work_and_school,-18.884178394306563,-0.19550000000000001,0,,,-18.884178394306563 -coef_pre_driving_age_child_school2_asc,7.0553066040769048,-3.9702999999999999,0,,,7.0553066040769048 -coef_pt_worker_work2_asc,-14.856919777024819,-3.0476000000000001,0,,,-14.856919777024819 -coef_round_trip_auto_time_to_work_school2,0.064396584422453232,-0.0033999999999999998,0,,,0.064396584422453232 -coef_round_trip_auto_time_to_work_work2,-0.025902525682352791,-0.0035000000000000001,0,,,-0.025902525682352791 -coef_round_trip_auto_time_to_work_work_and_school,-0.02180773780300839,-0.0030999999999999999,0,,,-0.02180773780300839 -coef_student_employed,61.497344836308677,3.0139999999999998,0,,,61.497344836308677 -coef_unavailable,-999,-999,0,-999,-999,-999 -coef_under_35_school1,0.7218,0.7218,0,,,0.7218 -coef_under_35_school2,27.445525973793494,1.2749999999999999,0,,,27.445525973793494 -coef_under_35_work1,1.0375724814647256,-0.46289999999999998,0,,,1.0375724814647256 -coef_under_35_work2,-0.25382084017500217,-0.13750000000000001,0,,,-0.25382084017500217 -coef_under_35_work_and_school,2.256932391854527,0.97609999999999997,0,,,2.256932391854527 -coef_univ_school2_asc,-19.291663549848391,-3.7429000000000001,0,,,-19.291663549848391 -coef_univ_work1_asc,26.703452792641354,2.1659999999999999,0,,,26.703452792641354 -coef_univ_work2_asc,35.174040311644283,-1.3965000000000001,0,,,35.174040311644283 -coef_univ_work_and_school_asc,51.163969004277625,0.10730000000000001,0,,,51.163969004277625 +param_name,value,best,initvalue,nullvalue +0,0,0,0,0 +coef_can_walk_to_work_and_school,0.071963467898163519,0.071963467898163519,0.13910000026226044,0 +coef_can_walk_to_work_school2,1.3682146498522929,1.3682146498522929,0.71139997243881226,0 +coef_can_walk_to_work_work2,0.65113800717686054,0.65113800717686054,0.52679997682571411,0 +coef_driving_age_child_school2_asc,-10.760724267208685,-10.760724267208685,-3.1359999179840088,0 +coef_driving_age_child_work_and_school_asc,-58.117542193724134,-58.117542193724134,-4.4362001419067383,0 +coef_female_school1,0.24693966666339068,0.24693966666339068,0.15919999778270721,0 +coef_female_school2,-0.30156175637229587,-0.30156175637229587,0.11400000005960464,0 +coef_female_work1,-2.2432731743178365,-2.2432731743178365,0.1737000048160553,0 +coef_female_work2,-0.38747695440528995,-0.38747695440528995,-0.22550000250339508,0 +coef_female_work_and_school,-3.2184568767600301,-3.2184568767600301,-0.34419998526573181,0 +coef_few_cars_than_drivers_school2,-0.86287494081897043,-0.86287494081897043,-0.57590001821517944,0 +coef_ft_worker_work2_asc,-15.631596564333391,-15.631596564333391,-3.3780999183654785,0 +coef_hh_income_gt_50k_school1,0.034699998903999218,0.034699998903999218,0.034699998795986176,0 +coef_hh_income_gt_50k_student_work_and_school,-0.92151889984559776,-0.92151889984559776,-0.052799999713897705,0 +coef_hh_income_gt_50k_work,-1.4188850438199954,-1.4188850438199954,-0.052799999713897705,0 +coef_hh_income_gt_50k_worker_work_and_school,0.034699998835916637,0.034699998835916637,0.034699998795986176,0 +coef_home_urban_school1,-0.1360999941739981,-0.1360999941739981,-0.13609999418258667,0 +coef_home_urban_school2,-11.831044462083096,-11.831044462083096,0.31700000166893005,0 +coef_home_urban_work1,24.254289912474832,24.254289912474832,-0.28310000896453857,0 +coef_home_urban_work2,12.738486792776087,12.738486792776087,0.23080000281333923,0 +coef_home_urban_work_and_school,-2.9754582133928946,-2.9754582133928946,-0.35089999437332153,0 +coef_no_cars_in_hh_school2,-0.62062284302473281,-0.62062284302473281,-1.4129999876022339,0 +coef_no_cars_in_hh_work2,-0.72590052527502258,-0.72590052527502258,-1.3059999942779541,0 +coef_no_cars_in_hh_work_and_school,-0.77649868370275865,-0.77649868370275865,-1.3020000457763672,0 +coef_non_family_hh_category1,-0.25000000001603168,-0.25000000001603168,-0.25,0 +coef_non_family_hh_category2,-18.224086954408719,-18.224086954408719,-0.17919999361038208,0 +coef_non_student_goes_to_school,3.8829998970263762,3.8829998970263762,3.8829998970031738,0 +coef_num_non_workers_in_hh_school1,0.2574000060601766,0.2574000060601766,0.25740000605583191,0 +coef_num_preschool_in_hh_school1,-0.1334999948664459,-0.1334999948664459,-0.13349999487400055,0 +coef_num_preschool_in_hh_school2,0.35679291623910225,0.35679291623910225,-0.55769997835159302,0 +coef_num_preschool_in_hh_work1,-2.9311650176345023,-2.9311650176345023,0.21909999847412109,0 +coef_num_preschool_in_hh_work2,0.22506526227617765,0.22506526227617765,-0.14779999852180481,0 +coef_num_preschool_in_hh_work_and_school,-1120.7076517335183,-1120.7076517335183,-0.12510000169277191,0 +coef_num_under_16_not_at_school_school2,-0.63850525564048144,-0.63850525564048144,0.08659999817609787,0 +coef_num_under_16_not_at_school_work2,-0.11879303600656704,-0.11879303600656704,0.18039999902248383,0 +coef_num_under_16_not_at_school_work_and_school,-72.572511675156974,-72.572511675156974,-0.19550000131130219,0 +coef_pre_driving_age_child_school2_asc,7.0553915333001287,7.0553915333001287,-3.9702999591827393,0 +coef_pt_worker_work2_asc,-14.85688697195045,-14.85688697195045,-3.0476000308990479,0 +coef_round_trip_auto_time_to_work_school2,0.064402441888329359,0.064402441888329359,-0.0034000000450760126,0 +coef_round_trip_auto_time_to_work_work2,-0.025903740882998762,-0.025903740882998762,-0.0035000001080334187,0 +coef_round_trip_auto_time_to_work_work_and_school,-0.021807011903415791,-0.021807011903415791,-0.0031000000890344381,0 +coef_student_employed,61.49730201724514,61.49730201724514,3.0139999389648438,0 +coef_unavailable,-999,-999,-999,0 +coef_under_35_school1,0.72180002927780151,0.72180002927780151,0.72180002927780151,0 +coef_under_35_school2,27.44527439728251,27.44527439728251,1.2749999761581421,0 +coef_under_35_work1,1.0377437873346973,1.0377437873346973,-0.46290001273155212,0 +coef_under_35_work2,-0.25384424363084357,-0.25384424363084357,-0.13750000298023224,0 +coef_under_35_work_and_school,2.2567986605803032,2.2567986605803032,0.97610002756118774,0 +coef_univ_school2_asc,-19.29191150330508,-19.29191150330508,-3.7428998947143555,0 +coef_univ_work1_asc,26.703389810868742,26.703389810868742,2.1659998893737793,0 +coef_univ_work2_asc,35.173970384221057,35.173970384221057,-1.3964999914169312,0 +coef_univ_work_and_school_asc,51.164083831710279,51.164083831710279,0.10729999840259552,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_.csv index fd8238225b..457583ab1e 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_.csv @@ -1,54 +1,54 @@ -,value,initvalue,nullvalue,minimum,maximum,best -0,0,0,0,0,0,0 -coef_can_walk_to_work_and_school,0.071630314724926147,0.1391,0,,,0.071630314724926147 -coef_can_walk_to_work_school2,1.3680922080710298,0.71140000000000003,0,,,1.3680922080710298 -coef_can_walk_to_work_work2,0.65112015867036332,0.52680000000000005,0,,,0.65112015867036332 -coef_driving_age_child_school2_asc,-13.067731116342266,-3.1360000000000001,0,,,-13.067731116342266 -coef_driving_age_child_work_and_school_asc,-12.605997463132249,-4.4362000000000004,0,,,-12.605997463132249 -coef_female_school1,0.24708609009193822,0.15920000000000001,0,,,0.24708609009193822 -coef_female_school2,-0.30133903622407632,0.114,0,,,-0.30133903622407632 -coef_female_work1,-2.2427478904369504,0.17369999999999999,0,,,-2.2427478904369504 -coef_female_work2,-0.38741093683095679,-0.22550000000000001,0,,,-0.38741093683095679 -coef_female_work_and_school,-3.2182798790112783,-0.34420000000000001,0,,,-3.2182798790112783 -coef_few_cars_than_drivers_school2,-0.86245057209463027,-0.57589999999999997,0,,,-0.86245057209463027 -coef_ft_worker_work2_asc,-5.5179331616722695,-3.3780999999999999,0,,,-5.5179331616722695 -coef_hh_income_gt_50k_school1,0.034700000000000002,0.034700000000000002,0,,,0.034700000000000002 -coef_hh_income_gt_50k_student_work_and_school,-0.92117498986723134,-0.0528,0,,,-0.92117498986723134 -coef_hh_income_gt_50k_work,-1.418550817944235,-0.0528,0,,,-1.418550817944235 -coef_hh_income_gt_50k_worker_work_and_school,0.034700000000000002,0.034700000000000002,0,,,0.034700000000000002 -coef_home_urban_school1,-0.1361,-0.1361,0,,,-0.1361 -coef_home_urban_school2,-8.4556148704436787,0.317,0,,,-8.4556148704436787 -coef_home_urban_work1,4.0267960749153291,-0.28310000000000002,0,,,4.0267960749153291 -coef_home_urban_work2,2.6248540229034232,0.23080000000000001,0,,,2.6248540229034232 -coef_home_urban_work_and_school,-0.44707001996924778,-0.35089999999999999,0,,,-0.44707001996924778 -coef_no_cars_in_hh_school2,-0.62046508539739775,-1.413,0,,,-0.62046508539739775 -coef_no_cars_in_hh_work2,-0.72596662468976358,-1.306,0,,,-0.72596662468976358 -coef_no_cars_in_hh_work_and_school,-0.77593437523044428,-1.302,0,,,-0.77593437523044428 -coef_non_family_hh_category1,-0.25,-0.25,0,,,-0.25 -coef_non_family_hh_category2,5.0007710032851715,-0.1792,0,,,5.0007710032851715 -coef_non_student_goes_to_school,3.883,3.883,0,,,3.883 -coef_num_non_workers_in_hh_school1,0.25740000000000002,0.25740000000000002,0,,,0.25740000000000002 -coef_num_preschool_in_hh_school1,-0.13350000000000001,-0.13350000000000001,0,,,-0.13350000000000001 -coef_num_preschool_in_hh_school2,0.35665962262224188,-0.55769999999999997,0,,,0.35665962262224188 -coef_num_preschool_in_hh_work1,-2.9305005867030882,0.21909999999999999,0,,,-2.9305005867030882 -coef_num_preschool_in_hh_work2,0.22517317861058084,-0.14779999999999999,0,,,0.22517317861058084 -coef_num_preschool_in_hh_work_and_school,-29.197568875282681,-0.12509999999999999,0,,,-29.197568875282681 -coef_num_under_16_not_at_school_school2,-0.63829340447675476,0.086599999999999996,0,,,-0.63829340447675476 -coef_num_under_16_not_at_school_work2,-0.11899114846640058,0.1804,0,,,-0.11899114846640058 -coef_num_under_16_not_at_school_work_and_school,-15.357906680125023,-0.19550000000000001,0,,,-15.357906680125023 -coef_pre_driving_age_child_school2_asc,3.6797262862761189,-3.9702999999999999,0,,,3.6797262862761189 -coef_pt_worker_work2_asc,-4.7432419645081501,-3.0476000000000001,0,,,-4.7432419645081501 -coef_round_trip_auto_time_to_work_school2,0.064410816444162883,-0.0033999999999999998,0,,,0.064410816444162883 -coef_round_trip_auto_time_to_work_work2,-0.025905460423305656,-0.0035000000000000001,0,,,-0.025905460423305656 -coef_round_trip_auto_time_to_work_work_and_school,-0.021803756470739271,-0.0030999999999999999,0,,,-0.021803756470739271 -coef_student_employed,13.457255204029515,3.0139999999999998,0,,,13.457255204029515 -coef_unavailable,-999,-999,0,-999,-999,-999 -coef_under_35_school1,0.7218,0.7218,0,,,0.7218 -coef_under_35_school2,15.011164063565253,1.2749999999999999,0,,,15.011164063565253 -coef_under_35_work1,1.0382419681378368,-0.46289999999999998,0,,,1.0382419681378368 -coef_under_35_work2,-0.25387498090125782,-0.13750000000000001,0,,,-0.25387498090125782 -coef_under_35_work_and_school,2.2570354258963481,0.97609999999999997,0,,,2.2570354258963481 -coef_univ_school2_asc,-10.233810040377643,-3.7429000000000001,0,,,-10.233810040377643 -coef_univ_work1_asc,6.475896074915326,2.1659999999999999,0,,,6.475896074915326 -coef_univ_work2_asc,4.8330291490834876,-1.3965000000000001,0,,,4.8330291490834876 -coef_univ_work_and_school_asc,8.1809274431629717,0.10730000000000001,0,,,8.1809274431629717 +param_name,value,best,initvalue,nullvalue +0,0,0,0,0 +coef_can_walk_to_work_and_school,0.20132027468875283,0.20132027468479921,0.13910000026226044,0 +coef_can_walk_to_work_school2,0.96010731757888212,0.96010731757834789,0.71139997243881226,0 +coef_can_walk_to_work_work2,0.73122607984567289,0.73122607983903865,0.52679997682571411,0 +coef_driving_age_child_school2_asc,-5.7172473630323095,-5.7172473630089566,-3.1359999179840088,0 +coef_driving_age_child_work_and_school_asc,-4.4775478428522311,-4.477547842857807,-4.4362001419067383,0 +coef_female_school1,-1.1179750622419278,-1.117975062224638,0.15919999778270721,0 +coef_female_school2,-0.48952027651274382,-0.48952027650816798,0.11400000005960464,0 +coef_female_work1,-2.0511063704156607,-2.0511063703883035,0.1737000048160553,0 +coef_female_work2,-0.41490266839201562,-0.4149026683981944,-0.22550000250339508,0 +coef_female_work_and_school,-2.9966902230088177,-2.9966902229830907,-0.34419998526573181,0 +coef_few_cars_than_drivers_school2,-0.55452509049148058,-0.55452509048768461,-0.57590001821517944,0 +coef_ft_worker_work2_asc,-3.8025869871232629,-3.8025869871213596,-3.3780999183654785,0 +coef_hh_income_gt_50k_school1,0.034699998795986176,0.034699998795986176,0.034699998795986176,0 +coef_hh_income_gt_50k_student_work_and_school,-0.17920043049685666,-0.17920043049604578,-0.052799999713897705,0 +coef_hh_income_gt_50k_work,-0.66212675917984232,-0.66212675918375841,-0.052799999713897705,0 +coef_hh_income_gt_50k_worker_work_and_school,0.034699998795986176,0.034699998795986176,0.034699998795986176,0 +coef_home_urban_school1,-0.13609999418258667,-0.13609999418258667,-0.13609999418258667,0 +coef_home_urban_school2,-2.1094338222937941,-2.1094338222674192,0.31700000166893005,0 +coef_home_urban_work1,0.34863867027519652,0.34863867026166911,-0.28310000896453857,0 +coef_home_urban_work2,0.76429400769443512,0.76429400769779043,0.23080000281333923,0 +coef_home_urban_work_and_school,-0.33420724299781907,-0.33420724299927923,-0.35089999437332153,0 +coef_no_cars_in_hh_school2,-0.3281459638377332,-0.32814596385333328,-1.4129999876022339,0 +coef_no_cars_in_hh_work2,-0.66209007579299561,-0.66209007577665158,-1.3059999942779541,0 +coef_no_cars_in_hh_work_and_school,-0.92494433055696312,-0.92494433055322656,-1.3020000457763672,0 +coef_non_family_hh_category1,-0.25,-0.25,-0.25,0 +coef_non_family_hh_category2,0.49350779245951959,0.49350779245352627,-0.17919999361038208,0 +coef_non_student_goes_to_school,3.8829998970031738,3.8829998970031738,3.8829998970031738,0 +coef_num_non_workers_in_hh_school1,0.25740000605583191,0.25740000605583191,0.25740000605583191,0 +coef_num_preschool_in_hh_school1,-0.13349999487400055,-0.13349999487400055,-0.13349999487400055,0 +coef_num_preschool_in_hh_school2,0.73590985242441875,0.73590985242576534,-0.55769997835159302,0 +coef_num_preschool_in_hh_work1,-1.6486490740351119,-1.6486490740275117,0.21909999847412109,0 +coef_num_preschool_in_hh_work2,0.24670839988109913,0.2467083998803998,-0.14779999852180481,0 +coef_num_preschool_in_hh_work_and_school,-2.4290259678800297,-2.4290259678663575,-0.12510000169277191,0 +coef_num_under_16_not_at_school_school2,-0.91329132598172835,-0.9132913259601626,0.08659999817609787,0 +coef_num_under_16_not_at_school_work2,-0.11459202927007101,-0.11459202927135456,0.18039999902248383,0 +coef_num_under_16_not_at_school_work_and_school,-2.3336601463508013,-2.3336601463331874,-0.19550000131130219,0 +coef_pre_driving_age_child_school2_asc,-2.6428722793019959,-2.6428722793097204,-3.9702999591827393,0 +coef_pt_worker_work2_asc,-3.0303892540032376,-3.0303892539968387,-3.0476000308990479,0 +coef_round_trip_auto_time_to_work_school2,0.064069158794420475,0.064069158793510952,-0.0034000000450760126,0 +coef_round_trip_auto_time_to_work_work2,-0.020202184952509333,-0.020202184952801051,-0.0035000001080334187,0 +coef_round_trip_auto_time_to_work_work_and_school,-0.015315391311272412,-0.015315391311409078,-0.0031000000890344381,0 +coef_student_employed,4.6032016663231561,4.6032016663032218,3.0139999389648438,0 +coef_unavailable,-999,-999,-999,0 +coef_under_35_school1,0.72180002927780151,0.72180002927780151,0.72180002927780151,0 +coef_under_35_school2,2.1398332789937333,2.1398332789898835,1.2749999761581421,0 +coef_under_35_work1,1.1194222799903866,1.1194222799688391,-0.46290001273155212,0 +coef_under_35_work2,-0.24674628169467161,-0.24674628168879748,-0.13750000298023224,0 +coef_under_35_work_and_school,2.7433316694686765,2.7433316694426049,0.97610002756118774,0 +coef_univ_school2_asc,-4.9155139535095262,-4.9155139534987793,-3.7428998947143555,0 +coef_univ_work1_asc,2.797738568613517,2.7977385685999896,2.1659998893737793,0 +coef_univ_work2_asc,-0.45572969467386271,-0.45572969467880964,-1.3964999914169312,0 +coef_univ_work_and_school_asc,0.16534045072359432,0.16534045072771017,0.10729999840259552,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_loglike.csv index f22660c1eb..a593ed1541 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_mandatory_tour_frequency_SLSQP_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-434.77803538920688,-410.22015020484497 +0,-434.77803587138652,-412.67417109099858 diff --git a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_trip_mode_choice_SLSQP_.csv b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_trip_mode_choice_SLSQP_.csv index 706cb99d17..a54d7e8e1c 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_trip_mode_choice_SLSQP_.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_simple_simulate_trip_mode_choice_SLSQP_.csv @@ -1,273 +1,273 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -1,1,1,1,1,1,1 -coef_age010_trn,0.36055093747501515,0,0,,,0.36055093747501515 -coef_age1619_da,0.34452990003908374,0,0,,,0.34452990003908374 -coef_age16p_sr,-0.15188561974324261,0,0,,,-0.15188561974324261 -coef_bike_ASC_rh,-7,-7,0,-7,-7,-7 -coef_bike_ASC_walk_work,-2.6591723273807126,-1.8332999999999999,0,,,-2.6591723273807126 -coef_drive_transit_ASC_commuter_work,0.42650515313601794,0.4012,0,,,0.42650515313601794 -coef_drive_transit_ASC_express_work,-0.35539999999999999,-0.35539999999999999,0,,,-0.35539999999999999 -coef_drive_transit_ASC_ferry_work,0.52769999999999995,0.52769999999999995,0,,,0.52769999999999995 -coef_drive_transit_ASC_heavyrail_work,-11.994127899898398,0.3538,0,,,-11.994127899898398 -coef_drive_transit_ASC_lightrail_work,-10.300240827222716,0.46210000000000001,0,,,-10.300240827222716 -coef_drive_transit_ASC_rh,-162.17158272141376,-4.25,0,,,-162.17158272141376 -coef_hhsize1_sr,-0.89092943199297148,-0.73460000000000003,0,,,-0.89092943199297148 -coef_hhsize2_sr,0.039652734237525036,0,0,,,0.039652734237525036 -coef_ivt_work,-0.010154908820341358,-0.021999999999999999,0,,,-0.010154908820341358 -coef_joint_auto_ASC_rh_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_auto_ASC_sr2_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_auto_ASC_sr3p_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_auto_ASC_walk_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_bike_ASC_rh_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_bike_ASC_walk_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_drive_transit_ASC_commuter_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_drive_transit_ASC_express_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_drive_transit_ASC_ferry_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_drive_transit_ASC_heavyrail_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_drive_transit_ASC_lightrail_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_drive_transit_ASC_rh_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_ride_hail_ASC_sr2_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_ride_hail_ASC_sr3p_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_ride_hail_ASC_taxi_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_ride_hail_ASC_tnc_shared,0,0,0,,,0 -coef_joint_ride_hail_ASC_tnc_single_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_ride_hail_ASC_walk_transit,0,0,0,,,0 -coef_joint_ride_hail_ASC_walk_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_ASC_rh_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_commuter_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_express_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_ferry_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_heavyrail_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_lightrail_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_rh_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_sr2_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_sr3p_work_univ_school_escort_atwork,0,0,0,,,0 -coef_joint_walk_transit_ASC_walk_work_univ_school_escort_atwork,0,0,0,,,0 -coef_nest_AUTO,0.71999999999999997,0.71999999999999997,1,0.71999999999999997,0.71999999999999997,0.71999999999999997 -coef_nest_AUTO_DRIVEALONE,0.34999999999999998,0.34999999999999998,1,0.34999999999999998,0.34999999999999998,0.34999999999999998 -coef_nest_AUTO_SHAREDRIDE2,0.34999999999999998,0.34999999999999998,1,0.34999999999999998,0.34999999999999998,0.34999999999999998 -coef_nest_AUTO_SHAREDRIDE3,0.34999999999999998,0.34999999999999998,1,0.34999999999999998,0.34999999999999998,0.34999999999999998 -coef_nest_NONMOTORIZED,0.71999999999999997,0.71999999999999997,1,0.71999999999999997,0.71999999999999997,0.71999999999999997 -coef_nest_RIDEHAIL,0.35999999999999999,0.35999999999999999,1,0.35999999999999999,0.35999999999999999,0.35999999999999999 -coef_nest_TRANSIT,0.71999999999999997,0.71999999999999997,1,0.71999999999999997,0.71999999999999997,0.71999999999999997 -coef_nest_TRANSIT_DRIVEACCESS,0.5,0.5,1,0.5,0.5,0.5 -coef_nest_TRANSIT_WALKACCESS,0.5,0.5,1,0.5,0.5,0.5 -coef_ride_hail_ASC_sr2_work,-24.964152977906629,-6.1079999999999997,0,,,-24.964152977906629 -coef_ride_hail_ASC_sr3p_work_escort_shopping_othmaint_atwork,-29.66185425032911,-7,0,,,-29.66185425032911 -coef_ride_hail_ASC_taxi_work,-2.5195664524629127,-2.3849999999999998,0,,,-2.5195664524629127 -coef_ride_hail_ASC_tnc_shared,23.268241084943003,0,0,,,23.268241084943003 -coef_ride_hail_ASC_tnc_single_work,23.984210218237873,0.73499999999999999,0,,,23.984210218237873 -coef_ride_hail_ASC_walk_transit,22.114586690634976,0,0,,,22.114586690634976 -coef_ride_hail_ASC_walk_work,21.771187507152565,0.2858,0,,,21.771187507152565 -coef_sov_ASC_rh_work_atwork,-7,-7,0,-7,-7,-7 -coef_sov_ASC_sr2_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork,-999,-999,0,-999,-999,-999 -coef_sov_ASC_sr3p_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork,-999,-999,0,-999,-999,-999 -coef_sov_ASC_walk_work,-3.007257338441883,-0.83979999999999999,0,,,-3.007257338441883 -coef_sr2_ASC_rh_work_school_escort_shopping_eatout_othmaint_social_othdiscr_atwork,-7,-7,0,-7,-7,-7 -coef_sr2_ASC_sr2_work,0.19745747776996045,0.0149,0,,,0.19745747776996045 -coef_sr2_ASC_sr3p,-999,-999,0,-999,-999,-999 -coef_sr2_ASC_walk_work,-2.2229353369557856,0.126,0,,,-2.2229353369557856 -coef_sr3p_ASC_rh_work,-71.917699999999996,-71.917699999999996,0,,,-71.917699999999996 -coef_sr3p_ASC_sr2_work,-0.95395070192885023,-1.1956,0,,,-0.95395070192885023 -coef_sr3p_ASC_sr3p_work,0.0022823628108995749,-0.18360000000000001,0,,,0.0022823628108995749 -coef_sr3p_ASC_walk_work,-2.3566238207046379,0.21709999999999999,0,,,-2.3566238207046379 -coef_walk_ASC_rh,-7,-7,0,-7,-7,-7 -coef_walk_transit_ASC_commuter_work,0.4012,0.4012,0,,,0.4012 -coef_walk_transit_ASC_express_work,-0.35539999999999999,-0.35539999999999999,0,,,-0.35539999999999999 -coef_walk_transit_ASC_ferry_work,0.52769999999999995,0.52769999999999995,0,,,0.52769999999999995 -coef_walk_transit_ASC_heavyrail_work,0.50253917060490561,0.3538,0,,,0.50253917060490561 -coef_walk_transit_ASC_lightrail_work,0.63259254210688642,0.46210000000000001,0,,,0.63259254210688642 -coef_walk_transit_ASC_rh_work,-2.8695420596058572,-3.7408000000000001,0,,,-2.8695420596058572 -coef_walk_transit_ASC_sr2_work,-2.2566861984974196,-3.6631,0,,,-2.2566861984974196 -coef_walk_transit_ASC_sr3p_work,-2.6534480546215211,-4.1314000000000002,0,,,-2.6534480546215211 -coef_walk_transit_ASC_walk_work,-0.39143818330516583,0.44319999999999998,0,,,-0.39143818330516583 -coef_bike_ASC_walk_univ,-0.33989999999999998,-0.33989999999999998,0,,,-0.33989999999999998 -coef_drive_transit_ASC_commuter_univ_school,0.90820000000000001,0.90820000000000001,0,,,0.90820000000000001 -coef_drive_transit_ASC_express_univ_school,0.32240000000000002,0.32240000000000002,0,,,0.32240000000000002 -coef_drive_transit_ASC_ferry_univ_school,1.7237,1.7237,0,,,1.7237 -coef_drive_transit_ASC_heavyrail_univ_school,0.84899999999999998,0.84899999999999998,0,,,0.84899999999999998 -coef_drive_transit_ASC_lightrail_univ_school,1.4436,1.4436,0,,,1.4436 -coef_ivt_univ_school,-0.0082670637836597533,-0.027099999999999999,0,,,-0.0082670637836597533 -coef_ride_hail_ASC_sr2_univ,-4.3372000000000002,-4.3372000000000002,0,,,-4.3372000000000002 -coef_ride_hail_ASC_sr3p_univ,-4.9219999999999997,-4.9219999999999997,0,,,-4.9219999999999997 -coef_ride_hail_ASC_taxi_univ,-1.5993999999999999,-1.5993999999999999,0,,,-1.5993999999999999 -coef_ride_hail_ASC_tnc_single_univ,0.1081,0.1081,0,,,0.1081 -coef_ride_hail_ASC_walk_univ_escort_shopping_othmaint,-26.322181448888141,-7,0,,,-26.322181448888141 -coef_sov_ASC_rh_univ,-6.649,-6.649,0,,,-6.649 -coef_sov_ASC_walk_univ,-1.0579000000000001,-1.0579000000000001,0,,,-1.0579000000000001 -coef_sr2_ASC_rh_univ,-6.6897000000000002,-6.6897000000000002,0,,,-6.6897000000000002 -coef_sr2_ASC_sr2_univ,0.4304,0.4304,0,,,0.4304 -coef_sr2_ASC_walk_univ,1.3041,1.3041,0,,,1.3041 -coef_sr3p_ASC_rh_univ_escort_shopping_eatout_othmaint_social_othdiscr,-7,-7,0,-7,-7,-7 -coef_sr3p_ASC_sr2_univ,-0.31169999999999998,-0.31169999999999998,0,,,-0.31169999999999998 -coef_sr3p_ASC_sr3p_univ,0.62929999999999997,0.62929999999999997,0,,,0.62929999999999997 -coef_sr3p_ASC_walk_univ,1.6793,1.6793,0,,,1.6793 -coef_walk_transit_ASC_commuter_univ_school,0.90820000000000001,0.90820000000000001,0,,,0.90820000000000001 -coef_walk_transit_ASC_express_univ_school,0.32240000000000002,0.32240000000000002,0,,,0.32240000000000002 -coef_walk_transit_ASC_ferry_univ_school,1.7237,1.7237,0,,,1.7237 -coef_walk_transit_ASC_heavyrail_univ_school,0.86656489716508533,0.84899999999999998,0,,,0.86656489716508533 -coef_walk_transit_ASC_lightrail_univ_school,1.5673078514811689,1.4436,0,,,1.5673078514811689 -coef_walk_transit_ASC_rh_univ,-4.2690999999999999,-4.2690999999999999,0,,,-4.2690999999999999 -coef_walk_transit_ASC_sr2_univ,-4.0049999999999999,-4.0049999999999999,0,,,-4.0049999999999999 -coef_walk_transit_ASC_sr3p_univ,-28.697199999999999,-28.697199999999999,0,,,-28.697199999999999 -coef_walk_transit_ASC_walk_univ,-1.0310999999999999,-1.0310999999999999,0,,,-1.0310999999999999 -coef_bike_ASC_walk_school,-106.06311357570786,-2.0331999999999999,0,,,-106.06311357570786 -coef_ride_hail_ASC_sr2_school,-17.561865296747161,-1.5869,0,,,-17.561865296747161 -coef_ride_hail_ASC_sr3p_school,-17.869354547202143,-2.5362,0,,,-17.869354547202143 -coef_ride_hail_ASC_taxi_school,-1.6385208722849591,-0.58689999999999998,0,,,-1.6385208722849591 -coef_ride_hail_ASC_tnc_single_school,23.693954665409134,0.3201,0,,,23.693954665409134 -coef_ride_hail_ASC_walk_school,-17.881587989266929,0.245,0,,,-17.881587989266929 -coef_sov_ASC_rh_school,-126.45615738910647,-5.6548999999999996,0,,,-126.45615738910647 -coef_sov_ASC_walk_school,-3.1154322443821076,-71.8977,0,,,-3.1154322443821076 -coef_sr2_ASC_sr2_school,-0.54053965419989403,-1.9335,0,,,-0.54053965419989403 -coef_sr2_ASC_walk_school,-2.0412093694429427,-1.0362,0,,,-2.0412093694429427 -coef_sr3p_ASC_rh_school,-167.92322922478724,-6.6714000000000002,0,,,-167.92322922478724 -coef_sr3p_ASC_sr2_school,-1.0175286592825254,-2.7406999999999999,0,,,-1.0175286592825254 -coef_sr3p_ASC_sr3p_school,0.19312416588689055,-1.7718,0,,,0.19312416588689055 -coef_sr3p_ASC_walk_school,-2.4447403802047534,-1.2126999999999999,0,,,-2.4447403802047534 -coef_walk_transit_ASC_rh_school,-4.3619869330314502,-7,0,,,-4.3619869330314502 -coef_walk_transit_ASC_sr2_school,-2.270274862864051,-4.2961,0,,,-2.270274862864051 -coef_walk_transit_ASC_sr3p_school,-2.4067383334703436,-3.8664999999999998,0,,,-2.4067383334703436 -coef_walk_transit_ASC_walk_school,-1.4950397618009084,-1.1828000000000001,0,,,-1.4950397618009084 -coef_bike_ASC_walk_escort,-13.520327232125497,-13.520300000000001,0,,,-13.520327232125497 -coef_drive_transit_ASC_commuter_escort,0.54420000000000002,0.54420000000000002,0,,,0.54420000000000002 -coef_drive_transit_ASC_express_escort,0.75470000000000004,0.75470000000000004,0,,,0.75470000000000004 -coef_drive_transit_ASC_ferry_escort,0.60050000000000003,0.60050000000000003,0,,,0.60050000000000003 -coef_drive_transit_ASC_heavyrail_escort,-14.234170107485145,0.56730000000000003,0,,,-14.234170107485145 -coef_drive_transit_ASC_lightrail_escort,0.5252,0.5252,0,,,0.5252 -coef_ivt_escort_shopping_eatout_othdiscr_atwork,-0.011935317751590301,-0.027900000000000001,0,,,-0.011935317751590301 -coef_ride_hail_ASC_sr2_escort_shopping_othmaint,-23.87393437250816,-5.6482999999999999,0,,,-23.87393437250816 -coef_ride_hail_ASC_taxi_escort_shopping_othmaint,-3.6645734090143596,-3.6629,0,,,-3.6645734090143596 -coef_ride_hail_ASC_tnc_single_escort_shopping_othmaint,24.219922267784732,0.78790000000000004,0,,,24.219922267784732 -coef_sov_ASC_rh_escort_shopping_eatout_othmaint_social_othdiscr,0,0,0,0,0,0 -coef_sov_ASC_sr2_escort,0,0,0,0,0,0 -coef_sov_ASC_sr3p_escort,0,0,0,0,0,0 -coef_sov_ASC_walk_escort,0,0,0,0,0,0 -coef_sr2_ASC_sr2_escort,0.48842589140865728,0.3533,0,,,0.48842589140865728 -coef_sr2_ASC_walk_escort,-25.486556155436141,-2.0528,0,,,-25.486556155436141 -coef_sr3p_ASC_sr2_escort,-0.20651374838779549,-0.26819999999999999,0,,,-0.20651374838779549 -coef_sr3p_ASC_sr3p_escort,0.89578731133543399,0.73580000000000001,0,,,0.89578731133543399 -coef_sr3p_ASC_walk_escort,-3.4160479693862773,-1.0075000000000001,0,,,-3.4160479693862773 -coef_walk_transit_ASC_commuter_escort,0.54420000000000002,0.54420000000000002,0,,,0.54420000000000002 -coef_walk_transit_ASC_express_escort,0.75470000000000004,0.75470000000000004,0,,,0.75470000000000004 -coef_walk_transit_ASC_ferry_escort,0.60050000000000003,0.60050000000000003,0,,,0.60050000000000003 -coef_walk_transit_ASC_heavyrail_escort,0.85028037339339679,0.56730000000000003,0,,,0.85028037339339679 -coef_walk_transit_ASC_lightrail_escort,1.12520509210183,0.5252,0,,,1.12520509210183 -coef_walk_transit_ASC_rh_escort_shopping_othmaint,-2.3595754865729988,-3.1135999999999999,0,,,-2.3595754865729988 -coef_walk_transit_ASC_sr2_escort,-1.0205990135822081,-2.6284000000000001,0,,,-1.0205990135822081 -coef_walk_transit_ASC_sr3p_escort,-184.69692639910483,-3.2073999999999998,0,,,-184.69692639910483 -coef_walk_transit_ASC_walk_escort,-88.120508523406016,-0.99739999999999995,0,,,-88.120508523406016 -coef_bike_ASC_walk_shopping,-23.156539428413335,-1.0245,0,,,-23.156539428413335 -coef_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork,0.51290000000000002,0.51290000000000002,0,,,0.51290000000000002 -coef_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork,0.66479999999999995,0.66479999999999995,0,,,0.66479999999999995 -coef_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork,0.64659999999999995,0.64659999999999995,0,,,0.64659999999999995 -coef_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.5403,0.5403,0,,,0.5403 -coef_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.53920000000000001,0.53920000000000001,0,,,0.53920000000000001 -coef_joint_auto_ASC_rh_shopping_eatout_othmaint_social_othdiscr,-7,-7,0,,,-7 -coef_joint_auto_ASC_sr2_shopping,2.7753435280365912,0.58750000000000002,0,,,2.7753435280365912 -coef_joint_auto_ASC_sr3p_shopping,-19.06434075519855,-19.045400000000001,0,,,-19.06434075519855 -coef_joint_auto_ASC_walk_shopping,-23.468835890078797,-23.468800000000002,0,,,-23.468835890078797 -coef_joint_bike_ASC_rh_shopping_eatout_othmaint_social_othdiscr,-12.3057,-12.3057,0,,,-12.3057 -coef_joint_bike_ASC_walk_shopping,-7.0476000000000001,-7.0476000000000001,0,,,-7.0476000000000001 -coef_joint_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr,0.51290000000000002,0.51290000000000002,0,,,0.51290000000000002 -coef_joint_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr,0.66479999999999995,0.66479999999999995,0,,,0.66479999999999995 -coef_joint_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr,0.64659999999999995,0.64659999999999995,0,,,0.64659999999999995 -coef_joint_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr,0.5403,0.5403,0,,,0.5403 -coef_joint_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr,0.53920000000000001,0.53920000000000001,0,,,0.53920000000000001 -coef_joint_drive_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr,4.6138000000000003,4.6138000000000003,0,,,4.6138000000000003 -coef_joint_ride_hail_ASC_sr2_shopping_eatout_othmaint_social_othdiscr,-7,-7,0,,,-7 -coef_joint_ride_hail_ASC_sr3p_shopping_eatout_othmaint_social_othdiscr,-7,-7,0,,,-7 -coef_joint_ride_hail_ASC_taxi_shopping_eatout_othmaint_social_othdiscr,-7,-7,0,,,-7 -coef_joint_ride_hail_ASC_tnc_single_shopping_eatout_othmaint_social_othdiscr,-4.7339000000000002,-4.7339000000000002,0,,,-4.7339000000000002 -coef_joint_ride_hail_ASC_walk_shopping_eatout_othmaint_social_othdiscr,-7,-7,0,,,-7 -coef_joint_walk_ASC_rh_shopping_eatout_othmaint_social_othdiscr,-3.0362,-3.0362,0,,,-3.0362 -coef_joint_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr,0.51290000000000002,0.51290000000000002,0,,,0.51290000000000002 -coef_joint_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr,0.66479999999999995,0.66479999999999995,0,,,0.66479999999999995 -coef_joint_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr,0.64659999999999995,0.64659999999999995,0,,,0.64659999999999995 -coef_joint_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr,21.596514314322803,0.5403,0,,,21.596514314322803 -coef_joint_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr,0.92447712850041863,0.53920000000000001,0,,,0.92447712850041863 -coef_joint_walk_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr,3.132294016909416,1.2265999999999999,0,,,3.132294016909416 -coef_joint_walk_transit_ASC_sr2_shopping,-21.576802070713498,-21.576799999999999,0,,,-21.576802070713498 -coef_joint_walk_transit_ASC_sr3p_shopping,-25.595300009753458,-25.595300000000002,0,,,-25.595300009753458 -coef_joint_walk_transit_ASC_walk_shopping,-101.70152139850924,-0.54720000000000002,0,,,-101.70152139850924 -coef_sov_ASC_walk_shopping,-3.7867423367407271,-1.4765999999999999,0,,,-3.7867423367407271 -coef_sr2_ASC_sr2_shopping,1.7463545613015568,1.3765000000000001,0,,,1.7463545613015568 -coef_sr2_ASC_walk_shopping,-3.6530756986244408,-0.11169999999999999,0,,,-3.6530756986244408 -coef_sr3p_ASC_sr2_shopping,0.33630630204547912,-0.078100000000000003,0,,,0.33630630204547912 -coef_sr3p_ASC_sr3p_shopping,1.6427397418976948,1.3394999999999999,0,,,1.6427397418976948 -coef_sr3p_ASC_walk_shopping,-3.9191286556878429,-0.50790000000000002,0,,,-3.9191286556878429 -coef_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork,0.51290000000000002,0.51290000000000002,0,,,0.51290000000000002 -coef_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork,0.66479999999999995,0.66479999999999995,0,,,0.66479999999999995 -coef_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork,0.64659999999999995,0.64659999999999995,0,,,0.64659999999999995 -coef_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.92621641961993106,0.5403,0,,,0.92621641961993106 -coef_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.83136133693868486,0.53920000000000001,0,,,0.83136133693868486 -coef_walk_transit_ASC_sr2_shopping,-2.1233168743409747,-4.0114999999999998,0,,,-2.1233168743409747 -coef_walk_transit_ASC_sr3p_shopping,-91.093530857559287,-4.3852000000000002,0,,,-91.093530857559287 -coef_walk_transit_ASC_walk_shopping,-0.81350805532373194,-0.1943,0,,,-0.81350805532373194 -coef_bike_ASC_walk_eatout,-174.81611515878677,-2.6989999999999998,0,,,-174.81611515878677 -coef_joint_auto_ASC_sr2_eatout_othmaint_social_othdiscr,0.38104442976260161,-0.063399999999999998,0,,,0.38104442976260161 -coef_joint_auto_ASC_sr3p_eatout_othmaint_social_othdiscr,0.38908646769235944,-0.16250000000000001,0,,,0.38908646769235944 -coef_joint_auto_ASC_walk_eatout_othmaint_social_othdiscr,-4.3794314087625494,-0.71440000000000003,0,,,-4.3794314087625494 -coef_joint_bike_ASC_walk_eatout,-15.5588,-15.5588,0,,,-15.5588 -coef_joint_walk_transit_ASC_sr2_eatout,-14.914319758236871,-14.913399999999999,0,,,-14.914319758236871 -coef_joint_walk_transit_ASC_sr3p_eatout,-16.758772307649721,-16.758700000000001,0,,,-16.758772307649721 -coef_joint_walk_transit_ASC_walk_eatout,1.1018580418672292,2.3016999999999999,0,,,1.1018580418672292 -coef_ride_hail_ASC_sr2_eatout_social_othdiscr,-16.851845961332685,-5.9691999999999998,0,,,-16.851845961332685 -coef_ride_hail_ASC_sr3p_eatout_social_othdiscr,-11.873288203497911,-6.7199,0,,,-11.873288203497911 -coef_ride_hail_ASC_taxi_eatout_social_othdiscr,-2.5632427324461902,-2.5543,0,,,-2.5632427324461902 -coef_ride_hail_ASC_tnc_single_eatout_social_othdiscr,23.995880104391915,0.82740000000000002,0,,,23.995880104391915 -coef_ride_hail_ASC_walk_eatout_social_othdiscr,-17.718714023749605,-3.3603000000000001,0,,,-17.718714023749605 -coef_sov_ASC_walk_eatout,-2.6337956057242367,-1.3443000000000001,0,,,-2.6337956057242367 -coef_sr2_ASC_sr2_eatout,1.1016400029858922,0.99709999999999999,0,,,1.1016400029858922 -coef_sr2_ASC_walk_eatout,-4.5471769897543872,-0.072900000000000006,0,,,-4.5471769897543872 -coef_sr3p_ASC_sr2_eatout,-0.21320006188848084,-0.31780000000000003,0,,,-0.21320006188848084 -coef_sr3p_ASC_sr3p_eatout,1.474080431715626,1.3323,0,,,1.474080431715626 -coef_sr3p_ASC_walk_eatout,-2.5082484326697068,-0.043400000000000001,0,,,-2.5082484326697068 -coef_walk_transit_ASC_rh_eatout_social_othdiscr,-2.6584911065153216,-4.7507999999999999,0,,,-2.6584911065153216 -coef_walk_transit_ASC_sr2_eatout,-2.0069285824622241,-3.1434000000000002,0,,,-2.0069285824622241 -coef_walk_transit_ASC_sr3p_eatout,-2.5231634577013793,-3.8519999999999999,0,,,-2.5231634577013793 -coef_walk_transit_ASC_walk_eatout,0.16642876905750803,1.3504,0,,,0.16642876905750803 -coef_bike_ASC_walk_othmaint,-0.784009151847747,-0.5706,0,,,-0.784009151847747 -coef_ivt_othmaint_social,-0.012116004199731105,-0.017500000000000002,0,,,-0.012116004199731105 -coef_joint_bike_ASC_walk_othmaint,-13.5192,-13.5192,0,,,-13.5192 -coef_joint_walk_transit_ASC_sr2_othmaint,-11.323061152682898,-5.3230000000000004,0,,,-11.323061152682898 -coef_joint_walk_transit_ASC_sr3p_othmaint,-44.010114035526179,-3.9563000000000001,0,,,-44.010114035526179 -coef_joint_walk_transit_ASC_walk_othmaint,-73.569639938685185,1.1876,0,,,-73.569639938685185 -coef_sov_ASC_walk_othmaint,-4.4263069274933464,-1.2992999999999999,0,,,-4.4263069274933464 -coef_sr2_ASC_sr2_othmaint,0.93483638812856495,0.85740000000000005,0,,,0.93483638812856495 -coef_sr2_ASC_walk_othmaint,-3.1386920238871761,-0.59889999999999999,0,,,-3.1386920238871761 -coef_sr3p_ASC_sr2_othmaint,0.042063533996837946,-0.29580000000000001,0,,,0.042063533996837946 -coef_sr3p_ASC_sr3p_othmaint,1.1283362600967743,0.95269999999999999,0,,,1.1283362600967743 -coef_sr3p_ASC_walk_othmaint,-5.0664087819757082,0.042200000000000001,0,,,-5.0664087819757082 -coef_walk_transit_ASC_sr2_othmaint,-34.674600000130781,-34.674599999999998,0,,,-34.674600000130781 -coef_walk_transit_ASC_sr3p_othmaint,-29.385800207697873,-29.3858,0,,,-29.385800207697873 -coef_walk_transit_ASC_walk_othmaint,-0.28811698789154394,0.7248,0,,,-0.28811698789154394 -coef_bike_ASC_walk_social,-13.240952180972661,-13.2384,0,,,-13.240952180972661 -coef_joint_bike_ASC_walk_social,-26.171399999999998,-26.171399999999998,0,,,-26.171399999999998 -coef_joint_walk_transit_ASC_sr2_social,-20.502199999999998,-20.502199999999998,0,,,-20.502199999999998 -coef_joint_walk_transit_ASC_sr3p_social,-34.7378,-34.7378,0,,,-34.7378 -coef_joint_walk_transit_ASC_walk_social,-2.3852136661883132,-2.1301000000000001,0,,,-2.3852136661883132 -coef_sov_ASC_walk_social,-25.269332635237305,-1.8077000000000001,0,,,-25.269332635237305 -coef_sr2_ASC_sr2_social,0.76826858270381293,0.56630000000000003,0,,,0.76826858270381293 -coef_sr2_ASC_walk_social,-2.8506566329280796,-0.79320000000000002,0,,,-2.8506566329280796 -coef_sr3p_ASC_sr2_social,-0.12234716443959485,-0.15559999999999999,0,,,-0.12234716443959485 -coef_sr3p_ASC_sr3p_social,1.2106868400711996,0.83309999999999995,0,,,1.2106868400711996 -coef_sr3p_ASC_walk_social,-3.0801131342084651,-0.31340000000000001,0,,,-3.0801131342084651 -coef_walk_transit_ASC_sr2_social,-2.0645343270844863,-3.3662999999999998,0,,,-2.0645343270844863 -coef_walk_transit_ASC_sr3p_social,-21.63090014868768,-21.6309,0,,,-21.63090014868768 -coef_walk_transit_ASC_walk_social,-2.3146041098832506,-0.7651,0,,,-2.3146041098832506 -coef_bike_ASC_walk_othdiscr,-1.3971872234012017,-1.3576999999999999,0,,,-1.3971872234012017 -coef_joint_bike_ASC_walk_othdiscr,-14.444000000000001,-14.444000000000001,0,,,-14.444000000000001 -coef_joint_walk_transit_ASC_sr2_othdiscr,-14.9801,-14.9801,0,,,-14.9801 -coef_joint_walk_transit_ASC_sr3p_othdiscr,-16.735567652638597,-16.735399999999998,0,,,-16.735567652638597 -coef_joint_walk_transit_ASC_walk_othdiscr,-25.948044836941285,-0.0023999999999999998,0,,,-25.948044836941285 -coef_sov_ASC_walk_othdiscr,-4.9397828512902819,-1.5507,0,,,-4.9397828512902819 -coef_sr2_ASC_sr2_othdiscr,0.85570357875378389,0.74780000000000002,0,,,0.85570357875378389 -coef_sr2_ASC_walk_othdiscr,-2.4704700572535603,-0.026800000000000001,0,,,-2.4704700572535603 -coef_sr3p_ASC_sr2_othdiscr,-0.41762969659077742,-0.61419999999999997,0,,,-0.41762969659077742 -coef_sr3p_ASC_sr3p_othdiscr,0.84494437659483101,0.73980000000000001,0,,,0.84494437659483101 -coef_sr3p_ASC_walk_othdiscr,-5.841028145506157,-0.5272,0,,,-5.841028145506157 -coef_walk_transit_ASC_sr2_othdiscr,-1.2322243797539427,-2.7589999999999999,0,,,-1.2322243797539427 -coef_walk_transit_ASC_sr3p_othdiscr,-1.1992638422488842,-2.5125999999999999,0,,,-1.1992638422488842 -coef_walk_transit_ASC_walk_othdiscr,-0.98049723049553139,-0.49969999999999998,0,,,-0.98049723049553139 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +1,1,1,1,0 +coef_age010_trn,0.35767627741145724,0.35767627741145724,0,0 +coef_age1619_da,0.34333578772407369,0.34333578772407369,0,0 +coef_age16p_sr,-0.14184142530409971,-0.14184142530409971,0,0 +coef_bike_ASC_rh,-7,-7,-7,0 +coef_bike_ASC_walk_eatout,-4.6307472236220351,-4.6307472236220351,-2.6989999999999998,0 +coef_bike_ASC_walk_escort,-13.520300035676909,-13.520300035676909,-13.520300000000001,0 +coef_bike_ASC_walk_othdiscr,-1.3990818215431378,-1.3990818215431378,-1.3576999999999999,0 +coef_bike_ASC_walk_othmaint,-0.7890931685689252,-0.7890931685689252,-0.5706,0 +coef_bike_ASC_walk_school,-4.9873103152859581,-4.9873103152859581,-2.0331999999999999,0 +coef_bike_ASC_walk_shopping,-11.171180371928761,-11.171180371928761,-1.0245,0 +coef_bike_ASC_walk_social,-13.238403309075121,-13.238403309075121,-13.2384,0 +coef_bike_ASC_walk_univ,-0.33989999999999998,-0.33989999999999998,-0.33989999999999998,0 +coef_bike_ASC_walk_work,-2.662073727736936,-2.662073727736936,-1.8332999999999999,0 +coef_drive_transit_ASC_commuter_escort,0.54420000000000002,0.54420000000000002,0.54420000000000002,0 +coef_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork,0.51290000000000002,0.51290000000000002,0.51290000000000002,0 +coef_drive_transit_ASC_commuter_univ_school,0.90820000000000001,0.90820000000000001,0.90820000000000001,0 +coef_drive_transit_ASC_commuter_work,0.42637620615946842,0.42637620615946842,0.4012,0 +coef_drive_transit_ASC_express_escort,0.75470000000000004,0.75470000000000004,0.75470000000000004,0 +coef_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork,0.66479999999999995,0.66479999999999995,0.66479999999999995,0 +coef_drive_transit_ASC_express_univ_school,0.32240000000000002,0.32240000000000002,0.32240000000000002,0 +coef_drive_transit_ASC_express_work,-0.35539999999999999,-0.35539999999999999,-0.35539999999999999,0 +coef_drive_transit_ASC_ferry_escort,0.60050000000000003,0.60050000000000003,0.60050000000000003,0 +coef_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork,0.64659999999999995,0.64659999999999995,0.64659999999999995,0 +coef_drive_transit_ASC_ferry_univ_school,1.7237,1.7237,1.7237,0 +coef_drive_transit_ASC_ferry_work,0.52769999999999995,0.52769999999999995,0.52769999999999995,0 +coef_drive_transit_ASC_heavyrail_escort,-9.989800530770955,-9.989800530770955,0.56730000000000003,0 +coef_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.5403,0.5403,0.5403,0 +coef_drive_transit_ASC_heavyrail_univ_school,0.84899999999999998,0.84899999999999998,0.84899999999999998,0 +coef_drive_transit_ASC_heavyrail_work,-8.7062475359294247,-8.7062475359294247,0.3538,0 +coef_drive_transit_ASC_lightrail_escort,0.5252,0.5252,0.5252,0 +coef_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.53920000000000001,0.53920000000000001,0.53920000000000001,0 +coef_drive_transit_ASC_lightrail_univ_school,1.4436,1.4436,1.4436,0 +coef_drive_transit_ASC_lightrail_work,-7.4071143692385562,-7.4071143692385562,0.46210000000000001,0 +coef_drive_transit_ASC_rh,-8.0109041770487206,-8.0109041770487206,-4.25,0 +coef_hhsize1_sr,-0.89082809363303117,-0.89082809363303117,-0.73460000000000003,0 +coef_hhsize2_sr,0.039673604461434211,0.039673604461434211,0,0 +coef_ivt_escort_shopping_eatout_othdiscr_atwork,-0.011939751587626005,-0.011939751587626005,-0.027900000000000001,0 +coef_ivt_othmaint_social,-0.012113122493485196,-0.012113122493485196,-0.017500000000000002,0 +coef_ivt_univ_school,-0.0082684909559910397,-0.0082684909559910397,-0.027099999999999999,0 +coef_ivt_work,-0.010154800227496609,-0.010154800227496609,-0.021999999999999999,0 +coef_joint_auto_ASC_rh_shopping_eatout_othmaint_social_othdiscr,-7,-7,-7,0 +coef_joint_auto_ASC_rh_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_auto_ASC_sr2_eatout_othmaint_social_othdiscr,0.37182968511460945,0.37182968511460945,-0.063399999999999998,0 +coef_joint_auto_ASC_sr2_shopping,2.7630522022711443,2.7630522022711443,0.58750000000000002,0 +coef_joint_auto_ASC_sr2_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_auto_ASC_sr3p_eatout_othmaint_social_othdiscr,0.37976650225041186,0.37976650225041186,-0.16250000000000001,0 +coef_joint_auto_ASC_sr3p_shopping,-19.04542622913733,-19.04542622913733,-19.045400000000001,0 +coef_joint_auto_ASC_sr3p_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_auto_ASC_walk_eatout_othmaint_social_othdiscr,-4.375671164481866,-4.375671164481866,-0.71440000000000003,0 +coef_joint_auto_ASC_walk_shopping,-23.468800060392848,-23.468800060392848,-23.468800000000002,0 +coef_joint_auto_ASC_walk_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_bike_ASC_rh_shopping_eatout_othmaint_social_othdiscr,-12.3057,-12.3057,-12.3057,0 +coef_joint_bike_ASC_rh_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_bike_ASC_walk_eatout,-15.5588,-15.5588,-15.5588,0 +coef_joint_bike_ASC_walk_othdiscr,-14.444000000000001,-14.444000000000001,-14.444000000000001,0 +coef_joint_bike_ASC_walk_othmaint,-13.5192,-13.5192,-13.5192,0 +coef_joint_bike_ASC_walk_shopping,-7.0476000000000001,-7.0476000000000001,-7.0476000000000001,0 +coef_joint_bike_ASC_walk_social,-26.171399999999998,-26.171399999999998,-26.171399999999998,0 +coef_joint_bike_ASC_walk_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr,0.51290000000000002,0.51290000000000002,0.51290000000000002,0 +coef_joint_drive_transit_ASC_commuter_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr,0.66479999999999995,0.66479999999999995,0.66479999999999995,0 +coef_joint_drive_transit_ASC_express_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr,0.64659999999999995,0.64659999999999995,0.64659999999999995,0 +coef_joint_drive_transit_ASC_ferry_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr,0.5403,0.5403,0.5403,0 +coef_joint_drive_transit_ASC_heavyrail_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr,0.53920000000000001,0.53920000000000001,0.53920000000000001,0 +coef_joint_drive_transit_ASC_lightrail_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_drive_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr,4.6138000000000003,4.6138000000000003,4.6138000000000003,0 +coef_joint_drive_transit_ASC_rh_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_ride_hail_ASC_sr2_shopping_eatout_othmaint_social_othdiscr,-7,-7,-7,0 +coef_joint_ride_hail_ASC_sr2_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_ride_hail_ASC_sr3p_shopping_eatout_othmaint_social_othdiscr,-7,-7,-7,0 +coef_joint_ride_hail_ASC_sr3p_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_ride_hail_ASC_taxi_shopping_eatout_othmaint_social_othdiscr,-7,-7,-7,0 +coef_joint_ride_hail_ASC_taxi_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_ride_hail_ASC_tnc_shared,0,0,0,0 +coef_joint_ride_hail_ASC_tnc_single_shopping_eatout_othmaint_social_othdiscr,-4.7339000000000002,-4.7339000000000002,-4.7339000000000002,0 +coef_joint_ride_hail_ASC_tnc_single_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_ride_hail_ASC_walk_shopping_eatout_othmaint_social_othdiscr,-7,-7,-7,0 +coef_joint_ride_hail_ASC_walk_transit,0,0,0,0 +coef_joint_ride_hail_ASC_walk_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_ASC_rh_shopping_eatout_othmaint_social_othdiscr,-3.0362,-3.0362,-3.0362,0 +coef_joint_walk_ASC_rh_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr,0.51290000000000002,0.51290000000000002,0.51290000000000002,0 +coef_joint_walk_transit_ASC_commuter_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr,0.66479999999999995,0.66479999999999995,0.66479999999999995,0 +coef_joint_walk_transit_ASC_express_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr,0.64659999999999995,0.64659999999999995,0.64659999999999995,0 +coef_joint_walk_transit_ASC_ferry_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr,13.389850481098479,13.389850481098479,0.5403,0 +coef_joint_walk_transit_ASC_heavyrail_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr,0.98274193315609293,0.98274193315609293,0.53920000000000001,0 +coef_joint_walk_transit_ASC_lightrail_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr,3.183325294252882,3.183325294252882,1.2265999999999999,0 +coef_joint_walk_transit_ASC_rh_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_sr2_eatout,-14.913401266030013,-14.913401266030013,-14.913399999999999,0 +coef_joint_walk_transit_ASC_sr2_othdiscr,-14.9801,-14.9801,-14.9801,0 +coef_joint_walk_transit_ASC_sr2_othmaint,-5.3309700517325718,-5.3309700517325718,-5.3230000000000004,0 +coef_joint_walk_transit_ASC_sr2_shopping,-21.576800002987216,-21.576800002987216,-21.576799999999999,0 +coef_joint_walk_transit_ASC_sr2_social,-20.502199999999998,-20.502199999999998,-20.502199999999998,0 +coef_joint_walk_transit_ASC_sr2_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_sr3p_eatout,-16.758700099536259,-16.758700099536259,-16.758700000000001,0 +coef_joint_walk_transit_ASC_sr3p_othdiscr,-16.735400264288284,-16.735400264288284,-16.735399999999998,0 +coef_joint_walk_transit_ASC_sr3p_othmaint,-4.0140180305790336,-4.0140180305790336,-3.9563000000000001,0 +coef_joint_walk_transit_ASC_sr3p_shopping,-25.595300000014014,-25.595300000014014,-25.595300000000002,0 +coef_joint_walk_transit_ASC_sr3p_social,-34.7378,-34.7378,-34.7378,0 +coef_joint_walk_transit_ASC_sr3p_work_univ_school_escort_atwork,0,0,0,0 +coef_joint_walk_transit_ASC_walk_eatout,1.1571199957158329,1.1571199957158329,2.3016999999999999,0 +coef_joint_walk_transit_ASC_walk_othdiscr,-0.043211155305850707,-0.043211155305850707,-0.0023999999999999998,0 +coef_joint_walk_transit_ASC_walk_othmaint,-5.4736104658626115,-5.4736104658626115,1.1876,0 +coef_joint_walk_transit_ASC_walk_shopping,-5.9428639820608167,-5.9428639820608167,-0.54720000000000002,0 +coef_joint_walk_transit_ASC_walk_social,-2.2714573374616625,-2.2714573374616625,-2.1301000000000001,0 +coef_joint_walk_transit_ASC_walk_work_univ_school_escort_atwork,0,0,0,0 +coef_nest_AUTO,0.71999999999999997,0.71999999999999997,0.71999999999999997,1 +coef_nest_AUTO_DRIVEALONE,0.34999999999999998,0.34999999999999998,0.34999999999999998,1 +coef_nest_AUTO_SHAREDRIDE2,0.34999999999999998,0.34999999999999998,0.34999999999999998,1 +coef_nest_AUTO_SHAREDRIDE3,0.34999999999999998,0.34999999999999998,0.34999999999999998,1 +coef_nest_NONMOTORIZED,0.71999999999999997,0.71999999999999997,0.71999999999999997,1 +coef_nest_RIDEHAIL,0.35999999999999999,0.35999999999999999,0.35999999999999999,1 +coef_nest_TRANSIT,0.71999999999999997,0.71999999999999997,0.71999999999999997,1 +coef_nest_TRANSIT_DRIVEACCESS,0.5,0.5,0.5,1 +coef_nest_TRANSIT_WALKACCESS,0.5,0.5,0.5,1 +coef_ride_hail_ASC_sr2_eatout_social_othdiscr,-6.4237650755245772,-6.4237650755245772,-5.9691999999999998,0 +coef_ride_hail_ASC_sr2_escort_shopping_othmaint,-8.6681239587327461,-8.6681239587327461,-5.6482999999999999,0 +coef_ride_hail_ASC_sr2_school,-9.8588042940530691,-9.8588042940530691,-1.5869,0 +coef_ride_hail_ASC_sr2_univ,-4.3372000000000002,-4.3372000000000002,-4.3372000000000002,0 +coef_ride_hail_ASC_sr2_work,-8.7370618957514985,-8.7370618957514985,-6.1079999999999997,0 +coef_ride_hail_ASC_sr3p_eatout_social_othdiscr,-6.8871198658131894,-6.8871198658131894,-6.7199,0 +coef_ride_hail_ASC_sr3p_school,-7.667321296853344,-7.667321296853344,-2.5362,0 +coef_ride_hail_ASC_sr3p_univ,-4.9219999999999997,-4.9219999999999997,-4.9219999999999997,0 +coef_ride_hail_ASC_sr3p_work_escort_shopping_othmaint_atwork,-8.4993141583621394,-8.4993141583621394,-7,0 +coef_ride_hail_ASC_taxi_eatout_social_othdiscr,-2.5631373967196924,-2.5631373967196924,-2.5543,0 +coef_ride_hail_ASC_taxi_escort_shopping_othmaint,-3.6639503613758153,-3.6639503613758153,-3.6629,0 +coef_ride_hail_ASC_taxi_school,-1.9540410876610597,-1.9540410876610597,-0.58689999999999998,0 +coef_ride_hail_ASC_taxi_univ,-1.5993999999999999,-1.5993999999999999,-1.5993999999999999,0 +coef_ride_hail_ASC_taxi_work,-2.4992280746351052,-2.4992280746351052,-2.3849999999999998,0 +coef_ride_hail_ASC_tnc_shared,6.4746604585247001,6.4746604585247001,0,0 +coef_ride_hail_ASC_tnc_single_eatout_social_othdiscr,7.2023280939010208,7.2023280939010208,0.82740000000000002,0 +coef_ride_hail_ASC_tnc_single_escort_shopping_othmaint,7.4263660209874116,7.4263660209874116,0.78790000000000004,0 +coef_ride_hail_ASC_tnc_single_school,6.9003930265898461,6.9003930265898461,0.3201,0 +coef_ride_hail_ASC_tnc_single_univ,0.1081,0.1081,0.1081,0 +coef_ride_hail_ASC_tnc_single_work,7.1906030498215934,7.1906030498215934,0.73499999999999999,0 +coef_ride_hail_ASC_walk_eatout_social_othdiscr,-8.2700256968569796,-8.2700256968569796,-3.3603000000000001,0 +coef_ride_hail_ASC_walk_school,-13.75516439764165,-13.75516439764165,0.245,0 +coef_ride_hail_ASC_walk_transit,5.320919867286813,5.320919867286813,0,0 +coef_ride_hail_ASC_walk_univ_escort_shopping_othmaint,-7.9623896833175234,-7.9623896833175234,-7,0 +coef_ride_hail_ASC_walk_work,4.9774767261374491,4.9774767261374491,0.2858,0 +coef_sov_ASC_rh_escort_shopping_eatout_othmaint_social_othdiscr,0,0,0,0 +coef_sov_ASC_rh_school,-10.949190676763848,-10.949190676763848,-5.6548999999999996,0 +coef_sov_ASC_rh_univ,-6.649,-6.649,-6.649,0 +coef_sov_ASC_rh_work_atwork,-7,-7,-7,0 +coef_sov_ASC_sr2_escort,0,0,0,0 +coef_sov_ASC_sr2_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork,-999,-999,-999,0 +coef_sov_ASC_sr3p_escort,0,0,0,0 +coef_sov_ASC_sr3p_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork,-999,-999,-999,0 +coef_sov_ASC_walk_eatout,-2.6351638383156661,-2.6351638383156661,-1.3443000000000001,0 +coef_sov_ASC_walk_escort,0,0,0,0 +coef_sov_ASC_walk_othdiscr,-4.9362989098824706,-4.9362989098824706,-1.5507,0 +coef_sov_ASC_walk_othmaint,-4.4203686413898176,-4.4203686413898176,-1.2992999999999999,0 +coef_sov_ASC_walk_school,-3.1150166914376998,-3.1150166914376998,-71.8977,0 +coef_sov_ASC_walk_shopping,-3.790711593714617,-3.790711593714617,-1.4765999999999999,0 +coef_sov_ASC_walk_social,-15.183480636962726,-15.183480636962726,-1.8077000000000001,0 +coef_sov_ASC_walk_univ,-1.0579000000000001,-1.0579000000000001,-1.0579000000000001,0 +coef_sov_ASC_walk_work,-3.0071231001560492,-3.0071231001560492,-0.83979999999999999,0 +coef_sr2_ASC_rh_univ,-6.6897000000000002,-6.6897000000000002,-6.6897000000000002,0 +coef_sr2_ASC_rh_work_school_escort_shopping_eatout_othmaint_social_othdiscr_atwork,-7,-7,-7,0 +coef_sr2_ASC_sr2_eatout,1.0915911218889376,1.0915911218889376,0.99709999999999999,0 +coef_sr2_ASC_sr2_escort,0.4783058163607723,0.4783058163607723,0.3533,0 +coef_sr2_ASC_sr2_othdiscr,0.84570279022463457,0.84570279022463457,0.74780000000000002,0 +coef_sr2_ASC_sr2_othmaint,0.92430088713320557,0.92430088713320557,0.85740000000000005,0 +coef_sr2_ASC_sr2_school,-0.55142467432696085,-0.55142467432696085,-1.9335,0 +coef_sr2_ASC_sr2_shopping,1.7357779332652892,1.7357779332652892,1.3765000000000001,0 +coef_sr2_ASC_sr2_social,0.75821484443712639,0.75821484443712639,0.56630000000000003,0 +coef_sr2_ASC_sr2_univ,0.4304,0.4304,0.4304,0 +coef_sr2_ASC_sr2_work,0.18734725740026195,0.18734725740026195,0.0149,0 +coef_sr2_ASC_sr3p,-999,-999,-999,0 +coef_sr2_ASC_walk_eatout,-4.5472515289454529,-4.5472515289454529,-0.072900000000000006,0 +coef_sr2_ASC_walk_escort,-23.318883212926828,-23.318883212926828,-2.0528,0 +coef_sr2_ASC_walk_othdiscr,-2.4714996184390858,-2.4714996184390858,-0.026800000000000001,0 +coef_sr2_ASC_walk_othmaint,-3.1513689512096983,-3.1513689512096983,-0.59889999999999999,0 +coef_sr2_ASC_walk_school,-2.0520641973448814,-2.0520641973448814,-1.0362,0 +coef_sr2_ASC_walk_shopping,-3.6616557867959929,-3.6616557867959929,-0.11169999999999999,0 +coef_sr2_ASC_walk_social,-2.8345061083015142,-2.8345061083015142,-0.79320000000000002,0 +coef_sr2_ASC_walk_univ,1.3041,1.3041,1.3041,0 +coef_sr2_ASC_walk_work,-2.2227044743770445,-2.2227044743770445,0.126,0 +coef_sr3p_ASC_rh_school,-10.730125238227592,-10.730125238227592,-6.6714000000000002,0 +coef_sr3p_ASC_rh_univ_escort_shopping_eatout_othmaint_social_othdiscr,-7,-7,-7,0 +coef_sr3p_ASC_rh_work,-71.917699999999996,-71.917699999999996,-71.917699999999996,0 +coef_sr3p_ASC_sr2_eatout,-0.22504507680807895,-0.22504507680807895,-0.31780000000000003,0 +coef_sr3p_ASC_sr2_escort,-0.21630087392009059,-0.21630087392009059,-0.26819999999999999,0 +coef_sr3p_ASC_sr2_othdiscr,-0.42794642963477558,-0.42794642963477558,-0.61419999999999997,0 +coef_sr3p_ASC_sr2_othmaint,0.027657045664653299,0.027657045664653299,-0.29580000000000001,0 +coef_sr3p_ASC_sr2_school,-1.0402498584452382,-1.0402498584452382,-2.7406999999999999,0 +coef_sr3p_ASC_sr2_shopping,0.32506049110749652,0.32506049110749652,-0.078100000000000003,0 +coef_sr3p_ASC_sr2_social,-0.13140596327177012,-0.13140596327177012,-0.15559999999999999,0 +coef_sr3p_ASC_sr2_univ,-0.31169999999999998,-0.31169999999999998,-0.31169999999999998,0 +coef_sr3p_ASC_sr2_work,-0.96412449274578071,-0.96412449274578071,-1.1956,0 +coef_sr3p_ASC_sr3p_eatout,1.4628559194582083,1.4628559194582083,1.3323,0 +coef_sr3p_ASC_sr3p_escort,0.88601825802048151,0.88601825802048151,0.73580000000000001,0 +coef_sr3p_ASC_sr3p_othdiscr,0.83449788039660122,0.83449788039660122,0.73980000000000001,0 +coef_sr3p_ASC_sr3p_othmaint,1.1154991293213483,1.1154991293213483,0.95269999999999999,0 +coef_sr3p_ASC_sr3p_school,0.17070219167279393,0.17070219167279393,-1.7718,0 +coef_sr3p_ASC_sr3p_shopping,1.6317620662617456,1.6317620662617456,1.3394999999999999,0 +coef_sr3p_ASC_sr3p_social,1.2011521984245512,1.2011521984245512,0.83309999999999995,0 +coef_sr3p_ASC_sr3p_univ,0.62929999999999997,0.62929999999999997,0.62929999999999997,0 +coef_sr3p_ASC_sr3p_work,-0.0078556547449509153,-0.0078556547449509153,-0.18360000000000001,0 +coef_sr3p_ASC_walk_eatout,-2.5139966855713642,-2.5139966855713642,-0.043400000000000001,0 +coef_sr3p_ASC_walk_escort,-3.4006765281458122,-3.4006765281458122,-1.0075000000000001,0 +coef_sr3p_ASC_walk_othdiscr,-5.8689862851164607,-5.8689862851164607,-0.5272,0 +coef_sr3p_ASC_walk_othmaint,-5.0613355985663731,-5.0613355985663731,0.042200000000000001,0 +coef_sr3p_ASC_walk_school,-2.4681086096393896,-2.4681086096393896,-1.2126999999999999,0 +coef_sr3p_ASC_walk_shopping,-3.9238148057027744,-3.9238148057027744,-0.50790000000000002,0 +coef_sr3p_ASC_walk_social,-3.0951059771109648,-3.0951059771109648,-0.31340000000000001,0 +coef_sr3p_ASC_walk_univ,1.6793,1.6793,1.6793,0 +coef_sr3p_ASC_walk_work,-2.3560377610366339,-2.3560377610366339,0.21709999999999999,0 +coef_walk_ASC_rh,-7,-7,-7,0 +coef_walk_transit_ASC_commuter_escort,0.54420000000000002,0.54420000000000002,0.54420000000000002,0 +coef_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork,0.51290000000000002,0.51290000000000002,0.51290000000000002,0 +coef_walk_transit_ASC_commuter_univ_school,0.90820000000000001,0.90820000000000001,0.90820000000000001,0 +coef_walk_transit_ASC_commuter_work,0.4012,0.4012,0.4012,0 +coef_walk_transit_ASC_express_escort,0.75470000000000004,0.75470000000000004,0.75470000000000004,0 +coef_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork,0.66479999999999995,0.66479999999999995,0.66479999999999995,0 +coef_walk_transit_ASC_express_univ_school,0.32240000000000002,0.32240000000000002,0.32240000000000002,0 +coef_walk_transit_ASC_express_work,-0.35539999999999999,-0.35539999999999999,-0.35539999999999999,0 +coef_walk_transit_ASC_ferry_escort,0.60050000000000003,0.60050000000000003,0.60050000000000003,0 +coef_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork,0.64659999999999995,0.64659999999999995,0.64659999999999995,0 +coef_walk_transit_ASC_ferry_univ_school,1.7237,1.7237,1.7237,0 +coef_walk_transit_ASC_ferry_work,0.52769999999999995,0.52769999999999995,0.52769999999999995,0 +coef_walk_transit_ASC_heavyrail_escort,0.82721493724592188,0.82721493724592188,0.56730000000000003,0 +coef_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.92635472370348126,0.92635472370348126,0.5403,0 +coef_walk_transit_ASC_heavyrail_univ_school,0.86667504489837865,0.86667504489837865,0.84899999999999998,0 +coef_walk_transit_ASC_heavyrail_work,0.5025352261009528,0.5025352261009528,0.3538,0 +coef_walk_transit_ASC_lightrail_escort,1.1220737240827967,1.1220737240827967,0.5252,0 +coef_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork,0.83144252485479686,0.83144252485479686,0.53920000000000001,0 +coef_walk_transit_ASC_lightrail_univ_school,1.5673106432418389,1.5673106432418389,1.4436,0 +coef_walk_transit_ASC_lightrail_work,0.63258933455537403,0.63258933455537403,0.46210000000000001,0 +coef_walk_transit_ASC_rh_eatout_social_othdiscr,-2.6606459058042766,-2.6606459058042766,-4.7507999999999999,0 +coef_walk_transit_ASC_rh_escort_shopping_othmaint,-2.3584519726828503,-2.3584519726828503,-3.1135999999999999,0 +coef_walk_transit_ASC_rh_school,-4.3651480868436616,-4.3651480868436616,-7,0 +coef_walk_transit_ASC_rh_univ,-4.2690999999999999,-4.2690999999999999,-4.2690999999999999,0 +coef_walk_transit_ASC_rh_work,-2.8694950408844635,-2.8694950408844635,-3.7408000000000001,0 +coef_walk_transit_ASC_sr2_eatout,-2.0166783466764731,-2.0166783466764731,-3.1434000000000002,0 +coef_walk_transit_ASC_sr2_escort,-0.99918778660088436,-0.99918778660088436,-2.6284000000000001,0 +coef_walk_transit_ASC_sr2_othdiscr,-1.239023652712798,-1.239023652712798,-2.7589999999999999,0 +coef_walk_transit_ASC_sr2_othmaint,-34.674600000000112,-34.674600000000112,-34.674599999999998,0 +coef_walk_transit_ASC_sr2_school,-2.274826392672725,-2.274826392672725,-4.2961,0 +coef_walk_transit_ASC_sr2_shopping,-2.1328304552358719,-2.1328304552358719,-4.0114999999999998,0 +coef_walk_transit_ASC_sr2_social,-2.0833502428062123,-2.0833502428062123,-3.3662999999999998,0 +coef_walk_transit_ASC_sr2_univ,-4.0049999999999999,-4.0049999999999999,-4.0049999999999999,0 +coef_walk_transit_ASC_sr2_work,-2.2668862736328519,-2.2668862736328519,-3.6631,0 +coef_walk_transit_ASC_sr3p_eatout,-2.5264121576158045,-2.5264121576158045,-3.8519999999999999,0 +coef_walk_transit_ASC_sr3p_escort,-5.8381371662273835,-5.8381371662273835,-3.2073999999999998,0 +coef_walk_transit_ASC_sr3p_othdiscr,-1.2063771576374867,-1.2063771576374867,-2.5125999999999999,0 +coef_walk_transit_ASC_sr3p_othmaint,-29.385800000282099,-29.385800000282099,-29.3858,0 +coef_walk_transit_ASC_sr3p_school,-2.4112913709689163,-2.4112913709689163,-3.8664999999999998,0 +coef_walk_transit_ASC_sr3p_shopping,-9.3867562985269899,-9.3867562985269899,-4.3852000000000002,0 +coef_walk_transit_ASC_sr3p_social,-21.630900000230085,-21.630900000230085,-21.6309,0 +coef_walk_transit_ASC_sr3p_univ,-28.697199999999999,-28.697199999999999,-28.697199999999999,0 +coef_walk_transit_ASC_sr3p_work,-2.6637022892916695,-2.6637022892916695,-4.1314000000000002,0 +coef_walk_transit_ASC_walk_eatout,0.16644232569851822,0.16644232569851822,1.3504,0 +coef_walk_transit_ASC_walk_escort,-7.2572232561591434,-7.2572232561591434,-0.99739999999999995,0 +coef_walk_transit_ASC_walk_othdiscr,-0.97975419372738359,-0.97975419372738359,-0.49969999999999998,0 +coef_walk_transit_ASC_walk_othmaint,-0.28905978556563527,-0.28905978556563527,0.7248,0 +coef_walk_transit_ASC_walk_school,-1.4956108248781241,-1.4956108248781241,-1.1828000000000001,0 +coef_walk_transit_ASC_walk_shopping,-0.81195924640993355,-0.81195924640993355,-0.1943,0 +coef_walk_transit_ASC_walk_social,-2.3212936443153995,-2.3212936443153995,-0.7651,0 +coef_walk_transit_ASC_walk_univ,-1.0310999999999999,-1.0310999999999999,-1.0310999999999999,0 +coef_walk_transit_ASC_walk_work,-0.39145632958685811,-0.39145632958685811,0.44319999999999998,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_stop_freq_model.csv b/activitysim/estimation/test/test_larch_estimation/test_stop_freq_model.csv index fe3ad8745e..8226d916b6 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_stop_freq_model.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_stop_freq_model.csv @@ -1,189 +1,189 @@ -,value,initvalue,nullvalue,minimum,maximum,best -coef_alternative_specific_constant_for_outbound_stops_1out_0in,-0.64459225156448208,-0.83299999999999996,0,,,-0.64459225156448208 -coef_alternative_specific_constant_for_outbound_stops_2out_0in,-2.4255802366653585,-2.613,0,,,-2.4255802366653585 -coef_alternative_specific_constant_for_outbound_stops_3out_0in,-3.5820111930695417,-3.9340000000000002,0,,,-3.5820111930695417 -coef_alternative_specific_constant_for_return_stops_0out_1in,-0.3361407605068179,-0.44500000000000001,0,,,-0.3361407605068179 -coef_alternative_specific_constant_for_return_stops_0out_2in,-1.4776978296906871,-1.7749999999999999,0,,,-1.4776978296906871 -coef_alternative_specific_constant_for_return_stops_0out_3in,-1.8942287851285506,-2.1389999999999998,0,,,-1.8942287851285506 -coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,-0.066082964956457158,0,0,,,-0.066082964956457158 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,0.39410022765664782,0.69499999999999995,0,,,0.39410022765664782 -coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,-1.9991650147668323,-1.9299999999999999,0,,,-1.9991650147668323 -coef_dummy_for_all_stops_made_by_transit,-0.67359832175323664,-0.69999999999999996,0,,,-0.67359832175323664 -coef_dummy_for_distance_in_miles,0.044964434234324543,0.01,0,,,0.044964434234324543 -coef_dummy_for_distance_less_than_20_miles,-0.706160965748858,-0.22,0,,,-0.706160965748858 -coef_dummy_for_female,0.28231394503923868,0.22,0,,,0.28231394503923868 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,0.71316112803981435,0.59999999999999998,0,,,0.71316112803981435 -coef_dummy_for_walking_to_all_stops,-1.4873003617932004,-1.54,0,,,-1.4873003617932004 -coef_evening_arrival_19_00_interacted_with_return_tours,0.60673724793430617,0.31,0,,,0.60673724793430617 -coef_high_income_hh,0.24000000000001878,0.23999999999999999,0,,,0.24000000000001878 -coef_mid_to_high_income_hh,0.22999999999999904,0.23000000000000001,0,,,0.22999999999999904 -coef_middle_to_low_income_hh,0.16999999999999266,0.17000000000000001,0,,,0.16999999999999266 -coef_no_stops_if_tour_mode_is_drivetransit,-999,-999,0,,,-999 -coef_num_kids_between_5_and_15_including_years_old,0.092924351471183969,0.080000000000000002,0,,,0.092924351471183969 -coef_number_of_adults_16_years_old_,0.077365010457442138,0.029999999999999999,0,,,0.077365010457442138 -coef_number_of_cars_number_of_workers,-0.066249623271299782,0.16,0,,,-0.066249623271299782 -coef_number_of_escort_tours_tours_undertaken_by_the_person,0.096330730862920849,0.20000000000000001,0,,,0.096330730862920849 -coef_number_of_hh_persons,-0.33185226515031135,-0.31,0,,,-0.33185226515031135 -coef_number_of_school_tours_tours_undertaken_by_the_person,0.08642823107614464,-1.55,0,,,0.08642823107614464 -coef_number_of_shop_tours_undertaken_by_the_houshold,-0.32741940681713538,-0.050000000000000003,0,,,-0.32741940681713538 -coef_number_of_students_in_hh,0.31650418957116999,0.20999999999999999,0,,,0.31650418957116999 -coef_number_of_subtours_in_the_tour,0.33666132961145517,0.19,0,,,0.33666132961145517 -coef_number_of_university_tours_tours_undertaken_by_the_person,-0.48000000000000226,-0.47999999999999998,0,,,-0.48000000000000226 -coef_number_of_work_tours_undertaken_by_the_person,-0.17466645120795349,-0.14999999999999999,0,,,-0.17466645120795349 -coef_presence_of_kids_between_0_and_4_including_years_old,0.56261491842472322,0.73999999999999999,0,,,0.56261491842472322 -coef_presence_of_kids_between_5_and_15_including_years_old,0.1165493262082634,0.26000000000000001,0,,,0.1165493262082634 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_school,-1.7790649191402255,-2.1230000000000002,0,,,-1.7790649191402255 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_school,-4.234917600900892,-3.798,0,,,-4.234917600900892 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_school,-6.1101913112134891,-5.8499999999999996,0,,,-6.1101913112134891 -coef_alternative_specific_constant_for_return_stops_0out_1in_school,-1.2575756776155038,-1.206,0,,,-1.2575756776155038 -coef_alternative_specific_constant_for_return_stops_0out_2in_school,-2.7008487973600204,-2.6720000000000002,0,,,-2.7008487973600204 -coef_alternative_specific_constant_for_return_stops_0out_3in_school,-3.1227065529446167,-3.3639999999999999,0,,,-3.1227065529446167 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,1.474045133717242,0.70099999999999996,0,,,1.474045133717242 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school,1.992911512841014,1.135,0,,,1.992911512841014 -coef_arrival_later_than_17_00_,1.3670454388826592,1.8376999999999999,0,,,1.3670454388826592 -coef_dummy_for_distance_in_miles_school,0.095083267583630102,0.043799999999999999,0,,,0.095083267583630102 -coef_dummy_for_female_school,0.68909925924380133,0.40989999999999999,0,,,0.68909925924380133 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,1.9145027660467904,0.95489999999999997,0,,,1.9145027660467904 -coef_dummy_for_walking_to_all_stops_school,-1.514711575382329,-1.8163,0,,,-1.514711575382329 -coef_number_of_cars_number_of_workers_school,0.29163991408877671,0.53310000000000002,0,,,0.29163991408877671 -coef_number_of_escort_tours_tours_undertaken_by_the_person_school,2.311650198005148,1.2364999999999999,0,,,2.311650198005148 -coef_number_of_hh_persons_school,-0.59762555971611364,-0.50600000000000001,0,,,-0.59762555971611364 -coef_presence_of_kids_between_5_and_15_including_years_old_school,0.025364840519008454,0.32990000000000003,0,,,0.025364840519008454 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ,-2.1635923818442357,-2.6280000000000001,0,,,-2.1635923818442357 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ,-3.922683293550401,-3.7410000000000001,0,,,-3.922683293550401 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ,-4.4868172910452992,-4.9809999999999999,0,,,-4.4868172910452992 -coef_alternative_specific_constant_for_return_stops_0out_1in_univ,-1.5298270302866508,-2.0030000000000001,0,,,-1.5298270302866508 -coef_alternative_specific_constant_for_return_stops_0out_2in_univ,-3.3006601948863188,-3.5099999999999998,0,,,-3.3006601948863188 -coef_alternative_specific_constant_for_return_stops_0out_3in_univ,-3.7075364155245767,-3.677,0,,,-3.7075364155245767 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ,1.8522535636157311,1.272,0,,,1.8522535636157311 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ,1.8679561735778554,1.871,0,,,1.8679561735778554 -coef_arrival_later_than_17_00__univ,0.38609418401942114,0.38900000000000001,0,,,0.38609418401942114 -coef_dummy_for_female_univ,0.51916166319197454,0.7349,0,,,0.51916166319197454 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ,1.0828661743516874,0.84340000000000004,0,,,1.0828661743516874 -coef_hh_accesibility_for_inbound_tours_interaction,0.24105038323693603,0.24809999999999999,0,,,0.24105038323693603 -coef_number_of_escort_tours_tours_undertaken_by_the_person_univ,1.8575681307186125,0.90180000000000005,0,,,1.8575681307186125 -coef_number_of_hh_persons_univ,-0.33066321661527537,-0.28270000000000001,0,,,-0.33066321661527537 -coef_number_of_vehicles,0.26364695030485774,0.17030000000000001,0,,,0.26364695030485774 -coef_presence_of_kids_between_5_and_15_including_years_old_univ,-0.044568923553371873,0.68230000000000002,0,,,-0.044568923553371873 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_social,-2.311541928791784,-1.081,0,,,-2.311541928791784 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_social,-3.971643113248795,-2.8740000000000001,0,,,-3.971643113248795 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_social,-14.469243911405519,-4.5519999999999996,0,,,-14.469243911405519 -coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,-2.3802074504942019,-1.7829999999999999,0,,,-2.3802074504942019 -coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,-4.719796104655666,-4.0670000000000002,0,,,-4.719796104655666 -coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,-11.749677886071149,-4.9980000000000002,0,,,-11.749677886071149 -coef_alternative_specific_constant_for_return_stops_0out_1in_social,0.20587483859873654,-1.1200000000000001,0,,,0.20587483859873654 -coef_alternative_specific_constant_for_return_stops_0out_2in_social,-2.5883343811753692,-2.7639999999999998,0,,,-2.5883343811753692 -coef_alternative_specific_constant_for_return_stops_0out_3in_social,-3.3206409198297089,-3.4510000000000001,0,,,-3.3206409198297089 -coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,-1.6522833462248787,-1.329,0,,,-1.6522833462248787 -coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,-4.1415469266822509,-2.7960000000000003,0,,,-4.1415469266822509 -coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,-4.0471873952483204,-3.379,0,,,-4.0471873952483204 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social,2.4766471322901977,0.496,0,,,2.4766471322901977 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_social,-1.7322797167348338,0.88200000000000001,0,,,-1.7322797167348338 -coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,0.33004028360351806,0,0,,,0.33004028360351806 -coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,-4.3574765624568856,0.51800000000000002,0,,,-4.3574765624568856 -coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,-1.4567085446015828,1.4970000000000001,0,,,-1.4567085446015828 -coef_arrival_later_than_17_00__social,-0.46270836809978866,-0.45000000000000001,0,,,-0.46270836809978866 -coef_at_least_one_kid_and_one_adult_participate_in_the_tour,1.9188835397128035,0.37,0,,,1.9188835397128035 -coef_dummy_for_a_return_visiting_tour,-1.5149329165512258,-0.64000000000000001,0,,,-1.5149329165512258 -coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,0.47207475952054606,0.44,0,,,0.47207475952054606 -coef_dummy_for_an_outbound_visiting_tour,0.23539836860732627,-0.68999999999999995,0,,,0.23539836860732627 -coef_dummy_for_distance_in_miles_social,-0.072029213636538594,-0.01,0,,,-0.072029213636538594 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,1.1553718422199695,1.3100000000000001,0,,,1.1553718422199695 -coef_dummy_for_walking_to_all_stops_social,-2.0728871373961595,-1.73,0,,,-2.0728871373961595 -coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,-0.050333392255275736,-0.46000000000000002,0,,,-0.050333392255275736 -coef_number_of_shop_tours_undertaken_by_the_person,-0.17568161789582087,-0.23999999999999999,0,,,-0.17568161789582087 -coef_number_of_vehicles_social,-0.15477794105663009,-0.19,0,,,-0.15477794105663009 -coef_number_of_work_tours_undertaken_by_the_person_social,-0.0089584068199000319,-0.28000000000000003,0,,,-0.0089584068199000319 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping,-1.4838947085615379,-1.339,0,,,-1.4838947085615379 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping,-3.2620315739726551,-3.1099999999999999,0,,,-3.2620315739726551 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping,-4.4603602952393357,-4.4870000000000001,0,,,-4.4603602952393357 -coef_alternative_specific_constant_for_return_stops_0out_1in_shopping,-1.2344970570494438,-1.179,0,,,-1.2344970570494438 -coef_alternative_specific_constant_for_return_stops_0out_2in_shopping,-2.4998350943155576,-2.3050000000000002,0,,,-2.4998350943155576 -coef_alternative_specific_constant_for_return_stops_0out_3in_shopping,-3.0179726864409173,-3.024,0,,,-3.0179726864409173 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping,0.51786237531360479,0.252,0,,,0.51786237531360479 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping,-0.13999198594270704,0.51400000000000001,0,,,-0.13999198594270704 -coef_dummy_for_distance_in_miles_shopping,0.023954475817419198,0.028899999999999999,0,,,0.023954475817419198 -coef_dummy_for_distance_less_than_5_miles,0.16375057013711192,0.37680000000000002,0,,,0.16375057013711192 -coef_dummy_for_female_shopping,0.33555842166191147,0.1721,0,,,0.33555842166191147 -coef_dummy_for_only_adults_participate_in_the_tour,1.8328392426308462,0.19020000000000001,0,,,1.8328392426308462 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping,3.0901330214553973,0.90559999999999996,0,,,3.0901330214553973 -coef_dummy_for_walking_to_all_stops_shopping,-1.8757970066056326,-1.4907999999999999,0,,,-1.8757970066056326 -coef_num_kids_between_5_and_15_including_years_old_shopping,-0.041573431257791199,0.0482,0,,,-0.041573431257791199 -coef_number_of_hh_persons_shopping,-0.086440021903122138,-0.1522,0,,,-0.086440021903122138 -coef_number_of_maintenace_tours_tours_undertaken_by_the_person,-0.32594276341704287,-0.19769999999999999,0,,,-0.32594276341704287 -coef_number_of_shop_tours_undertaken_by_the_houshold_shopping,0.148064037095576,-0.073300000000000004,0,,,0.148064037095576 -coef_number_of_university_tours_tours_undertaken_by_the_person_shopping,-0.67089999999999317,-0.67090000000000005,0,,,-0.67089999999999317 -coef_number_of_work_tours_undertaken_by_the_person_shopping,-0.43852307225491766,-0.54799999999999993,0,,,-0.43852307225491766 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout,-1.8947544828670186,-2.1899999999999999,0,,,-1.8947544828670186 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout,-12.056169150387893,-4.516,0,,,-12.056169150387893 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout,-3.6615505167080027,-5.2549999999999999,0,,,-3.6615505167080027 -coef_alternative_specific_constant_for_return_stops_0out_1in_eatout,-1.7027857761249232,-1.7609999999999999,0,,,-1.7027857761249232 -coef_alternative_specific_constant_for_return_stops_0out_2in_eatout,-2.9579857808480341,-3.6970000000000001,0,,,-2.9579857808480341 -coef_alternative_specific_constant_for_return_stops_0out_3in_eatout,-4.7624817489674145,-4.7169999999999996,0,,,-4.7624817489674145 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout,-7.6049863056520284,0.93999999999999995,0,,,-7.6049863056520284 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout,-6.3461221747969292,2.0259999999999998,0,,,-6.3461221747969292 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort,-2.2250757954780624,-2.173,0,,,-2.2250757954780624 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort,-4.7895755848191897,-4.2939999999999996,0,,,-4.7895755848191897 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort,-4.7102122245889007,-4.758,0,,,-4.7102122245889007 -coef_alternative_specific_constant_for_return_stops_0out_1in_escort,-1.2974740994833047,-0.96799999999999997,0,,,-1.2974740994833047 -coef_alternative_specific_constant_for_return_stops_0out_2in_escort,-2.384295363242952,-2.4100000000000001,0,,,-2.384295363242952 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort,-4.200646186979724,-1.8069999999999999,0,,,-4.200646186979724 -coef_dummy_for_distance_less_than_5_miles_escort,0.28109442258515044,0.32000000000000001,0,,,0.28109442258515044 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__escort,1.399121270410205,0.58999999999999997,0,,,1.399121270410205 -coef_dummy_for_walking_to_all_stops_escort,-0.33797126945456507,-1.9099999999999999,0,,,-0.33797126945456507 -coef_number_of_escort_tours_tours_undertaken_by_the_person_escort,-0.25348532544405961,-0.14999999999999999,0,,,-0.25348532544405961 -coef_number_of_hh_persons_escort,-0.17640711121772834,-0.23999999999999999,0,,,-0.17640711121772834 -coef_number_of_students_in_hh_escort,0.15956368405077143,0.19,0,,,0.15956368405077143 -coef_number_of_work_tours_undertaken_by_the_person_escort,-0.15468473406122965,-0.28999999999999998,0,,,-0.15468473406122965 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint,-1.7954028317479085,-1.7609999999999999,0,,,-1.7954028317479085 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint,-2.9624065700982283,-3.661,0,,,-2.9624065700982283 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint,-3.5347850519252844,-5.4260000000000002,0,,,-3.5347850519252844 -coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint,-0.31247075228968507,-0.58499999999999996,0,,,-0.31247075228968507 -coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint,-0.55780644045958916,-1.48,0,,,-0.55780644045958916 -coef_alternative_specific_constant_for_return_stops_0out_3in_othmaint,-1.4356606147103497,-2.4620000000000002,0,,,-1.4356606147103497 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint,-0.36815794089018233,0.41399999999999998,0,,,-0.36815794089018233 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint,-9.3990479541430307,0.48799999999999999,0,,,-9.3990479541430307 -coef_dummy_for_distance_in_miles_othmaint,0.10402635612778853,0.027300000000000001,0,,,0.10402635612778853 -coef_dummy_for_distance_less_than_20_miles_,-1.1515269947782993,-0.40799999999999997,0,,,-1.1515269947782993 -coef_dummy_for_female_othmaint,0.25071759069742156,0.30120000000000002,0,,,0.25071759069742156 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint,0.15444184637909519,0.51339999999999997,0,,,0.15444184637909519 -coef_dummy_for_walking_to_all_stops_othmaint,-2.3023894742054765,-1.4329000000000001,0,,,-2.3023894742054765 -coef_middle_to_low_income_hh_,0.16999999999999751,0.17000000000000001,0,,,0.16999999999999751 -coef_number_of_maintenace_tours_undertaken_by_the_houshold,-0.28330678921194874,-0.046800000000000001,0,,,-0.28330678921194874 -coef_number_of_persons_participating_in_the_tour_return_stops_interaction,0.86852209944195768,0.4904,0,,,0.86852209944195768 -coef_number_of_shool_tours_tours_undertaken_by_the_person,-8.3600381729826356,-1.4135,0,,,-8.3600381729826356 -coef_number_of_shop_tours_undertaken_by_the_person_othmaint,-0.2135166522304546,-0.14280000000000001,0,,,-0.2135166522304546 -coef_number_of_university_tours_tours_undertaken_by_the_person_othmaint,-0.62519999999999998,-0.62519999999999998,0,,,-0.62519999999999998 -coef_number_of_work_tours_undertaken_by_the_person_othmaint,-0.37594536286827945,-0.36399999999999999,0,,,-0.37594536286827945 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr,-1.4221678878531314,-1.581,0,,,-1.4221678878531314 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr,-2.8848210033043737,-3.323,0,,,-2.8848210033043737 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr,-4.252076697537615,-4.6230000000000002,0,,,-4.252076697537615 -coef_alternative_specific_constant_for_return_stops_0out_1in_othdiscr,-1.0106467090607749,-0.92100000000000004,0,,,-1.0106467090607749 -coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr,-2.4905380449227463,-2.3359999999999999,0,,,-2.4905380449227463 -coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr,-2.9393725902979506,-2.927,0,,,-2.9393725902979506 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr,0.93746509917896281,0.86299999999999999,0,,,0.93746509917896281 -coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othdiscr,0.1562826739961867,0.93899999999999995,0,,,0.1562826739961867 -coef_arrival_later_than_17_00__othdiscr,-0.36444328163105733,-0.63829999999999998,0,,,-0.36444328163105733 -coef_dummy_for_distance_in_miles_othdiscr,0.050978615843919654,-0.022499999999999999,0,,,0.050978615843919654 -coef_dummy_for_distance_less_than_10_miles_,-0.041691234906133287,0.37559999999999999,0,,,-0.041691234906133287 -coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othdiscr,0.24111825316596613,0.83350000000000002,0,,,0.24111825316596613 -coef_dummy_for_walking_to_all_stops_othdiscr,-2.0711353545710396,-2.4578000000000002,0,,,-2.0711353545710396 -coef_number_of_maintenace_tours_tours_undertaken_by_the_person_othdiscr,-0.63013749629210336,-0.3715,0,,,-0.63013749629210336 -coef_number_of_shool_tours_tours_undertaken_by_the_person_othdiscr,-0.60717706214459344,-0.81759999999999999,0,,,-0.60717706214459344 -coef_number_of_shop_tours_undertaken_by_the_person_othdiscr,-0.61831778671278581,-0.629,0,,,-0.61831778671278581 -coef_number_of_work_tours_undertaken_by_the_person_othdiscr,-0.48765485585276552,-0.61529999999999996,0,,,-0.48765485585276552 -coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork,-3.4794056872571746,-3.8959999999999999,0,,,-3.4794056872571746 -coef_alternative_specific_constant_for_outbound_stops_2out_0in_atwork,-5.1779127909529326,-5.7089999999999996,0,,,-5.1779127909529326 -coef_alternative_specific_constant_for_outbound_stops_3out_0in_atwork,-6.8972239265618009,-7.3610000000000007,0,,,-6.8972239265618009 -coef_alternative_specific_constant_for_return_stops_0out_1in_atwork,-3.4084307916883305,-3.6709999999999998,0,,,-3.4084307916883305 -coef_alternative_specific_constant_for_return_stops_0out_2in_atwork,-5.5993645137153578,-5.3879999999999999,0,,,-5.5993645137153578 -coef_alternative_specific_constant_for_return_stops_0out_3in_atwork,-7.6012050846491075,-6.21,0,,,-7.6012050846491075 -coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_atwork,2.1051947898870833,2.1269999999999998,0,,,2.1051947898870833 -coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,0.27000000000000002,0.27000000000000002,0,,,0.27000000000000002 -coef_middle_to_low_income_hh__atwork,0.60212861030437703,0.45000000000000001,0,,,0.60212861030437703 -coef_number_of_eating_tours_tours_undertaken_by_the_person,-0.2548910504386343,-0.28000000000000003,0,,,-0.2548910504386343 -coef_primary_destination_accessibility_log_of_it_,0.17070563536066408,0.17999999999999999,0,,,0.17070563536066408 -coef_subtour_departure_less_than_or_equal_to_11am,0.2690647561783549,0.31,0,,,0.2690647561783549 -coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,-0.15252472133506134,0.02,0,,,-0.15252472133506134 -coef_subtour_duration_in_hours_integer_,0.68175040347788651,0.56000000000000005,0,,,0.68175040347788651 -coef_subtour_return_time_greater_or_equal_to_2pm,1.5555725209500013,0.34000000000000002,0,,,1.5555725209500013 +param_name,value,best,initvalue,nullvalue +coef_alternative_specific_constant_for_outbound_stops_1out_0in,-0.64664292294462322,-0.64664292294462322,-0.83300000429153442,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork,-3.4646908445777185,-3.4646908445777185,-3.8959999084472656,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout,-1.9002755809567333,-1.9002755809567333,-2.190000057220459,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort,-2.2236727981595754,-2.2236727981595754,-2.1730000972747803,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr,-1.4225160376379478,-1.4225160376379478,-1.5809999704360962,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint,-1.7982672995567395,-1.7982672995567395,-1.7610000371932983,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_school,-1.7843866978905401,-1.7843866978905401,-2.122999906539917,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping,-1.4865329275382189,-1.4865329275382189,-1.3389999866485596,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_social,-2.4183081291403341,-2.4183081291403341,-1.0809999704360962,0 +coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ,-2.1599604516652944,-2.1599604516652944,-2.628000020980835,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in,-2.4294460976186265,-2.4294460976186265,-2.6129999160766602,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_atwork,-5.162092909819334,-5.162092909819334,-5.7090001106262207,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout,-12.348235464701929,-12.348235464701929,-4.5159997940063477,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort,-4.7869220327015922,-4.7869220327015922,-4.2940001487731934,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr,-2.8878858646220089,-2.8878858646220089,-3.3229999542236328,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint,-2.9665698636514888,-2.9665698636514888,-3.6610000133514404,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_school,-4.2448419370273394,-4.2448419370273394,-3.7980000972747803,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping,-3.2674224134549679,-3.2674224134549679,-3.1099998950958252,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_social,-4.083061579914375,-4.083061579914375,-2.874000072479248,0 +coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ,-3.9124510768537806,-3.9124510768537806,-3.7409999370574951,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in,-3.5882985172757271,-3.5882985172757271,-3.9340000152587891,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_atwork,-6.8802912288167422,-6.8802912288167422,-7.3610000610351562,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout,-3.6740988604843077,-3.6740988604843077,-5.255000114440918,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort,-4.7119993374061275,-4.7119993374061275,-4.7579998970031738,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr,-4.2547457784063534,-4.2547457784063534,-4.6230001449584961,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint,-3.5457191991847736,-3.5457191991847736,-5.4260001182556152,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_school,-6.1404126227480189,-6.1404126227480189,-5.8499999046325684,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping,-4.4728129557270648,-4.4728129557270648,-4.4869999885559082,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_social,-14.181486266726454,-14.181486266726454,-4.5520000457763672,0 +coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ,-4.4862998759715698,-4.4862998759715698,-4.9809999465942383,0 +coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in,-2.3818839828880933,-2.3818839828880933,-1.7829999923706055,0 +coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in,-4.7211128189494742,-4.7211128189494742,-4.0669999122619629,0 +coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in,-11.512801227605346,-11.512801227605346,-4.9980001449584961,0 +coef_alternative_specific_constant_for_return_stops_0out_1in,-0.33835620803188837,-0.33835620803188837,-0.44499999284744263,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_atwork,-3.3927653179415662,-3.3927653179415662,-3.6710000038146973,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_eatout,-1.7128159843448749,-1.7128159843448749,-1.7610000371932983,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_escort,-1.2952840546510411,-1.2952840546510411,-0.96799999475479126,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_othdiscr,-1.0109237306426695,-1.0109237306426695,-0.92100000381469727,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint,-0.31261766892443127,-0.31261766892443127,-0.58499997854232788,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_school,-1.263001355242064,-1.263001355242064,-1.2059999704360962,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_shopping,-1.2369710581521229,-1.2369710581521229,-1.1790000200271606,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_social,0.17362744454328641,0.17362744454328641,-1.1200000047683716,0 +coef_alternative_specific_constant_for_return_stops_0out_1in_univ,-1.5271071961686673,-1.5271071961686673,-2.003000020980835,0 +coef_alternative_specific_constant_for_return_stops_0out_2in,-1.4815144916155247,-1.4815144916155247,-1.7749999761581421,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_atwork,-5.5866314294429644,-5.5866314294429644,-5.3880000114440918,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_eatout,-2.9694905231062454,-2.9694905231062454,-3.6970000267028809,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_escort,-2.382475901775571,-2.382475901775571,-2.4100000858306885,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr,-2.4932337056093066,-2.4932337056093066,-2.3359999656677246,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint,-0.559549687865037,-0.559549687865037,-1.4800000190734863,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_school,-2.7043554260825506,-2.7043554260825506,-2.6719999313354492,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_shopping,-2.5050450260948609,-2.5050450260948609,-2.3050000667572021,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_social,-2.6165947696422243,-2.6165947696422243,-2.7639999389648438,0 +coef_alternative_specific_constant_for_return_stops_0out_2in_univ,-3.3021693186962295,-3.3021693186962295,-3.5099999904632568,0 +coef_alternative_specific_constant_for_return_stops_0out_3in,-1.8983525585535195,-1.8983525585535195,-2.1389999389648438,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_atwork,-7.5705523838869704,-7.5705523838869704,-6.2100000381469727,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_eatout,-4.796417149118664,-4.796417149118664,-4.7170000076293945,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr,-2.9420323763794887,-2.9420323763794887,-2.9270000457763672,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_othmaint,-1.4387779329032728,-1.4387779329032728,-2.4619998931884766,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_school,-3.1370907253356171,-3.1370907253356171,-3.3640000820159912,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_shopping,-3.0227423732428385,-3.0227423732428385,-3.0239999294281006,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_social,-3.365589571395263,-3.365589571395263,-3.4509999752044678,0 +coef_alternative_specific_constant_for_return_stops_0out_3in_univ,-3.7066457714767771,-3.7066457714767771,-3.6770000457763672,0 +coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in,-1.6502808127233042,-1.6502808127233042,-1.3289999961853027,0 +coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in,-4.1259435861090799,-4.1259435861090799,-2.7960000038146973,0 +coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in,-4.0391454462416165,-4.0391454462416165,-3.3789999485015869,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in,-0.063749295179186538,-0.063749295179186538,0,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in,1.488754315051501,1.488754315051501,0.70099997520446777,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_atwork,2.075702350960686,2.075702350960686,2.127000093460083,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout,-7.6034666858545901,-7.6034666858545901,0.93999999761581421,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr,0.94069720899608722,0.94069720899608722,0.86299997568130493,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint,-0.36444527841576962,-0.36444527841576962,0.414000004529953,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping,0.52573690053968991,0.52573690053968991,0.25200000405311584,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social,2.4865763319224596,2.4865763319224596,0.49599999189376831,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ,1.8527731047949576,1.8527731047949576,1.2719999551773071,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in,0.4026073230010761,0.4026073230010761,0.69499999284744263,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout,-6.1299064789603026,-6.1299064789603026,2.0260000228881836,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort,-3.7134719215772845,-3.7134719215772845,-1.8070000410079956,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othdiscr,0.17361191938538348,0.17361191938538348,0.93900001049041748,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint,-9.4707846262842086,-9.4707846262842086,0.48800000548362732,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school,2.0224057350148397,2.0224057350148397,1.1349999904632568,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping,-0.12757312512048605,-0.12757312512048605,0.51399999856948853,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_social,-1.3744527120467618,-1.3744527120467618,0.88200002908706665,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ,1.8597722433784889,1.8597722433784889,1.8710000514984131,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in,0.32162618139688331,0.32162618139688331,0,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in,-4.2949115331788805,-4.2949115331788805,0.51800000667572021,0 +coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in,-1.3125472274782721,-1.3125472274782721,1.496999979019165,0 +coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours,-1.9993783388249304,-1.9993783388249304,-1.9299999475479126,0 +coef_arrival_later_than_17_00_,1.3712401433259278,1.3712401433259278,1.8377000093460083,0 +coef_arrival_later_than_17_00__othdiscr,-0.36182578620009853,-0.36182578620009853,-0.63830000162124634,0 +coef_arrival_later_than_17_00__social,-0.45958068481537828,-0.45958068481537828,-0.44999998807907104,0 +coef_arrival_later_than_17_00__univ,0.3855005796150392,0.3855005796150392,0.38899999856948853,0 +coef_at_least_one_kid_and_one_adult_participate_in_the_tour,1.8868763384626477,1.8868763384626477,0.37000000476837158,0 +coef_dummy_for_a_return_visiting_tour,-1.4902612723336455,-1.4902612723336455,-0.63999998569488525,0 +coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg,0.4774633750050547,0.4774633750050547,0.43999999761581421,0 +coef_dummy_for_all_stops_made_by_transit,-0.67365004739838075,-0.67365004739838075,-0.69999998807907104,0 +coef_dummy_for_an_outbound_visiting_tour,0.33371222702629083,0.33371222702629083,-0.68999999761581421,0 +coef_dummy_for_distance_in_miles,0.04459896461230884,0.04459896461230884,0.0099999997764825821,0 +coef_dummy_for_distance_in_miles_othdiscr,0.050994204723024648,0.050994204723024648,-0.022500000894069672,0 +coef_dummy_for_distance_in_miles_othmaint,0.10425996435025388,0.10425996435025388,0.027300000190734863,0 +coef_dummy_for_distance_in_miles_school,0.095129477447622096,0.095129477447622096,0.043800000101327896,0 +coef_dummy_for_distance_in_miles_shopping,0.024311169311439184,0.024311169311439184,0.028899999335408211,0 +coef_dummy_for_distance_in_miles_social,-0.071016613228709641,-0.071016613228709641,-0.0099999997764825821,0 +coef_dummy_for_distance_less_than_10_miles_,-0.042062943317224627,-0.042062943317224627,0.37560001015663147,0 +coef_dummy_for_distance_less_than_20_miles,-0.69994214881030159,-0.69994214881030159,-0.2199999988079071,0 +coef_dummy_for_distance_less_than_20_miles_,-1.1529791304602006,-1.1529791304602006,-0.40799999237060547,0 +coef_dummy_for_distance_less_than_5_miles,0.16605718344134035,0.16605718344134035,0.37680000066757202,0 +coef_dummy_for_distance_less_than_5_miles_escort,0.27909765242825124,0.27909765242825124,0.31999999284744263,0 +coef_dummy_for_female,0.28229839587426708,0.28229839587426708,0.2199999988079071,0 +coef_dummy_for_female_othmaint,0.25086336764451594,0.25086336764451594,0.30120000243186951,0 +coef_dummy_for_female_school,0.69500027366692196,0.69500027366692196,0.4099000096321106,0 +coef_dummy_for_female_shopping,0.33523175540304978,0.33523175540304978,0.17209999263286591,0 +coef_dummy_for_female_univ,0.51700991234531735,0.51700991234531735,0.73489999771118164,0 +coef_dummy_for_only_adults_participate_in_the_tour,1.823935137209177,1.823935137209177,0.19020000100135803,0 +coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_,0.27000001072883606,0.27000001072883606,0.27000001072883606,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours,0.71310000535072282,0.71310000535072282,0.60000002384185791,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_,1.1559795820114029,1.1559795820114029,1.309999942779541,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_,1.9112642049973785,1.9112642049973785,0.95490002632141113,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__escort,1.4016231431941613,1.4016231431941613,0.5899999737739563,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othdiscr,0.23693003799296872,0.23693003799296872,0.83350002765655518,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint,0.15461331684851626,0.15461331684851626,0.51340001821517944,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping,3.0905565103897032,3.0905565103897032,0.90560001134872437,0 +coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ,1.0776052104148675,1.0776052104148675,0.84340000152587891,0 +coef_dummy_for_walking_to_all_stops,-1.4898874048260706,-1.4898874048260706,-1.5399999618530273,0 +coef_dummy_for_walking_to_all_stops_escort,-0.34448329904128916,-0.34448329904128916,-1.9099999666213989,0 +coef_dummy_for_walking_to_all_stops_othdiscr,-2.0623131666705032,-2.0623131666705032,-2.4577999114990234,0 +coef_dummy_for_walking_to_all_stops_othmaint,-2.2984130311760791,-2.2984130311760791,-1.4328999519348145,0 +coef_dummy_for_walking_to_all_stops_school,-1.5166153192565364,-1.5166153192565364,-1.8163000345230103,0 +coef_dummy_for_walking_to_all_stops_shopping,-1.8770352536650028,-1.8770352536650028,-1.4908000230789185,0 +coef_dummy_for_walking_to_all_stops_social,-2.069393000920547,-2.069393000920547,-1.7300000190734863,0 +coef_evening_arrival_19_00_interacted_with_return_tours,0.606474995858996,0.606474995858996,0.31000000238418579,0 +coef_hh_accesibility_for_inbound_tours_interaction,0.24072323322804254,0.24072323322804254,0.24809999763965607,0 +coef_high_income_hh,0.23999999463558197,0.23999999463558197,0.23999999463558197,0 +coef_mid_to_high_income_hh,0.23000000417232513,0.23000000417232513,0.23000000417232513,0 +coef_middle_to_low_income_hh,0.17000000178813934,0.17000000178813934,0.17000000178813934,0 +coef_middle_to_low_income_hh_,0.17000000178813934,0.17000000178813934,0.17000000178813934,0 +coef_middle_to_low_income_hh__atwork,0.5862265411484473,0.5862265411484473,0.44999998807907104,0 +coef_no_stops_if_tour_mode_is_drivetransit,-999,-999,-999,0 +coef_num_kids_between_5_and_15_including_years_old,0.091794962859674076,0.091794962859674076,0.079999998211860657,0 +coef_num_kids_between_5_and_15_including_years_old_shopping,-0.041937522351757225,-0.041937522351757225,0.048200000077486038,0 +coef_number_of_adults_16_years_old_,0.077287692445891232,0.077287692445891232,0.029999999329447746,0 +coef_number_of_cars_number_of_workers,-0.066693087232745682,-0.066693087232745682,0.15999999642372131,0 +coef_number_of_cars_number_of_workers_school,0.29115625638327919,0.29115625638327919,0.53310000896453857,0 +coef_number_of_eating_tours_tours_undertaken_by_the_person,-0.25037573867732094,-0.25037573867732094,-0.2800000011920929,0 +coef_number_of_escort_tours_tours_undertaken_by_the_person,0.09806050880270635,0.09806050880270635,0.20000000298023224,0 +coef_number_of_escort_tours_tours_undertaken_by_the_person_escort,-0.25353945421619623,-0.25353945421619623,-0.15000000596046448,0 +coef_number_of_escort_tours_tours_undertaken_by_the_person_school,2.3120956941836317,2.3120956941836317,1.2365000247955322,0 +coef_number_of_escort_tours_tours_undertaken_by_the_person_univ,1.830804423840976,1.830804423840976,0.90179997682571411,0 +coef_number_of_hh_persons,-0.33185754187761923,-0.33185754187761923,-0.31000000238418579,0 +coef_number_of_hh_persons_escort,-0.17646847789383999,-0.17646847789383999,-0.23999999463558197,0 +coef_number_of_hh_persons_school,-0.59789398459025822,-0.59789398459025822,-0.50599998235702515,0 +coef_number_of_hh_persons_shopping,-0.086269571752504437,-0.086269571752504437,-0.15219999849796295,0 +coef_number_of_hh_persons_univ,-0.3306926246943428,-0.3306926246943428,-0.28270000219345093,0 +coef_number_of_maintenace_tours_tours_undertaken_by_the_person,-0.32592941005324705,-0.32592941005324705,-0.19769999384880066,0 +coef_number_of_maintenace_tours_tours_undertaken_by_the_person_othdiscr,-0.63285637282069895,-0.63285637282069895,-0.37149998545646667,0 +coef_number_of_maintenace_tours_undertaken_by_the_houshold,-0.28437497321740485,-0.28437497321740485,-0.046799998730421066,0 +coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction,-0.073331456006935747,-0.073331456006935747,-0.46000000834465027,0 +coef_number_of_persons_participating_in_the_tour_return_stops_interaction,0.86868810623690584,0.86868810623690584,0.49039998650550842,0 +coef_number_of_school_tours_tours_undertaken_by_the_person,0.13029516970232355,0.13029516970232355,-1.5499999523162842,0 +coef_number_of_shool_tours_tours_undertaken_by_the_person,-8.132575401995668,-8.132575401995668,-1.4134999513626099,0 +coef_number_of_shool_tours_tours_undertaken_by_the_person_othdiscr,-0.6130966929400905,-0.6130966929400905,-0.81760001182556152,0 +coef_number_of_shop_tours_undertaken_by_the_houshold,-0.32765232732837346,-0.32765232732837346,-0.05000000074505806,0 +coef_number_of_shop_tours_undertaken_by_the_houshold_shopping,0.14836308529667627,0.14836308529667627,-0.073299996554851532,0 +coef_number_of_shop_tours_undertaken_by_the_person,-0.17448403101684509,-0.17448403101684509,-0.23999999463558197,0 +coef_number_of_shop_tours_undertaken_by_the_person_othdiscr,-0.61731020325001096,-0.61731020325001096,-0.62900000810623169,0 +coef_number_of_shop_tours_undertaken_by_the_person_othmaint,-0.20994314934766711,-0.20994314934766711,-0.14280000329017639,0 +coef_number_of_students_in_hh,0.31655751614649219,0.31655751614649219,0.20999999344348907,0 +coef_number_of_students_in_hh_escort,0.15949746173262461,0.15949746173262461,0.18999999761581421,0 +coef_number_of_subtours_in_the_tour,0.33681763025289185,0.33681763025289185,0.18999999761581421,0 +coef_number_of_university_tours_tours_undertaken_by_the_person,-0.47999998927116394,-0.47999998927116394,-0.47999998927116394,0 +coef_number_of_university_tours_tours_undertaken_by_the_person_othmaint,-0.62519997358322144,-0.62519997358322144,-0.62519997358322144,0 +coef_number_of_university_tours_tours_undertaken_by_the_person_shopping,-0.67089998722076416,-0.67089998722076416,-0.67089998722076416,0 +coef_number_of_vehicles,0.26376160794948833,0.26376160794948833,0.17030000686645508,0 +coef_number_of_vehicles_social,-0.15425969899603631,-0.15425969899603631,-0.18999999761581421,0 +coef_number_of_work_tours_undertaken_by_the_person,-0.17654196807499356,-0.17654196807499356,-0.15000000596046448,0 +coef_number_of_work_tours_undertaken_by_the_person_escort,-0.1509302945890815,-0.1509302945890815,-0.28999999165534973,0 +coef_number_of_work_tours_undertaken_by_the_person_othdiscr,-0.49002581553862051,-0.49002581553862051,-0.61529999971389771,0 +coef_number_of_work_tours_undertaken_by_the_person_othmaint,-0.37756033423406382,-0.37756033423406382,-0.36399999260902405,0 +coef_number_of_work_tours_undertaken_by_the_person_shopping,-0.43906376866089675,-0.43906376866089675,-0.54799997806549072,0 +coef_number_of_work_tours_undertaken_by_the_person_social,-0.0062244254965315264,-0.0062244254965315264,-0.2800000011920929,0 +coef_presence_of_kids_between_0_and_4_including_years_old,0.56347076554717646,0.56347076554717646,0.74000000953674316,0 +coef_presence_of_kids_between_5_and_15_including_years_old,0.1180989290788108,0.1180989290788108,0.25999999046325684,0 +coef_presence_of_kids_between_5_and_15_including_years_old_school,0.02961104507186792,0.02961104507186792,0.32989999651908875,0 +coef_presence_of_kids_between_5_and_15_including_years_old_univ,-0.046419488763570535,-0.046419488763570535,0.68229997158050537,0 +coef_primary_destination_accessibility_log_of_it_,0.17066218967701827,0.17066218967701827,0.18000000715255737,0 +coef_subtour_departure_less_than_or_equal_to_11am,0.26747568517112724,0.26747568517112724,0.31000000238418579,0 +coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_,-0.15255084910340322,-0.15255084910340322,0.019999999552965164,0 +coef_subtour_duration_in_hours_integer_,0.68168671797342328,0.68168671797342328,0.56000000238418579,0 +coef_subtour_return_time_greater_or_equal_to_2pm,1.5553971310301449,1.5553971310301449,0.34000000357627869,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_stop_frequency_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_stop_frequency_loglike.csv index c888032785..d60deb239c 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_stop_frequency_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_stop_frequency_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-6184.4158915598673,-6090.2588006296801 +0,-6184.4158913795764,-6090.2665603871901 diff --git a/activitysim/estimation/test/test_larch_estimation/test_tour_and_subtour_mode_choice.csv b/activitysim/estimation/test/test_larch_estimation/test_tour_and_subtour_mode_choice.csv index eb7dd67ad4..8d8064cbb4 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_tour_and_subtour_mode_choice.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_tour_and_subtour_mode_choice.csv @@ -1,302 +1,302 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -1,1,1,1,1,1,1 -bike_ASC_auto_deficient_eatout,-95.05184217157273,-1.5691105999999999,0,,,-95.05184217157273 -bike_ASC_auto_sufficient_eatout,-1.464117844170342,-1.2003470999999999,0,,,-1.464117844170342 -bike_ASC_no_auto_eatout,7.2018489969700603,0.86807095999999995,0,,,7.2018489969700603 -coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0.71970855821176882,0,0,,,0.71970855821176882 -coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,-0.019079686463276232,0,0,,,-0.019079686463276232 -coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.1399641109136807,-1.3660000000000001,0,,,-1.1399641109136807 -coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0.020999157406492247,0,0,,,0.020999157406492247 -coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,-0.0039274834138578957,0,0,,,-0.0039274834138578957 -coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.016936905425988552,-0.017500000000000002,0,,,-0.016936905425988552 -coef_nest_AUTO,0.71999999999999997,0.71999999999999997,1,0.71999999999999997,0.71999999999999997,0.71999999999999997 -coef_nest_AUTO_DRIVEALONE,0.34999999999999998,0.34999999999999998,1,0.34999999999999998,0.34999999999999998,0.34999999999999998 -coef_nest_AUTO_SHAREDRIDE2,0.34999999999999998,0.34999999999999998,1,0.34999999999999998,0.34999999999999998,0.34999999999999998 -coef_nest_AUTO_SHAREDRIDE3,0.34999999999999998,0.34999999999999998,1,0.34999999999999998,0.34999999999999998,0.34999999999999998 -coef_nest_NONMOTORIZED,0.71999999999999997,0.71999999999999997,1,0.71999999999999997,0.71999999999999997,0.71999999999999997 -coef_nest_RIDEHAIL,0.35999999999999999,0.35999999999999999,1,0.35999999999999999,0.35999999999999999,0.35999999999999999 -coef_nest_TRANSIT,0.71999999999999997,0.71999999999999997,1,0.71999999999999997,0.71999999999999997,0.71999999999999997 -coef_nest_TRANSIT_DRIVEACCESS,0.5,0.5,1,0.5,0.5,0.5 -coef_nest_TRANSIT_WALKACCESS,0.5,0.5,1,0.5,0.5,0.5 -commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-38.790613234584711,0.72701850000000001,0,,,-38.790613234584711 -drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.94012379999999995,0.94012379999999995,0,,,0.94012379999999995 -drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-25.934367224180519,0.76895475000000002,0,,,-25.934367224180519 -drive_transit_ASC_auto_deficient_eatout,14.683282344019709,0.59980610000000001,0,,,14.683282344019709 -drive_transit_ASC_auto_sufficient_eatout,12.110507359997341,-0.96951586000000001,0,,,12.110507359997341 -drive_transit_ASC_no_auto_all,0,0,0,,,0 -drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.5534042664919846,0.52500000000000002,0,,,0.5534042664919846 -express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-61.526574455540178,0.96923159999999997,0,,,-61.526574455540178 -heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-11.341188179738285,0.77061210000000002,0,,,-11.341188179738285 -joint_bike_ASC_auto_deficient_all,-154.53293340147803,-6.0764149999999999,0,,,-154.53293340147803 -joint_bike_ASC_auto_sufficient_all,-108.23845192023519,-6.3760656999999998,0,,,-108.23845192023519 -joint_bike_ASC_no_auto_all,-47.716366793432869,-2.8671598,0,,,-47.716366793432869 -joint_drive_transit_ASC_auto_deficient_all,-36.092321468347947,-5.9632215000000004,0,,,-36.092321468347947 -joint_drive_transit_ASC_auto_sufficient_all,-24.112388481073179,-8.0452849999999998,0,,,-24.112388481073179 -joint_drive_transit_ASC_no_auto_all,0,0,0,0,0,0 -joint_sr2_ASC_auto_deficient_all,0,0,0,0,0,0 -joint_sr2_ASC_auto_sufficient_all,0,0,0,0,0,0 -joint_sr2_ASC_no_auto_all,0,0,0,0,0,0 -joint_sr3p_ASC_auto_deficient_all,-5.3440373640659899,-1.8841692000000001,0,,,-5.3440373640659899 -joint_sr3p_ASC_auto_sufficient_all,-3.3313993635418799,-2.234826,0,,,-3.3313993635418799 -joint_sr3p_ASC_no_auto_all,0.97681497679456009,0.56306710000000004,0,,,0.97681497679456009 -joint_taxi_ASC_auto_deficient_all,-45.521802684568371,-9.8156999999999996,0,,,-45.521802684568371 -joint_taxi_ASC_auto_sufficient_all,-11.709899999999999,-11.709899999999999,0,-11.709899999999999,-11.709899999999999,-11.709899999999999 -joint_taxi_ASC_no_auto_all,-12.836772689761483,-4.5792000000000002,0,,,-12.836772689761483 -joint_tnc_shared_ASC_auto_deficient_all,-37.818891366620065,-11.1572,0,,,-37.818891366620065 -joint_tnc_shared_ASC_auto_sufficient_all,-13.205,-13.205,0,-13.205,-13.205,-13.205 -joint_tnc_shared_ASC_no_auto_all,-24.544220954818456,-4.3002000000000002,0,,,-24.544220954818456 -joint_tnc_single_ASC_auto_deficient_all,-51.742391051044301,-9.8961000000000006,0,,,-51.742391051044301 -joint_tnc_single_ASC_auto_sufficient_all,-14.0159,-14.0159,0,-14.0159,-14.0159,-14.0159 -joint_tnc_single_ASC_no_auto_all,-16.690109945634145,-4.4916999999999998,0,,,-16.690109945634145 -joint_walk_ASC_auto_deficient_all,-1.3109169286458668,-1.9607706,0,,,-1.3109169286458668 -joint_walk_ASC_auto_sufficient_all,-3.6267323534729816,-3.2352156999999999,0,,,-3.6267323534729816 -joint_walk_ASC_no_auto_all,0.96782165602250947,-0.21274700999999999,0,,,0.96782165602250947 -joint_walk_transit_ASC_auto_deficient_all,4.47692459281539,-5.1634482999999998,0,,,4.47692459281539 -joint_walk_transit_ASC_auto_sufficient_all,-18.266087843452436,-18.264534000000001,0,,,-18.266087843452436 -joint_walk_transit_ASC_no_auto_all,13.576834007614055,0.62292415000000001,0,,,13.576834007614055 -local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-12.586753852403735,-0.090703264000000006,0,,,-12.586753852403735 -sr2_ASC_auto_deficient_eatout,0.23544005287595454,0.58823449999999999,0,,,0.23544005287595454 -sr2_ASC_auto_sufficient_eatout,0.45709380576692815,0.86280555000000003,0,,,0.45709380576692815 -sr2_ASC_no_auto_all,5.3017687689662525,0,0,,,5.3017687689662525 -sr3p_ASC_auto_deficient_eatout,-0.084427303598265835,0.046052360000000001,0,,,-0.084427303598265835 -sr3p_ASC_auto_sufficient_eatout,0.5022326090064464,0.84685960000000005,0,,,0.5022326090064464 -sr3p_ASC_no_auto_eatout,5.8000711004385348,0.3219998,0,,,5.8000711004385348 -taxi_ASC_auto_deficient_eatout_othdiscr_social,-34.267917620077426,-3.1316999999999999,0,,,-34.267917620077426 -taxi_ASC_auto_sufficient_eatout_othdiscr_social,-2.3771385102751132,-3.0373999999999999,0,,,-2.3771385102751132 -taxi_ASC_no_auto_eatout_othdiscr_social,-25.802108248945846,0.99229999999999996,0,,,-25.802108248945846 -tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-2.9229081712184213,-4.3575999999999997,0,,,-2.9229081712184213 -tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.4681630975727953,-3.6638000000000002,0,,,-3.4681630975727953 -tnc_shared_ASC_no_auto_eatout_othdiscr_social,5.5019116236636574,0.64639999999999997,0,,,5.5019116236636574 -tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-30.032668619901013,-2.9622999999999999,0,,,-30.032668619901013 -tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.0882587225999232,-2.3239000000000001,0,,,-2.0882587225999232 -tnc_single_ASC_no_auto_eatout_othdiscr_social,6.6972430765281166,1.6852,0,,,6.6972430765281166 -walk_ASC_auto_deficient_eatout,2.9644622570813723,3.2746050000000002,0,,,2.9644622570813723 -walk_ASC_auto_sufficient_eatout,0.80408030631941008,1.5516903,0,,,0.80408030631941008 -walk_ASC_no_auto_eatout,10.214310524095858,5.1251173000000003,0,,,10.214310524095858 -walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.94012379999999995,0.94012379999999995,0,,,0.94012379999999995 -walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-11.57159128546653,0.76895475000000002,0,,,-11.57159128546653 -walk_transit_ASC_auto_deficient_eatout,12.342017037167501,-0.038963240000000003,0,,,12.342017037167501 -walk_transit_ASC_auto_sufficient_eatout,10.754005322077003,-1.1126906000000001,0,,,10.754005322077003 -walk_transit_ASC_no_auto_eatout,20.50374109308898,2.5936368000000001,0,,,20.50374109308898 -walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.69084760274299939,0.52500000000000002,0,,,0.69084760274299939 -bike_ASC_auto_deficient_escort,-80.815682010385316,-4.5279280000000002,0,,,-80.815682010385316 -bike_ASC_auto_sufficient_escort,-4.2321601413248553,-5.0631084,0,,,-4.2321601413248553 -bike_ASC_no_auto_escort,-82.613015375494186,-0.71621199999999996,0,,,-82.613015375494186 -drive_transit_ASC_auto_deficient_escort,12.026459792966101,-1.1537067000000001,0,,,12.026459792966101 -drive_transit_ASC_auto_sufficient_escort,-38.422040468436286,-4.6014246999999999,0,,,-38.422040468436286 -sr2_ASC_auto_deficient_escort,-0.028872449257253224,0,0,,,-0.028872449257253224 -sr2_ASC_auto_sufficient_escort,-0.44574542118914673,0,0,,,-0.44574542118914673 -sr3p_ASC_auto_deficient_escort,-0.24177007008960669,-0.40818766000000001,0,,,-0.24177007008960669 -sr3p_ASC_auto_sufficient_escort,-0.57332666707255331,-0.057412530000000003,0,,,-0.57332666707255331 -sr3p_ASC_no_auto_escort,-48.558116584856315,-1.8129267,0,,,-48.558116584856315 -taxi_ASC_auto_deficient_escort_othmaint_shopping,0.22119642025031605,0.17660000000000001,0,,,0.22119642025031605 -taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8747078913779172,-1.8055000000000001,0,,,-1.8747078913779172 -taxi_ASC_no_auto_escort_othmaint_shopping,7.3609237955139406,1.8938999999999999,0,,,7.3609237955139406 -tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.24413343003260302,-0.38629999999999998,0,,,-0.24413343003260302 -tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.7057545852498914,-2.4365000000000001,0,,,-2.7057545852498914 -tnc_shared_ASC_no_auto_escort_othmaint_shopping,6.659950525915475,0.93610000000000004,0,,,6.659950525915475 -tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.8961844796143621,0.67479999999999996,0,,,0.8961844796143621 -tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.7457428565324109,-1.45,0,,,-1.7457428565324109 -tnc_single_ASC_no_auto_escort_othmaint_shopping,7.4428007891764407,1.8605,0,,,7.4428007891764407 -walk_ASC_auto_deficient_escort,-103.0336443777916,-0.90204656000000005,0,,,-103.0336443777916 -walk_ASC_auto_sufficient_escort,-2.1951400279195017,-0.81160659999999996,0,,,-2.1951400279195017 -walk_ASC_no_auto_escort,7.3688522520263184,2.8012068000000001,0,,,7.3688522520263184 -walk_transit_ASC_auto_deficient_escort,8.7582882394391213,-4.9607039999999998,0,,,8.7582882394391213 -walk_transit_ASC_auto_sufficient_escort,7.5679819008069709,-4.9348470000000004,0,,,7.5679819008069709 -walk_transit_ASC_no_auto_escort,-44.292550039991539,-2.2172081000000001,0,,,-44.292550039991539 -bike_ASC_auto_deficient_othdiscr,0.22855250662092905,-0.092468339999999996,0,,,0.22855250662092905 -bike_ASC_auto_sufficient_othdiscr,-1.9167540400011813,-1.0714596999999999,0,,,-1.9167540400011813 -bike_ASC_no_auto_othdiscr,5.4577562038310869,-0.37642320000000001,0,,,5.4577562038310869 -drive_transit_ASC_auto_deficient_othdiscr,-61.370153497008388,0.31993080000000002,0,,,-61.370153497008388 -drive_transit_ASC_auto_sufficient_othdiscr,11.330970889251063,-0.37859169999999998,0,,,11.330970889251063 -sr2_ASC_auto_deficient_othdiscr,0.73863776841688022,0.6601513,0,,,0.73863776841688022 -sr2_ASC_auto_sufficient_othdiscr,0.1229837233438586,0.49684616999999998,0,,,0.1229837233438586 -sr3p_ASC_auto_deficient_othdiscr,0.935476440999231,1.0470965999999999,0,,,0.935476440999231 -sr3p_ASC_auto_sufficient_othdiscr,0.24598820830201615,0.58850205,0,,,0.24598820830201615 -sr3p_ASC_no_auto_othdiscr,6.4061820861081387,0.27216901999999998,0,,,6.4061820861081387 -walk_ASC_auto_deficient_othdiscr,1.6258274516143363,2.2494074999999998,0,,,1.6258274516143363 -walk_ASC_auto_sufficient_othdiscr,1.0941805514117351,1.2633475999999999,0,,,1.0941805514117351 -walk_ASC_no_auto_othdiscr,8.7710297682817657,3.2665945999999999,0,,,8.7710297682817657 -walk_transit_ASC_auto_deficient_othdiscr,13.266819655208037,0.95308839999999995,0,,,13.266819655208037 -walk_transit_ASC_auto_sufficient_othdiscr,11.302586771209716,-0.80636792999999996,0,,,11.302586771209716 -walk_transit_ASC_no_auto_othdiscr,20.427662059570103,2.2437784999999999,0,,,20.427662059570103 -bike_ASC_auto_deficient_othmaint,-1.5527604047004422,-1.5184648999999999,0,,,-1.5527604047004422 -bike_ASC_auto_sufficient_othmaint,-2.4841948233012818,-2.8083024000000001,0,,,-2.4841948233012818 -bike_ASC_no_auto_othmaint,7.0881917223708513,1.5394334000000001,0,,,7.0881917223708513 -drive_transit_ASC_auto_deficient_othmaint,-65.867616159128147,-0.29943228,0,,,-65.867616159128147 -drive_transit_ASC_auto_sufficient_othmaint,-32.152894844805871,-2.6249478000000002,0,,,-32.152894844805871 -sr2_ASC_auto_deficient_othmaint,-0.17432511879711185,0.26215270000000002,0,,,-0.17432511879711185 -sr2_ASC_auto_sufficient_othmaint,0.22153081726667476,0.25817883000000003,0,,,0.22153081726667476 -sr3p_ASC_auto_deficient_othmaint,-1.7678027401708742,-1.3493925,0,,,-1.7678027401708742 -sr3p_ASC_auto_sufficient_othmaint,-0.25110618149419051,-0.075498670000000004,0,,,-0.25110618149419051 -sr3p_ASC_no_auto_othmaint,4.5931064543486562,-0.80318540000000005,0,,,4.5931064543486562 -walk_ASC_auto_deficient_othmaint,1.8335810139202195,1.3690404,0,,,1.8335810139202195 -walk_ASC_auto_sufficient_othmaint,0.88973692144242589,0.79996339999999999,0,,,0.88973692144242589 -walk_ASC_no_auto_othmaint,6.2906587601839359,1.287299,0,,,6.2906587601839359 -walk_transit_ASC_auto_deficient_othmaint,9.407080733783447,-3.0597257999999998,0,,,9.407080733783447 -walk_transit_ASC_auto_sufficient_othmaint,10.958165434769871,-1.5471172,0,,,10.958165434769871 -walk_transit_ASC_no_auto_othmaint,20.617092448325504,2.5643455999999998,0,,,20.617092448325504 -bike_ASC_auto_deficient_school,-0.41778199451832254,-0.52806779999999998,0,,,-0.41778199451832254 -bike_ASC_auto_sufficient_school,-3.5078886893560863,-2.1134686,0,,,-3.5078886893560863 -bike_ASC_no_auto_school,-28.724176190313326,12.098735,0,,,-28.724176190313326 -coef_age010_trn_multiplier_school_univ,-1.1120244370243391,-1.5548,0,,,-1.1120244370243391 -coef_age1619_da_multiplier_school_univ,-1.5951711270563249,-1.3813,0,,,-1.5951711270563249 -coef_age16p_sr_multiplier_school_univ_work_atwork,-0.72100839095028546,0,0,,,-0.72100839095028546 -coef_hhsize2_sr_multiplier_school_univ,-0.53400766253899179,-0.63590000000000002,0,,,-0.53400766253899179 -coef_ivt_school_univ,-0.021192297603561172,-0.0224,0,,,-0.021192297603561172 -commuter_rail_ASC_school_univ,-32.353210620182892,1.0336205999999999,0,,,-32.353210620182892 -drive_ferry_ASC_school_univ,2.0202317000000001,2.0202317000000001,0,,,2.0202317000000001 -drive_light_rail_ASC_school_univ,-19.108400881329299,1.6814003,0,,,-19.108400881329299 -drive_transit_ASC_auto_deficient_school,-95.982095698903436,5.3252654000000001,0,,,-95.982095698903436 -drive_transit_ASC_auto_sufficient_school,-46.151623937860776,1.4013500000000001,0,,,-46.151623937860776 -drive_transit_CBD_ASC_school_univ,62.459413404223476,0.67200000000000004,0,,,62.459413404223476 -express_bus_ASC_school_univ,0.32496937999999997,0.32496937999999997,0,,,0.32496937999999997 -heavy_rail_ASC_school_univ,-12.380133019206522,0.96200377000000004,0,,,-12.380133019206522 -local_bus_ASC_school_univ,-13.872178494981991,-0.065086210000000005,0,,,-13.872178494981991 -sr2_ASC_auto_deficient_school,0.11627333557951013,0.12474365,0,,,0.11627333557951013 -sr2_ASC_auto_sufficient_school,-1.6466117937376035,-1.6062657,0,,,-1.6466117937376035 -sr3p_ASC_auto_deficient_school,0.72079329001052006,0.71495710000000001,0,,,0.72079329001052006 -sr3p_ASC_auto_sufficient_school,-1.1869716638057384,-1.0201935,0,,,-1.1869716638057384 -sr3p_ASC_no_auto_school,-6.0240829874137232,-6.0240827000000001,0,,,-6.0240829874137232 -taxi_ASC_auto_deficient_school,1.5929016972992514,-0.33379999999999999,0,,,1.5929016972992514 -taxi_ASC_auto_sufficient_school,-1.6320342977823235,-2.4293999999999998,0,,,-1.6320342977823235 -taxi_ASC_no_auto_school_univ,-7,-7,0,-7,-7,-7 -tnc_shared_ASC_auto_deficient_school,0.96832753972334895,-1.4745999999999999,0,,,0.96832753972334895 -tnc_shared_ASC_auto_sufficient_school,-2.7739120530297634,-3.7219000000000002,0,,,-2.7739120530297634 -tnc_shared_ASC_no_auto_school,-7,-7,0,-7,-7,-7 -tnc_single_ASC_auto_deficient_school,-3.3648846265737218,-0.5524,0,,,-3.3648846265737218 -tnc_single_ASC_auto_sufficient_school,-2.3704008497944398,-2.8374999999999999,0,,,-2.3704008497944398 -tnc_single_ASC_no_auto_school,-7,-7,0,-7,-7,-7 -walk_ASC_auto_deficient_school,3.2349172133905824,3.2573623999999999,0,,,3.2349172133905824 -walk_ASC_auto_sufficient_school,0.69222756273388997,0.64768559999999997,0,,,0.69222756273388997 -walk_ASC_no_auto_school,31.884868503149736,18.414556999999999,0,,,31.884868503149736 -walk_ferry_ASC_school_univ,2.0202317000000001,2.0202317000000001,0,,,2.0202317000000001 -walk_light_rail_ASC_school_univ,-12.140651343565191,1.6814003,0,,,-12.140651343565191 -walk_transit_ASC_auto_deficient_school,17.064091325381543,4.1207079999999996,0,,,17.064091325381543 -walk_transit_ASC_auto_sufficient_school,14.160601613148902,0.74590873999999996,0,,,14.160601613148902 -walk_transit_ASC_no_auto_school,48.738094718979411,21.383749000000002,0,,,48.738094718979411 -walk_transit_CBD_ASC_school_univ,0.78788966521195125,0.67200000000000004,0,,,0.78788966521195125 -bike_ASC_auto_deficient_shopping,-0.98208504158712495,-0.87584466000000005,0,,,-0.98208504158712495 -bike_ASC_auto_sufficient_shopping,-4.7656989395473435,-2.5662102999999998,0,,,-4.7656989395473435 -bike_ASC_no_auto_shopping,6.3473397917421011,0.83415550000000005,0,,,6.3473397917421011 -drive_transit_ASC_auto_deficient_shopping,-69.120302733575414,-0.41849177999999998,0,,,-69.120302733575414 -drive_transit_ASC_auto_sufficient_shopping,-74.360368326848842,-2.1718937999999999,0,,,-74.360368326848842 -sr2_ASC_auto_deficient_shopping,0.34099442656713952,0.24409755999999999,0,,,0.34099442656713952 -sr2_ASC_auto_sufficient_shopping,0.14451282933821491,0.19770707000000001,0,,,0.14451282933821491 -sr3p_ASC_auto_deficient_shopping,-0.26249087797911946,-0.073370166000000001,0,,,-0.26249087797911946 -sr3p_ASC_auto_sufficient_shopping,-0.2963226415957782,-0.077571294999999998,0,,,-0.2963226415957782 -sr3p_ASC_no_auto_shopping,5.6484952721990362,-0.27978947999999998,0,,,5.6484952721990362 -walk_ASC_auto_deficient_shopping,2.8036057394781277,2.2701733000000002,0,,,2.8036057394781277 -walk_ASC_auto_sufficient_shopping,0.47346256501524292,0.73126630000000004,0,,,0.47346256501524292 -walk_ASC_no_auto_shopping,8.2155350323455529,2.3768772999999999,0,,,8.2155350323455529 -walk_transit_ASC_auto_deficient_shopping,11.256552971319946,-0.84765690000000005,0,,,11.256552971319946 -walk_transit_ASC_auto_sufficient_shopping,9.7542972055490029,-2.2036798000000002,0,,,9.7542972055490029 -walk_transit_ASC_no_auto_shopping,19.703542063063956,2.1067475999999998,0,,,19.703542063063956 -bike_ASC_auto_deficient_social,-0.26949163972926454,0.63452140000000001,0,,,-0.26949163972926454 -bike_ASC_auto_sufficient_social,-0.85401921523485835,-1.368071,0,,,-0.85401921523485835 -bike_ASC_no_auto_social,5.5137426913975727,0.020583210000000001,0,,,5.5137426913975727 -drive_transit_ASC_auto_deficient_social,-52.758046290102875,1.5627195,0,,,-52.758046290102875 -drive_transit_ASC_auto_sufficient_social,-47.996801808437127,-0.61585575000000004,0,,,-47.996801808437127 -sr2_ASC_auto_deficient_social,1.429304838044295,1.8558528000000001,0,,,1.429304838044295 -sr2_ASC_auto_sufficient_social,0.97730089864256342,0.52360249999999997,0,,,0.97730089864256342 -sr3p_ASC_auto_deficient_social,0.67630143929989783,1.5007242999999999,0,,,0.67630143929989783 -sr3p_ASC_auto_sufficient_social,0.75744513577301464,0.50617886000000001,0,,,0.75744513577301464 -sr3p_ASC_no_auto_social,3.972071101285167,-1.4036902,0,,,3.972071101285167 -walk_ASC_auto_deficient_social,3.1688988708714376,2.8701840000000001,0,,,3.1688988708714376 -walk_ASC_auto_sufficient_social,2.6607837167356201,1.7072186,0,,,2.6607837167356201 -walk_ASC_no_auto_social,6.4445317299061999,1.8680915,0,,,6.4445317299061999 -walk_transit_ASC_auto_deficient_social,12.875988741851154,0.97444487000000002,0,,,12.875988741851154 -walk_transit_ASC_auto_sufficient_social,12.613259624057664,-0.34537590000000001,0,,,12.613259624057664 -walk_transit_ASC_no_auto_social,18.966818965194228,1.3814651,0,,,18.966818965194228 -bike_ASC_auto_deficient_univ,-0.66923500000000002,-0.66923500000000002,0,,,-0.66923500000000002 -bike_ASC_auto_sufficient_univ,-1.9397831999999999,-1.9397831999999999,0,,,-1.9397831999999999 -bike_ASC_no_auto_univ,4.2945156000000004,4.2945156000000004,0,,,4.2945156000000004 -drive_transit_ASC_auto_deficient_univ,1.8501175999999999,1.8501175999999999,0,,,1.8501175999999999 -drive_transit_ASC_auto_sufficient_univ,1.3587753,1.3587753,0,,,1.3587753 -sr2_ASC_auto_deficient_univ,-1.6922345999999999,-1.6922345999999999,0,,,-1.6922345999999999 -sr2_ASC_auto_sufficient_univ,-1.8594269999999999,-1.8594269999999999,0,,,-1.8594269999999999 -sr3p_ASC_auto_deficient_univ,-1.7277422,-1.7277422,0,,,-1.7277422 -sr3p_ASC_auto_sufficient_univ,-1.9047098,-1.9047098,0,,,-1.9047098 -sr3p_ASC_no_auto_univ,-6.0560010000000002,-6.0560010000000002,0,,,-6.0560010000000002 -taxi_ASC_auto_deficient_univ,4.2492000000000001,4.2492000000000001,0,,,4.2492000000000001 -taxi_ASC_auto_sufficient_univ,-0.31309999999999999,-0.31309999999999999,0,,,-0.31309999999999999 -tnc_shared_ASC_auto_deficient_univ,3.25,3.25,0,,,3.25 -tnc_shared_ASC_auto_sufficient_univ,-0.90680000000000005,-0.90680000000000005,0,,,-0.90680000000000005 -tnc_shared_ASC_no_auto_univ,-5.8116000000000003,-5.8116000000000003,0,,,-5.8116000000000003 -tnc_single_ASC_auto_deficient_univ,1.0221,1.0221,0,,,1.0221 -tnc_single_ASC_auto_sufficient_univ,0.20880000000000001,0.20880000000000001,0,,,0.20880000000000001 -tnc_single_ASC_no_auto_univ,-2.5190000000000001,-2.5190000000000001,0,,,-2.5190000000000001 -walk_ASC_auto_deficient_univ,4.5059100000000001,4.5059100000000001,0,,,4.5059100000000001 -walk_ASC_auto_sufficient_univ,1.0607664999999999,1.0607664999999999,0,,,1.0607664999999999 -walk_ASC_no_auto_univ,6.4089669999999996,6.4089669999999996,0,,,6.4089669999999996 -walk_transit_ASC_auto_deficient_univ,3.1362554999999999,3.1362554999999999,0,,,3.1362554999999999 -walk_transit_ASC_auto_sufficient_univ,0.47311629999999999,0.47311629999999999,0,,,0.47311629999999999 -walk_transit_ASC_no_auto_univ,8.7860370000000003,8.7860370000000003,0,,,8.7860370000000003 -bike_ASC_auto_deficient_work,0.1093970633711833,0.25318967999999997,0,,,0.1093970633711833 -bike_ASC_auto_sufficient_work,-1.7147321677779845,-1.5800232000000001,0,,,-1.7147321677779845 -bike_ASC_no_auto_work,7.916152782688358,3.1940088000000002,0,,,7.916152782688358 -coef_hhsize1_sr_multiplier_work,-0.6790273309383883,-0.73458800000000002,0,,,-0.6790273309383883 -coef_ivt_work,-0.013061382875350648,-0.0134,0,,,-0.013061382875350648 -commuter_rail_ASC_work,12.116488014803043,0.72550300000000001,0,,,12.116488014803043 -drive_ferry_ASC_work,0.93322605000000003,0.93322605000000003,0,,,0.93322605000000003 -drive_light_rail_ASC_work,-36.000808246088518,0.82555670000000003,0,,,-36.000808246088518 -drive_transit_ASC_auto_deficient_work,-10.817730116154191,0.10081567,0,,,-10.817730116154191 -drive_transit_ASC_auto_sufficient_work,-11.922033211338155,-1.0045459000000001,0,,,-11.922033211338155 -drive_transit_CBD_ASC_work,1.4326312016814422,1.1000000000000001,0,,,1.4326312016814422 -express_bus_ASC_work,-54.606930216243668,-0.51654739999999999,0,,,-54.606930216243668 -heavy_rail_ASC_work,11.025029288750547,0.64772974999999999,0,,,11.025029288750547 -local_bus_ASC_work,10.288307853536837,0.066895070000000001,0,,,10.288307853536837 -sr2_ASC_auto_deficient_work,0.46312369609634996,-0.33803123000000002,0,,,0.46312369609634996 -sr2_ASC_auto_sufficient_work,-0.39096777455191095,-1.0857458,0,,,-0.39096777455191095 -sr3p_ASC_auto_deficient_work,-0.050072021249821817,-0.85270420000000002,0,,,-0.050072021249821817 -sr3p_ASC_auto_sufficient_work,-0.71640219472524347,-1.4699701999999999,0,,,-0.71640219472524347 -sr3p_ASC_no_auto_work,5.2865327465387555,-0.5831269,0,,,5.2865327465387555 -taxi_ASC_auto_deficient_work,-1.3594600760328026,-1.4765999999999999,0,,,-1.3594600760328026 -taxi_ASC_auto_sufficient_work,-55.988330866418792,-4.8509000000000002,0,,,-55.988330866418792 -taxi_ASC_no_auto_work,9.43236827804283,4.7290999999999999,0,,,9.43236827804283 -tnc_shared_ASC_auto_deficient_work,-2.4348067005945464,-2.1435,0,,,-2.4348067005945464 -tnc_shared_ASC_auto_sufficient_work,-63.386791172965488,-5.3574999999999999,0,,,-63.386791172965488 -tnc_shared_ASC_no_auto_work,-47.309135186133922,3.2429000000000001,0,,,-47.309135186133922 -tnc_single_ASC_auto_deficient_work,-0.78812679103952732,-0.80130000000000001,0,,,-0.78812679103952732 -tnc_single_ASC_auto_sufficient_work,-4.8755977035592224,-4.1946000000000003,0,,,-4.8755977035592224 -tnc_single_ASC_no_auto_work,10.295672942342067,5.7854999999999999,0,,,10.295672942342067 -walk_ASC_auto_deficient_work,2.1275102930594469,2.4010416999999999,0,,,2.1275102930594469 -walk_ASC_auto_sufficient_work,0.17637095807617828,0.053265337000000003,0,,,0.17637095807617828 -walk_ASC_no_auto_work,10.356832861443557,5.7672157000000004,0,,,10.356832861443557 -walk_ferry_ASC_work,0.93322605000000003,0.93322605000000003,0,,,0.93322605000000003 -walk_light_rail_ASC_work,11.204407103182628,0.82555670000000003,0,,,11.204407103182628 -walk_transit_ASC_auto_deficient_work,-9.7699412444923333,0.65302855000000004,0,,,-9.7699412444923333 -walk_transit_ASC_auto_sufficient_work,-11.327002198633336,-0.89165070000000002,0,,,-11.327002198633336 -walk_transit_ASC_no_auto_work,-0.8184290314119107,5.0354165999999996,0,,,-0.8184290314119107 -walk_transit_CBD_ASC_work,0.97698021606324725,0.80400000000000005,0,,,0.97698021606324725 -bike_ASC_auto_deficient_atwork,-1.3755365229511889,-0.80740829999999997,0,,,-1.3755365229511889 -bike_ASC_auto_sufficient_atwork,15.727786619761421,15.72017,0,,,15.727786619761421 -bike_ASC_no_auto_atwork,-28.399708381108002,-0.90725845000000005,0,,,-28.399708381108002 -coef_age010_trn_multiplier_atwork,0.00072199999999999999,0.00072199999999999999,0,,,0.00072199999999999999 -coef_age1619_da_multiplier_atwork,-0.80694718452625747,0.0032336000000000001,0,,,-0.80694718452625747 -coef_ivt_atwork,-0.021019454866270101,-0.018800000000000001,0,,,-0.021019454866270101 -drive_transit_ASC_auto_deficient_atwork,-998.81960000000004,-998.81960000000004,0,,,-998.81960000000004 -drive_transit_ASC_auto_sufficient_atwork,-999.21465999999998,-999.21465999999998,0,,,-999.21465999999998 -drive_transit_CBD_ASC_atwork,0.56399999999999995,0.56399999999999995,0,,,0.56399999999999995 -sr2_ASC_auto_deficient_atwork,-1.892695657391221,-2.1102420999999998,0,,,-1.892695657391221 -sr2_ASC_auto_sufficient_atwork,-0.65249983963035285,-1.4450618,0,,,-0.65249983963035285 -sr3p_ASC_auto_deficient_atwork,-2.383158561544211,-2.5146579999999998,0,,,-2.383158561544211 -sr3p_ASC_auto_sufficient_atwork,-0.82204450780308591,-1.652174,0,,,-0.82204450780308591 -sr3p_ASC_no_auto_atwork,6.2362869423849405,0.58266260000000003,0,,,6.2362869423849405 -taxi_ASC_auto_deficient_atwork,-28.540908119718239,-4.4046000000000003,0,,,-28.540908119718239 -taxi_ASC_auto_sufficient_atwork,-28.643971281649424,-2.8803999999999998,0,,,-28.643971281649424 -taxi_ASC_no_auto_atwork,10.212879604829233,4.1021000000000001,0,,,10.212879604829233 -tnc_shared_ASC_auto_deficient_atwork,-3.912212918042727,-4.5088999999999997,0,,,-3.912212918042727 -tnc_shared_ASC_auto_sufficient_atwork,-2.6881377816112022,-3.5396999999999998,0,,,-2.6881377816112022 -tnc_shared_ASC_no_auto_atwork,9.0603158795129275,3.3672,0,,,9.0603158795129275 -tnc_single_ASC_auto_deficient_atwork,-3.1078735470600058,-3.7625999999999999,0,,,-3.1078735470600058 -tnc_single_ASC_auto_sufficient_atwork,-2.6270251391593975,-2.7988,0,,,-2.6270251391593975 -tnc_single_ASC_no_auto_atwork,10.48194785788473,4.4981999999999998,0,,,10.48194785788473 -walk_ASC_auto_deficient_atwork,1.82422508083581,0.92546092999999996,0,,,1.82422508083581 -walk_ASC_auto_sufficient_atwork,1.428722206355812,0.67721600000000004,0,,,1.428722206355812 -walk_ASC_no_auto_atwork,12.824108683156906,6.6692130000000001,0,,,12.824108683156906 -walk_transit_ASC_auto_deficient_atwork,8.9851772342267946,-2.9988291,0,,,8.9851772342267946 -walk_transit_ASC_auto_sufficient_atwork,9.1279962633935359,-3.401027,0,,,9.1279962633935359 -walk_transit_ASC_no_auto_atwork,21.234895822541418,2.7041876,0,,,21.234895822541418 -walk_transit_CBD_ASC_atwork,0.35194949771152162,0.56399999999999995,0,,,0.35194949771152162 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +1,1,1,1,0 +bike_ASC_auto_deficient_atwork,-1.3785489288550536,-1.3785489288550536,-0.80740827322006226,0 +bike_ASC_auto_deficient_eatout,-13.478289059952681,-13.478289059952681,-1.569110631942749,0 +bike_ASC_auto_deficient_escort,-12.549532816371221,-12.549532816371221,-4.5279278755187988,0 +bike_ASC_auto_deficient_othdiscr,0.23084257669564343,0.23084257669564343,-0.092468343675136566,0 +bike_ASC_auto_deficient_othmaint,-1.5508409145945901,-1.5508409145945901,-1.5184649229049683,0 +bike_ASC_auto_deficient_school,-0.4215455293721031,-0.4215455293721031,-0.52806782722473145,0 +bike_ASC_auto_deficient_shopping,-0.98162820928675221,-0.98162820928675221,-0.87584465742111206,0 +bike_ASC_auto_deficient_social,-0.26248269451457257,-0.26248269451457257,0.63452142477035522,0 +bike_ASC_auto_deficient_univ,-0.6692349910736084,-0.6692349910736084,-0.6692349910736084,0 +bike_ASC_auto_deficient_work,0.10890208866785125,0.10890208866785125,0.25318968296051025,0 +bike_ASC_auto_sufficient_atwork,15.720436744241237,15.720436744241237,15.720170021057129,0 +bike_ASC_auto_sufficient_eatout,-1.4652985888010346,-1.4652985888010346,-1.2003470659255981,0 +bike_ASC_auto_sufficient_escort,-4.2292784418487299,-4.2292784418487299,-5.0631084442138672,0 +bike_ASC_auto_sufficient_othdiscr,-1.9171536174387396,-1.9171536174387396,-1.0714596509933472,0 +bike_ASC_auto_sufficient_othmaint,-2.480889498913136,-2.480889498913136,-2.8083024024963379,0 +bike_ASC_auto_sufficient_school,-3.5085898921949115,-3.5085898921949115,-2.1134686470031738,0 +bike_ASC_auto_sufficient_shopping,-4.7741583038198092,-4.7741583038198092,-2.5662102699279785,0 +bike_ASC_auto_sufficient_social,-0.84654185177446273,-0.84654185177446273,-1.3680709600448608,0 +bike_ASC_auto_sufficient_univ,-1.9397832155227661,-1.9397832155227661,-1.9397832155227661,0 +bike_ASC_auto_sufficient_work,-1.7160300530004202,-1.7160300530004202,-1.5800231695175171,0 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+walk_ferry_ASC_school_univ,2.0202317237854004,2.0202317237854004,2.0202317237854004,0 +walk_ferry_ASC_work,0.93322604894638062,0.93322604894638062,0.93322604894638062,0 +walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.4992168746737472,-0.4992168746737472,0.76895475387573242,0 +walk_light_rail_ASC_school_univ,-1.1206738073437195,-1.1206738073437195,1.6814002990722656,0 +walk_light_rail_ASC_work,1.9458205366633916,1.9458205366633916,0.82555669546127319,0 +walk_transit_ASC_auto_deficient_atwork,-2.087745734137894,-2.087745734137894,-2.9988291263580322,0 +walk_transit_ASC_auto_deficient_eatout,1.2702715178798751,1.2702715178798751,-0.038963239639997482,0 +walk_transit_ASC_auto_deficient_escort,-2.3202251183894047,-2.3202251183894047,-4.9607038497924805,0 +walk_transit_ASC_auto_deficient_othdiscr,2.1954218795721774,2.1954218795721774,0.95308840274810791,0 +walk_transit_ASC_auto_deficient_othmaint,-1.660576732282677,-1.660576732282677,-3.0597257614135742,0 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a/activitysim/estimation/test/test_larch_estimation/test_workplace_location.csv b/activitysim/estimation/test/test_larch_estimation/test_workplace_location.csv index 4fbab98906..528d77a9ba 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_workplace_location.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_workplace_location.csv @@ -1,35 +1,35 @@ -,value,initvalue,nullvalue,minimum,maximum,best --999,-999,-999,-999,-999,-999,-999 -1,1,1,1,1,1,1 -coef_dist_0_1,-1.0822922946274103,-0.84279999999999999,0,-25,25,-1.0822922946274103 -coef_dist_0_5_high,0.1207879915621165,0.14999999999999999,0,-25,25,0.1207879915621165 -coef_dist_15_up,-0.091700000000000004,-0.091700000000000004,0,-25,25,-0.091700000000000004 -coef_dist_1_2,-0.24766620079389307,-0.31040000000000001,0,-25,25,-0.24766620079389307 -coef_dist_2_5,-0.3685512775196465,-0.37830000000000003,0,-25,25,-0.3685512775196465 -coef_dist_5_15,-0.15169975841932976,-0.1285,0,-25,25,-0.15169975841932976 -coef_dist_5_up_high,-0.015068815366553389,0.02,0,-25,25,-0.015068815366553389 -coef_mode_logsum,0.1608428973358158,0.29999999999999999,0,-25,25,0.1608428973358158 -work_high_AGREMPN,-5.414473039484391,-5.1159958097540823,0,-6,6,-5.414473039484391 -work_high_FPSEMPN,-2.0198354749074623,-1.575036485716768,0,-6,6,-2.0198354749074623 -work_high_HEREMPN,-1.5774962961301324,-1.258781040820931,0,-6,6,-1.5774962961301324 -work_high_MWTEMPN,-2.0728064576402221,-1.4312917270506265,0,-6,6,-2.0728064576402221 -work_high_OTHEMPN,-2.1440619487784449,-1.870802676568508,0,-6,6,-2.1440619487784449 -work_high_RETEMPN,-2.2072749131897207,-2.2072749131897207,0,-2.2072749131897207,-2.2072749131897207,-2.2072749131897207 -work_low_AGREMPN,-5.8479826351063204,-4.6051701859880909,0,-6,6,-5.8479826351063204 -work_low_FPSEMPN,-0.64550992870675994,-1.6450650900772514,0,-6,6,-0.64550992870675994 -work_low_HEREMPN,0.10934025159883133,-0.95972028980149104,0,-6,6,0.10934025159883133 -work_low_MWTEMPN,-0.35351399513998311,-1.8078888511579385,0,-6,6,-0.35351399513998311 -work_low_OTHEMPN,-0.68434316293298214,-2.120263536200091,0,-6,6,-0.68434316293298214 -work_low_RETEMPN,-2.0479428746204649,-2.0479428746204649,0,-2.0479428746204649,-2.0479428746204649,-2.0479428746204649 -work_med_AGREMPN,-6,-4.8283137373023015,0,-6,6,-6 -work_med_FPSEMPN,-1.9402118373687092,-1.6245515502441485,0,-6,6,-1.9402118373687092 -work_med_HEREMPN,-1.3812045016840873,-1.1239300966523995,0,-6,6,-1.3812045016840873 -work_med_MWTEMPN,-0.90824261771266779,-1.5606477482646683,0,-6,6,-0.90824261771266779 -work_med_OTHEMPN,-2.533158060495702,-1.9732813458514451,0,-6,6,-2.533158060495702 -work_med_RETEMPN,-2.120263536200091,-2.120263536200091,0,-2.120263536200091,-2.120263536200091,-2.120263536200091 -work_veryhigh_AGREMPN,-0.27766271475654764,-5.521460917862246,0,-6,6,-0.27766271475654764 -work_veryhigh_FPSEMPN,-1.7531684994723506,-1.3093333199837622,0,-6,6,-1.7531684994723506 -work_veryhigh_HEREMPN,-1.6190143529862893,-1.422958345491482,0,-6,6,-1.6190143529862893 -work_veryhigh_MWTEMPN,-1.7198536938187046,-1.4024237430497744,0,-6,6,-1.7198536938187046 -work_veryhigh_OTHEMPN,-2.4806660321461727,-1.9241486572738007,0,-6,6,-2.4806660321461727 -work_veryhigh_RETEMPN,-2.375155785828881,-2.375155785828881,0,-2.375155785828881,-2.375155785828881,-2.375155785828881 +param_name,value,best,initvalue,nullvalue +-999,-999,-999,-999,0 +1,1,1,1,0 +coef_dist_0_1,-0.30581956658755333,-0.30581956658755333,-0.84280002117156982,0 +coef_dist_0_5_high,-0.037591580411684179,-0.037591580411684179,0.15000000596046448,0 +coef_dist_15_up,-0.091700002551078796,-0.091700002551078796,-0.091700002551078796,0 +coef_dist_1_2,0.36403861650270469,0.36403861650270469,-0.31040000915527344,0 +coef_dist_2_5,0.074928140378863833,0.074928140378863833,-0.3783000111579895,0 +coef_dist_5_15,0.042013884570839009,0.042013884570839009,-0.12849999964237213,0 +coef_dist_5_up_high,-0.091028422362310263,-0.091028422362310263,0.019999999552965164,0 +coef_mode_logsum,0.30841772929564437,0.30841772929564437,0.30000001192092896,0 +work_high_AGREMPN,-5.1363934448871946,-5.1363934448871946,-5.1159958839416504,0 +work_high_FPSEMPN,-6,-6,-1.5750365257263184,0 +work_high_HEREMPN,-5.8758079453903402,-5.8758079453903402,-1.2587810754776001,0 +work_high_MWTEMPN,-6,-6,-1.4312916994094849,0 +work_high_OTHEMPN,-4.7871339627162968,-4.7871339627162968,-1.870802640914917,0 +work_high_RETEMPN,-2.2072749137878418,-2.2072749137878418,-2.2072749137878418,0 +work_low_AGREMPN,-4.62261674278023,-4.62261674278023,-4.6051702499389648,0 +work_low_FPSEMPN,-6,-6,-1.6450650691986084,0 +work_low_HEREMPN,-5.0606787642354085,-5.0606787642354085,-0.95972031354904175,0 +work_low_MWTEMPN,-5.9999999999999991,-5.9999999999999991,-1.8078888654708862,0 +work_low_OTHEMPN,-4.6880188560666634,-4.6880188560666634,-2.1202635765075684,0 +work_low_RETEMPN,-2.0479428768157959,-2.0479428768157959,-2.0479428768157959,0 +work_med_AGREMPN,-4.8408567797905402,-4.8408567797905402,-4.8283138275146484,0 +work_med_FPSEMPN,-6,-6,-1.62455153465271,0 +work_med_HEREMPN,-5.4208328700623296,-5.4208328700623296,-1.1239300966262817,0 +work_med_MWTEMPN,-5.8367726149021344,-5.8367726149021344,-1.56064772605896,0 +work_med_OTHEMPN,-4.8724459817438781,-4.8724459817438781,-1.9732813835144043,0 +work_med_RETEMPN,-2.1202635765075684,-2.1202635765075684,-2.1202635765075684,0 +work_veryhigh_AGREMPN,-5.543114854785494,-5.543114854785494,-5.521461009979248,0 +work_veryhigh_FPSEMPN,-6,-6,-1.309333324432373,0 +work_veryhigh_HEREMPN,-5.6530572756846489,-5.6530572756846489,-1.4229583740234375,0 +work_veryhigh_MWTEMPN,-6,-6,-1.4024237394332886,0 +work_veryhigh_OTHEMPN,-5.8240005358464035,-5.8240005358464035,-1.9241486787796021,0 +work_veryhigh_RETEMPN,-2.3751556873321533,-2.3751556873321533,-2.3751556873321533,0 diff --git a/activitysim/estimation/test/test_larch_estimation/test_workplace_location_loglike.csv b/activitysim/estimation/test/test_larch_estimation/test_workplace_location_loglike.csv index 71e9b13b18..dcd02282b7 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_workplace_location_loglike.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_workplace_location_loglike.csv @@ -1,2 +1,2 @@ ,loglike_prior,loglike_converge -0,-13535.154991016063,-13520.930381371694 +0,-8694.16639312677943963,-7268.65904321087054996 diff --git a/activitysim/estimation/test/test_larch_estimation/test_workplace_location_size_spec.csv b/activitysim/estimation/test/test_larch_estimation/test_workplace_location_size_spec.csv index b568bafc7b..f6d6b17462 100644 --- a/activitysim/estimation/test/test_larch_estimation/test_workplace_location_size_spec.csv +++ b/activitysim/estimation/test/test_larch_estimation/test_workplace_location_size_spec.csv @@ -1,8 +1,8 @@ ,segment,model_selector,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE -0,work_low,workplace,0,0.04331096924066484,0.17606247084331519,0.37453642466033488,0.16935644669165648,0.000968861288590913,0.23576482727543777,0,0,0,0 -1,work_med,workplace,0,0.11999183383359351,0.14366373395357587,0.25125860903337882,0.079402445282216183,0.0024785834941433989,0.40320479440309209,0,0,0,0 -2,work_high,workplace,0,0.15790294154771911,0.19045576988955706,0.29641460514585227,0.1682066889662423,0.0063903077215986892,0.18062968672903063,0,0,0,0 -3,work_veryhigh,workplace,0,0.062641019242734242,0.11667678718376452,0.13342796655219047,0.056368476597385232,0.51025642529636983,0.12062932512755588,0,0,0,0 +0,work_low,workplace,0,0.8096367605074406,0.015557278192424053,0.039799319567405213,0.057772317445170702,0.06167704609513544,0.015557278192424065,0,0,0,0 +1,work_med,workplace,0,0.8254493658270311,0.017050704122867678,0.030427859824865577,0.052654174261774579,0.054344027646221538,0.020073868317239382,0,0,0,0 +2,work_high,workplace,0,0.83346467076784692,0.018781385164327268,0.02126490971714939,0.063163857701673223,0.044543791484676111,0.018781385164327268,0,0,0,0 +3,work_veryhigh,workplace,0,0.85845344910642152,0.022880571461814655,0.032369961125427262,0.027283649594894748,0.03613179724962709,0.022880571461814655,0,0,0,0 4,university,school,0,0,0,0,0,0,0,0,0,0.59199999999999997,0.40799999999999997 5,gradeschool,school,0,0,0,0,0,0,0,1,0,0,0 6,highschool,school,0,0,0,0,0,0,0,0,1,0,0 diff --git a/activitysim/examples/example_estimation/build_full_mtc_example.py b/activitysim/examples/example_estimation/build_full_mtc_example.py new file mode 100644 index 0000000000..63878a7156 --- /dev/null +++ b/activitysim/examples/example_estimation/build_full_mtc_example.py @@ -0,0 +1,196 @@ +""" +This script builds estimation data bundles for the full MTC example for ActivitySim. + +It does so by running ActivitySim twice: once to create synthetic survey data and then +a second time to create the estimation data bundles. For "real" applications, the +synthetic survey data would not be generated by ActivitySim; it would be real survey data. + +It downloads the necessary example data, runs the ActivitySim model with specified configurations, +and processes the output data. The script accepts the working directory and household sample size +as command-line arguments. + +Functions: + download_example(download_dir: Path) -> Path: + Downloads the prototype MTC extended example data to the specified directory. + + main(working_dir: Path, household_sample_size: int): + Main function that sets up directories, runs the ActivitySim model, and processes the output data. + +Usage: + python build-full-mtc.py -d -s + +Arguments: + -d, --directory: Directory to use as the working directory. Defaults to '/tmp/edb-test'. + -s, --household_sample_size: Household sample size. Defaults to 2000. +""" + +from __future__ import annotations + +import argparse +import os +import shutil +import subprocess +import sys +import time +from pathlib import Path + +from activitysim.examples.external import download_external_example + + +def download_example(download_dir): + example_dir = download_external_example( + name="prototype_mtc_extended", + working_dir=download_dir, + url="https://github.com/ActivitySim/activitysim-prototype-mtc/archive/refs/heads/extended.tar.gz", + assets={ + "data_full.tar.zst": { + "url": "https://github.com/ActivitySim/activitysim-prototype-mtc/releases/download/v1.3.4/data_full.tar.zst", + "sha256": "b402506a61055e2d38621416dd9a5c7e3cf7517c0a9ae5869f6d760c03284ef3", + "unpack": "data_full", + }, + "test/prototype_mtc_reference_pipeline.zip": { + "url": "https://github.com/ActivitySim/activitysim-prototype-mtc/releases/download/v1.3.2/prototype_mtc_extended_reference_pipeline.zip", + "sha256": "4d94b6a8a83225dda17e9ca19c9110bc1df2df5b4b362effa153d1c8d31524f5", + }, + }, + ) + return example_dir + + +def main(working_dir: Path, household_sample_size: int, skip_to_edb: bool = False): + working_dir = Path(working_dir) + working_dir.mkdir(parents=True, exist_ok=True) + + script_dir = Path(__file__).resolve().parent + infer_py = script_dir / "scripts" / "infer.py" + + print(f'The current CONDA environment is {os.getenv("CONDA_DEFAULT_ENV")}') + + configs_dir = working_dir / "activitysim-prototype-mtc-extended" / "configs" + full_data_dir = working_dir / "activitysim-prototype-mtc-extended" / "data_full" + pseudosurvey_dir = working_dir / "activitysim-prototype-mtc-extended" / "output" + + if not skip_to_edb: + download_example(working_dir) + + subprocess.run( + [ + sys.executable, + "-m", + "activitysim", + "run", + "-c", + str(configs_dir), + "-d", + str(full_data_dir), + "-o", + str(pseudosurvey_dir), + "--households_sample_size", + str(household_sample_size), + ], + check=True, + ) + + survey_data_dir = pseudosurvey_dir / "survey_data" + survey_data_dir.mkdir(parents=True, exist_ok=True) + + for file in pseudosurvey_dir.glob("final_*.csv"): + shutil.copy(str(file), str(survey_data_dir)) + + files_to_copy = [ + "final_households.csv", + "final_persons.csv", + "final_tours.csv", + "final_joint_tour_participants.csv", + "final_trips.csv", + ] + + for file_name in files_to_copy: + src = pseudosurvey_dir / file_name + dest = survey_data_dir / file_name.replace("final_", "survey_") + shutil.copy(src, dest) + + output_dir = working_dir / "infer-output" + output_dir.mkdir(parents=True, exist_ok=True) + output_dir.joinpath(".gitignore").write_text("**\n") + + subprocess.run( + [ + sys.executable, + str(infer_py), + str(pseudosurvey_dir), + str(configs_dir), + str(output_dir), + ], + check=True, + ) + + for file in output_dir.glob("override_*.csv"): + shutil.copy(str(file), str(full_data_dir)) + + edb_dir = working_dir / "activitysim-prototype-mtc-extended" / "output-est-mode" + edb_dir.mkdir(parents=True, exist_ok=True) + + subprocess.run( + [ + sys.executable, + "-m", + "activitysim", + "run", + "-c", + str(script_dir / "configs_estimation"), + "-c", + str(configs_dir), + "-d", + str(full_data_dir), + "-o", + str(edb_dir), + ], + check=True, + ) + + # mark the entire created directory as ignored for git + edb_dir.parent.joinpath(".gitignore").write_text("**\n") + + # create a success.txt file to indicate that the EDB was successfully built + completed_at = time.strftime("%Y-%m-%d %H:%M:%S") + edb_dir.joinpath("success.txt").write_text( + f"Successfully built the full MTC estimation data bundle example with {household_sample_size} households.\n" + f"Completed at {completed_at}" + ) + + +def as_needed(working_dir: Path, household_sample_size: int): + """Check if the EDB directory has already been built. If not, build it.""" + edb_dir = ( + Path(working_dir) / "activitysim-prototype-mtc-extended" / "output-est-mode" + ) + if not edb_dir.joinpath("success.txt").exists(): + main(working_dir, household_sample_size) + else: + print("EDB directory already populated.") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Build the full MTC example.") + parser.add_argument( + "-d", + "--directory", + type=str, + default="/tmp/edb-test", + help="Directory to use as the working directory. Defaults to '/tmp/edb-test'.", + ) + parser.add_argument( + "-s", + "--household_sample_size", + type=int, + default=2000, + help="Household sample size. Defaults to 2000.", + ) + parser.add_argument( + "--skip_to_edb", + action="store_true", + help="Skip to EDB step. Defaults to False.", + ) + args = parser.parse_args() + main(args.directory, args.household_sample_size, args.skip_to_edb) diff --git a/activitysim/examples/example_estimation/configs/estimation.yaml b/activitysim/examples/example_estimation/configs/estimation.yaml index 7e8112c0f0..0c86d786d3 100644 --- a/activitysim/examples/example_estimation/configs/estimation.yaml +++ b/activitysim/examples/example_estimation/configs/estimation.yaml @@ -49,55 +49,7 @@ survey_tables: file_name: survey_data/override_trips.csv index_col: trip_id -estimation_table_recipes: - - interaction_sample_simulate: - omnibus_tables: - choosers_combined: - - choices - - override_choices - - choosers - alternatives_combined: - - interaction_sample_alternatives - - interaction_expression_values - omnibus_tables_append_columns: [choosers_combined] - - interaction_simulate: - omnibus_tables: - choosers_combined: - - choices - - override_choices - - choosers - omnibus_tables_append_columns: [choosers_combined] - - simple_simulate: - omnibus_tables: - values_combined: - - choices - - override_choices - - expression_values - - choosers - omnibus_tables_append_columns: [values_combined] - - cdap_simulate: - omnibus_tables: - values_combined: - - choices - - override_choices - - choosers - omnibus_tables_append_columns: [values_combined] - - simple_probabilistic: - omnibus_tables: - values_combined: - - choices - - override_choices - - choosers - - probs - omnibus_tables_append_columns: [values_combined] - - -model_estimation_table_types: +estimation_table_types: school_location: interaction_sample_simulate workplace_location: interaction_sample_simulate auto_ownership: simple_simulate diff --git a/activitysim/examples/example_estimation/configs_estimation/estimation.yaml b/activitysim/examples/example_estimation/configs_estimation/estimation.yaml new file mode 100644 index 0000000000..dd9cd0c24c --- /dev/null +++ b/activitysim/examples/example_estimation/configs_estimation/estimation.yaml @@ -0,0 +1,80 @@ +EDB_FILETYPE: parquet # options: csv, parquet, pkl + +enable: True + +bundles: + - school_location + - workplace_location + - auto_ownership + - vehicle_type_choice + - free_parking + - cdap + - mandatory_tour_frequency + - mandatory_tour_scheduling_work + - mandatory_tour_scheduling_school + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_scheduling + - trip_mode_choice + +# - atwork_subtour_mode_choice subtours.tour_mode + +survey_tables: + households: + file_name: override_households.csv + index_col: household_id + persons: + file_name: override_persons.csv + index_col: person_id + tours: + file_name: override_tours.csv + index_col: tour_id + joint_tour_participants: + file_name: override_joint_tour_participants.csv + index_col: participant_id + trips: + file_name: override_trips.csv + index_col: trip_id + +estimation_table_types: + school_location: interaction_sample_simulate + workplace_location: interaction_sample_simulate + auto_ownership: simple_simulate + vehicle_type_choice: interaction_simulate + free_parking: simple_simulate + cdap: cdap_simulate + mandatory_tour_frequency: simple_simulate + mandatory_tour_scheduling_work: interaction_sample_simulate + mandatory_tour_scheduling_school: interaction_sample_simulate + joint_tour_frequency: simple_simulate + joint_tour_composition: simple_simulate + joint_tour_participation: simple_simulate + joint_tour_destination: interaction_sample_simulate + joint_tour_scheduling: interaction_sample_simulate + non_mandatory_tour_frequency: interaction_simulate + non_mandatory_tour_destination: interaction_sample_simulate + non_mandatory_tour_scheduling: interaction_sample_simulate + tour_mode_choice: simple_simulate + atwork_subtour_frequency: simple_simulate + atwork_subtour_destination: interaction_sample_simulate + atwork_subtour_scheduling: interaction_sample_simulate + atwork_subtour_mode_choice: simple_simulate + stop_frequency: simple_simulate + trip_purpose: simple_probabilistic + trip_destination: interaction_sample_simulate + trip_scheduling: simple_probabilistic + trip_mode_choice: simple_simulate diff --git a/activitysim/examples/example_estimation/configs_estimation/logging.yaml b/activitysim/examples/example_estimation/configs_estimation/logging.yaml new file mode 100644 index 0000000000..f4902943d6 --- /dev/null +++ b/activitysim/examples/example_estimation/configs_estimation/logging.yaml @@ -0,0 +1,67 @@ +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile, elogfile] + + loggers: + + estimation: + level: DEBUG + handlers: [console, elogfile] + propagate: false + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + elogfile: + class: logging.FileHandler + filename: + get_log_file_path: 'estimation.log' + mode: w + formatter: fileFormatter + level: NOTSET + + logfile: + class: logging.FileHandler + filename: + get_log_file_path: 'activitysim.log' + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/example_estimation/configs_estimation/settings.yaml b/activitysim/examples/example_estimation/configs_estimation/settings.yaml new file mode 100644 index 0000000000..1e96b0f1a5 --- /dev/null +++ b/activitysim/examples/example_estimation/configs_estimation/settings.yaml @@ -0,0 +1,165 @@ + +inherit_settings: True + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# input tables +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: override_households.csv + index_col: household_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + # + # persons (table index 'person_id') + # + - tablename: persons + filename: override_persons.csv + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: land_use.csv + rename_columns: + # accept either TAZ or ZONE (but not both) + TAZ: zone_id + ZONE: zone_id + COUNTY: county_id + keep_columns: + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL + +write_raw_tables: False +rng_base_seed: 42 + +fail_fast: True + +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# number of households to simulate +households_sample_size: 0 + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: initialize_households + +trace_hh_id: + +multiprocess: true +num_processes: 2 + + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +resume_after: + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination +# - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice +# - write_data_dictionary +# - track_skim_usage +# - write_trip_matrices + - write_tables + - coalesce_estimation_data_bundles + + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_accessibility + begin: compute_accessibility + slice: + tables: + - accessibility + # don't slice any tables not explicitly listed above in slice.tables + exclude: True + - name: mp_households + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_tables diff --git a/activitysim/examples/example_estimation/notebooks/02_school_location.ipynb b/activitysim/examples/example_estimation/notebooks/02_school_location.ipynb index f838803020..8562928ae1 100644 --- a/activitysim/examples/example_estimation/notebooks/02_school_location.ipynb +++ b/activitysim/examples/example_estimation/notebooks/02_school_location.ipynb @@ -26,30 +26,73 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.31.dev37+gd94e3a6',\n", + " 'sharrow': '2.12.2.dev4+gea8174f',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import larch # !conda install larch #for estimation\n", + "import larch as lx\n", "import pandas as pd\n", - "import numpy as np\n", - "import yaml \n", - "import larch.util.excel\n", - "import os" + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "prepare()" ] }, { @@ -72,10 +115,27 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/school_location/school_location_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/school_location/school_location_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/school_location/school_location_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/school_location/school_location_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/school_location/school_location_landuse.csv\n", + "loading from output-est-mode/estimation_data_bundle/school_location/school_location_size_terms.csv\n" + ] + } + ], "source": [ "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname, return_data=True, \n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + ")" ] }, { @@ -266,148 +326,130 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -429,202 +471,197 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
person_idvariable59101213143739...178179180181182184186187189190alt_destprobpick_countmode_choice_logsumsize_termshadow_price_size_term_adjustmentshadow_price_utility_adjustmentutil_dist_0_1util_dist_1_2util_dist_2_5util_dist_5_15util_dist_15_uputil_size_variableutil_utility_adjustmentutil_no_attractionsutil_mode_choice_logsumutil_sample_of_corrections_factor
0629mode_choice_logsum-1.2112310566711697-0.932235390015777-0.9960110406421955-1.2780595287661722-1.4547553701284834-1.4573412829661365-3.8602604963599583-3.8673170163655084...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1918130.0066881-0.7486112578.276039101.01.003.002.180.0000007.8552640False-0.7486115.007436
1629pick_count1.01.01.01.01.01.01.01918690.0346972-1.2289934541.220657101.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.003.000.020.0000008.4211710False-1.2289934.054244
2629prob3.964160543594042e-050.00167816373630599280.00066438955743874150.002793114898696190.00218095283685440940.0004625352628872690.00014199354993792870.00020956937572710045...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN19181330.2493565-0.91645113419.153743101.01.001.460.000.0000009.5045130False-0.9164512.998311
3629shadow_price_size_term_adjustment1.01.01.01.01.01.01.019181850.07378360.075435146.752243101.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.090.000.000.0000004.9955370False0.0754354.398386
4629shadow_price_utility_adjustment0.00.00.00.00.00.00.00.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN19181880.56315716-0.49483014849.890384101.01.000.210.000.0000009.6058150False-0.4948303.346785
.....................
157397541072util_mode_choice_logsum0.401352799399892270.6886076440937283-0.196874267615671370.29845718751131060.128933525367319560.11661882054650644-0.6296937346519611-1.8050089284615745...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
157407541072util_no_attractionsFalseFalseFalseFalseFalseFalseFalse28036975601739610.0095872-7.6515462344.192182101.01.003.0010.0012.8400007.7601230False...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
157417541072util_sample_of_corrections_factor7.3899157826177583.631716891606514.0865671414586033.06858033169584983.3293709346992134.8949702670889666.1574555626588955.794602181057687...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
157427541072util_size_variable3.77759816877277157.1020650866127866.01568252532117858.0076215688752837.8552640336705346.4095117009260145.5236883616663575.897955675114184...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
157437541072util_utility_adjustment-7.6515465.340517
280370756017310190.0662763-8.96283319615.000247101.01.003.0010.0015.4500019.8841010False-8.9628333.812534
280371756017311270.0467072-7.2689939729.629299101.01.003.0010.0010.6399999.1830340False-7.2689933.757005
280372756017311980.0076981-10.4703743124.400431101.01.003.0010.0019.7799998.0473180False-10.4703744.866794
280373756190311270.981543314.2188819729.629299101.01.000.320.000.0000009.1830340...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFalse4.2188813.452616
\n", - "

15744 rows × 192 columns

\n", + "

280374 rows × 18 columns

\n", "" ], "text/plain": [ - " person_id variable 5 \\\n", - "0 629 mode_choice_logsum -1.2112310566711697 \n", - "1 629 pick_count 1.0 \n", - "2 629 prob 3.964160543594042e-05 \n", - "3 629 shadow_price_size_term_adjustment 1.0 \n", - "4 629 shadow_price_utility_adjustment 0.0 \n", - "... ... ... ... \n", - "15739 7541072 util_mode_choice_logsum 0.40135279939989227 \n", - "15740 7541072 util_no_attractions False \n", - "15741 7541072 util_sample_of_corrections_factor 7.389915782617758 \n", - "15742 7541072 util_size_variable 3.7775981687727715 \n", - "15743 7541072 util_utility_adjustment 0 \n", + " person_id alt_dest prob pick_count mode_choice_logsum \\\n", + "0 1918 13 0.006688 1 -0.748611 \n", + "1 1918 69 0.034697 2 -1.228993 \n", + "2 1918 133 0.249356 5 -0.916451 \n", + "3 1918 185 0.073783 6 0.075435 \n", + "4 1918 188 0.563157 16 -0.494830 \n", + "... ... ... ... ... ... \n", + "280369 7560173 961 0.009587 2 -7.651546 \n", + "280370 7560173 1019 0.066276 3 -8.962833 \n", + "280371 7560173 1127 0.046707 2 -7.268993 \n", + "280372 7560173 1198 0.007698 1 -10.470374 \n", + "280373 7561903 1127 0.981543 31 4.218881 \n", "\n", - " 9 10 12 \\\n", - "0 -0.932235390015777 -0.9960110406421955 -1.2780595287661722 \n", - "1 1.0 1.0 1.0 \n", - "2 0.0016781637363059928 0.0006643895574387415 0.00279311489869619 \n", - "3 1.0 1.0 1.0 \n", - "4 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "15739 0.6886076440937283 -0.19687426761567137 0.2984571875113106 \n", - "15740 False False False \n", - "15741 3.63171689160651 4.086567141458603 3.0685803316958498 \n", - "15742 7.102065086612786 6.0156825253211785 8.007621568875283 \n", - "15743 0 0 0 \n", + " size_term shadow_price_size_term_adjustment \\\n", + "0 2578.276039 1 \n", + "1 4541.220657 1 \n", + "2 13419.153743 1 \n", + "3 146.752243 1 \n", + "4 14849.890384 1 \n", + "... ... ... \n", + "280369 2344.192182 1 \n", + "280370 19615.000247 1 \n", + "280371 9729.629299 1 \n", + "280372 3124.400431 1 \n", + "280373 9729.629299 1 \n", "\n", - " 13 14 37 \\\n", - "0 -1.4547553701284834 -1.4573412829661365 -3.8602604963599583 \n", - "1 1.0 1.0 1.0 \n", - "2 0.0021809528368544094 0.000462535262887269 0.0001419935499379287 \n", - "3 1.0 1.0 1.0 \n", - "4 0.0 0.0 0.0 \n", - "... ... ... ... \n", - "15739 0.12893352536731956 0.11661882054650644 -0.6296937346519611 \n", - "15740 False False False \n", - "15741 3.329370934699213 4.894970267088966 6.157455562658895 \n", - "15742 7.855264033670534 6.409511700926014 5.523688361666357 \n", - "15743 0 0 0 \n", + " shadow_price_utility_adjustment util_dist_0_1 util_dist_1_2 \\\n", + "0 0 1.0 1.00 \n", + "1 0 1.0 1.00 \n", + "2 0 1.0 1.00 \n", + "3 0 1.0 0.09 \n", + "4 0 1.0 1.00 \n", + "... ... ... ... \n", + "280369 0 1.0 1.00 \n", + "280370 0 1.0 1.00 \n", + "280371 0 1.0 1.00 \n", + "280372 0 1.0 1.00 \n", + "280373 0 1.0 1.00 \n", "\n", - " 39 ... 178 179 180 181 182 184 186 187 \\\n", - "0 -3.8673170163655084 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "1 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "2 0.00020956937572710045 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "3 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "4 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "... ... ... ... ... ... ... ... ... ... ... \n", - "15739 -1.8050089284615745 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "15740 False ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "15741 5.794602181057687 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "15742 5.897955675114184 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", - "15743 0 ... NaN NaN NaN NaN NaN NaN NaN NaN \n", + " util_dist_2_5 util_dist_5_15 util_dist_15_up util_size_variable \\\n", + "0 3.00 2.18 0.000000 7.855264 \n", + "1 3.00 0.02 0.000000 8.421171 \n", + "2 1.46 0.00 0.000000 9.504513 \n", + "3 0.00 0.00 0.000000 4.995537 \n", + "4 0.21 0.00 0.000000 9.605815 \n", + "... ... ... ... ... \n", + "280369 3.00 10.00 12.840000 7.760123 \n", + "280370 3.00 10.00 15.450001 9.884101 \n", + "280371 3.00 10.00 10.639999 9.183034 \n", + "280372 3.00 10.00 19.779999 8.047318 \n", + "280373 0.32 0.00 0.000000 9.183034 \n", "\n", - " 189 190 \n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "... ... ... \n", - "15739 NaN NaN \n", - "15740 NaN NaN \n", - "15741 NaN NaN \n", - "15742 NaN NaN \n", - "15743 NaN NaN \n", + " util_utility_adjustment util_no_attractions util_mode_choice_logsum \\\n", + "0 0 False -0.748611 \n", + "1 0 False -1.228993 \n", + "2 0 False -0.916451 \n", + "3 0 False 0.075435 \n", + "4 0 False -0.494830 \n", + "... ... ... ... \n", + "280369 0 False -7.651546 \n", + "280370 0 False -8.962833 \n", + "280371 0 False -7.268993 \n", + "280372 0 False -10.470374 \n", + "280373 0 False 4.218881 \n", "\n", - "[15744 rows x 192 columns]" + " util_sample_of_corrections_factor \n", + "0 5.007436 \n", + "1 4.054244 \n", + "2 2.998311 \n", + "3 4.398386 \n", + "4 3.346785 \n", + "... ... \n", + "280369 5.340517 \n", + "280370 3.812534 \n", + "280371 3.757005 \n", + "280372 4.866794 \n", + "280373 3.452616 \n", + "\n", + "[280374 rows x 18 columns]" ] }, "execution_count": 6, @@ -680,48 +717,48 @@ " \n", " \n", " 0\n", - " 629\n", - " 13\n", - " 12\n", - " 131\n", + " 1918\n", + " 188\n", + " 188\n", + " 186\n", " 3\n", - " 629\n", + " 1918\n", " \n", " \n", " 1\n", - " 1274\n", - " 10\n", - " 10\n", - " 166\n", + " 2256\n", + " 133\n", + " 229\n", + " 208\n", " 3\n", - " 1274\n", + " 2256\n", " \n", " \n", " 2\n", - " 27266\n", - " 10\n", - " 12\n", - " 9\n", + " 3215\n", + " 291\n", + " 229\n", + " 252\n", " 3\n", - " 27266\n", + " 3215\n", " \n", " \n", " 3\n", - " 28012\n", - " 5\n", - " 5\n", - " 10\n", + " 4362\n", + " 300\n", + " 342\n", + " 313\n", " 3\n", - " 28012\n", + " 4362\n", " \n", " \n", " 4\n", - " 29368\n", - " 185\n", - " 185\n", - " 16\n", + " 5859\n", + " 490\n", + " 490\n", + " 400\n", " 3\n", - " 29368\n", + " 5859\n", " \n", " \n", " ...\n", @@ -733,83 +770,83 @@ " ...\n", " \n", " \n", - " 979\n", - " 7514214\n", - " 185\n", - " 185\n", - " 56\n", - " 3\n", - " 2822879\n", + " 14984\n", + " 7550994\n", + " 1091\n", + " 1091\n", + " 1124\n", + " 2\n", + " 2859659\n", " \n", " \n", - " 980\n", - " 7514777\n", - " 185\n", - " 185\n", - " 106\n", - " 3\n", - " 2823442\n", + " 14985\n", + " 7552418\n", + " 1270\n", + " 1263\n", + " 1268\n", + " 2\n", + " 2861083\n", " \n", " \n", - " 981\n", - " 7515185\n", - " 188\n", - " 188\n", - " 142\n", - " 3\n", - " 2823850\n", + " 14986\n", + " 7553232\n", + " 1324\n", + " 1324\n", + " 1320\n", + " 2\n", + " 2861897\n", " \n", " \n", - " 982\n", - " 7527597\n", - " 69\n", - " 13\n", - " 188\n", + " 14987\n", + " 7560173\n", + " 557\n", + " 811\n", + " 743\n", " 3\n", - " 2836262\n", + " 2868838\n", " \n", " \n", - " 983\n", - " 7541072\n", - " 112\n", - " 107\n", - " 117\n", + " 14988\n", + " 7561903\n", + " 1127\n", + " 1127\n", + " 1121\n", " 3\n", - " 2849737\n", + " 2870568\n", " \n", " \n", "\n", - "

984 rows × 6 columns

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14989 rows × 6 columns

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"979 2822879 \n", - "980 2823442 \n", - "981 2823850 \n", - "982 2836262 \n", - "983 2849737 \n", + " household_id \n", + "0 1918 \n", + "1 2256 \n", + "2 3215 \n", + "3 4362 \n", + "4 5859 \n", + "... ... \n", + "14984 2859659 \n", + "14985 2861083 \n", + "14986 2861897 \n", + "14987 2868838 \n", + "14988 2870568 \n", "\n", - "[984 rows x 6 columns]" + "[14989 rows x 6 columns]" ] }, "execution_count": 7, @@ -916,9 +953,9 @@ " 7\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 3\n", " 5.89564\n", " 2.875000\n", @@ -940,9 +977,9 @@ " 19\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.84871\n", " 5.195214\n", @@ -964,9 +1001,9 @@ " 38\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.53231\n", " 80.470405\n", @@ -988,9 +1025,9 @@ " 20\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 2\n", " 5.64330\n", " 7.947368\n", @@ -1012,9 +1049,9 @@ " 86\n", " ...\n", " 0\n", - " 0.00000\n", + " 0.0\n", " 72.14684\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.52555\n", " 38.187500\n", @@ -1047,188 +1084,188 @@ " ...\n", " \n", " \n", - " 186\n", - " 4\n", - " 4\n", - " 1\n", - " 2779\n", - " 8062\n", - " 376.0\n", - " 172.0\n", - " 15.00000\n", - " 1760\n", - " 1178\n", + " 1450\n", + " 34\n", + " 34\n", + " 9\n", + " 2724\n", + " 6493\n", + " 1320.0\n", + " 630.0\n", + " 69.00000\n", + " 1046\n", + " 1013\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 4\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.04173\n", - " 14.860963\n", - " 9.411765\n", - " 5.762347\n", + " 1.12116\n", + " 3.896996\n", + " 1.496423\n", + " 1.081235\n", " False\n", " \n", " \n", - " 187\n", - " 4\n", - " 4\n", - " 1\n", - " 1492\n", - " 4139\n", - " 214.0\n", - " 116.0\n", - " 10.00000\n", - " 808\n", - " 603\n", + " 1451\n", + " 34\n", + " 34\n", + " 9\n", + " 2016\n", + " 4835\n", + " 664.0\n", + " 379.0\n", + " 43.00000\n", + " 757\n", + " 757\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 4\n", + " 0.0\n", " 0.00000\n", - " 2\n", - " 1.73676\n", - " 11.841270\n", - " 6.412698\n", - " 4.159890\n", + " 0.0\n", + " 1\n", + " 1.17116\n", + " 4.777251\n", + " 1.793839\n", + " 1.304140\n", " False\n", " \n", " \n", - " 188\n", - " 4\n", - " 4\n", - " 1\n", - " 753\n", - " 4072\n", - " 232.0\n", - " 11.0\n", - " 178.00000\n", - " 4502\n", - " 1117\n", + " 1452\n", + " 34\n", + " 34\n", + " 9\n", + " 2178\n", + " 5055\n", + " 1068.0\n", + " 602.0\n", + " 35.00000\n", + " 2110\n", + " 789\n", " ...\n", - " 2\n", - " 3961.04761\n", - " 17397.79102\n", - " 11152.93652\n", + " 4\n", + " 0.0\n", + " 0.00000\n", + " 0.0\n", " 1\n", - " 2.28992\n", - " 3.984127\n", - " 23.820106\n", - " 3.413233\n", + " 1.17587\n", + " 3.419152\n", + " 3.312402\n", + " 1.682465\n", " False\n", " \n", " \n", - " 189\n", - " 4\n", - " 4\n", - " 1\n", - " 3546\n", - " 8476\n", - " 201.0\n", - " 72.0\n", - " 6.00000\n", - " 226\n", - " 1057\n", + " 1453\n", + " 34\n", + " 34\n", + " 9\n", + " 298\n", + " 779\n", + " 14195.0\n", + " 429.0\n", + " 4.00000\n", + " 922\n", + " 88\n", " ...\n", - " 2\n", - " 0.00000\n", - " 0.00000\n", + " 5\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.88773\n", - " 45.461538\n", - " 2.897436\n", - " 2.723836\n", + " 1.01972\n", + " 0.688222\n", + " 2.129330\n", + " 0.520115\n", " False\n", " \n", " \n", - " 190\n", - " 4\n", - " 4\n", - " 1\n", - " 968\n", - " 1647\n", - " 1381.0\n", - " 14.0\n", - " 28.00000\n", - " 1010\n", - " 114\n", + " 1454\n", + " 34\n", + " 34\n", + " 9\n", + " 1068\n", + " 2337\n", + " 10469.0\n", + " 1114.0\n", + " 27.00000\n", + " 607\n", + " 418\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 5\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.60309\n", - " 23.047619\n", - " 24.047619\n", - " 11.768501\n", + " 0.95542\n", + " 0.936021\n", + " 0.531989\n", + " 0.339203\n", " False\n", " \n", " \n", "\n", - "

190 rows × 28 columns

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1454 rows × 28 columns

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keyvalue
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['AGE0519*(school_segment==1)',\n",
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+       " 'util_no_attractions*(school_segment==3)',\n",
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+       " 'util_sample_of_corrections_factor*(school_segment==3)']
choice_ca'override_choice == _original_zone_id'
avail_ca'_avail_'
" + ], + "text/plain": [ + "┣ ca: ['AGE0519*(school_segment==1)',\n", + "┃ 'COLLFTE*(school_segment==3)',\n", + "┃ 'COLLPTE*(school_segment==3)',\n", + "┃ 'HSENROLL*(school_segment==2)',\n", + "┃ 'util_dist_0_1*(school_segment==1)',\n", + "┃ 'util_dist_0_1*(school_segment==2)',\n", + "┃ 'util_dist_0_1*(school_segment==3)',\n", + "┃ 'util_dist_15_up*(school_segment==1)',\n", + "┃ 'util_dist_15_up*(school_segment==2)',\n", + "┃ 'util_dist_15_up*(school_segment==3)',\n", + "┃ 'util_dist_1_2*(school_segment==1)',\n", + "┃ 'util_dist_1_2*(school_segment==2)',\n", + "┃ 'util_dist_1_2*(school_segment==3)',\n", + "┃ 'util_dist_2_5*(school_segment==1)',\n", + "┃ 'util_dist_2_5*(school_segment==2)',\n", + "┃ 'util_dist_2_5*(school_segment==3)',\n", + "┃ 'util_dist_5_15*(school_segment==1)',\n", + "┃ 'util_dist_5_15*(school_segment==2)',\n", + "┃ 'util_dist_5_15*(school_segment==3)',\n", + "┃ 'util_mode_choice_logsum*(school_segment==1)',\n", + "┃ 'util_mode_choice_logsum*(school_segment==2)',\n", + "┃ 'util_mode_choice_logsum*(school_segment==3)',\n", + "┃ 'util_no_attractions*(school_segment==1)',\n", + "┃ 'util_no_attractions*(school_segment==2)',\n", + "┃ 'util_no_attractions*(school_segment==3)',\n", + "┃ 'util_sample_of_corrections_factor*(school_segment==1)',\n", + "┃ 'util_sample_of_corrections_factor*(school_segment==2)',\n", + "┃ 'util_sample_of_corrections_factor*(school_segment==3)']\n", + "┣ choice_ca: 'override_choice == _original_zone_id'\n", + "┣ avail_ca: '_avail_'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.required_data()" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Estimate\n", - "\n", - "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has a built-in estimation methods including BHHH, and also offers access to more advanced general purpose non-linear optimizers in the `scipy` package, including SLSQP, which allows for bounds and constraints on parameters. BHHH is the default and typically runs faster, but does not follow constraints on parameters." + "Running the `doctor` method will check the model specification for potential issues." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "dictx()\n" ] - }, + } + ], + "source": [ + "model, problems = model.doctor()\n", + "\n", + "print(problems)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Estimate\n", + "\n", + "With the model setup for estimation, the next step is to estimate the model coefficients. Make sure to use a sufficiently large enough household sample and set of zones to avoid an over-specified model, which does not have a numerically stable likelihood maximizing solution. Larch has a built-in estimation methods including BHHH, and also offers access to more advanced general purpose non-linear optimizers in the `scipy` package, including SLSQP, which allows for bounds and constraints on parameters. BHHH is the default and typically runs faster, but does not follow constraints on parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "

Iteration 006 [Optimization terminated successfully.]

" + "

Iteration 013 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1534,7 +1669,7 @@ { "data": { "text/html": [ - "

Best LL = -4058.719620832402

" + "

Best LL = -30424.700914962996

" ], "text/plain": [ "" @@ -1565,13 +1700,22 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1579,301 +1723,261 @@ " -999\n", " -999.000000\n", " -999.000000\n", - " -999.0\n", - " -999.0\n", - " -999.0\n", - " 1\n", - " \n", " -999.000000\n", + " -999.000000\n", + " -999.000000\n", + " 0.0\n", + " 1\n", " \n", " \n", " 1\n", " 1.000000\n", " 1.000000\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1\n", - " \n", " 1.000000\n", + " 1.000000\n", + " 1.000000\n", + " 0.0\n", + " 1\n", " \n", " \n", " coef_grade_dist_0_1\n", - " -1.824840\n", + " -1.886212\n", + " -1.886212\n", " -1.641900\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.824840\n", " \n", " \n", " coef_grade_dist_15_up\n", + " -0.103616\n", + " -0.103616\n", " -0.046000\n", - " -0.046000\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.046000\n", " \n", " \n", " coef_grade_dist_5_15\n", - " -0.144663\n", + " -0.285452\n", + " -0.285452\n", " -0.203100\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.144663\n", " \n", " \n", " coef_high_dist_0_1\n", - " -2.106211\n", + " -0.869799\n", + " -0.869799\n", " -0.952300\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.106211\n", " \n", " \n", " coef_high_dist_15_up\n", + " -0.233127\n", + " -0.233127\n", " -0.188200\n", - " -0.188200\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.188200\n", " \n", " \n", " coef_high_dist_5_15\n", - " -0.101747\n", + " -0.285690\n", + " -0.285690\n", " -0.193000\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.101747\n", " \n", " \n", " coef_high_grade_dist_1_2\n", - 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" \n", - " -0.073000\n", " \n", " \n", " coef_univ_dist_1_2\n", - " -2.878224\n", + " -3.082102\n", + " -3.082102\n", " -2.701100\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.878224\n", " \n", " \n", " coef_univ_dist_2_5\n", - " -0.456009\n", + " -0.668470\n", + " -0.668470\n", " -0.570700\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.456009\n", " \n", " \n", " coef_univ_dist_5_15\n", - " -0.572553\n", + " -0.563338\n", + " -0.563338\n", " -0.500200\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.572553\n", " \n", " \n", " gradeschool_AGE0519\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " highschool_HSENROLL\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " university_COLLFTE\n", " -0.524249\n", " -0.524249\n", + " -0.524249\n", + " -0.524249\n", + " -0.524249\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -0.524249\n", " \n", " \n", " university_COLLPTE\n", - " -1.208828\n", + " -0.900765\n", + " -0.900765\n", " -0.896488\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -1.208828\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue nullvalue minimum maximum \\\n", - "-999 -999.000000 -999.000000 -999.0 -999.0 -999.0 \n", - "1 1.000000 1.000000 1.0 1.0 1.0 \n", - "coef_grade_dist_0_1 -1.824840 -1.641900 0.0 NaN NaN \n", - "coef_grade_dist_15_up -0.046000 -0.046000 0.0 NaN NaN \n", - "coef_grade_dist_5_15 -0.144663 -0.203100 0.0 NaN NaN \n", - "coef_high_dist_0_1 -2.106211 -0.952300 0.0 NaN NaN \n", - "coef_high_dist_15_up -0.188200 -0.188200 0.0 NaN NaN \n", - "coef_high_dist_5_15 -0.101747 -0.193000 0.0 NaN NaN \n", - "coef_high_grade_dist_1_2 -0.751852 -0.570000 0.0 NaN NaN \n", - "coef_high_grade_dist_2_5 -0.667119 -0.570000 0.0 NaN NaN \n", - "coef_mode_logsum 0.372245 0.535800 0.0 NaN NaN \n", - "coef_univ_dist_0_1 -3.311744 -3.245100 0.0 NaN NaN \n", - "coef_univ_dist_15_up -0.073000 -0.073000 0.0 NaN NaN \n", - "coef_univ_dist_1_2 -2.878224 -2.701100 0.0 NaN NaN \n", - "coef_univ_dist_2_5 -0.456009 -0.570700 0.0 NaN NaN \n", - "coef_univ_dist_5_15 -0.572553 -0.500200 0.0 NaN NaN \n", - "gradeschool_AGE0519 0.000000 0.000000 0.0 -6.0 6.0 \n", - "highschool_HSENROLL 0.000000 0.000000 0.0 -6.0 6.0 \n", - "university_COLLFTE -0.524249 -0.524249 0.0 -6.0 6.0 \n", - "university_COLLPTE -1.208828 -0.896488 0.0 -6.0 6.0 \n", + " value best initvalue minimum \\\n", + "param_name \n", + "-999 -999.000000 -999.000000 -999.000000 -999.000000 \n", + "1 1.000000 1.000000 1.000000 1.000000 \n", + "coef_grade_dist_0_1 -1.886212 -1.886212 -1.641900 -25.000000 \n", + "coef_grade_dist_15_up -0.103616 -0.103616 -0.046000 -25.000000 \n", + "coef_grade_dist_5_15 -0.285452 -0.285452 -0.203100 -25.000000 \n", + "coef_high_dist_0_1 -0.869799 -0.869799 -0.952300 -25.000000 \n", + "coef_high_dist_15_up -0.233127 -0.233127 -0.188200 -25.000000 \n", + "coef_high_dist_5_15 -0.285690 -0.285690 -0.193000 -25.000000 \n", + "coef_high_grade_dist_1_2 -0.931692 -0.931692 -0.570000 -25.000000 \n", + "coef_high_grade_dist_2_5 -0.694687 -0.694687 -0.570000 -25.000000 \n", + "coef_mode_logsum 0.074994 0.074994 0.535800 -25.000000 \n", + "coef_univ_dist_0_1 -2.780799 -2.780799 -3.245100 -25.000000 \n", + "coef_univ_dist_15_up -0.145257 -0.145257 -0.073000 -25.000000 \n", + "coef_univ_dist_1_2 -3.082102 -3.082102 -2.701100 -25.000000 \n", + "coef_univ_dist_2_5 -0.668470 -0.668470 -0.570700 -25.000000 \n", + "coef_univ_dist_5_15 -0.563338 -0.563338 -0.500200 -25.000000 \n", + "gradeschool_AGE0519 0.000000 0.000000 0.000000 0.000000 \n", + "highschool_HSENROLL 0.000000 0.000000 0.000000 0.000000 \n", + "university_COLLFTE -0.524249 -0.524249 -0.524249 -0.524249 \n", + "university_COLLPTE -0.900765 -0.900765 -0.896488 -6.000000 \n", "\n", - 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tolerance4.213233078884777e-06stepsarray([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])message'Optimization terminated successfully'elapsed_time0:00:00.994749method'BHHH'n_cases14989iteration_number13
" ], "text/plain": [ - "┣ loglike: -4058.719620832402\n", + "┣ loglike: -30424.700914962996\n", "┣ x: -999 -999.000000\n", "┃ 1 1.000000\n", - "┃ coef_grade_dist_0_1 -1.824840\n", - "┃ coef_grade_dist_15_up -0.046000\n", - "┃ coef_grade_dist_5_15 -0.144663\n", - "┃ coef_high_dist_0_1 -2.106211\n", - "┃ coef_high_dist_15_up -0.188200\n", - "┃ coef_high_dist_5_15 -0.101747\n", - "┃ coef_high_grade_dist_1_2 -0.751852\n", - "┃ coef_high_grade_dist_2_5 -0.667119\n", - "┃ coef_mode_logsum 0.372245\n", - "┃ coef_univ_dist_0_1 -3.311744\n", - "┃ coef_univ_dist_15_up -0.073000\n", - "┃ coef_univ_dist_1_2 -2.878224\n", - "┃ coef_univ_dist_2_5 -0.456009\n", - "┃ coef_univ_dist_5_15 -0.572553\n", + "┃ coef_grade_dist_0_1 -1.886212\n", + "┃ coef_grade_dist_15_up -0.103616\n", + "┃ coef_grade_dist_5_15 -0.285452\n", + "┃ coef_high_dist_0_1 -0.869799\n", + "┃ coef_high_dist_15_up -0.233127\n", + "┃ coef_high_dist_5_15 -0.285690\n", + "┃ coef_high_grade_dist_1_2 -0.931692\n", + "┃ coef_high_grade_dist_2_5 -0.694687\n", + "┃ coef_mode_logsum 0.074994\n", + "┃ coef_univ_dist_0_1 -2.780799\n", + "┃ coef_univ_dist_15_up -0.145257\n", + "┃ coef_univ_dist_1_2 -3.082102\n", + "┃ coef_univ_dist_2_5 -0.668470\n", + "┃ coef_univ_dist_5_15 -0.563338\n", "┃ gradeschool_AGE0519 0.000000\n", "┃ highschool_HSENROLL 0.000000\n", "┃ university_COLLFTE -0.524249\n", - "┃ university_COLLPTE -1.208828\n", + "┃ university_COLLPTE -0.900765\n", "┃ dtype: float64\n", - "┣ tolerance: 7.272237631812952e-06\n", - "┣ steps: array([1., 1., 1., 1., 1., 1.])\n", - "┣ message: 'Optimization terminated successfully.'\n", - "┣ elapsed_time: datetime.timedelta(microseconds=218468)\n", + "┣ tolerance: 4.213233078884777e-06\n", + "┣ steps: array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ elapsed_time: datetime.timedelta(microseconds=994749)\n", "┣ method: 'BHHH'\n", - "┣ n_cases: 984\n", - "┣ iteration_number: 6\n", - "┣ logloss: 4.124715061821547" + "┣ n_cases: 14989\n", + "┣ iteration_number: 13" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -2015,209 +2118,235 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Value Std Err t Stat Signif Null Value Constrained
-999-999. NA NA-999.00fixed value
1 1.00 NA NA 1.00fixed value
coef_grade_dist_0_1-1.82 0.435-4.19*** 0.00
coef_grade_dist_15_up-0.0460 4.66e-15-BIG*** 0.00
coef_grade_dist_5_15-0.145 0.0638-2.27* 0.00
coef_high_dist_0_1-2.11 0.801-2.63** 0.00
coef_high_dist_15_up-0.188 3.34e-15-BIG*** 0.00
coef_high_dist_5_15-0.102 0.143-0.71 0.00
coef_high_grade_dist_1_2-0.752 0.186-4.05*** 0.00
coef_high_grade_dist_2_5-0.667 0.0510-13.09*** 0.00
coef_mode_logsum 0.372 0.0461 8.08*** 0.00
coef_univ_dist_0_1-3.31 0.740-4.47*** 0.00
coef_univ_dist_15_up-0.0730 3.48e-16-BIG*** 0.00
coef_univ_dist_1_2-2.88 0.299-9.64*** 0.00
coef_univ_dist_2_5-0.456 0.0773-5.90*** 0.00
coef_univ_dist_5_15-0.573 0.0938-6.11*** 0.00
gradeschool_AGE0519 0.00 NA NA 0.00fixed value
highschool_HSENROLL 0.00 NA NA 0.00fixed value
university_COLLFTE-0.524 NA NA 0.00fixed value
university_COLLPTE-1.21 0.314-3.85*** 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
-999-999. 0.00 NA 0.00fixed value
1 1.00 0.00 NA 0.00fixed value
coef_grade_dist_0_1-1.89 0.0843-22.37*** 0.00
coef_grade_dist_15_up-0.104 0.00536-19.34*** 0.00
coef_grade_dist_5_15-0.285 0.00724-39.45*** 0.00
coef_high_dist_0_1-0.870 0.302-2.88** 0.00
coef_high_dist_15_up-0.233 0.0225-10.36*** 0.00
coef_high_dist_5_15-0.286 0.0124-23.10*** 0.00
coef_high_grade_dist_1_2-0.932 0.0424-22.00*** 0.00
coef_high_grade_dist_2_5-0.695 0.0138-50.31*** 0.00
coef_mode_logsum 0.0750 0.0118 6.35*** 0.00
coef_univ_dist_0_1-2.78 0.391-7.11*** 0.00
coef_univ_dist_15_up-0.145 0.0105-13.85*** 0.00
coef_univ_dist_1_2-3.08 0.168-18.34*** 0.00
coef_univ_dist_2_5-0.668 0.0383-17.46*** 0.00
coef_univ_dist_5_15-0.563 0.0167-33.63*** 0.00
gradeschool_AGE0519 0.00 0.00 NA 0.00fixed value
highschool_HSENROLL 0.00 0.00 NA 0.00fixed value
university_COLLFTE-0.524 0.00 NA 0.00fixed value
university_COLLPTE-0.901 0.0959-9.39*** 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -2226,6 +2355,29 @@ "model.parameter_summary()" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
StatisticAggregatePer Case
Number of Cases14989
Log Likelihood at Convergence-30424.70-2.03
Log Likelihood at Null Parameters-71673.19-4.78
Rho Squared w.r.t. Null Parameters0.576
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.estimation_statistics()" + ] + }, { "cell_type": "markdown", "metadata": { @@ -2238,7 +2390,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -2255,7 +2407,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -2274,7 +2426,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2391,8 +2543,8 @@ " 0.000000\n", " 0.000\n", " 0.000000\n", - " 0.664760\n", - " 0.335240\n", + " 0.593032\n", + " 0.406968\n", " \n", " \n", " 5\n", @@ -2700,7 +2852,7 @@ "1 0.139139 0.008008 0.210210 0.000 0.000000 0.000000 0.000000 \n", "2 0.154000 0.006000 0.239000 0.000 0.000000 0.000000 0.000000 \n", "3 0.146000 0.004000 0.246000 0.000 0.000000 0.000000 0.000000 \n", - "4 0.000000 0.000000 0.000000 0.000 0.000000 0.664760 0.335240 \n", + "4 0.000000 0.000000 0.000000 0.000 0.000000 0.593032 0.406968 \n", "5 0.000000 0.000000 0.000000 1.000 0.000000 0.000000 0.000000 \n", "6 0.000000 0.000000 0.000000 0.000 1.000000 0.000000 0.000000 \n", "7 0.000000 0.000000 0.000000 0.465 0.166000 0.000000 0.000000 \n", @@ -2720,7 +2872,7 @@ "21 0.000000 0.000000 0.000000 0.000 0.000000 0.591409 0.407592 " ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2741,7 +2893,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -2765,7 +2917,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2798,85 +2950,85 @@ " \n", " 0\n", " coef_univ_dist_0_1\n", - " -3.311744\n", + " -2.780799\n", " F\n", " \n", " \n", " 1\n", " coef_univ_dist_1_2\n", - " -2.878224\n", + " -3.082102\n", " F\n", " \n", " \n", " 2\n", " coef_univ_dist_2_5\n", - " -0.456009\n", + " -0.668470\n", " F\n", " \n", " \n", " 3\n", " coef_univ_dist_5_15\n", - " -0.572553\n", + " -0.563338\n", " F\n", " \n", " \n", " 4\n", " coef_univ_dist_15_up\n", - " -0.073000\n", + " -0.145257\n", " F\n", " \n", " \n", " 5\n", " coef_high_dist_0_1\n", - " -2.106211\n", + " -0.869799\n", " F\n", " \n", " \n", " 6\n", " coef_high_grade_dist_1_2\n", - " -0.751852\n", + " -0.931692\n", " F\n", " \n", " \n", " 7\n", " coef_high_grade_dist_2_5\n", - " -0.667119\n", + " -0.694687\n", " F\n", " \n", " \n", " 8\n", " coef_high_dist_5_15\n", - " -0.101747\n", + " -0.285690\n", " F\n", " \n", " \n", " 9\n", " coef_high_dist_15_up\n", - " -0.188200\n", + " -0.233127\n", " F\n", " \n", " \n", " 10\n", " coef_grade_dist_0_1\n", - " -1.824840\n", + " -1.886212\n", " F\n", " \n", " \n", " 11\n", " coef_grade_dist_5_15\n", - " -0.144663\n", + " -0.285452\n", " F\n", " \n", " \n", " 12\n", " coef_grade_dist_15_up\n", - " -0.046000\n", + " -0.103616\n", " F\n", " \n", " \n", " 13\n", " coef_mode_logsum\n", - " 0.372245\n", + " 0.074994\n", " F\n", " \n", " \n", @@ -2885,23 +3037,23 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coef_univ_dist_0_1 -3.311744 F\n", - "1 coef_univ_dist_1_2 -2.878224 F\n", - "2 coef_univ_dist_2_5 -0.456009 F\n", - "3 coef_univ_dist_5_15 -0.572553 F\n", - "4 coef_univ_dist_15_up -0.073000 F\n", - "5 coef_high_dist_0_1 -2.106211 F\n", - "6 coef_high_grade_dist_1_2 -0.751852 F\n", - "7 coef_high_grade_dist_2_5 -0.667119 F\n", - "8 coef_high_dist_5_15 -0.101747 F\n", - "9 coef_high_dist_15_up -0.188200 F\n", - "10 coef_grade_dist_0_1 -1.824840 F\n", - "11 coef_grade_dist_5_15 -0.144663 F\n", - "12 coef_grade_dist_15_up -0.046000 F\n", - "13 coef_mode_logsum 0.372245 F" + "0 coef_univ_dist_0_1 -2.780799 F\n", + "1 coef_univ_dist_1_2 -3.082102 F\n", + "2 coef_univ_dist_2_5 -0.668470 F\n", + "3 coef_univ_dist_5_15 -0.563338 F\n", + "4 coef_univ_dist_15_up -0.145257 F\n", + "5 coef_high_dist_0_1 -0.869799 F\n", + "6 coef_high_grade_dist_1_2 -0.931692 F\n", + "7 coef_high_grade_dist_2_5 -0.694687 F\n", + "8 coef_high_dist_5_15 -0.285690 F\n", + "9 coef_high_dist_15_up -0.233127 F\n", + "10 coef_grade_dist_0_1 -1.886212 F\n", + "11 coef_grade_dist_5_15 -0.285452 F\n", + "12 coef_grade_dist_15_up -0.103616 F\n", + "13 coef_mode_logsum 0.074994 F" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2912,7 +3064,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3035,8 +3187,8 @@ " 0.000000\n", " 0.000\n", " 0.000000\n", - " 0.664760\n", - " 0.335240\n", + " 0.593032\n", + " 0.406968\n", " \n", " \n", " 5\n", @@ -3361,7 +3513,7 @@ "1 0.325325 0.139139 0.008008 0.210210 0.000 0.000000 0.000000 \n", "2 0.284000 0.154000 0.006000 0.239000 0.000 0.000000 0.000000 \n", "3 0.241000 0.146000 0.004000 0.246000 0.000 0.000000 0.000000 \n", - "4 0.000000 0.000000 0.000000 0.000000 0.000 0.000000 0.664760 \n", + "4 0.000000 0.000000 0.000000 0.000000 0.000 0.000000 0.593032 \n", "5 0.000000 0.000000 0.000000 0.000000 1.000 0.000000 0.000000 \n", "6 0.000000 0.000000 0.000000 0.000000 0.000 1.000000 0.000000 \n", "7 0.144000 0.000000 0.000000 0.000000 0.465 0.166000 0.000000 \n", @@ -3385,7 +3537,7 @@ "1 0.000000 \n", "2 0.000000 \n", "3 0.000000 \n", - "4 0.335240 \n", + "4 0.406968 \n", "5 0.000000 \n", "6 0.000000 \n", "7 0.000000 \n", @@ -3405,7 +3557,7 @@ "21 0.407592 " ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -3422,7 +3574,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -3436,7 +3588,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/03_work_location.ipynb b/activitysim/examples/example_estimation/notebooks/03_work_location.ipynb index 2a9479023c..4fc0fbc480 100644 --- a/activitysim/examples/example_estimation/notebooks/03_work_location.ipynb +++ b/activitysim/examples/example_estimation/notebooks/03_work_location.ipynb @@ -25,38 +25,88 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "jupyter": { + "is_executing": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.33',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import larch # !conda install larch #for estimation\n", + "import larch as lx # !conda install larch #for estimation\n", "import pandas as pd\n", - "import numpy as np\n", - "import yaml \n", - "import larch.util.excel\n", - "import os" + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare, backup\n", + "\n", + "prepare()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Load data and prep model for estimation" + "In this demo notebook, we will (later) edit the model spec file. But for demo purposes, we want to\n", + "make sure we are starting from the \"original\" spec file, so we'll check that now. For actual \n", + "applications, this step would not be necessary." ] }, { @@ -65,7 +115,14 @@ "metadata": {}, "outputs": [], "source": [ - "modelname=\"workplace_location\"" + "backup(\"output-est-mode/estimation_data_bundle/workplace_location/workplace_location_SPEC.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data and prep model for estimation" ] }, { @@ -73,9 +130,36 @@ "execution_count": 4, "metadata": {}, "outputs": [], + "source": [ + "modelname = \"workplace_location\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_landuse.csv\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_size_terms.csv\n" + ] + } + ], "source": [ "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -96,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -187,7 +271,7 @@ "coef_mode_logsum 0.3000 F" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -205,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -230,148 +314,142 @@ " \n", " \n", " person_id\n", - " variable\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " ...\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", - " 190\n", + " alt_dest\n", + " prob\n", + " pick_count\n", + " mode_choice_logsum\n", + " size_term\n", + " shadow_price_size_term_adjustment\n", + " shadow_price_utility_adjustment\n", + " util_dist_0_1\n", + " util_dist_1_2\n", + " util_dist_2_5\n", + " util_dist_5_15\n", + " util_dist_15_up\n", + " util_dist_0_5_high\n", + " util_dist_15_up_high\n", + " util_size_variable\n", + " util_utility_adjustment\n", + " util_no_attractions\n", + " util_mode_logsum\n", + " util_sample_of_corrections_factor\n", " \n", " \n", " \n", " \n", " 0\n", - " 72241\n", - " mode_choice_logsum\n", - " -0.6000073826104492\n", - " -0.4656491845430101\n", - " -0.41794791684660004\n", - " -0.4545880924383482\n", - " -0.3870339464573669\n", - " -0.713189384898449\n", - " -0.6002723120707204\n", - " -0.7303789868909905\n", - " ...\n", - " -1.2551998198820722\n", - " -1.444611856370077\n", - " -1.309689434695158\n", - " -1.2118895631639268\n", - " -1.116133290115392\n", - " -1.1401586021041086\n", - " -1.268242046936546\n", - " -1.1992258811031282\n", - " -1.332212687016352\n", - " -1.4322381548918224\n", + " 72355\n", + " 2\n", + " 0.033018\n", + " 4\n", + " -1.930058\n", + " 8679.220\n", + " 1\n", + " 0\n", + " 1.0\n", + " 1.0\n", + " 3.0\n", + " 1.01\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", + " 9.068802\n", + " 0\n", + " False\n", + " -1.930058\n", + " 4.796986\n", " \n", " \n", " 1\n", - " 72241\n", - " pick_count\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 1.0\n", - " 1.0\n", - 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46476 rows × 192 columns

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766224 rows × 20 columns

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2582 rows × 5 columns

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28281 rows × 5 columns

\n", "" ], "text/plain": [ - " person_id model_choice override_choice income_segment home_zone_id\n", - "0 72241 13 9 1 17\n", - "1 72441 100 100 1 60\n", - "2 72528 139 126 1 69\n", - "3 73144 77 77 1 125\n", - "4 73493 117 117 1 133\n", - "... ... ... ... ... ...\n", - "2577 7514214 80 71 1 56\n", - "2578 7514284 187 186 1 72\n", - "2579 7514404 105 106 1 81\n", - "2580 7514777 87 87 1 106\n", - "2581 7515185 16 16 1 142\n", + " person_id model_choice override_choice income_segment home_zone_id\n", + "0 72355 14 17 1 55\n", + "1 72384 1 16 1 59\n", + "2 72407 70 70 1 59\n", + "3 72459 193 30 1 61\n", + "4 72529 16 57 1 69\n", + "... ... ... ... ... ...\n", + "28276 7539071 1006 1019 1 1006\n", + "28277 7539203 1059 1152 1 1159\n", + "28278 7539217 940 991 1 1159\n", + "28279 7539270 1162 1145 1 1161\n", + "28280 7539317 1390 1381 1 1390\n", "\n", - "[2582 rows x 5 columns]" + "[28281 rows x 5 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -795,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -881,9 +953,9 @@ " 7\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 3\n", " 5.89564\n", " 2.875000\n", @@ -905,9 +977,9 @@ " 19\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.84871\n", " 5.195214\n", @@ -929,9 +1001,9 @@ " 38\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.53231\n", " 80.470405\n", @@ -953,9 +1025,9 @@ " 20\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 2\n", " 5.64330\n", " 7.947368\n", @@ -977,9 +1049,9 @@ " 86\n", " ...\n", " 0\n", - " 0.00000\n", + " 0.0\n", " 72.14684\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.52555\n", " 38.187500\n", @@ -1012,191 +1084,191 @@ " ...\n", " \n", " \n", - " 186\n", - " 4\n", - " 4\n", - " 1\n", - " 2779\n", - " 8062\n", - " 376.0\n", - " 172.0\n", - " 15.00000\n", - " 1760\n", - " 1178\n", + " 1450\n", + " 34\n", + " 34\n", + " 9\n", + " 2724\n", + " 6493\n", + " 1320.0\n", + " 630.0\n", + " 69.00000\n", + " 1046\n", + " 1013\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 4\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.04173\n", - " 14.860963\n", - " 9.411765\n", - " 5.762347\n", + " 1.12116\n", + " 3.896996\n", + " 1.496423\n", + " 1.081235\n", " False\n", " \n", " \n", - " 187\n", - " 4\n", - " 4\n", - " 1\n", - " 1492\n", - " 4139\n", - " 214.0\n", - " 116.0\n", - " 10.00000\n", - " 808\n", - " 603\n", + " 1451\n", + " 34\n", + " 34\n", + " 9\n", + " 2016\n", + " 4835\n", + " 664.0\n", + " 379.0\n", + " 43.00000\n", + " 757\n", + " 757\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 4\n", + " 0.0\n", " 0.00000\n", - " 2\n", - " 1.73676\n", - " 11.841270\n", - " 6.412698\n", - " 4.159890\n", + " 0.0\n", + " 1\n", + " 1.17116\n", + " 4.777251\n", + " 1.793839\n", + " 1.304140\n", " False\n", " \n", " \n", - " 188\n", - " 4\n", - " 4\n", - " 1\n", - " 753\n", - " 4072\n", - " 232.0\n", - " 11.0\n", - " 178.00000\n", - " 4502\n", - " 1117\n", + " 1452\n", + " 34\n", + " 34\n", + " 9\n", + " 2178\n", + " 5055\n", + " 1068.0\n", + " 602.0\n", + " 35.00000\n", + " 2110\n", + " 789\n", " ...\n", - " 2\n", - " 3961.04761\n", - " 17397.79102\n", - " 11152.93652\n", + " 4\n", + " 0.0\n", + " 0.00000\n", + " 0.0\n", " 1\n", - " 2.28992\n", - " 3.984127\n", - " 23.820106\n", - " 3.413233\n", + " 1.17587\n", + " 3.419152\n", + " 3.312402\n", + " 1.682465\n", " False\n", " \n", " \n", - " 189\n", - " 4\n", - " 4\n", - " 1\n", - " 3546\n", - " 8476\n", - " 201.0\n", - " 72.0\n", - " 6.00000\n", - " 226\n", - " 1057\n", + " 1453\n", + " 34\n", + " 34\n", + " 9\n", + " 298\n", + " 779\n", + " 14195.0\n", + " 429.0\n", + " 4.00000\n", + " 922\n", + " 88\n", " ...\n", - " 2\n", - " 0.00000\n", - " 0.00000\n", + " 5\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.88773\n", - " 45.461538\n", - " 2.897436\n", - " 2.723836\n", + " 1.01972\n", + " 0.688222\n", + " 2.129330\n", + " 0.520115\n", " False\n", " \n", " \n", - " 190\n", - " 4\n", - " 4\n", - " 1\n", - " 968\n", - " 1647\n", - " 1381.0\n", - " 14.0\n", - " 28.00000\n", - " 1010\n", - " 114\n", + " 1454\n", + " 34\n", + " 34\n", + " 9\n", + " 1068\n", + " 2337\n", + " 10469.0\n", + " 1114.0\n", + " 27.00000\n", + " 607\n", + " 418\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 5\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.60309\n", - " 23.047619\n", - " 24.047619\n", - " 11.768501\n", + " 0.95542\n", + " 0.936021\n", + " 0.531989\n", + " 0.339203\n", " False\n", " \n", " \n", "\n", - "

190 rows × 28 columns

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1454 rows × 28 columns

\n", "" ], "text/plain": [ - " DISTRICT SD county_id TOTHH TOTPOP TOTACRE RESACRE CIACRE \\\n", - "zone_id \n", - "1 1 1 1 46 82 20.3 1.0 15.00000 \n", - "2 1 1 1 134 240 31.1 1.0 24.79297 \n", - "3 1 1 1 267 476 14.7 1.0 2.31799 \n", - "4 1 1 1 151 253 19.3 1.0 18.00000 \n", - "5 1 1 1 611 1069 52.7 1.0 15.00000 \n", - "... ... .. ... ... ... ... ... ... \n", - "186 4 4 1 2779 8062 376.0 172.0 15.00000 \n", - "187 4 4 1 1492 4139 214.0 116.0 10.00000 \n", - "188 4 4 1 753 4072 232.0 11.0 178.00000 \n", - "189 4 4 1 3546 8476 201.0 72.0 6.00000 \n", - "190 4 4 1 968 1647 1381.0 14.0 28.00000 \n", + " DISTRICT SD county_id TOTHH TOTPOP TOTACRE RESACRE CIACRE \\\n", + "zone_id \n", + "1 1 1 1 46 82 20.3 1.0 15.00000 \n", + "2 1 1 1 134 240 31.1 1.0 24.79297 \n", + "3 1 1 1 267 476 14.7 1.0 2.31799 \n", + "4 1 1 1 151 253 19.3 1.0 18.00000 \n", + "5 1 1 1 611 1069 52.7 1.0 15.00000 \n", + "... ... .. ... ... ... ... ... ... \n", + "1450 34 34 9 2724 6493 1320.0 630.0 69.00000 \n", + "1451 34 34 9 2016 4835 664.0 379.0 43.00000 \n", + "1452 34 34 9 2178 5055 1068.0 602.0 35.00000 \n", + "1453 34 34 9 298 779 14195.0 429.0 4.00000 \n", + "1454 34 34 9 1068 2337 10469.0 1114.0 27.00000 \n", "\n", - " TOTEMP AGE0519 ... area_type HSENROLL COLLFTE \\\n", - "zone_id ... \n", - "1 27318 7 ... 0 0.00000 0.00000 \n", - "2 42078 19 ... 0 0.00000 0.00000 \n", - "3 2445 38 ... 0 0.00000 0.00000 \n", - "4 22434 20 ... 0 0.00000 0.00000 \n", - "5 15662 86 ... 0 0.00000 72.14684 \n", - "... ... ... ... ... ... ... \n", - "186 1760 1178 ... 3 0.00000 0.00000 \n", - "187 808 603 ... 3 0.00000 0.00000 \n", - "188 4502 1117 ... 2 3961.04761 17397.79102 \n", - "189 226 1057 ... 2 0.00000 0.00000 \n", - "190 1010 114 ... 3 0.00000 0.00000 \n", + " TOTEMP AGE0519 ... area_type HSENROLL COLLFTE COLLPTE \\\n", + "zone_id ... \n", + "1 27318 7 ... 0 0.0 0.00000 0.0 \n", + "2 42078 19 ... 0 0.0 0.00000 0.0 \n", + "3 2445 38 ... 0 0.0 0.00000 0.0 \n", + "4 22434 20 ... 0 0.0 0.00000 0.0 \n", + "5 15662 86 ... 0 0.0 72.14684 0.0 \n", + "... ... ... ... ... ... ... ... \n", + "1450 1046 1013 ... 4 0.0 0.00000 0.0 \n", + "1451 757 757 ... 4 0.0 0.00000 0.0 \n", + "1452 2110 789 ... 4 0.0 0.00000 0.0 \n", + "1453 922 88 ... 5 0.0 0.00000 0.0 \n", + "1454 607 418 ... 5 0.0 0.00000 0.0 \n", "\n", - " COLLPTE TOPOLOGY TERMINAL household_density \\\n", - "zone_id \n", - "1 0.00000 3 5.89564 2.875000 \n", - "2 0.00000 1 5.84871 5.195214 \n", - "3 0.00000 1 5.53231 80.470405 \n", - "4 0.00000 2 5.64330 7.947368 \n", - "5 0.00000 1 5.52555 38.187500 \n", - "... ... ... ... ... \n", - "186 0.00000 1 2.04173 14.860963 \n", - "187 0.00000 2 1.73676 11.841270 \n", - "188 11152.93652 1 2.28992 3.984127 \n", - "189 0.00000 1 2.88773 45.461538 \n", - "190 0.00000 1 2.60309 23.047619 \n", + " TOPOLOGY TERMINAL household_density employment_density \\\n", + "zone_id \n", + "1 3 5.89564 2.875000 1707.375000 \n", + "2 1 5.84871 5.195214 1631.374751 \n", + "3 1 5.53231 80.470405 736.891913 \n", + "4 2 5.64330 7.947368 1180.736842 \n", + "5 1 5.52555 38.187500 978.875000 \n", + "... ... ... ... ... \n", + "1450 1 1.12116 3.896996 1.496423 \n", + "1451 1 1.17116 4.777251 1.793839 \n", + "1452 1 1.17587 3.419152 3.312402 \n", + "1453 1 1.01972 0.688222 2.129330 \n", + "1454 1 0.95542 0.936021 0.531989 \n", "\n", - " employment_density density_index is_cbd \n", - "zone_id \n", - "1 1707.375000 2.870167 False \n", - "2 1631.374751 5.178722 False \n", - "3 736.891913 72.547987 False \n", - "4 1180.736842 7.894233 False \n", - "5 978.875000 36.753679 False \n", - "... ... ... ... \n", - "186 9.411765 5.762347 False \n", - "187 6.412698 4.159890 False \n", - "188 23.820106 3.413233 False \n", - "189 2.897436 2.723836 False \n", - "190 24.047619 11.768501 False \n", + " density_index is_cbd \n", + "zone_id \n", + "1 2.870167 False \n", + "2 5.178722 False \n", + "3 72.547987 False \n", + "4 7.894233 False \n", + "5 36.753679 False \n", + "... ... ... \n", + "1450 1.081235 False \n", + "1451 1.304140 False \n", + "1452 1.682465 False \n", + "1453 0.520115 False \n", + "1454 0.339203 False \n", "\n", - "[190 rows x 28 columns]" + "[1454 rows x 28 columns]" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1214,7 +1286,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1357,7 +1429,7 @@ "9 @np.minimum(np.log(df.pick_count/df.prob), 60) 1 " ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1375,7 +1447,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1466,7 +1538,7 @@ "work_veryhigh 0.093 0.270 0.241 0.146 0.004 0.246" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1486,20 +1558,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 475 [Optimization terminated successfully.]

" + "

Iteration 009 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1511,7 +1576,7 @@ { "data": { "text/html": [ - "

Best LL = -13521.35475538419

" + "

Best LL = -88683.85038605405

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"work_veryhigh_RETEMPN -2.375156 -2.375156 0.0 -6.0 6.0 \n", + " value best initvalue minimum \\\n", + "param_name \n", + "-999 -9.990000e+02 -9.990000e+02 -999.000000 -999.000000 \n", + "1 1.000000e+00 1.000000e+00 1.000000 1.000000 \n", + "coef_dist_0_1 -1.035381e+00 -1.035381e+00 -0.842800 -25.000000 \n", + "coef_dist_0_5_high 1.469662e-01 1.469662e-01 0.150000 -25.000000 \n", + "coef_dist_15_up -1.170512e-01 -1.170512e-01 -0.091700 -25.000000 \n", + "coef_dist_1_2 -4.652491e-01 -4.652491e-01 -0.310400 -25.000000 \n", + "coef_dist_2_5 -4.177477e-01 -4.177477e-01 -0.378300 -25.000000 \n", + "coef_dist_5_15 -1.558734e-01 -1.558734e-01 -0.128500 -25.000000 \n", + "coef_dist_5_up_high 2.506728e-02 2.506728e-02 0.020000 -25.000000 \n", + "coef_mode_logsum 7.423654e-02 7.423654e-02 0.300000 -25.000000 \n", + "work_high_AGREMPN -1.856268e+00 -1.856268e+00 -5.115996 -6.000000 \n", + "work_high_FPSEMPN -1.895722e+00 -1.895722e+00 -1.575037 -6.000000 \n", + "work_high_HEREMPN -1.698263e+00 -1.698263e+00 -1.258781 -6.000000 \n", + "work_high_MWTEMPN -1.815687e+00 -1.815687e+00 -1.431292 -6.000000 \n", + "work_high_OTHEMPN -2.187482e+00 -2.187482e+00 -1.870803 -6.000000 \n", + "work_high_RETEMPN -2.207275e+00 -2.207275e+00 -2.207275 -2.207275 \n", + "work_low_AGREMPN -7.178467e+01 -7.178467e+01 -4.605170 -6.000000 \n", + "work_low_FPSEMPN -1.507959e+00 -1.507959e+00 -1.645065 -6.000000 \n", + "work_low_HEREMPN -8.190703e-01 -8.190703e-01 -0.959720 -6.000000 \n", + "work_low_MWTEMPN -1.980587e+00 -1.980587e+00 -1.807889 -6.000000 \n", + "work_low_OTHEMPN -2.021455e+00 -2.021455e+00 -2.120264 -6.000000 \n", + "work_low_RETEMPN -2.047943e+00 -2.047943e+00 -2.047943 -2.047943 \n", + "work_med_AGREMPN -8.087999e+64 -8.087999e+64 -4.828314 -6.000000 \n", + "work_med_FPSEMPN -2.151306e+00 -2.151306e+00 -1.624552 -6.000000 \n", + "work_med_HEREMPN -1.606036e+00 -1.606036e+00 -1.123930 -6.000000 \n", + "work_med_MWTEMPN -2.043992e+00 -2.043992e+00 -1.560648 -6.000000 \n", + "work_med_OTHEMPN -2.172089e+00 -2.172089e+00 -1.973281 -6.000000 \n", + "work_med_RETEMPN -2.120264e+00 -2.120264e+00 -2.120264 -2.120264 \n", + "work_veryhigh_AGREMPN -6.042012e+00 -6.042012e+00 -5.521461 -6.000000 \n", + "work_veryhigh_FPSEMPN -1.838134e+00 -1.838134e+00 -1.309333 -6.000000 \n", + "work_veryhigh_HEREMPN -1.992130e+00 -1.992130e+00 -1.422958 -6.000000 \n", + "work_veryhigh_MWTEMPN -1.763131e+00 -1.763131e+00 -1.402424 -6.000000 \n", + "work_veryhigh_OTHEMPN -2.509024e+00 -2.509024e+00 -1.924149 -6.000000 \n", + "work_veryhigh_RETEMPN -2.375156e+00 -2.375156e+00 -2.375156 -2.375156 \n", "\n", - " holdfast note best \n", - "-999 1 -999.000000 \n", - "1 1 1.000000 \n", - "coef_dist_0_1 0 -1.084778 \n", - "coef_dist_0_5_high 0 0.120981 \n", - "coef_dist_15_up 0 -0.091700 \n", - "coef_dist_1_2 0 -0.252580 \n", - "coef_dist_2_5 0 -0.369689 \n", - "coef_dist_5_15 0 -0.152259 \n", - "coef_dist_5_up_high 0 -0.015225 \n", - "coef_mode_logsum 0 0.155426 \n", - "work_high_AGREMPN 0 -14.042031 \n", - "work_high_FPSEMPN 0 -2.021234 \n", - "work_high_HEREMPN 0 -1.578781 \n", - "work_high_MWTEMPN 0 -2.074250 \n", - "work_high_OTHEMPN 0 -2.145558 \n", - "work_high_RETEMPN 1 -2.207275 \n", - "work_low_AGREMPN 0 -40.734929 \n", - "work_low_FPSEMPN 0 -0.670108 \n", - "work_low_HEREMPN 0 0.087905 \n", - "work_low_MWTEMPN 0 -0.373028 \n", - "work_low_OTHEMPN 0 -0.705270 \n", - "work_low_RETEMPN 1 -2.047943 \n", - "work_med_AGREMPN 0 -14.886841 \n", - "work_med_FPSEMPN 0 -1.942050 \n", - "work_med_HEREMPN 0 -1.382809 \n", - "work_med_MWTEMPN 0 -0.910436 \n", - "work_med_OTHEMPN 0 -2.535326 \n", - "work_med_RETEMPN 1 -2.120264 \n", - "work_veryhigh_AGREMPN 0 -0.277725 \n", - "work_veryhigh_FPSEMPN 0 -1.757306 \n", - "work_veryhigh_HEREMPN 0 -1.621894 \n", - "work_veryhigh_MWTEMPN 0 -1.722069 \n", - "work_veryhigh_OTHEMPN 0 -2.483268 \n", - "work_veryhigh_RETEMPN 1 -2.375156 " + " maximum nullvalue holdfast \n", + "param_name \n", + "-999 -999.000000 0.0 1 \n", + "1 1.000000 0.0 1 \n", + "coef_dist_0_1 25.000000 0.0 0 \n", + "coef_dist_0_5_high 25.000000 0.0 0 \n", + "coef_dist_15_up 25.000000 0.0 0 \n", + "coef_dist_1_2 25.000000 0.0 0 \n", + "coef_dist_2_5 25.000000 0.0 0 \n", + "coef_dist_5_15 25.000000 0.0 0 \n", + "coef_dist_5_up_high 25.000000 0.0 0 \n", + "coef_mode_logsum 25.000000 0.0 0 \n", + "work_high_AGREMPN 6.000000 0.0 0 \n", + "work_high_FPSEMPN 6.000000 0.0 0 \n", + "work_high_HEREMPN 6.000000 0.0 0 \n", + "work_high_MWTEMPN 6.000000 0.0 0 \n", + "work_high_OTHEMPN 6.000000 0.0 0 \n", + "work_high_RETEMPN -2.207275 0.0 1 \n", + "work_low_AGREMPN 6.000000 0.0 0 \n", + "work_low_FPSEMPN 6.000000 0.0 0 \n", + "work_low_HEREMPN 6.000000 0.0 0 \n", + "work_low_MWTEMPN 6.000000 0.0 0 \n", + "work_low_OTHEMPN 6.000000 0.0 0 \n", + "work_low_RETEMPN -2.047943 0.0 1 \n", + "work_med_AGREMPN 6.000000 0.0 0 \n", + "work_med_FPSEMPN 6.000000 0.0 0 \n", + "work_med_HEREMPN 6.000000 0.0 0 \n", + "work_med_MWTEMPN 6.000000 0.0 0 \n", + "work_med_OTHEMPN 6.000000 0.0 0 \n", + "work_med_RETEMPN -2.120264 0.0 1 \n", + "work_veryhigh_AGREMPN 6.000000 0.0 0 \n", + "work_veryhigh_FPSEMPN 6.000000 0.0 0 \n", + "work_veryhigh_HEREMPN 6.000000 0.0 0 \n", + "work_veryhigh_MWTEMPN 6.000000 0.0 0 \n", + "work_veryhigh_OTHEMPN 6.000000 0.0 0 \n", + "work_veryhigh_RETEMPN -2.375156 0.0 1 " ] }, "metadata": {}, @@ -2011,1040 +2053,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 6.58075738982826e-14 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 8.609108514589023e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 4.858994087802833e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 4.590724503258719e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.764703716398322e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 7.95965356556717e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.818442093095742e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 5.03392554043634e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.5381467152499962e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - 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" warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.1462882066859337e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.2236450588660233e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 8.957773169475355e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.403629593864148e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - 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" warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.8356027731239294e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.1421351948148873e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.2122137179899207e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.361811071723201e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 7.424246392034423e-17 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 9.833431869070201e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.3560892176329117e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.3002356046314505e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 8.14760641170999e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.5893755977713855e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.2242791423085704e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 7.840228498303649e-17 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.821310614405388e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - 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" warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.874082949306298e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 7.371793040685402e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.938572491694241e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.246124180526511e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.141265756002516e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.100401477984635e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.5378782864186965e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.9313922953760897e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.1977191783387483e-15 in general_inverse\n", - 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"/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.4275891045055421e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.4393823211036024e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 4.186384064873585e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 4.824617816113209e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.85880587925525e-15 in general_inverse\n", - 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"/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.4897864931953783e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.0509512337158769e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.1377543116819802e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.800744497149333e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.761798985494412e-15 in general_inverse\n", - 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" warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.8734374809662115e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.232343583659919e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.0482842256784803e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.467379347282302e-18 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.5759888124606325e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.225617744485029e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 7.703772751192879e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 6.883109514984853e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.455312980236373e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 6.23895606701099e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.0603077714105237e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate(method='BHHH', options={'maxiter':1000})\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 8.886410808092305e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { "data": { "text/html": [ - 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work_veryhigh_OTHEMPN-2.483268-2.509024e+00
work_veryhigh_RETEMPN-2.375156-2.375156e+00
tolerance9.957944723905177e-06
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message'Optimization terminated successfully.'
elapsed_time0:00:22.109298
method'BHHH'
n_cases2582
iteration_number475
logloss5.2367756604896165
" + "
tolerance8.618081038971004e-07stepsarray([0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 1. , 1. , 1. ])message'Optimization terminated successfully'elapsed_time0:00:02.528580method'BHHH'n_cases28281iteration_number9
" ], "text/plain": [ - "┣ loglike: -13521.35475538419\n", - "┣ x: -999 -999.000000\n", - "┃ 1 1.000000\n", - "┃ coef_dist_0_1 -1.084778\n", - "┃ coef_dist_0_5_high 0.120981\n", - "┃ coef_dist_15_up -0.091700\n", - "┃ coef_dist_1_2 -0.252580\n", - "┃ coef_dist_2_5 -0.369689\n", - "┃ coef_dist_5_15 -0.152259\n", - "┃ coef_dist_5_up_high -0.015225\n", - "┃ coef_mode_logsum 0.155426\n", - "┃ work_high_AGREMPN -14.042031\n", - "┃ work_high_FPSEMPN -2.021234\n", - "┃ work_high_HEREMPN -1.578781\n", - "┃ work_high_MWTEMPN -2.074250\n", - "┃ work_high_OTHEMPN -2.145558\n", - "┃ work_high_RETEMPN -2.207275\n", - "┃ work_low_AGREMPN -40.734929\n", - "┃ work_low_FPSEMPN -0.670108\n", - "┃ work_low_HEREMPN 0.087905\n", - "┃ work_low_MWTEMPN -0.373028\n", - "┃ work_low_OTHEMPN -0.705270\n", - "┃ work_low_RETEMPN -2.047943\n", - "┃ work_med_AGREMPN -14.886841\n", - "┃ work_med_FPSEMPN -1.942050\n", - "┃ work_med_HEREMPN -1.382809\n", - "┃ work_med_MWTEMPN -0.910436\n", - "┃ work_med_OTHEMPN -2.535326\n", - "┃ work_med_RETEMPN -2.120264\n", - "┃ work_veryhigh_AGREMPN -0.277725\n", - "┃ work_veryhigh_FPSEMPN -1.757306\n", - "┃ work_veryhigh_HEREMPN -1.621894\n", - "┃ work_veryhigh_MWTEMPN -1.722069\n", - "┃ work_veryhigh_OTHEMPN -2.483268\n", - "┃ work_veryhigh_RETEMPN -2.375156\n", + "┣ loglike: -88683.85038605405\n", + "┣ x: -999 -9.990000e+02\n", + "┃ 1 1.000000e+00\n", + "┃ coef_dist_0_1 -1.035381e+00\n", + "┃ coef_dist_0_5_high 1.469662e-01\n", + "┃ coef_dist_15_up -1.170512e-01\n", + "┃ coef_dist_1_2 -4.652491e-01\n", + "┃ coef_dist_2_5 -4.177477e-01\n", + "┃ coef_dist_5_15 -1.558734e-01\n", + "┃ coef_dist_5_up_high 2.506728e-02\n", + "┃ coef_mode_logsum 7.423654e-02\n", + "┃ work_high_AGREMPN -1.856268e+00\n", + "┃ work_high_FPSEMPN -1.895722e+00\n", + "┃ work_high_HEREMPN -1.698263e+00\n", + "┃ work_high_MWTEMPN -1.815687e+00\n", + "┃ work_high_OTHEMPN -2.187482e+00\n", + "┃ work_high_RETEMPN -2.207275e+00\n", + "┃ work_low_AGREMPN -7.178467e+01\n", + "┃ work_low_FPSEMPN -1.507959e+00\n", + "┃ work_low_HEREMPN -8.190703e-01\n", + "┃ work_low_MWTEMPN -1.980587e+00\n", + "┃ work_low_OTHEMPN -2.021455e+00\n", + "┃ work_low_RETEMPN -2.047943e+00\n", + "┃ work_med_AGREMPN -8.087999e+64\n", + "┃ work_med_FPSEMPN -2.151306e+00\n", + "┃ work_med_HEREMPN -1.606036e+00\n", + "┃ work_med_MWTEMPN -2.043992e+00\n", + "┃ work_med_OTHEMPN -2.172089e+00\n", + "┃ work_med_RETEMPN -2.120264e+00\n", + "┃ work_veryhigh_AGREMPN -6.042012e+00\n", + "┃ work_veryhigh_FPSEMPN -1.838134e+00\n", + "┃ work_veryhigh_HEREMPN -1.992130e+00\n", + "┃ work_veryhigh_MWTEMPN -1.763131e+00\n", + "┃ work_veryhigh_OTHEMPN -2.509024e+00\n", + "┃ work_veryhigh_RETEMPN -2.375156e+00\n", "┃ dtype: float64\n", - "┣ tolerance: 9.957944723905177e-06\n", - "┣ steps: array([0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625, 0.015625, 0.015625, 0.015625, 0.015625, 0.015625,\n", - "┃ 0.015625])\n", - "┣ message: 'Optimization terminated successfully.'\n", - "┣ elapsed_time: datetime.timedelta(seconds=22, microseconds=109298)\n", + "┣ tolerance: 8.618081038971004e-07\n", + "┣ steps: array([0.125, 0.25 , 0.375, 0.5 , 0.625, 0.75 , 1. , 1. , 1. ])\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ elapsed_time: datetime.timedelta(seconds=2, microseconds=528580)\n", "┣ method: 'BHHH'\n", - "┣ n_cases: 2582\n", - "┣ iteration_number: 475\n", - "┣ logloss: 5.2367756604896165" + "┣ n_cases: 28281\n", + "┣ iteration_number: 9" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.estimate(method='BHHH', options={'maxiter':1000})" + "model.estimate(method=\"BHHH\", options={\"maxiter\": 1000})" ] }, { @@ -3414,335 +2271,361 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Null Value Constrained
-999-999. NA NA-999.00fixed value
1 1.00 NA NA 1.00fixed value
coef_dist_0_1-1.08 0.270-4.02*** 0.00
coef_dist_0_5_high 0.121 0.0339 3.57*** 0.00
coef_dist_15_up-0.0917 0.000603-152.16*** 0.00
coef_dist_1_2-0.253 0.115-2.20* 0.00
coef_dist_2_5-0.370 0.0329-11.23*** 0.00
coef_dist_5_15-0.152 0.0419-3.63*** 0.00
coef_dist_5_up_high-0.0152 0.0576-0.26 0.00
coef_mode_logsum 0.155 0.0455 3.41*** 0.00
work_high_AGREMPN-14.0 9.45e+03-0.00 0.00
work_high_FPSEMPN-2.02 0.419-4.83*** 0.00
work_high_HEREMPN-1.58 0.398-3.97*** 0.00
work_high_MWTEMPN-2.07 1.07-1.94 0.00
work_high_OTHEMPN-2.15 0.463-4.64*** 0.00
work_high_RETEMPN-2.21 NA NA 0.00fixed value
work_low_AGREMPN-40.7 3.95e-06-BIG*** 0.00
work_low_FPSEMPN-0.670 1.46-0.46 0.00
work_low_HEREMPN 0.0879 1.47 0.06 0.00
work_low_MWTEMPN-0.373 1.66-0.22 0.00
work_low_OTHEMPN-0.705 1.49-0.47 0.00
work_low_RETEMPN-2.05 NA NA 0.00fixed value
work_med_AGREMPN-14.9 2.37e+03-0.01 0.00
work_med_FPSEMPN-1.94 0.484-4.01*** 0.00
work_med_HEREMPN-1.38 0.448-3.09** 0.00
work_med_MWTEMPN-0.910 0.564-1.61 0.00
work_med_OTHEMPN-2.54 0.640-3.96*** 0.00
work_med_RETEMPN-2.12 NA NA 0.00fixed value
work_veryhigh_AGREMPN-0.278 2.35-0.12 0.00
work_veryhigh_FPSEMPN-1.76 0.410-4.29*** 0.00
work_veryhigh_HEREMPN-1.62 0.414-3.92*** 0.00
work_veryhigh_MWTEMPN-1.72 0.739-2.33* 0.00
work_veryhigh_OTHEMPN-2.48 0.493-5.03*** 0.00
work_veryhigh_RETEMPN-2.38 NA NA 0.00fixed value
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
-999-999. 0.00 NA 0.00fixed value
1 1.00 0.00 NA 0.00fixed value
coef_dist_0_1-1.04 0.105-9.89*** 0.00
coef_dist_0_5_high 0.147 0.0114 12.86*** 0.00
coef_dist_15_up-0.117 0.00211-55.56*** 0.00
coef_dist_1_2-0.465 0.0428-10.86*** 0.00
coef_dist_2_5-0.418 0.0116-36.15*** 0.00
coef_dist_5_15-0.156 0.00276-56.40*** 0.00
coef_dist_5_up_high 0.0251 0.00176 14.22*** 0.00
coef_mode_logsum 0.0742 0.00837 8.87*** 0.00
work_high_AGREMPN-1.86 0.596-3.11** 0.00
work_high_FPSEMPN-1.90 0.178-10.67*** 0.00
work_high_HEREMPN-1.70 0.193-8.81*** 0.00
work_high_MWTEMPN-1.82 0.188-9.68*** 0.00
work_high_OTHEMPN-2.19 0.207-10.56*** 0.00
work_high_RETEMPN-2.21 0.00 NA 0.00fixed value
work_low_AGREMPN-71.8 7.07e-12-BIG*** 0.00
work_low_FPSEMPN-1.51 0.346-4.36*** 0.00
work_low_HEREMPN-0.819 0.360-2.27* 0.00
work_low_MWTEMPN-1.98 0.387-5.12*** 0.00
work_low_OTHEMPN-2.02 0.407-4.97*** 0.00
work_low_RETEMPN-2.05 0.00 NA 0.00fixed value
work_med_AGREMPN-8.09e+64 NA NA 0.00
work_med_FPSEMPN-2.15 0.194-11.06*** 0.00
work_med_HEREMPN-1.61 0.205-7.85*** 0.00
work_med_MWTEMPN-2.04 0.210-9.74*** 0.00
work_med_OTHEMPN-2.17 0.222-9.77*** 0.00
work_med_RETEMPN-2.12 0.00 NA 0.00fixed value
work_veryhigh_AGREMPN-6.04 24.2-0.25 0.00
work_veryhigh_FPSEMPN-1.84 0.152-12.10*** 0.00
work_veryhigh_HEREMPN-1.99 0.170-11.71*** 0.00
work_veryhigh_MWTEMPN-1.76 0.159-11.08*** 0.00
work_veryhigh_OTHEMPN-2.51 0.189-13.24*** 0.00
work_veryhigh_RETEMPN-2.38 0.00 NA 0.00fixed value
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3763,12 +2646,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from activitysim.estimation.larch import update_coefficients, update_size_spec\n", - "result_dir = data.edb_directory/\"estimated\"" + "\n", + "result_dir = data.edb_directory / \"estimated\"" ] }, { @@ -3780,12 +2664,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "update_coefficients(\n", - " model, data, result_dir,\n", + " model,\n", + " data,\n", + " result_dir,\n", " output_file=f\"{modelname}_coefficients_revised.csv\",\n", ");" ] @@ -3799,7 +2685,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -3844,12 +2730,12 @@ " work_low\n", " workplace\n", " 0.000000\n", - " 0.044251\n", - " 0.175515\n", - " 0.374554\n", - " 0.169451\n", - " 6.988481e-19\n", - " 0.236229\n", + " 0.121509\n", + " 0.208506\n", + " 0.415240\n", + " 0.124770\n", + " 6.285395e-32\n", + " 0.129975\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -3860,12 +2746,12 @@ " work_med\n", " workplace\n", " 0.000000\n", - " 0.120498\n", - " 0.144005\n", - " 0.251914\n", - " 0.079565\n", - " 3.439740e-07\n", - " 0.404018\n", + " 0.176350\n", + " 0.170960\n", + " 0.294919\n", + " 0.167444\n", + " 0.000000e+00\n", + " 0.190327\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -3876,12 +2762,12 @@ " work_high\n", " workplace\n", " 0.000000\n", - " 0.159104\n", - " 0.191636\n", - " 0.298286\n", - " 0.169233\n", - " 1.153217e-06\n", - " 0.181741\n", + " 0.125802\n", + " 0.171788\n", + " 0.209290\n", + " 0.128317\n", + " 1.787012e-01\n", + " 0.186102\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -3892,12 +2778,12 @@ " work_veryhigh\n", " workplace\n", " 0.000000\n", - " 0.062723\n", - " 0.116348\n", - " 0.133219\n", - " 0.056296\n", - " 5.108942e-01\n", - " 0.120520\n", + " 0.144466\n", + " 0.247167\n", + " 0.211891\n", + " 0.126365\n", + " 3.692074e-03\n", + " 0.266419\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -4197,10 +3083,10 @@ ], "text/plain": [ " segment model_selector TOTHH RETEMPN FPSEMPN HEREMPN \\\n", - "0 work_low workplace 0.000000 0.044251 0.175515 0.374554 \n", - "1 work_med workplace 0.000000 0.120498 0.144005 0.251914 \n", - "2 work_high workplace 0.000000 0.159104 0.191636 0.298286 \n", - "3 work_veryhigh workplace 0.000000 0.062723 0.116348 0.133219 \n", + "0 work_low workplace 0.000000 0.121509 0.208506 0.415240 \n", + "1 work_med workplace 0.000000 0.176350 0.170960 0.294919 \n", + "2 work_high workplace 0.000000 0.125802 0.171788 0.209290 \n", + "3 work_veryhigh workplace 0.000000 0.144466 0.247167 0.211891 \n", "4 university school 0.000000 0.000000 0.000000 0.000000 \n", "5 gradeschool school 0.000000 0.000000 0.000000 0.000000 \n", "6 highschool school 0.000000 0.000000 0.000000 0.000000 \n", @@ -4221,10 +3107,10 @@ "21 univ trip 0.000999 0.000000 0.000000 0.000000 \n", "\n", " OTHEMPN AGREMPN MWTEMPN AGE0519 HSENROLL COLLFTE COLLPTE \n", - "0 0.169451 6.988481e-19 0.236229 0.000 0.000000 0.000000 0.000000 \n", - "1 0.079565 3.439740e-07 0.404018 0.000 0.000000 0.000000 0.000000 \n", - "2 0.169233 1.153217e-06 0.181741 0.000 0.000000 0.000000 0.000000 \n", - "3 0.056296 5.108942e-01 0.120520 0.000 0.000000 0.000000 0.000000 \n", + "0 0.124770 6.285395e-32 0.129975 0.000 0.000000 0.000000 0.000000 \n", + "1 0.167444 0.000000e+00 0.190327 0.000 0.000000 0.000000 0.000000 \n", + "2 0.128317 1.787012e-01 0.186102 0.000 0.000000 0.000000 0.000000 \n", + "3 0.126365 3.692074e-03 0.266419 0.000 0.000000 0.000000 0.000000 \n", "4 0.000000 0.000000e+00 0.000000 0.000 0.000000 0.592000 0.408000 \n", "5 0.000000 0.000000e+00 0.000000 1.000 0.000000 0.000000 0.000000 \n", "6 0.000000 0.000000e+00 0.000000 0.000 1.000000 0.000000 0.000000 \n", @@ -4245,14 +3131,16 @@ "21 0.000000 0.000000e+00 0.000000 0.000 0.000000 0.591409 0.407592 " ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "update_size_spec(\n", - " model, data, result_dir, \n", + " model,\n", + " data,\n", + " result_dir,\n", " output_file=f\"{modelname}_size_terms.csv\",\n", ")" ] @@ -4266,12 +3154,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "model.to_xlsx(\n", - " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", + " result_dir / f\"{modelname}_model_estimation.xlsx\",\n", " data_statistics=False,\n", ");" ] @@ -4290,7 +3178,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -4323,49 +3211,49 @@ " \n", " 0\n", " coef_dist_0_1\n", - " -1.084778\n", + " -1.035381\n", " F\n", " \n", " \n", " 1\n", " coef_dist_1_2\n", - " -0.252580\n", + " -0.465249\n", " F\n", " \n", " \n", " 2\n", " coef_dist_2_5\n", - " -0.369689\n", + " -0.417748\n", " F\n", " \n", " \n", " 3\n", " coef_dist_5_15\n", - " -0.152259\n", + " -0.155873\n", " F\n", " \n", " \n", " 4\n", " coef_dist_15_up\n", - " -0.091700\n", + " -0.117051\n", " F\n", " \n", " \n", " 5\n", " coef_dist_0_5_high\n", - " 0.120981\n", + " 0.146966\n", " F\n", " \n", " \n", " 6\n", " coef_dist_5_up_high\n", - " -0.015225\n", + " 0.025067\n", " F\n", " \n", " \n", " 7\n", " coef_mode_logsum\n", - " 0.155426\n", + " 0.074237\n", " F\n", " \n", " \n", @@ -4374,28 +3262,28 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coef_dist_0_1 -1.084778 F\n", - "1 coef_dist_1_2 -0.252580 F\n", - "2 coef_dist_2_5 -0.369689 F\n", - "3 coef_dist_5_15 -0.152259 F\n", - "4 coef_dist_15_up -0.091700 F\n", - "5 coef_dist_0_5_high 0.120981 F\n", - "6 coef_dist_5_up_high -0.015225 F\n", - "7 coef_mode_logsum 0.155426 F" + "0 coef_dist_0_1 -1.035381 F\n", + "1 coef_dist_1_2 -0.465249 F\n", + "2 coef_dist_2_5 -0.417748 F\n", + "3 coef_dist_5_15 -0.155873 F\n", + "4 coef_dist_15_up -0.117051 F\n", + "5 coef_dist_0_5_high 0.146966 F\n", + "6 coef_dist_5_up_high 0.025067 F\n", + "7 coef_mode_logsum 0.074237 F" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.read_csv(result_dir/f\"{modelname}_coefficients_revised.csv\")" + "pd.read_csv(result_dir / f\"{modelname}_coefficients_revised.csv\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -4442,12 +3330,12 @@ " work_low\n", " workplace\n", " 0.000000\n", - " 0.044251\n", - " 0.175515\n", - " 0.374554\n", - " 0.169451\n", - " 6.988481e-19\n", - " 0.236229\n", + " 0.121509\n", + " 0.208506\n", + " 0.415240\n", + " 0.124770\n", + " 6.285395e-32\n", + " 0.129975\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -4459,12 +3347,12 @@ " work_med\n", " workplace\n", " 0.000000\n", - " 0.120498\n", - " 0.144005\n", - " 0.251914\n", - " 0.079565\n", - " 3.439740e-07\n", - " 0.404018\n", + " 0.176350\n", + " 0.170960\n", + " 0.294919\n", + " 0.167444\n", + " 0.000000e+00\n", + " 0.190327\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -4476,12 +3364,12 @@ " work_high\n", " workplace\n", " 0.000000\n", - " 0.159104\n", - " 0.191636\n", - " 0.298286\n", - " 0.169233\n", - " 1.153217e-06\n", - " 0.181741\n", + " 0.125802\n", + " 0.171788\n", + " 0.209290\n", + " 0.128317\n", + " 1.787012e-01\n", + " 0.186102\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -4493,12 +3381,12 @@ " work_veryhigh\n", " workplace\n", " 0.000000\n", - " 0.062723\n", - " 0.116348\n", - " 0.133219\n", - " 0.056296\n", - " 5.108942e-01\n", - " 0.120520\n", + " 0.144466\n", + " 0.247167\n", + " 0.211891\n", + " 0.126365\n", + " 3.692074e-03\n", + " 0.266419\n", " 0.000\n", " 0.000000\n", " 0.000000\n", @@ -4816,10 +3704,10 @@ ], "text/plain": [ " index segment model_selector TOTHH RETEMPN FPSEMPN \\\n", - "0 0 work_low workplace 0.000000 0.044251 0.175515 \n", - "1 1 work_med workplace 0.000000 0.120498 0.144005 \n", - "2 2 work_high workplace 0.000000 0.159104 0.191636 \n", - "3 3 work_veryhigh workplace 0.000000 0.062723 0.116348 \n", + "0 0 work_low workplace 0.000000 0.121509 0.208506 \n", + "1 1 work_med workplace 0.000000 0.176350 0.170960 \n", + "2 2 work_high workplace 0.000000 0.125802 0.171788 \n", + "3 3 work_veryhigh workplace 0.000000 0.144466 0.247167 \n", "4 4 university school 0.000000 0.000000 0.000000 \n", "5 5 gradeschool school 0.000000 0.000000 0.000000 \n", "6 6 highschool school 0.000000 0.000000 0.000000 \n", @@ -4840,10 +3728,10 @@ "21 21 univ trip 0.000999 0.000000 0.000000 \n", "\n", " HEREMPN OTHEMPN AGREMPN MWTEMPN AGE0519 HSENROLL COLLFTE \\\n", - "0 0.374554 0.169451 6.988481e-19 0.236229 0.000 0.000000 0.000000 \n", - "1 0.251914 0.079565 3.439740e-07 0.404018 0.000 0.000000 0.000000 \n", - "2 0.298286 0.169233 1.153217e-06 0.181741 0.000 0.000000 0.000000 \n", - "3 0.133219 0.056296 5.108942e-01 0.120520 0.000 0.000000 0.000000 \n", + "0 0.415240 0.124770 6.285395e-32 0.129975 0.000 0.000000 0.000000 \n", + "1 0.294919 0.167444 0.000000e+00 0.190327 0.000 0.000000 0.000000 \n", + "2 0.209290 0.128317 1.787012e-01 0.186102 0.000 0.000000 0.000000 \n", + "3 0.211891 0.126365 3.692074e-03 0.266419 0.000 0.000000 0.000000 \n", "4 0.000000 0.000000 0.000000e+00 0.000000 0.000 0.000000 0.592000 \n", "5 0.000000 0.000000 0.000000e+00 0.000000 1.000 0.000000 0.000000 \n", "6 0.000000 0.000000 0.000000e+00 0.000000 0.000 1.000000 0.000000 \n", @@ -4888,13 +3776,1784 @@ "21 0.407592 " ] }, - "execution_count": 18, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_csv(result_dir / f\"{modelname}_size_terms.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modify Spec\n", + "\n", + "Here, we will demonstrate the process of re-estimating the model with a modified\n", + "SPEC file. This does *not* require re-running ActivitySim, it just requires\n", + "changing the SPEC file and re-running the Larch estimation only.\n", + "\n", + "The `backup` command we ran earlier made a backup copy of the \n", + "original spec file in the EDB directory.\n", + "This was not strictly necessary, but since we're about to modify it and\n", + "we may want undo our changes, it can be handy to keep a copy of the\n", + "original spec file around. Since we already have a backup copy, we'll make some \n", + "changes directly in the SPEC file. As an example here, we're going\n", + "to change one of the breakpoints on the piecewise distance function in the\n", + "utility calculation. For this demo we are editing \n", + "the SPEC file using Python code to make the changes, but a user does not need\n", + "to change the file using Python; any CSV editor (e.g. Excel) can be used. \n", + "\n", + "The raw contents of the SPEC file can be loaded and viewed in Python like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Label,Description,Expression,coefficient\n", + "local_dist,,_DIST@skims['DIST'],1\n", + "util_dist_0_1,\"Distance, piecewise linear from 0 to 1 miles\",\"@_DIST.clip(0,1)\",coef_dist_0_1\n", + "util_dist_1_2,\"Distance, piecewise linear from 1 to 2 miles\",\"@(_DIST-1).clip(0,1)\",coef_dist_1_2\n", + "util_dist_2_5,\"Distance, piecewise linear from 2 to 5 miles\",\"@(_DIST-2).clip(0,3)\",coef_dist_2_5\n", + "util_dist_5_15,\"Distance, piecewise linear from 5 to 15 miles\",\"@(_DIST-5).clip(0,10)\",coef_dist_5_15\n", + "util_dist_15_up,\"Distance, piecewise linear for 15+ miles\",@(_DIST-15.0).clip(0),coef_dist_15_up\n", + "util_dist_0_5_high,\"Distance 0 to 5 mi, high and very high income\",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * _DIST.clip(upper=5),coef_dist_0_5_high\n", + "util_dist_15_up_high,\"Distance 5+ mi, high and very high income\",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * (_DIST-5).clip(0),coef_dist_5_up_high\n", + "util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1\n", + "util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1\n", + "util_no_attractions,No attractions,@df['size_term']==0,-999\n", + "util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum\n", + "util_sample_of_corrections_factor,Sample of alternatives correction factor,\"@np.minimum(np.log(df.pick_count/df.prob), 60)\",1\n" + ] + } + ], + "source": [ + "with open(data.edb_directory / \"workplace_location_SPEC.csv\") as f:\n", + " raw_spec = f.read()\n", + "\n", + "print(raw_spec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's move the 2 mile breakpoint to 3 miles. To do so, we will need to change two lines\n", + "of the SPEC file. As we change the file, we will edit all four columns: label, description, \n", + "expression, and coefficient. \n", + "\n", + "## WARNING\n", + "\n", + "**It is particularly important to make changes to the label when \n", + "changing the expression.** The estimation tools will prefer the pre-calculated variables computed\n", + "and stored with the given label, so if the label is not changed the expression changes will be ignored.\n", + "\n", + "After we make the changes, we'll write the modified SPEC file back to disk, overwriting the original." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "orig_lines = \"\"\"util_dist_1_2,\"Distance, piecewise linear from 1 to 2 miles\",\"@(_DIST-1).clip(0,1)\",coef_dist_1_2\n", + "util_dist_2_5,\"Distance, piecewise linear from 2 to 5 miles\",\"@(_DIST-2).clip(0,3)\",coef_dist_2_5\"\"\"\n", + "\n", + "repl_lines = \"\"\"util_dist_1_3,\"Distance, piecewise linear from 1 to 3 miles\",\"@(_DIST-1).clip(0,2)\",coef_dist_1_3\n", + "util_dist_3_5,\"Distance, piecewise linear from 3 to 5 miles\",\"@(_DIST-3).clip(0,2)\",coef_dist_3_5\"\"\"\n", + "\n", + "raw_spec = raw_spec.replace(orig_lines, repl_lines)\n", + "\n", + "with open(data.edb_directory / \"workplace_location_SPEC.csv\", mode=\"w\") as f:\n", + " f.write(raw_spec)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reloading the model and getting set for re-estimation can be done using the same commands as above." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_landuse.csv\n", + "loading from output-est-mode/estimation_data_bundle/workplace_location/workplace_location_size_terms.csv\n" + ] + } + ], + "source": [ + "model2, data2 = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may notice in the utility functions shown below, all of the unadulterated lines of the \n", + "spec file correlate with utility terms that are simple `X.label` data items, which are \n", + "stored as pre-computed data variables in the EDB. Our modified lines, however, are now\n", + "showing the complete expression that will be freshly evaluated by Larch using Sharrow." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
P.coef_dist_0_1 * X.util_dist_0_1
 + P.coef_dist_1_3 * X('(_DIST-1).clip(0,2)')
 + P.coef_dist_3_5 * X('(_DIST-3).clip(0,2)')
 + P.coef_dist_5_15 * X.util_dist_5_15
 + P.coef_dist_15_up * X.util_dist_15_up
 + P.coef_dist_0_5_high * X.util_dist_0_5_high
 + P.coef_dist_5_up_high * X.util_dist_15_up_high
 + P('-999') * X.util_no_attractions
 + P.coef_mode_logsum * X.util_mode_logsum
 + P('1') * X.util_sample_of_corrections_factor
" + ], + "text/plain": [ + " P.coef_dist_0_1 * X.util_dist_0_1\n", + "+ P.coef_dist_1_3 * X('(_DIST-1).clip(0,2)')\n", + "+ P.coef_dist_3_5 * X('(_DIST-3).clip(0,2)')\n", + "+ P.coef_dist_5_15 * X.util_dist_5_15\n", + "+ P.coef_dist_15_up * X.util_dist_15_up\n", + "+ P.coef_dist_0_5_high * X.util_dist_0_5_high\n", + "+ P.coef_dist_5_up_high * X.util_dist_15_up_high\n", + "+ P('-999') * X.util_no_attractions\n", + "+ P.coef_mode_logsum * X.util_mode_logsum\n", + "+ P('1') * X.util_sample_of_corrections_factor" + ] + }, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.read_csv(result_dir/f\"{modelname}_size_terms.csv\")" + "model2.utility_ca" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are almost ready to go with this model. But if we attempt to estimate the model now, we will get an error:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NameError(\"name '_DIST' is not defined\")\n" + ] + } + ], + "source": [ + "from larch.util.shush import shush\n", + "\n", + "with shush(stderr=True):\n", + " try:\n", + " model2.estimate()\n", + " except NameError as e:\n", + " print(repr(e))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The error arises because the model SPEC includes a temporary `_DIST` variable, which was not stored in the EDB data files.\n", + "There are several ways to solve this problem. The most robust way would be to return to ActivitySim and edit the model specification\n", + "and configurations so that this variable is not a temporary value (i.e. add it to a preprocessor or annotator). This process \n", + "may take some time and effort, but it should be able to allow the user to access any variable from ActivitySim.\n", + "\n", + "Alternatively, it may be possible to reconstruct the value of the temporary variable, or a suitable proxy, by processing \n", + "values that did get stored in the EDB. We can do so here, by reconstituting the distance value as the sum of the piecewise\n", + "parts that did get stored in the EDB. Rather than returning to ActivitySim, we can simply compute the temporary variable\n", + "directly in the model's data object, giving it the same name as from the specification file, and otherwise proceed as normal." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "model2.data[\"_DIST\"] = (\n", + " model2.data.util_dist_0_1\n", + " + model2.data.util_dist_1_2\n", + " + model2.data.util_dist_2_5\n", + " + model2.data.util_dist_5_15\n", + " + model2.data.util_dist_15_up\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Iteration 008 [Optimization terminated successfully]

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Best LL = -88684.18177107332

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0
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coef_dist_0_5_high0.147163
coef_dist_15_up-0.117017
coef_dist_1_3-0.433865
coef_dist_3_5-0.417069
coef_dist_5_15-0.155760
coef_dist_5_up_high0.025050
coef_mode_logsum0.074530
work_high_AGREMPN-1.857041
work_high_FPSEMPN-1.895566
work_high_HEREMPN-1.698595
work_high_MWTEMPN-1.816036
work_high_OTHEMPN-2.187730
work_high_RETEMPN-2.207275
work_low_AGREMPN-286.802086
work_low_FPSEMPN-1.507131
work_low_HEREMPN-0.818251
work_low_MWTEMPN-1.979965
work_low_OTHEMPN-2.020885
work_low_RETEMPN-2.047943
work_med_AGREMPN-1490.624349
work_med_FPSEMPN-2.150429
work_med_HEREMPN-1.605331
work_med_MWTEMPN-2.043346
work_med_OTHEMPN-2.171601
work_med_RETEMPN-2.120264
work_veryhigh_AGREMPN-6.028955
work_veryhigh_FPSEMPN-1.838036
work_veryhigh_HEREMPN-1.992113
work_veryhigh_MWTEMPN-1.763204
work_veryhigh_OTHEMPN-2.509234
work_veryhigh_RETEMPN-2.375156
tolerance2.5082874166498626e-07
stepsarray([0.25, 0.5 , 0.75, 1. , 1. , 1. , 1. , 1. ])
message'Optimization terminated successfully'
elapsed_time0:00:02.337866
method'BHHH'
n_cases28281
iteration_number8
" + ], + "text/plain": [ + "┣ loglike: -88684.18177107332\n", + "┣ x: -999 -999.000000\n", + "┃ 1 1.000000\n", + "┃ coef_dist_0_1 -1.072002\n", + "┃ coef_dist_0_5_high 0.147163\n", + "┃ coef_dist_15_up -0.117017\n", + "┃ coef_dist_1_3 -0.433865\n", + "┃ coef_dist_3_5 -0.417069\n", + "┃ coef_dist_5_15 -0.155760\n", + "┃ coef_dist_5_up_high 0.025050\n", + "┃ coef_mode_logsum 0.074530\n", + "┃ work_high_AGREMPN -1.857041\n", + "┃ work_high_FPSEMPN -1.895566\n", + "┃ work_high_HEREMPN -1.698595\n", + "┃ work_high_MWTEMPN -1.816036\n", + "┃ work_high_OTHEMPN -2.187730\n", + "┃ work_high_RETEMPN -2.207275\n", + "┃ work_low_AGREMPN -286.802086\n", + "┃ work_low_FPSEMPN -1.507131\n", + "┃ work_low_HEREMPN -0.818251\n", + "┃ work_low_MWTEMPN -1.979965\n", + "┃ work_low_OTHEMPN -2.020885\n", + "┃ work_low_RETEMPN -2.047943\n", + "┃ work_med_AGREMPN -1490.624349\n", + "┃ work_med_FPSEMPN -2.150429\n", + "┃ work_med_HEREMPN -1.605331\n", + "┃ work_med_MWTEMPN -2.043346\n", + "┃ work_med_OTHEMPN -2.171601\n", + "┃ work_med_RETEMPN -2.120264\n", + "┃ work_veryhigh_AGREMPN -6.028955\n", + "┃ work_veryhigh_FPSEMPN -1.838036\n", + "┃ work_veryhigh_HEREMPN -1.992113\n", + "┃ work_veryhigh_MWTEMPN -1.763204\n", + "┃ work_veryhigh_OTHEMPN -2.509234\n", + "┃ work_veryhigh_RETEMPN -2.375156\n", + "┃ dtype: float64\n", + "┣ tolerance: 2.5082874166498626e-07\n", + "┣ steps: array([0.25, 0.5 , 0.75, 1. , 1. , 1. , 1. , 1. ])\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ elapsed_time: datetime.timedelta(seconds=2, microseconds=337866)\n", + "┣ method: 'BHHH'\n", + "┣ n_cases: 28281\n", + "┣ iteration_number: 8" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with shush(stderr=True):\n", + " # swallow a bunch of error logging related to recovery from our previous estimation failure\n", + " result2 = model2.estimate(method=\"BHHH\", options={\"maxiter\": 1000})\n", + "\n", + "result2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then review the original and revised models side-by-side to see the differences." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelmodel2
Number of CasesAggregate28281.00000028281.000000
Log Likelihood at ConvergenceAggregate-88683.850386-88684.181771
Per Case-3.135810-3.135822
Log Likelihood at Null ParametersAggregate-124263.519724-124263.519724
Per Case-4.393887-4.393887
Rho Squared w.r.t. Null ParametersAggregate0.2863240.286322
\n", + "
" + ], + "text/plain": [ + " model model2\n", + "Number of Cases Aggregate 28281.000000 28281.000000\n", + "Log Likelihood at Convergence Aggregate -88683.850386 -88684.181771\n", + " Per Case -3.135810 -3.135822\n", + "Log Likelihood at Null Parameters Aggregate -124263.519724 -124263.519724\n", + " Per Case -4.393887 -4.393887\n", + "Rho Squared w.r.t. Null Parameters Aggregate 0.286324 0.286322" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.estimation_statistics_raw(),\n", + " \"model2\": model2.estimation_statistics_raw(),\n", + " }, axis=1).fillna(\"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelmodel2
ValueStd Errt StatSignifNull ValueConstrainedValueStd Errt StatSignifNull ValueConstrained
Parameter
-999-999.0.00NA0.0fixed value-999.0.00NA0.0fixed value
11.000.00NA0.0fixed value1.000.00NA0.0fixed value
coef_dist_0_1-1.040.105-9.89***0.0-1.070.0946-11.33***0.0
coef_dist_0_5_high0.1470.011412.86***0.00.1470.011412.86***0.0
coef_dist_15_up-0.1170.00211-55.56***0.0-0.1170.00211-55.55***0.0
coef_dist_1_2-0.4650.0428-10.86***0.0
coef_dist_2_5-0.4180.0116-36.15***0.0
coef_dist_5_15-0.1560.00276-56.40***0.0-0.1560.00283-55.09***0.0
coef_dist_5_up_high0.02510.0017614.22***0.00.02500.0017614.20***0.0
coef_mode_logsum0.07420.008378.87***0.00.07450.008368.91***0.0
work_high_AGREMPN-1.860.596-3.11**0.0-1.860.597-3.11**0.0
work_high_FPSEMPN-1.900.178-10.67***0.0-1.900.178-10.67***0.0
work_high_HEREMPN-1.700.193-8.81***0.0-1.700.193-8.81***0.0
work_high_MWTEMPN-1.820.188-9.68***0.0-1.820.188-9.68***0.0
work_high_OTHEMPN-2.190.207-10.56***0.0-2.190.207-10.56***0.0
work_high_RETEMPN-2.210.00NA0.0fixed value-2.210.00NA0.0fixed value
work_low_AGREMPN-71.87.07e-12-BIG***0.0-287.NANA0.0
work_low_FPSEMPN-1.510.346-4.36***0.0-1.510.346-4.36***0.0
work_low_HEREMPN-0.8190.360-2.27*0.0-0.8180.361-2.27*0.0
work_low_MWTEMPN-1.980.387-5.12***0.0-1.980.387-5.11***0.0
work_low_OTHEMPN-2.020.407-4.97***0.0-2.020.407-4.96***0.0
work_low_RETEMPN-2.050.00NA0.0fixed value-2.050.00NA0.0fixed value
work_med_AGREMPN-8.09e+64NANA0.0-1.49e+03NANA0.0
work_med_FPSEMPN-2.150.194-11.06***0.0-2.150.195-11.05***0.0
work_med_HEREMPN-1.610.205-7.85***0.0-1.610.205-7.84***0.0
work_med_MWTEMPN-2.040.210-9.74***0.0-2.040.210-9.73***0.0
work_med_OTHEMPN-2.170.222-9.77***0.0-2.170.222-9.77***0.0
work_med_RETEMPN-2.120.00NA0.0fixed value-2.120.00NA0.0fixed value
work_veryhigh_AGREMPN-6.0424.2-0.250.0-6.0323.7-0.250.0
work_veryhigh_FPSEMPN-1.840.152-12.10***0.0-1.840.152-12.10***0.0
work_veryhigh_HEREMPN-1.990.170-11.71***0.0-1.990.170-11.71***0.0
work_veryhigh_MWTEMPN-1.760.159-11.08***0.0-1.760.159-11.08***0.0
work_veryhigh_OTHEMPN-2.510.189-13.24***0.0-2.510.189-13.24***0.0
work_veryhigh_RETEMPN-2.380.00NA0.0fixed value-2.380.00NA0.0fixed value
coef_dist_1_3-0.4340.0202-21.48***0.0
coef_dist_3_5-0.4170.0156-26.81***0.0
\n", + "
" + ], + "text/plain": [ + " model \\\n", + " Value Std Err t Stat Signif Null Value \n", + "Parameter \n", + "-999 -999.  0.00  NA 0.0 \n", + "1  1.00  0.00  NA 0.0 \n", + "coef_dist_0_1 -1.04  0.105 -9.89 *** 0.0 \n", + "coef_dist_0_5_high  0.147  0.0114  12.86 *** 0.0 \n", + "coef_dist_15_up -0.117  0.00211 -55.56 *** 0.0 \n", + "coef_dist_1_2 -0.465  0.0428 -10.86 *** 0.0 \n", + "coef_dist_2_5 -0.418  0.0116 -36.15 *** 0.0 \n", + "coef_dist_5_15 -0.156  0.00276 -56.40 *** 0.0 \n", + "coef_dist_5_up_high  0.0251  0.00176  14.22 *** 0.0 \n", + "coef_mode_logsum  0.0742  0.00837  8.87 *** 0.0 \n", + "work_high_AGREMPN -1.86  0.596 -3.11 ** 0.0 \n", + "work_high_FPSEMPN -1.90  0.178 -10.67 *** 0.0 \n", + "work_high_HEREMPN -1.70  0.193 -8.81 *** 0.0 \n", + "work_high_MWTEMPN -1.82  0.188 -9.68 *** 0.0 \n", + "work_high_OTHEMPN -2.19  0.207 -10.56 *** 0.0 \n", + "work_high_RETEMPN -2.21  0.00  NA 0.0 \n", + "work_low_AGREMPN -71.8  7.07e-12 -BIG *** 0.0 \n", + "work_low_FPSEMPN -1.51  0.346 -4.36 *** 0.0 \n", + "work_low_HEREMPN -0.819  0.360 -2.27 * 0.0 \n", + "work_low_MWTEMPN -1.98  0.387 -5.12 *** 0.0 \n", + "work_low_OTHEMPN -2.02  0.407 -4.97 *** 0.0 \n", + "work_low_RETEMPN -2.05  0.00  NA 0.0 \n", + "work_med_AGREMPN -8.09e+64  NA  NA 0.0 \n", + "work_med_FPSEMPN -2.15  0.194 -11.06 *** 0.0 \n", + "work_med_HEREMPN -1.61  0.205 -7.85 *** 0.0 \n", + "work_med_MWTEMPN -2.04  0.210 -9.74 *** 0.0 \n", + "work_med_OTHEMPN -2.17  0.222 -9.77 *** 0.0 \n", + "work_med_RETEMPN -2.12  0.00  NA 0.0 \n", + "work_veryhigh_AGREMPN -6.04  24.2 -0.25 0.0 \n", + "work_veryhigh_FPSEMPN -1.84  0.152 -12.10 *** 0.0 \n", + "work_veryhigh_HEREMPN -1.99  0.170 -11.71 *** 0.0 \n", + "work_veryhigh_MWTEMPN -1.76  0.159 -11.08 *** 0.0 \n", + "work_veryhigh_OTHEMPN -2.51  0.189 -13.24 *** 0.0 \n", + "work_veryhigh_RETEMPN -2.38  0.00  NA 0.0 \n", + "coef_dist_1_3 \n", + "coef_dist_3_5 \n", + "\n", + " model2 \\\n", + " Constrained Value Std Err t Stat Signif \n", + "Parameter \n", + "-999 fixed value -999.  0.00  NA \n", + "1 fixed value  1.00  0.00  NA \n", + "coef_dist_0_1 -1.07  0.0946 -11.33 *** \n", + "coef_dist_0_5_high  0.147  0.0114  12.86 *** \n", + "coef_dist_15_up -0.117  0.00211 -55.55 *** \n", + "coef_dist_1_2 \n", + "coef_dist_2_5 \n", + "coef_dist_5_15 -0.156  0.00283 -55.09 *** \n", + "coef_dist_5_up_high  0.0250  0.00176  14.20 *** \n", + "coef_mode_logsum  0.0745  0.00836  8.91 *** \n", + "work_high_AGREMPN -1.86  0.597 -3.11 ** \n", + "work_high_FPSEMPN -1.90  0.178 -10.67 *** \n", + "work_high_HEREMPN -1.70  0.193 -8.81 *** \n", + "work_high_MWTEMPN -1.82  0.188 -9.68 *** \n", + "work_high_OTHEMPN -2.19  0.207 -10.56 *** \n", + "work_high_RETEMPN fixed value -2.21  0.00  NA \n", + "work_low_AGREMPN -287.  NA  NA \n", + "work_low_FPSEMPN -1.51  0.346 -4.36 *** \n", + "work_low_HEREMPN -0.818  0.361 -2.27 * \n", + "work_low_MWTEMPN -1.98  0.387 -5.11 *** \n", + "work_low_OTHEMPN -2.02  0.407 -4.96 *** \n", + "work_low_RETEMPN fixed value -2.05  0.00  NA \n", + "work_med_AGREMPN -1.49e+03  NA  NA \n", + "work_med_FPSEMPN -2.15  0.195 -11.05 *** \n", + "work_med_HEREMPN -1.61  0.205 -7.84 *** \n", + "work_med_MWTEMPN -2.04  0.210 -9.73 *** \n", + "work_med_OTHEMPN -2.17  0.222 -9.77 *** \n", + "work_med_RETEMPN fixed value -2.12  0.00  NA \n", + "work_veryhigh_AGREMPN -6.03  23.7 -0.25 \n", + "work_veryhigh_FPSEMPN -1.84  0.152 -12.10 *** \n", + "work_veryhigh_HEREMPN -1.99  0.170 -11.71 *** \n", + "work_veryhigh_MWTEMPN -1.76  0.159 -11.08 *** \n", + "work_veryhigh_OTHEMPN -2.51  0.189 -13.24 *** \n", + "work_veryhigh_RETEMPN fixed value -2.38  0.00  NA \n", + "coef_dist_1_3 -0.434  0.0202 -21.48 *** \n", + "coef_dist_3_5 -0.417  0.0156 -26.81 *** \n", + "\n", + " \n", + " Null Value Constrained \n", + "Parameter \n", + "-999 0.0 fixed value \n", + "1 0.0 fixed value \n", + "coef_dist_0_1 0.0 \n", + "coef_dist_0_5_high 0.0 \n", + "coef_dist_15_up 0.0 \n", + "coef_dist_1_2 \n", + "coef_dist_2_5 \n", + "coef_dist_5_15 0.0 \n", + "coef_dist_5_up_high 0.0 \n", + "coef_mode_logsum 0.0 \n", + "work_high_AGREMPN 0.0 \n", + "work_high_FPSEMPN 0.0 \n", + "work_high_HEREMPN 0.0 \n", + "work_high_MWTEMPN 0.0 \n", + "work_high_OTHEMPN 0.0 \n", + "work_high_RETEMPN 0.0 fixed value \n", + "work_low_AGREMPN 0.0 \n", + "work_low_FPSEMPN 0.0 \n", + "work_low_HEREMPN 0.0 \n", + "work_low_MWTEMPN 0.0 \n", + "work_low_OTHEMPN 0.0 \n", + "work_low_RETEMPN 0.0 fixed value \n", + "work_med_AGREMPN 0.0 \n", + "work_med_FPSEMPN 0.0 \n", + "work_med_HEREMPN 0.0 \n", + "work_med_MWTEMPN 0.0 \n", + "work_med_OTHEMPN 0.0 \n", + "work_med_RETEMPN 0.0 fixed value \n", + "work_veryhigh_AGREMPN 0.0 \n", + "work_veryhigh_FPSEMPN 0.0 \n", + "work_veryhigh_HEREMPN 0.0 \n", + "work_veryhigh_MWTEMPN 0.0 \n", + "work_veryhigh_OTHEMPN 0.0 \n", + "work_veryhigh_RETEMPN 0.0 fixed value \n", + "coef_dist_1_3 0.0 \n", + "coef_dist_3_5 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.parameter_summary().data,\n", + " \"model2\": model2.parameter_summary().data,\n", + " }, axis=1).fillna(\"\"))" ] } ], @@ -4905,7 +5564,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -4919,7 +5578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/04_auto_ownership.ipynb b/activitysim/examples/example_estimation/notebooks/04_auto_ownership.ipynb index 8a4206918f..c637ed5407 100644 --- a/activitysim/examples/example_estimation/notebooks/04_auto_ownership.ipynb +++ b/activitysim/examples/example_estimation/notebooks/04_auto_ownership.ipynb @@ -34,34 +34,82 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare, backup\n", + "prepare()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Load data and prep model for estimation" + "In this demo notebook, we will (later) edit the model spec file. But for demo purposes, we want to\n", + "make sure we are starting from the \"original\" spec file, so we'll check that now. For actual \n", + "applications, this step would not be necessary." ] }, { @@ -69,11 +117,41 @@ "execution_count": 3, "metadata": {}, "outputs": [], + "source": [ + "backup(\"output-est-mode/estimation_data_bundle/auto_ownership/auto_ownership_SPEC.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data and prep model for estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/auto_ownership/auto_ownership_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/auto_ownership/auto_ownership_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/auto_ownership/auto_ownership_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"auto_ownership\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "model, data = component_model(\n", + " modelname, \n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -94,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -206,7 +284,7 @@ "[67 rows x 2 columns]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -224,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -801,7 +879,7 @@ "28 coef_cars4_auto_time_saving_per_worker " ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -819,7 +897,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -854,15 +932,15 @@ " util_presence_children_0_4\n", " util_presence_children_5_17\n", " ...\n", - " HSENROLL\n", - " COLLFTE\n", - " COLLPTE\n", - " TOPOLOGY\n", - " TERMINAL\n", - " household_density\n", - " employment_density\n", - " density_index\n", - " is_cbd\n", + " auPkTotal\n", + " auOpRetail\n", + " auOpTotal\n", + " trPkRetail\n", + " trPkTotal\n", + " trOpRetail\n", + " trOpTotal\n", + " nmRetail\n", + " nmTotal\n", " override_choice_code\n", " \n", " \n", @@ -892,9 +970,9 @@ " \n", " \n", " \n", - " 166\n", - " 0\n", - " 0\n", + " 45\n", + " 1\n", + " 1\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -904,21 +982,21 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.21263\n", - " 24.783133\n", - " 31.566265\n", - " 13.883217\n", - " False\n", - " 1\n", + " 12.513805\n", + " 9.924660\n", + " 12.562639\n", + " 4.193237\n", + " 6.875144\n", + " 3.952128\n", + " 6.590585\n", + " 2.194792\n", + " 6.359507\n", + " 2\n", " \n", " \n", - " 197\n", - " 0\n", - " 0\n", + " 499\n", + " 1\n", + " 1\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -928,19 +1006,19 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.68156\n", - " 56.783784\n", - " 10.459459\n", - " 8.832526\n", - " False\n", - " 1\n", + " 12.823009\n", + " 10.284673\n", + " 12.868645\n", + " 6.639963\n", + " 9.364105\n", + " 6.531079\n", + " 9.259002\n", + " 5.955868\n", + " 7.795004\n", + " 2\n", " \n", " \n", - " 268\n", + " 659\n", " 1\n", " 1\n", " 0.0\n", @@ -952,19 +1030,19 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 3598.08521\n", - " 0.00000\n", - " 1\n", - " 3.29100\n", - " 11.947644\n", - " 45.167539\n", - " 9.448375\n", - " True\n", + " 12.663406\n", + " 10.247505\n", + " 12.762286\n", + " 6.001466\n", + " 8.409169\n", + " 5.786652\n", + " 8.279842\n", + " 5.798886\n", + " 7.900061\n", " 2\n", " \n", " \n", - " 375\n", + " 948\n", " 1\n", " 1\n", " 0.0\n", @@ -976,20 +1054,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 4.11499\n", - " 73.040169\n", - " 28.028350\n", - " 20.255520\n", - " True\n", + " 12.710919\n", + " 10.150335\n", + " 12.777635\n", + " 5.172974\n", + " 7.850360\n", + " 4.893929\n", + " 7.571579\n", + " 4.895220\n", + " 7.409345\n", " 2\n", " \n", " \n", - " 387\n", - " 1\n", + " 1276\n", + " 0\n", " 1\n", " 0.0\n", " 0.0\n", @@ -1000,15 +1078,15 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 227.78223\n", - " 41.22827\n", - " 1\n", - " 3.83527\n", - " 26.631579\n", - " 45.868421\n", - " 16.848945\n", - " False\n", + " 12.661307\n", + " 10.258471\n", + " 12.759529\n", + " 6.039019\n", + " 8.348963\n", + " 5.778785\n", + " 8.070525\n", + " 6.073537\n", + " 7.851667\n", " 2\n", " \n", " \n", @@ -1036,7 +1114,7 @@ " ...\n", " \n", " \n", - " 2863464\n", + " 2874468\n", " 1\n", " 1\n", " 0.0\n", @@ -1048,21 +1126,21 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 72.14684\n", - " 0.00000\n", - " 1\n", - " 5.52555\n", - " 38.187500\n", - " 978.875000\n", - " 36.753679\n", - " False\n", + " 10.036845\n", + " 8.113608\n", + " 10.265845\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 2\n", " \n", " \n", - " 2863483\n", - " 1\n", + " 2874567\n", " 1\n", + " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -1072,20 +1150,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 3\n", - " 3.99027\n", - " 39.838272\n", - " 71.693001\n", - " 25.608291\n", - " True\n", - " 2\n", + " 8.811126\n", + " 6.560015\n", + " 8.886403\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 1\n", " \n", " \n", - " 2863806\n", - " 1\n", + " 2874576\n", + " 0\n", " 1\n", " 0.0\n", " 0.0\n", @@ -1096,19 +1174,19 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 4.27539\n", - " 51.675676\n", - " 47.216216\n", - " 24.672699\n", - " False\n", + " 8.811126\n", + " 6.560015\n", + " 8.886403\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 2\n", " \n", " \n", - " 2864518\n", + " 2874826\n", " 1\n", " 1\n", " 0.0\n", @@ -1120,20 +1198,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 25.52083\n", - " 15.938148\n", - " 551.353820\n", - " 15.490363\n", - " True\n", + " 11.356335\n", + " 9.298380\n", + " 11.721935\n", + " 1.052528\n", + " 2.925968\n", + " 0.494776\n", + " 2.006432\n", + " 3.782008\n", + " 6.208875\n", " 2\n", " \n", " \n", - " 2864688\n", - " 1\n", + " 2875013\n", + " 0\n", " 1\n", " 0.0\n", " 0.0\n", @@ -1144,111 +1222,111 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.29134\n", - " 16.000000\n", - " 31.644068\n", - " 10.626823\n", - " False\n", + " 11.663277\n", + " 9.588527\n", + " 12.019407\n", + " 3.053361\n", + " 5.871246\n", + " 0.141304\n", + " 0.776133\n", + " 4.090554\n", + " 6.388513\n", " 2\n", " \n", " \n", "\n", - "

2000 rows × 95 columns

\n", + "

20000 rows × 95 columns

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Iteration 015 [Optimization terminated successfully.]

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Iteration 067 [Optimization terminated successfully]

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Best LL = -1732.441809546802

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66 rows × 8 columns

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66 rows × 7 columns

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"coef_retail_non_motor NaN NaN 1 \n", - "coef_retail_transit_no_workers NaN NaN 0 \n", - "coef_retail_transit_workers NaN NaN 0 \n", + " value best initvalue \\\n", + "param_name \n", + "coef_cars1_asc 1.337433 1.337433 1.1865 \n", + "coef_cars1_asc_county -0.655949 -0.655949 -0.5660 \n", + "coef_cars1_asc_marin -0.168475 -0.168475 -0.2434 \n", + "coef_cars1_asc_san_francisco 0.324519 0.324519 0.4259 \n", + "coef_cars1_auto_time_saving_per_worker 0.394451 0.394451 0.4707 \n", + "... ... ... ... \n", + "coef_retail_auto_no_workers 0.039844 0.039844 0.0626 \n", + "coef_retail_auto_workers 0.155792 0.155792 0.1646 \n", + "coef_retail_non_motor -0.030000 -0.030000 -0.0300 \n", + "coef_retail_transit_no_workers -0.307701 -0.307701 -0.3053 \n", + "coef_retail_transit_workers -0.524658 -0.524658 -0.5117 \n", "\n", - " best \n", - "coef_cars1_asc 4.744711 \n", - "coef_cars1_asc_county -0.566000 \n", - "coef_cars1_asc_marin -0.243396 \n", - "coef_cars1_asc_san_francisco 3.984111 \n", - "coef_cars1_auto_time_saving_per_worker -0.039384 \n", - 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"text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 9.359944885353407e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 8.107476782860855e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 7.627843595130013e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.990378290025734e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.0563705701904108e-16 in general_inverse\n", - 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x\n", + "
keyvalue
x\n", " \n", " \n", " \n", @@ -1563,23 +1577,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1587,11 +1601,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1603,11 +1617,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1623,7 +1637,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1631,11 +1645,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1643,175 +1657,175 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1819,816 +1833,3969 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coef_cars1_asc4.7447111.337433
coef_cars1_asc_county-0.566000-0.655949
coef_cars1_asc_marin-0.243396-0.168475
coef_cars1_asc_san_francisco3.9841110.324519
coef_cars1_auto_time_saving_per_worker-0.0393840.394451
coef_cars1_density_0_10_no_workers
coef_cars1_density_10_up_no_workers-0.006930-0.014457
coef_cars1_density_10_up_workers-0.016448-0.018280
coef_cars1_drivers_2
coef_cars1_drivers_4_up2.0455452.010834
coef_cars1_hh_income_0_30k0.0285640.045617
coef_cars1_hh_income_30_up
coef_cars1_persons_18_24-0.448145-0.475768
coef_cars1_persons_25_34
coef_cars1_presence_children_0_40.4863210.486627
coef_cars1_presence_children_5_17-0.2342660.038980
coef_cars234_asc_marin
coef_cars234_presence_children_0_40.5901130.834181
coef_cars2_asc2.466763-0.816912
coef_cars2_asc_county-0.442900-0.534108
coef_cars2_asc_san_francisco4.0196630.395165
coef_cars2_auto_time_saving_per_worker0.0106060.625959
coef_cars2_density_0_10_no_workers-0.189423-0.188961
coef_cars2_density_10_up_no_workers-0.121021-0.118760
coef_cars2_drivers_22.9451532.883946
coef_cars2_drivers_33.5950543.464192
coef_cars2_drivers_4_up6.8564056.182053
coef_cars2_hh_income_0_30k0.0622690.055723
coef_cars2_hh_income_30_up0.0072880.007093
coef_cars2_num_workers_clip_3-0.0282580.316983
coef_cars2_persons_16_17-1.439330-0.845210
coef_cars2_persons_18_24-0.942633-0.990269
coef_cars2_persons_25_34-0.471640-0.379371
coef_cars2_presence_children_5_170.1645260.327420
coef_cars34_asc_county-0.237200-0.260727
coef_cars34_asc_san_francisco-180.6108020.055894
coef_cars34_density_0_10_no_workers-0.377292-0.342857
coef_cars34_density_10_up_no_workers-0.211272-0.255253
coef_cars34_persons_16_17-1.955221-1.816227
coef_cars34_persons_18_24-0.988280-1.014724
coef_cars34_persons_25_34-0.914993-0.745197
coef_cars34_presence_children_5_170.4654180.541541
coef_cars3_asc185.820626-3.427743
coef_cars3_auto_time_saving_per_worker0.0094180.538213
coef_cars3_drivers_22.3528283.329318
coef_cars3_drivers_35.0484885.702014
coef_cars3_drivers_4_up8.3179508.643466
coef_cars3_hh_income_0_30k0.0519870.064430
coef_cars3_hh_income_30_up0.0047160.009129
coef_cars3_num_workers_clip_30.3741280.650136
coef_cars4_asc-375.140428-5.526492
coef_cars4_auto_time_saving_per_worker-0.1472990.834093
coef_cars4_drivers_2561.0150572.649806
coef_cars4_drivers_3564.4901585.329905
coef_cars4_drivers_4_up568.2725149.423154
coef_cars4_hh_income_0_30k0.0358320.071838
coef_cars4_hh_income_30_up0.0130760.013164
coef_cars4_num_workers_clip_30.6996560.948575
coef_retail_auto_no_workers-0.6377040.039844
coef_retail_auto_workers-0.5311120.155792
coef_retail_non_motor
coef_retail_transit_no_workers-0.333447-0.307701
coef_retail_transit_workers-0.464382-0.524658
tolerance4.356665489100602e-06
steps
array([1. , 1. , 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
-       "       0.5, 0.5])
message'Optimization terminated successfully.'
elapsed_time0:00:00.484879
method'bhhh'
n_cases2000
iteration_number15
logloss0.866220904773401
" - ], - "text/plain": [ - "┣ loglike: -1732.441809546802\n", - "┣ x: coef_cars1_asc 4.744711\n", - "┃ coef_cars1_asc_county -0.566000\n", - "┃ coef_cars1_asc_marin -0.243396\n", - "┃ coef_cars1_asc_san_francisco 3.984111\n", - "┃ coef_cars1_auto_time_saving_per_worker -0.039384\n", - "┃ ... \n", - "┃ coef_retail_auto_no_workers -0.637704\n", - "┃ coef_retail_auto_workers -0.531112\n", - "┃ coef_retail_non_motor -0.030000\n", - "┃ coef_retail_transit_no_workers -0.333447\n", - "┃ coef_retail_transit_workers -0.464382\n", - "┃ Length: 66, dtype: float64\n", - "┣ tolerance: 4.356665489100602e-06\n", - "┣ steps: array([1. , 1. , 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n", - "┃ 0.5, 0.5])\n", - "┣ message: 'Optimization terminated successfully.'\n", - "┣ elapsed_time: datetime.timedelta(microseconds=484879)\n", - "┣ method: 'bhhh'\n", - "┣ n_cases: 2000\n", - "┣ iteration_number: 15\n", - "┣ logloss: 0.866220904773401" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.estimate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Estimated coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_cars1_asc 4.74 2.66 1.78 NA 0.00
coef_cars1_asc_county-0.566 NA NA[] 0.00 0.00
coef_cars1_asc_marin-0.243 0.0141-17.28*** NA 0.00
coef_cars1_asc_san_francisco 3.98 2.66 1.50 NA 0.00
coef_cars1_auto_time_saving_per_worker-0.0394 0.561-0.07 NA 0.00
coef_cars1_density_0_10_no_workers 0.00 NA NA NA 0.00fixed value
coef_cars1_density_10_up_no_workers-0.00693 0.00514-1.35 NA 0.00
coef_cars1_density_10_up_workers-0.0164 0.00390-4.21*** NA 0.00
coef_cars1_drivers_2 0.00 NA NA NA 0.00fixed value
coef_cars1_drivers_3 0.00 NA NA NA 0.00fixed value
coef_cars1_drivers_4_up 2.05 0.504 4.06*** NA 0.00
coef_cars1_hh_income_0_30k 0.0286 0.00628 4.55*** NA 0.00
coef_cars1_hh_income_30_up 0.00 NA NA NA 0.00fixed value
coef_cars1_num_workers_clip_3 0.00 NA NA NA 0.00fixed value
coef_cars1_persons_16_17 0.00 NA NA NA 0.00fixed value
coef_cars1_persons_18_24-0.448 0.125-3.59*** NA 0.00
coef_cars1_persons_25_34 0.00 NA NA NA 0.00fixed value
coef_cars1_presence_children_0_4 0.486 0.262 1.85 NA 0.00
coef_cars1_presence_children_5_17-0.234 0.196-1.19 NA 0.00
coef_cars234_asc_marin 0.00 NA NA NA 0.00fixed value
coef_cars234_presence_children_0_4 0.590 0.323 1.83 NA 0.00
coef_cars2_asc 2.47 2.66 0.93 NA 0.00
coef_cars2_asc_county-0.443 0.000441-BIG*** NA 0.00
coef_cars2_asc_san_francisco 4.02 2.66 1.51 NA 0.00
coef_cars2_auto_time_saving_per_worker 0.0106 0.709 0.01 NA 0.00
coef_cars2_density_0_10_no_workers-0.189 0.0342-5.54*** NA 0.00
coef_cars2_density_10_up_no_workers-0.121 0.0176-6.87*** NA 0.00
coef_cars2_drivers_2 2.95 0.265 11.12*** NA 0.00
coef_cars2_drivers_3 3.60 0.354 10.14*** NA 0.00
coef_cars2_drivers_4_up 6.86 0.647 10.60*** NA 0.00
coef_cars2_hh_income_0_30k 0.0623 0.0149 4.17*** NA 0.00
coef_cars2_hh_income_30_up 0.00729 0.00256 2.85** NA 0.00
coef_cars2_num_workers_clip_3-0.0283 0.144-0.20 NA 0.00
coef_cars2_persons_16_17-1.44 0.381-3.78*** NA 0.00
coef_cars2_persons_18_24-0.943 0.176-5.36*** NA 0.00
coef_cars2_persons_25_34-0.472 0.0988-4.78*** NA 0.00
coef_cars2_presence_children_5_17 0.165 0.260 0.63 NA 0.00
coef_cars34_asc_county-0.237 8.28e-05-BIG*** NA 0.00
coef_cars34_asc_san_francisco-181. NA NA[***] BIG 0.00
coef_cars34_density_0_10_no_workers-0.377 0.0616-6.12*** NA 0.00
coef_cars34_density_10_up_no_workers-0.211 0.0736-2.87** NA 0.00
coef_cars34_persons_16_17-1.96 0.507-3.86*** NA 0.00
coef_cars34_persons_18_24-0.988 0.215-4.61*** NA 0.00
coef_cars34_persons_25_34-0.915 0.168-5.46*** NA 0.00
coef_cars34_presence_children_5_17 0.465 0.364 1.28 NA 0.00
coef_cars3_asc 186. NA NA[***] BIG 0.00
coef_cars3_auto_time_saving_per_worker 0.00942 1.06 0.01 NA 0.00
coef_cars3_drivers_2 2.35 0.790 2.98** NA 0.00
coef_cars3_drivers_3 5.05 0.811 6.23*** NA 0.00
coef_cars3_drivers_4_up 8.32 1.01 8.25*** NA 0.00
coef_cars3_hh_income_0_30k 0.0520 0.0286 1.82 NA 0.00
coef_cars3_hh_income_30_up 0.00472 0.00485 0.97 NA 0.00
coef_cars3_num_workers_clip_3 0.374 0.234 1.60 NA 0.00
coef_cars4_asc-375. 2.15e+03-0.17 NA 0.00
coef_cars4_auto_time_saving_per_worker-0.147 1.49-0.10 NA 0.00
coef_cars4_drivers_2 561. 2.30e+03 0.24 NA 0.00
coef_cars4_drivers_3 564. 2.30e+03 0.25 NA 0.00
coef_cars4_drivers_4_up 568. 2.30e+03 0.25 NA 0.00
coef_cars4_hh_income_0_30k 0.0358 0.0460 0.78 NA 0.00
coef_cars4_hh_income_30_up 0.0131 0.00635 2.06* NA 0.00
coef_cars4_num_workers_clip_3 0.700 0.321 2.18* NA 0.00
coef_retail_auto_no_workers-0.638 0.595-1.07 NA 0.00
coef_retail_auto_workers-0.531 0.589-0.90 NA 0.00
coef_retail_non_motor-0.0300 NA NA NA 0.00fixed value
coef_retail_transit_no_workers-0.333 0.193-1.73 NA 0.00
coef_retail_transit_workers-0.464 0.162-2.86** NA 0.00
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.parameter_summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "TojXWivZsx7M" - }, - "source": [ - "# Output Estimation Results" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from activitysim.estimation.larch import update_coefficients\n", - "result_dir = data.edb_directory/\"estimated\"\n", - "update_coefficients(\n", - " model, data, result_dir,\n", - " output_file=f\"{modelname}_coefficients_revised.csv\",\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, + "
logloss0.924274751334728d_logloss\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0
coef_cars1_asc5.272591e-05
coef_cars1_asc_county-8.675234e-06
coef_cars1_asc_marin-6.400057e-05
coef_cars1_asc_san_francisco-1.587832e-04
coef_cars1_auto_time_saving_per_worker1.403290e-04
coef_cars1_density_0_10_no_workers0.000000e+00
coef_cars1_density_10_up_no_workers6.620783e-06
coef_cars1_density_10_up_workers-2.503582e-06
coef_cars1_drivers_20.000000e+00
coef_cars1_drivers_30.000000e+00
coef_cars1_drivers_4_up-7.541270e-05
coef_cars1_hh_income_0_30k1.049198e-05
coef_cars1_hh_income_30_up0.000000e+00
coef_cars1_num_workers_clip_30.000000e+00
coef_cars1_persons_16_170.000000e+00
coef_cars1_persons_18_241.878411e-05
coef_cars1_persons_25_340.000000e+00
coef_cars1_presence_children_0_4-2.694541e-05
coef_cars1_presence_children_5_171.274960e-04
coef_cars234_asc_marin0.000000e+00
coef_cars234_presence_children_0_4-3.830304e-05
coef_cars2_asc-1.829182e-05
coef_cars2_asc_county-3.904999e-05
coef_cars2_asc_san_francisco1.268645e-04
coef_cars2_auto_time_saving_per_worker-3.296868e-05
coef_cars2_density_0_10_no_workers-4.356619e-06
coef_cars2_density_10_up_no_workers-1.094392e-05
coef_cars2_drivers_2-4.226757e-05
coef_cars2_drivers_36.818523e-05
coef_cars2_drivers_4_up3.745028e-05
coef_cars2_hh_income_0_30k3.076831e-05
coef_cars2_hh_income_30_up4.120039e-07
coef_cars2_num_workers_clip_3-9.781070e-05
coef_cars2_persons_16_171.620926e-04
coef_cars2_persons_18_24-1.615064e-04
coef_cars2_persons_25_34-1.420584e-05
coef_cars2_presence_children_5_176.079833e-05
coef_cars34_asc_county2.795941e-05
coef_cars34_asc_san_francisco2.027922e-05
coef_cars34_density_0_10_no_workers5.432376e-06
coef_cars34_density_10_up_no_workers4.880993e-05
coef_cars34_persons_16_17-1.804202e-04
coef_cars34_persons_18_247.896584e-05
coef_cars34_persons_25_34-3.944173e-06
coef_cars34_presence_children_5_17-2.364296e-04
coef_cars3_asc4.510100e-05
coef_cars3_auto_time_saving_per_worker-6.354019e-05
coef_cars3_drivers_21.322705e-04
coef_cars3_drivers_37.346112e-05
coef_cars3_drivers_4_up-1.013529e-04
coef_cars3_hh_income_0_30k5.005154e-06
coef_cars3_hh_income_30_up-1.522397e-05
coef_cars3_num_workers_clip_3-2.420821e-04
coef_cars4_asc-4.863863e-05
coef_cars4_auto_time_saving_per_worker-3.102682e-05
coef_cars4_drivers_21.784363e-05
coef_cars4_drivers_32.136643e-05
coef_cars4_drivers_4_up1.397419e-06
coef_cars4_hh_income_0_30k7.719776e-07
coef_cars4_hh_income_30_up-1.121368e-05
coef_cars4_num_workers_clip_3-7.913124e-05
coef_retail_auto_no_workers1.803616e-05
coef_retail_auto_workers-1.355341e-06
coef_retail_non_motor0.000000e+00
coef_retail_transit_no_workers-6.122156e-05
coef_retail_transit_workers-7.132030e-06
nit67nfev84njev67status0message'Optimization terminated successfully'successTrueelapsed_time0:00:01.497985method'slsqp'n_cases20000iteration_number67loglike-18485.49502669456
" + ], + "text/plain": [ + "┣ x: coef_cars1_asc 1.337433\n", + "┃ coef_cars1_asc_county -0.655949\n", + "┃ coef_cars1_asc_marin -0.168475\n", + "┃ coef_cars1_asc_san_francisco 0.324519\n", + "┃ coef_cars1_auto_time_saving_per_worker 0.394451\n", + "┃ ... \n", + "┃ coef_retail_auto_no_workers 0.039844\n", + "┃ coef_retail_auto_workers 0.155792\n", + "┃ coef_retail_non_motor -0.030000\n", + "┃ coef_retail_transit_no_workers -0.307701\n", + "┃ coef_retail_transit_workers -0.524658\n", + "┃ Length: 66, dtype: float64\n", + "┣ logloss: 0.924274751334728\n", + "┣ d_logloss: coef_cars1_asc 0.000053\n", + "┃ coef_cars1_asc_county -0.000009\n", + "┃ coef_cars1_asc_marin -0.000064\n", + "┃ coef_cars1_asc_san_francisco -0.000159\n", + "┃ coef_cars1_auto_time_saving_per_worker 0.000140\n", + "┃ ... \n", + "┃ coef_retail_auto_no_workers 0.000018\n", + "┃ coef_retail_auto_workers -0.000001\n", + "┃ coef_retail_non_motor 0.000000\n", + "┃ coef_retail_transit_no_workers -0.000061\n", + "┃ coef_retail_transit_workers -0.000007\n", + "┃ Length: 66, dtype: float64\n", + "┣ nit: 67\n", + "┣ nfev: 84\n", + "┣ njev: 67\n", + "┣ status: 0\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ success: True\n", + "┣ elapsed_time: datetime.timedelta(seconds=1, microseconds=497985)\n", + "┣ method: 'slsqp'\n", + "┣ n_cases: 20000\n", + "┣ iteration_number: 67\n", + "┣ loglike: -18485.49502669456" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Write the model estimation report, including coefficient t-statistic and log likelihood" + "model.estimate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimated coefficients" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_cars1_asc 1.34 0.941 1.42 0.00
coef_cars1_asc_county-0.656 0.158-4.14*** 0.00
coef_cars1_asc_marin-0.168 0.106-1.58 0.00
coef_cars1_asc_san_francisco 0.325 0.0979 3.31*** 0.00
coef_cars1_auto_time_saving_per_worker 0.394 0.188 2.10* 0.00
coef_cars1_density_0_10_no_workers 0.00 0.00 NA 0.00fixed value
coef_cars1_density_10_up_no_workers-0.0145 0.00339-4.27*** 0.00
coef_cars1_density_10_up_workers-0.0183 0.00271-6.75*** 0.00
coef_cars1_drivers_2 0.00 0.00 NA 0.00fixed value
coef_cars1_drivers_3 0.00 0.00 NA 0.00fixed value
coef_cars1_drivers_4_up 2.01 0.317 6.35*** 0.00
coef_cars1_hh_income_0_30k 0.0456 0.00298 15.29*** 0.00
coef_cars1_hh_income_30_up 0.00 0.00 NA 0.00fixed value
coef_cars1_num_workers_clip_3 0.00 0.00 NA 0.00fixed value
coef_cars1_persons_16_17 0.00 0.00 NA 0.00fixed value
coef_cars1_persons_18_24-0.476 0.0586-8.11*** 0.00
coef_cars1_persons_25_34 0.00 0.00 NA 0.00fixed value
coef_cars1_presence_children_0_4 0.487 0.130 3.75*** 0.00
coef_cars1_presence_children_5_17 0.0390 0.0934 0.42 0.00
coef_cars234_asc_marin 0.00 0.00 NA 0.00fixed value
coef_cars234_presence_children_0_4 0.834 0.137 6.10*** 0.00
coef_cars2_asc-0.817 0.944-0.87 0.00
coef_cars2_asc_county-0.534 0.166-3.22** 0.00
coef_cars2_asc_san_francisco 0.395 0.123 3.22** 0.00
coef_cars2_auto_time_saving_per_worker 0.626 0.197 3.18** 0.00
coef_cars2_density_0_10_no_workers-0.189 0.0118-16.03*** 0.00
coef_cars2_density_10_up_no_workers-0.119 0.0117-10.12*** 0.00
coef_cars2_drivers_2 2.88 0.0602 47.87*** 0.00
coef_cars2_drivers_3 3.46 0.0971 35.68*** 0.00
coef_cars2_drivers_4_up 6.18 0.332 18.61*** 0.00
coef_cars2_hh_income_0_30k 0.0557 0.00419 13.31*** 0.00
coef_cars2_hh_income_30_up 0.00709 0.000782 9.07*** 0.00
coef_cars2_num_workers_clip_3 0.317 0.0420 7.55*** 0.00
coef_cars2_persons_16_17-0.845 0.0976-8.66*** 0.00
coef_cars2_persons_18_24-0.990 0.0659-15.04*** 0.00
coef_cars2_persons_25_34-0.379 0.0322-11.77*** 0.00
coef_cars2_presence_children_5_17 0.327 0.104 3.16** 0.00
coef_cars34_asc_county-0.261 0.173-1.50 0.00
coef_cars34_asc_san_francisco 0.0559 0.161 0.35 0.00
coef_cars34_density_0_10_no_workers-0.343 0.0182-18.81*** 0.00
coef_cars34_density_10_up_no_workers-0.255 0.0544-4.69*** 0.00
coef_cars34_persons_16_17-1.82 0.113-16.13*** 0.00
coef_cars34_persons_18_24-1.01 0.0695-14.60*** 0.00
coef_cars34_persons_25_34-0.745 0.0402-18.56*** 0.00
coef_cars34_presence_children_5_17 0.542 0.112 4.83*** 0.00
coef_cars3_asc-3.43 0.962-3.56*** 0.00
coef_cars3_auto_time_saving_per_worker 0.538 0.215 2.50* 0.00
coef_cars3_drivers_2 3.33 0.151 21.99*** 0.00
coef_cars3_drivers_3 5.70 0.170 33.48*** 0.00
coef_cars3_drivers_4_up 8.64 0.364 23.72*** 0.00
coef_cars3_hh_income_0_30k 0.0644 0.00668 9.65*** 0.00
coef_cars3_hh_income_30_up 0.00913 0.00103 8.88*** 0.00
coef_cars3_num_workers_clip_3 0.650 0.0519 12.53*** 0.00
coef_cars4_asc-5.53 1.02-5.42*** 0.00
coef_cars4_auto_time_saving_per_worker 0.834 0.249 3.35*** 0.00
coef_cars4_drivers_2 2.65 0.287 9.23*** 0.00
coef_cars4_drivers_3 5.33 0.297 17.95*** 0.00
coef_cars4_drivers_4_up 9.42 0.436 21.59*** 0.00
coef_cars4_hh_income_0_30k 0.0718 0.0111 6.46*** 0.00
coef_cars4_hh_income_30_up 0.0132 0.00132 10.00*** 0.00
coef_cars4_num_workers_clip_3 0.949 0.0646 14.69*** 0.00
coef_retail_auto_no_workers 0.0398 0.101 0.39 0.00
coef_retail_auto_workers 0.156 0.102 1.52 0.00
coef_retail_non_motor-0.0300 0.00 NA 0.00fixed value
coef_retail_transit_no_workers-0.308 0.0295-10.44*** 0.00
coef_retail_transit_workers-0.525 0.0396-13.24*** 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.parameter_summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TojXWivZsx7M" + }, + "source": [ + "# Output Estimation Results" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from activitysim.estimation.larch import update_coefficients\n", + "result_dir = data.edb_directory/\"estimated\"\n", + "update_coefficients(\n", + " model, data, result_dir,\n", + " output_file=f\"{modelname}_coefficients_revised.csv\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write the model estimation report, including coefficient t-statistic and log likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model.to_xlsx(\n", + " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", + " data_statistics=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Steps\n", + "\n", + "The final step is to either manually or automatically copy the `*_coefficients_revised.csv` file to the configs folder, rename it to `*_coefficients.csv`, and run ActivitySim in simulation mode." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefficient_namevalueconstrain
0coef_cars1_drivers_20.000000T
1coef_cars1_drivers_30.000000T
2coef_cars1_persons_16_170.000000T
3coef_cars234_asc_marin0.000000T
4coef_cars1_persons_25_340.000000T
............
62coef_cars4_drivers_35.329905F
63coef_cars3_drivers_35.702014F
64coef_cars2_drivers_4_up6.182053F
65coef_cars3_drivers_4_up8.643466F
66coef_cars4_drivers_4_up9.423154F
\n", + "

67 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " coefficient_name value constrain\n", + "0 coef_cars1_drivers_2 0.000000 T\n", + "1 coef_cars1_drivers_3 0.000000 T\n", + "2 coef_cars1_persons_16_17 0.000000 T\n", + "3 coef_cars234_asc_marin 0.000000 T\n", + "4 coef_cars1_persons_25_34 0.000000 T\n", + ".. ... ... ...\n", + "62 coef_cars4_drivers_3 5.329905 F\n", + "63 coef_cars3_drivers_3 5.702014 F\n", + "64 coef_cars2_drivers_4_up 6.182053 F\n", + "65 coef_cars3_drivers_4_up 8.643466 F\n", + "66 coef_cars4_drivers_4_up 9.423154 F\n", + "\n", + "[67 rows x 3 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_csv(result_dir/f\"{modelname}_coefficients_revised.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modify Spec\n", + "\n", + "Here, we will demonstrate the process of re-estimating the model with a modified\n", + "SPEC file. This does *not* require re-running ActivitySim, it just requires\n", + "changing the SPEC file and re-running the Larch estimation only.\n", + "\n", + "The `backup` command we ran earlier made a backup copy of the\n", + "original spec file in the EDB directory.\n", + "This was not strictly necessary, but since we're about to modify it and\n", + "we may want undo our changes, it can be handy to keep a copy of the\n", + "original spec file around. Since we already have a backup copy, we'll make some \n", + "changes directly in the SPEC file. As an example here, we're going\n", + "to re-write the household income section of the file, to change the piecewise \n", + "linear utility from 3 segments to 4. We'll move the breakpoints and rename some\n", + "coefficients to accomodate the change. As above, for this demo we are editing \n", + "the SPEC file using Python code to make the changes, but a user does not need\n", + "to change the file using Python; any CSV editor (e.g. Excel) can be used. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "with open(data.edb_directory / \"auto_ownership_SPEC.csv\") as f:\n", + " raw_spec = f.read()\n", + "\n", + "orig_lines = \"\"\"util_hh_income_0_30k,\"Piecewise Linear household income, $0-30k\",\"@df.income_in_thousands.clip(0, 30)\",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k\n", + "util_hh_income_30_75k,\"Piecewise Linear household income, $30-75k\",\"@(df.income_in_thousands-30).clip(0, 45)\",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up\n", + "util_hh_income_75k_up,\"Piecewise Linear household income, $75k+, capped at $125k\",\"@(df.income_in_thousands-75).clip(0, 50)\",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up\"\"\"\n", + "\n", + "repl_lines = \"\"\"util_hh_income_0_25k,\"Piecewise Linear household income, $0-25k\",\"@df.income_in_thousands.clip(0, 25)\",,coef_cars1_hh_income_0_25k,coef_cars2_hh_income_0_25k,coef_cars3_hh_income_0_25k,coef_cars4_hh_income_0_25k\n", + "util_hh_income_25_50k,\"Piecewise Linear household income, $25-50k\",\"@(df.income_in_thousands-25).clip(0, 25)\",,coef_cars1_hh_income_25_50,coef_cars2_hh_income_25_50,coef_cars3_hh_income_25_50,coef_cars4_hh_income_25_50\n", + "util_hh_income_50_75k,\"Piecewise Linear household income, $50-75k\",\"@(df.income_in_thousands-50).clip(0, 25)\",,coef_cars1_hh_income_50_75,coef_cars2_hh_income_50_75,coef_cars3_hh_income_50_75,coef_cars4_hh_income_50_75\n", + "util_hh_income_75k_150k,\"Piecewise Linear household income, $75k+, capped at $150k\",\"@(df.income_in_thousands-75).clip(0, 75)\",,coef_cars1_hh_income_75_up,coef_cars2_hh_income_75_up,coef_cars3_hh_income_75_up,coef_cars4_hh_income_75_up\"\"\"\n", + "\n", + "raw_spec = raw_spec.replace(orig_lines, repl_lines)\n", + "\n", + "with open(data.edb_directory / \"auto_ownership_SPEC.csv\", mode=\"w\") as f:\n", + " f.write(raw_spec)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### WARNING\n", + "\n", + "The re-estimation process will use the variable in the estimation data bundle (EDB) given by the \"Label\" \n", + "column of the SPEC, if a variable with that exact name exists in the EDB. Otherwise, it will attempt to\n", + "re-evaluate the contents of the \"Expression\" column using Sharrow, and the other data in the EDB. Thus,\n", + "the expression must only reference other data that is available explicitly in the EDB; to use \n", + "variables that ActivitySim could access but which have not been written to the EDB, it will be necessary\n", + "to go back to ActivitySim and re-run in estimation mode.\n", + "\n", + "Also, the estimation functions do not inherently know what the \"original\" spec file contained, and rely\n", + "entirely on the presence or absence of an exact match on the \"Label\" column to find pre-evaluated expressions.\n", + "It is imcumbent on the user to ensure that any material changes the Expression column are also reflected\n", + "by a new unique name in the \"Label\" column." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to re-estimate the model, we just re-run the same steps as the original estimation above." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/auto_ownership/auto_ownership_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/auto_ownership/auto_ownership_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/auto_ownership/auto_ownership_values_combined.parquet\n" + ] + } + ], + "source": [ + "model2, data2 = component_model(modelname, edb_directory=data.edb_directory, return_data=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may notice in the utility functions shown below, all of the unadulterated lines of the \n", + "spec file correlate with utility terms that are simple `X.label` data items, which are \n", + "stored as pre-computed data variables in the EDB. Our modified lines, however, are now\n", + "showing the complete expression that will be freshly evaluated by Larch using Sharrow." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
<larch.DictOfLinearFunction>
altformula
1
<Empty LinearFunction>
2
P.coef_cars1_drivers_2 * X.util_drivers_2
 + P.coef_cars1_drivers_3 * X.util_drivers_3
 + P.coef_cars1_drivers_4_up * X.util_drivers_4_up
 + P.coef_cars1_persons_16_17 * X.util_persons_16_17
 + P.coef_cars1_persons_18_24 * X.util_persons_18_24
 + P.coef_cars1_persons_25_34 * X.util_persons_25_34
 + P.coef_cars1_presence_children_0_4 * X.util_presence_children_0_4
 + P.coef_cars1_presence_children_5_17 * X.util_presence_children_5_17
 + P.coef_cars1_num_workers_clip_3 * X.util_num_workers_clip_3
 + P.coef_cars1_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')
 + P.coef_cars1_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')
 + P.coef_cars1_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')
 + P.coef_cars1_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')
 + P.coef_cars1_density_0_10_no_workers * X.util_density_0_10_no_workers
 + P.coef_cars1_density_10_up_no_workers * X.util_density_10_up_no_workers
 + P.coef_cars1_density_0_10_no_workers * X.util_density_0_10_workers
 + P.coef_cars1_density_10_up_workers * X.util_density_10_up_workers
 + P.coef_cars1_asc * X.util_asc
 + P.coef_cars1_asc_san_francisco * X.util_asc_san_francisco
 + P.coef_cars1_asc_county * X.util_asc_solano
 + P.coef_cars1_asc_county * X.util_asc_napa
 + P.coef_cars1_asc_county * X.util_asc_sonoma
 + P.coef_cars1_asc_marin * X.util_asc_marin
 + P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers
 + P.coef_retail_auto_workers * X.util_retail_auto_workers
 + P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers
 + P.coef_retail_transit_workers * X.util_retail_transit_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_no_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_workers
 + P.coef_cars1_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker
3
P.coef_cars2_drivers_2 * X.util_drivers_2
 + P.coef_cars2_drivers_3 * X.util_drivers_3
 + P.coef_cars2_drivers_4_up * X.util_drivers_4_up
 + P.coef_cars2_persons_16_17 * X.util_persons_16_17
 + P.coef_cars2_persons_18_24 * X.util_persons_18_24
 + P.coef_cars2_persons_25_34 * X.util_persons_25_34
 + P.coef_cars234_presence_children_0_4 * X.util_presence_children_0_4
 + P.coef_cars2_presence_children_5_17 * X.util_presence_children_5_17
 + P.coef_cars2_num_workers_clip_3 * X.util_num_workers_clip_3
 + P.coef_cars2_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')
 + P.coef_cars2_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')
 + P.coef_cars2_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')
 + P.coef_cars2_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')
 + P.coef_cars2_density_0_10_no_workers * X.util_density_0_10_no_workers
 + P.coef_cars2_density_10_up_no_workers * X.util_density_10_up_no_workers
 + P.coef_cars2_density_0_10_no_workers * X.util_density_0_10_workers
 + P.coef_cars2_density_10_up_no_workers * X.util_density_10_up_workers
 + P.coef_cars2_asc * X.util_asc
 + P.coef_cars2_asc_san_francisco * X.util_asc_san_francisco
 + P.coef_cars2_asc_county * X.util_asc_solano
 + P.coef_cars2_asc_county * X.util_asc_napa
 + P.coef_cars2_asc_county * X.util_asc_sonoma
 + P.coef_cars234_asc_marin * X.util_asc_marin
 + P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers
 + P.coef_retail_auto_workers * X.util_retail_auto_workers
 + P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers
 + P.coef_retail_transit_workers * X.util_retail_transit_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_no_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_workers
 + P.coef_cars2_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker
4
P.coef_cars3_drivers_2 * X.util_drivers_2
 + P.coef_cars3_drivers_3 * X.util_drivers_3
 + P.coef_cars3_drivers_4_up * X.util_drivers_4_up
 + P.coef_cars34_persons_16_17 * X.util_persons_16_17
 + P.coef_cars34_persons_18_24 * X.util_persons_18_24
 + P.coef_cars34_persons_25_34 * X.util_persons_25_34
 + P.coef_cars234_presence_children_0_4 * X.util_presence_children_0_4
 + P.coef_cars34_presence_children_5_17 * X.util_presence_children_5_17
 + P.coef_cars3_num_workers_clip_3 * X.util_num_workers_clip_3
 + P.coef_cars3_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')
 + P.coef_cars3_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')
 + P.coef_cars3_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')
 + P.coef_cars3_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')
 + P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_no_workers
 + P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_no_workers
 + P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_workers
 + P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_workers
 + P.coef_cars3_asc * X.util_asc
 + P.coef_cars34_asc_san_francisco * X.util_asc_san_francisco
 + P.coef_cars34_asc_county * X.util_asc_solano
 + P.coef_cars34_asc_county * X.util_asc_napa
 + P.coef_cars34_asc_county * X.util_asc_sonoma
 + P.coef_cars234_asc_marin * X.util_asc_marin
 + P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers
 + P.coef_retail_auto_workers * X.util_retail_auto_workers
 + P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers
 + P.coef_retail_transit_workers * X.util_retail_transit_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_no_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_workers
 + P.coef_cars3_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker
5
P.coef_cars4_drivers_2 * X.util_drivers_2
 + P.coef_cars4_drivers_3 * X.util_drivers_3
 + P.coef_cars4_drivers_4_up * X.util_drivers_4_up
 + P.coef_cars34_persons_16_17 * X.util_persons_16_17
 + P.coef_cars34_persons_18_24 * X.util_persons_18_24
 + P.coef_cars34_persons_25_34 * X.util_persons_25_34
 + P.coef_cars234_presence_children_0_4 * X.util_presence_children_0_4
 + P.coef_cars34_presence_children_5_17 * X.util_presence_children_5_17
 + P.coef_cars4_num_workers_clip_3 * X.util_num_workers_clip_3
 + P.coef_cars4_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')
 + P.coef_cars4_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')
 + P.coef_cars4_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')
 + P.coef_cars4_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')
 + P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_no_workers
 + P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_no_workers
 + P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_workers
 + P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_workers
 + P.coef_cars4_asc * X.util_asc
 + P.coef_cars34_asc_san_francisco * X.util_asc_san_francisco
 + P.coef_cars34_asc_county * X.util_asc_solano
 + P.coef_cars34_asc_county * X.util_asc_napa
 + P.coef_cars34_asc_county * X.util_asc_sonoma
 + P.coef_cars234_asc_marin * X.util_asc_marin
 + P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers
 + P.coef_retail_auto_workers * X.util_retail_auto_workers
 + P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers
 + P.coef_retail_transit_workers * X.util_retail_transit_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_no_workers
 + P.coef_retail_non_motor * X.util_retail_non_motor_workers
 + P.coef_cars4_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker
" + ], + "text/plain": [ + "DictOfLinearFunction({1: , 2: P.coef_cars1_drivers_2 * X.util_drivers_2\n", + "+ P.coef_cars1_drivers_3 * X.util_drivers_3\n", + "+ P.coef_cars1_drivers_4_up * X.util_drivers_4_up\n", + "+ P.coef_cars1_persons_16_17 * X.util_persons_16_17\n", + "+ P.coef_cars1_persons_18_24 * X.util_persons_18_24\n", + "+ P.coef_cars1_persons_25_34 * X.util_persons_25_34\n", + "+ P.coef_cars1_presence_children_0_4 * X.util_presence_children_0_4\n", + "+ P.coef_cars1_presence_children_5_17 * X.util_presence_children_5_17\n", + "+ P.coef_cars1_num_workers_clip_3 * X.util_num_workers_clip_3\n", + "+ P.coef_cars1_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')\n", + "+ P.coef_cars1_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')\n", + "+ P.coef_cars1_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')\n", + "+ P.coef_cars1_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')\n", + "+ P.coef_cars1_density_0_10_no_workers * X.util_density_0_10_no_workers\n", + "+ P.coef_cars1_density_10_up_no_workers * X.util_density_10_up_no_workers\n", + "+ P.coef_cars1_density_0_10_no_workers * X.util_density_0_10_workers\n", + "+ P.coef_cars1_density_10_up_workers * X.util_density_10_up_workers\n", + "+ P.coef_cars1_asc * X.util_asc\n", + "+ P.coef_cars1_asc_san_francisco * X.util_asc_san_francisco\n", + "+ P.coef_cars1_asc_county * X.util_asc_solano\n", + "+ P.coef_cars1_asc_county * X.util_asc_napa\n", + "+ P.coef_cars1_asc_county * X.util_asc_sonoma\n", + "+ P.coef_cars1_asc_marin * X.util_asc_marin\n", + "+ P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers\n", + "+ P.coef_retail_auto_workers * X.util_retail_auto_workers\n", + "+ P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers\n", + "+ P.coef_retail_transit_workers * X.util_retail_transit_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_no_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_workers\n", + "+ P.coef_cars1_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker, 3: P.coef_cars2_drivers_2 * X.util_drivers_2\n", + "+ P.coef_cars2_drivers_3 * X.util_drivers_3\n", + "+ P.coef_cars2_drivers_4_up * X.util_drivers_4_up\n", + "+ P.coef_cars2_persons_16_17 * X.util_persons_16_17\n", + "+ P.coef_cars2_persons_18_24 * X.util_persons_18_24\n", + "+ P.coef_cars2_persons_25_34 * X.util_persons_25_34\n", + "+ P.coef_cars234_presence_children_0_4 * X.util_presence_children_0_4\n", + "+ P.coef_cars2_presence_children_5_17 * X.util_presence_children_5_17\n", + "+ P.coef_cars2_num_workers_clip_3 * X.util_num_workers_clip_3\n", + "+ P.coef_cars2_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')\n", + "+ P.coef_cars2_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')\n", + "+ P.coef_cars2_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')\n", + "+ P.coef_cars2_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')\n", + "+ P.coef_cars2_density_0_10_no_workers * X.util_density_0_10_no_workers\n", + "+ P.coef_cars2_density_10_up_no_workers * X.util_density_10_up_no_workers\n", + "+ P.coef_cars2_density_0_10_no_workers * X.util_density_0_10_workers\n", + "+ P.coef_cars2_density_10_up_no_workers * X.util_density_10_up_workers\n", + "+ P.coef_cars2_asc * X.util_asc\n", + "+ P.coef_cars2_asc_san_francisco * X.util_asc_san_francisco\n", + "+ P.coef_cars2_asc_county * X.util_asc_solano\n", + "+ P.coef_cars2_asc_county * X.util_asc_napa\n", + "+ P.coef_cars2_asc_county * X.util_asc_sonoma\n", + "+ P.coef_cars234_asc_marin * X.util_asc_marin\n", + "+ P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers\n", + "+ P.coef_retail_auto_workers * X.util_retail_auto_workers\n", + "+ P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers\n", + "+ P.coef_retail_transit_workers * X.util_retail_transit_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_no_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_workers\n", + "+ P.coef_cars2_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker, 4: P.coef_cars3_drivers_2 * X.util_drivers_2\n", + "+ P.coef_cars3_drivers_3 * X.util_drivers_3\n", + "+ P.coef_cars3_drivers_4_up * X.util_drivers_4_up\n", + "+ P.coef_cars34_persons_16_17 * X.util_persons_16_17\n", + "+ P.coef_cars34_persons_18_24 * X.util_persons_18_24\n", + "+ P.coef_cars34_persons_25_34 * X.util_persons_25_34\n", + "+ P.coef_cars234_presence_children_0_4 * X.util_presence_children_0_4\n", + "+ P.coef_cars34_presence_children_5_17 * X.util_presence_children_5_17\n", + "+ P.coef_cars3_num_workers_clip_3 * X.util_num_workers_clip_3\n", + "+ P.coef_cars3_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')\n", + "+ P.coef_cars3_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')\n", + "+ P.coef_cars3_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')\n", + "+ P.coef_cars3_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')\n", + "+ P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_no_workers\n", + "+ P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_no_workers\n", + "+ P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_workers\n", + "+ P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_workers\n", + "+ P.coef_cars3_asc * X.util_asc\n", + "+ P.coef_cars34_asc_san_francisco * X.util_asc_san_francisco\n", + "+ P.coef_cars34_asc_county * X.util_asc_solano\n", + "+ P.coef_cars34_asc_county * X.util_asc_napa\n", + "+ P.coef_cars34_asc_county * X.util_asc_sonoma\n", + "+ P.coef_cars234_asc_marin * X.util_asc_marin\n", + "+ P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers\n", + "+ P.coef_retail_auto_workers * X.util_retail_auto_workers\n", + "+ P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers\n", + "+ P.coef_retail_transit_workers * X.util_retail_transit_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_no_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_workers\n", + "+ P.coef_cars3_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker, 5: P.coef_cars4_drivers_2 * X.util_drivers_2\n", + "+ P.coef_cars4_drivers_3 * X.util_drivers_3\n", + "+ P.coef_cars4_drivers_4_up * X.util_drivers_4_up\n", + "+ P.coef_cars34_persons_16_17 * X.util_persons_16_17\n", + "+ P.coef_cars34_persons_18_24 * X.util_persons_18_24\n", + "+ P.coef_cars34_persons_25_34 * X.util_persons_25_34\n", + "+ P.coef_cars234_presence_children_0_4 * X.util_presence_children_0_4\n", + "+ P.coef_cars34_presence_children_5_17 * X.util_presence_children_5_17\n", + "+ P.coef_cars4_num_workers_clip_3 * X.util_num_workers_clip_3\n", + "+ P.coef_cars4_hh_income_0_25k * X('df.income_in_thousands.clip(0, 25)')\n", + "+ P.coef_cars4_hh_income_25_50 * X('(df.income_in_thousands-25).clip(0, 25)')\n", + "+ P.coef_cars4_hh_income_50_75 * X('(df.income_in_thousands-50).clip(0, 25)')\n", + "+ P.coef_cars4_hh_income_75_up * X('(df.income_in_thousands-75).clip(0, 75)')\n", + "+ P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_no_workers\n", + "+ P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_no_workers\n", + "+ P.coef_cars34_density_0_10_no_workers * X.util_density_0_10_workers\n", + "+ P.coef_cars34_density_10_up_no_workers * X.util_density_10_up_workers\n", + "+ P.coef_cars4_asc * X.util_asc\n", + "+ P.coef_cars34_asc_san_francisco * X.util_asc_san_francisco\n", + "+ P.coef_cars34_asc_county * X.util_asc_solano\n", + "+ P.coef_cars34_asc_county * X.util_asc_napa\n", + "+ P.coef_cars34_asc_county * X.util_asc_sonoma\n", + "+ P.coef_cars234_asc_marin * X.util_asc_marin\n", + "+ P.coef_retail_auto_no_workers * X.util_retail_auto_no_workers\n", + "+ P.coef_retail_auto_workers * X.util_retail_auto_workers\n", + "+ P.coef_retail_transit_no_workers * X.util_retail_transit_no_workers\n", + "+ P.coef_retail_transit_workers * X.util_retail_transit_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_no_workers\n", + "+ P.coef_retail_non_motor * X.util_retail_non_motor_workers\n", + "+ P.coef_cars4_auto_time_saving_per_worker * X.util_auto_time_saving_per_worker})" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model2.utility_co" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Iteration 077 [Optimization terminated successfully]

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "

Best LL = -18480.69200834759

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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valuebestinitvalueminimummaximumnullvalueholdfast
param_name
coef_cars1_asc1.4519301.4519301.1865-50.0050.000.00
coef_cars1_asc_county-0.669928-0.669928-0.5660-50.0050.000.00
coef_cars1_asc_marin-0.170886-0.170886-0.2434-50.0050.000.00
coef_cars1_asc_san_francisco0.3172280.3172280.4259-50.0050.000.00
coef_cars1_auto_time_saving_per_worker0.3802890.3802890.4707-50.0050.000.00
........................
coef_retail_auto_no_workers0.0278530.0278530.0626-50.0050.000.00
coef_retail_auto_workers0.1430320.1430320.1646-50.0050.000.00
coef_retail_non_motor-0.030000-0.030000-0.0300-0.03-0.030.01
coef_retail_transit_no_workers-0.307531-0.307531-0.3053-50.0050.000.00
coef_retail_transit_workers-0.522486-0.522486-0.5117-50.0050.000.00
\n", + "

74 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " value best initvalue \\\n", + "param_name \n", + "coef_cars1_asc 1.451930 1.451930 1.1865 \n", + "coef_cars1_asc_county -0.669928 -0.669928 -0.5660 \n", + "coef_cars1_asc_marin -0.170886 -0.170886 -0.2434 \n", + "coef_cars1_asc_san_francisco 0.317228 0.317228 0.4259 \n", + "coef_cars1_auto_time_saving_per_worker 0.380289 0.380289 0.4707 \n", + "... ... ... ... \n", + "coef_retail_auto_no_workers 0.027853 0.027853 0.0626 \n", + "coef_retail_auto_workers 0.143032 0.143032 0.1646 \n", + "coef_retail_non_motor -0.030000 -0.030000 -0.0300 \n", + "coef_retail_transit_no_workers -0.307531 -0.307531 -0.3053 \n", + "coef_retail_transit_workers -0.522486 -0.522486 -0.5117 \n", + "\n", + " minimum maximum nullvalue holdfast \n", + "param_name \n", + "coef_cars1_asc -50.00 50.00 0.0 0 \n", + "coef_cars1_asc_county -50.00 50.00 0.0 0 \n", + "coef_cars1_asc_marin -50.00 50.00 0.0 0 \n", + "coef_cars1_asc_san_francisco -50.00 50.00 0.0 0 \n", + "coef_cars1_auto_time_saving_per_worker -50.00 50.00 0.0 0 \n", + "... ... ... ... ... \n", + "coef_retail_auto_no_workers -50.00 50.00 0.0 0 \n", + "coef_retail_auto_workers -50.00 50.00 0.0 0 \n", + "coef_retail_non_motor -0.03 -0.03 0.0 1 \n", + "coef_retail_transit_no_workers -50.00 50.00 0.0 0 \n", + "coef_retail_transit_workers -50.00 50.00 0.0 0 \n", + "\n", + "[74 rows x 7 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
keyvalue
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0
coef_cars1_asc1.451930
coef_cars1_asc_county-0.669928
coef_cars1_asc_marin-0.170886
coef_cars1_asc_san_francisco0.317228
coef_cars1_auto_time_saving_per_worker0.380289
coef_cars1_density_0_10_no_workers0.000000
coef_cars1_density_10_up_no_workers-0.014252
coef_cars1_density_10_up_workers-0.018450
coef_cars1_drivers_20.000000
coef_cars1_drivers_30.000000
coef_cars1_drivers_4_up1.993329
coef_cars1_hh_income_0_25k0.047122
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coef_cars1_hh_income_75_up-0.002331
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coef_cars1_persons_16_170.000000
coef_cars1_persons_18_24-0.473008
coef_cars1_persons_25_340.000000
coef_cars1_presence_children_0_40.494212
coef_cars1_presence_children_5_170.044213
coef_cars234_asc_marin0.000000
coef_cars234_presence_children_0_40.839737
coef_cars2_asc-0.751717
coef_cars2_asc_county-0.552884
coef_cars2_asc_san_francisco0.394107
coef_cars2_auto_time_saving_per_worker0.608005
coef_cars2_density_0_10_no_workers-0.190732
coef_cars2_density_10_up_no_workers-0.118593
coef_cars2_drivers_22.883229
coef_cars2_drivers_33.462629
coef_cars2_drivers_4_up6.177691
coef_cars2_hh_income_0_25k0.062963
coef_cars2_hh_income_25_500.007705
coef_cars2_hh_income_50_750.019152
coef_cars2_hh_income_75_up0.002677
coef_cars2_num_workers_clip_30.316577
coef_cars2_persons_16_17-0.851694
coef_cars2_persons_18_24-0.988962
coef_cars2_persons_25_34-0.375153
coef_cars2_presence_children_5_170.333678
coef_cars34_asc_county-0.272462
coef_cars34_asc_san_francisco0.042635
coef_cars34_density_0_10_no_workers-0.343379
coef_cars34_density_10_up_no_workers-0.254388
coef_cars34_persons_16_17-1.828782
coef_cars34_persons_18_24-1.015850
coef_cars34_persons_25_34-0.741201
coef_cars34_presence_children_5_170.545134
coef_cars3_asc-3.502778
coef_cars3_auto_time_saving_per_worker0.528275
coef_cars3_drivers_23.337821
coef_cars3_drivers_35.712040
coef_cars3_drivers_4_up8.649770
coef_cars3_hh_income_0_25k0.081492
coef_cars3_hh_income_25_500.001622
coef_cars3_hh_income_50_750.026796
coef_cars3_hh_income_75_up0.003811
coef_cars3_num_workers_clip_30.651693
coef_cars4_asc-5.633922
coef_cars4_auto_time_saving_per_worker0.825818
coef_cars4_drivers_22.636133
coef_cars4_drivers_35.304881
coef_cars4_drivers_4_up9.400424
coef_cars4_hh_income_0_25k0.095108
coef_cars4_hh_income_25_500.002764
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coef_retail_auto_no_workers0.027853
coef_retail_auto_workers0.143032
coef_retail_non_motor-0.030000
coef_retail_transit_no_workers-0.307531
coef_retail_transit_workers-0.522486
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coef_cars1_asc0.000104
coef_cars1_asc_county-0.000032
coef_cars1_asc_marin-0.000051
coef_cars1_asc_san_francisco-0.000097
coef_cars1_auto_time_saving_per_worker0.000056
coef_cars1_density_0_10_no_workers0.000000
coef_cars1_density_10_up_no_workers-0.000020
coef_cars1_density_10_up_workers0.000007
coef_cars1_drivers_20.000000
coef_cars1_drivers_30.000000
coef_cars1_drivers_4_up-0.000038
coef_cars1_hh_income_0_25k0.000119
coef_cars1_hh_income_25_500.000125
coef_cars1_hh_income_50_750.000092
coef_cars1_hh_income_75_up0.000120
coef_cars1_num_workers_clip_30.000000
coef_cars1_persons_16_170.000000
coef_cars1_persons_18_240.000042
coef_cars1_persons_25_340.000000
coef_cars1_presence_children_0_40.000002
coef_cars1_presence_children_5_170.000109
coef_cars234_asc_marin0.000000
coef_cars234_presence_children_0_4-0.000091
coef_cars2_asc-0.000074
coef_cars2_asc_county0.000006
coef_cars2_asc_san_francisco0.000069
coef_cars2_auto_time_saving_per_worker0.000086
coef_cars2_density_0_10_no_workers0.000073
coef_cars2_density_10_up_no_workers-0.000101
coef_cars2_drivers_2-0.000008
coef_cars2_drivers_30.000049
coef_cars2_drivers_4_up-0.000019
coef_cars2_hh_income_0_25k0.000382
coef_cars2_hh_income_25_500.000289
coef_cars2_hh_income_50_750.000444
coef_cars2_hh_income_75_up0.000750
coef_cars2_num_workers_clip_30.000106
coef_cars2_persons_16_170.000130
coef_cars2_persons_18_24-0.000274
coef_cars2_persons_25_34-0.000109
coef_cars2_presence_children_5_17-0.000037
coef_cars34_asc_county0.000005
coef_cars34_asc_san_francisco0.000055
coef_cars34_density_0_10_no_workers0.000004
coef_cars34_density_10_up_no_workers0.000024
coef_cars34_persons_16_17-0.000129
coef_cars34_persons_18_240.000166
coef_cars34_persons_25_34-0.000067
coef_cars34_presence_children_5_17-0.000139
coef_cars3_asc0.000107
coef_cars3_auto_time_saving_per_worker-0.000071
coef_cars3_drivers_20.000149
coef_cars3_drivers_30.000024
coef_cars3_drivers_4_up-0.000038
coef_cars3_hh_income_0_25k0.000005
coef_cars3_hh_income_25_500.000096
coef_cars3_hh_income_50_750.000072
coef_cars3_hh_income_75_up0.000185
coef_cars3_num_workers_clip_3-0.000169
coef_cars4_asc-0.000103
coef_cars4_auto_time_saving_per_worker-0.000058
coef_cars4_drivers_2-0.000040
coef_cars4_drivers_30.000093
coef_cars4_drivers_4_up-0.000046
coef_cars4_hh_income_0_25k-0.000523
coef_cars4_hh_income_25_50-0.000691
coef_cars4_hh_income_50_75-0.000513
coef_cars4_hh_income_75_up-0.001026
coef_cars4_num_workers_clip_3-0.000170
coef_retail_auto_no_workers0.000014
coef_retail_auto_workers0.000098
coef_retail_non_motor0.000000
coef_retail_transit_no_workers0.000133
coef_retail_transit_workers-0.000083
nit77
nfev102
njev77
status0
message'Optimization terminated successfully'
successTrue
elapsed_time0:00:01.723675
method'slsqp'
n_cases20000
iteration_number77
loglike-18480.69200834759
" + ], + "text/plain": [ + "┣ x: coef_cars1_asc 1.451930\n", + "┃ coef_cars1_asc_county -0.669928\n", + "┃ coef_cars1_asc_marin -0.170886\n", + "┃ coef_cars1_asc_san_francisco 0.317228\n", + "┃ coef_cars1_auto_time_saving_per_worker 0.380289\n", + "┃ ... \n", + "┃ coef_retail_auto_no_workers 0.027853\n", + "┃ coef_retail_auto_workers 0.143032\n", + "┃ coef_retail_non_motor -0.030000\n", + "┃ coef_retail_transit_no_workers -0.307531\n", + "┃ coef_retail_transit_workers -0.522486\n", + "┃ Length: 74, dtype: float64\n", + "┣ logloss: 0.9240346004173794\n", + "┣ d_logloss: coef_cars1_asc 0.000104\n", + "┃ coef_cars1_asc_county -0.000032\n", + "┃ coef_cars1_asc_marin -0.000051\n", + "┃ coef_cars1_asc_san_francisco -0.000097\n", + "┃ coef_cars1_auto_time_saving_per_worker 0.000056\n", + "┃ ... \n", + "┃ coef_retail_auto_no_workers 0.000014\n", + "┃ coef_retail_auto_workers 0.000098\n", + "┃ coef_retail_non_motor 0.000000\n", + "┃ coef_retail_transit_no_workers 0.000133\n", + "┃ coef_retail_transit_workers -0.000083\n", + "┃ Length: 74, dtype: float64\n", + "┣ nit: 77\n", + "┣ nfev: 102\n", + "┣ njev: 77\n", + "┣ status: 0\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ success: True\n", + "┣ elapsed_time: datetime.timedelta(seconds=1, microseconds=723675)\n", + "┣ method: 'slsqp'\n", + "┣ n_cases: 20000\n", + "┣ iteration_number: 77\n", + "┣ loglike: -18480.69200834759" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model2.estimate(maxiter=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily review the parameter estimates from the original and\n", + "revised models side by side to see what changed." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelmodel2
ValueStd Errt StatSignifNull ValueConstrainedValueStd Errt StatSignifNull ValueConstrained
Parameter
coef_cars1_asc1.340.9411.420.01.450.9461.540.0
coef_cars1_asc_county-0.6560.158-4.14***0.0-0.6700.159-4.22***0.0
coef_cars1_asc_marin-0.1680.106-1.580.0-0.1710.106-1.610.0
coef_cars1_asc_san_francisco0.3250.09793.31***0.00.3170.09823.23**0.0
coef_cars1_auto_time_saving_per_worker0.3940.1882.10*0.00.3800.1892.01*0.0
coef_cars1_density_0_10_no_workers0.000.00NA0.0fixed value0.000.00NA0.0fixed value
coef_cars1_density_10_up_no_workers-0.01450.00339-4.27***0.0-0.01430.00339-4.21***0.0
coef_cars1_density_10_up_workers-0.01830.00271-6.75***0.0-0.01840.00271-6.80***0.0
coef_cars1_drivers_20.000.00NA0.0fixed value0.000.00NA0.0fixed value
coef_cars1_drivers_30.000.00NA0.0fixed value0.000.00NA0.0fixed value
coef_cars1_drivers_4_up2.010.3176.35***0.01.990.3186.27***0.0
coef_cars1_hh_income_0_30k0.04560.0029815.29***0.0
coef_cars1_hh_income_30_up0.000.00NA0.0fixed value
coef_cars1_num_workers_clip_30.000.00NA0.0fixed value0.000.00NA0.0fixed value
coef_cars1_persons_16_170.000.00NA0.0fixed value0.000.00NA0.0fixed value
coef_cars1_persons_18_24-0.4760.0586-8.11***0.0-0.4730.0586-8.07***0.0
coef_cars1_persons_25_340.000.00NA0.0fixed value0.000.00NA0.0fixed value
coef_cars1_presence_children_0_40.4870.1303.75***0.00.4940.1303.80***0.0
coef_cars1_presence_children_5_170.03900.09340.420.00.04420.09350.470.0
coef_cars234_asc_marin0.000.00NA0.0fixed value0.000.00NA0.0fixed value
coef_cars234_presence_children_0_40.8340.1376.10***0.00.8400.1376.13***0.0
coef_cars2_asc-0.8170.944-0.870.0-0.7520.949-0.790.0
coef_cars2_asc_county-0.5340.166-3.22**0.0-0.5530.166-3.33***0.0
coef_cars2_asc_san_francisco0.3950.1233.22**0.00.3940.1233.20**0.0
coef_cars2_auto_time_saving_per_worker0.6260.1973.18**0.00.6080.1983.07**0.0
coef_cars2_density_0_10_no_workers-0.1890.0118-16.03***0.0-0.1910.0118-16.16***0.0
coef_cars2_density_10_up_no_workers-0.1190.0117-10.12***0.0-0.1190.0117-10.13***0.0
coef_cars2_drivers_22.880.060247.87***0.02.880.060347.83***0.0
coef_cars2_drivers_33.460.097135.68***0.03.460.097135.66***0.0
coef_cars2_drivers_4_up6.180.33218.61***0.06.180.33318.53***0.0
coef_cars2_hh_income_0_30k0.05570.0041913.31***0.0
coef_cars2_hh_income_30_up0.007090.0007829.07***0.0
coef_cars2_num_workers_clip_30.3170.04207.55***0.00.3170.04207.54***0.0
coef_cars2_persons_16_17-0.8450.0976-8.66***0.0-0.8520.0977-8.72***0.0
coef_cars2_persons_18_24-0.9900.0659-15.04***0.0-0.9890.0659-15.01***0.0
coef_cars2_persons_25_34-0.3790.0322-11.77***0.0-0.3750.0322-11.64***0.0
coef_cars2_presence_children_5_170.3270.1043.16**0.00.3340.1043.21**0.0
coef_cars34_asc_county-0.2610.173-1.500.0-0.2720.174-1.570.0
coef_cars34_asc_san_francisco0.05590.1610.350.00.04260.1610.260.0
coef_cars34_density_0_10_no_workers-0.3430.0182-18.81***0.0-0.3430.0182-18.84***0.0
coef_cars34_density_10_up_no_workers-0.2550.0544-4.69***0.0-0.2540.0542-4.70***0.0
coef_cars34_persons_16_17-1.820.113-16.13***0.0-1.830.113-16.23***0.0
coef_cars34_persons_18_24-1.010.0695-14.60***0.0-1.020.0696-14.60***0.0
coef_cars34_persons_25_34-0.7450.0402-18.56***0.0-0.7410.0402-18.44***0.0
coef_cars34_presence_children_5_170.5420.1124.83***0.00.5450.1124.86***0.0
coef_cars3_asc-3.430.962-3.56***0.0-3.500.972-3.60***0.0
coef_cars3_auto_time_saving_per_worker0.5380.2152.50*0.00.5280.2162.45*0.0
coef_cars3_drivers_23.330.15121.99***0.03.340.15221.93***0.0
coef_cars3_drivers_35.700.17033.48***0.05.710.17133.40***0.0
coef_cars3_drivers_4_up8.640.36423.72***0.08.650.36623.64***0.0
coef_cars3_hh_income_0_30k0.06440.006689.65***0.0
coef_cars3_hh_income_30_up0.009130.001038.88***0.0
coef_cars3_num_workers_clip_30.6500.051912.53***0.00.6520.051912.55***0.0
coef_cars4_asc-5.531.02-5.42***0.0-5.631.04-5.40***0.0
coef_cars4_auto_time_saving_per_worker0.8340.2493.35***0.00.8260.2503.30***0.0
coef_cars4_drivers_22.650.2879.23***0.02.640.2839.30***0.0
coef_cars4_drivers_35.330.29717.95***0.05.300.29318.07***0.0
coef_cars4_drivers_4_up9.420.43621.59***0.09.400.43521.61***0.0
coef_cars4_hh_income_0_30k0.07180.01116.46***0.0
coef_cars4_hh_income_30_up0.01320.0013210.00***0.0
coef_cars4_num_workers_clip_30.9490.064614.69***0.00.9580.064614.82***0.0
coef_retail_auto_no_workers0.03980.1010.390.00.02790.1020.270.0
coef_retail_auto_workers0.1560.1021.520.00.1430.1031.390.0
coef_retail_non_motor-0.03000.00NA0.0fixed value-0.03000.00NA0.0fixed value
coef_retail_transit_no_workers-0.3080.0295-10.44***0.0-0.3080.0295-10.42***0.0
coef_retail_transit_workers-0.5250.0396-13.24***0.0-0.5220.0397-13.15***0.0
coef_cars1_hh_income_0_25k0.04710.0043610.81***0.0
coef_cars1_hh_income_25_500.006620.005371.230.0
coef_cars1_hh_income_50_750.007920.006661.190.0
coef_cars1_hh_income_75_up-0.002330.00246-0.950.0
coef_cars2_hh_income_0_25k0.06300.0060710.37***0.0
coef_cars2_hh_income_25_500.007700.006081.270.0
coef_cars2_hh_income_50_750.01920.007142.68**0.0
coef_cars2_hh_income_75_up0.002680.002591.030.0
coef_cars3_hh_income_0_25k0.08150.01018.05***0.0
coef_cars3_hh_income_25_500.001620.007460.220.0
coef_cars3_hh_income_50_750.02680.007933.38***0.0
coef_cars3_hh_income_75_up0.003810.002751.390.0
coef_cars4_hh_income_0_25k0.09510.01755.45***0.0
coef_cars4_hh_income_25_500.002760.009820.280.0
coef_cars4_hh_income_50_750.02740.009073.02**0.0
coef_cars4_hh_income_75_up0.008110.002922.78**0.0
\n", + "
" + ], "text/plain": [ - "" + " model \\\n", + " Value Std Err t Stat Signif \n", + "Parameter \n", + "coef_cars1_asc  1.34  0.941  1.42 \n", + "coef_cars1_asc_county -0.656  0.158 -4.14 *** \n", + "coef_cars1_asc_marin -0.168  0.106 -1.58 \n", + "coef_cars1_asc_san_francisco  0.325  0.0979  3.31 *** \n", + "coef_cars1_auto_time_saving_per_worker  0.394  0.188  2.10 * \n", + "coef_cars1_density_0_10_no_workers  0.00  0.00  NA \n", + "coef_cars1_density_10_up_no_workers -0.0145  0.00339 -4.27 *** \n", + "coef_cars1_density_10_up_workers -0.0183  0.00271 -6.75 *** \n", + "coef_cars1_drivers_2  0.00  0.00  NA \n", + "coef_cars1_drivers_3  0.00  0.00  NA \n", + "coef_cars1_drivers_4_up  2.01  0.317  6.35 *** \n", + "coef_cars1_hh_income_0_30k  0.0456  0.00298  15.29 *** \n", + "coef_cars1_hh_income_30_up  0.00  0.00  NA \n", + "coef_cars1_num_workers_clip_3  0.00  0.00  NA \n", + "coef_cars1_persons_16_17  0.00  0.00  NA \n", + "coef_cars1_persons_18_24 -0.476  0.0586 -8.11 *** \n", + "coef_cars1_persons_25_34  0.00  0.00  NA \n", + "coef_cars1_presence_children_0_4  0.487  0.130  3.75 *** \n", + "coef_cars1_presence_children_5_17  0.0390  0.0934  0.42 \n", + "coef_cars234_asc_marin  0.00  0.00  NA \n", + "coef_cars234_presence_children_0_4  0.834  0.137  6.10 *** \n", + "coef_cars2_asc -0.817  0.944 -0.87 \n", + "coef_cars2_asc_county -0.534  0.166 -3.22 ** \n", + "coef_cars2_asc_san_francisco  0.395  0.123  3.22 ** \n", + "coef_cars2_auto_time_saving_per_worker  0.626  0.197  3.18 ** \n", + "coef_cars2_density_0_10_no_workers -0.189  0.0118 -16.03 *** \n", + "coef_cars2_density_10_up_no_workers -0.119  0.0117 -10.12 *** \n", + "coef_cars2_drivers_2  2.88  0.0602  47.87 *** \n", + "coef_cars2_drivers_3  3.46  0.0971  35.68 *** \n", + "coef_cars2_drivers_4_up  6.18  0.332  18.61 *** \n", + "coef_cars2_hh_income_0_30k  0.0557  0.00419  13.31 *** \n", + "coef_cars2_hh_income_30_up  0.00709  0.000782  9.07 *** \n", + "coef_cars2_num_workers_clip_3  0.317  0.0420  7.55 *** \n", + "coef_cars2_persons_16_17 -0.845  0.0976 -8.66 *** \n", + "coef_cars2_persons_18_24 -0.990  0.0659 -15.04 *** \n", + "coef_cars2_persons_25_34 -0.379  0.0322 -11.77 *** \n", + "coef_cars2_presence_children_5_17  0.327  0.104  3.16 ** \n", + "coef_cars34_asc_county -0.261  0.173 -1.50 \n", + "coef_cars34_asc_san_francisco  0.0559  0.161  0.35 \n", + "coef_cars34_density_0_10_no_workers -0.343  0.0182 -18.81 *** \n", + "coef_cars34_density_10_up_no_workers -0.255  0.0544 -4.69 *** \n", + "coef_cars34_persons_16_17 -1.82  0.113 -16.13 *** \n", + "coef_cars34_persons_18_24 -1.01  0.0695 -14.60 *** \n", + "coef_cars34_persons_25_34 -0.745  0.0402 -18.56 *** \n", + "coef_cars34_presence_children_5_17  0.542  0.112  4.83 *** \n", + "coef_cars3_asc -3.43  0.962 -3.56 *** \n", + "coef_cars3_auto_time_saving_per_worker  0.538  0.215  2.50 * \n", + "coef_cars3_drivers_2  3.33  0.151  21.99 *** \n", + "coef_cars3_drivers_3  5.70  0.170  33.48 *** \n", + "coef_cars3_drivers_4_up  8.64  0.364  23.72 *** \n", + "coef_cars3_hh_income_0_30k  0.0644  0.00668  9.65 *** \n", + "coef_cars3_hh_income_30_up  0.00913  0.00103  8.88 *** \n", + "coef_cars3_num_workers_clip_3  0.650  0.0519  12.53 *** \n", + "coef_cars4_asc -5.53  1.02 -5.42 *** \n", + "coef_cars4_auto_time_saving_per_worker  0.834  0.249  3.35 *** \n", + "coef_cars4_drivers_2  2.65  0.287  9.23 *** \n", + "coef_cars4_drivers_3  5.33  0.297  17.95 *** \n", + "coef_cars4_drivers_4_up  9.42  0.436  21.59 *** \n", + "coef_cars4_hh_income_0_30k  0.0718  0.0111  6.46 *** \n", + "coef_cars4_hh_income_30_up  0.0132  0.00132  10.00 *** \n", + "coef_cars4_num_workers_clip_3  0.949  0.0646  14.69 *** \n", + "coef_retail_auto_no_workers  0.0398  0.101  0.39 \n", + "coef_retail_auto_workers  0.156  0.102  1.52 \n", + "coef_retail_non_motor -0.0300  0.00  NA \n", + "coef_retail_transit_no_workers -0.308  0.0295 -10.44 *** \n", + "coef_retail_transit_workers -0.525  0.0396 -13.24 *** \n", + "coef_cars1_hh_income_0_25k \n", + "coef_cars1_hh_income_25_50 \n", + "coef_cars1_hh_income_50_75 \n", + "coef_cars1_hh_income_75_up \n", + "coef_cars2_hh_income_0_25k \n", + "coef_cars2_hh_income_25_50 \n", + "coef_cars2_hh_income_50_75 \n", + "coef_cars2_hh_income_75_up \n", + "coef_cars3_hh_income_0_25k \n", + "coef_cars3_hh_income_25_50 \n", + "coef_cars3_hh_income_50_75 \n", + "coef_cars3_hh_income_75_up \n", + "coef_cars4_hh_income_0_25k \n", + "coef_cars4_hh_income_25_50 \n", + "coef_cars4_hh_income_50_75 \n", + "coef_cars4_hh_income_75_up \n", + "\n", + " model2 \\\n", + " Null Value Constrained Value \n", + "Parameter \n", + "coef_cars1_asc 0.0  1.45 \n", + "coef_cars1_asc_county 0.0 -0.670 \n", + "coef_cars1_asc_marin 0.0 -0.171 \n", + "coef_cars1_asc_san_francisco 0.0  0.317 \n", + "coef_cars1_auto_time_saving_per_worker 0.0  0.380 \n", + "coef_cars1_density_0_10_no_workers 0.0 fixed value  0.00 \n", + "coef_cars1_density_10_up_no_workers 0.0 -0.0143 \n", + "coef_cars1_density_10_up_workers 0.0 -0.0184 \n", + "coef_cars1_drivers_2 0.0 fixed value  0.00 \n", + "coef_cars1_drivers_3 0.0 fixed value  0.00 \n", + "coef_cars1_drivers_4_up 0.0  1.99 \n", + "coef_cars1_hh_income_0_30k 0.0 \n", + "coef_cars1_hh_income_30_up 0.0 fixed value \n", + "coef_cars1_num_workers_clip_3 0.0 fixed value  0.00 \n", + "coef_cars1_persons_16_17 0.0 fixed value  0.00 \n", + "coef_cars1_persons_18_24 0.0 -0.473 \n", + "coef_cars1_persons_25_34 0.0 fixed value  0.00 \n", + "coef_cars1_presence_children_0_4 0.0  0.494 \n", + "coef_cars1_presence_children_5_17 0.0  0.0442 \n", + "coef_cars234_asc_marin 0.0 fixed value  0.00 \n", + "coef_cars234_presence_children_0_4 0.0  0.840 \n", + "coef_cars2_asc 0.0 -0.752 \n", + "coef_cars2_asc_county 0.0 -0.553 \n", + "coef_cars2_asc_san_francisco 0.0  0.394 \n", + "coef_cars2_auto_time_saving_per_worker 0.0  0.608 \n", + "coef_cars2_density_0_10_no_workers 0.0 -0.191 \n", + "coef_cars2_density_10_up_no_workers 0.0 -0.119 \n", + "coef_cars2_drivers_2 0.0  2.88 \n", + "coef_cars2_drivers_3 0.0  3.46 \n", + "coef_cars2_drivers_4_up 0.0  6.18 \n", + "coef_cars2_hh_income_0_30k 0.0 \n", + "coef_cars2_hh_income_30_up 0.0 \n", + "coef_cars2_num_workers_clip_3 0.0  0.317 \n", + "coef_cars2_persons_16_17 0.0 -0.852 \n", + "coef_cars2_persons_18_24 0.0 -0.989 \n", + "coef_cars2_persons_25_34 0.0 -0.375 \n", + "coef_cars2_presence_children_5_17 0.0  0.334 \n", + "coef_cars34_asc_county 0.0 -0.272 \n", + "coef_cars34_asc_san_francisco 0.0  0.0426 \n", + "coef_cars34_density_0_10_no_workers 0.0 -0.343 \n", + "coef_cars34_density_10_up_no_workers 0.0 -0.254 \n", + "coef_cars34_persons_16_17 0.0 -1.83 \n", + "coef_cars34_persons_18_24 0.0 -1.02 \n", + "coef_cars34_persons_25_34 0.0 -0.741 \n", + "coef_cars34_presence_children_5_17 0.0  0.545 \n", + "coef_cars3_asc 0.0 -3.50 \n", + "coef_cars3_auto_time_saving_per_worker 0.0  0.528 \n", + "coef_cars3_drivers_2 0.0  3.34 \n", + "coef_cars3_drivers_3 0.0  5.71 \n", + "coef_cars3_drivers_4_up 0.0  8.65 \n", + "coef_cars3_hh_income_0_30k 0.0 \n", + "coef_cars3_hh_income_30_up 0.0 \n", + "coef_cars3_num_workers_clip_3 0.0  0.652 \n", + "coef_cars4_asc 0.0 -5.63 \n", + "coef_cars4_auto_time_saving_per_worker 0.0  0.826 \n", + "coef_cars4_drivers_2 0.0  2.64 \n", + "coef_cars4_drivers_3 0.0  5.30 \n", + "coef_cars4_drivers_4_up 0.0  9.40 \n", + "coef_cars4_hh_income_0_30k 0.0 \n", + "coef_cars4_hh_income_30_up 0.0 \n", + "coef_cars4_num_workers_clip_3 0.0  0.958 \n", + "coef_retail_auto_no_workers 0.0  0.0279 \n", + "coef_retail_auto_workers 0.0  0.143 \n", + "coef_retail_non_motor 0.0 fixed value -0.0300 \n", + "coef_retail_transit_no_workers 0.0 -0.308 \n", + "coef_retail_transit_workers 0.0 -0.522 \n", + "coef_cars1_hh_income_0_25k  0.0471 \n", + "coef_cars1_hh_income_25_50  0.00662 \n", + "coef_cars1_hh_income_50_75  0.00792 \n", + "coef_cars1_hh_income_75_up -0.00233 \n", + "coef_cars2_hh_income_0_25k  0.0630 \n", + "coef_cars2_hh_income_25_50  0.00770 \n", + "coef_cars2_hh_income_50_75  0.0192 \n", + "coef_cars2_hh_income_75_up  0.00268 \n", + "coef_cars3_hh_income_0_25k  0.0815 \n", + "coef_cars3_hh_income_25_50  0.00162 \n", + "coef_cars3_hh_income_50_75  0.0268 \n", + "coef_cars3_hh_income_75_up  0.00381 \n", + "coef_cars4_hh_income_0_25k  0.0951 \n", + "coef_cars4_hh_income_25_50  0.00276 \n", + "coef_cars4_hh_income_50_75  0.0274 \n", + "coef_cars4_hh_income_75_up  0.00811 \n", + "\n", + " \\\n", + " Std Err t Stat Signif Null Value \n", + "Parameter \n", + "coef_cars1_asc  0.946  1.54 0.0 \n", + "coef_cars1_asc_county  0.159 -4.22 *** 0.0 \n", + "coef_cars1_asc_marin  0.106 -1.61 0.0 \n", + "coef_cars1_asc_san_francisco  0.0982  3.23 ** 0.0 \n", + "coef_cars1_auto_time_saving_per_worker  0.189  2.01 * 0.0 \n", + "coef_cars1_density_0_10_no_workers  0.00  NA 0.0 \n", + "coef_cars1_density_10_up_no_workers  0.00339 -4.21 *** 0.0 \n", + "coef_cars1_density_10_up_workers  0.00271 -6.80 *** 0.0 \n", + "coef_cars1_drivers_2  0.00  NA 0.0 \n", + "coef_cars1_drivers_3  0.00  NA 0.0 \n", + "coef_cars1_drivers_4_up  0.318  6.27 *** 0.0 \n", + "coef_cars1_hh_income_0_30k \n", + "coef_cars1_hh_income_30_up \n", + "coef_cars1_num_workers_clip_3  0.00  NA 0.0 \n", + "coef_cars1_persons_16_17  0.00  NA 0.0 \n", + "coef_cars1_persons_18_24  0.0586 -8.07 *** 0.0 \n", + "coef_cars1_persons_25_34  0.00  NA 0.0 \n", + "coef_cars1_presence_children_0_4  0.130  3.80 *** 0.0 \n", + "coef_cars1_presence_children_5_17  0.0935  0.47 0.0 \n", + "coef_cars234_asc_marin  0.00  NA 0.0 \n", + "coef_cars234_presence_children_0_4  0.137  6.13 *** 0.0 \n", + "coef_cars2_asc  0.949 -0.79 0.0 \n", + "coef_cars2_asc_county  0.166 -3.33 *** 0.0 \n", + "coef_cars2_asc_san_francisco  0.123  3.20 ** 0.0 \n", + "coef_cars2_auto_time_saving_per_worker  0.198  3.07 ** 0.0 \n", + "coef_cars2_density_0_10_no_workers  0.0118 -16.16 *** 0.0 \n", + "coef_cars2_density_10_up_no_workers  0.0117 -10.13 *** 0.0 \n", + "coef_cars2_drivers_2  0.0603  47.83 *** 0.0 \n", + "coef_cars2_drivers_3  0.0971  35.66 *** 0.0 \n", + "coef_cars2_drivers_4_up  0.333  18.53 *** 0.0 \n", + "coef_cars2_hh_income_0_30k \n", + "coef_cars2_hh_income_30_up \n", + "coef_cars2_num_workers_clip_3  0.0420  7.54 *** 0.0 \n", + "coef_cars2_persons_16_17  0.0977 -8.72 *** 0.0 \n", + "coef_cars2_persons_18_24  0.0659 -15.01 *** 0.0 \n", + "coef_cars2_persons_25_34  0.0322 -11.64 *** 0.0 \n", + "coef_cars2_presence_children_5_17  0.104  3.21 ** 0.0 \n", + "coef_cars34_asc_county  0.174 -1.57 0.0 \n", + "coef_cars34_asc_san_francisco  0.161  0.26 0.0 \n", + "coef_cars34_density_0_10_no_workers  0.0182 -18.84 *** 0.0 \n", + "coef_cars34_density_10_up_no_workers  0.0542 -4.70 *** 0.0 \n", + "coef_cars34_persons_16_17  0.113 -16.23 *** 0.0 \n", + "coef_cars34_persons_18_24  0.0696 -14.60 *** 0.0 \n", + "coef_cars34_persons_25_34  0.0402 -18.44 *** 0.0 \n", + "coef_cars34_presence_children_5_17  0.112  4.86 *** 0.0 \n", + "coef_cars3_asc  0.972 -3.60 *** 0.0 \n", + "coef_cars3_auto_time_saving_per_worker  0.216  2.45 * 0.0 \n", + "coef_cars3_drivers_2  0.152  21.93 *** 0.0 \n", + "coef_cars3_drivers_3  0.171  33.40 *** 0.0 \n", + "coef_cars3_drivers_4_up  0.366  23.64 *** 0.0 \n", + "coef_cars3_hh_income_0_30k \n", + "coef_cars3_hh_income_30_up \n", + "coef_cars3_num_workers_clip_3  0.0519  12.55 *** 0.0 \n", + "coef_cars4_asc  1.04 -5.40 *** 0.0 \n", + "coef_cars4_auto_time_saving_per_worker  0.250  3.30 *** 0.0 \n", + "coef_cars4_drivers_2  0.283  9.30 *** 0.0 \n", + "coef_cars4_drivers_3  0.293  18.07 *** 0.0 \n", + "coef_cars4_drivers_4_up  0.435  21.61 *** 0.0 \n", + "coef_cars4_hh_income_0_30k \n", + "coef_cars4_hh_income_30_up \n", + "coef_cars4_num_workers_clip_3  0.0646  14.82 *** 0.0 \n", + "coef_retail_auto_no_workers  0.102  0.27 0.0 \n", + "coef_retail_auto_workers  0.103  1.39 0.0 \n", + "coef_retail_non_motor  0.00  NA 0.0 \n", + "coef_retail_transit_no_workers  0.0295 -10.42 *** 0.0 \n", + "coef_retail_transit_workers  0.0397 -13.15 *** 0.0 \n", + "coef_cars1_hh_income_0_25k  0.00436  10.81 *** 0.0 \n", + "coef_cars1_hh_income_25_50  0.00537  1.23 0.0 \n", + "coef_cars1_hh_income_50_75  0.00666  1.19 0.0 \n", + "coef_cars1_hh_income_75_up  0.00246 -0.95 0.0 \n", + "coef_cars2_hh_income_0_25k  0.00607  10.37 *** 0.0 \n", + "coef_cars2_hh_income_25_50  0.00608  1.27 0.0 \n", + "coef_cars2_hh_income_50_75  0.00714  2.68 ** 0.0 \n", + "coef_cars2_hh_income_75_up  0.00259  1.03 0.0 \n", + "coef_cars3_hh_income_0_25k  0.0101  8.05 *** 0.0 \n", + "coef_cars3_hh_income_25_50  0.00746  0.22 0.0 \n", + "coef_cars3_hh_income_50_75  0.00793  3.38 *** 0.0 \n", + "coef_cars3_hh_income_75_up  0.00275  1.39 0.0 \n", + "coef_cars4_hh_income_0_25k  0.0175  5.45 *** 0.0 \n", + "coef_cars4_hh_income_25_50  0.00982  0.28 0.0 \n", + "coef_cars4_hh_income_50_75  0.00907  3.02 ** 0.0 \n", + "coef_cars4_hh_income_75_up  0.00292  2.78 ** 0.0 \n", + "\n", + " \n", + " Constrained \n", + "Parameter \n", + "coef_cars1_asc \n", + "coef_cars1_asc_county \n", + "coef_cars1_asc_marin \n", + "coef_cars1_asc_san_francisco \n", + "coef_cars1_auto_time_saving_per_worker \n", + "coef_cars1_density_0_10_no_workers fixed value \n", + "coef_cars1_density_10_up_no_workers \n", + "coef_cars1_density_10_up_workers \n", + "coef_cars1_drivers_2 fixed value \n", + "coef_cars1_drivers_3 fixed value \n", + "coef_cars1_drivers_4_up \n", + "coef_cars1_hh_income_0_30k \n", + "coef_cars1_hh_income_30_up \n", + "coef_cars1_num_workers_clip_3 fixed value \n", + "coef_cars1_persons_16_17 fixed value \n", + "coef_cars1_persons_18_24 \n", + "coef_cars1_persons_25_34 fixed value \n", + "coef_cars1_presence_children_0_4 \n", + "coef_cars1_presence_children_5_17 \n", + "coef_cars234_asc_marin fixed value \n", + "coef_cars234_presence_children_0_4 \n", + "coef_cars2_asc \n", + "coef_cars2_asc_county \n", + "coef_cars2_asc_san_francisco \n", + "coef_cars2_auto_time_saving_per_worker \n", + "coef_cars2_density_0_10_no_workers \n", + "coef_cars2_density_10_up_no_workers \n", + "coef_cars2_drivers_2 \n", + "coef_cars2_drivers_3 \n", + "coef_cars2_drivers_4_up \n", + "coef_cars2_hh_income_0_30k \n", + "coef_cars2_hh_income_30_up \n", + "coef_cars2_num_workers_clip_3 \n", + "coef_cars2_persons_16_17 \n", + "coef_cars2_persons_18_24 \n", + "coef_cars2_persons_25_34 \n", + "coef_cars2_presence_children_5_17 \n", + "coef_cars34_asc_county \n", + "coef_cars34_asc_san_francisco \n", + "coef_cars34_density_0_10_no_workers \n", + "coef_cars34_density_10_up_no_workers \n", + "coef_cars34_persons_16_17 \n", + "coef_cars34_persons_18_24 \n", + "coef_cars34_persons_25_34 \n", + "coef_cars34_presence_children_5_17 \n", + "coef_cars3_asc \n", + "coef_cars3_auto_time_saving_per_worker \n", + "coef_cars3_drivers_2 \n", + "coef_cars3_drivers_3 \n", + "coef_cars3_drivers_4_up \n", + "coef_cars3_hh_income_0_30k \n", + "coef_cars3_hh_income_30_up \n", + "coef_cars3_num_workers_clip_3 \n", + "coef_cars4_asc \n", + "coef_cars4_auto_time_saving_per_worker \n", + "coef_cars4_drivers_2 \n", + "coef_cars4_drivers_3 \n", + "coef_cars4_drivers_4_up \n", + "coef_cars4_hh_income_0_30k \n", + "coef_cars4_hh_income_30_up \n", + "coef_cars4_num_workers_clip_3 \n", + "coef_retail_auto_no_workers \n", + "coef_retail_auto_workers \n", + "coef_retail_non_motor fixed value \n", + "coef_retail_transit_no_workers \n", + "coef_retail_transit_workers \n", + "coef_cars1_hh_income_0_25k \n", + "coef_cars1_hh_income_25_50 \n", + "coef_cars1_hh_income_50_75 \n", + "coef_cars1_hh_income_75_up \n", + "coef_cars2_hh_income_0_25k \n", + "coef_cars2_hh_income_25_50 \n", + "coef_cars2_hh_income_50_75 \n", + "coef_cars2_hh_income_75_up \n", + "coef_cars3_hh_income_0_25k \n", + "coef_cars3_hh_income_25_50 \n", + "coef_cars3_hh_income_50_75 \n", + "coef_cars3_hh_income_75_up \n", + "coef_cars4_hh_income_0_25k \n", + "coef_cars4_hh_income_25_50 \n", + "coef_cars4_hh_income_50_75 \n", + "coef_cars4_hh_income_75_up " ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "model.to_xlsx(\n", - " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", - " data_statistics=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "The final step is to either manually or automatically copy the `*_coefficients_revised.csv` file to the configs folder, rename it to `*_coefficients.csv`, and run ActivitySim in simulation mode." + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.parameter_summary().data,\n", + " \"model2\": model2.parameter_summary().data,\n", + " }, axis=1).fillna(\"\"))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2652,107 +5819,70 @@ " \n", " \n", " \n", - " coefficient_name\n", - " value\n", - " constrain\n", + " \n", + " model\n", + " model2\n", " \n", " \n", " \n", " \n", - " 0\n", - " coef_cars1_drivers_2\n", - " 0.000000\n", - " T\n", - " \n", - " \n", - " 1\n", - " coef_cars1_drivers_3\n", - " 0.000000\n", - " T\n", - " \n", - " \n", - " 2\n", - " coef_cars1_persons_16_17\n", - " 0.000000\n", - " T\n", - " \n", - " \n", - " 3\n", - " coef_cars234_asc_marin\n", - " 0.000000\n", - " T\n", - " \n", - " \n", - " 4\n", - " coef_cars1_persons_25_34\n", - " 0.000000\n", - " T\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " Number of Cases\n", + " Aggregate\n", + " 20000.000000\n", + " 20000.000000\n", " \n", " \n", - " 62\n", - " coef_cars4_drivers_3\n", - " 564.490158\n", - " F\n", + " Log Likelihood at Convergence\n", + " Aggregate\n", + " -18485.495027\n", + " -18480.692008\n", " \n", " \n", - " 63\n", - " coef_cars3_drivers_3\n", - " 5.048488\n", - " F\n", + " Per Case\n", + " -0.924275\n", + " -0.924035\n", " \n", " \n", - " 64\n", - " coef_cars2_drivers_4_up\n", - " 6.856405\n", - " F\n", + " Log Likelihood at Null Parameters\n", + " Aggregate\n", + " -32431.882743\n", + " -32431.882743\n", " \n", " \n", - " 65\n", - " coef_cars3_drivers_4_up\n", - " 8.317950\n", - " F\n", + " Per Case\n", + " -1.621594\n", + " -1.621594\n", " \n", " \n", - " 66\n", - " coef_cars4_drivers_4_up\n", - " 568.272514\n", - " F\n", + " Rho Squared w.r.t. Null Parameters\n", + " Aggregate\n", + " 0.430021\n", + " 0.430169\n", " \n", " \n", "\n", - "

67 rows × 3 columns

\n", "" ], "text/plain": [ - " coefficient_name value constrain\n", - "0 coef_cars1_drivers_2 0.000000 T\n", - "1 coef_cars1_drivers_3 0.000000 T\n", - "2 coef_cars1_persons_16_17 0.000000 T\n", - "3 coef_cars234_asc_marin 0.000000 T\n", - "4 coef_cars1_persons_25_34 0.000000 T\n", - ".. ... ... ...\n", - "62 coef_cars4_drivers_3 564.490158 F\n", - "63 coef_cars3_drivers_3 5.048488 F\n", - "64 coef_cars2_drivers_4_up 6.856405 F\n", - "65 coef_cars3_drivers_4_up 8.317950 F\n", - "66 coef_cars4_drivers_4_up 568.272514 F\n", - "\n", - "[67 rows x 3 columns]" + " model model2\n", + "Number of Cases Aggregate 20000.000000 20000.000000\n", + "Log Likelihood at Convergence Aggregate -18485.495027 -18480.692008\n", + " Per Case -0.924275 -0.924035\n", + "Log Likelihood at Null Parameters Aggregate -32431.882743 -32431.882743\n", + " Per Case -1.621594 -1.621594\n", + "Rho Squared w.r.t. Null Parameters Aggregate 0.430021 0.430169" ] }, - "execution_count": 11, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "pd.read_csv(result_dir/f\"{modelname}_coefficients_revised.csv\")" + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.estimation_statistics_raw(),\n", + " \"model2\": model2.estimation_statistics_raw(),\n", + " }, axis=1).fillna(\"\"))" ] } ], @@ -2763,7 +5893,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -2777,7 +5907,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/05_free_parking.ipynb b/activitysim/examples/example_estimation/notebooks/05_free_parking.ipynb index fd0fe5714f..464f32106c 100644 --- a/activitysim/examples/example_estimation/notebooks/05_free_parking.ipynb +++ b/activitysim/examples/example_estimation/notebooks/05_free_parking.ipynb @@ -34,27 +34,75 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +116,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/free_parking/free_parking_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/free_parking/free_parking_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/free_parking/free_parking_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"free_parking\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -388,14 +451,14 @@ " util_hh_size_4_up\n", " util_more_autos_than_workers\n", " ...\n", - " COLLFTE\n", - " COLLPTE\n", - " TOPOLOGY\n", - " TERMINAL\n", - " household_density\n", - " employment_density\n", - " density_index\n", - " is_cbd\n", + " auOpRetail\n", + " auOpTotal\n", + " trPkRetail\n", + " trPkTotal\n", + " trOpRetail\n", + " trOpTotal\n", + " nmRetail\n", + " nmTotal\n", " workplace_county_id\n", " override_choice_code\n", " \n", @@ -426,8 +489,8 @@ " \n", " \n", " \n", - " 72241\n", - " 72241\n", + " 72355\n", + " 72355\n", " False\n", " False\n", " 1.0\n", @@ -436,22 +499,22 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 4.58156\n", - " 68.092368\n", - " 151.674705\n", - " 46.994710\n", - " True\n", + " 9.915345\n", + " 12.430580\n", + " 6.550726\n", + " 9.119016\n", + " 6.446184\n", + " 9.035333\n", + " 5.256966\n", + " 6.831275\n", " 1\n", " 2\n", " \n", " \n", - " 72441\n", - " 72441\n", + " 72384\n", + " 72384\n", " False\n", " False\n", " 1.0\n", @@ -462,20 +525,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.28482\n", - " 28.491228\n", - " 4.052632\n", - " 3.547964\n", - " False\n", + " 9.971763\n", + " 12.488100\n", + " 6.384615\n", + " 8.910521\n", + " 6.292135\n", + " 8.845287\n", + " 5.447277\n", + " 7.062559\n", " 1\n", " 2\n", " \n", " \n", - " 72528\n", - " 72528\n", + " 72407\n", + " 72407\n", " False\n", " False\n", " 1.0\n", @@ -486,20 +549,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 6861.20508\n", - " 1174.96875\n", - " 1\n", - " 3.49553\n", - " 19.033424\n", - " 37.235052\n", - " 12.595161\n", - " False\n", + " 9.971763\n", + " 12.488100\n", + " 6.384615\n", + " 8.910521\n", + " 6.292135\n", + " 8.845287\n", + " 5.447277\n", + " 7.062559\n", " 1\n", " 2\n", " \n", " \n", - " 73144\n", - " 73144\n", + " 72459\n", + " 72459\n", " False\n", " False\n", " 1.0\n", @@ -510,20 +573,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.48345\n", - " 26.073171\n", - " 8.048780\n", - " 6.150212\n", - " False\n", + " 10.086939\n", + " 12.619691\n", + " 6.787018\n", + " 9.400117\n", + " 6.687820\n", + " 9.307625\n", + " 5.877288\n", + " 7.691985\n", " 1\n", " 2\n", " \n", " \n", - " 73493\n", - " 73493\n", + " 72529\n", + " 72529\n", " False\n", " False\n", " 1.0\n", @@ -534,14 +597,14 @@ " 0.0\n", " 0.0\n", " ...\n", - " 7144.64307\n", - " 22523.34570\n", - " 1\n", - " 3.20356\n", - " 13.672131\n", - " 39.852459\n", - " 10.179771\n", - " False\n", + " 10.263491\n", + " 12.913804\n", + " 6.830450\n", + " 9.558905\n", + " 6.611336\n", + " 9.308290\n", + " 6.696709\n", + " 8.925142\n", " 1\n", " 2\n", " \n", @@ -570,35 +633,35 @@ " ...\n", " \n", " \n", - " 2822879\n", - " 7514214\n", - " False\n", + " 2847736\n", + " 7539071\n", " False\n", - " 1.0\n", - " 0.0\n", + " True\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 1.08765\n", - " 0.415094\n", - " 3.578616\n", - " 0.371951\n", - " False\n", + " 9.875413\n", + " 12.586445\n", + " 4.800090\n", + " 8.088658\n", + " 4.581187\n", + " 7.858361\n", + " 5.697172\n", + " 9.212853\n", + " 4\n", " 1\n", - " 2\n", " \n", " \n", - " 2822949\n", - " 7514284\n", + " 2847868\n", + " 7539203\n", " False\n", " False\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -606,47 +669,47 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.86254\n", - " 33.841404\n", - " 40.299687\n", - " 18.394631\n", - " True\n", - " 1\n", + " 9.121001\n", + " 11.606573\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 1.324505\n", + " 4.959246\n", + " 5\n", " 2\n", " \n", " \n", - " 2823069\n", - " 7514404\n", - " False\n", - " False\n", - " 1.0\n", + " 2847882\n", + " 7539217\n", + " True\n", + " True\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.95752\n", - " 65.596535\n", - " 32.655666\n", - " 21.802041\n", - " True\n", + " 9.121001\n", + " 11.606573\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 1.324505\n", + " 4.959246\n", + " 4\n", " 1\n", - " 2\n", " \n", " \n", - " 2823442\n", - " 7514777\n", + " 2847935\n", + " 7539270\n", " False\n", " False\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -654,23 +717,23 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 2\n", - " 4.51942\n", - " 56.706023\n", - " 144.861886\n", - " 40.753220\n", - " True\n", - " 1\n", + " 9.653875\n", + " 12.007751\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 5.523983\n", + " 7.598532\n", + " 5\n", " 2\n", " \n", " \n", - " 2823850\n", - " 7515185\n", - " False\n", + " 2847982\n", + " 7539317\n", + " True\n", " False\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -678,108 +741,108 @@ " 0.0\n", " 0.0\n", " ...\n", - 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2582 rows × 116 columns

\n", + "

28281 rows × 116 columns

\n", "" ], "text/plain": [ " person_id model_choice override_choice \\\n", "household_id \n", - "72241 72241 False False \n", - "72441 72441 False False \n", - "72528 72528 False False \n", - "73144 73144 False False \n", - "73493 73493 False False \n", + "72355 72355 False False \n", + "72384 72384 False False \n", + "72407 72407 False False \n", + "72459 72459 False False \n", + "72529 72529 False False \n", "... ... ... ... \n", - "2822879 7514214 False False \n", - "2822949 7514284 False False \n", - "2823069 7514404 False False \n", - "2823442 7514777 False False \n", - "2823850 7515185 False False \n", + "2847736 7539071 False True \n", + "2847868 7539203 False False \n", + "2847882 7539217 True True \n", + "2847935 7539270 False False \n", + "2847982 7539317 True False \n", "\n", " util_asc_san_francisco util_asc_santa_clara util_asc_alameda \\\n", "household_id \n", - "72241 1.0 0.0 0.0 \n", - "72441 1.0 0.0 0.0 \n", - "72528 1.0 0.0 0.0 \n", - "73144 1.0 0.0 0.0 \n", - "73493 1.0 0.0 0.0 \n", + "72355 1.0 0.0 0.0 \n", + "72384 1.0 0.0 0.0 \n", + "72407 1.0 0.0 0.0 \n", + "72459 1.0 0.0 0.0 \n", + "72529 1.0 0.0 0.0 \n", "... ... ... ... \n", - "2822879 1.0 0.0 0.0 \n", - "2822949 1.0 0.0 0.0 \n", - "2823069 1.0 0.0 0.0 \n", - "2823442 1.0 0.0 0.0 \n", - "2823850 1.0 0.0 0.0 \n", + "2847736 0.0 0.0 1.0 \n", + "2847868 0.0 0.0 0.0 \n", + "2847882 0.0 0.0 1.0 \n", + "2847935 0.0 0.0 0.0 \n", + "2847982 0.0 0.0 0.0 \n", "\n", " util_income_very_high util_income_high util_hh_size_4_up \\\n", "household_id \n", - "72241 0.0 0.0 0.0 \n", - "72441 0.0 0.0 0.0 \n", - "72528 0.0 0.0 0.0 \n", - "73144 0.0 0.0 0.0 \n", - "73493 0.0 0.0 0.0 \n", + "72355 0.0 0.0 0.0 \n", + "72384 0.0 0.0 0.0 \n", + "72407 0.0 0.0 0.0 \n", + "72459 0.0 0.0 0.0 \n", + "72529 0.0 0.0 0.0 \n", "... ... ... ... \n", - "2822879 0.0 0.0 0.0 \n", - "2822949 0.0 0.0 0.0 \n", - "2823069 0.0 0.0 0.0 \n", - "2823442 0.0 0.0 0.0 \n", - "2823850 0.0 0.0 0.0 \n", + "2847736 0.0 0.0 0.0 \n", + "2847868 0.0 0.0 0.0 \n", + "2847882 0.0 0.0 0.0 \n", + "2847935 0.0 0.0 0.0 \n", + "2847982 0.0 0.0 0.0 \n", "\n", - " util_more_autos_than_workers ... COLLFTE COLLPTE \\\n", - "household_id ... \n", - "72241 0.0 ... 0.00000 0.00000 \n", - "72441 0.0 ... 0.00000 0.00000 \n", - "72528 0.0 ... 6861.20508 1174.96875 \n", - "73144 0.0 ... 0.00000 0.00000 \n", - "73493 0.0 ... 7144.64307 22523.34570 \n", - "... ... ... ... ... \n", - "2822879 0.0 ... 0.00000 0.00000 \n", - "2822949 0.0 ... 0.00000 0.00000 \n", - "2823069 0.0 ... 0.00000 0.00000 \n", - "2823442 0.0 ... 0.00000 0.00000 \n", - "2823850 0.0 ... 0.00000 0.00000 \n", + " util_more_autos_than_workers ... auOpRetail auOpTotal \\\n", + "household_id ... \n", + "72355 1.0 ... 9.915345 12.430580 \n", + "72384 0.0 ... 9.971763 12.488100 \n", + "72407 0.0 ... 9.971763 12.488100 \n", + "72459 0.0 ... 10.086939 12.619691 \n", + "72529 0.0 ... 10.263491 12.913804 \n", + "... ... ... ... ... \n", + "2847736 1.0 ... 9.875413 12.586445 \n", + "2847868 0.0 ... 9.121001 11.606573 \n", + "2847882 0.0 ... 9.121001 11.606573 \n", + "2847935 0.0 ... 9.653875 12.007751 \n", + "2847982 0.0 ... 6.494711 8.713819 \n", "\n", - " TOPOLOGY TERMINAL household_density employment_density \\\n", - "household_id \n", - "72241 1 4.58156 68.092368 151.674705 \n", - "72441 1 2.28482 28.491228 4.052632 \n", - "72528 1 3.49553 19.033424 37.235052 \n", - "73144 1 2.48345 26.073171 8.048780 \n", - "73493 1 3.20356 13.672131 39.852459 \n", - "... ... ... ... ... \n", - "2822879 1 1.08765 0.415094 3.578616 \n", - "2822949 1 3.86254 33.841404 40.299687 \n", - "2823069 1 3.95752 65.596535 32.655666 \n", - "2823442 2 4.51942 56.706023 144.861886 \n", - "2823850 3 2.46800 0.296858 36.296472 \n", + " trPkRetail trPkTotal trOpRetail trOpTotal nmRetail \\\n", + "household_id \n", + "72355 6.550726 9.119016 6.446184 9.035333 5.256966 \n", + "72384 6.384615 8.910521 6.292135 8.845287 5.447277 \n", + "72407 6.384615 8.910521 6.292135 8.845287 5.447277 \n", + "72459 6.787018 9.400117 6.687820 9.307625 5.877288 \n", + "72529 6.830450 9.558905 6.611336 9.308290 6.696709 \n", + "... ... ... ... ... ... \n", + "2847736 4.800090 8.088658 4.581187 7.858361 5.697172 \n", + "2847868 0.000000 0.000000 0.000000 0.000000 1.324505 \n", + "2847882 0.000000 0.000000 0.000000 0.000000 1.324505 \n", + "2847935 0.000000 0.000000 0.000000 0.000000 5.523983 \n", + "2847982 0.019735 0.222696 0.016077 0.153189 0.000000 \n", "\n", - " density_index is_cbd workplace_county_id override_choice_code \n", - "household_id \n", - "72241 46.994710 True 1 2 \n", - "72441 3.547964 False 1 2 \n", - "72528 12.595161 False 1 2 \n", - "73144 6.150212 False 1 2 \n", - "73493 10.179771 False 1 2 \n", - "... ... ... ... ... \n", - "2822879 0.371951 False 1 2 \n", - "2822949 18.394631 True 1 2 \n", - "2823069 21.802041 True 1 2 \n", - "2823442 40.753220 True 1 2 \n", - "2823850 0.294449 True 1 2 \n", + " nmTotal workplace_county_id override_choice_code \n", + "household_id \n", + "72355 6.831275 1 2 \n", + "72384 7.062559 1 2 \n", + "72407 7.062559 1 2 \n", + "72459 7.691985 1 2 \n", + "72529 8.925142 1 2 \n", + "... ... ... ... \n", + "2847736 9.212853 4 1 \n", + "2847868 4.959246 5 2 \n", + "2847882 4.959246 4 1 \n", + "2847935 7.598532 5 2 \n", + "2847982 0.000000 8 2 \n", "\n", - "[2582 rows x 116 columns]" + "[28281 rows x 116 columns]" ] }, "execution_count": 6, @@ -802,20 +865,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 010 [Optimization terminated successfully]

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Iteration 007 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -827,7 +883,7 @@ { "data": { "text/html": [ - "

Best LL = -484.0908426547178

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Best LL = -15941.804810325377

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coef_asc_san_francisco0.0003990.000074
coef_asc_santa_clara0.0000000.000008
coef_fewer_autos_than_workers0.0001830.000150
coef_hh_size_4_up0.000210-0.000075
coef_income_high0.0003640.000317
coef_income_very_high-0.000061-0.000092
coef_more_autos_than_workers0.0001070.000781
nit10nfev19njev10status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.810470method'slsqp'n_cases2582iteration_number10logloss0.1874867709739418" + "nit7nfev7njev7status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.092472method'slsqp'n_cases28281iteration_number7loglike-15941.804810325377" ], "text/plain": [ "┣ x: 0.0 0.000000\n", - "┃ coef_asc_alameda -0.109200\n", - "┃ coef_asc_san_francisco -2.456059\n", - "┃ coef_asc_santa_clara 0.211800\n", - "┃ coef_fewer_autos_than_workers -1.619489\n", - "┃ coef_hh_size_4_up 0.279145\n", - "┃ coef_income_high -0.053120\n", - "┃ coef_income_very_high 0.296661\n", - "┃ coef_more_autos_than_workers 0.223434\n", + "┃ coef_asc_alameda -0.141547\n", + "┃ coef_asc_san_francisco -2.630212\n", + "┃ coef_asc_santa_clara 0.188205\n", + "┃ coef_fewer_autos_than_workers -1.407827\n", + "┃ coef_hh_size_4_up 0.264779\n", + "┃ coef_income_high 0.217818\n", + "┃ coef_income_very_high 0.235389\n", + "┃ coef_more_autos_than_workers 0.225131\n", "┃ dtype: float64\n", - "┣ loglike: -484.0908426547178\n", - "┣ d_loglike: 0.0 0.000000\n", - "┃ coef_asc_alameda 0.000000\n", - "┃ coef_asc_san_francisco 0.000399\n", - "┃ coef_asc_santa_clara 0.000000\n", - "┃ coef_fewer_autos_than_workers 0.000183\n", - "┃ coef_hh_size_4_up 0.000210\n", - "┃ coef_income_high 0.000364\n", - "┃ coef_income_very_high -0.000061\n", - "┃ coef_more_autos_than_workers 0.000107\n", + "┣ logloss: 0.5636931088124669\n", + "┣ d_logloss: 0.0 0.000000\n", + "┃ coef_asc_alameda 0.000061\n", + "┃ coef_asc_san_francisco 0.000074\n", + "┃ coef_asc_santa_clara 0.000008\n", + "┃ coef_fewer_autos_than_workers 0.000150\n", + "┃ coef_hh_size_4_up -0.000075\n", + "┃ coef_income_high 0.000317\n", + "┃ coef_income_very_high -0.000092\n", + "┃ coef_more_autos_than_workers 0.000781\n", "┃ dtype: float64\n", - "┣ nit: 10\n", - "┣ nfev: 19\n", - "┣ njev: 10\n", + "┣ nit: 7\n", + "┣ nfev: 7\n", + "┣ njev: 7\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(microseconds=810470)\n", + "┣ elapsed_time: datetime.timedelta(microseconds=92472)\n", "┣ method: 'slsqp'\n", - "┣ n_cases: 2582\n", - "┣ iteration_number: 10\n", - "┣ logloss: 0.1874867709739418" + "┣ n_cases: 28281\n", + "┣ iteration_number: 7\n", + "┣ loglike: -15941.804810325377" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1154,119 +1204,136 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Value Std Err t Stat Signif Like Ratio Null Value Constrained
0.0 0.00 NA NA NA 0.00fixed value
coef_asc_alameda-0.109 0.00 NA[] 0.00 0.00
coef_asc_san_francisco-2.46 0.168-14.65*** NA 0.00
coef_asc_santa_clara 0.212 1.43e-16 BIG*** NA 0.00
coef_fewer_autos_than_workers-1.62 0.220-7.37*** NA 0.00
coef_hh_size_4_up 0.279 0.196 1.43 NA 0.00
coef_income_high-0.0531 0.242-0.22 NA 0.00
coef_income_very_high 0.297 0.208 1.43 NA 0.00
coef_more_autos_than_workers 0.223 0.241 0.93 NA 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
0.0 0.00 0.00 NA 0.00fixed value
coef_asc_alameda-0.142 0.0310-4.56*** 0.00
coef_asc_san_francisco-2.63 0.0591-44.52*** 0.00
coef_asc_santa_clara 0.188 0.0294 6.40*** 0.00
coef_fewer_autos_than_workers-1.41 0.0366-38.48*** 0.00
coef_hh_size_4_up 0.265 0.0278 9.51*** 0.00
coef_income_high 0.218 0.0307 7.09*** 0.00
coef_income_very_high 0.235 0.0282 8.35*** 0.00
coef_more_autos_than_workers 0.225 0.0294 7.65*** 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1287,14 +1354,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from activitysim.estimation.larch import update_coefficients\n", - "result_dir = data.edb_directory/\"estimated\"\n", + "\n", + "result_dir = data.edb_directory / \"estimated\"\n", "update_coefficients(\n", - " model, data, result_dir,\n", + " model,\n", + " data,\n", + " result_dir,\n", " output_file=f\"{modelname}_coefficients_revised.csv\",\n", ");" ] @@ -1308,23 +1378,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", - " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", + " result_dir / f\"{modelname}_model_estimation.xlsx\",\n", " data_statistics=False,\n", ")" ] @@ -1340,7 +1399,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1373,49 +1432,49 @@ " \n", " 0\n", " coef_asc_san_francisco\n", - " -2.456059\n", + " -2.630212\n", " F\n", " \n", " \n", " 1\n", " coef_asc_santa_clara\n", - " 0.211800\n", + " 0.188205\n", " F\n", " \n", " \n", " 2\n", " coef_asc_alameda\n", - " -0.109200\n", + " -0.141547\n", " F\n", " \n", " \n", " 3\n", " coef_income_very_high\n", - " 0.296661\n", + " 0.235389\n", " F\n", " \n", " \n", " 4\n", " coef_income_high\n", - " -0.053120\n", + " 0.217818\n", " F\n", " \n", " \n", " 5\n", " coef_hh_size_4_up\n", - " 0.279145\n", + " 0.264779\n", " F\n", " \n", " \n", " 6\n", " coef_more_autos_than_workers\n", - " 0.223434\n", + " 0.225131\n", " F\n", " \n", " \n", " 7\n", " coef_fewer_autos_than_workers\n", - " -1.619489\n", + " -1.407827\n", " F\n", " \n", " \n", @@ -1424,23 +1483,23 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coef_asc_san_francisco -2.456059 F\n", - "1 coef_asc_santa_clara 0.211800 F\n", - "2 coef_asc_alameda -0.109200 F\n", - "3 coef_income_very_high 0.296661 F\n", - "4 coef_income_high -0.053120 F\n", - "5 coef_hh_size_4_up 0.279145 F\n", - "6 coef_more_autos_than_workers 0.223434 F\n", - "7 coef_fewer_autos_than_workers -1.619489 F" + "0 coef_asc_san_francisco -2.630212 F\n", + "1 coef_asc_santa_clara 0.188205 F\n", + "2 coef_asc_alameda -0.141547 F\n", + "3 coef_income_very_high 0.235389 F\n", + "4 coef_income_high 0.217818 F\n", + "5 coef_hh_size_4_up 0.264779 F\n", + "6 coef_more_autos_than_workers 0.225131 F\n", + "7 coef_fewer_autos_than_workers -1.407827 F" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pd.read_csv(result_dir/f\"{modelname}_coefficients_revised.csv\")" + "pd.read_csv(result_dir / f\"{modelname}_coefficients_revised.csv\")" ] } ], @@ -1451,7 +1510,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -1465,7 +1524,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/06_cdap.ipynb b/activitysim/examples/example_estimation/notebooks/06_cdap.ipynb index 72169b9e3d..a29ea0a7b1 100644 --- a/activitysim/examples/example_estimation/notebooks/06_cdap.ipynb +++ b/activitysim/examples/example_estimation/notebooks/06_cdap.ipynb @@ -23,21 +23,84 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import numpy as np\n", "import pandas as pd\n", - "import larch\n", - "import os" + "import larch as lx\n", + "import os\n", + "\n", + "lx.versions()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from est_mode_setup import prepare, backup\n", + "\n", + "prepare()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "os.chdir('test')" + "backup(\"output-est-mode/estimation_data_bundle/cdap/cdap_INDIV_AND_HHSIZE1_SPEC.csv\")\n" ] }, { @@ -49,31 +112,35 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "one-hot encoding choice array\n", - "converting data_ch to \n", - "one-hot encoding choice array\n", - "converting data_ch to \n", - "one-hot encoding choice array\n", - "converting data_ch to \n", - "one-hot encoding choice array\n", - "converting data_ch to \n", - "one-hot encoding choice array\n", - "converting data_ch to \n" + "Reading output-est-mode/estimation_data_bundle/cdap/cdap_model_settings.yaml\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/final_households.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/final_persons.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_coefficients.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_interaction_coefficients.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_joint_tour_coefficients.csv\n", + "Including joint tour utility?: False\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_INDIV_AND_HHSIZE1_SPEC.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_values_combined.parquet\n" ] } ], "source": [ + "from activitysim.estimation.larch import component_model\n", + "\n", "modelname = \"cdap\"\n", "\n", - "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -92,34 +159,51 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "┣ INDIV_AND_HHSIZE1_SPEC: 'cdap_indiv_and_hhsize1.csv'\n", + "┣ ADD_JOINT_TOUR_UTILITY: False\n", "┣ COEFFICIENTS: 'cdap_coefficients.csv'\n", - "┣ INTERACTION_COEFFICIENTS: 'cdap_interaction_coefficients.csv'\n", - "┣ FIXED_RELATIVE_PROPORTIONS_SPEC: 'cdap_fixed_relative_proportions.csv'\n", - "┣ CONSTANTS: ┣ FULL: 1\n", - "┃ ┣ PART: 2\n", - "┃ ┣ UNIVERSITY: 3\n", + "┣ CONSTANTS: ┣ DRIVING: 6\n", + "┃ ┣ FULL: 1\n", "┃ ┣ NONWORK: 4\n", + "┃ ┣ PART: 2\n", + "┃ ┣ PRESCHOOL: 8\n", "┃ ┣ RETIRED: 5\n", - "┃ ┣ DRIVING: 6\n", "┃ ┣ SCHOOL: 7\n", - "┃ ┣ PRESCHOOL: 8\n", - "┣ PERSON_TYPE_MAP: ┣ WORKER: [1, 2]\n", - "┃ ┣ CHILD: [6, 7, 8]\n", - "┣ annotate_persons: ┣ SPEC: 'annotate_persons_cdap'\n", - "┃ ┣ DF: 'persons'\n", - "┣ annotate_households: ┣ SPEC: 'annotate_households_cdap'\n", - "┃ ┣ DF: 'households'\n", - "┃ ┣ TABLES: ['persons']" + "┃ ┣ UNIVERSITY: 3\n", + "┣ FIXED_RELATIVE_PROPORTIONS_SPEC: 'cdap_fixed_relative_proportions.csv'\n", + "┣ INDIV_AND_HHSIZE1_SPEC: 'cdap_indiv_and_hhsize1.csv'\n", + "┣ INTERACTION_COEFFICIENTS: 'cdap_interaction_coefficients.csv'\n", + "┣ JOINT_TOUR_COEFFICIENTS: 'cdap_joint_tour_coefficients.csv'\n", + "┣ JOINT_TOUR_USEFUL_COLUMNS: None\n", + "┣ PERSON_TYPE_MAP: ┣ CHILD: ---\n", + "┃ ┃ - 6\n", + "┃ ┃ - 7\n", + "┃ ┃ - 8\n", + "┃ ┃ ...\n", + "┃ ┣ WORKER: ---\n", + "┃ ┃ - 1\n", + "┃ ┃ - 2\n", + "┃ ┃ ...\n", + "┣ annotate_households: ┣ DF: 'households'\n", + "┃ ┣ SPEC: 'annotate_households_cdap'\n", + "┃ ┣ TABLES: ---\n", + "┃ ┃ - persons\n", + "┃ ┃ ...\n", + "┣ annotate_persons: ┣ DF: 'persons'\n", + "┃ ┣ SPEC: 'annotate_persons_cdap'\n", + "┃ ┣ TABLES: None\n", + "┣ compute_settings: None\n", + "┣ source_file_paths: ---\n", + "┃ - /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/test-estimation-data/activitysim-prototype-mtc-extended/configs/cdap.yaml\n", + "┃ ..." ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -137,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -161,7 +245,6 @@ " \n", " \n", " \n", - " household_id\n", " home_zone_id\n", " income\n", " hhsize\n", @@ -171,8 +254,8 @@ " sample_rate\n", " income_in_thousands\n", " income_segment\n", + " median_value_of_time\n", " ...\n", - " hh_work_auto_savings_ratio\n", " num_under16_not_at_school\n", " num_travel_active\n", " num_travel_active_adults\n", @@ -182,23 +265,23 @@ " participates_in_jtf_model\n", " joint_tour_frequency\n", " num_hh_joint_tours\n", + " household_id\n", " \n", " \n", " \n", " \n", " 0\n", - " 166\n", - " 71\n", - " 9000\n", - " 1\n", - " 6\n", + " 52\n", " 0\n", + " 1\n", + " 4\n", + " 1\n", " 0\n", " 1\n", - " 9.00\n", + " 0.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -208,21 +291,21 @@ " False\n", " 0_tours\n", " 0\n", + " 45\n", " \n", " \n", " 1\n", - " 197\n", - " 80\n", - " 4400\n", + " 124\n", + " 14000\n", + " 1\n", + " 4\n", " 1\n", - " 6\n", - " 0\n", " 0\n", " 1\n", - " 4.40\n", + " 14.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -232,21 +315,21 @@ " False\n", " 0_tours\n", " 0\n", + " 499\n", " \n", " \n", " 2\n", - " 268\n", - " 91\n", - " 1200\n", + " 132\n", + " 9600\n", " 1\n", " 4\n", " 1\n", " 0\n", " 1\n", - " 1.20\n", + " 9.6\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -256,21 +339,21 @@ " False\n", " 0_tours\n", " 0\n", + " 659\n", " \n", " \n", " 3\n", - " 375\n", - " 105\n", - " 0\n", + " 148\n", + " 18000\n", " 1\n", " 6\n", " 1\n", " 0\n", " 1\n", - " 0.00\n", + " 18.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -280,21 +363,21 @@ " False\n", " 0_tours\n", " 0\n", + " 948\n", " \n", " \n", " 4\n", - " 387\n", - " 112\n", - " 15770\n", + " 166\n", + " 14000\n", " 1\n", - " 6\n", + " 4\n", " 1\n", " 0\n", " 1\n", - " 15.77\n", + " 14.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -304,6 +387,7 @@ " False\n", " 0_tours\n", " 0\n", + " 1276\n", " \n", " \n", " ...\n", @@ -330,19 +414,18 @@ " ...\n", " \n", " \n", - " 1995\n", - " 2863464\n", - " 5\n", + " 19995\n", + " 1386\n", " 0\n", " 1\n", " 0\n", " 1\n", " 0\n", " 1\n", - " 0.00\n", + " 0.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -352,45 +435,45 @@ " False\n", " 0_tours\n", " 0\n", + " 2874468\n", " \n", " \n", - " 1996\n", - " 2863483\n", - " 20\n", + " 19996\n", + " 1402\n", " 0\n", " 1\n", " 0\n", - " 1\n", + " 0\n", " 0\n", " 1\n", - " 0.00\n", + " 0.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", - " 1\n", - " 1\n", " 0\n", " 0\n", - " 1\n", + " 0\n", + " 0\n", + " 0\n", " False\n", " 0_tours\n", " 0\n", + " 2874567\n", " \n", " \n", - " 1997\n", - " 2863806\n", - " 85\n", + " 19997\n", + " 1402\n", " 0\n", " 1\n", " 0\n", " 1\n", " 0\n", " 1\n", - " 0.00\n", + " 0.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -400,21 +483,21 @@ " False\n", " 0_tours\n", " 0\n", + " 2874576\n", " \n", " \n", - " 1998\n", - " 2864518\n", - " 115\n", + " 19998\n", + " 1421\n", " 0\n", " 1\n", " 0\n", " 1\n", " 0\n", " 1\n", - " 0.00\n", + " 0.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -424,21 +507,21 @@ " False\n", " 0_tours\n", " 0\n", + " 2874826\n", " \n", " \n", - " 1999\n", - " 2864688\n", - " 136\n", + " 19999\n", + " 1437\n", " 0\n", " 1\n", " 0\n", " 1\n", " 0\n", " 1\n", - " 0.00\n", + " 0.0\n", " 1\n", + " 6.01\n", " ...\n", - " 0.0\n", " 0\n", " 1\n", " 1\n", @@ -448,108 +531,109 @@ " False\n", " 0_tours\n", " 0\n", + " 2875013\n", " \n", " \n", "\n", - "

2000 rows × 35 columns

\n", + "

20000 rows × 35 columns

\n", "" ], "text/plain": [ - " household_id home_zone_id income hhsize HHT auto_ownership \\\n", - "0 166 71 9000 1 6 0 \n", - "1 197 80 4400 1 6 0 \n", - "2 268 91 1200 1 4 1 \n", - "3 375 105 0 1 6 1 \n", - "4 387 112 15770 1 6 1 \n", - "... ... ... ... ... ... ... \n", - "1995 2863464 5 0 1 0 1 \n", - "1996 2863483 20 0 1 0 1 \n", - "1997 2863806 85 0 1 0 1 \n", - "1998 2864518 115 0 1 0 1 \n", - "1999 2864688 136 0 1 0 1 \n", + " home_zone_id income hhsize HHT auto_ownership num_workers \\\n", + "0 52 0 1 4 1 0 \n", + "1 124 14000 1 4 1 0 \n", + "2 132 9600 1 4 1 0 \n", + "3 148 18000 1 6 1 0 \n", + "4 166 14000 1 4 1 0 \n", + "... ... ... ... ... ... ... \n", + "19995 1386 0 1 0 1 0 \n", + "19996 1402 0 1 0 0 0 \n", + "19997 1402 0 1 0 1 0 \n", + "19998 1421 0 1 0 1 0 \n", + "19999 1437 0 1 0 1 0 \n", "\n", - " num_workers sample_rate income_in_thousands income_segment ... \\\n", - "0 0 1 9.00 1 ... \n", - "1 0 1 4.40 1 ... \n", - "2 0 1 1.20 1 ... \n", - "3 0 1 0.00 1 ... \n", - "4 0 1 15.77 1 ... \n", - "... ... ... ... ... ... \n", - "1995 0 1 0.00 1 ... \n", - "1996 0 1 0.00 1 ... \n", - "1997 0 1 0.00 1 ... \n", - "1998 0 1 0.00 1 ... \n", - "1999 0 1 0.00 1 ... \n", + " sample_rate income_in_thousands income_segment median_value_of_time \\\n", + "0 1 0.0 1 6.01 \n", + "1 1 14.0 1 6.01 \n", + "2 1 9.6 1 6.01 \n", + "3 1 18.0 1 6.01 \n", + "4 1 14.0 1 6.01 \n", + "... ... ... ... ... \n", + "19995 1 0.0 1 6.01 \n", + "19996 1 0.0 1 6.01 \n", + "19997 1 0.0 1 6.01 \n", + "19998 1 0.0 1 6.01 \n", + "19999 1 0.0 1 6.01 \n", "\n", - " hh_work_auto_savings_ratio num_under16_not_at_school \\\n", - "0 0.0 0 \n", - "1 0.0 0 \n", - "2 0.0 0 \n", - "3 0.0 0 \n", - "4 0.0 0 \n", - "... ... ... \n", - "1995 0.0 0 \n", - "1996 0.0 0 \n", - "1997 0.0 0 \n", - "1998 0.0 0 \n", - "1999 0.0 0 \n", + " ... num_under16_not_at_school num_travel_active \\\n", + "0 ... 0 1 \n", + "1 ... 0 1 \n", + "2 ... 0 1 \n", + "3 ... 0 1 \n", + "4 ... 0 1 \n", + "... ... ... ... \n", + "19995 ... 0 1 \n", + "19996 ... 0 0 \n", + "19997 ... 0 1 \n", + "19998 ... 0 1 \n", + "19999 ... 0 1 \n", "\n", - " num_travel_active num_travel_active_adults \\\n", - "0 1 1 \n", - "1 1 1 \n", - "2 1 1 \n", - "3 1 1 \n", - "4 1 1 \n", - "... ... ... \n", - "1995 1 1 \n", - "1996 1 1 \n", - "1997 1 1 \n", - "1998 1 1 \n", - "1999 1 1 \n", + " num_travel_active_adults num_travel_active_preschoolers \\\n", + "0 1 0 \n", + "1 1 0 \n", + "2 1 0 \n", + "3 1 0 \n", + "4 1 0 \n", + "... ... ... \n", + "19995 1 0 \n", + "19996 0 0 \n", + "19997 1 0 \n", + "19998 1 0 \n", + "19999 1 0 \n", "\n", - " num_travel_active_preschoolers num_travel_active_children \\\n", - "0 0 0 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 \n", - "... ... ... \n", - "1995 0 0 \n", - "1996 0 0 \n", - "1997 0 0 \n", - "1998 0 0 \n", - "1999 0 0 \n", + " num_travel_active_children num_travel_active_non_preschoolers \\\n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 1 \n", + "3 0 1 \n", + "4 0 1 \n", + "... ... ... \n", + "19995 0 1 \n", + "19996 0 0 \n", + "19997 0 1 \n", + "19998 0 1 \n", + "19999 0 1 \n", "\n", - " num_travel_active_non_preschoolers participates_in_jtf_model \\\n", - "0 1 False \n", - "1 1 False \n", - "2 1 False \n", - "3 1 False \n", - "4 1 False \n", - "... ... ... \n", - "1995 1 False \n", - "1996 1 False \n", - "1997 1 False \n", - "1998 1 False \n", - "1999 1 False \n", + " participates_in_jtf_model joint_tour_frequency num_hh_joint_tours \\\n", + "0 False 0_tours 0 \n", + "1 False 0_tours 0 \n", + "2 False 0_tours 0 \n", + "3 False 0_tours 0 \n", + "4 False 0_tours 0 \n", + "... ... ... ... \n", + "19995 False 0_tours 0 \n", + "19996 False 0_tours 0 \n", + "19997 False 0_tours 0 \n", + "19998 False 0_tours 0 \n", + "19999 False 0_tours 0 \n", "\n", - " joint_tour_frequency num_hh_joint_tours \n", - "0 0_tours 0 \n", - "1 0_tours 0 \n", - "2 0_tours 0 \n", - "3 0_tours 0 \n", - "4 0_tours 0 \n", - "... ... ... \n", - "1995 0_tours 0 \n", - "1996 0_tours 0 \n", - "1997 0_tours 0 \n", - "1998 0_tours 0 \n", - "1999 0_tours 0 \n", + " household_id \n", + "0 45 \n", + "1 499 \n", + "2 659 \n", + "3 948 \n", + "4 1276 \n", + "... ... \n", + "19995 2874468 \n", + "19996 2874567 \n", + "19997 2874576 \n", + "19998 2874826 \n", + "19999 2875013 \n", "\n", - "[2000 rows x 35 columns]" + "[20000 rows x 35 columns]" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -567,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -602,14 +686,14 @@ " pstudent\n", " ptype\n", " ...\n", - " COLLFTE\n", - " COLLPTE\n", - " TOPOLOGY\n", - " TERMINAL\n", - " household_density\n", - " employment_density\n", - " density_index\n", - " is_cbd\n", + " auOpRetail\n", + " auOpTotal\n", + " trPkRetail\n", + " trPkTotal\n", + " trOpRetail\n", + " trOpTotal\n", + " nmRetail\n", + " nmTotal\n", " chunk_id\n", " cdap_rank\n", " \n", @@ -617,122 +701,122 @@ " \n", " \n", " 0\n", - " 166\n", + " 45\n", " N\n", " N\n", - " 166\n", - " 54\n", + " 45\n", + " 48\n", + " 1\n", " 1\n", - " 2\n", " 3\n", " 3\n", " 4\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.21263\n", - " 24.783133\n", - " 31.566265\n", - " 13.883217\n", - " False\n", + " 9.924660\n", + " 12.562639\n", + " 4.193237\n", + " 6.875144\n", + " 3.952128\n", + " 6.590585\n", + " 2.194792\n", + " 6.359507\n", " 0\n", " 1\n", " \n", " \n", " 1\n", - " 197\n", + " 499\n", " N\n", " N\n", - " 197\n", - " 46\n", + " 499\n", + " 50\n", + " 1\n", " 1\n", - " 2\n", " 3\n", " 3\n", " 4\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.68156\n", - " 56.783784\n", - " 10.459459\n", - " 8.832526\n", - " False\n", + " 10.284673\n", + " 12.868645\n", + " 6.639963\n", + " 9.364105\n", + " 6.531079\n", + " 9.259002\n", + " 5.955868\n", + " 7.795004\n", " 1\n", " 1\n", " \n", " \n", " 2\n", - " 268\n", + " 659\n", " N\n", " N\n", - " 268\n", - " 46\n", + " 659\n", + " 52\n", " 1\n", " 1\n", " 3\n", " 3\n", " 4\n", " ...\n", - " 3598.08521\n", - " 0.00000\n", - " 1\n", - " 3.29100\n", - " 11.947644\n", - " 45.167539\n", - " 9.448375\n", - " True\n", - " 2\n", + " 10.247505\n", + " 12.762286\n", + " 6.001466\n", + " 8.409169\n", + " 5.786652\n", + " 8.279842\n", + " 5.798886\n", + " 7.900061\n", + " 0\n", " 1\n", " \n", " \n", " 3\n", - " 375\n", + " 948\n", " N\n", " N\n", - " 375\n", - " 54\n", + " 948\n", + " 61\n", " 1\n", " 2\n", " 3\n", " 3\n", " 4\n", " ...\n", - " 0.00000\n", - " 0.00000\n", + " 10.150335\n", + " 12.777635\n", + " 5.172974\n", + " 7.850360\n", + " 4.893929\n", + " 7.571579\n", + " 4.895220\n", + " 7.409345\n", " 1\n", - " 4.11499\n", - " 73.040169\n", - " 28.028350\n", - " 20.255520\n", - " True\n", - " 3\n", " 1\n", " \n", " \n", " 4\n", - " 387\n", - " N\n", + " 1276\n", + " H\n", " N\n", - " 387\n", - " 44\n", + " 1276\n", + " 46\n", + " 1\n", " 1\n", - " 2\n", " 3\n", " 3\n", " 4\n", " ...\n", - " 227.78223\n", - " 41.22827\n", - " 1\n", - " 3.83527\n", - " 26.631579\n", - " 45.868421\n", - " 16.848945\n", - " False\n", - " 4\n", + " 10.258471\n", + " 12.759529\n", + " 6.039019\n", + " 8.348963\n", + " 5.778785\n", + " 8.070525\n", + " 6.073537\n", + " 7.851667\n", + " 2\n", " 1\n", " \n", " \n", @@ -760,187 +844,187 @@ " ...\n", " \n", " \n", - " 4400\n", - " 7554799\n", + " 53069\n", + " 7565803\n", " N\n", " N\n", - " 2863464\n", - " 93\n", + " 2874468\n", + " 85\n", " 1\n", " 2\n", " 3\n", " 3\n", " 5\n", " ...\n", - " 72.14684\n", - " 0.00000\n", - " 1\n", - " 5.52555\n", - " 38.187500\n", - " 978.875000\n", - " 36.753679\n", - " False\n", - " 1995\n", + " 8.113608\n", + " 10.265845\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 9998\n", " 1\n", " \n", " \n", - " 4401\n", - " 7554818\n", - " N\n", - " N\n", - " 2863483\n", - " 68\n", - " 1\n", + " 53070\n", + " 7565902\n", + " H\n", + " H\n", + " 2874567\n", + " 87\n", " 1\n", + " 2\n", " 3\n", " 3\n", " 5\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 3\n", - " 3.99027\n", - " 39.838272\n", - " 71.693001\n", - " 25.608291\n", - " True\n", - " 1996\n", + " 6.560015\n", + " 8.886403\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 9997\n", " 1\n", " \n", " \n", - " 4402\n", - " 7555141\n", + " 53071\n", + " 7565911\n", " N\n", " N\n", - " 2863806\n", - " 93\n", + " 2874576\n", + " 85\n", " 1\n", " 2\n", " 3\n", " 3\n", " 5\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 4.27539\n", - " 51.675676\n", - " 47.216216\n", - " 24.672699\n", - " False\n", - " 1997\n", + " 6.560015\n", + " 8.886403\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 9999\n", " 1\n", " \n", " \n", - " 4403\n", - " 7555853\n", + " 53072\n", + " 7566161\n", " N\n", " N\n", - " 2864518\n", - " 71\n", + " 2874826\n", + " 79\n", " 1\n", " 1\n", " 3\n", " 3\n", " 5\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 25.52083\n", - " 15.938148\n", - " 551.353820\n", - " 15.490363\n", - " True\n", - " 1998\n", + " 9.298380\n", + " 11.721935\n", + " 1.052528\n", + " 2.925968\n", + " 0.494776\n", + " 2.006432\n", + " 3.782008\n", + " 6.208875\n", + " 9998\n", " 1\n", " \n", " \n", - " 4404\n", - " 7556023\n", + " 53073\n", + " 7566348\n", " N\n", " N\n", - " 2864688\n", + " 2875013\n", " 93\n", " 1\n", - " 1\n", + " 2\n", " 3\n", " 3\n", " 5\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.29134\n", - " 16.000000\n", - " 31.644068\n", - " 10.626823\n", - " False\n", - " 1999\n", + " 9.588527\n", + " 12.019407\n", + " 3.053361\n", + " 5.871246\n", + " 0.141304\n", + " 0.776133\n", + " 4.090554\n", + " 6.388513\n", + " 9999\n", " 1\n", " \n", " \n", "\n", - "

4405 rows × 110 columns

\n", + "

53074 rows × 110 columns

\n", "" ], "text/plain": [ - " person_id model_choice override_choice household_id age PNUM sex \\\n", - "0 166 N N 166 54 1 2 \n", - "1 197 N N 197 46 1 2 \n", - "2 268 N N 268 46 1 1 \n", - "3 375 N N 375 54 1 2 \n", - "4 387 N N 387 44 1 2 \n", - "... ... ... ... ... ... ... ... \n", - "4400 7554799 N N 2863464 93 1 2 \n", - "4401 7554818 N N 2863483 68 1 1 \n", - "4402 7555141 N N 2863806 93 1 2 \n", - "4403 7555853 N N 2864518 71 1 1 \n", - "4404 7556023 N N 2864688 93 1 1 \n", + " person_id model_choice override_choice household_id age PNUM sex \\\n", + "0 45 N N 45 48 1 1 \n", + "1 499 N N 499 50 1 1 \n", + "2 659 N N 659 52 1 1 \n", + "3 948 N N 948 61 1 2 \n", + "4 1276 H N 1276 46 1 1 \n", + "... ... ... ... ... ... ... ... \n", + "53069 7565803 N N 2874468 85 1 2 \n", + "53070 7565902 H H 2874567 87 1 2 \n", + "53071 7565911 N N 2874576 85 1 2 \n", + "53072 7566161 N N 2874826 79 1 1 \n", + "53073 7566348 N N 2875013 93 1 2 \n", "\n", - " pemploy pstudent ptype ... COLLFTE COLLPTE TOPOLOGY TERMINAL \\\n", - "0 3 3 4 ... 0.00000 0.00000 1 3.21263 \n", - "1 3 3 4 ... 0.00000 0.00000 1 3.68156 \n", - "2 3 3 4 ... 3598.08521 0.00000 1 3.29100 \n", - "3 3 3 4 ... 0.00000 0.00000 1 4.11499 \n", - "4 3 3 4 ... 227.78223 41.22827 1 3.83527 \n", - "... ... ... ... ... ... ... ... ... \n", - "4400 3 3 5 ... 72.14684 0.00000 1 5.52555 \n", - "4401 3 3 5 ... 0.00000 0.00000 3 3.99027 \n", - "4402 3 3 5 ... 0.00000 0.00000 1 4.27539 \n", - "4403 3 3 5 ... 0.00000 0.00000 1 25.52083 \n", - "4404 3 3 5 ... 0.00000 0.00000 1 3.29134 \n", + " pemploy pstudent ptype ... auOpRetail auOpTotal trPkRetail \\\n", + "0 3 3 4 ... 9.924660 12.562639 4.193237 \n", + "1 3 3 4 ... 10.284673 12.868645 6.639963 \n", + "2 3 3 4 ... 10.247505 12.762286 6.001466 \n", + "3 3 3 4 ... 10.150335 12.777635 5.172974 \n", + "4 3 3 4 ... 10.258471 12.759529 6.039019 \n", + "... ... ... ... ... ... ... ... \n", + "53069 3 3 5 ... 8.113608 10.265845 0.000000 \n", + "53070 3 3 5 ... 6.560015 8.886403 0.000000 \n", + "53071 3 3 5 ... 6.560015 8.886403 0.000000 \n", + "53072 3 3 5 ... 9.298380 11.721935 1.052528 \n", + "53073 3 3 5 ... 9.588527 12.019407 3.053361 \n", "\n", - " household_density employment_density density_index is_cbd chunk_id \\\n", - "0 24.783133 31.566265 13.883217 False 0 \n", - "1 56.783784 10.459459 8.832526 False 1 \n", - "2 11.947644 45.167539 9.448375 True 2 \n", - "3 73.040169 28.028350 20.255520 True 3 \n", - "4 26.631579 45.868421 16.848945 False 4 \n", - "... ... ... ... ... ... \n", - "4400 38.187500 978.875000 36.753679 False 1995 \n", - "4401 39.838272 71.693001 25.608291 True 1996 \n", - "4402 51.675676 47.216216 24.672699 False 1997 \n", - "4403 15.938148 551.353820 15.490363 True 1998 \n", - "4404 16.000000 31.644068 10.626823 False 1999 \n", + " trPkTotal trOpRetail trOpTotal nmRetail nmTotal chunk_id \\\n", + "0 6.875144 3.952128 6.590585 2.194792 6.359507 0 \n", + "1 9.364105 6.531079 9.259002 5.955868 7.795004 1 \n", + "2 8.409169 5.786652 8.279842 5.798886 7.900061 0 \n", + "3 7.850360 4.893929 7.571579 4.895220 7.409345 1 \n", + "4 8.348963 5.778785 8.070525 6.073537 7.851667 2 \n", + "... ... ... ... ... ... ... \n", + "53069 0.000000 0.000000 0.000000 0.000000 0.000000 9998 \n", + "53070 0.000000 0.000000 0.000000 0.000000 0.000000 9997 \n", + "53071 0.000000 0.000000 0.000000 0.000000 0.000000 9999 \n", + "53072 2.925968 0.494776 2.006432 3.782008 6.208875 9998 \n", + "53073 5.871246 0.141304 0.776133 4.090554 6.388513 9999 \n", "\n", - " cdap_rank \n", - "0 1 \n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 \n", - "... ... \n", - "4400 1 \n", - "4401 1 \n", - "4402 1 \n", - "4403 1 \n", - "4404 1 \n", + " cdap_rank \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n", + "... ... \n", + "53069 1 \n", + "53070 1 \n", + "53071 1 \n", + "53072 1 \n", + "53073 1 \n", "\n", - "[4405 rows x 110 columns]" + "[53074 rows x 110 columns]" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -958,7 +1042,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1100,7 +1184,7 @@ "[133 rows x 5 columns]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1118,7 +1202,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1210,7 +1294,7 @@ "4 coef_UNAVAILABLE coef_retired_asc_N NaN " ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1219,6 +1303,26 @@ "data.spec1.head()" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['edb_directory', 'person_data', 'spec1', 'interaction_coef', 'coefficients', 'households', 'settings', 'joint_coef', 'add_joint'])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.keys()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1303,7 +1407,7 @@ { "data": { "text/html": [ - "

Iteration 124 [Optimization terminated successfully]

" + "

Iteration 138 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1315,7 +1419,7 @@ { "data": { "text/html": [ - "

Best LL = -2354.458830163564

" + "

Best LL = -28223.515541250054

" ], "text/plain": [ "" @@ -1346,70 +1450,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " coef_UNAVAILABLE\n", + " -999.0\n", " -999.000000\n", " -999.000000\n", - " 0.0\n", - " NaN\n", - " NaN\n", - " True\n", - " \n", " -999.000000\n", + " -999.0\n", + " -999.0\n", + " 0.0\n", + " 1\n", " \n", " \n", - " coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N\n", - " 0.856930\n", - " 0.082330\n", + " coef_H_11\n", + " 1.446554\n", + " 1.446554\n", + " 1.626000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " 0.856930\n", + " 0\n", " \n", " \n", - " coef_driving_age_child_who_is_in_school_asc_M\n", - " 0.738480\n", - " 2.330919\n", + " coef_H_12\n", + " 0.954554\n", + " 0.954554\n", + " 0.740700\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " 0.738480\n", + " 0\n", " \n", " \n", - " coef_driving_age_child_who_is_in_school_asc_N\n", - " -8.930886\n", - " -0.599119\n", + " coef_H_124_122_144\n", + " 0.353981\n", + " 0.353981\n", + " 0.957300\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " -8.930886\n", + " 0\n", " \n", " \n", - " coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H\n", - " -0.938531\n", - " -0.503100\n", + " coef_H_126_146\n", + " -0.013374\n", + " -0.013374\n", + " 0.293900\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " -0.938531\n", + " 0\n", " \n", " \n", " ...\n", @@ -1420,122 +1528,120 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " coef_N_124_122_144\n", - " 0.996710\n", - " 0.349100\n", + " coef_retired_interaction_with_more_cars_than_workers_M\n", + " 2.992000\n", + " 2.992000\n", + " 2.992000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " 0.996710\n", + " 0\n", " \n", " \n", - " coef_N_166\n", - " -1.976101\n", - " 0.355300\n", + " coef_retired_interaction_with_more_cars_than_workers_N\n", + " 1.015905\n", + " 1.015905\n", + " 1.056000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " -1.976101\n", + " 0\n", " \n", " \n", - " coef_N_222_224_444\n", - " -2.046233\n", - " -1.386000\n", + " coef_retired_interaction_with_peak_accessibility_to_all_employment_M\n", + " 0.279200\n", + " 0.279200\n", + " 0.279200\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " -2.046233\n", + " 0\n", " \n", " \n", - " coef_N_246_226_446\n", - " -0.859376\n", - " -0.857100\n", + " coef_university_student_asc_M\n", + " 2.169416\n", + " 2.169416\n", + " 2.353595\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " -0.859376\n", + " 0\n", " \n", " \n", - " coef_N_xxxxx\n", - " -0.825691\n", - " -3.453000\n", + " coef_university_student_asc_N\n", + " 0.489431\n", + " 0.489431\n", + " 0.609710\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", - " False\n", - " \n", - " -0.825691\n", + " 0\n", " \n", " \n", "\n", - "

161 rows × 8 columns

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161 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_UNAVAILABLE -999.000000 -999.000000 \n", - "coef_child_who_is_in_school_or_too_young_for_sc... 0.856930 0.082330 \n", - "coef_driving_age_child_who_is_in_school_asc_M 0.738480 2.330919 \n", - "coef_driving_age_child_who_is_in_school_asc_N -8.930886 -0.599119 \n", - "coef_driving_age_child_who_is_in_school_interac... -0.938531 -0.503100 \n", + " value best \\\n", + "param_name \n", + "-999.0 -999.000000 -999.000000 \n", + "coef_H_11 1.446554 1.446554 \n", + "coef_H_12 0.954554 0.954554 \n", + "coef_H_124_122_144 0.353981 0.353981 \n", + "coef_H_126_146 -0.013374 -0.013374 \n", "... ... ... \n", - "coef_N_124_122_144 0.996710 0.349100 \n", - "coef_N_166 -1.976101 0.355300 \n", - "coef_N_222_224_444 -2.046233 -1.386000 \n", - "coef_N_246_226_446 -0.859376 -0.857100 \n", - "coef_N_xxxxx -0.825691 -3.453000 \n", + "coef_retired_interaction_with_more_cars_than_wo... 2.992000 2.992000 \n", + "coef_retired_interaction_with_more_cars_than_wo... 1.015905 1.015905 \n", + "coef_retired_interaction_with_peak_accessibilit... 0.279200 0.279200 \n", + "coef_university_student_asc_M 2.169416 2.169416 \n", + "coef_university_student_asc_N 0.489431 0.489431 \n", "\n", - " nullvalue minimum \\\n", - "coef_UNAVAILABLE 0.0 NaN \n", - "coef_child_who_is_in_school_or_too_young_for_sc... 0.0 NaN \n", - "coef_driving_age_child_who_is_in_school_asc_M 0.0 NaN \n", - "coef_driving_age_child_who_is_in_school_asc_N 0.0 NaN \n", - "coef_driving_age_child_who_is_in_school_interac... 0.0 NaN \n", - "... ... ... \n", - "coef_N_124_122_144 0.0 NaN \n", - "coef_N_166 0.0 NaN \n", - "coef_N_222_224_444 0.0 NaN \n", - "coef_N_246_226_446 0.0 NaN \n", - "coef_N_xxxxx 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "-999.0 -999.000000 -999.0 \n", + "coef_H_11 1.626000 -inf \n", + "coef_H_12 0.740700 -inf \n", + "coef_H_124_122_144 0.957300 -inf \n", + "coef_H_126_146 0.293900 -inf \n", + "... ... ... \n", + "coef_retired_interaction_with_more_cars_than_wo... 2.992000 -inf \n", + "coef_retired_interaction_with_more_cars_than_wo... 1.056000 -inf \n", + "coef_retired_interaction_with_peak_accessibilit... 0.279200 -inf \n", + "coef_university_student_asc_M 2.353595 -inf \n", + "coef_university_student_asc_N 0.609710 -inf \n", "\n", - " maximum holdfast note \\\n", - "coef_UNAVAILABLE NaN True \n", - "coef_child_who_is_in_school_or_too_young_for_sc... NaN False \n", - "coef_driving_age_child_who_is_in_school_asc_M NaN False \n", - "coef_driving_age_child_who_is_in_school_asc_N NaN False \n", - "coef_driving_age_child_who_is_in_school_interac... NaN False \n", - "... ... ... ... \n", - "coef_N_124_122_144 NaN False \n", - "coef_N_166 NaN False \n", - "coef_N_222_224_444 NaN False \n", - "coef_N_246_226_446 NaN False \n", - "coef_N_xxxxx NaN False \n", + " maximum nullvalue \\\n", + "param_name \n", + "-999.0 -999.0 0.0 \n", + "coef_H_11 inf 0.0 \n", + "coef_H_12 inf 0.0 \n", + "coef_H_124_122_144 inf 0.0 \n", + "coef_H_126_146 inf 0.0 \n", + "... ... ... \n", + "coef_retired_interaction_with_more_cars_than_wo... inf 0.0 \n", + "coef_retired_interaction_with_more_cars_than_wo... inf 0.0 \n", + "coef_retired_interaction_with_peak_accessibilit... inf 0.0 \n", + "coef_university_student_asc_M inf 0.0 \n", + "coef_university_student_asc_N inf 0.0 \n", "\n", - " best \n", - "coef_UNAVAILABLE -999.000000 \n", - "coef_child_who_is_in_school_or_too_young_for_sc... 0.856930 \n", - "coef_driving_age_child_who_is_in_school_asc_M 0.738480 \n", - "coef_driving_age_child_who_is_in_school_asc_N -8.930886 \n", - "coef_driving_age_child_who_is_in_school_interac... -0.938531 \n", - "... ... \n", - "coef_N_124_122_144 0.996710 \n", - "coef_N_166 -1.976101 \n", - "coef_N_222_224_444 -2.046233 \n", - "coef_N_246_226_446 -0.859376 \n", - "coef_N_xxxxx -0.825691 \n", + " holdfast \n", + "param_name \n", + "-999.0 1 \n", + "coef_H_11 0 \n", + "coef_H_12 0 \n", + "coef_H_124_122_144 0 \n", + "coef_H_126_146 0 \n", + "... ... \n", + "coef_retired_interaction_with_more_cars_than_wo... 0 \n", + "coef_retired_interaction_with_more_cars_than_wo... 0 \n", + "coef_retired_interaction_with_peak_accessibilit... 0 \n", + "coef_university_student_asc_M 0 \n", + "coef_university_student_asc_N 0 \n", "\n", - "[161 rows x 8 columns]" + "[161 rows x 7 columns]" ] }, "metadata": {}, @@ -1545,12 +1651,9 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " r = model.estimate(method='SLSQP', options={'maxiter':1000})\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.7787895067513534e-43 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: RuntimeWarning: invalid value encountered in sqrt\n", - " r = model.estimate(method='SLSQP', options={'maxiter':1000})\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/optimization.py:338: UserWarning: SLSQP may not play nicely with unbounded parameters\n", + "if you get poor results, consider setting global bounds with model.set_cap()\n", + " warnings.warn( # infinite bounds # )\n" ] } ], @@ -1566,1704 +1669,4882 @@ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value
coef_UNAVAILABLE-999. 0.00 NA NA 0.00
coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N 0.857 0.618 1.39 NA 0.00
coef_driving_age_child_who_is_in_school_asc_M 0.738 0.649 1.14 NA 0.00
coef_driving_age_child_who_is_in_school_asc_N-8.93 6.17-1.45 NA 0.00
coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H-0.939 0.760-1.23 NA 0.00
coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H 0.409 0.669 0.61 NA 0.00
coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H-1.75 0.887-1.97* NA 0.00
coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H 0.456 0.807 0.57 NA 0.00
coef_full_time_worker_asc_M 1.03 2.64 0.39 NA 0.00
coef_full_time_worker_asc_N 0.742 0.188 3.95*** NA 0.00
coef_full_time_worker_interaction_with_age_less_than_40_M 0.439 0.131 3.35*** NA 0.00
coef_full_time_worker_interaction_with_female_gender_M 0.0529 0.130 0.41 NA 0.00
coef_full_time_worker_interaction_with_fewer_cars_than_workers_H 0.418 0.192 2.18* NA 0.00
coef_full_time_worker_interaction_with_income_less_than_20k_H 0.359 0.326 1.10 NA 0.00
coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M 0.147 0.209 0.70 NA 0.00
coef_non_working_adult_asc_N 0.874 3.96 0.22 NA 0.00
coef_non_working_adult_interaction_with_female_gender_M-0.743 0.0120-62.04*** NA 0.00
coef_non_working_adult_interaction_with_fewer_cars_than_workers_H 0.797 0.336 2.37* NA 0.00
coef_non_working_adult_interaction_with_income_between_50k_and_100k_H-1.23 0.374-3.28** NA 0.00
coef_non_working_adult_interaction_with_income_more_than_100k_H-0.683 0.344-1.98* NA 0.00
coef_non_working_adult_interaction_with_more_cars_than_workers_M 0.651 0.0188 34.73*** NA 0.00
coef_non_working_adult_interaction_with_more_cars_than_workers_N 1.33 0.300 4.42*** NA 0.00
coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M 0.231 0.00933 24.80*** NA 0.00
coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N 0.0176 0.396 0.04 NA 0.00
coef_part_time_worker_asc_M 4.90 4.19 1.17 NA 0.00
coef_part_time_worker_asc_N 0.659 0.316 2.09* NA 0.00
coef_part_time_worker_interaction_with_income_between_50k_and_100k_H 0.172 0.421 0.41 NA 0.00
coef_part_time_worker_interaction_with_income_less_than_20k_H 0.380 0.411 0.93 NA 0.00
coef_part_time_worker_interaction_with_income_more_than_100k_H-1.36 0.780-1.75 NA 0.00
coef_part_time_worker_interaction_with_income_more_than_100k_N 0.493 0.272 1.81 NA 0.00
coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M-0.247 0.330-0.75 NA 0.00
coef_pre_driving_age_child_who_is_in_school_asc_M 3.95 0.527 7.49*** NA 0.00
coef_pre_driving_age_child_who_is_in_school_asc_N-6.81 6.14-1.11 NA 0.00
coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M-1.55 0.475-3.26** NA 0.00
coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N-1.15 0.513-2.25* NA 0.00
coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M-0.670 0.371-1.81 NA 0.00
coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H 1.10 0.381 2.89** NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_asc_M 0.938 0.408 2.30* NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_asc_N-8.47 6.15-1.38 NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M-0.944 0.359-2.63** NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M 0.239 0.412 0.58 NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H-0.0626 0.407-0.15 NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H-0.723 0.506-1.43 NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H-0.783 0.465-1.68 NA 0.00
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N 0.772 0.420 1.84 NA 0.00
coef_retired_asc_N 1.54 3.97 0.39 NA 0.00
coef_retired_interaction_with_age_more_than_80_H 1.17 0.248 4.71*** NA 0.00
coef_retired_interaction_with_female_M 0.477 0.00219 218.04*** NA 0.00
coef_retired_interaction_with_fewer_cars_than_workers_H 0.849 0.444 1.91 NA 0.00
coef_retired_interaction_with_income_less_than_20k_H 0.635 0.259 2.45* NA 0.00
coef_retired_interaction_with_more_cars_than_workers_M 2.99 0.00983 304.22*** NA 0.00
coef_retired_interaction_with_more_cars_than_workers_N 0.713 0.256 2.79** NA 0.00
coef_retired_interaction_with_peak_accessibility_to_all_employment_M 0.279 0.0173 16.15*** NA 0.00
coef_university_student_asc_M 2.12 0.255 8.29*** NA 0.00
coef_university_student_asc_N 0.810 3.94 0.21 NA 0.00
-999.0-999. 0.00 NA NA-999.00
coef_H_11 1.48 0.210 7.04*** NA 0.00
coef_H_12-0.0642 0.955-0.07 NA 0.00
coef_H_13 1.49 0.327 4.56*** NA 0.00
coef_H_14 1.21 0.276 4.37*** NA 0.00
coef_H_15 0.966 0.369 2.62** NA 0.00
coef_H_16 1.54 0.345 4.47*** NA 0.00
coef_H_17 1.46 0.227 6.46*** NA 0.00
coef_H_18 1.21 0.309 3.92*** NA 0.00
coef_H_22-20.0 1.16e+03-0.02 NA 0.00
coef_H_23 0.761 1.06 0.72 NA 0.00
coef_H_24 0.966 0.806 1.20 NA 0.00
coef_H_25-18.4 1.22e+04-0.00 NA 0.00
coef_H_26 4.14 1.29 3.20** NA 0.00
coef_H_27 0.402 1.00 0.40 NA 0.00
coef_H_28-17.8 9.45e+03-0.00 NA 0.00
coef_H_33 0.955 0.848 1.13 NA 0.00
coef_H_34 1.56 0.392 3.97*** NA 0.00
coef_H_35-18.3 1.34e+04-0.00 NA 0.00
coef_H_36 1.55 0.0159 97.45*** NA 0.00
coef_H_37 1.55 0.00264 587.28*** NA 0.00
coef_H_38 1.34 0.0149 90.11*** NA 0.00
coef_H_44 1.52 0.412 3.69*** NA 0.00
coef_H_45 2.05 0.499 4.11*** NA 0.00
coef_H_46 0.524 0.00131 399.66*** NA 0.00
coef_H_47 0.811 0.00179 453.41*** NA 0.00
coef_H_48 1.17 0.0131 89.09*** NA 0.00
coef_H_55 1.01 0.517 1.96 NA 0.00
coef_H_56_57_58 0.863 0.0203 42.43*** NA 0.00
coef_H_66 20.8 NA NA[***] 55.85 0.00
coef_H_67-22.2 0.00635-BIG*** NA 0.00
coef_H_68 1.47 0.00299 490.95*** NA 0.00
coef_H_77 2.42 0.504 4.81*** NA 0.00
coef_H_78 18.7 NA NA[***] 7.25 0.00
coef_H_88 1.18 0.582 2.03* NA 0.00
coef_M_11-0.103 0.147-0.70 NA 0.00
coef_M_12 0.243 0.225 1.08 NA 0.00
coef_M_13 0.235 0.156 1.51 NA 0.00
coef_M_16 0.374 0.290 1.29 NA 0.00
coef_M_17-0.124 0.116-1.06 NA 0.00
coef_M_18 0.262 0.167 1.57 NA 0.00
coef_M_22 0.888 0.287 3.10** NA 0.00
coef_M_23 0.170 0.330 0.51 NA 0.00
coef_M_26 1.90 1.13 1.68 NA 0.00
coef_M_27 0.0987 0.189 0.52 NA 0.00
coef_M_28 0.378 0.294 1.28 NA 0.00
coef_M_33 0.470 0.212 2.21* NA 0.00
coef_M_36-0.00210 NA NA[] 0.00 0.00
coef_M_37 0.297 0.000362 822.44*** NA 0.00
coef_M_38 0.225 NA NA[] 0.00 0.00
coef_M_66 19.2 NA NA[***] 35.09 0.00
coef_M_67-21.0 2.08-10.10*** NA 0.00
coef_M_68 0.552 0.000386 BIG*** NA 0.00
coef_M_77 1.17 0.316 3.71*** NA 0.00
coef_M_78-1.62 0.000121-BIG*** NA 0.00
coef_M_88 1.14 0.349 3.28** NA 0.00
coef_N_11 0.776 0.323 2.41* NA 0.00
coef_N_12 0.488 0.429 1.14 NA 0.00
coef_N_13 0.497 0.436 1.14 NA 0.00
coef_N_14-0.360 0.326-1.11 NA 0.00
coef_N_15 0.366 0.242 1.51 NA 0.00
coef_N_16 0.864 0.738 1.17 NA 0.00
coef_N_17-0.155 0.514-0.30 NA 0.00
coef_N_18 0.0616 0.502 0.12 NA 0.00
coef_N_22 0.782 0.611 1.28 NA 0.00
coef_N_23 0.518 0.622 0.83 NA 0.00
coef_N_24 1.21 0.329 3.66*** NA 0.00
coef_N_25-0.304 0.471-0.65 NA 0.00
coef_N_26-17.9 0.143-125.60*** NA 0.00
coef_N_27 0.291 0.521 0.56 NA 0.00
coef_N_28 0.932 0.453 2.06* NA 0.00
coef_N_33 0.976 0.277 3.52*** NA 0.00
coef_N_34-0.197 0.399-0.49 NA 0.00
coef_N_35 0.0721 0.504 0.14 NA 0.00
coef_N_36 1.62 0.00 NA[] 0.00 0.00
coef_N_37 0.516 0.00 NA[] 0.00 0.00
coef_N_38 0.897 0.00 NA[] 0.00 0.00
coef_N_44 0.492 0.450 1.09 NA 0.00
coef_N_45 0.0795 0.354 0.22 NA 0.00
coef_N_46 0.680 0.00 NA[] 0.00 0.00
coef_N_47 0.565 0.00 NA[] 0.00 0.00
coef_N_48 1.16 0.00 NA[] 0.00 0.00
coef_N_55 0.712 0.348 2.04* NA 0.00
coef_N_56_57_58 0.292 0.00 NA[] 0.00 0.00
coef_N_66-3.08 0.00112-BIG*** NA 0.00
coef_N_67-17.1 2.19e+04-0.00 NA 0.00
coef_N_68 1.27 0.00 NA[] 0.00 0.00
coef_N_77 2.27 0.413 5.49*** NA 0.00
coef_N_78-0.396 1.75e-15-BIG*** NA 0.00
coef_N_88-0.184 0.811-0.23 NA 0.00
coef_H_124_122_144 0.693 0.513 1.35 NA 0.00
coef_H_126_146 1.70 1.76 0.97 NA 0.00
coef_H_222_224_244-9.07 1.44e+03-0.01 NA 0.00
coef_H_226_246_446 39.5 1.16e+03 0.03 NA 0.00
coef_H_266_466 0.475 0.00 NA[] 0.00 0.00
coef_H_xxxxx-4.45 1.19-3.74*** NA 0.00
coef_M_111 0.265 0.175 1.51 NA 0.00
coef_M_112_114-0.133 0.365-0.37 NA 0.00
coef_M_666-0.391 0.00 NA[] 0.00 0.00
coef_M_xxxxx-0.0402 0.190-0.21 NA 0.00
coef_N_112_114-0.452 0.881-0.51 NA 0.00
coef_N_124_122_144 0.997 0.535 1.86 NA 0.00
coef_N_166-1.98 1.09e-05-BIG*** NA 0.00
coef_N_222_224_444-2.05 0.981-2.09* NA 0.00
coef_N_246_226_446-0.859 2.50e-18-BIG*** NA 0.00
coef_N_xxxxx-0.826 0.514-1.61 NA 0.00
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.parameter_summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "TojXWivZsx7M" - }, - "source": [ - "# Output Estimation Results" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "from larch.util.activitysim import update_coefficients\n", - "coefficients = update_coefficients(model, data)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "result_dir = data.edb_directory/'estimated'" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "os.makedirs(result_dir, exist_ok=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write the re-estimated coefficients file" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "coefficients.reset_index().to_csv(\n", - " result_dir/\"cdap_coefficients_revised.csv\", \n", - " index=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write the model estimation report, including coefficient t-statistic and log likelihood" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "for hh_size, submodel in enumerate(model, 1):\n", - " if hh_size > 3: \n", - " break \n", - " # the largest HH sizes have massive model reports that \n", - " # Excel doesn't handle well; review them in Jupyter \n", - " # instead if you are interested\n", - " submodel.to_xlsx(\n", - " result_dir/f\"cdap_model_estimation_hhsize{hh_size}.xlsx\", \n", - " data_statistics=False,\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "The final step is to either manually or automatically copy the `cdap_coefficients.csv_revised.csv` file to the configs folder, rename it to `cdap_coefficients.csv.csv`, and run ActivitySim in simulation mode." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefficient_namevalueconstrain
 ValueStd Errt StatSignifNull Value
Parameter     
0coef_UNAVAILABLE-999.000000T-999.0-999. 0.00 NA 0.00
1coef_full_time_worker_asc_M1.034987Fcoef_H_11 1.45 0.0614 23.55*** 0.00
2coef_full_time_worker_asc_N0.742053Fcoef_H_12 0.955 0.132 7.21*** 0.00
3coef_part_time_worker_asc_M4.900043Fcoef_H_124_122_144 0.354 0.153 2.31* 0.00
4coef_part_time_worker_asc_N0.659214Fcoef_H_126_146-0.0134 0.336-0.04 0.00
............coef_H_13 1.20 0.130 9.21*** 0.00
161coef_N_xxxx-1.346000Fcoef_H_14 0.998 0.0903 11.05*** 0.00
162coef_H_xxxx-3.733000Fcoef_H_15 1.18 0.0922 12.76*** 0.00
163coef_M_xxxxx-0.040214Fcoef_H_16 1.85 0.102 18.08*** 0.00
164coef_N_xxxxx-0.825691Fcoef_H_17 1.34 0.0557 24.07*** 0.00
165coef_H_xxxxx-4.449197Fcoef_H_18 0.803 0.109 7.36*** 0.00
\n", - "

166 rows × 3 columns

\n", - "
" - ], - "text/plain": [ - " coefficient_name value constrain\n", - "0 coef_UNAVAILABLE -999.000000 T\n", - "1 coef_full_time_worker_asc_M 1.034987 F\n", - "2 coef_full_time_worker_asc_N 0.742053 F\n", - "3 coef_part_time_worker_asc_M 4.900043 F\n", - "4 coef_part_time_worker_asc_N 0.659214 F\n", - ".. ... ... ...\n", - "161 coef_N_xxxx -1.346000 F\n", - "162 coef_H_xxxx -3.733000 F\n", - "163 coef_M_xxxxx -0.040214 F\n", - "164 coef_N_xxxxx -0.825691 F\n", - "165 coef_H_xxxxx -4.449197 F\n", - "\n", - "[166 rows x 3 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.read_csv(result_dir/\"cdap_coefficients_revised.csv\")" + " \n", + " coef_H_22\n", + "  1.66\n", + "  0.215\n", + "  7.76\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_222_224_244\n", + "  0.0747\n", + "  0.225\n", + "  0.33\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_226_246_446\n", + "  0.0745\n", + "  0.683\n", + "  0.11\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_23\n", + "  1.68\n", + "  0.209\n", + "  8.02\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_24\n", + "  1.20\n", + "  0.165\n", + "  7.27\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_25\n", + " -0.0811\n", + "  0.278\n", + " -0.29\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_26\n", + "  2.83\n", + "  0.239\n", + "  11.83\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_266_466\n", + "  1.24\n", + "  0.650\n", + "  1.91\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_27\n", + "  0.803\n", + "  0.158\n", + "  5.08\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_28\n", + "  0.941\n", + "  0.187\n", + "  5.05\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_33\n", + "  0.359\n", + "  0.448\n", + "  0.80\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_34\n", + "  1.54\n", + "  0.131\n", + "  11.80\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_35\n", + "  0.211\n", + "  0.389\n", + "  0.54\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_36\n", + "  1.55\n", + "  2.55e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_37\n", + "  1.55\n", + "  NA\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_38\n", + "  1.34\n", + "  NA\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_44\n", + "  1.09\n", + "  0.0884\n", + "  12.29\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_45\n", + "  1.13\n", + "  0.139\n", + "  8.18\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_46\n", + "  0.524\n", + "  9.04e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_47\n", + "  0.811\n", + "  4.46e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_48\n", + "  1.17\n", + "  2.26e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_55\n", + "  1.22\n", + "  0.166\n", + "  7.37\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_56_57_58\n", + "  0.863\n", + "  1.52e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_66\n", + "  1.36\n", + "  0.299\n", + "  4.56\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_67\n", + "  1.29\n", + "  0.385\n", + "  3.35\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_68\n", + "  1.39\n", + "  0.755\n", + "  1.85\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_H_77\n", + "  2.43\n", + "  0.0802\n", + "  30.27\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_78\n", + "  1.21\n", + "  0.157\n", + "  7.72\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_88\n", + "  0.944\n", + "  0.218\n", + "  4.33\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_H_xxxxx\n", + " -3.46\n", + "  0.269\n", + " -12.86\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_11\n", + "  0.00775\n", + "  0.0439\n", + "  0.18\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_111\n", + "  0.158\n", + "  0.0551\n", + "  2.87\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_M_112_114\n", + "  0.297\n", + "  0.0900\n", + "  3.30\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_12\n", + " -0.109\n", + "  0.0595\n", + " -1.83\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_13\n", + "  0.195\n", + "  0.0448\n", + "  4.36\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_16\n", + "  0.158\n", + "  0.0607\n", + "  2.61\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_M_17\n", + "  0.0366\n", + "  0.0286\n", + "  1.28\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_18\n", + "  0.373\n", + "  0.0438\n", + "  8.53\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_22\n", + "  0.964\n", + "  0.0932\n", + "  10.34\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_23\n", + "  0.107\n", + "  0.0905\n", + "  1.18\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_26\n", + "  0.775\n", + "  0.129\n", + "  5.99\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_27\n", + "  0.0971\n", + "  0.0483\n", + "  2.01\n", + " *\n", + " 0.00\n", + " \n", + " \n", + " coef_M_28\n", + "  0.515\n", + "  0.0771\n", + "  6.68\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_33\n", + "  0.671\n", + "  0.0861\n", + "  7.79\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_36\n", + " -0.00210\n", + "  NA\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_37\n", + "  0.297\n", + "  3.78e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_38\n", + "  0.225\n", + "  9.09e-14\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_66\n", + "  0.0385\n", + "  0.239\n", + "  0.16\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_666\n", + " -0.673\n", + "  1.46\n", + " -0.46\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_67\n", + "  0.274\n", + "  0.359\n", + "  0.76\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_68\n", + "  0.691\n", + "  0.457\n", + "  1.51\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_77\n", + "  0.700\n", + "  0.0584\n", + "  11.99\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_78\n", + "  0.141\n", + "  0.0957\n", + "  1.48\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_M_88\n", + "  1.33\n", + "  0.0903\n", + "  14.70\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_M_xxxxx\n", + "  0.103\n", + "  0.0498\n", + "  2.07\n", + " *\n", + " 0.00\n", + " \n", + " \n", + " coef_N_11\n", + "  0.914\n", + "  0.0818\n", + "  11.17\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_112_114\n", + "  0.103\n", + "  0.173\n", + "  0.60\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_12\n", + "  0.465\n", + "  0.111\n", + "  4.21\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_124_122_144\n", + "  0.132\n", + "  0.142\n", + "  0.93\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_13\n", + "  0.216\n", + "  0.135\n", + "  1.60\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_14\n", + "  0.0331\n", + "  0.0719\n", + "  0.46\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_15\n", + "  0.0871\n", + "  0.0882\n", + "  0.99\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_16\n", + "  0.308\n", + "  0.222\n", + "  1.38\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_166\n", + "  0.434\n", + "  0.521\n", + "  0.83\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_17\n", + "  0.277\n", + "  0.0921\n", + "  3.01\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_N_18\n", + "  0.297\n", + "  0.0989\n", + "  3.01\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_N_22\n", + "  0.867\n", + "  0.169\n", + "  5.14\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_222_224_444\n", + " -1.34\n", + "  0.194\n", + " -6.91\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_23\n", + "  0.160\n", + "  0.200\n", + "  0.80\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_24\n", + "  0.596\n", + "  0.0737\n", + "  8.09\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_246_226_446\n", + " -0.872\n", + "  5.63\n", + " -0.15\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_25\n", + " -0.0263\n", + "  0.107\n", + " -0.25\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_26\n", + "  0.125\n", + "  0.436\n", + "  0.29\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_27\n", + "  0.733\n", + "  0.0964\n", + "  7.60\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_28\n", + "  1.05\n", + "  0.107\n", + "  9.85\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_33\n", + "  0.781\n", + "  0.165\n", + "  4.72\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_34\n", + " -0.0522\n", + "  0.0980\n", + " -0.53\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_35\n", + "  0.390\n", + "  0.151\n", + "  2.59\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_N_36\n", + "  1.62\n", + "  NA\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_37\n", + "  0.516\n", + "  1.11e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_38\n", + "  0.897\n", + "  8.33e-14\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_44\n", + "  0.302\n", + "  0.0996\n", + "  3.03\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_N_45\n", + " -0.0876\n", + "  0.0854\n", + " -1.03\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_46\n", + "  0.680\n", + "  3.06e-14\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_47\n", + "  0.565\n", + "  1.01e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_48\n", + "  1.16\n", + "  2.00e-13\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_55\n", + "  0.731\n", + "  0.114\n", + "  6.42\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_56_57_58\n", + "  0.292\n", + "  NA\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_66\n", + "  1.60\n", + "  0.492\n", + "  3.25\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_N_67\n", + "  1.29\n", + "  0.666\n", + "  1.93\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_68\n", + "  1.14\n", + "  0.897\n", + "  1.27\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_77\n", + "  1.27\n", + "  0.109\n", + "  11.65\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_78\n", + " -0.429\n", + "  0.549\n", + " -0.78\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_N_88\n", + "  0.645\n", + "  0.151\n", + "  4.26\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_N_xxxxx\n", + " -0.0469\n", + "  0.100\n", + " -0.47\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_UNAVAILABLE\n", + " -999.\n", + "  0.00\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N\n", + "  0.138\n", + "  0.0415\n", + "  3.32\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_driving_age_child_who_is_in_school_asc_M\n", + "  2.27\n", + "  0.177\n", + "  12.78\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_driving_age_child_who_is_in_school_asc_N\n", + " -1.09\n", + "  0.445\n", + " -2.45\n", + " *\n", + " 0.00\n", + " \n", + " \n", + " coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H\n", + " -0.159\n", + "  0.188\n", + " -0.85\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H\n", + "  0.839\n", + "  0.168\n", + "  4.98\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H\n", + " -1.57\n", + "  0.241\n", + " -6.53\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H\n", + "  1.46\n", + "  0.217\n", + "  6.75\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_full_time_worker_asc_M\n", + "  2.13\n", + "  0.243\n", + "  8.76\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_full_time_worker_asc_N\n", + "  0.651\n", + "  0.0476\n", + "  13.66\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_full_time_worker_interaction_with_age_less_than_40_M\n", + "  0.210\n", + "  0.0388\n", + "  5.41\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_full_time_worker_interaction_with_female_gender_M\n", + " -0.128\n", + "  0.0394\n", + " -3.24\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_full_time_worker_interaction_with_fewer_cars_than_workers_H\n", + "  0.520\n", + "  0.0567\n", + "  9.16\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_full_time_worker_interaction_with_income_less_than_20k_H\n", + "  0.406\n", + "  0.109\n", + "  3.72\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M\n", + "  0.0656\n", + "  0.0203\n", + "  3.23\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_asc_N\n", + "  0.697\n", + "  0.354\n", + "  1.97\n", + " *\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_interaction_with_female_gender_M\n", + " -0.743\n", + "  NA\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_interaction_with_fewer_cars_than_workers_H\n", + "  0.977\n", + "  0.136\n", + "  7.19\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_interaction_with_income_between_50k_and_100k_H\n", + " -0.422\n", + "  0.0839\n", + " -5.03\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_interaction_with_income_more_than_100k_H\n", + " -0.721\n", + "  0.104\n", + " -6.94\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_interaction_with_more_cars_than_workers_M\n", + "  0.651\n", + "  7.21e-15\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_interaction_with_more_cars_than_workers_N\n", + "  0.783\n", + "  0.0828\n", + "  9.45\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M\n", + "  0.231\n", + "  1.73e-14\n", + "  BIG\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N\n", + "  0.0628\n", + "  0.0353\n", + "  1.78\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_part_time_worker_asc_M\n", + " -0.903\n", + "  0.394\n", + " -2.29\n", + " *\n", + " 0.00\n", + " \n", + " \n", + " coef_part_time_worker_asc_N\n", + "  0.677\n", + "  0.0943\n", + "  7.18\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_part_time_worker_interaction_with_income_between_50k_and_100k_H\n", + " -0.470\n", + "  0.120\n", + " -3.93\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_part_time_worker_interaction_with_income_less_than_20k_H\n", + "  0.274\n", + "  0.126\n", + "  2.18\n", + " *\n", + " 0.00\n", + " \n", + " \n", + " coef_part_time_worker_interaction_with_income_more_than_100k_H\n", + " -0.492\n", + "  0.135\n", + " -3.64\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_part_time_worker_interaction_with_income_more_than_100k_N\n", + "  0.334\n", + "  0.0733\n", + "  4.56\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M\n", + "  0.218\n", + "  0.0323\n", + "  6.76\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_in_school_asc_M\n", + "  3.24\n", + "  0.104\n", + "  31.22\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_in_school_asc_N\n", + " -0.0676\n", + "  0.411\n", + " -0.16\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M\n", + " -0.694\n", + "  0.112\n", + " -6.17\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N\n", + " -0.607\n", + "  0.129\n", + " -4.70\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M\n", + " -0.400\n", + "  0.0833\n", + " -4.80\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H\n", + "  0.674\n", + "  0.0861\n", + "  7.83\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_asc_M\n", + "  0.583\n", + "  0.107\n", + "  5.43\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_asc_N\n", + " -1.39\n", + "  0.416\n", + " -3.34\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M\n", + " -0.393\n", + "  0.0920\n", + " -4.27\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M\n", + "  0.669\n", + "  0.0978\n", + "  6.84\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H\n", + "  0.243\n", + "  0.134\n", + "  1.81\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H\n", + " -0.707\n", + "  0.128\n", + " -5.51\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H\n", + " -0.695\n", + "  0.131\n", + " -5.31\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N\n", + "  0.685\n", + "  0.0858\n", + "  7.99\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_retired_asc_N\n", + "  0.434\n", + "  0.359\n", + "  1.21\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_retired_interaction_with_age_more_than_80_H\n", + "  0.730\n", + "  0.0826\n", + "  8.84\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_retired_interaction_with_female_M\n", + "  0.477\n", + "  0.00\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_retired_interaction_with_fewer_cars_than_workers_H\n", + "  0.650\n", + "  0.218\n", + "  2.98\n", + " **\n", + " 0.00\n", + " \n", + " \n", + " coef_retired_interaction_with_income_less_than_20k_H\n", + "  0.476\n", + "  0.0845\n", + "  5.63\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_retired_interaction_with_more_cars_than_workers_M\n", + "  2.99\n", + "  0.00\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_retired_interaction_with_more_cars_than_workers_N\n", + "  1.02\n", + "  0.0885\n", + "  11.48\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_retired_interaction_with_peak_accessibility_to_all_employment_M\n", + "  0.279\n", + "  0.00\n", + "  NA\n", + " \n", + " 0.00\n", + " \n", + " \n", + " coef_university_student_asc_M\n", + "  2.17\n", + "  0.0864\n", + "  25.12\n", + " ***\n", + " 0.00\n", + " \n", + " \n", + " coef_university_student_asc_N\n", + "  0.489\n", + "  0.356\n", + "  1.38\n", + " \n", + " 0.00\n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.parameter_summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TojXWivZsx7M" + }, + "source": [ + "# Output Estimation Results" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from activitysim.estimation.larch import update_coefficients\n", + "coefficients = update_coefficients(model, data)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "result_dir = data.edb_directory/'estimated'" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "os.makedirs(result_dir, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write the re-estimated coefficients file" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "coefficients.reset_index().to_csv(\n", + " result_dir/\"cdap_coefficients_revised.csv\", \n", + " index=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write the model estimation report, including coefficient t-statistic and log likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "for hh_size, submodel in enumerate(model, 1):\n", + " if hh_size > 3: \n", + " break \n", + " # the largest HH sizes have massive model reports that \n", + " # Excel doesn't handle well; review them in Jupyter \n", + " # instead if you are interested\n", + " submodel.to_xlsx(\n", + " result_dir/f\"cdap_model_estimation_hhsize{hh_size}.xlsx\", \n", + " data_statistics=False,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Steps\n", + "\n", + "The final step is to either manually or automatically copy the `cdap_coefficients.csv_revised.csv` file to the configs folder, rename it to `cdap_coefficients.csv.csv`, and run ActivitySim in simulation mode." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefficient_namevalueconstrain
0coef_UNAVAILABLE-999.000000T
1coef_full_time_worker_asc_M2.126748F
2coef_full_time_worker_asc_N0.650847F
3coef_part_time_worker_asc_M-0.902594F
4coef_part_time_worker_asc_N0.677144F
............
161coef_N_xxxx-1.346000F
162coef_H_xxxx-3.733000F
163coef_M_xxxxx0.103269F
164coef_N_xxxxx-0.046852F
165coef_H_xxxxx-3.460857F
\n", + "

166 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " coefficient_name value constrain\n", + "0 coef_UNAVAILABLE -999.000000 T\n", + "1 coef_full_time_worker_asc_M 2.126748 F\n", + "2 coef_full_time_worker_asc_N 0.650847 F\n", + "3 coef_part_time_worker_asc_M -0.902594 F\n", + "4 coef_part_time_worker_asc_N 0.677144 F\n", + ".. ... ... ...\n", + "161 coef_N_xxxx -1.346000 F\n", + "162 coef_H_xxxx -3.733000 F\n", + "163 coef_M_xxxxx 0.103269 F\n", + "164 coef_N_xxxxx -0.046852 F\n", + "165 coef_H_xxxxx -3.460857 F\n", + "\n", + "[166 rows x 3 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_csv(result_dir/\"cdap_coefficients_revised.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modify Spec\n", + "\n", + "Here, we will demonstrate the process of re-estimating the CDAP model with a modified\n", + "SPEC file. This does *not* require re-running ActivitySim, it just requires\n", + "changing the SPEC file and re-running the Larch estimation only.\n", + "\n", + "The `backup` command we ran earlier made a backup copy of the\n", + "original spec file in the EDB directory.\n", + "This was not strictly necessary, but since we're about to modify it and\n", + "we may want undo our changes, it can be handy to keep a copy of the\n", + "original spec file around. Since we already have a backup copy, we'll make some \n", + "changes directly in the SPEC file. As an example here, we're going\n", + "to re-write the household income section of the file, to change the piecewise \n", + "linear utility from 3 segments to 4. We'll move the breakpoints and rename some\n", + "coefficients to accomodate the change. As above, for this demo we are editing \n", + "the SPEC file using Python code to make the changes, but a user does not need\n", + "to change the file using Python; any CSV editor (e.g. Excel) can be used. " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "with open(data.edb_directory / \"cdap_INDIV_AND_HHSIZE1_SPEC.csv\") as f:\n", + " raw_spec = f.read()\n", + "\n", + "repl_lines = \"\"\"Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,coef_full_time_worker_interaction_with_income_less_than_20k_H\n", + "\"\"\"\n", + "\n", + "new_lines = \"\"\"Full-time worker interaction with income less than $35k,(ptype == 1) & (income_in_thousands < 35),,,coef_full_time_worker_interaction_with_income_less_than_35k_H\n", + "Full-time worker interaction with income between $35k and $100k,(ptype == 1) & (income_in_thousands >= 35) & (income_in_thousands <= 100),,,coef_full_time_worker_interaction_with_income_between_35k_and_100k_H\n", + "\"\"\"\n", + "\n", + "raw_spec = raw_spec.replace(repl_lines, new_lines)\n", + "\n", + "with open(data.edb_directory / \"cdap_INDIV_AND_HHSIZE1_SPEC.csv\", \"w\") as f:\n", + " f.write(raw_spec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Unlike other components, the CDAP estimation process is built fundamentally against \n", + "a re-evaluation of utility functions, instead of using pre-computed values in the\n", + "EDB. Thus, the user does not need to worry about the \"Label\" column of the utility\n", + "specification file (which in this example does not even exist.) \n", + "\n", + "Estimation of the revised model can be completed using the same process as the \n", + "original estimation." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading output-est-mode/estimation_data_bundle/cdap/cdap_model_settings.yaml\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/final_households.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/final_persons.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_coefficients.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_interaction_coefficients.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_joint_tour_coefficients.csv\n", + "Including joint tour utility?: False\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_INDIV_AND_HHSIZE1_SPEC.csv\n", + "Reading /Users/jpn/Git/est-mode/activitysim/activitysim/examples/example_estimation/notebooks/test-estimation-data/activitysim-prototype-mtc-extended/output-est-mode/estimation_data_bundle/cdap/cdap_values_combined.parquet\n" + ] + } + ], + "source": [ + "model2, data2 = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

Iteration 138 [Optimization terminated successfully]

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Best LL = -28225.10715069874

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valuebestinitvalueminimummaximumnullvalueholdfast
param_name
-999.0-999.000000-999.000000-999.000000-999.0-999.00.01
coef_H_111.4550121.4550121.626000-infinf0.00
coef_H_120.9496080.9496080.740700-infinf0.00
coef_H_124_122_1440.3674960.3674960.957300-infinf0.00
coef_H_126_146-0.009021-0.0090210.293900-infinf0.00
........................
coef_retired_interaction_with_more_cars_than_workers_M2.9920002.9920002.992000-infinf0.00
coef_retired_interaction_with_more_cars_than_workers_N1.0128061.0128061.056000-infinf0.00
coef_retired_interaction_with_peak_accessibility_to_all_employment_M0.2792000.2792000.279200-infinf0.00
coef_university_student_asc_M2.1683682.1683682.353595-infinf0.00
coef_university_student_asc_N0.4898480.4898480.609710-infinf0.00
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162 rows × 7 columns

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modelmodel2
Number of CasesAggregate20000.00000020000.000000
Log Likelihood at ConvergenceAggregate-28223.515541-28225.107151
Per Case-1.411176-1.411255
Log Likelihood at Null ParametersAggregate-51107.531825-51107.531825
Per Case-2.555377-2.555377
Rho Squared w.r.t. Null ParametersAggregate0.4477620.447731
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" + ], + "text/plain": [ + " model model2\n", + "Number of Cases Aggregate 20000.000000 20000.000000\n", + "Log Likelihood at Convergence Aggregate -28223.515541 -28225.107151\n", + " Per Case -1.411176 -1.411255\n", + "Log Likelihood at Null Parameters Aggregate -51107.531825 -51107.531825\n", + " Per Case -2.555377 -2.555377\n", + "Rho Squared w.r.t. Null Parameters Aggregate 0.447762 0.447731" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.estimation_statistics_raw(),\n", + " \"model2\": model2.estimation_statistics_raw(),\n", + " }, axis=1).fillna(\"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelmodel2
ValueStd Errt StatSignifNull ValueValueStd Errt StatSignifNull Value
Parameter
-999.0-999.0.00NA0.0-999.0.00NA0.0
coef_H_111.450.061423.55***0.01.460.062223.40***0.0
coef_H_120.9550.1327.21***0.00.9500.1327.18***0.0
coef_H_124_122_1440.3540.1532.31*0.00.3670.1532.40*0.0
coef_H_126_146-0.01340.336-0.040.0-0.009020.336-0.030.0
coef_H_131.200.1309.21***0.01.200.1319.17***0.0
coef_H_140.9980.090311.05***0.00.9910.090410.96***0.0
coef_H_151.180.092212.76***0.01.180.092112.86***0.0
coef_H_161.850.10218.08***0.01.850.10317.98***0.0
coef_H_171.340.055724.07***0.01.340.055824.04***0.0
coef_H_180.8030.1097.36***0.00.8080.1097.42***0.0
coef_H_221.660.2157.76***0.01.660.2157.73***0.0
coef_H_222_224_2440.07470.2250.330.00.08040.2250.360.0
coef_H_226_246_4460.07450.6830.110.00.07260.6870.110.0
coef_H_231.680.2098.02***0.01.680.2098.03***0.0
coef_H_241.200.1657.27***0.01.200.1657.24***0.0
coef_H_25-0.08110.278-0.290.0-0.07370.277-0.270.0
coef_H_262.830.23911.83***0.02.840.23911.86***0.0
coef_H_266_4661.240.6501.910.01.250.6521.910.0
coef_H_270.8030.1585.08***0.00.8040.1585.07***0.0
coef_H_280.9410.1875.05***0.00.9460.1865.08***0.0
coef_H_330.3590.4480.800.00.3660.4480.820.0
coef_H_341.540.13111.80***0.01.540.13111.75***0.0
coef_H_350.2110.3890.540.00.2130.3890.550.0
coef_H_361.552.55e-13BIG***0.01.55NANA0.0
coef_H_371.55NANA0.01.55NANA0.0
coef_H_381.34NANA0.01.344.18e-13BIG***0.0
coef_H_441.090.088412.29***0.01.090.088312.30***0.0
coef_H_451.130.1398.18***0.01.130.1398.16***0.0
coef_H_460.5249.04e-13BIG***0.00.5243.12e-13BIG***0.0
coef_H_470.8114.46e-13BIG***0.00.811NANA0.0
coef_H_481.172.26e-13BIG***0.01.176.30e-13BIG***0.0
coef_H_551.220.1667.37***0.01.220.1657.39***0.0
coef_H_56_57_580.8631.52e-13BIG***0.00.863NANA0.0
coef_H_661.360.2994.56***0.01.370.2984.60***0.0
coef_H_671.290.3853.35***0.01.280.3863.32***0.0
coef_H_681.390.7551.850.01.390.7561.830.0
coef_H_772.430.080230.27***0.02.420.080430.16***0.0
coef_H_781.210.1577.72***0.01.210.1577.69***0.0
coef_H_880.9440.2184.33***0.00.9560.2174.40***0.0
coef_H_xxxxx-3.460.269-12.86***0.0-3.470.269-12.88***0.0
coef_M_110.007750.04390.180.09.36e-050.04430.000.0
coef_M_1110.1580.05512.87**0.00.1640.05522.96**0.0
coef_M_112_1140.2970.09003.30***0.00.2950.09003.28**0.0
coef_M_12-0.1090.0595-1.830.0-0.1090.0595-1.830.0
coef_M_130.1950.04484.36***0.00.1970.04484.39***0.0
coef_M_160.1580.06072.61**0.00.1590.06072.63**0.0
coef_M_170.03660.02861.280.00.03720.02861.300.0
coef_M_180.3730.04388.53***0.00.3720.04388.49***0.0
coef_M_220.9640.093210.34***0.00.9660.093310.36***0.0
coef_M_230.1070.09051.180.00.1070.09051.180.0
coef_M_260.7750.1295.99***0.00.7760.1296.00***0.0
coef_M_270.09710.04832.01*0.00.09430.04821.950.0
coef_M_280.5150.07716.68***0.00.5150.07716.68***0.0
coef_M_330.6710.08617.79***0.00.6730.08627.81***0.0
coef_M_36-0.00210NANA0.0-0.00210NANA0.0
coef_M_370.2973.78e-13BIG***0.00.2973.67e-13BIG***0.0
coef_M_380.2259.09e-14BIG***0.00.2254.32e-13BIG***0.0
coef_M_660.03850.2390.160.00.04220.2400.180.0
coef_M_666-0.6731.46-0.460.0-0.6711.46-0.460.0
coef_M_670.2740.3590.760.00.2880.3600.800.0
coef_M_680.6910.4571.510.00.6950.4571.520.0
coef_M_770.7000.058411.99***0.00.7030.058412.03***0.0
coef_M_780.1410.09571.480.00.1410.09571.480.0
coef_M_881.330.090314.70***0.01.330.090314.70***0.0
coef_M_xxxxx0.1030.04982.07*0.00.1050.04982.10*0.0
coef_N_110.9140.081811.17***0.00.9150.081611.21***0.0
coef_N_112_1140.1030.1730.600.00.1040.1740.600.0
coef_N_120.4650.1114.21***0.00.4670.1114.22***0.0
coef_N_124_122_1440.1320.1420.930.00.1280.1430.900.0
coef_N_130.2160.1351.600.00.2110.1351.570.0
coef_N_140.03310.07190.460.00.02710.07200.380.0
coef_N_150.08710.08820.990.00.08940.08811.010.0
coef_N_160.3080.2221.380.00.3090.2231.390.0
coef_N_1660.4340.5210.830.00.4330.5250.820.0
coef_N_170.2770.09213.01**0.00.2800.09193.04**0.0
coef_N_180.2970.09893.01**0.00.2990.09893.02**0.0
coef_N_220.8670.1695.14***0.00.8670.1695.14***0.0
coef_N_222_224_444-1.340.194-6.91***0.0-1.350.195-6.91***0.0
coef_N_230.1600.2000.800.00.1650.2000.830.0
coef_N_240.5960.07378.09***0.00.5950.07378.07***0.0
coef_N_246_226_446-0.8725.63-0.150.0-0.8725.62-0.160.0
coef_N_25-0.02630.107-0.250.0-0.02750.107-0.260.0
coef_N_260.1250.4360.290.00.1260.4360.290.0
coef_N_270.7330.09647.60***0.00.7340.09617.64***0.0
coef_N_281.050.1079.85***0.01.050.1079.85***0.0
coef_N_330.7810.1654.72***0.00.7860.1654.77***0.0
coef_N_34-0.05220.0980-0.530.0-0.05110.0979-0.520.0
coef_N_350.3900.1512.59**0.00.3900.1502.59**0.0
coef_N_361.62NANA0.01.62NANA0.0
coef_N_370.5161.11e-13BIG***0.00.516NANA0.0
coef_N_380.8978.33e-14BIG***0.00.8972.96e-13BIG***0.0
coef_N_440.3020.09963.03**0.00.3000.09963.01**0.0
coef_N_45-0.08760.0854-1.030.0-0.09020.0854-1.060.0
coef_N_460.6803.06e-14BIG***0.00.6802.86e-13BIG***0.0
coef_N_470.5651.01e-13BIG***0.00.5652.67e-13BIG***0.0
coef_N_481.162.00e-13BIG***0.01.162.34e-13BIG***0.0
coef_N_550.7310.1146.42***0.00.7290.1146.41***0.0
coef_N_56_57_580.292NANA0.00.292NANA0.0
coef_N_661.600.4923.25**0.01.600.4953.23**0.0
coef_N_671.290.6661.930.01.290.6731.920.0
coef_N_681.140.8971.270.01.140.8991.270.0
coef_N_771.270.10911.65***0.01.280.10911.68***0.0
coef_N_78-0.4290.549-0.780.0-0.4210.546-0.770.0
coef_N_880.6450.1514.26***0.00.6450.1514.26***0.0
coef_N_xxxxx-0.04690.100-0.470.0-0.04460.100-0.440.0
coef_UNAVAILABLE-999.0.00NA0.0-999.0.00NA0.0
coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N0.1380.04153.32***0.00.1390.04163.35***0.0
coef_driving_age_child_who_is_in_school_asc_M2.270.17712.78***0.02.300.17812.94***0.0
coef_driving_age_child_who_is_in_school_asc_N-1.090.445-2.45*0.0-1.060.445-2.39*0.0
coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H-0.1590.188-0.850.0-0.1320.188-0.700.0
coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H0.8390.1684.98***0.00.8360.1684.96***0.0
coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H-1.570.241-6.53***0.0-1.510.242-6.24***0.0
coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H1.460.2176.75***0.01.530.2167.05***0.0
coef_full_time_worker_asc_M2.130.2438.76***0.02.180.2468.88***0.0
coef_full_time_worker_asc_N0.6510.047613.66***0.00.6930.057412.07***0.0
coef_full_time_worker_interaction_with_age_less_than_40_M0.2100.03885.41***0.00.2160.03895.54***0.0
coef_full_time_worker_interaction_with_female_gender_M-0.1280.0394-3.24**0.0-0.1240.0394-3.15**0.0
coef_full_time_worker_interaction_with_fewer_cars_than_workers_H0.5200.05679.16***0.00.5280.05689.29***0.0
coef_full_time_worker_interaction_with_income_less_than_20k_H0.4060.1093.72***0.0
coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M0.06560.02033.23**0.00.06420.02033.16**0.0
coef_non_working_adult_asc_N0.6970.3541.97*0.00.6980.3541.97*0.0
coef_non_working_adult_interaction_with_female_gender_M-0.743NANA0.0-0.7433.86e-14-BIG***0.0
coef_non_working_adult_interaction_with_fewer_cars_than_workers_H0.9770.1367.19***0.00.9780.1367.19***0.0
coef_non_working_adult_interaction_with_income_between_50k_and_100k_H-0.4220.0839-5.03***0.0-0.4240.0839-5.05***0.0
coef_non_working_adult_interaction_with_income_more_than_100k_H-0.7210.104-6.94***0.0-0.7160.104-6.89***0.0
coef_non_working_adult_interaction_with_more_cars_than_workers_M0.6517.21e-15BIG***0.00.651NANA0.0
coef_non_working_adult_interaction_with_more_cars_than_workers_N0.7830.08289.45***0.00.7810.08289.43***0.0
coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M0.2311.73e-14BIG***0.00.2318.76e-15BIG***0.0
coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N0.06280.03531.780.00.06290.03521.790.0
coef_part_time_worker_asc_M-0.9030.394-2.29*0.0-0.9000.394-2.29*0.0
coef_part_time_worker_asc_N0.6770.09437.18***0.00.6790.09447.19***0.0
coef_part_time_worker_interaction_with_income_between_50k_and_100k_H-0.4700.120-3.93***0.0-0.4710.120-3.93***0.0
coef_part_time_worker_interaction_with_income_less_than_20k_H0.2740.1262.18*0.00.2780.1262.21*0.0
coef_part_time_worker_interaction_with_income_more_than_100k_H-0.4920.135-3.64***0.0-0.4880.135-3.61***0.0
coef_part_time_worker_interaction_with_income_more_than_100k_N0.3340.07334.56***0.00.3340.07344.55***0.0
coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M0.2180.03236.76***0.00.2180.03236.75***0.0
coef_pre_driving_age_child_who_is_in_school_asc_M3.240.10431.22***0.03.240.10431.19***0.0
coef_pre_driving_age_child_who_is_in_school_asc_N-0.06760.411-0.160.0-0.07780.412-0.190.0
coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M-0.6940.112-6.17***0.0-0.6970.112-6.19***0.0
coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N-0.6070.129-4.70***0.0-0.6050.129-4.69***0.0
coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M-0.4000.0833-4.80***0.0-0.3980.0834-4.77***0.0
coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H0.6740.08617.83***0.00.6760.08627.84***0.0
coef_pre_driving_age_child_who_is_too_young_for_school_asc_M0.5830.1075.43***0.00.5870.1075.47***0.0
coef_pre_driving_age_child_who_is_too_young_for_school_asc_N-1.390.416-3.34***0.0-1.400.417-3.36***0.0
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M-0.3930.0920-4.27***0.0-0.3920.0920-4.27***0.0
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M0.6690.09786.84***0.00.6700.09786.85***0.0
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H0.2430.1341.810.00.2430.1341.820.0
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H-0.7070.128-5.51***0.0-0.7080.128-5.52***0.0
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H-0.6950.131-5.31***0.0-0.6880.131-5.26***0.0
coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N0.6850.08587.99***0.00.6860.08587.99***0.0
coef_retired_asc_N0.4340.3591.210.00.4390.3591.220.0
coef_retired_interaction_with_age_more_than_80_H0.7300.08268.84***0.00.7320.08258.87***0.0
coef_retired_interaction_with_female_M0.4770.00NA0.00.4770.00NA0.0
coef_retired_interaction_with_fewer_cars_than_workers_H0.6500.2182.98**0.00.6490.2182.97**0.0
coef_retired_interaction_with_income_less_than_20k_H0.4760.08455.63***0.00.4830.08445.72***0.0
coef_retired_interaction_with_more_cars_than_workers_M2.990.00NA0.02.990.00NA0.0
coef_retired_interaction_with_more_cars_than_workers_N1.020.088511.48***0.01.010.088411.45***0.0
coef_retired_interaction_with_peak_accessibility_to_all_employment_M0.2790.00NA0.00.2790.00NA0.0
coef_university_student_asc_M2.170.086425.12***0.02.170.086425.10***0.0
coef_university_student_asc_N0.4890.3561.380.00.4900.3561.380.0
coef_full_time_worker_interaction_with_income_between_35k_and_100k_H0.05410.05421.000.0
coef_full_time_worker_interaction_with_income_less_than_35k_H0.2450.07953.08**0.0
\n", + "
" + ], + "text/plain": [ + " model \\\n", + " Value Std Err \n", + "Parameter \n", + "-999.0 -999.  0.00 \n", + "coef_H_11  1.45  0.0614 \n", + "coef_H_12  0.955  0.132 \n", + "coef_H_124_122_144  0.354  0.153 \n", + "coef_H_126_146 -0.0134  0.336 \n", + "coef_H_13  1.20  0.130 \n", + "coef_H_14  0.998  0.0903 \n", + "coef_H_15  1.18  0.0922 \n", + "coef_H_16  1.85  0.102 \n", + "coef_H_17  1.34  0.0557 \n", + "coef_H_18  0.803  0.109 \n", + "coef_H_22  1.66  0.215 \n", + "coef_H_222_224_244  0.0747  0.225 \n", + "coef_H_226_246_446  0.0745  0.683 \n", + "coef_H_23  1.68  0.209 \n", + "coef_H_24  1.20  0.165 \n", + "coef_H_25 -0.0811  0.278 \n", + "coef_H_26  2.83  0.239 \n", + "coef_H_266_466  1.24  0.650 \n", + "coef_H_27  0.803  0.158 \n", + "coef_H_28  0.941  0.187 \n", + "coef_H_33  0.359  0.448 \n", + "coef_H_34  1.54  0.131 \n", + "coef_H_35  0.211  0.389 \n", + "coef_H_36  1.55  2.55e-13 \n", + "coef_H_37  1.55  NA \n", + "coef_H_38  1.34  NA \n", + "coef_H_44  1.09  0.0884 \n", + "coef_H_45  1.13  0.139 \n", + "coef_H_46  0.524  9.04e-13 \n", + "coef_H_47  0.811  4.46e-13 \n", + "coef_H_48  1.17  2.26e-13 \n", + "coef_H_55  1.22  0.166 \n", + "coef_H_56_57_58  0.863  1.52e-13 \n", + "coef_H_66  1.36  0.299 \n", + "coef_H_67  1.29  0.385 \n", + "coef_H_68  1.39  0.755 \n", + "coef_H_77  2.43  0.0802 \n", + "coef_H_78  1.21  0.157 \n", + "coef_H_88  0.944  0.218 \n", + "coef_H_xxxxx -3.46  0.269 \n", + "coef_M_11  0.00775  0.0439 \n", + "coef_M_111  0.158  0.0551 \n", + "coef_M_112_114  0.297  0.0900 \n", + "coef_M_12 -0.109  0.0595 \n", + "coef_M_13  0.195  0.0448 \n", + "coef_M_16  0.158  0.0607 \n", + "coef_M_17  0.0366  0.0286 \n", + "coef_M_18  0.373  0.0438 \n", + "coef_M_22  0.964  0.0932 \n", + "coef_M_23  0.107  0.0905 \n", + "coef_M_26  0.775  0.129 \n", + "coef_M_27  0.0971  0.0483 \n", + "coef_M_28  0.515  0.0771 \n", + "coef_M_33  0.671  0.0861 \n", + "coef_M_36 -0.00210  NA \n", + "coef_M_37  0.297  3.78e-13 \n", + "coef_M_38  0.225  9.09e-14 \n", + "coef_M_66  0.0385  0.239 \n", + "coef_M_666 -0.673  1.46 \n", + "coef_M_67  0.274  0.359 \n", + "coef_M_68  0.691  0.457 \n", + "coef_M_77  0.700  0.0584 \n", + "coef_M_78  0.141  0.0957 \n", + "coef_M_88  1.33  0.0903 \n", + "coef_M_xxxxx  0.103  0.0498 \n", + "coef_N_11  0.914  0.0818 \n", + "coef_N_112_114  0.103  0.173 \n", + "coef_N_12  0.465  0.111 \n", + "coef_N_124_122_144  0.132  0.142 \n", + "coef_N_13  0.216  0.135 \n", + "coef_N_14  0.0331  0.0719 \n", + "coef_N_15  0.0871  0.0882 \n", + "coef_N_16  0.308  0.222 \n", + "coef_N_166  0.434  0.521 \n", + "coef_N_17  0.277  0.0921 \n", + "coef_N_18  0.297  0.0989 \n", + "coef_N_22  0.867  0.169 \n", + "coef_N_222_224_444 -1.34  0.194 \n", + "coef_N_23  0.160  0.200 \n", + "coef_N_24  0.596  0.0737 \n", + "coef_N_246_226_446 -0.872  5.63 \n", + "coef_N_25 -0.0263  0.107 \n", + "coef_N_26  0.125  0.436 \n", + "coef_N_27  0.733  0.0964 \n", + "coef_N_28  1.05  0.107 \n", + "coef_N_33  0.781  0.165 \n", + "coef_N_34 -0.0522  0.0980 \n", + "coef_N_35  0.390  0.151 \n", + "coef_N_36  1.62  NA \n", + "coef_N_37  0.516  1.11e-13 \n", + "coef_N_38  0.897  8.33e-14 \n", + "coef_N_44  0.302  0.0996 \n", + "coef_N_45 -0.0876  0.0854 \n", + "coef_N_46  0.680  3.06e-14 \n", + "coef_N_47  0.565  1.01e-13 \n", + "coef_N_48  1.16  2.00e-13 \n", + "coef_N_55  0.731  0.114 \n", + "coef_N_56_57_58  0.292  NA \n", + "coef_N_66  1.60  0.492 \n", + "coef_N_67  1.29  0.666 \n", + "coef_N_68  1.14  0.897 \n", + "coef_N_77  1.27  0.109 \n", + "coef_N_78 -0.429  0.549 \n", + "coef_N_88  0.645  0.151 \n", + "coef_N_xxxxx -0.0469  0.100 \n", + "coef_UNAVAILABLE -999.  0.00 \n", + "coef_child_who_is_in_school_or_too_young_for_sc...  0.138  0.0415 \n", + "coef_driving_age_child_who_is_in_school_asc_M  2.27  0.177 \n", + "coef_driving_age_child_who_is_in_school_asc_N -1.09  0.445 \n", + "coef_driving_age_child_who_is_in_school_interac... -0.159  0.188 \n", + "coef_driving_age_child_who_is_in_school_interac...  0.839  0.168 \n", + "coef_driving_age_child_who_is_in_school_interac... -1.57  0.241 \n", + "coef_driving_age_child_who_is_in_school_interac...  1.46  0.217 \n", + "coef_full_time_worker_asc_M  2.13  0.243 \n", + "coef_full_time_worker_asc_N  0.651  0.0476 \n", + "coef_full_time_worker_interaction_with_age_less...  0.210  0.0388 \n", + "coef_full_time_worker_interaction_with_female_g... -0.128  0.0394 \n", + "coef_full_time_worker_interaction_with_fewer_ca...  0.520  0.0567 \n", + "coef_full_time_worker_interaction_with_income_l...  0.406  0.109 \n", + "coef_full_time_worker_intraction_with_peak_acce...  0.0656  0.0203 \n", + "coef_non_working_adult_asc_N  0.697  0.354 \n", + "coef_non_working_adult_interaction_with_female_... -0.743  NA \n", + "coef_non_working_adult_interaction_with_fewer_c...  0.977  0.136 \n", + "coef_non_working_adult_interaction_with_income_... -0.422  0.0839 \n", + "coef_non_working_adult_interaction_with_income_... -0.721  0.104 \n", + "coef_non_working_adult_interaction_with_more_ca...  0.651  7.21e-15 \n", + "coef_non_working_adult_interaction_with_more_ca...  0.783  0.0828 \n", + "coef_non_working_adult_interaction_with_peak_ac...  0.231  1.73e-14 \n", + "coef_non_working_adult_retired_or_univ_student_...  0.0628  0.0353 \n", + "coef_part_time_worker_asc_M -0.903  0.394 \n", + "coef_part_time_worker_asc_N  0.677  0.0943 \n", + "coef_part_time_worker_interaction_with_income_b... -0.470  0.120 \n", + "coef_part_time_worker_interaction_with_income_l...  0.274  0.126 \n", + "coef_part_time_worker_interaction_with_income_m... -0.492  0.135 \n", + "coef_part_time_worker_interaction_with_income_m...  0.334  0.0733 \n", + "coef_part_time_worker_interaction_with_peak_acc...  0.218  0.0323 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_M  3.24  0.104 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_N -0.0676  0.411 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -0.694  0.112 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -0.607  0.129 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -0.400  0.0833 \n", + "coef_pre_driving_age_child_who_is_in_school_int...  0.674  0.0861 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.583  0.107 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -1.39  0.416 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -0.393  0.0920 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.669  0.0978 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.243  0.134 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -0.707  0.128 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -0.695  0.131 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.685  0.0858 \n", + "coef_retired_asc_N  0.434  0.359 \n", + "coef_retired_interaction_with_age_more_than_80_H  0.730  0.0826 \n", + "coef_retired_interaction_with_female_M  0.477  0.00 \n", + "coef_retired_interaction_with_fewer_cars_than_w...  0.650  0.218 \n", + "coef_retired_interaction_with_income_less_than_...  0.476  0.0845 \n", + "coef_retired_interaction_with_more_cars_than_wo...  2.99  0.00 \n", + "coef_retired_interaction_with_more_cars_than_wo...  1.02  0.0885 \n", + "coef_retired_interaction_with_peak_accessibilit...  0.279  0.00 \n", + "coef_university_student_asc_M  2.17  0.0864 \n", + "coef_university_student_asc_N  0.489  0.356 \n", + "coef_full_time_worker_interaction_with_income_b... \n", + "coef_full_time_worker_interaction_with_income_l... \n", + "\n", + " \\\n", + " t Stat Signif Null Value \n", + "Parameter \n", + "-999.0  NA 0.0 \n", + "coef_H_11  23.55 *** 0.0 \n", + "coef_H_12  7.21 *** 0.0 \n", + "coef_H_124_122_144  2.31 * 0.0 \n", + "coef_H_126_146 -0.04 0.0 \n", + "coef_H_13  9.21 *** 0.0 \n", + "coef_H_14  11.05 *** 0.0 \n", + "coef_H_15  12.76 *** 0.0 \n", + "coef_H_16  18.08 *** 0.0 \n", + "coef_H_17  24.07 *** 0.0 \n", + "coef_H_18  7.36 *** 0.0 \n", + "coef_H_22  7.76 *** 0.0 \n", + "coef_H_222_224_244  0.33 0.0 \n", + "coef_H_226_246_446  0.11 0.0 \n", + "coef_H_23  8.02 *** 0.0 \n", + "coef_H_24  7.27 *** 0.0 \n", + "coef_H_25 -0.29 0.0 \n", + "coef_H_26  11.83 *** 0.0 \n", + "coef_H_266_466  1.91 0.0 \n", + "coef_H_27  5.08 *** 0.0 \n", + "coef_H_28  5.05 *** 0.0 \n", + "coef_H_33  0.80 0.0 \n", + "coef_H_34  11.80 *** 0.0 \n", + "coef_H_35  0.54 0.0 \n", + "coef_H_36  BIG *** 0.0 \n", + "coef_H_37  NA 0.0 \n", + "coef_H_38  NA 0.0 \n", + "coef_H_44  12.29 *** 0.0 \n", + "coef_H_45  8.18 *** 0.0 \n", + "coef_H_46  BIG *** 0.0 \n", + "coef_H_47  BIG *** 0.0 \n", + "coef_H_48  BIG *** 0.0 \n", + "coef_H_55  7.37 *** 0.0 \n", + "coef_H_56_57_58  BIG *** 0.0 \n", + "coef_H_66  4.56 *** 0.0 \n", + "coef_H_67  3.35 *** 0.0 \n", + "coef_H_68  1.85 0.0 \n", + "coef_H_77  30.27 *** 0.0 \n", + "coef_H_78  7.72 *** 0.0 \n", + "coef_H_88  4.33 *** 0.0 \n", + "coef_H_xxxxx -12.86 *** 0.0 \n", + "coef_M_11  0.18 0.0 \n", + "coef_M_111  2.87 ** 0.0 \n", + "coef_M_112_114  3.30 *** 0.0 \n", + "coef_M_12 -1.83 0.0 \n", + "coef_M_13  4.36 *** 0.0 \n", + "coef_M_16  2.61 ** 0.0 \n", + "coef_M_17  1.28 0.0 \n", + "coef_M_18  8.53 *** 0.0 \n", + "coef_M_22  10.34 *** 0.0 \n", + "coef_M_23  1.18 0.0 \n", + "coef_M_26  5.99 *** 0.0 \n", + "coef_M_27  2.01 * 0.0 \n", + "coef_M_28  6.68 *** 0.0 \n", + "coef_M_33  7.79 *** 0.0 \n", + "coef_M_36  NA 0.0 \n", + "coef_M_37  BIG *** 0.0 \n", + "coef_M_38  BIG *** 0.0 \n", + "coef_M_66  0.16 0.0 \n", + "coef_M_666 -0.46 0.0 \n", + "coef_M_67  0.76 0.0 \n", + "coef_M_68  1.51 0.0 \n", + "coef_M_77  11.99 *** 0.0 \n", + "coef_M_78  1.48 0.0 \n", + "coef_M_88  14.70 *** 0.0 \n", + "coef_M_xxxxx  2.07 * 0.0 \n", + "coef_N_11  11.17 *** 0.0 \n", + "coef_N_112_114  0.60 0.0 \n", + "coef_N_12  4.21 *** 0.0 \n", + "coef_N_124_122_144  0.93 0.0 \n", + "coef_N_13  1.60 0.0 \n", + "coef_N_14  0.46 0.0 \n", + "coef_N_15  0.99 0.0 \n", + "coef_N_16  1.38 0.0 \n", + "coef_N_166  0.83 0.0 \n", + "coef_N_17  3.01 ** 0.0 \n", + "coef_N_18  3.01 ** 0.0 \n", + "coef_N_22  5.14 *** 0.0 \n", + "coef_N_222_224_444 -6.91 *** 0.0 \n", + "coef_N_23  0.80 0.0 \n", + "coef_N_24  8.09 *** 0.0 \n", + "coef_N_246_226_446 -0.15 0.0 \n", + "coef_N_25 -0.25 0.0 \n", + "coef_N_26  0.29 0.0 \n", + "coef_N_27  7.60 *** 0.0 \n", + "coef_N_28  9.85 *** 0.0 \n", + "coef_N_33  4.72 *** 0.0 \n", + "coef_N_34 -0.53 0.0 \n", + "coef_N_35  2.59 ** 0.0 \n", + "coef_N_36  NA 0.0 \n", + "coef_N_37  BIG *** 0.0 \n", + "coef_N_38  BIG *** 0.0 \n", + "coef_N_44  3.03 ** 0.0 \n", + "coef_N_45 -1.03 0.0 \n", + "coef_N_46  BIG *** 0.0 \n", + "coef_N_47  BIG *** 0.0 \n", + "coef_N_48  BIG *** 0.0 \n", + "coef_N_55  6.42 *** 0.0 \n", + "coef_N_56_57_58  NA 0.0 \n", + "coef_N_66  3.25 ** 0.0 \n", + "coef_N_67  1.93 0.0 \n", + "coef_N_68  1.27 0.0 \n", + "coef_N_77  11.65 *** 0.0 \n", + "coef_N_78 -0.78 0.0 \n", + "coef_N_88  4.26 *** 0.0 \n", + "coef_N_xxxxx -0.47 0.0 \n", + "coef_UNAVAILABLE  NA 0.0 \n", + "coef_child_who_is_in_school_or_too_young_for_sc...  3.32 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_asc_M  12.78 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_asc_N -2.45 * 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac... -0.85 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac...  4.98 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac... -6.53 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac...  6.75 *** 0.0 \n", + "coef_full_time_worker_asc_M  8.76 *** 0.0 \n", + "coef_full_time_worker_asc_N  13.66 *** 0.0 \n", + "coef_full_time_worker_interaction_with_age_less...  5.41 *** 0.0 \n", + "coef_full_time_worker_interaction_with_female_g... -3.24 ** 0.0 \n", + "coef_full_time_worker_interaction_with_fewer_ca...  9.16 *** 0.0 \n", + "coef_full_time_worker_interaction_with_income_l...  3.72 *** 0.0 \n", + "coef_full_time_worker_intraction_with_peak_acce...  3.23 ** 0.0 \n", + "coef_non_working_adult_asc_N  1.97 * 0.0 \n", + "coef_non_working_adult_interaction_with_female_...  NA 0.0 \n", + "coef_non_working_adult_interaction_with_fewer_c...  7.19 *** 0.0 \n", + "coef_non_working_adult_interaction_with_income_... -5.03 *** 0.0 \n", + "coef_non_working_adult_interaction_with_income_... -6.94 *** 0.0 \n", + "coef_non_working_adult_interaction_with_more_ca...  BIG *** 0.0 \n", + "coef_non_working_adult_interaction_with_more_ca...  9.45 *** 0.0 \n", + "coef_non_working_adult_interaction_with_peak_ac...  BIG *** 0.0 \n", + "coef_non_working_adult_retired_or_univ_student_...  1.78 0.0 \n", + "coef_part_time_worker_asc_M -2.29 * 0.0 \n", + "coef_part_time_worker_asc_N  7.18 *** 0.0 \n", + "coef_part_time_worker_interaction_with_income_b... -3.93 *** 0.0 \n", + "coef_part_time_worker_interaction_with_income_l...  2.18 * 0.0 \n", + "coef_part_time_worker_interaction_with_income_m... -3.64 *** 0.0 \n", + "coef_part_time_worker_interaction_with_income_m...  4.56 *** 0.0 \n", + "coef_part_time_worker_interaction_with_peak_acc...  6.76 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_M  31.22 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_N -0.16 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -6.17 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -4.70 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -4.80 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int...  7.83 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  5.43 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -3.34 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -4.27 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  6.84 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  1.81 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -5.51 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -5.31 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  7.99 *** 0.0 \n", + "coef_retired_asc_N  1.21 0.0 \n", + "coef_retired_interaction_with_age_more_than_80_H  8.84 *** 0.0 \n", + "coef_retired_interaction_with_female_M  NA 0.0 \n", + "coef_retired_interaction_with_fewer_cars_than_w...  2.98 ** 0.0 \n", + "coef_retired_interaction_with_income_less_than_...  5.63 *** 0.0 \n", + "coef_retired_interaction_with_more_cars_than_wo...  NA 0.0 \n", + "coef_retired_interaction_with_more_cars_than_wo...  11.48 *** 0.0 \n", + "coef_retired_interaction_with_peak_accessibilit...  NA 0.0 \n", + "coef_university_student_asc_M  25.12 *** 0.0 \n", + "coef_university_student_asc_N  1.38 0.0 \n", + "coef_full_time_worker_interaction_with_income_b... \n", + "coef_full_time_worker_interaction_with_income_l... \n", + "\n", + " model2 \\\n", + " Value Std Err \n", + "Parameter \n", + "-999.0 -999.  0.00 \n", + "coef_H_11  1.46  0.0622 \n", + "coef_H_12  0.950  0.132 \n", + "coef_H_124_122_144  0.367  0.153 \n", + "coef_H_126_146 -0.00902  0.336 \n", + "coef_H_13  1.20  0.131 \n", + "coef_H_14  0.991  0.0904 \n", + "coef_H_15  1.18  0.0921 \n", + "coef_H_16  1.85  0.103 \n", + "coef_H_17  1.34  0.0558 \n", + "coef_H_18  0.808  0.109 \n", + "coef_H_22  1.66  0.215 \n", + "coef_H_222_224_244  0.0804  0.225 \n", + "coef_H_226_246_446  0.0726  0.687 \n", + "coef_H_23  1.68  0.209 \n", + "coef_H_24  1.20  0.165 \n", + "coef_H_25 -0.0737  0.277 \n", + "coef_H_26  2.84  0.239 \n", + "coef_H_266_466  1.25  0.652 \n", + "coef_H_27  0.804  0.158 \n", + "coef_H_28  0.946  0.186 \n", + "coef_H_33  0.366  0.448 \n", + "coef_H_34  1.54  0.131 \n", + "coef_H_35  0.213  0.389 \n", + "coef_H_36  1.55  NA \n", + "coef_H_37  1.55  NA \n", + "coef_H_38  1.34  4.18e-13 \n", + "coef_H_44  1.09  0.0883 \n", + "coef_H_45  1.13  0.139 \n", + "coef_H_46  0.524  3.12e-13 \n", + "coef_H_47  0.811  NA \n", + "coef_H_48  1.17  6.30e-13 \n", + "coef_H_55  1.22  0.165 \n", + "coef_H_56_57_58  0.863  NA \n", + "coef_H_66  1.37  0.298 \n", + "coef_H_67  1.28  0.386 \n", + "coef_H_68  1.39  0.756 \n", + "coef_H_77  2.42  0.0804 \n", + "coef_H_78  1.21  0.157 \n", + "coef_H_88  0.956  0.217 \n", + "coef_H_xxxxx -3.47  0.269 \n", + "coef_M_11  9.36e-05  0.0443 \n", + "coef_M_111  0.164  0.0552 \n", + "coef_M_112_114  0.295  0.0900 \n", + "coef_M_12 -0.109  0.0595 \n", + "coef_M_13  0.197  0.0448 \n", + "coef_M_16  0.159  0.0607 \n", + "coef_M_17  0.0372  0.0286 \n", + "coef_M_18  0.372  0.0438 \n", + "coef_M_22  0.966  0.0933 \n", + "coef_M_23  0.107  0.0905 \n", + "coef_M_26  0.776  0.129 \n", + "coef_M_27  0.0943  0.0482 \n", + "coef_M_28  0.515  0.0771 \n", + "coef_M_33  0.673  0.0862 \n", + "coef_M_36 -0.00210  NA \n", + "coef_M_37  0.297  3.67e-13 \n", + "coef_M_38  0.225  4.32e-13 \n", + "coef_M_66  0.0422  0.240 \n", + "coef_M_666 -0.671  1.46 \n", + "coef_M_67  0.288  0.360 \n", + "coef_M_68  0.695  0.457 \n", + "coef_M_77  0.703  0.0584 \n", + "coef_M_78  0.141  0.0957 \n", + "coef_M_88  1.33  0.0903 \n", + "coef_M_xxxxx  0.105  0.0498 \n", + "coef_N_11  0.915  0.0816 \n", + "coef_N_112_114  0.104  0.174 \n", + "coef_N_12  0.467  0.111 \n", + "coef_N_124_122_144  0.128  0.143 \n", + "coef_N_13  0.211  0.135 \n", + "coef_N_14  0.0271  0.0720 \n", + "coef_N_15  0.0894  0.0881 \n", + "coef_N_16  0.309  0.223 \n", + "coef_N_166  0.433  0.525 \n", + "coef_N_17  0.280  0.0919 \n", + "coef_N_18  0.299  0.0989 \n", + "coef_N_22  0.867  0.169 \n", + "coef_N_222_224_444 -1.35  0.195 \n", + "coef_N_23  0.165  0.200 \n", + "coef_N_24  0.595  0.0737 \n", + "coef_N_246_226_446 -0.872  5.62 \n", + "coef_N_25 -0.0275  0.107 \n", + "coef_N_26  0.126  0.436 \n", + "coef_N_27  0.734  0.0961 \n", + "coef_N_28  1.05  0.107 \n", + "coef_N_33  0.786  0.165 \n", + "coef_N_34 -0.0511  0.0979 \n", + "coef_N_35  0.390  0.150 \n", + "coef_N_36  1.62  NA \n", + "coef_N_37  0.516  NA \n", + "coef_N_38  0.897  2.96e-13 \n", + "coef_N_44  0.300  0.0996 \n", + "coef_N_45 -0.0902  0.0854 \n", + "coef_N_46  0.680  2.86e-13 \n", + "coef_N_47  0.565  2.67e-13 \n", + "coef_N_48  1.16  2.34e-13 \n", + "coef_N_55  0.729  0.114 \n", + "coef_N_56_57_58  0.292  NA \n", + "coef_N_66  1.60  0.495 \n", + "coef_N_67  1.29  0.673 \n", + "coef_N_68  1.14  0.899 \n", + "coef_N_77  1.28  0.109 \n", + "coef_N_78 -0.421  0.546 \n", + "coef_N_88  0.645  0.151 \n", + "coef_N_xxxxx -0.0446  0.100 \n", + "coef_UNAVAILABLE -999.  0.00 \n", + "coef_child_who_is_in_school_or_too_young_for_sc...  0.139  0.0416 \n", + "coef_driving_age_child_who_is_in_school_asc_M  2.30  0.178 \n", + "coef_driving_age_child_who_is_in_school_asc_N -1.06  0.445 \n", + "coef_driving_age_child_who_is_in_school_interac... -0.132  0.188 \n", + "coef_driving_age_child_who_is_in_school_interac...  0.836  0.168 \n", + "coef_driving_age_child_who_is_in_school_interac... -1.51  0.242 \n", + "coef_driving_age_child_who_is_in_school_interac...  1.53  0.216 \n", + "coef_full_time_worker_asc_M  2.18  0.246 \n", + "coef_full_time_worker_asc_N  0.693  0.0574 \n", + "coef_full_time_worker_interaction_with_age_less...  0.216  0.0389 \n", + "coef_full_time_worker_interaction_with_female_g... -0.124  0.0394 \n", + "coef_full_time_worker_interaction_with_fewer_ca...  0.528  0.0568 \n", + "coef_full_time_worker_interaction_with_income_l... \n", + "coef_full_time_worker_intraction_with_peak_acce...  0.0642  0.0203 \n", + "coef_non_working_adult_asc_N  0.698  0.354 \n", + "coef_non_working_adult_interaction_with_female_... -0.743  3.86e-14 \n", + "coef_non_working_adult_interaction_with_fewer_c...  0.978  0.136 \n", + "coef_non_working_adult_interaction_with_income_... -0.424  0.0839 \n", + "coef_non_working_adult_interaction_with_income_... -0.716  0.104 \n", + "coef_non_working_adult_interaction_with_more_ca...  0.651  NA \n", + "coef_non_working_adult_interaction_with_more_ca...  0.781  0.0828 \n", + "coef_non_working_adult_interaction_with_peak_ac...  0.231  8.76e-15 \n", + "coef_non_working_adult_retired_or_univ_student_...  0.0629  0.0352 \n", + "coef_part_time_worker_asc_M -0.900  0.394 \n", + "coef_part_time_worker_asc_N  0.679  0.0944 \n", + "coef_part_time_worker_interaction_with_income_b... -0.471  0.120 \n", + "coef_part_time_worker_interaction_with_income_l...  0.278  0.126 \n", + "coef_part_time_worker_interaction_with_income_m... -0.488  0.135 \n", + "coef_part_time_worker_interaction_with_income_m...  0.334  0.0734 \n", + "coef_part_time_worker_interaction_with_peak_acc...  0.218  0.0323 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_M  3.24  0.104 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_N -0.0778  0.412 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -0.697  0.112 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -0.605  0.129 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -0.398  0.0834 \n", + "coef_pre_driving_age_child_who_is_in_school_int...  0.676  0.0862 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.587  0.107 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -1.40  0.417 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -0.392  0.0920 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.670  0.0978 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.243  0.134 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -0.708  0.128 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -0.688  0.131 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  0.686  0.0858 \n", + "coef_retired_asc_N  0.439  0.359 \n", + "coef_retired_interaction_with_age_more_than_80_H  0.732  0.0825 \n", + "coef_retired_interaction_with_female_M  0.477  0.00 \n", + "coef_retired_interaction_with_fewer_cars_than_w...  0.649  0.218 \n", + "coef_retired_interaction_with_income_less_than_...  0.483  0.0844 \n", + "coef_retired_interaction_with_more_cars_than_wo...  2.99  0.00 \n", + "coef_retired_interaction_with_more_cars_than_wo...  1.01  0.0884 \n", + "coef_retired_interaction_with_peak_accessibilit...  0.279  0.00 \n", + "coef_university_student_asc_M  2.17  0.0864 \n", + "coef_university_student_asc_N  0.490  0.356 \n", + "coef_full_time_worker_interaction_with_income_b...  0.0541  0.0542 \n", + "coef_full_time_worker_interaction_with_income_l...  0.245  0.0795 \n", + "\n", + " \n", + " t Stat Signif Null Value \n", + "Parameter \n", + "-999.0  NA 0.0 \n", + "coef_H_11  23.40 *** 0.0 \n", + "coef_H_12  7.18 *** 0.0 \n", + "coef_H_124_122_144  2.40 * 0.0 \n", + "coef_H_126_146 -0.03 0.0 \n", + "coef_H_13  9.17 *** 0.0 \n", + "coef_H_14  10.96 *** 0.0 \n", + "coef_H_15  12.86 *** 0.0 \n", + "coef_H_16  17.98 *** 0.0 \n", + "coef_H_17  24.04 *** 0.0 \n", + "coef_H_18  7.42 *** 0.0 \n", + "coef_H_22  7.73 *** 0.0 \n", + "coef_H_222_224_244  0.36 0.0 \n", + "coef_H_226_246_446  0.11 0.0 \n", + "coef_H_23  8.03 *** 0.0 \n", + "coef_H_24  7.24 *** 0.0 \n", + "coef_H_25 -0.27 0.0 \n", + "coef_H_26  11.86 *** 0.0 \n", + "coef_H_266_466  1.91 0.0 \n", + "coef_H_27  5.07 *** 0.0 \n", + "coef_H_28  5.08 *** 0.0 \n", + "coef_H_33  0.82 0.0 \n", + "coef_H_34  11.75 *** 0.0 \n", + "coef_H_35  0.55 0.0 \n", + "coef_H_36  NA 0.0 \n", + "coef_H_37  NA 0.0 \n", + "coef_H_38  BIG *** 0.0 \n", + "coef_H_44  12.30 *** 0.0 \n", + "coef_H_45  8.16 *** 0.0 \n", + "coef_H_46  BIG *** 0.0 \n", + "coef_H_47  NA 0.0 \n", + "coef_H_48  BIG *** 0.0 \n", + "coef_H_55  7.39 *** 0.0 \n", + "coef_H_56_57_58  NA 0.0 \n", + "coef_H_66  4.60 *** 0.0 \n", + "coef_H_67  3.32 *** 0.0 \n", + "coef_H_68  1.83 0.0 \n", + "coef_H_77  30.16 *** 0.0 \n", + "coef_H_78  7.69 *** 0.0 \n", + "coef_H_88  4.40 *** 0.0 \n", + "coef_H_xxxxx -12.88 *** 0.0 \n", + "coef_M_11  0.00 0.0 \n", + "coef_M_111  2.96 ** 0.0 \n", + "coef_M_112_114  3.28 ** 0.0 \n", + "coef_M_12 -1.83 0.0 \n", + "coef_M_13  4.39 *** 0.0 \n", + "coef_M_16  2.63 ** 0.0 \n", + "coef_M_17  1.30 0.0 \n", + "coef_M_18  8.49 *** 0.0 \n", + "coef_M_22  10.36 *** 0.0 \n", + "coef_M_23  1.18 0.0 \n", + "coef_M_26  6.00 *** 0.0 \n", + "coef_M_27  1.95 0.0 \n", + "coef_M_28  6.68 *** 0.0 \n", + "coef_M_33  7.81 *** 0.0 \n", + "coef_M_36  NA 0.0 \n", + "coef_M_37  BIG *** 0.0 \n", + "coef_M_38  BIG *** 0.0 \n", + "coef_M_66  0.18 0.0 \n", + "coef_M_666 -0.46 0.0 \n", + "coef_M_67  0.80 0.0 \n", + "coef_M_68  1.52 0.0 \n", + "coef_M_77  12.03 *** 0.0 \n", + "coef_M_78  1.48 0.0 \n", + "coef_M_88  14.70 *** 0.0 \n", + "coef_M_xxxxx  2.10 * 0.0 \n", + "coef_N_11  11.21 *** 0.0 \n", + "coef_N_112_114  0.60 0.0 \n", + "coef_N_12  4.22 *** 0.0 \n", + "coef_N_124_122_144  0.90 0.0 \n", + "coef_N_13  1.57 0.0 \n", + "coef_N_14  0.38 0.0 \n", + "coef_N_15  1.01 0.0 \n", + "coef_N_16  1.39 0.0 \n", + "coef_N_166  0.82 0.0 \n", + "coef_N_17  3.04 ** 0.0 \n", + "coef_N_18  3.02 ** 0.0 \n", + "coef_N_22  5.14 *** 0.0 \n", + "coef_N_222_224_444 -6.91 *** 0.0 \n", + "coef_N_23  0.83 0.0 \n", + "coef_N_24  8.07 *** 0.0 \n", + "coef_N_246_226_446 -0.16 0.0 \n", + "coef_N_25 -0.26 0.0 \n", + "coef_N_26  0.29 0.0 \n", + "coef_N_27  7.64 *** 0.0 \n", + "coef_N_28  9.85 *** 0.0 \n", + "coef_N_33  4.77 *** 0.0 \n", + "coef_N_34 -0.52 0.0 \n", + "coef_N_35  2.59 ** 0.0 \n", + "coef_N_36  NA 0.0 \n", + "coef_N_37  NA 0.0 \n", + "coef_N_38  BIG *** 0.0 \n", + "coef_N_44  3.01 ** 0.0 \n", + "coef_N_45 -1.06 0.0 \n", + "coef_N_46  BIG *** 0.0 \n", + "coef_N_47  BIG *** 0.0 \n", + "coef_N_48  BIG *** 0.0 \n", + "coef_N_55  6.41 *** 0.0 \n", + "coef_N_56_57_58  NA 0.0 \n", + "coef_N_66  3.23 ** 0.0 \n", + "coef_N_67  1.92 0.0 \n", + "coef_N_68  1.27 0.0 \n", + "coef_N_77  11.68 *** 0.0 \n", + "coef_N_78 -0.77 0.0 \n", + "coef_N_88  4.26 *** 0.0 \n", + "coef_N_xxxxx -0.44 0.0 \n", + "coef_UNAVAILABLE  NA 0.0 \n", + "coef_child_who_is_in_school_or_too_young_for_sc...  3.35 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_asc_M  12.94 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_asc_N -2.39 * 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac... -0.70 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac...  4.96 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac... -6.24 *** 0.0 \n", + "coef_driving_age_child_who_is_in_school_interac...  7.05 *** 0.0 \n", + "coef_full_time_worker_asc_M  8.88 *** 0.0 \n", + "coef_full_time_worker_asc_N  12.07 *** 0.0 \n", + "coef_full_time_worker_interaction_with_age_less...  5.54 *** 0.0 \n", + "coef_full_time_worker_interaction_with_female_g... -3.15 ** 0.0 \n", + "coef_full_time_worker_interaction_with_fewer_ca...  9.29 *** 0.0 \n", + "coef_full_time_worker_interaction_with_income_l... \n", + "coef_full_time_worker_intraction_with_peak_acce...  3.16 ** 0.0 \n", + "coef_non_working_adult_asc_N  1.97 * 0.0 \n", + "coef_non_working_adult_interaction_with_female_... -BIG *** 0.0 \n", + "coef_non_working_adult_interaction_with_fewer_c...  7.19 *** 0.0 \n", + "coef_non_working_adult_interaction_with_income_... -5.05 *** 0.0 \n", + "coef_non_working_adult_interaction_with_income_... -6.89 *** 0.0 \n", + "coef_non_working_adult_interaction_with_more_ca...  NA 0.0 \n", + "coef_non_working_adult_interaction_with_more_ca...  9.43 *** 0.0 \n", + "coef_non_working_adult_interaction_with_peak_ac...  BIG *** 0.0 \n", + "coef_non_working_adult_retired_or_univ_student_...  1.79 0.0 \n", + "coef_part_time_worker_asc_M -2.29 * 0.0 \n", + "coef_part_time_worker_asc_N  7.19 *** 0.0 \n", + "coef_part_time_worker_interaction_with_income_b... -3.93 *** 0.0 \n", + "coef_part_time_worker_interaction_with_income_l...  2.21 * 0.0 \n", + "coef_part_time_worker_interaction_with_income_m... -3.61 *** 0.0 \n", + "coef_part_time_worker_interaction_with_income_m...  4.55 *** 0.0 \n", + "coef_part_time_worker_interaction_with_peak_acc...  6.75 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_M  31.19 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_asc_N -0.19 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -6.19 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -4.69 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int... -4.77 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_in_school_int...  7.84 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  5.47 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -3.36 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -4.27 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  6.85 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  1.82 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -5.52 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for... -5.26 *** 0.0 \n", + "coef_pre_driving_age_child_who_is_too_young_for...  7.99 *** 0.0 \n", + "coef_retired_asc_N  1.22 0.0 \n", + "coef_retired_interaction_with_age_more_than_80_H  8.87 *** 0.0 \n", + "coef_retired_interaction_with_female_M  NA 0.0 \n", + "coef_retired_interaction_with_fewer_cars_than_w...  2.97 ** 0.0 \n", + "coef_retired_interaction_with_income_less_than_...  5.72 *** 0.0 \n", + "coef_retired_interaction_with_more_cars_than_wo...  NA 0.0 \n", + "coef_retired_interaction_with_more_cars_than_wo...  11.45 *** 0.0 \n", + "coef_retired_interaction_with_peak_accessibilit...  NA 0.0 \n", + "coef_university_student_asc_M  25.10 *** 0.0 \n", + "coef_university_student_asc_N  1.38 0.0 \n", + "coef_full_time_worker_interaction_with_income_b...  1.00 0.0 \n", + "coef_full_time_worker_interaction_with_income_l...  3.08 ** 0.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.parameter_summary().data,\n", + " \"model2\": model2.parameter_summary().data,\n", + " }, axis=1).fillna(\"\"))" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -3277,7 +6558,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb b/activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb index 12091fc166..5e444a544c 100644 --- a/activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb +++ b/activitysim/examples/example_estimation/notebooks/07_mand_tour_freq.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +115,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_frequency/mandatory_tour_frequency_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/mandatory_tour_frequency/mandatory_tour_frequency_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_frequency/mandatory_tour_frequency_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"mandatory_tour_frequency\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -743,15 +805,15 @@ " util_driving_age_child\n", " util_pre_driving_age_child\n", " ...\n", - " HSENROLL\n", - " COLLFTE\n", - " COLLPTE\n", - " TOPOLOGY\n", - " TERMINAL\n", - " household_density\n", - " employment_density\n", - " density_index\n", - " is_cbd\n", + " auPkTotal\n", + " auOpRetail\n", + " auOpTotal\n", + " trPkRetail\n", + " trPkTotal\n", + " trOpRetail\n", + " trOpTotal\n", + " nmRetail\n", + " nmTotal\n", " override_choice_code\n", " \n", " \n", @@ -781,8 +843,8 @@ " \n", " \n", " \n", - " 629\n", - " 629\n", + " 1918\n", + " 1918\n", " school1\n", " school1\n", " 0.0\n", @@ -793,20 +855,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 1.64217\n", - " 13.280000\n", - " 4.535000\n", - " 3.380567\n", - " False\n", + " 12.295952\n", + " 10.006297\n", + " 12.411522\n", + " 5.033643\n", + " 7.242282\n", + " 4.864150\n", + " 7.063680\n", + " 5.626389\n", + " 7.133756\n", " 3\n", " \n", " \n", - " 1274\n", - " 1274\n", + " 3215\n", + " 3215\n", " school1\n", " school1\n", " 0.0\n", @@ -817,20 +879,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 506.23721\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.44180\n", - " 19.776119\n", - " 7.179104\n", - " 5.267062\n", - " False\n", + " 12.036005\n", + " 9.698240\n", + " 12.156041\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 1.360774\n", + " 5.139792\n", " 3\n", " \n", " \n", - " 27266\n", - " 27266\n", + " 4362\n", + " 4362\n", " school1\n", " school1\n", " 0.0\n", @@ -841,20 +903,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 26.92893\n", - " 2035.58118\n", - " 20.60887\n", - " 2\n", - " 5.22542\n", - " 97.634722\n", - " 550.205552\n", - " 82.920387\n", - " False\n", + " 11.954228\n", + " 9.566058\n", + " 12.139587\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 3.086628\n", + " 5.647842\n", " 3\n", " \n", " \n", - " 28012\n", - " 28012\n", + " 5859\n", + " 5859\n", " school1\n", " school1\n", " 0.0\n", @@ -865,20 +927,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 690.54974\n", - " 0.00000\n", - " 3\n", - " 4.73802\n", - " 117.769796\n", - " 246.205869\n", - " 79.663609\n", - " False\n", + " 12.484185\n", + " 9.994051\n", + " 12.608786\n", + " 2.273060\n", + " 4.760981\n", + " 2.104966\n", + " 4.545843\n", + " 4.254230\n", + " 6.941860\n", " 3\n", " \n", " \n", - " 29476\n", - " 29476\n", + " 6100\n", + " 6100\n", " school1\n", " school1\n", " 0.0\n", @@ -889,15 +951,15 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 0.00000\n", - " 2\n", - " 4.75017\n", - " 71.898080\n", - " 273.023745\n", - " 56.911108\n", - " False\n", + " 12.718182\n", + " 10.233345\n", + " 12.809847\n", + " 3.534863\n", + " 6.375527\n", + " 3.401762\n", + " 6.258096\n", + " 5.240538\n", + " 7.175776\n", " 3\n", " \n", " \n", @@ -925,106 +987,106 @@ " ...\n", " \n", " \n", - " 2823069\n", - " 7514404\n", - " work1\n", - " work1\n", - " 1.0\n", + " 2857869\n", + " 7549204\n", + " school1\n", + " school1\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 3.95752\n", - " 65.596535\n", - " 32.655666\n", - " 21.802041\n", - " True\n", - " 1\n", + " 12.307902\n", + " 10.005144\n", + " 12.585021\n", + " 3.812385\n", + " 6.519807\n", + " 3.632087\n", + " 6.178083\n", + " 3.718425\n", + " 5.556368\n", + " 3\n", " \n", " \n", - " 2823442\n", - " 7514777\n", - " work1\n", - " work1\n", + " 2857903\n", + " 7549238\n", + " school1\n", + " school1\n", " 0.0\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 0.00000\n", - " 2\n", - " 4.51942\n", - " 56.706023\n", - " 144.861886\n", - " 40.753220\n", - " True\n", - " 1\n", + " 12.309276\n", + " 9.986443\n", + " 12.582868\n", + " 3.319335\n", + " 6.046532\n", + " 3.207581\n", + " 5.829832\n", + " 4.139571\n", + " 6.510036\n", + " 3\n", " \n", " \n", - " 2823850\n", - " 7515185\n", - " work_and_school\n", - " work_and_school\n", + " 2859659\n", + " 7550994\n", + " school1\n", + " school1\n", " 0.0\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 11.638333\n", + " 9.598840\n", + " 11.963516\n", + " 0.916326\n", + " 3.279901\n", + " 0.315332\n", + " 2.429541\n", + " 3.824743\n", + " 6.768218\n", " 3\n", - " 2.46800\n", - " 0.296858\n", - " 36.296472\n", - " 0.294449\n", - " True\n", - " 5\n", " \n", " \n", - " 2836262\n", - " 7527597\n", + " 2861083\n", + " 7552418\n", " school1\n", " school1\n", " 0.0\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " ...\n", - " 3961.04761\n", - " 17397.79102\n", - " 11152.93652\n", - " 1\n", - " 2.28992\n", - " 3.984127\n", - " 23.820106\n", - " 3.413233\n", - " False\n", + " 10.447823\n", + " 8.697696\n", + " 10.794293\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 3.679297\n", + " 5.376192\n", " 3\n", " \n", " \n", - " 2849737\n", - " 7541072\n", - " school2\n", - " school2\n", + " 2870568\n", + " 7561903\n", + " school1\n", + " school1\n", " 0.0\n", " 0.0\n", " 1.0\n", @@ -1033,108 +1095,94 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 11.802080\n", + " 9.800589\n", + " 12.206363\n", + " 1.183362\n", + " 3.611805\n", + " 0.897289\n", + " 3.179071\n", + " 5.769691\n", + " 8.087817\n", " 3\n", - " 3.35329\n", - " 34.477273\n", - " 15.454545\n", - " 10.671163\n", - " True\n", - " 4\n", " \n", " \n", "\n", - "

2620 rows × 220 columns

\n", + "

31768 rows × 220 columns

\n", "" ], "text/plain": [ - " person_id model_choice override_choice util_ft_worker \\\n", - "household_id \n", - "629 629 school1 school1 0.0 \n", - "1274 1274 school1 school1 0.0 \n", - "27266 27266 school1 school1 0.0 \n", - "28012 28012 school1 school1 0.0 \n", - "29476 29476 school1 school1 0.0 \n", - "... ... ... ... ... \n", - "2823069 7514404 work1 work1 1.0 \n", - "2823442 7514777 work1 work1 0.0 \n", - "2823850 7515185 work_and_school work_and_school 0.0 \n", - "2836262 7527597 school1 school1 0.0 \n", - "2849737 7541072 school2 school2 0.0 \n", + " person_id model_choice override_choice util_ft_worker \\\n", + "household_id \n", + "1918 1918 school1 school1 0.0 \n", + "3215 3215 school1 school1 0.0 \n", + "4362 4362 school1 school1 0.0 \n", + "5859 5859 school1 school1 0.0 \n", + "6100 6100 school1 school1 0.0 \n", + "... ... ... ... ... \n", + "2857869 7549204 school1 school1 0.0 \n", + "2857903 7549238 school1 school1 0.0 \n", + "2859659 7550994 school1 school1 0.0 \n", + "2861083 7552418 school1 school1 0.0 \n", + "2870568 7561903 school1 school1 0.0 \n", "\n", " util_pt_worker util_univ util_non_working_adult util_retired \\\n", "household_id \n", - "629 0.0 1.0 0.0 0.0 \n", - "1274 0.0 1.0 0.0 0.0 \n", - "27266 0.0 1.0 0.0 0.0 \n", - "28012 0.0 1.0 0.0 0.0 \n", - "29476 0.0 1.0 0.0 0.0 \n", + "1918 0.0 1.0 0.0 0.0 \n", + "3215 0.0 1.0 0.0 0.0 \n", + "4362 0.0 1.0 0.0 0.0 \n", + "5859 0.0 1.0 0.0 0.0 \n", + "6100 0.0 1.0 0.0 0.0 \n", "... ... ... ... ... \n", - "2823069 0.0 0.0 0.0 0.0 \n", - "2823442 0.0 1.0 0.0 0.0 \n", - "2823850 0.0 1.0 0.0 0.0 \n", - "2836262 0.0 1.0 0.0 0.0 \n", - "2849737 0.0 1.0 0.0 0.0 \n", + "2857869 0.0 0.0 0.0 0.0 \n", + "2857903 0.0 0.0 0.0 0.0 \n", + "2859659 0.0 0.0 0.0 0.0 \n", + "2861083 0.0 0.0 0.0 0.0 \n", + "2870568 0.0 1.0 0.0 0.0 \n", "\n", " util_driving_age_child util_pre_driving_age_child ... \\\n", "household_id ... \n", - "629 0.0 0.0 ... \n", - "1274 0.0 0.0 ... \n", - "27266 0.0 0.0 ... \n", - "28012 0.0 0.0 ... \n", - "29476 0.0 0.0 ... \n", + "1918 0.0 0.0 ... \n", + "3215 0.0 0.0 ... \n", + "4362 0.0 0.0 ... \n", + "5859 0.0 0.0 ... \n", + "6100 0.0 0.0 ... \n", "... ... ... ... \n", - "2823069 0.0 0.0 ... \n", - "2823442 0.0 0.0 ... \n", - "2823850 0.0 0.0 ... \n", - "2836262 0.0 0.0 ... \n", - "2849737 0.0 0.0 ... \n", + "2857869 1.0 0.0 ... \n", + "2857903 1.0 0.0 ... \n", + "2859659 1.0 0.0 ... \n", + "2861083 0.0 1.0 ... \n", + "2870568 0.0 0.0 ... \n", "\n", - " HSENROLL COLLFTE COLLPTE TOPOLOGY TERMINAL \\\n", - "household_id \n", - "629 0.00000 0.00000 0.00000 1 1.64217 \n", - "1274 506.23721 0.00000 0.00000 1 2.44180 \n", - "27266 26.92893 2035.58118 20.60887 2 5.22542 \n", - "28012 0.00000 690.54974 0.00000 3 4.73802 \n", - "29476 0.00000 0.00000 0.00000 2 4.75017 \n", - "... ... ... ... ... ... \n", - "2823069 0.00000 0.00000 0.00000 1 3.95752 \n", - "2823442 0.00000 0.00000 0.00000 2 4.51942 \n", - "2823850 0.00000 0.00000 0.00000 3 2.46800 \n", - "2836262 3961.04761 17397.79102 11152.93652 1 2.28992 \n", - "2849737 0.00000 0.00000 0.00000 3 3.35329 \n", + " auPkTotal auOpRetail auOpTotal trPkRetail trPkTotal \\\n", + "household_id \n", + "1918 12.295952 10.006297 12.411522 5.033643 7.242282 \n", + "3215 12.036005 9.698240 12.156041 0.000000 0.000000 \n", + "4362 11.954228 9.566058 12.139587 0.000000 0.000000 \n", + "5859 12.484185 9.994051 12.608786 2.273060 4.760981 \n", + "6100 12.718182 10.233345 12.809847 3.534863 6.375527 \n", + "... ... ... ... ... ... \n", + "2857869 12.307902 10.005144 12.585021 3.812385 6.519807 \n", + "2857903 12.309276 9.986443 12.582868 3.319335 6.046532 \n", + "2859659 11.638333 9.598840 11.963516 0.916326 3.279901 \n", + "2861083 10.447823 8.697696 10.794293 0.000000 0.000000 \n", + "2870568 11.802080 9.800589 12.206363 1.183362 3.611805 \n", "\n", - " household_density employment_density density_index is_cbd \\\n", - "household_id \n", - "629 13.280000 4.535000 3.380567 False \n", - "1274 19.776119 7.179104 5.267062 False \n", - "27266 97.634722 550.205552 82.920387 False \n", - "28012 117.769796 246.205869 79.663609 False \n", - "29476 71.898080 273.023745 56.911108 False \n", - "... ... ... ... ... \n", - "2823069 65.596535 32.655666 21.802041 True \n", - "2823442 56.706023 144.861886 40.753220 True \n", - "2823850 0.296858 36.296472 0.294449 True \n", - "2836262 3.984127 23.820106 3.413233 False \n", - "2849737 34.477273 15.454545 10.671163 True \n", + " trOpRetail trOpTotal nmRetail nmTotal override_choice_code \n", + "household_id \n", + "1918 4.864150 7.063680 5.626389 7.133756 3 \n", + "3215 0.000000 0.000000 1.360774 5.139792 3 \n", + "4362 0.000000 0.000000 3.086628 5.647842 3 \n", + "5859 2.104966 4.545843 4.254230 6.941860 3 \n", + "6100 3.401762 6.258096 5.240538 7.175776 3 \n", + "... ... ... ... ... ... \n", + "2857869 3.632087 6.178083 3.718425 5.556368 3 \n", + "2857903 3.207581 5.829832 4.139571 6.510036 3 \n", + "2859659 0.315332 2.429541 3.824743 6.768218 3 \n", + "2861083 0.000000 0.000000 3.679297 5.376192 3 \n", + "2870568 0.897289 3.179071 5.769691 8.087817 3 \n", "\n", - " override_choice_code \n", - "household_id \n", - "629 3 \n", - "1274 3 \n", - "27266 3 \n", - "28012 3 \n", - "29476 3 \n", - "... ... \n", - "2823069 1 \n", - "2823442 1 \n", - "2823850 5 \n", - "2836262 3 \n", - "2849737 4 \n", - "\n", - "[2620 rows x 220 columns]" + "[31768 rows x 220 columns]" ] }, "execution_count": 6, @@ -1160,17 +1208,10 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

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Best LL = -410.2201502048449

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Best LL = -4677.236180934496

" ], "text/plain": [ "" @@ -1213,823 +1254,783 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " 0\n", " 0.000000\n", + " 0.000000\n", " 0.0000\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_can_walk_to_work_and_school\n", - " 0.071630\n", + " 0.284832\n", + " 0.284832\n", " 0.1391\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.071630\n", " \n", " \n", " coef_can_walk_to_work_school2\n", - " 1.368092\n", + " 0.811379\n", + " 0.811379\n", " 0.7114\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.368092\n", " \n", " \n", " coef_can_walk_to_work_work2\n", - " 0.651120\n", + " 0.600928\n", + " 0.600928\n", " 0.5268\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.651120\n", " \n", " \n", " coef_driving_age_child_school2_asc\n", - " -13.067731\n", + " -3.209418\n", + " -3.209418\n", " -3.1360\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -13.067731\n", " \n", " \n", " coef_driving_age_child_work_and_school_asc\n", - " -12.605997\n", + " -4.565091\n", + " -4.565091\n", " -4.4362\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -12.605997\n", " \n", " \n", " coef_female_school1\n", - " 0.247086\n", + " 0.000463\n", + " 0.000463\n", " 0.1592\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.247086\n", " \n", " \n", " coef_female_school2\n", - " -0.301339\n", + " -0.016856\n", + " -0.016856\n", " 0.1140\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.301339\n", " \n", " \n", " coef_female_work1\n", - " -2.242748\n", + " 0.333342\n", + " 0.333342\n", " 0.1737\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.242748\n", " \n", " \n", " coef_female_work2\n", - " -0.387411\n", + " -0.219862\n", + " -0.219862\n", " -0.2255\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.387411\n", " \n", " \n", " coef_female_work_and_school\n", - " -3.218280\n", + " -0.265847\n", + " -0.265847\n", " -0.3442\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -3.218280\n", " \n", " \n", " coef_few_cars_than_drivers_school2\n", - " -0.862451\n", + " -0.297623\n", + " -0.297623\n", " -0.5759\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.862451\n", " \n", " \n", " coef_ft_worker_work2_asc\n", - " -5.517933\n", + " -3.494595\n", + " -3.494595\n", " -3.3781\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -5.517933\n", " \n", " \n", " coef_hh_income_gt_50k_school1\n", " 0.034700\n", + " 0.034700\n", " 0.0347\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.034700\n", " \n", " \n", " coef_hh_income_gt_50k_student_work_and_school\n", - " -0.921175\n", + " -0.128428\n", + " -0.128428\n", " -0.0528\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.921175\n", " \n", " \n", " coef_hh_income_gt_50k_work\n", - " -1.418551\n", + " -0.286159\n", + " -0.286159\n", " -0.0528\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.418551\n", " \n", " \n", " coef_hh_income_gt_50k_worker_work_and_school\n", " 0.034700\n", + " 0.034700\n", " 0.0347\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.034700\n", " \n", " \n", " coef_home_urban_school1\n", " -0.136100\n", + " -0.136100\n", " -0.1361\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.136100\n", " \n", " \n", " coef_home_urban_school2\n", - " -8.455615\n", + " 0.161745\n", + " 0.161745\n", " 0.3170\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -8.455615\n", " \n", " \n", " coef_home_urban_work1\n", - " 4.026796\n", + " -0.122770\n", + " -0.122770\n", " -0.2831\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4.026796\n", " \n", " \n", " coef_home_urban_work2\n", - " 2.624854\n", + " 0.142218\n", + " 0.142218\n", " 0.2308\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.624854\n", " \n", " \n", " coef_home_urban_work_and_school\n", - " -0.447070\n", + " -0.436835\n", + " -0.436835\n", " -0.3509\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.447070\n", " \n", " \n", " coef_no_cars_in_hh_school2\n", - " -0.620465\n", + " -1.479517\n", + " -1.479517\n", " -1.4130\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.620465\n", " \n", " \n", " coef_no_cars_in_hh_work2\n", - " -0.725967\n", + " -1.524960\n", + " -1.524960\n", " -1.3060\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.725967\n", " \n", " \n", " coef_no_cars_in_hh_work_and_school\n", - " -0.775934\n", + " -1.244512\n", + " -1.244512\n", " -1.3020\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.775934\n", " \n", " \n", " coef_non_family_hh_category1\n", " -0.250000\n", + " -0.250000\n", " -0.2500\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.250000\n", " \n", " \n", " coef_non_family_hh_category2\n", - " 5.000771\n", + " -0.091298\n", + " -0.091298\n", " -0.1792\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 5.000771\n", " \n", " \n", " coef_non_student_goes_to_school\n", " 3.883000\n", + " 3.883000\n", " 3.8830\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 3.883000\n", " \n", " \n", " coef_num_non_workers_in_hh_school1\n", " 0.257400\n", + " 0.257400\n", " 0.2574\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.257400\n", " \n", " \n", " coef_num_preschool_in_hh_school1\n", " -0.133500\n", + " -0.133500\n", " -0.1335\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.133500\n", " \n", " \n", " coef_num_preschool_in_hh_school2\n", - " 0.356660\n", + " -0.796350\n", + " -0.796350\n", " -0.5577\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.356660\n", " \n", " \n", " coef_num_preschool_in_hh_work1\n", - " -2.930501\n", + " 0.125045\n", + " 0.125045\n", " 0.2191\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.930501\n", " \n", " \n", " coef_num_preschool_in_hh_work2\n", - " 0.225173\n", + " -0.126183\n", + " -0.126183\n", " -0.1478\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.225173\n", " \n", " \n", " coef_num_preschool_in_hh_work_and_school\n", - " -29.197569\n", + " -0.070125\n", + " -0.070125\n", " -0.1251\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -29.197569\n", " \n", " \n", " coef_num_under_16_not_at_school_school2\n", - " -0.638293\n", + " -0.015345\n", + " -0.015345\n", " 0.0866\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.638293\n", " \n", " \n", " coef_num_under_16_not_at_school_work2\n", - " -0.118991\n", + " 0.168260\n", + " 0.168260\n", " 0.1804\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.118991\n", " \n", " \n", " coef_num_under_16_not_at_school_work_and_school\n", - " -15.357907\n", + " -0.192330\n", + " -0.192330\n", " -0.1955\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -15.357907\n", " \n", " \n", " coef_pre_driving_age_child_school2_asc\n", - " 3.679726\n", + " -4.210709\n", + " -4.210709\n", " -3.9703\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 3.679726\n", " \n", " \n", " coef_pt_worker_work2_asc\n", - " -4.743242\n", + " -3.079547\n", + " -3.079547\n", " -3.0476\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.743242\n", " \n", " \n", " coef_round_trip_auto_time_to_work_school2\n", - " 0.064411\n", + " -0.005959\n", + " -0.005959\n", " -0.0034\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.064411\n", " \n", " \n", " coef_round_trip_auto_time_to_work_work2\n", - " -0.025905\n", + " -0.001855\n", + " -0.001855\n", " -0.0035\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.025905\n", " \n", " \n", " coef_round_trip_auto_time_to_work_work_and_school\n", - " -0.021804\n", + " -0.002208\n", + " -0.002208\n", " -0.0031\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.021804\n", " \n", " \n", " coef_student_employed\n", - " 13.457255\n", + " 2.918973\n", + " 2.918973\n", " 3.0140\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 13.457255\n", " \n", " \n", " coef_unavailable\n", " -999.000000\n", + " -999.000000\n", " -999.0000\n", + " -999.0\n", + " -999.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_under_35_school1\n", " 0.721800\n", + " 0.721800\n", " 0.7218\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.721800\n", " \n", " \n", " coef_under_35_school2\n", - " 15.011164\n", + " 1.230550\n", + " 1.230550\n", " 1.2750\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 15.011164\n", " \n", " \n", " coef_under_35_work1\n", - " 1.038242\n", + " -0.458863\n", + " -0.458863\n", " -0.4629\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.038242\n", " \n", " \n", " coef_under_35_work2\n", - " -0.253875\n", + " 0.005614\n", + " 0.005614\n", " -0.1375\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.253875\n", " \n", " \n", " coef_under_35_work_and_school\n", - " 2.257035\n", + " 1.149726\n", + " 1.149726\n", " 0.9761\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.257035\n", " \n", " \n", " coef_univ_school2_asc\n", - " -10.233810\n", + " -3.640737\n", + " -3.640737\n", " -3.7429\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -10.233810\n", " \n", " \n", " coef_univ_work1_asc\n", - " 6.475896\n", + " 2.209708\n", + " 2.209708\n", " 2.1660\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 6.475896\n", " \n", " \n", " coef_univ_work2_asc\n", - " 4.833029\n", + " -1.472430\n", + " -1.472430\n", " -1.3965\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4.833029\n", " \n", " \n", " coef_univ_work_and_school_asc\n", - " 8.180927\n", + " 0.173386\n", + " 0.173386\n", " 0.1073\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 8.180927\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "0 0.000000 0.0000 \n", - "coef_can_walk_to_work_and_school 0.071630 0.1391 \n", - "coef_can_walk_to_work_school2 1.368092 0.7114 \n", - "coef_can_walk_to_work_work2 0.651120 0.5268 \n", - "coef_driving_age_child_school2_asc -13.067731 -3.1360 \n", - "coef_driving_age_child_work_and_school_asc -12.605997 -4.4362 \n", - "coef_female_school1 0.247086 0.1592 \n", - "coef_female_school2 -0.301339 0.1140 \n", - "coef_female_work1 -2.242748 0.1737 \n", - "coef_female_work2 -0.387411 -0.2255 \n", - "coef_female_work_and_school -3.218280 -0.3442 \n", - "coef_few_cars_than_drivers_school2 -0.862451 -0.5759 \n", - "coef_ft_worker_work2_asc -5.517933 -3.3781 \n", - "coef_hh_income_gt_50k_school1 0.034700 0.0347 \n", - "coef_hh_income_gt_50k_student_work_and_school -0.921175 -0.0528 \n", - "coef_hh_income_gt_50k_work -1.418551 -0.0528 \n", - "coef_hh_income_gt_50k_worker_work_and_school 0.034700 0.0347 \n", - "coef_home_urban_school1 -0.136100 -0.1361 \n", - "coef_home_urban_school2 -8.455615 0.3170 \n", - "coef_home_urban_work1 4.026796 -0.2831 \n", - "coef_home_urban_work2 2.624854 0.2308 \n", - "coef_home_urban_work_and_school -0.447070 -0.3509 \n", - "coef_no_cars_in_hh_school2 -0.620465 -1.4130 \n", - "coef_no_cars_in_hh_work2 -0.725967 -1.3060 \n", - "coef_no_cars_in_hh_work_and_school -0.775934 -1.3020 \n", - "coef_non_family_hh_category1 -0.250000 -0.2500 \n", - "coef_non_family_hh_category2 5.000771 -0.1792 \n", - "coef_non_student_goes_to_school 3.883000 3.8830 \n", - "coef_num_non_workers_in_hh_school1 0.257400 0.2574 \n", - "coef_num_preschool_in_hh_school1 -0.133500 -0.1335 \n", - "coef_num_preschool_in_hh_school2 0.356660 -0.5577 \n", - "coef_num_preschool_in_hh_work1 -2.930501 0.2191 \n", - "coef_num_preschool_in_hh_work2 0.225173 -0.1478 \n", - "coef_num_preschool_in_hh_work_and_school -29.197569 -0.1251 \n", - "coef_num_under_16_not_at_school_school2 -0.638293 0.0866 \n", - "coef_num_under_16_not_at_school_work2 -0.118991 0.1804 \n", - "coef_num_under_16_not_at_school_work_and_school -15.357907 -0.1955 \n", - "coef_pre_driving_age_child_school2_asc 3.679726 -3.9703 \n", - "coef_pt_worker_work2_asc -4.743242 -3.0476 \n", - "coef_round_trip_auto_time_to_work_school2 0.064411 -0.0034 \n", - "coef_round_trip_auto_time_to_work_work2 -0.025905 -0.0035 \n", - "coef_round_trip_auto_time_to_work_work_and_school -0.021804 -0.0031 \n", - "coef_student_employed 13.457255 3.0140 \n", - "coef_unavailable -999.000000 -999.0000 \n", - "coef_under_35_school1 0.721800 0.7218 \n", - "coef_under_35_school2 15.011164 1.2750 \n", - "coef_under_35_work1 1.038242 -0.4629 \n", - "coef_under_35_work2 -0.253875 -0.1375 \n", - "coef_under_35_work_and_school 2.257035 0.9761 \n", - "coef_univ_school2_asc -10.233810 -3.7429 \n", - "coef_univ_work1_asc 6.475896 2.1660 \n", - "coef_univ_work2_asc 4.833029 -1.3965 \n", - "coef_univ_work_and_school_asc 8.180927 0.1073 \n", + " value best \\\n", + "param_name \n", + "0 0.000000 0.000000 \n", + "coef_can_walk_to_work_and_school 0.284832 0.284832 \n", + "coef_can_walk_to_work_school2 0.811379 0.811379 \n", + "coef_can_walk_to_work_work2 0.600928 0.600928 \n", + "coef_driving_age_child_school2_asc -3.209418 -3.209418 \n", + "coef_driving_age_child_work_and_school_asc -4.565091 -4.565091 \n", + "coef_female_school1 0.000463 0.000463 \n", + "coef_female_school2 -0.016856 -0.016856 \n", + "coef_female_work1 0.333342 0.333342 \n", + "coef_female_work2 -0.219862 -0.219862 \n", + "coef_female_work_and_school -0.265847 -0.265847 \n", + "coef_few_cars_than_drivers_school2 -0.297623 -0.297623 \n", + "coef_ft_worker_work2_asc -3.494595 -3.494595 \n", + "coef_hh_income_gt_50k_school1 0.034700 0.034700 \n", + "coef_hh_income_gt_50k_student_work_and_school -0.128428 -0.128428 \n", + "coef_hh_income_gt_50k_work -0.286159 -0.286159 \n", + "coef_hh_income_gt_50k_worker_work_and_school 0.034700 0.034700 \n", + "coef_home_urban_school1 -0.136100 -0.136100 \n", + "coef_home_urban_school2 0.161745 0.161745 \n", + "coef_home_urban_work1 -0.122770 -0.122770 \n", + "coef_home_urban_work2 0.142218 0.142218 \n", + "coef_home_urban_work_and_school -0.436835 -0.436835 \n", + "coef_no_cars_in_hh_school2 -1.479517 -1.479517 \n", + "coef_no_cars_in_hh_work2 -1.524960 -1.524960 \n", + "coef_no_cars_in_hh_work_and_school -1.244512 -1.244512 \n", + "coef_non_family_hh_category1 -0.250000 -0.250000 \n", + "coef_non_family_hh_category2 -0.091298 -0.091298 \n", + "coef_non_student_goes_to_school 3.883000 3.883000 \n", + "coef_num_non_workers_in_hh_school1 0.257400 0.257400 \n", + "coef_num_preschool_in_hh_school1 -0.133500 -0.133500 \n", + "coef_num_preschool_in_hh_school2 -0.796350 -0.796350 \n", + "coef_num_preschool_in_hh_work1 0.125045 0.125045 \n", + "coef_num_preschool_in_hh_work2 -0.126183 -0.126183 \n", + "coef_num_preschool_in_hh_work_and_school -0.070125 -0.070125 \n", + "coef_num_under_16_not_at_school_school2 -0.015345 -0.015345 \n", + "coef_num_under_16_not_at_school_work2 0.168260 0.168260 \n", + "coef_num_under_16_not_at_school_work_and_school -0.192330 -0.192330 \n", + "coef_pre_driving_age_child_school2_asc -4.210709 -4.210709 \n", + "coef_pt_worker_work2_asc -3.079547 -3.079547 \n", + "coef_round_trip_auto_time_to_work_school2 -0.005959 -0.005959 \n", + "coef_round_trip_auto_time_to_work_work2 -0.001855 -0.001855 \n", + "coef_round_trip_auto_time_to_work_work_and_school -0.002208 -0.002208 \n", + "coef_student_employed 2.918973 2.918973 \n", + "coef_unavailable -999.000000 -999.000000 \n", + "coef_under_35_school1 0.721800 0.721800 \n", + "coef_under_35_school2 1.230550 1.230550 \n", + "coef_under_35_work1 -0.458863 -0.458863 \n", + "coef_under_35_work2 0.005614 0.005614 \n", + "coef_under_35_work_and_school 1.149726 1.149726 \n", + "coef_univ_school2_asc -3.640737 -3.640737 \n", + "coef_univ_work1_asc 2.209708 2.209708 \n", + "coef_univ_work2_asc -1.472430 -1.472430 \n", + "coef_univ_work_and_school_asc 0.173386 0.173386 \n", "\n", - " nullvalue minimum \\\n", - "0 0.0 0.0 \n", - "coef_can_walk_to_work_and_school 0.0 NaN \n", - "coef_can_walk_to_work_school2 0.0 NaN \n", - "coef_can_walk_to_work_work2 0.0 NaN \n", - "coef_driving_age_child_school2_asc 0.0 NaN \n", - "coef_driving_age_child_work_and_school_asc 0.0 NaN \n", - "coef_female_school1 0.0 NaN \n", - "coef_female_school2 0.0 NaN \n", - "coef_female_work1 0.0 NaN \n", - "coef_female_work2 0.0 NaN \n", - "coef_female_work_and_school 0.0 NaN \n", - "coef_few_cars_than_drivers_school2 0.0 NaN \n", - "coef_ft_worker_work2_asc 0.0 NaN \n", - "coef_hh_income_gt_50k_school1 0.0 NaN \n", - "coef_hh_income_gt_50k_student_work_and_school 0.0 NaN \n", - "coef_hh_income_gt_50k_work 0.0 NaN \n", - "coef_hh_income_gt_50k_worker_work_and_school 0.0 NaN \n", - "coef_home_urban_school1 0.0 NaN \n", - "coef_home_urban_school2 0.0 NaN \n", - "coef_home_urban_work1 0.0 NaN \n", - "coef_home_urban_work2 0.0 NaN \n", - "coef_home_urban_work_and_school 0.0 NaN \n", - "coef_no_cars_in_hh_school2 0.0 NaN \n", - "coef_no_cars_in_hh_work2 0.0 NaN \n", - "coef_no_cars_in_hh_work_and_school 0.0 NaN \n", - "coef_non_family_hh_category1 0.0 NaN \n", - "coef_non_family_hh_category2 0.0 NaN \n", - "coef_non_student_goes_to_school 0.0 NaN \n", - "coef_num_non_workers_in_hh_school1 0.0 NaN \n", - "coef_num_preschool_in_hh_school1 0.0 NaN \n", - "coef_num_preschool_in_hh_school2 0.0 NaN \n", - "coef_num_preschool_in_hh_work1 0.0 NaN \n", - "coef_num_preschool_in_hh_work2 0.0 NaN \n", - "coef_num_preschool_in_hh_work_and_school 0.0 NaN \n", - "coef_num_under_16_not_at_school_school2 0.0 NaN \n", - "coef_num_under_16_not_at_school_work2 0.0 NaN \n", - "coef_num_under_16_not_at_school_work_and_school 0.0 NaN \n", - "coef_pre_driving_age_child_school2_asc 0.0 NaN \n", - "coef_pt_worker_work2_asc 0.0 NaN \n", - "coef_round_trip_auto_time_to_work_school2 0.0 NaN \n", - "coef_round_trip_auto_time_to_work_work2 0.0 NaN \n", - "coef_round_trip_auto_time_to_work_work_and_school 0.0 NaN \n", - "coef_student_employed 0.0 NaN \n", - "coef_unavailable 0.0 NaN \n", - "coef_under_35_school1 0.0 NaN \n", - "coef_under_35_school2 0.0 NaN \n", - "coef_under_35_work1 0.0 NaN \n", - "coef_under_35_work2 0.0 NaN \n", - "coef_under_35_work_and_school 0.0 NaN \n", - "coef_univ_school2_asc 0.0 NaN \n", - "coef_univ_work1_asc 0.0 NaN \n", - "coef_univ_work2_asc 0.0 NaN \n", - "coef_univ_work_and_school_asc 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "0 0.0000 0.0 \n", + "coef_can_walk_to_work_and_school 0.1391 -50.0 \n", + "coef_can_walk_to_work_school2 0.7114 -50.0 \n", + "coef_can_walk_to_work_work2 0.5268 -50.0 \n", + "coef_driving_age_child_school2_asc -3.1360 -50.0 \n", + "coef_driving_age_child_work_and_school_asc -4.4362 -50.0 \n", + "coef_female_school1 0.1592 -50.0 \n", + "coef_female_school2 0.1140 -50.0 \n", + "coef_female_work1 0.1737 -50.0 \n", + "coef_female_work2 -0.2255 -50.0 \n", + "coef_female_work_and_school -0.3442 -50.0 \n", + "coef_few_cars_than_drivers_school2 -0.5759 -50.0 \n", + "coef_ft_worker_work2_asc -3.3781 -50.0 \n", + "coef_hh_income_gt_50k_school1 0.0347 -50.0 \n", + "coef_hh_income_gt_50k_student_work_and_school -0.0528 -50.0 \n", + "coef_hh_income_gt_50k_work -0.0528 -50.0 \n", + "coef_hh_income_gt_50k_worker_work_and_school 0.0347 -50.0 \n", + "coef_home_urban_school1 -0.1361 -50.0 \n", + "coef_home_urban_school2 0.3170 -50.0 \n", + "coef_home_urban_work1 -0.2831 -50.0 \n", + "coef_home_urban_work2 0.2308 -50.0 \n", + "coef_home_urban_work_and_school -0.3509 -50.0 \n", + "coef_no_cars_in_hh_school2 -1.4130 -50.0 \n", + "coef_no_cars_in_hh_work2 -1.3060 -50.0 \n", + "coef_no_cars_in_hh_work_and_school -1.3020 -50.0 \n", + "coef_non_family_hh_category1 -0.2500 -50.0 \n", + "coef_non_family_hh_category2 -0.1792 -50.0 \n", + "coef_non_student_goes_to_school 3.8830 -50.0 \n", + "coef_num_non_workers_in_hh_school1 0.2574 -50.0 \n", + "coef_num_preschool_in_hh_school1 -0.1335 -50.0 \n", + "coef_num_preschool_in_hh_school2 -0.5577 -50.0 \n", + "coef_num_preschool_in_hh_work1 0.2191 -50.0 \n", + "coef_num_preschool_in_hh_work2 -0.1478 -50.0 \n", + "coef_num_preschool_in_hh_work_and_school -0.1251 -50.0 \n", + "coef_num_under_16_not_at_school_school2 0.0866 -50.0 \n", + "coef_num_under_16_not_at_school_work2 0.1804 -50.0 \n", + "coef_num_under_16_not_at_school_work_and_school -0.1955 -50.0 \n", + "coef_pre_driving_age_child_school2_asc -3.9703 -50.0 \n", + "coef_pt_worker_work2_asc -3.0476 -50.0 \n", + "coef_round_trip_auto_time_to_work_school2 -0.0034 -50.0 \n", + "coef_round_trip_auto_time_to_work_work2 -0.0035 -50.0 \n", + "coef_round_trip_auto_time_to_work_work_and_school -0.0031 -50.0 \n", + "coef_student_employed 3.0140 -50.0 \n", + "coef_unavailable -999.0000 -999.0 \n", + "coef_under_35_school1 0.7218 -50.0 \n", + "coef_under_35_school2 1.2750 -50.0 \n", + "coef_under_35_work1 -0.4629 -50.0 \n", + "coef_under_35_work2 -0.1375 -50.0 \n", + "coef_under_35_work_and_school 0.9761 -50.0 \n", + "coef_univ_school2_asc -3.7429 -50.0 \n", + "coef_univ_work1_asc 2.1660 -50.0 \n", + "coef_univ_work2_asc -1.3965 -50.0 \n", + "coef_univ_work_and_school_asc 0.1073 -50.0 \n", "\n", - " maximum holdfast note \\\n", - "0 0.0 1 \n", - "coef_can_walk_to_work_and_school NaN 0 \n", - "coef_can_walk_to_work_school2 NaN 0 \n", - "coef_can_walk_to_work_work2 NaN 0 \n", - "coef_driving_age_child_school2_asc NaN 0 \n", - "coef_driving_age_child_work_and_school_asc NaN 0 \n", - "coef_female_school1 NaN 0 \n", - "coef_female_school2 NaN 0 \n", - "coef_female_work1 NaN 0 \n", - "coef_female_work2 NaN 0 \n", - "coef_female_work_and_school NaN 0 \n", - "coef_few_cars_than_drivers_school2 NaN 0 \n", - "coef_ft_worker_work2_asc NaN 0 \n", - "coef_hh_income_gt_50k_school1 NaN 0 \n", - "coef_hh_income_gt_50k_student_work_and_school NaN 0 \n", - "coef_hh_income_gt_50k_work NaN 0 \n", - "coef_hh_income_gt_50k_worker_work_and_school NaN 0 \n", - "coef_home_urban_school1 NaN 0 \n", - "coef_home_urban_school2 NaN 0 \n", - "coef_home_urban_work1 NaN 0 \n", - "coef_home_urban_work2 NaN 0 \n", - "coef_home_urban_work_and_school NaN 0 \n", - "coef_no_cars_in_hh_school2 NaN 0 \n", - "coef_no_cars_in_hh_work2 NaN 0 \n", - "coef_no_cars_in_hh_work_and_school NaN 0 \n", - "coef_non_family_hh_category1 NaN 0 \n", - "coef_non_family_hh_category2 NaN 0 \n", - "coef_non_student_goes_to_school NaN 0 \n", - "coef_num_non_workers_in_hh_school1 NaN 0 \n", - "coef_num_preschool_in_hh_school1 NaN 0 \n", - "coef_num_preschool_in_hh_school2 NaN 0 \n", - "coef_num_preschool_in_hh_work1 NaN 0 \n", - "coef_num_preschool_in_hh_work2 NaN 0 \n", - "coef_num_preschool_in_hh_work_and_school NaN 0 \n", - "coef_num_under_16_not_at_school_school2 NaN 0 \n", - "coef_num_under_16_not_at_school_work2 NaN 0 \n", - "coef_num_under_16_not_at_school_work_and_school NaN 0 \n", - "coef_pre_driving_age_child_school2_asc NaN 0 \n", - "coef_pt_worker_work2_asc NaN 0 \n", - "coef_round_trip_auto_time_to_work_school2 NaN 0 \n", - "coef_round_trip_auto_time_to_work_work2 NaN 0 \n", - "coef_round_trip_auto_time_to_work_work_and_school NaN 0 \n", - "coef_student_employed NaN 0 \n", - "coef_unavailable NaN 1 \n", - "coef_under_35_school1 NaN 0 \n", - "coef_under_35_school2 NaN 0 \n", - "coef_under_35_work1 NaN 0 \n", - "coef_under_35_work2 NaN 0 \n", - "coef_under_35_work_and_school NaN 0 \n", - "coef_univ_school2_asc NaN 0 \n", - "coef_univ_work1_asc NaN 0 \n", - "coef_univ_work2_asc NaN 0 \n", - "coef_univ_work_and_school_asc NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "0 0.0 0.0 \n", + "coef_can_walk_to_work_and_school 50.0 0.0 \n", + "coef_can_walk_to_work_school2 50.0 0.0 \n", + "coef_can_walk_to_work_work2 50.0 0.0 \n", + "coef_driving_age_child_school2_asc 50.0 0.0 \n", + "coef_driving_age_child_work_and_school_asc 50.0 0.0 \n", + "coef_female_school1 50.0 0.0 \n", + "coef_female_school2 50.0 0.0 \n", + "coef_female_work1 50.0 0.0 \n", + "coef_female_work2 50.0 0.0 \n", + "coef_female_work_and_school 50.0 0.0 \n", + "coef_few_cars_than_drivers_school2 50.0 0.0 \n", + "coef_ft_worker_work2_asc 50.0 0.0 \n", + "coef_hh_income_gt_50k_school1 50.0 0.0 \n", + "coef_hh_income_gt_50k_student_work_and_school 50.0 0.0 \n", + "coef_hh_income_gt_50k_work 50.0 0.0 \n", + "coef_hh_income_gt_50k_worker_work_and_school 50.0 0.0 \n", + "coef_home_urban_school1 50.0 0.0 \n", + "coef_home_urban_school2 50.0 0.0 \n", + "coef_home_urban_work1 50.0 0.0 \n", + "coef_home_urban_work2 50.0 0.0 \n", + "coef_home_urban_work_and_school 50.0 0.0 \n", + "coef_no_cars_in_hh_school2 50.0 0.0 \n", + "coef_no_cars_in_hh_work2 50.0 0.0 \n", + "coef_no_cars_in_hh_work_and_school 50.0 0.0 \n", + "coef_non_family_hh_category1 50.0 0.0 \n", + "coef_non_family_hh_category2 50.0 0.0 \n", + "coef_non_student_goes_to_school 50.0 0.0 \n", + "coef_num_non_workers_in_hh_school1 50.0 0.0 \n", + "coef_num_preschool_in_hh_school1 50.0 0.0 \n", + "coef_num_preschool_in_hh_school2 50.0 0.0 \n", + "coef_num_preschool_in_hh_work1 50.0 0.0 \n", + "coef_num_preschool_in_hh_work2 50.0 0.0 \n", + "coef_num_preschool_in_hh_work_and_school 50.0 0.0 \n", + "coef_num_under_16_not_at_school_school2 50.0 0.0 \n", + "coef_num_under_16_not_at_school_work2 50.0 0.0 \n", + "coef_num_under_16_not_at_school_work_and_school 50.0 0.0 \n", + "coef_pre_driving_age_child_school2_asc 50.0 0.0 \n", + "coef_pt_worker_work2_asc 50.0 0.0 \n", + "coef_round_trip_auto_time_to_work_school2 50.0 0.0 \n", + "coef_round_trip_auto_time_to_work_work2 50.0 0.0 \n", + "coef_round_trip_auto_time_to_work_work_and_school 50.0 0.0 \n", + "coef_student_employed 50.0 0.0 \n", + "coef_unavailable -999.0 0.0 \n", + "coef_under_35_school1 50.0 0.0 \n", + "coef_under_35_school2 50.0 0.0 \n", + "coef_under_35_work1 50.0 0.0 \n", + "coef_under_35_work2 50.0 0.0 \n", + "coef_under_35_work_and_school 50.0 0.0 \n", + "coef_univ_school2_asc 50.0 0.0 \n", + "coef_univ_work1_asc 50.0 0.0 \n", + "coef_univ_work2_asc 50.0 0.0 \n", + "coef_univ_work_and_school_asc 50.0 0.0 \n", "\n", - " best \n", - "0 0.000000 \n", - "coef_can_walk_to_work_and_school 0.071630 \n", - "coef_can_walk_to_work_school2 1.368092 \n", - "coef_can_walk_to_work_work2 0.651120 \n", - "coef_driving_age_child_school2_asc -13.067731 \n", - "coef_driving_age_child_work_and_school_asc -12.605997 \n", - "coef_female_school1 0.247086 \n", - "coef_female_school2 -0.301339 \n", - "coef_female_work1 -2.242748 \n", - "coef_female_work2 -0.387411 \n", - "coef_female_work_and_school -3.218280 \n", - "coef_few_cars_than_drivers_school2 -0.862451 \n", - "coef_ft_worker_work2_asc -5.517933 \n", - "coef_hh_income_gt_50k_school1 0.034700 \n", - "coef_hh_income_gt_50k_student_work_and_school -0.921175 \n", - "coef_hh_income_gt_50k_work -1.418551 \n", - "coef_hh_income_gt_50k_worker_work_and_school 0.034700 \n", - "coef_home_urban_school1 -0.136100 \n", - "coef_home_urban_school2 -8.455615 \n", - "coef_home_urban_work1 4.026796 \n", - "coef_home_urban_work2 2.624854 \n", - "coef_home_urban_work_and_school -0.447070 \n", - "coef_no_cars_in_hh_school2 -0.620465 \n", - "coef_no_cars_in_hh_work2 -0.725967 \n", - "coef_no_cars_in_hh_work_and_school -0.775934 \n", - "coef_non_family_hh_category1 -0.250000 \n", - "coef_non_family_hh_category2 5.000771 \n", - "coef_non_student_goes_to_school 3.883000 \n", - "coef_num_non_workers_in_hh_school1 0.257400 \n", - "coef_num_preschool_in_hh_school1 -0.133500 \n", - "coef_num_preschool_in_hh_school2 0.356660 \n", - "coef_num_preschool_in_hh_work1 -2.930501 \n", - "coef_num_preschool_in_hh_work2 0.225173 \n", - "coef_num_preschool_in_hh_work_and_school -29.197569 \n", - "coef_num_under_16_not_at_school_school2 -0.638293 \n", - "coef_num_under_16_not_at_school_work2 -0.118991 \n", - "coef_num_under_16_not_at_school_work_and_school -15.357907 \n", - "coef_pre_driving_age_child_school2_asc 3.679726 \n", - "coef_pt_worker_work2_asc -4.743242 \n", - "coef_round_trip_auto_time_to_work_school2 0.064411 \n", - "coef_round_trip_auto_time_to_work_work2 -0.025905 \n", - "coef_round_trip_auto_time_to_work_work_and_school -0.021804 \n", - "coef_student_employed 13.457255 \n", - "coef_unavailable -999.000000 \n", - "coef_under_35_school1 0.721800 \n", - "coef_under_35_school2 15.011164 \n", - "coef_under_35_work1 1.038242 \n", - "coef_under_35_work2 -0.253875 \n", - "coef_under_35_work_and_school 2.257035 \n", - "coef_univ_school2_asc -10.233810 \n", - "coef_univ_work1_asc 6.475896 \n", - "coef_univ_work2_asc 4.833029 \n", - "coef_univ_work_and_school_asc 8.180927 " + " holdfast \n", + "param_name \n", + "0 1 \n", + "coef_can_walk_to_work_and_school 0 \n", + "coef_can_walk_to_work_school2 0 \n", + "coef_can_walk_to_work_work2 0 \n", + "coef_driving_age_child_school2_asc 0 \n", + "coef_driving_age_child_work_and_school_asc 0 \n", + "coef_female_school1 0 \n", + "coef_female_school2 0 \n", + "coef_female_work1 0 \n", + "coef_female_work2 0 \n", + "coef_female_work_and_school 0 \n", + "coef_few_cars_than_drivers_school2 0 \n", + "coef_ft_worker_work2_asc 0 \n", + "coef_hh_income_gt_50k_school1 0 \n", + "coef_hh_income_gt_50k_student_work_and_school 0 \n", + "coef_hh_income_gt_50k_work 0 \n", + "coef_hh_income_gt_50k_worker_work_and_school 0 \n", + "coef_home_urban_school1 0 \n", + "coef_home_urban_school2 0 \n", + "coef_home_urban_work1 0 \n", + "coef_home_urban_work2 0 \n", + "coef_home_urban_work_and_school 0 \n", + "coef_no_cars_in_hh_school2 0 \n", + "coef_no_cars_in_hh_work2 0 \n", + "coef_no_cars_in_hh_work_and_school 0 \n", + "coef_non_family_hh_category1 0 \n", + "coef_non_family_hh_category2 0 \n", + "coef_non_student_goes_to_school 0 \n", + "coef_num_non_workers_in_hh_school1 0 \n", + "coef_num_preschool_in_hh_school1 0 \n", + "coef_num_preschool_in_hh_school2 0 \n", + "coef_num_preschool_in_hh_work1 0 \n", + "coef_num_preschool_in_hh_work2 0 \n", + "coef_num_preschool_in_hh_work_and_school 0 \n", + "coef_num_under_16_not_at_school_school2 0 \n", + "coef_num_under_16_not_at_school_work2 0 \n", + "coef_num_under_16_not_at_school_work_and_school 0 \n", + "coef_pre_driving_age_child_school2_asc 0 \n", + "coef_pt_worker_work2_asc 0 \n", + "coef_round_trip_auto_time_to_work_school2 0 \n", + "coef_round_trip_auto_time_to_work_work2 0 \n", + "coef_round_trip_auto_time_to_work_work_and_school 0 \n", + "coef_student_employed 0 \n", + "coef_unavailable 1 \n", + "coef_under_35_school1 0 \n", + "coef_under_35_school2 0 \n", + "coef_under_35_work1 0 \n", + "coef_under_35_work2 0 \n", + "coef_under_35_work_and_school 0 \n", + "coef_univ_school2_asc 0 \n", + "coef_univ_work1_asc 0 \n", + "coef_univ_work2_asc 0 \n", + "coef_univ_work_and_school_asc 0 " ] }, "metadata": {}, @@ -2039,12 +2040,8 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate()\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.771611294971759e-29 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: RuntimeWarning: invalid value encountered in sqrt\n", - " model.estimate()\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -2064,51 +2061,51 @@ " \n", " \n", " coef_can_walk_to_work_and_school\n", - " 0.071630\n", + " 0.284832\n", " \n", " \n", " coef_can_walk_to_work_school2\n", - " 1.368092\n", + " 0.811379\n", " \n", " \n", " coef_can_walk_to_work_work2\n", - " 0.651120\n", + " 0.600928\n", " \n", " \n", " coef_driving_age_child_school2_asc\n", - " -13.067731\n", + " -3.209418\n", " \n", " \n", " coef_driving_age_child_work_and_school_asc\n", - " -12.605997\n", + " -4.565091\n", " \n", " \n", " coef_female_school1\n", - " 0.247086\n", + " 0.000463\n", " \n", " \n", " coef_female_school2\n", - " -0.301339\n", + " -0.016856\n", " \n", " \n", " coef_female_work1\n", - " -2.242748\n", + " 0.333342\n", " \n", " \n", " coef_female_work2\n", - " -0.387411\n", + " -0.219862\n", " \n", " \n", " coef_female_work_and_school\n", - " -3.218280\n", + " -0.265847\n", " \n", " \n", " coef_few_cars_than_drivers_school2\n", - " -0.862451\n", + " -0.297623\n", " \n", " \n", " coef_ft_worker_work2_asc\n", - " -5.517933\n", + " -3.494595\n", " \n", " \n", " coef_hh_income_gt_50k_school1\n", @@ -2116,11 +2113,11 @@ " \n", " \n", " coef_hh_income_gt_50k_student_work_and_school\n", - " -0.921175\n", + " -0.128428\n", " \n", " \n", " coef_hh_income_gt_50k_work\n", - " -1.418551\n", + " -0.286159\n", " \n", " \n", " coef_hh_income_gt_50k_worker_work_and_school\n", @@ -2132,31 +2129,31 @@ " \n", " \n", " coef_home_urban_school2\n", - " -8.455615\n", + " 0.161745\n", " \n", " \n", " coef_home_urban_work1\n", - " 4.026796\n", + " -0.122770\n", " \n", " \n", " coef_home_urban_work2\n", - " 2.624854\n", + " 0.142218\n", " \n", " \n", " coef_home_urban_work_and_school\n", - " -0.447070\n", + " -0.436835\n", " \n", " \n", " coef_no_cars_in_hh_school2\n", - " -0.620465\n", + " -1.479517\n", " \n", " \n", " coef_no_cars_in_hh_work2\n", - " -0.725967\n", + " -1.524960\n", " \n", " \n", " coef_no_cars_in_hh_work_and_school\n", - " -0.775934\n", + " -1.244512\n", " \n", " \n", " coef_non_family_hh_category1\n", @@ -2164,7 +2161,7 @@ " \n", " \n", " coef_non_family_hh_category2\n", - " 5.000771\n", + " -0.091298\n", " \n", " \n", " coef_non_student_goes_to_school\n", @@ -2180,55 +2177,55 @@ " \n", " \n", " coef_num_preschool_in_hh_school2\n", - " 0.356660\n", + " -0.796350\n", " \n", " \n", " coef_num_preschool_in_hh_work1\n", - " -2.930501\n", + " 0.125045\n", " \n", " \n", " coef_num_preschool_in_hh_work2\n", - " 0.225173\n", + " -0.126183\n", " \n", " \n", " coef_num_preschool_in_hh_work_and_school\n", - " -29.197569\n", + " -0.070125\n", " \n", " \n", " coef_num_under_16_not_at_school_school2\n", - " -0.638293\n", + " -0.015345\n", " \n", " \n", " coef_num_under_16_not_at_school_work2\n", - " -0.118991\n", + " 0.168260\n", " \n", " \n", " coef_num_under_16_not_at_school_work_and_school\n", - " -15.357907\n", + " -0.192330\n", " \n", " \n", " coef_pre_driving_age_child_school2_asc\n", - " 3.679726\n", + " -4.210709\n", " \n", " \n", " coef_pt_worker_work2_asc\n", - " -4.743242\n", + " -3.079547\n", " \n", " \n", " coef_round_trip_auto_time_to_work_school2\n", - " 0.064411\n", + " -0.005959\n", " \n", " \n", " coef_round_trip_auto_time_to_work_work2\n", - " -0.025905\n", + " -0.001855\n", " \n", " \n", " coef_round_trip_auto_time_to_work_work_and_school\n", - " -0.021804\n", + " -0.002208\n", " \n", " \n", " coef_student_employed\n", - " 13.457255\n", + " 2.918973\n", " \n", " \n", " coef_unavailable\n", @@ -2240,38 +2237,38 @@ " \n", " \n", " coef_under_35_school2\n", - " 15.011164\n", + " 1.230550\n", " \n", " \n", " coef_under_35_work1\n", - " 1.038242\n", + " -0.458863\n", " \n", " \n", " coef_under_35_work2\n", - " -0.253875\n", + " 0.005614\n", " \n", " \n", " coef_under_35_work_and_school\n", - " 2.257035\n", + " 1.149726\n", " \n", " \n", " coef_univ_school2_asc\n", - " -10.233810\n", + " -3.640737\n", " \n", " \n", " coef_univ_work1_asc\n", - " 6.475896\n", + " 2.209708\n", " \n", " \n", " coef_univ_work2_asc\n", - " 4.833029\n", + " -1.472430\n", " \n", " \n", " coef_univ_work_and_school_asc\n", - " 8.180927\n", + " 0.173386\n", " \n", " \n", - "loglike-410.2201502048449d_loglike\n", + "
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00.000000e+000.000000
coef_can_walk_to_work_and_school-2.159761e-05-0.000048
coef_can_walk_to_work_school2-7.694778e-050.000193
coef_can_walk_to_work_work24.202731e-05-0.000216
coef_driving_age_child_school2_asc-4.255256e-08-0.000081
coef_driving_age_child_work_and_school_asc2.167724e-050.000044
coef_female_school1-6.957308e-05-0.000091
coef_female_school2-2.554309e-040.000133
coef_female_work1-7.357671e-050.000084
coef_female_work28.686757e-05-0.000073
coef_female_work_and_school1.188196e-050.000059
coef_few_cars_than_drivers_school2-2.286678e-040.000044
coef_ft_worker_work2_asc5.501466e-05-0.000349
coef_hh_income_gt_50k_school10.000000e+000.000000
coef_hh_income_gt_50k_student_work_and_school6.367109e-06-0.000180
coef_hh_income_gt_50k_work-1.223654e-050.000049
coef_hh_income_gt_50k_worker_work_and_school0.000000e+000.000000
coef_home_urban_school10.000000e+000.000000
coef_home_urban_school2-1.028755e-040.000081
coef_home_urban_work1-4.465713e-05-0.000036
coef_home_urban_work21.557017e-04-0.000060
coef_home_urban_work_and_school1.077213e-040.000041
coef_no_cars_in_hh_school28.945645e-05-0.000061
coef_no_cars_in_hh_work23.899664e-05-0.000129
coef_no_cars_in_hh_work_and_school-3.695621e-050.000036
coef_non_family_hh_category10.000000e+000.000000
coef_non_family_hh_category21.251878e-100.000057
coef_non_student_goes_to_school0.000000e+000.000000
coef_num_non_workers_in_hh_school10.000000e+000.000000
coef_num_preschool_in_hh_school10.000000e+000.000000
coef_num_preschool_in_hh_school23.508996e-050.000013
coef_num_preschool_in_hh_work1-2.217197e-050.000012
coef_num_preschool_in_hh_work28.498540e-050.000204
coef_num_preschool_in_hh_work_and_school-3.475886e-12-0.000049
coef_num_under_16_not_at_school_school2-8.486532e-050.000086
coef_num_under_16_not_at_school_work2-3.959448e-050.000236
coef_num_under_16_not_at_school_work_and_school-2.351049e-07-0.000025
coef_pre_driving_age_child_school2_asc6.171829e-060.000137
coef_pt_worker_work2_asc1.420463e-04-0.000007
coef_round_trip_auto_time_to_work_school2-4.638296e-030.000126
coef_round_trip_auto_time_to_work_work23.215960e-030.000199
coef_round_trip_auto_time_to_work_work_and_school1.679772e-030.000093
coef_student_employed2.170492e-050.000041
coef_unavailable0.000000e+000.000000
coef_under_35_school10.000000e+000.000000
coef_under_35_school2-1.082319e-04-0.000063
coef_under_35_work1-9.399930e-050.000101
coef_under_35_work27.788570e-05-0.000136
coef_under_35_work_and_school-1.237585e-04-0.000093
coef_univ_school2_asc-1.090048e-040.000045
coef_univ_work1_asc-4.465713e-050.000111
coef_univ_work2_asc-4.135926e-050.000065
coef_univ_work_and_school_asc8.604407e-05-0.000179
nit50nfev84njev50status0message'Optimization terminated successfully'successTrueelapsed_time0:00:03.886165method'slsqp'n_cases2620iteration_number50logloss0.15657257641406294" + "nit34nfev39njev34status0message'Optimization terminated successfully'successTrueelapsed_time0:00:01.181912method'slsqp'n_cases31768iteration_number34loglike-4677.236180934496" ], "text/plain": [ "┣ x: 0 0.000000\n", - "┃ coef_can_walk_to_work_and_school 0.071630\n", - "┃ coef_can_walk_to_work_school2 1.368092\n", - "┃ coef_can_walk_to_work_work2 0.651120\n", - "┃ coef_driving_age_child_school2_asc -13.067731\n", - "┃ coef_driving_age_child_work_and_school_asc -12.605997\n", - "┃ coef_female_school1 0.247086\n", - "┃ coef_female_school2 -0.301339\n", - "┃ coef_female_work1 -2.242748\n", - "┃ coef_female_work2 -0.387411\n", - "┃ coef_female_work_and_school -3.218280\n", - "┃ coef_few_cars_than_drivers_school2 -0.862451\n", - "┃ coef_ft_worker_work2_asc -5.517933\n", + "┃ coef_can_walk_to_work_and_school 0.284832\n", + "┃ coef_can_walk_to_work_school2 0.811379\n", + "┃ coef_can_walk_to_work_work2 0.600928\n", + "┃ coef_driving_age_child_school2_asc -3.209418\n", + "┃ coef_driving_age_child_work_and_school_asc -4.565091\n", + "┃ coef_female_school1 0.000463\n", + "┃ coef_female_school2 -0.016856\n", + "┃ coef_female_work1 0.333342\n", + "┃ coef_female_work2 -0.219862\n", + "┃ coef_female_work_and_school -0.265847\n", + "┃ coef_few_cars_than_drivers_school2 -0.297623\n", + "┃ coef_ft_worker_work2_asc -3.494595\n", "┃ coef_hh_income_gt_50k_school1 0.034700\n", - "┃ coef_hh_income_gt_50k_student_work_and_school -0.921175\n", - "┃ coef_hh_income_gt_50k_work -1.418551\n", + "┃ coef_hh_income_gt_50k_student_work_and_school -0.128428\n", + "┃ coef_hh_income_gt_50k_work -0.286159\n", "┃ coef_hh_income_gt_50k_worker_work_and_school 0.034700\n", "┃ coef_home_urban_school1 -0.136100\n", - "┃ coef_home_urban_school2 -8.455615\n", - "┃ coef_home_urban_work1 4.026796\n", - "┃ coef_home_urban_work2 2.624854\n", - "┃ coef_home_urban_work_and_school -0.447070\n", - "┃ coef_no_cars_in_hh_school2 -0.620465\n", - "┃ coef_no_cars_in_hh_work2 -0.725967\n", - "┃ coef_no_cars_in_hh_work_and_school -0.775934\n", + "┃ coef_home_urban_school2 0.161745\n", + "┃ coef_home_urban_work1 -0.122770\n", + "┃ coef_home_urban_work2 0.142218\n", + "┃ coef_home_urban_work_and_school -0.436835\n", + "┃ coef_no_cars_in_hh_school2 -1.479517\n", + "┃ coef_no_cars_in_hh_work2 -1.524960\n", + "┃ coef_no_cars_in_hh_work_and_school -1.244512\n", "┃ coef_non_family_hh_category1 -0.250000\n", - "┃ coef_non_family_hh_category2 5.000771\n", + "┃ coef_non_family_hh_category2 -0.091298\n", "┃ coef_non_student_goes_to_school 3.883000\n", "┃ coef_num_non_workers_in_hh_school1 0.257400\n", "┃ coef_num_preschool_in_hh_school1 -0.133500\n", - "┃ coef_num_preschool_in_hh_school2 0.356660\n", - "┃ coef_num_preschool_in_hh_work1 -2.930501\n", - "┃ coef_num_preschool_in_hh_work2 0.225173\n", - "┃ coef_num_preschool_in_hh_work_and_school -29.197569\n", - "┃ coef_num_under_16_not_at_school_school2 -0.638293\n", - "┃ coef_num_under_16_not_at_school_work2 -0.118991\n", - "┃ coef_num_under_16_not_at_school_work_and_school -15.357907\n", - "┃ coef_pre_driving_age_child_school2_asc 3.679726\n", - "┃ coef_pt_worker_work2_asc -4.743242\n", - "┃ coef_round_trip_auto_time_to_work_school2 0.064411\n", - "┃ coef_round_trip_auto_time_to_work_work2 -0.025905\n", - "┃ coef_round_trip_auto_time_to_work_work_and_school -0.021804\n", - "┃ coef_student_employed 13.457255\n", + "┃ coef_num_preschool_in_hh_school2 -0.796350\n", + "┃ coef_num_preschool_in_hh_work1 0.125045\n", + "┃ coef_num_preschool_in_hh_work2 -0.126183\n", + "┃ coef_num_preschool_in_hh_work_and_school -0.070125\n", + "┃ coef_num_under_16_not_at_school_school2 -0.015345\n", + "┃ coef_num_under_16_not_at_school_work2 0.168260\n", + "┃ coef_num_under_16_not_at_school_work_and_school -0.192330\n", + "┃ coef_pre_driving_age_child_school2_asc -4.210709\n", + "┃ coef_pt_worker_work2_asc -3.079547\n", + "┃ coef_round_trip_auto_time_to_work_school2 -0.005959\n", + "┃ coef_round_trip_auto_time_to_work_work2 -0.001855\n", + "┃ coef_round_trip_auto_time_to_work_work_and_school -0.002208\n", + "┃ coef_student_employed 2.918973\n", "┃ coef_unavailable -999.000000\n", "┃ coef_under_35_school1 0.721800\n", - "┃ coef_under_35_school2 15.011164\n", - "┃ coef_under_35_work1 1.038242\n", - "┃ coef_under_35_work2 -0.253875\n", - "┃ coef_under_35_work_and_school 2.257035\n", - "┃ coef_univ_school2_asc -10.233810\n", - "┃ coef_univ_work1_asc 6.475896\n", - "┃ coef_univ_work2_asc 4.833029\n", - "┃ coef_univ_work_and_school_asc 8.180927\n", + "┃ coef_under_35_school2 1.230550\n", + "┃ coef_under_35_work1 -0.458863\n", + "┃ coef_under_35_work2 0.005614\n", + "┃ coef_under_35_work_and_school 1.149726\n", + "┃ coef_univ_school2_asc -3.640737\n", + "┃ coef_univ_work1_asc 2.209708\n", + "┃ coef_univ_work2_asc -1.472430\n", + "┃ coef_univ_work_and_school_asc 0.173386\n", "┃ dtype: float64\n", - "┣ loglike: -410.2201502048449\n", - "┣ d_loglike: 0 0.000000e+00\n", - "┃ coef_can_walk_to_work_and_school -2.159761e-05\n", - "┃ coef_can_walk_to_work_school2 -7.694778e-05\n", - "┃ coef_can_walk_to_work_work2 4.202731e-05\n", - "┃ coef_driving_age_child_school2_asc -4.255256e-08\n", - "┃ coef_driving_age_child_work_and_school_asc 2.167724e-05\n", - "┃ coef_female_school1 -6.957308e-05\n", - "┃ coef_female_school2 -2.554309e-04\n", - "┃ coef_female_work1 -7.357671e-05\n", - "┃ coef_female_work2 8.686757e-05\n", - "┃ coef_female_work_and_school 1.188196e-05\n", - "┃ coef_few_cars_than_drivers_school2 -2.286678e-04\n", - "┃ coef_ft_worker_work2_asc 5.501466e-05\n", - "┃ coef_hh_income_gt_50k_school1 0.000000e+00\n", - "┃ coef_hh_income_gt_50k_student_work_and_school 6.367109e-06\n", - "┃ coef_hh_income_gt_50k_work -1.223654e-05\n", - "┃ coef_hh_income_gt_50k_worker_work_and_school 0.000000e+00\n", - "┃ coef_home_urban_school1 0.000000e+00\n", - "┃ coef_home_urban_school2 -1.028755e-04\n", - "┃ coef_home_urban_work1 -4.465713e-05\n", - "┃ coef_home_urban_work2 1.557017e-04\n", - "┃ coef_home_urban_work_and_school 1.077213e-04\n", - "┃ coef_no_cars_in_hh_school2 8.945645e-05\n", - "┃ coef_no_cars_in_hh_work2 3.899664e-05\n", - "┃ coef_no_cars_in_hh_work_and_school -3.695621e-05\n", - "┃ coef_non_family_hh_category1 0.000000e+00\n", - "┃ coef_non_family_hh_category2 1.251878e-10\n", - "┃ coef_non_student_goes_to_school 0.000000e+00\n", - "┃ coef_num_non_workers_in_hh_school1 0.000000e+00\n", - "┃ coef_num_preschool_in_hh_school1 0.000000e+00\n", - "┃ coef_num_preschool_in_hh_school2 3.508996e-05\n", - "┃ coef_num_preschool_in_hh_work1 -2.217197e-05\n", - "┃ coef_num_preschool_in_hh_work2 8.498540e-05\n", - "┃ coef_num_preschool_in_hh_work_and_school -3.475886e-12\n", - "┃ coef_num_under_16_not_at_school_school2 -8.486532e-05\n", - "┃ coef_num_under_16_not_at_school_work2 -3.959448e-05\n", - "┃ coef_num_under_16_not_at_school_work_and_school -2.351049e-07\n", - "┃ coef_pre_driving_age_child_school2_asc 6.171829e-06\n", - "┃ coef_pt_worker_work2_asc 1.420463e-04\n", - "┃ coef_round_trip_auto_time_to_work_school2 -4.638296e-03\n", - "┃ coef_round_trip_auto_time_to_work_work2 3.215960e-03\n", - "┃ coef_round_trip_auto_time_to_work_work_and_school 1.679772e-03\n", - "┃ coef_student_employed 2.170492e-05\n", - "┃ coef_unavailable 0.000000e+00\n", - "┃ coef_under_35_school1 0.000000e+00\n", - "┃ coef_under_35_school2 -1.082319e-04\n", - "┃ coef_under_35_work1 -9.399930e-05\n", - "┃ coef_under_35_work2 7.788570e-05\n", - "┃ coef_under_35_work_and_school -1.237585e-04\n", - "┃ coef_univ_school2_asc -1.090048e-04\n", - "┃ coef_univ_work1_asc -4.465713e-05\n", - "┃ coef_univ_work2_asc -4.135926e-05\n", - "┃ coef_univ_work_and_school_asc 8.604407e-05\n", + "┣ logloss: 0.1472310558088169\n", + "┣ d_logloss: 0 0.000000\n", + "┃ coef_can_walk_to_work_and_school -0.000048\n", + "┃ coef_can_walk_to_work_school2 0.000193\n", + "┃ coef_can_walk_to_work_work2 -0.000216\n", + "┃ coef_driving_age_child_school2_asc -0.000081\n", + "┃ coef_driving_age_child_work_and_school_asc 0.000044\n", + "┃ coef_female_school1 -0.000091\n", + "┃ coef_female_school2 0.000133\n", + "┃ coef_female_work1 0.000084\n", + "┃ coef_female_work2 -0.000073\n", + "┃ coef_female_work_and_school 0.000059\n", + "┃ coef_few_cars_than_drivers_school2 0.000044\n", + "┃ coef_ft_worker_work2_asc -0.000349\n", + "┃ coef_hh_income_gt_50k_school1 0.000000\n", + "┃ coef_hh_income_gt_50k_student_work_and_school -0.000180\n", + "┃ coef_hh_income_gt_50k_work 0.000049\n", + "┃ coef_hh_income_gt_50k_worker_work_and_school 0.000000\n", + "┃ coef_home_urban_school1 0.000000\n", + "┃ coef_home_urban_school2 0.000081\n", + "┃ coef_home_urban_work1 -0.000036\n", + "┃ coef_home_urban_work2 -0.000060\n", + "┃ coef_home_urban_work_and_school 0.000041\n", + "┃ coef_no_cars_in_hh_school2 -0.000061\n", + "┃ coef_no_cars_in_hh_work2 -0.000129\n", + "┃ coef_no_cars_in_hh_work_and_school 0.000036\n", + "┃ coef_non_family_hh_category1 0.000000\n", + "┃ coef_non_family_hh_category2 0.000057\n", + "┃ coef_non_student_goes_to_school 0.000000\n", + "┃ coef_num_non_workers_in_hh_school1 0.000000\n", + "┃ coef_num_preschool_in_hh_school1 0.000000\n", + "┃ coef_num_preschool_in_hh_school2 0.000013\n", + "┃ coef_num_preschool_in_hh_work1 0.000012\n", + "┃ coef_num_preschool_in_hh_work2 0.000204\n", + "┃ coef_num_preschool_in_hh_work_and_school -0.000049\n", + "┃ coef_num_under_16_not_at_school_school2 0.000086\n", + "┃ coef_num_under_16_not_at_school_work2 0.000236\n", + "┃ coef_num_under_16_not_at_school_work_and_school -0.000025\n", + "┃ coef_pre_driving_age_child_school2_asc 0.000137\n", + "┃ coef_pt_worker_work2_asc -0.000007\n", + "┃ coef_round_trip_auto_time_to_work_school2 0.000126\n", + "┃ coef_round_trip_auto_time_to_work_work2 0.000199\n", + "┃ coef_round_trip_auto_time_to_work_work_and_school 0.000093\n", + "┃ coef_student_employed 0.000041\n", + "┃ coef_unavailable 0.000000\n", + "┃ coef_under_35_school1 0.000000\n", + "┃ coef_under_35_school2 -0.000063\n", + "┃ coef_under_35_work1 0.000101\n", + "┃ coef_under_35_work2 -0.000136\n", + "┃ coef_under_35_work_and_school -0.000093\n", + "┃ coef_univ_school2_asc 0.000045\n", + "┃ coef_univ_work1_asc 0.000111\n", + "┃ coef_univ_work2_asc 0.000065\n", + "┃ coef_univ_work_and_school_asc -0.000179\n", "┃ dtype: float64\n", - "┣ nit: 50\n", - "┣ nfev: 84\n", - "┣ njev: 50\n", + "┣ nit: 34\n", + "┣ nfev: 39\n", + "┣ njev: 34\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=3, microseconds=886165)\n", + "┣ elapsed_time: datetime.timedelta(seconds=1, microseconds=181912)\n", "┣ method: 'slsqp'\n", - "┣ n_cases: 2620\n", - "┣ iteration_number: 50\n", - "┣ logloss: 0.15657257641406294" + "┣ n_cases: 31768\n", + "┣ iteration_number: 34\n", + "┣ loglike: -4677.236180934496" ] }, "execution_count": 7, @@ -2641,550 +2638,523 @@ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
0 0.00 NA NA NA 0.00fixed value
coef_can_walk_to_work_and_school 0.0716 0.710 0.10 NA 0.00
coef_can_walk_to_work_school2 1.37 0.878 1.56 NA 0.00
coef_can_walk_to_work_work2 0.651 0.397 1.64 NA 0.00
coef_driving_age_child_school2_asc-13.1 4.56e+03-0.00 NA 0.00
coef_driving_age_child_work_and_school_asc-12.6 NA NA[***] 68.07 0.00
coef_female_school1 0.247 1.35 0.18 NA 0.00
coef_female_school2-0.301 0.785-0.38 NA 0.00
coef_female_work1-2.24 1.34-1.68 NA 0.00
coef_female_work2-0.387 0.246-1.57 NA 0.00
coef_female_work_and_school-3.22 1.40-2.30* NA 0.00
coef_few_cars_than_drivers_school2-0.862 0.715-1.21 NA 0.00
coef_ft_worker_work2_asc-5.52 NA NA[***] BIG 0.00
coef_hh_income_gt_50k_school1 0.0347 NA NA[] 0.00 0.00
coef_hh_income_gt_50k_student_work_and_school-0.921 1.85-0.50 NA 0.00
coef_hh_income_gt_50k_work-1.42 1.92-0.74 NA 0.00
coef_hh_income_gt_50k_worker_work_and_school 0.0347 NA NA[] 0.00 0.00
coef_home_urban_school1-0.136 NA NA[] 0.00 0.00
coef_home_urban_school2-8.46 NA NA[***] BIG 0.00
coef_home_urban_work1 4.03 NA NA[***] 165.42 0.00
coef_home_urban_work2 2.62 NA NA[***] 134.83 0.00
coef_home_urban_work_and_school-0.447 NA NA[] 1.59 0.00
coef_no_cars_in_hh_school2-0.620 1.18-0.52 NA 0.00
coef_no_cars_in_hh_work2-0.726 0.336-2.16* NA 0.00
coef_no_cars_in_hh_work_and_school-0.776 0.703-1.10 NA 0.00
coef_non_family_hh_category1-0.250 NA NA[] 0.00 0.00
coef_non_family_hh_category2 5.00 NA NA[] 0.00 0.00
coef_non_student_goes_to_school 3.88 NA NA[] 0.00 0.00
coef_num_non_workers_in_hh_school1 0.257 NA NA[] 0.00 0.00
coef_num_preschool_in_hh_school1-0.134 NA NA[] 0.00 0.00
coef_num_preschool_in_hh_school2 0.357 0.576 0.62 NA 0.00
coef_num_preschool_in_hh_work1-2.93 1.55-1.89 NA 0.00
coef_num_preschool_in_hh_work2 0.225 0.192 1.17 NA 0.00
coef_num_preschool_in_hh_work_and_school-29.2 NA NA[**] 4.26 0.00
coef_num_under_16_not_at_school_school2-0.638 1.02-0.63 NA 0.00
coef_num_under_16_not_at_school_work2-0.119 0.306-0.39 NA 0.00
coef_num_under_16_not_at_school_work_and_school-15.4 2.03e+03-0.01 NA 0.00
coef_pre_driving_age_child_school2_asc 3.68 NA NA[***] 18.81 0.00
coef_pt_worker_work2_asc-4.74 NA NA[***] 562.79 0.00
coef_round_trip_auto_time_to_work_school2 0.0644 0.0331 1.95 NA 0.00
coef_round_trip_auto_time_to_work_work2-0.0259 0.0249-1.04 NA 0.00
coef_round_trip_auto_time_to_work_work_and_school-0.0218 0.0402-0.54 NA 0.00
coef_student_employed 13.5 1.31e+03 0.01 NA 0.00
coef_unavailable-999. NA NA NA 0.00fixed value
coef_under_35_school1 0.722 0.00 NA[] 0.00 0.00
coef_under_35_school2 15.0 1.14e+03 0.01 NA 0.00
coef_under_35_work1 1.04 0.886 1.17 NA 0.00
coef_under_35_work2-0.254 0.245-1.03 NA 0.00
coef_under_35_work_and_school 2.26 1.21 1.87 NA 0.00
coef_univ_school2_asc-10.2 NA NA[***] 556.91 0.00
coef_univ_work1_asc 6.48 NA NA[***] 349.23 0.00
coef_univ_work2_asc 4.83 NA NA[***] 26.10 0.00
coef_univ_work_and_school_asc 8.18 NA NA[***] 132.81 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
0 0.00 0.00 NA 0.00fixed value
coef_can_walk_to_work_and_school 0.285 0.165 1.73 0.00
coef_can_walk_to_work_school2 0.811 0.163 4.97*** 0.00
coef_can_walk_to_work_work2 0.601 0.103 5.81*** 0.00
coef_driving_age_child_school2_asc-3.21 0.230-13.96*** 0.00
coef_driving_age_child_work_and_school_asc-4.57 889.-0.01 0.00
coef_female_school1 0.000463 0.265 0.00 0.00
coef_female_school2-0.0169 0.166-0.10 0.00
coef_female_work1 0.333 0.257 1.30 0.00
coef_female_work2-0.220 0.0801-2.75** 0.00
coef_female_work_and_school-0.266 0.240-1.11 0.00
coef_few_cars_than_drivers_school2-0.298 0.146-2.04* 0.00
coef_ft_worker_work2_asc-3.49 0.110-31.78*** 0.00
coef_hh_income_gt_50k_school1 0.0347 1.34e-05 BIG*** 0.00
coef_hh_income_gt_50k_student_work_and_school-0.128 0.325-0.39 0.00
coef_hh_income_gt_50k_work-0.286 0.351-0.81 0.00
coef_hh_income_gt_50k_worker_work_and_school 0.0347 NA NA 0.00
coef_home_urban_school1-0.136 NA NA 0.00
coef_home_urban_school2 0.162 0.147 1.10 0.00
coef_home_urban_work1-0.123 0.280-0.44 0.00
coef_home_urban_work2 0.142 0.0850 1.67 0.00
coef_home_urban_work_and_school-0.437 0.269-1.62 0.00
coef_no_cars_in_hh_school2-1.48 0.438-3.37*** 0.00
coef_no_cars_in_hh_work2-1.52 0.310-4.92*** 0.00
coef_no_cars_in_hh_work_and_school-1.24 0.334-3.72*** 0.00
coef_non_family_hh_category1-0.250 NA NA 0.00
coef_non_family_hh_category2-0.0913 0.741-0.12 0.00
coef_non_student_goes_to_school 3.88 7.74e-06 BIG*** 0.00
coef_num_non_workers_in_hh_school1 0.257 2.54e-06 BIG*** 0.00
coef_num_preschool_in_hh_school1-0.133 1.09e-06-BIG*** 0.00
coef_num_preschool_in_hh_school2-0.796 0.214-3.72*** 0.00
coef_num_preschool_in_hh_work1 0.125 0.323 0.39 0.00
coef_num_preschool_in_hh_work2-0.126 0.0781-1.62 0.00
coef_num_preschool_in_hh_work_and_school-0.0701 0.322-0.22 0.00
coef_num_under_16_not_at_school_school2-0.0153 0.176-0.09 0.00
coef_num_under_16_not_at_school_work2 0.168 0.0783 2.15* 0.00
coef_num_under_16_not_at_school_work_and_school-0.192 0.210-0.92 0.00
coef_pre_driving_age_child_school2_asc-4.21 0.204-20.62*** 0.00
coef_pt_worker_work2_asc-3.08 0.135-22.84*** 0.00
coef_round_trip_auto_time_to_work_school2-0.00596 0.00317-1.88 0.00
coef_round_trip_auto_time_to_work_work2-0.00185 0.00188-0.99 0.00
coef_round_trip_auto_time_to_work_work_and_school-0.00221 0.00565-0.39 0.00
coef_student_employed 2.92 889. 0.00 0.00
coef_unavailable-999. 0.00 NA 0.00fixed value
coef_under_35_school1 0.722 0.00 NA 0.00
coef_under_35_school2 1.23 0.389 3.16** 0.00
coef_under_35_work1-0.459 0.574-0.80 0.00
coef_under_35_work2 0.00561 0.0823 0.07 0.00
coef_under_35_work_and_school 1.15 0.646 1.78 0.00
coef_univ_school2_asc-3.64 0.415-8.77*** 0.00
coef_univ_work1_asc 2.21 889. 0.00 0.00
coef_univ_work2_asc-1.47 889.-0.00 0.00
coef_univ_work_and_school_asc 0.173 889. 0.00 0.00
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -3231,18 +3201,7 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -3300,85 +3259,85 @@ " \n", " 1\n", " coef_ft_worker_work2_asc\n", - " -5.517933\n", + " -3.494595\n", " F\n", " \n", " \n", " 2\n", " coef_pt_worker_work2_asc\n", - " -4.743242\n", + " -3.079547\n", " F\n", " \n", " \n", " 3\n", " coef_univ_work1_asc\n", - " 6.475896\n", + " 2.209708\n", " F\n", " \n", " \n", " 4\n", " coef_univ_work2_asc\n", - " 4.833029\n", + " -1.472430\n", " F\n", " \n", " \n", " 5\n", " coef_univ_school2_asc\n", - " -10.233810\n", + " -3.640737\n", " F\n", " \n", " \n", " 6\n", " coef_univ_work_and_school_asc\n", - " 8.180927\n", + " 0.173386\n", " F\n", " \n", " \n", " 7\n", " coef_driving_age_child_school2_asc\n", - " -13.067731\n", + " -3.209418\n", " F\n", " \n", " \n", " 8\n", " coef_driving_age_child_work_and_school_asc\n", - " -12.605997\n", + " -4.565091\n", " F\n", " \n", " \n", " 9\n", " coef_pre_driving_age_child_school2_asc\n", - " 3.679726\n", + " -4.210709\n", " F\n", " \n", " \n", " 10\n", " coef_female_work1\n", - " -2.242748\n", + " 0.333342\n", " F\n", " \n", " \n", " 11\n", " coef_female_work2\n", - " -0.387411\n", + " -0.219862\n", " F\n", " \n", " \n", " 12\n", " coef_female_school1\n", - " 0.247086\n", + " 0.000463\n", " F\n", " \n", " \n", " 13\n", " coef_female_school2\n", - " -0.301339\n", + " -0.016856\n", " F\n", " \n", " \n", " 14\n", " coef_female_work_and_school\n", - " -3.218280\n", + " -0.265847\n", " F\n", " \n", " \n", @@ -3390,13 +3349,13 @@ " \n", " 16\n", " coef_under_35_work1\n", - " 1.038242\n", + " -0.458863\n", " F\n", " \n", " \n", " 17\n", " coef_under_35_work2\n", - " -0.253875\n", + " 0.005614\n", " F\n", " \n", " \n", @@ -3408,55 +3367,55 @@ " \n", " 19\n", " coef_under_35_school2\n", - " 15.011164\n", + " 1.230550\n", " F\n", " \n", " \n", " 20\n", " coef_under_35_work_and_school\n", - " 2.257035\n", + " 1.149726\n", " F\n", " \n", " \n", " 21\n", " coef_can_walk_to_work_work2\n", - " 0.651120\n", + " 0.600928\n", " F\n", " \n", " \n", " 22\n", " coef_can_walk_to_work_school2\n", - " 1.368092\n", + " 0.811379\n", " F\n", " \n", " \n", " 23\n", " coef_can_walk_to_work_and_school\n", - " 0.071630\n", + " 0.284832\n", " F\n", " \n", " \n", " 24\n", " coef_round_trip_auto_time_to_work_work2\n", - " -0.025905\n", + " -0.001855\n", " F\n", " \n", " \n", " 25\n", " coef_round_trip_auto_time_to_work_school2\n", - " 0.064411\n", + " -0.005959\n", " F\n", " \n", " \n", " 26\n", " coef_round_trip_auto_time_to_work_work_and_school\n", - " -0.021804\n", + " -0.002208\n", " F\n", " \n", " \n", " 27\n", " coef_student_employed\n", - " 13.457255\n", + " 2.918973\n", " F\n", " \n", " \n", @@ -3468,37 +3427,37 @@ " \n", " 29\n", " coef_no_cars_in_hh_work2\n", - " -0.725967\n", + " -1.524960\n", " F\n", " \n", " \n", " 30\n", " coef_no_cars_in_hh_school2\n", - " -0.620465\n", + " -1.479517\n", " F\n", " \n", " \n", " 31\n", " coef_no_cars_in_hh_work_and_school\n", - " -0.775934\n", + " -1.244512\n", " F\n", " \n", " \n", " 32\n", " coef_few_cars_than_drivers_school2\n", - " -0.862451\n", + " -0.297623\n", " F\n", " \n", " \n", " 33\n", " coef_num_preschool_in_hh_work1\n", - " -2.930501\n", + " 0.125045\n", " F\n", " \n", " \n", " 34\n", " coef_num_preschool_in_hh_work2\n", - " 0.225173\n", + " -0.126183\n", " F\n", " \n", " \n", @@ -3510,13 +3469,13 @@ " \n", " 36\n", " coef_num_preschool_in_hh_school2\n", - " 0.356660\n", + " -0.796350\n", " F\n", " \n", " \n", " 37\n", " coef_num_preschool_in_hh_work_and_school\n", - " -29.197569\n", + " -0.070125\n", " F\n", " \n", " \n", @@ -3528,7 +3487,7 @@ " \n", " 39\n", " coef_hh_income_gt_50k_work\n", - " -1.418551\n", + " -0.286159\n", " F\n", " \n", " \n", @@ -3546,7 +3505,7 @@ " \n", " 42\n", " coef_hh_income_gt_50k_student_work_and_school\n", - " -0.921175\n", + " -0.128428\n", " F\n", " \n", " \n", @@ -3558,37 +3517,37 @@ " \n", " 44\n", " coef_non_family_hh_category2\n", - " 5.000771\n", + " -0.091298\n", " F\n", " \n", " \n", " 45\n", " coef_num_under_16_not_at_school_work2\n", - " -0.118991\n", + " 0.168260\n", " NaN\n", " \n", " \n", " 46\n", " coef_num_under_16_not_at_school_school2\n", - " -0.638293\n", + " -0.015345\n", " NaN\n", " \n", " \n", " 47\n", " coef_num_under_16_not_at_school_work_and_school\n", - " -15.357907\n", + " -0.192330\n", " NaN\n", " \n", " \n", " 48\n", " coef_home_urban_work1\n", - " 4.026796\n", + " -0.122770\n", " NaN\n", " \n", " \n", " 49\n", " coef_home_urban_work2\n", - " 2.624854\n", + " 0.142218\n", " NaN\n", " \n", " \n", @@ -3600,13 +3559,13 @@ " \n", " 51\n", " coef_home_urban_school2\n", - " -8.455615\n", + " 0.161745\n", " NaN\n", " \n", " \n", " 52\n", " coef_home_urban_work_and_school\n", - " -0.447070\n", + " -0.436835\n", " NaN\n", " \n", " \n", @@ -3616,58 +3575,58 @@ "text/plain": [ " coefficient_name value constrain\n", "0 coef_unavailable -999.000000 T\n", - "1 coef_ft_worker_work2_asc -5.517933 F\n", - "2 coef_pt_worker_work2_asc -4.743242 F\n", - "3 coef_univ_work1_asc 6.475896 F\n", - "4 coef_univ_work2_asc 4.833029 F\n", - "5 coef_univ_school2_asc -10.233810 F\n", - "6 coef_univ_work_and_school_asc 8.180927 F\n", - "7 coef_driving_age_child_school2_asc -13.067731 F\n", - "8 coef_driving_age_child_work_and_school_asc -12.605997 F\n", - "9 coef_pre_driving_age_child_school2_asc 3.679726 F\n", - "10 coef_female_work1 -2.242748 F\n", - "11 coef_female_work2 -0.387411 F\n", - "12 coef_female_school1 0.247086 F\n", - "13 coef_female_school2 -0.301339 F\n", - "14 coef_female_work_and_school -3.218280 F\n", + "1 coef_ft_worker_work2_asc -3.494595 F\n", + "2 coef_pt_worker_work2_asc -3.079547 F\n", + "3 coef_univ_work1_asc 2.209708 F\n", + "4 coef_univ_work2_asc -1.472430 F\n", + "5 coef_univ_school2_asc -3.640737 F\n", + "6 coef_univ_work_and_school_asc 0.173386 F\n", + "7 coef_driving_age_child_school2_asc -3.209418 F\n", + "8 coef_driving_age_child_work_and_school_asc -4.565091 F\n", + "9 coef_pre_driving_age_child_school2_asc -4.210709 F\n", + "10 coef_female_work1 0.333342 F\n", + "11 coef_female_work2 -0.219862 F\n", + "12 coef_female_school1 0.000463 F\n", + "13 coef_female_school2 -0.016856 F\n", + "14 coef_female_work_and_school -0.265847 F\n", "15 coef_female_univ_work1 0.173700 F\n", - "16 coef_under_35_work1 1.038242 F\n", - "17 coef_under_35_work2 -0.253875 F\n", + "16 coef_under_35_work1 -0.458863 F\n", + "17 coef_under_35_work2 0.005614 F\n", "18 coef_under_35_school1 0.721800 F\n", - "19 coef_under_35_school2 15.011164 F\n", - "20 coef_under_35_work_and_school 2.257035 F\n", - "21 coef_can_walk_to_work_work2 0.651120 F\n", - "22 coef_can_walk_to_work_school2 1.368092 F\n", - "23 coef_can_walk_to_work_and_school 0.071630 F\n", - "24 coef_round_trip_auto_time_to_work_work2 -0.025905 F\n", - "25 coef_round_trip_auto_time_to_work_school2 0.064411 F\n", - "26 coef_round_trip_auto_time_to_work_work_and_school -0.021804 F\n", - "27 coef_student_employed 13.457255 F\n", + "19 coef_under_35_school2 1.230550 F\n", + "20 coef_under_35_work_and_school 1.149726 F\n", + "21 coef_can_walk_to_work_work2 0.600928 F\n", + "22 coef_can_walk_to_work_school2 0.811379 F\n", + "23 coef_can_walk_to_work_and_school 0.284832 F\n", + "24 coef_round_trip_auto_time_to_work_work2 -0.001855 F\n", + "25 coef_round_trip_auto_time_to_work_school2 -0.005959 F\n", + "26 coef_round_trip_auto_time_to_work_work_and_school -0.002208 F\n", + "27 coef_student_employed 2.918973 F\n", "28 coef_non_student_goes_to_school 3.883000 F\n", - "29 coef_no_cars_in_hh_work2 -0.725967 F\n", - "30 coef_no_cars_in_hh_school2 -0.620465 F\n", - "31 coef_no_cars_in_hh_work_and_school -0.775934 F\n", - "32 coef_few_cars_than_drivers_school2 -0.862451 F\n", - "33 coef_num_preschool_in_hh_work1 -2.930501 F\n", - "34 coef_num_preschool_in_hh_work2 0.225173 F\n", + "29 coef_no_cars_in_hh_work2 -1.524960 F\n", + "30 coef_no_cars_in_hh_school2 -1.479517 F\n", + "31 coef_no_cars_in_hh_work_and_school -1.244512 F\n", + "32 coef_few_cars_than_drivers_school2 -0.297623 F\n", + "33 coef_num_preschool_in_hh_work1 0.125045 F\n", + "34 coef_num_preschool_in_hh_work2 -0.126183 F\n", "35 coef_num_preschool_in_hh_school1 -0.133500 F\n", - "36 coef_num_preschool_in_hh_school2 0.356660 F\n", - "37 coef_num_preschool_in_hh_work_and_school -29.197569 F\n", + "36 coef_num_preschool_in_hh_school2 -0.796350 F\n", + "37 coef_num_preschool_in_hh_work_and_school -0.070125 F\n", "38 coef_num_non_workers_in_hh_school1 0.257400 F\n", - "39 coef_hh_income_gt_50k_work -1.418551 F\n", + "39 coef_hh_income_gt_50k_work -0.286159 F\n", "40 coef_hh_income_gt_50k_school1 0.034700 F\n", "41 coef_hh_income_gt_50k_worker_work_and_school 0.034700 F\n", - "42 coef_hh_income_gt_50k_student_work_and_school -0.921175 F\n", + "42 coef_hh_income_gt_50k_student_work_and_school -0.128428 F\n", "43 coef_non_family_hh_category1 -0.250000 F\n", - "44 coef_non_family_hh_category2 5.000771 F\n", - "45 coef_num_under_16_not_at_school_work2 -0.118991 NaN\n", - "46 coef_num_under_16_not_at_school_school2 -0.638293 NaN\n", - "47 coef_num_under_16_not_at_school_work_and_school -15.357907 NaN\n", - "48 coef_home_urban_work1 4.026796 NaN\n", - "49 coef_home_urban_work2 2.624854 NaN\n", + "44 coef_non_family_hh_category2 -0.091298 F\n", + "45 coef_num_under_16_not_at_school_work2 0.168260 NaN\n", + "46 coef_num_under_16_not_at_school_school2 -0.015345 NaN\n", + "47 coef_num_under_16_not_at_school_work_and_school -0.192330 NaN\n", + "48 coef_home_urban_work1 -0.122770 NaN\n", + "49 coef_home_urban_work2 0.142218 NaN\n", "50 coef_home_urban_school1 -0.136100 NaN\n", - "51 coef_home_urban_school2 -8.455615 NaN\n", - "52 coef_home_urban_work_and_school -0.447070 NaN" + "51 coef_home_urban_school2 0.161745 NaN\n", + "52 coef_home_urban_work_and_school -0.436835 NaN" ] }, "execution_count": 11, @@ -3687,7 +3646,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -3701,7 +3660,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/08_work_tour_scheduling.ipynb b/activitysim/examples/example_estimation/notebooks/08_work_tour_scheduling.ipynb index 3cd81e8a23..b592a29746 100644 --- a/activitysim/examples/example_estimation/notebooks/08_work_tour_scheduling.ipynb +++ b/activitysim/examples/example_estimation/notebooks/08_work_tour_scheduling.ipynb @@ -34,27 +34,75 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +116,28 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_work/tour_scheduling_work_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_work/mandatory_tour_scheduling_work_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_work/mandatory_tour_scheduling_work_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_work/mandatory_tour_scheduling_work_choosers_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"mandatory_tour_scheduling_work\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -461,10 +525,10 @@ " \n", " \n", " 0\n", - " 2961920\n", - " 32\n", - " 35\n", - " 72241\n", + " 2966594\n", + " 7\n", + " 96\n", + " 72355\n", " work\n", " 1\n", " 1\n", @@ -477,18 +541,18 @@ " True\n", " False\n", " False\n", - " 9\n", - " -1\n", " 17\n", + " -1\n", + " 55\n", " 5\n", " 5\n", " \n", " \n", " 1\n", - " 2970120\n", - " 51\n", - " 51\n", - " 72441\n", + " 2967783\n", + " 50\n", + " 30\n", + " 72384\n", " work\n", " 1\n", " 1\n", @@ -501,18 +565,18 @@ " True\n", " False\n", " False\n", - " 100\n", + " 16\n", " -1\n", - " 60\n", + " 59\n", " 5\n", " 5\n", " \n", " \n", " 2\n", - " 2998943\n", + " 2968726\n", + " 64\n", " 29\n", - " 28\n", - " 73144\n", + " 72407\n", " work\n", " 1\n", " 1\n", @@ -525,18 +589,18 @@ " True\n", " False\n", " False\n", - " 77\n", + " 70\n", " -1\n", - " 125\n", + " 59\n", " 5\n", " 5\n", " \n", " \n", " 3\n", - " 3013252\n", - " 47\n", - " 47\n", - " 73493\n", + " 2970858\n", + " 31\n", + " 80\n", + " 72459\n", " work\n", " 1\n", " 1\n", @@ -549,18 +613,18 @@ " True\n", " False\n", " False\n", - " 117\n", + " 30\n", " -1\n", - " 133\n", + " 61\n", " 5\n", " 5\n", " \n", " \n", " 4\n", - " 3015794\n", - " 59\n", - " 59\n", - " 73555\n", + " 2973728\n", + " 61\n", + " 115\n", + " 72529\n", " work\n", " 1\n", " 1\n", @@ -573,9 +637,9 @@ " True\n", " False\n", " False\n", - " 167\n", + " 57\n", " -1\n", - " 136\n", + " 69\n", " 5\n", " 5\n", " \n", @@ -604,35 +668,35 @@ " ...\n", " \n", " \n", - " 2119\n", - " 308070309\n", - " 184\n", - " 184\n", - " 7513909\n", + " 22632\n", + " 309081532\n", + " 32\n", + " 10\n", + " 7538573\n", " work\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", " mandatory\n", " ...\n", " False\n", - " work2\n", + " work1\n", " True\n", " False\n", " False\n", - " 11\n", + " 135\n", " -1\n", - " 21\n", - " 17\n", - " 21\n", + " 200\n", + " 5\n", + " 5\n", " \n", " \n", - " 2120\n", - " 308073875\n", - " 41\n", - " 38\n", - " 7513996\n", + " 22633\n", + " 309090634\n", + " 48\n", + " 79\n", + " 7538795\n", " work\n", " 1\n", " 1\n", @@ -645,18 +709,18 @@ " True\n", " False\n", " False\n", - " 67\n", + " 1018\n", " -1\n", - " 21\n", + " 1006\n", " 5\n", " 5\n", " \n", " \n", - " 2121\n", - " 308090603\n", - " 27\n", - " 27\n", - " 7514404\n", + " 22634\n", + " 309101950\n", + " 69\n", + " 78\n", + " 7539071\n", " work\n", " 1\n", " 1\n", @@ -669,18 +733,18 @@ " True\n", " False\n", " False\n", - " 106\n", + " 1019\n", " -1\n", - " 81\n", + " 1006\n", " 5\n", " 5\n", " \n", " \n", - " 2122\n", - " 308105896\n", - " 6\n", - " 6\n", - " 7514777\n", + " 22635\n", + " 309107362\n", + " 139\n", + " 53\n", + " 7539203\n", " work\n", " 1\n", " 1\n", @@ -691,110 +755,110 @@ " False\n", " work1\n", " True\n", - " True\n", - " True\n", - " 87\n", - " 185\n", - " 106\n", + " False\n", + " False\n", + " 1152\n", + " -1\n", + " 1159\n", " 5\n", " 5\n", " \n", " \n", - " 2123\n", - " 308122624\n", - " 44\n", - " 44\n", - " 7515185\n", + " 22636\n", + " 309112036\n", + " 64\n", + " 36\n", + " 7539317\n", " work\n", " 1\n", " 1\n", " 1\n", - " 2\n", + " 1\n", " mandatory\n", " ...\n", " False\n", - " work_and_school\n", - " True\n", - " True\n", + " work1\n", " True\n", - " 16\n", - " 188\n", - " 142\n", + " False\n", + " False\n", + " 1381\n", + " -1\n", + " 1390\n", " 5\n", " 5\n", " \n", " \n", "\n", - "

2124 rows × 40 columns

\n", + "

22637 rows × 40 columns

\n", "" ], "text/plain": [ - " tour_id model_choice override_choice person_id tour_type \\\n", - "0 2961920 32 35 72241 work \n", - "1 2970120 51 51 72441 work \n", - "2 2998943 29 28 73144 work \n", - "3 3013252 47 47 73493 work \n", - "4 3015794 59 59 73555 work \n", - "... ... ... ... ... ... \n", - "2119 308070309 184 184 7513909 work \n", - "2120 308073875 41 38 7513996 work \n", - "2121 308090603 27 27 7514404 work \n", - "2122 308105896 6 6 7514777 work \n", - "2123 308122624 44 44 7515185 work \n", + " tour_id model_choice override_choice person_id tour_type \\\n", + "0 2966594 7 96 72355 work \n", + "1 2967783 50 30 72384 work \n", + "2 2968726 64 29 72407 work \n", + "3 2970858 31 80 72459 work \n", + "4 2973728 61 115 72529 work \n", + "... ... ... ... ... ... \n", + "22632 309081532 32 10 7538573 work \n", + "22633 309090634 48 79 7538795 work \n", + "22634 309101950 69 78 7539071 work \n", + "22635 309107362 139 53 7539203 work \n", + "22636 309112036 64 36 7539317 work \n", "\n", - " tour_type_count tour_type_num tour_num tour_count tour_category ... \\\n", - "0 1 1 1 1 mandatory ... \n", - "1 1 1 1 1 mandatory ... \n", - "2 1 1 1 1 mandatory ... \n", - "3 1 1 1 1 mandatory ... \n", - "4 1 1 1 1 mandatory ... \n", - "... ... ... ... ... ... ... \n", - "2119 2 2 2 2 mandatory ... \n", - "2120 1 1 1 1 mandatory ... \n", - "2121 1 1 1 1 mandatory ... \n", - "2122 1 1 1 1 mandatory ... \n", - "2123 1 1 1 2 mandatory ... \n", + " tour_type_count tour_type_num tour_num tour_count tour_category \\\n", + "0 1 1 1 1 mandatory \n", + "1 1 1 1 1 mandatory \n", + "2 1 1 1 1 mandatory \n", + "3 1 1 1 1 mandatory \n", + "4 1 1 1 1 mandatory \n", + "... ... ... ... ... ... \n", + "22632 1 1 1 1 mandatory \n", + "22633 1 1 1 1 mandatory \n", + "22634 1 1 1 1 mandatory \n", + "22635 1 1 1 1 mandatory \n", + "22636 1 1 1 1 mandatory \n", "\n", - " home_is_rural mandatory_tour_frequency is_worker is_student \\\n", - "0 False work1 True False \n", - "1 False work1 True False \n", - "2 False work1 True False \n", - "3 False work1 True False \n", - "4 False work1 True False \n", - "... ... ... ... ... \n", - "2119 False work2 True False \n", - "2120 False work1 True False \n", - "2121 False work1 True False \n", - "2122 False work1 True True \n", - "2123 False work_and_school True True \n", + " ... home_is_rural mandatory_tour_frequency is_worker is_student \\\n", + "0 ... False work1 True False \n", + "1 ... False work1 True False \n", + "2 ... False work1 True False \n", + "3 ... False work1 True False \n", + "4 ... False work1 True False \n", + "... ... ... ... ... ... \n", + "22632 ... False work1 True False \n", + "22633 ... False work1 True False \n", + "22634 ... False work1 True False \n", + "22635 ... False work1 True False \n", + "22636 ... False work1 True False \n", "\n", - " is_university workplace_zone_id school_zone_id home_zone_id \\\n", - "0 False 9 -1 17 \n", - "1 False 100 -1 60 \n", - "2 False 77 -1 125 \n", - "3 False 117 -1 133 \n", - "4 False 167 -1 136 \n", - "... ... ... ... ... \n", - "2119 False 11 -1 21 \n", - "2120 False 67 -1 21 \n", - "2121 False 106 -1 81 \n", - "2122 True 87 185 106 \n", - "2123 True 16 188 142 \n", + " is_university workplace_zone_id school_zone_id home_zone_id \\\n", + "0 False 17 -1 55 \n", + "1 False 16 -1 59 \n", + "2 False 70 -1 59 \n", + "3 False 30 -1 61 \n", + "4 False 57 -1 69 \n", + "... ... ... ... ... \n", + "22632 False 135 -1 200 \n", + "22633 False 1018 -1 1006 \n", + "22634 False 1019 -1 1006 \n", + "22635 False 1152 -1 1159 \n", + "22636 False 1381 -1 1390 \n", "\n", - " start_previous end_previous \n", - "0 5 5 \n", - "1 5 5 \n", - "2 5 5 \n", - "3 5 5 \n", - "4 5 5 \n", - "... ... ... \n", - "2119 17 21 \n", - "2120 5 5 \n", - "2121 5 5 \n", - "2122 5 5 \n", - "2123 5 5 \n", + " start_previous end_previous \n", + "0 5 5 \n", + "1 5 5 \n", + "2 5 5 \n", + "3 5 5 \n", + "4 5 5 \n", + "... ... ... \n", + "22632 5 5 \n", + "22633 5 5 \n", + "22634 5 5 \n", + "22635 5 5 \n", + "22636 5 5 \n", "\n", - "[2124 rows x 40 columns]" + "[22637 rows x 40 columns]" ] }, "execution_count": 6, @@ -840,148 +904,148 @@ " \n", " \n", " tour_id\n", - " variable\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", + " start\n", + " end\n", + " duration\n", + " tdd\n", + " out_period\n", + " in_period\n", + " mode_choice_logsum\n", + " util_free_flow_round_trip_auto_time_shift_effects_departure\n", + " util_free_flow_round_trip_auto_time_shift_effects_duration\n", " ...\n", - " 180\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", + " util_arrival_constants_late\n", + " util_duration_constants_0_to_2_hours\n", + " util_duration_constants_3_to_4_hours\n", + " util_duration_constants_5_to_6_hours\n", + " util_duration_constants_7_to_8_hours\n", + " util_duration_constants_9_hours\n", + " util_duration_constants_10_hours\n", + " util_duration_constants_11_hours\n", + " util_duration_constants_12_to_13_hours\n", + " util_duration_constants_14_to_18_hours\n", " \n", " \n", " \n", " \n", " 0\n", - " 2961920\n", - " duration\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", + " 2966594\n", + " 5\n", " 5\n", - " 6\n", - " 7\n", - " ...\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", " 0\n", - " 1\n", - " 2\n", - " 0\n", - " 1\n", " 0\n", + " EA\n", + " EA\n", + " -0.897101\n", + " 163.399994\n", + " 0.000000\n", + " ...\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 1\n", - " 2961920\n", - " end\n", + " 2966594\n", " 5\n", " 6\n", - " 7\n", - " 8\n", - " 9\n", - " 10\n", - " 11\n", - " 12\n", + " 1\n", + " 1\n", + " EA\n", + " AM\n", + " -1.333629\n", + " 163.399994\n", + " 32.680000\n", " ...\n", - " 20\n", - " 21\n", - " 22\n", - " 23\n", - " 21\n", - " 22\n", - " 23\n", - " 22\n", - " 23\n", - " 23\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 2\n", - " 2961920\n", - " in_period\n", + " 2966594\n", + " 5\n", + " 7\n", + " 2\n", + " 2\n", " EA\n", " AM\n", - " AM\n", - " AM\n", - " AM\n", - " MD\n", - " MD\n", - " MD\n", + " -1.333629\n", + " 163.399994\n", + " 65.360001\n", " ...\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 3\n", - " 2961920\n", - " mode_choice_logsum\n", - " -0.6808670711507444\n", - " -0.9609052826735235\n", - " -0.9609052826735235\n", - " -0.9609052826735235\n", - " -0.9609052826735235\n", - " -1.1106688167107288\n", - " -1.1106688167107288\n", - " -1.1106688167107288\n", - " ...\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", - " -0.8117834942924501\n", + " 2966594\n", + " 5\n", + " 8\n", + " 3\n", + " 3\n", + " EA\n", + " AM\n", + " -1.333629\n", + " 163.399994\n", + " 98.040001\n", + " ...\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 4\n", - " 2961920\n", - " out_period\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", + " 2966594\n", + " 5\n", + " 9\n", + " 4\n", + " 4\n", " EA\n", + " AM\n", + " -1.333629\n", + " 163.399994\n", + " 130.720001\n", " ...\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " ...\n", @@ -1008,9 +1072,20 @@ " ...\n", " \n", " \n", - " 146551\n", - " 308122624\n", - " util_subsequent_tour_must_start_after_previous...\n", + " 4222354\n", + " 309112036\n", + " 21\n", + " 22\n", + " 1\n", + " 185\n", + " EV\n", + " EV\n", + " -2.780379\n", + " 1388.099976\n", + " 66.099998\n", + " ...\n", + " True\n", + " True\n", " False\n", " False\n", " False\n", @@ -1019,9 +1094,22 @@ " False\n", " False\n", " False\n", + " \n", + " \n", + " 4222355\n", + " 309112036\n", + " 21\n", + " 23\n", + " 2\n", + " 186\n", + " EV\n", + " EV\n", + " -2.780379\n", + " 1388.099976\n", + " 132.199997\n", " ...\n", - " False\n", - " False\n", + " True\n", + " True\n", " False\n", " False\n", " False\n", @@ -1032,81 +1120,44 @@ " False\n", " \n", " \n", - " 146552\n", - " 308122624\n", - " util_tours_by_student_duration_lt_8_hrs\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", + " 4222356\n", + " 309112036\n", + " 22\n", + " 22\n", + " 0\n", + " 187\n", + " EV\n", + " EV\n", + " -2.780379\n", + " 1454.199951\n", + " 0.000000\n", " ...\n", " True\n", " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 146553\n", - " 308122624\n", - " util_tours_by_worker_duration_lt_8_hrs\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", + " 4222357\n", + " 309112036\n", + " 22\n", + " 23\n", + " 1\n", + " 188\n", + " EV\n", + " EV\n", + " -2.780379\n", + " 1454.199951\n", + " 66.099998\n", " ...\n", " True\n", " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " \n", - " \n", - " 146554\n", - " 308122624\n", - " util_university_student_departure_shift_effects\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " ...\n", - " 20\n", - " 20\n", - " 20\n", - " 20\n", - " 21\n", - " 21\n", - " 21\n", - " 22\n", - " 22\n", - " 23\n", - " \n", - " \n", - " 146555\n", - " 308122624\n", - " util_worker_13_to_15_arrival_interaction\n", " False\n", " False\n", " False\n", @@ -1115,9 +1166,22 @@ " False\n", " False\n", " False\n", + " \n", + " \n", + " 4222358\n", + " 309112036\n", + " 23\n", + " 23\n", + " 0\n", + " 189\n", + " EV\n", + " EV\n", + " -2.780379\n", + " 1520.299927\n", + " 0.000000\n", " ...\n", - " False\n", - " False\n", + " True\n", + " True\n", " False\n", " False\n", " False\n", @@ -1129,102 +1193,167 @@ " \n", " \n", "\n", - "

146556 rows × 192 columns

\n", + "

4222359 rows × 71 columns

\n", "" ], "text/plain": [ - " tour_id variable \\\n", - "0 2961920 duration \n", - "1 2961920 end \n", - "2 2961920 in_period \n", - "3 2961920 mode_choice_logsum \n", - "4 2961920 out_period \n", - "... ... ... \n", - "146551 308122624 util_subsequent_tour_must_start_after_previous... \n", - "146552 308122624 util_tours_by_student_duration_lt_8_hrs \n", - "146553 308122624 util_tours_by_worker_duration_lt_8_hrs \n", - "146554 308122624 util_university_student_departure_shift_effects \n", - "146555 308122624 util_worker_13_to_15_arrival_interaction \n", + " tour_id start end duration tdd out_period in_period \\\n", + "0 2966594 5 5 0 0 EA EA \n", + "1 2966594 5 6 1 1 EA AM \n", + "2 2966594 5 7 2 2 EA AM \n", + "3 2966594 5 8 3 3 EA AM \n", + "4 2966594 5 9 4 4 EA AM \n", + "... ... ... ... ... ... ... ... \n", + "4222354 309112036 21 22 1 185 EV EV \n", + "4222355 309112036 21 23 2 186 EV EV \n", + "4222356 309112036 22 22 0 187 EV EV \n", + "4222357 309112036 22 23 1 188 EV EV \n", + "4222358 309112036 23 23 0 189 EV EV \n", "\n", - " 0 1 2 \\\n", - "0 0 1 2 \n", - "1 5 6 7 \n", - "2 EA AM AM \n", - "3 -0.6808670711507444 -0.9609052826735235 -0.9609052826735235 \n", - "4 EA EA EA \n", - "... ... ... ... \n", - "146551 False False False \n", - "146552 True True True \n", - "146553 True True True \n", - "146554 5 5 5 \n", - "146555 False False False \n", + " mode_choice_logsum \\\n", + "0 -0.897101 \n", + "1 -1.333629 \n", + "2 -1.333629 \n", + "3 -1.333629 \n", + "4 -1.333629 \n", + "... ... \n", + "4222354 -2.780379 \n", + "4222355 -2.780379 \n", + "4222356 -2.780379 \n", + "4222357 -2.780379 \n", + "4222358 -2.780379 \n", "\n", - " 3 4 5 \\\n", - "0 3 4 5 \n", - "1 8 9 10 \n", - "2 AM AM MD \n", - "3 -0.9609052826735235 -0.9609052826735235 -1.1106688167107288 \n", - "4 EA EA EA \n", - "... ... ... ... \n", - "146551 False False False \n", - "146552 True True True \n", - "146553 True True True \n", - "146554 5 5 5 \n", - "146555 False False False \n", + " util_free_flow_round_trip_auto_time_shift_effects_departure \\\n", + "0 163.399994 \n", + "1 163.399994 \n", + "2 163.399994 \n", + "3 163.399994 \n", + "4 163.399994 \n", + "... ... \n", + "4222354 1388.099976 \n", + "4222355 1388.099976 \n", + "4222356 1454.199951 \n", + "4222357 1454.199951 \n", + "4222358 1520.299927 \n", "\n", - " 6 7 ... 180 \\\n", - "0 6 7 ... 0 \n", - "1 11 12 ... 20 \n", - "2 MD MD ... EV \n", - "3 -1.1106688167107288 -1.1106688167107288 ... -0.8117834942924501 \n", - "4 EA EA ... EV \n", - "... ... ... ... ... \n", - "146551 False False ... False \n", - "146552 True True ... True \n", - "146553 True True ... True \n", - "146554 5 5 ... 20 \n", - "146555 False False ... False \n", + " util_free_flow_round_trip_auto_time_shift_effects_duration ... \\\n", + "0 0.000000 ... \n", + "1 32.680000 ... \n", + "2 65.360001 ... \n", + "3 98.040001 ... \n", + "4 130.720001 ... \n", + "... ... ... \n", + "4222354 66.099998 ... \n", + "4222355 132.199997 ... \n", + "4222356 0.000000 ... \n", + "4222357 66.099998 ... \n", + "4222358 0.000000 ... \n", "\n", - " 181 182 183 \\\n", - "0 1 2 3 \n", - "1 21 22 23 \n", - "2 EV EV EV \n", - "3 -0.8117834942924501 -0.8117834942924501 -0.8117834942924501 \n", - "4 EV EV EV \n", - "... ... ... ... \n", - "146551 False False False \n", - "146552 True True True \n", - "146553 True True True \n", - "146554 20 20 20 \n", - "146555 False False False \n", + " util_arrival_constants_late util_duration_constants_0_to_2_hours \\\n", + "0 False True \n", + "1 False True \n", + "2 False True \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "4222354 True True \n", + "4222355 True True \n", + "4222356 True True \n", + "4222357 True True \n", + "4222358 True True \n", "\n", - " 184 185 186 \\\n", - "0 0 1 2 \n", - "1 21 22 23 \n", - "2 EV EV EV \n", - "3 -0.8117834942924501 -0.8117834942924501 -0.8117834942924501 \n", - "4 EV EV EV \n", - "... ... ... ... \n", - "146551 False False False \n", - "146552 True True True \n", - "146553 True True True \n", - "146554 21 21 21 \n", - "146555 False False False \n", + " util_duration_constants_3_to_4_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 True \n", + "4 True \n", + "... ... \n", + "4222354 False \n", + "4222355 False \n", + "4222356 False \n", + "4222357 False \n", + "4222358 False \n", "\n", - " 187 188 189 \n", - "0 0 1 0 \n", - "1 22 23 23 \n", - "2 EV EV EV \n", - "3 -0.8117834942924501 -0.8117834942924501 -0.8117834942924501 \n", - "4 EV EV EV \n", - "... ... ... ... \n", - "146551 False False False \n", - "146552 True True True \n", - "146553 True True True \n", - "146554 22 22 23 \n", - "146555 False False False \n", + " util_duration_constants_5_to_6_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "4222354 False \n", + "4222355 False \n", + "4222356 False \n", + "4222357 False \n", + "4222358 False \n", "\n", - "[146556 rows x 192 columns]" + " util_duration_constants_7_to_8_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "4222354 False \n", + "4222355 False \n", + "4222356 False \n", + "4222357 False \n", + "4222358 False \n", + "\n", + " util_duration_constants_9_hours util_duration_constants_10_hours \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "4222354 False False \n", + "4222355 False False \n", + "4222356 False False \n", + "4222357 False False \n", + "4222358 False False \n", + "\n", + " util_duration_constants_11_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "4222354 False \n", + "4222355 False \n", + "4222356 False \n", + "4222357 False \n", + "4222358 False \n", + "\n", + " util_duration_constants_12_to_13_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "4222354 False \n", + "4222355 False \n", + "4222356 False \n", + "4222357 False \n", + "4222358 False \n", + "\n", + " util_duration_constants_14_to_18_hours \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "4222354 False \n", + "4222355 False \n", + "4222356 False \n", + "4222357 False \n", + "4222358 False \n", + "\n", + "[4222359 rows x 71 columns]" ] }, "execution_count": 7, @@ -1254,13 +1383,47 @@ "name": "stderr", "output_type": "stream", "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "problem: chosen_but_not_available has (83 issues)\n" ] }, + { + "data": { + "text/plain": [ + "(,\n", + " ┣ chosen_but_not_available: altid n example rows\n", + " ┃ 0 23 1 7136\n", + " ┃ 1 40 1 7611\n", + " ┃ 2 41 2 2673, 3979\n", + " ┃ 3 42 1 15549\n", + " ┃ 4 44 2 10390, 17539\n", + " ┃ .. ... .. ...\n", + " ┃ 78 175 1 15501\n", + " ┃ 79 177 1 16777\n", + " ┃ 80 178 1 1665\n", + " ┃ 81 180 1 6817\n", + " ┃ 82 184 1 10510\n", + " ┃ \n", + " ┃ [83 rows x 3 columns])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.doctor(repair_ch_av=\"-\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "

Iteration 064 [Optimization terminated successfully]

" + "

Iteration 055 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1272,7 +1435,7 @@ { "data": { "text/html": [ - "

Best LL = -8399.82196783851

" + "

Best LL = -89082.99255547294

" ], "text/plain": [ "" @@ -1303,70 +1466,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " 0.326676\n", + " 0.346120\n", + " 0.346120\n", " 0.362700\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.326676\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction\n", - " 0.298901\n", + " -0.097167\n", + " -0.097167\n", " -0.101200\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.298901\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " -0.038388\n", + " 0.103758\n", + " 0.103758\n", " 0.177100\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.038388\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction\n", - " -0.012605\n", + " -0.534895\n", + " -0.534895\n", " -0.212300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.012605\n", " \n", " \n", " coef_arrival_constants_am_peak\n", - " -2.527138\n", + " -1.859817\n", + " -1.859817\n", " -1.854521\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.527138\n", " \n", " \n", " ...\n", @@ -1377,122 +1544,120 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs\n", - " 5.443390\n", + " 2.609918\n", + " 2.609918\n", " 2.582000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 5.443390\n", " \n", " \n", " coef_subsequent_tour_must_start_after_previous_tour_ends\n", " -100.000000\n", " -100.000000\n", + " -100.000000\n", + " -100.0\n", + " -100.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " -100.000000\n", " \n", " \n", " coef_tours_by_student_duration_lt_8_hrs\n", - " 20.927636\n", + " 2.619822\n", + " 2.619822\n", " 2.582000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 20.927636\n", " \n", " \n", " coef_tours_by_worker_duration_lt_8_hrs\n", - " 19.258236\n", + " 0.950422\n", + " 0.950422\n", " 0.912600\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 19.258236\n", " \n", " \n", " coef_university_student_departure_shift_effects\n", - " 0.076480\n", + " 0.045387\n", + " 0.045387\n", " 0.057470\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.076480\n", " \n", " \n", "\n", - "

63 rows × 8 columns

\n", + "

63 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_adjacent_window_exists_after_this_arrival_... 0.326676 0.362700 \n", - "coef_adjacent_window_exists_after_this_arrival_... 0.298901 -0.101200 \n", - "coef_adjacent_window_exists_before_this_departu... -0.038388 0.177100 \n", - "coef_adjacent_window_exists_before_this_departu... -0.012605 -0.212300 \n", - "coef_arrival_constants_am_peak -2.527138 -1.854521 \n", + " value best \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 0.346120 0.346120 \n", + "coef_adjacent_window_exists_after_this_arrival_... -0.097167 -0.097167 \n", + "coef_adjacent_window_exists_before_this_departu... 0.103758 0.103758 \n", + "coef_adjacent_window_exists_before_this_departu... -0.534895 -0.534895 \n", + "coef_arrival_constants_am_peak -1.859817 -1.859817 \n", "... ... ... \n", - "coef_subsequent_of_2plus_work_tours_duration_lt... 5.443390 2.582000 \n", + "coef_subsequent_of_2plus_work_tours_duration_lt... 2.609918 2.609918 \n", "coef_subsequent_tour_must_start_after_previous_... -100.000000 -100.000000 \n", - "coef_tours_by_student_duration_lt_8_hrs 20.927636 2.582000 \n", - "coef_tours_by_worker_duration_lt_8_hrs 19.258236 0.912600 \n", - "coef_university_student_departure_shift_effects 0.076480 0.057470 \n", + "coef_tours_by_student_duration_lt_8_hrs 2.619822 2.619822 \n", + "coef_tours_by_worker_duration_lt_8_hrs 0.950422 0.950422 \n", + "coef_university_student_departure_shift_effects 0.045387 0.045387 \n", "\n", - " nullvalue minimum \\\n", - "coef_adjacent_window_exists_after_this_arrival_... 0.0 -25.0 \n", - "coef_adjacent_window_exists_after_this_arrival_... 0.0 -25.0 \n", - "coef_adjacent_window_exists_before_this_departu... 0.0 -25.0 \n", - "coef_adjacent_window_exists_before_this_departu... 0.0 -25.0 \n", - "coef_arrival_constants_am_peak 0.0 -25.0 \n", - "... ... ... \n", - "coef_subsequent_of_2plus_work_tours_duration_lt... 0.0 -25.0 \n", - "coef_subsequent_tour_must_start_after_previous_... 0.0 -25.0 \n", - "coef_tours_by_student_duration_lt_8_hrs 0.0 -25.0 \n", - "coef_tours_by_worker_duration_lt_8_hrs 0.0 -25.0 \n", - "coef_university_student_departure_shift_effects 0.0 -25.0 \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 0.362700 -25.0 \n", + "coef_adjacent_window_exists_after_this_arrival_... -0.101200 -25.0 \n", + "coef_adjacent_window_exists_before_this_departu... 0.177100 -25.0 \n", + "coef_adjacent_window_exists_before_this_departu... -0.212300 -25.0 \n", + "coef_arrival_constants_am_peak -1.854521 -25.0 \n", + "... ... ... \n", + "coef_subsequent_of_2plus_work_tours_duration_lt... 2.582000 -25.0 \n", + "coef_subsequent_tour_must_start_after_previous_... -100.000000 -100.0 \n", + "coef_tours_by_student_duration_lt_8_hrs 2.582000 -25.0 \n", + "coef_tours_by_worker_duration_lt_8_hrs 0.912600 -25.0 \n", + "coef_university_student_departure_shift_effects 0.057470 -25.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_adjacent_window_exists_after_this_arrival_... 25.0 0 \n", - "coef_adjacent_window_exists_after_this_arrival_... 25.0 0 \n", - "coef_adjacent_window_exists_before_this_departu... 25.0 0 \n", - "coef_adjacent_window_exists_before_this_departu... 25.0 0 \n", - "coef_arrival_constants_am_peak 25.0 0 \n", - "... ... ... ... \n", - "coef_subsequent_of_2plus_work_tours_duration_lt... 25.0 0 \n", - "coef_subsequent_tour_must_start_after_previous_... 25.0 1 \n", - "coef_tours_by_student_duration_lt_8_hrs 25.0 0 \n", - "coef_tours_by_worker_duration_lt_8_hrs 25.0 0 \n", - "coef_university_student_departure_shift_effects 25.0 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 25.0 0.0 \n", + "coef_adjacent_window_exists_after_this_arrival_... 25.0 0.0 \n", + "coef_adjacent_window_exists_before_this_departu... 25.0 0.0 \n", + "coef_adjacent_window_exists_before_this_departu... 25.0 0.0 \n", + "coef_arrival_constants_am_peak 25.0 0.0 \n", + "... ... ... \n", + "coef_subsequent_of_2plus_work_tours_duration_lt... 25.0 0.0 \n", + "coef_subsequent_tour_must_start_after_previous_... -100.0 0.0 \n", + "coef_tours_by_student_duration_lt_8_hrs 25.0 0.0 \n", + "coef_tours_by_worker_duration_lt_8_hrs 25.0 0.0 \n", + "coef_university_student_departure_shift_effects 25.0 0.0 \n", "\n", - " best \n", - "coef_adjacent_window_exists_after_this_arrival_... 0.326676 \n", - "coef_adjacent_window_exists_after_this_arrival_... 0.298901 \n", - "coef_adjacent_window_exists_before_this_departu... -0.038388 \n", - "coef_adjacent_window_exists_before_this_departu... -0.012605 \n", - "coef_arrival_constants_am_peak -2.527138 \n", - "... ... \n", - "coef_subsequent_of_2plus_work_tours_duration_lt... 5.443390 \n", - "coef_subsequent_tour_must_start_after_previous_... -100.000000 \n", - "coef_tours_by_student_duration_lt_8_hrs 20.927636 \n", - "coef_tours_by_worker_duration_lt_8_hrs 19.258236 \n", - "coef_university_student_departure_shift_effects 0.076480 \n", + " holdfast \n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 0 \n", + "coef_adjacent_window_exists_after_this_arrival_... 0 \n", + "coef_adjacent_window_exists_before_this_departu... 0 \n", + "coef_adjacent_window_exists_before_this_departu... 0 \n", + "coef_arrival_constants_am_peak 0 \n", + "... ... \n", + "coef_subsequent_of_2plus_work_tours_duration_lt... 0 \n", + "coef_subsequent_tour_must_start_after_previous_... 1 \n", + "coef_tours_by_student_duration_lt_8_hrs 0 \n", + "coef_tours_by_worker_duration_lt_8_hrs 0 \n", + "coef_university_student_departure_shift_effects 0 \n", "\n", - "[63 rows x 8 columns]" + "[63 rows x 7 columns]" ] }, "metadata": {}, @@ -1502,10 +1667,8 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate()\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.580800976238575e-22 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -1521,23 +1684,23 @@ " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " 0.326676\n", + " 0.346120\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction\n", - " 0.298901\n", + " -0.097167\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " -0.038388\n", + " 0.103758\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction\n", - " -0.012605\n", + " -0.534895\n", " \n", " \n", " coef_arrival_constants_am_peak\n", - " -2.527138\n", + " -1.859817\n", " \n", " \n", " coef_arrival_constants_early\n", @@ -1545,19 +1708,19 @@ " \n", " \n", " coef_arrival_constants_evening\n", - " -0.085796\n", + " 0.108541\n", " \n", " \n", " coef_arrival_constants_late\n", - " -1.221408\n", + " -0.888178\n", " \n", " \n", " coef_arrival_constants_midday_1\n", - " -0.489736\n", + " -0.478024\n", " \n", " \n", " coef_arrival_constants_midday_2\n", - " -0.383260\n", + " -0.405187\n", " \n", " \n", " coef_arrival_constants_pm_peak_1\n", @@ -1565,19 +1728,19 @@ " \n", " \n", " coef_arrival_constants_pm_peak_2\n", - " 0.202005\n", + " 0.320157\n", " \n", " \n", " coef_arrival_constants_pm_peak_3\n", - " 0.572524\n", + " 0.736762\n", " \n", " \n", " coef_arrival_constants_pm_peak_4\n", - " 0.705592\n", + " 0.834430\n", " \n", " \n", " coef_departure_constants_am_peak_1\n", - " -0.641898\n", + " -0.600877\n", " \n", " \n", " coef_departure_constants_am_peak_2\n", @@ -1585,55 +1748,55 @@ " \n", " \n", " coef_departure_constants_am_peak_3\n", - " -0.323006\n", + " -0.221080\n", " \n", " \n", " coef_departure_constants_am_peak_4\n", - " -1.220042\n", + " -1.207929\n", " \n", " \n", " coef_departure_constants_early\n", - " -0.889376\n", + " -0.930812\n", " \n", " \n", " coef_departure_constants_evening\n", - " -1.989402\n", + " -1.498679\n", " \n", " \n", " coef_departure_constants_late\n", - " -2.936685\n", + " -2.893575\n", " \n", " \n", " coef_departure_constants_midday_1\n", - " -1.826700\n", + " -1.684758\n", " \n", " \n", " coef_departure_constants_midday_2\n", - " -1.878783\n", + " -1.708659\n", " \n", " \n", " coef_departure_constants_pm_peak\n", - " -1.691847\n", + " -1.375804\n", " \n", " \n", " coef_destination_in_cbd_departure_shift_effects\n", - " 0.028678\n", + " 0.057883\n", " \n", " \n", " coef_destination_in_cbd_duration_shift_effects\n", - " 0.096673\n", + " 0.070217\n", " \n", " \n", " coef_destination_in_cbd_early_departure_interaction\n", - " -0.419959\n", + " -0.365371\n", " \n", " \n", " coef_destination_in_cbd_late_arrival_interaction\n", - " -0.266935\n", + " -0.156618\n", " \n", " \n", " coef_duration_constants_0_to_2_hours\n", - " -2.062403\n", + " -2.598564\n", " \n", " \n", " coef_duration_constants_10_hours\n", @@ -1641,75 +1804,75 @@ " \n", " \n", " coef_duration_constants_11_hours\n", - " -0.358479\n", + " -0.316543\n", " \n", " \n", " coef_duration_constants_12_to_13_hours\n", - " -1.132000\n", + " -0.998700\n", " \n", " \n", " coef_duration_constants_14_to_18_hours\n", - " -1.854875\n", + " -1.641305\n", " \n", " \n", " coef_duration_constants_3_to_4_hours\n", - " -0.600278\n", + " -0.976620\n", " \n", " \n", " coef_duration_constants_5_to_6_hours\n", - " -0.343562\n", + " -0.786160\n", " \n", " \n", " coef_duration_constants_7_to_8_hours\n", - " -0.033981\n", + " -0.191835\n", " \n", " \n", " coef_duration_constants_9_hours\n", - " 0.137251\n", + " 0.041325\n", " \n", " \n", " coef_first_of_2plus_work_tours_departure_shift_effects\n", - " -0.289684\n", + " -0.286911\n", " \n", " \n", " coef_first_of_2plus_work_tours_duration_lt_8_hrs\n", - " 1.916784\n", + " 2.160421\n", " \n", " \n", " coef_first_of_2plus_work_tours_duration_shift_effects\n", - " -0.286185\n", + " -0.159326\n", " \n", " \n", " coef_free_flow_round_trip_auto_time_shift_effects_departure\n", - " 0.000599\n", + " -0.001158\n", " \n", " \n", " coef_free_flow_round_trip_auto_time_shift_effects_duration\n", - " 0.004216\n", + " 0.002097\n", " \n", " \n", " coef_full_time_worker_10_to_12_departure_interaction\n", - " -0.678250\n", + " -0.474411\n", " \n", " \n", " coef_full_time_worker_duration_lt_9_hours_interaction\n", - " -1.357293\n", + " -1.190464\n", " \n", " \n", " coef_household_income_departure_shift_effects\n", - " 0.000111\n", + " 0.000219\n", " \n", " \n", " coef_household_income_early_departure_interaction\n", - " -0.582774\n", + " -0.462242\n", " \n", " \n", " coef_household_income_late_arrival_interaction\n", - " 0.029954\n", + " -0.560334\n", " \n", " \n", " coef_mode_choice_logsum\n", - " 0.247114\n", + " 0.666366\n", " \n", " \n", " coef_non_working_adult_duration_shift_effects\n", @@ -1717,23 +1880,23 @@ " \n", " \n", " coef_part_time_worker_13_to_15_arrival_interaction\n", - " 0.375322\n", + " 0.687690\n", " \n", " \n", " coef_part_time_worker_departure_shift_effects\n", - " 0.095094\n", + " 0.058805\n", " \n", " \n", " coef_previously_scheduled_tour_begins_in_this_arrival_hour\n", - " -6.724955\n", + " -1.250685\n", " \n", " \n", " coef_previously_scheduled_tour_ends_in_this_departure_hour\n", - " 0.258853\n", + " -0.563627\n", " \n", " \n", " coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours\n", - " -6.865187\n", + " -18.677135\n", " \n", " \n", " coef_rural_household_early_departure_interaction\n", @@ -1745,34 +1908,34 @@ " \n", " \n", " coef_subsequent_2plus_work_departure_tours_shift_effects\n", - " 0.060824\n", + " 0.059853\n", " \n", " \n", " coef_subsequent_2plus_work_duration_tours_shift_effects\n", - " -0.080957\n", + " -0.352513\n", " \n", " \n", " coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs\n", - " 5.443390\n", + " 2.609918\n", " \n", " \n", " coef_subsequent_tour_must_start_after_previous_tour_ends\n", - " -25.000000\n", + " -100.000000\n", " \n", " \n", " coef_tours_by_student_duration_lt_8_hrs\n", - " 20.927636\n", + " 2.619822\n", " \n", " \n", " coef_tours_by_worker_duration_lt_8_hrs\n", - " 19.258236\n", + " 0.950422\n", " \n", " \n", " coef_university_student_departure_shift_effects\n", - " 0.076480\n", + " 0.045387\n", " \n", " \n", - "loglike-8399.82196783851d_loglike\n", + "
logloss3.971246101795334d_logloss\n", " \n", " \n", " \n", @@ -1782,23 +1945,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1806,19 +1969,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1826,19 +1989,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1846,55 +2009,55 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1902,75 +2065,75 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1978,23 +2141,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2006,15 +2169,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2022,65 +2185,65 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction6.227709e-05-2.476569e-05
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction-3.094051e-042.401362e-05
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction2.047044e-04-3.242733e-07
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction1.044967e-041.721606e-05
coef_arrival_constants_am_peak-2.094941e-056.908123e-05
coef_arrival_constants_early
coef_arrival_constants_evening-2.523831e-047.288184e-05
coef_arrival_constants_late-1.447395e-03-9.483020e-05
coef_arrival_constants_midday_12.766105e-04-4.017493e-05
coef_arrival_constants_midday_27.345342e-04-6.167975e-05
coef_arrival_constants_pm_peak_1
coef_arrival_constants_pm_peak_27.327399e-04-3.095877e-05
coef_arrival_constants_pm_peak_34.196985e-04-1.941954e-04
coef_arrival_constants_pm_peak_4-6.906812e-049.466248e-05
coef_departure_constants_am_peak_11.225866e-035.666160e-05
coef_departure_constants_am_peak_2
coef_departure_constants_am_peak_31.473105e-03-1.055621e-04
coef_departure_constants_am_peak_4-1.160737e-031.197416e-05
coef_departure_constants_early1.279229e-03-4.303027e-05
coef_departure_constants_evening-7.494769e-044.847181e-05
coef_departure_constants_late-3.382858e-04-1.550132e-05
coef_departure_constants_midday_13.335414e-054.834936e-05
coef_departure_constants_midday_2-2.645412e-03-1.698437e-04
coef_departure_constants_pm_peak-1.917919e-031.852010e-04
coef_destination_in_cbd_departure_shift_effects-4.480046e-022.109060e-04
coef_destination_in_cbd_duration_shift_effects2.390663e-025.183584e-04
coef_destination_in_cbd_early_departure_interaction1.038321e-038.561721e-05
coef_destination_in_cbd_late_arrival_interaction-7.345503e-043.056000e-06
coef_duration_constants_0_to_2_hours-1.367149e-037.551894e-05
coef_duration_constants_10_hours
coef_duration_constants_11_hours7.690066e-04-5.843846e-05
coef_duration_constants_12_to_13_hours1.653562e-036.715821e-05
coef_duration_constants_14_to_18_hours1.225821e-031.601895e-04
coef_duration_constants_3_to_4_hours-1.640102e-03-3.312036e-05
coef_duration_constants_5_to_6_hours-5.618970e-04-1.248871e-04
coef_duration_constants_7_to_8_hours4.597377e-041.212099e-05
coef_duration_constants_9_hours-7.212915e-04-1.193587e-04
coef_first_of_2plus_work_tours_departure_shift_effects-6.512833e-036.406568e-05
coef_first_of_2plus_work_tours_duration_lt_8_hrs-2.321194e-041.190483e-05
coef_first_of_2plus_work_tours_duration_shift_effects3.977570e-03-9.017286e-05
coef_free_flow_round_trip_auto_time_shift_effects_departure-9.773028e-01-1.930899e-03
coef_free_flow_round_trip_auto_time_shift_effects_duration6.851835e-013.916590e-04
coef_full_time_worker_10_to_12_departure_interaction-1.959177e-042.789068e-05
coef_full_time_worker_duration_lt_9_hours_interaction-2.085279e-03-5.147377e-05
coef_household_income_departure_shift_effects-6.023402e+00-8.126657e-03
coef_household_income_early_departure_interaction9.200981e-05-4.696739e-05
coef_household_income_late_arrival_interaction-2.749490e-045.039132e-05
coef_mode_choice_logsum-1.198356e-031.368051e-05
coef_non_working_adult_duration_shift_effects
coef_part_time_worker_13_to_15_arrival_interaction3.494690e-042.401302e-05
coef_part_time_worker_departure_shift_effects-7.745591e-031.505693e-04
coef_previously_scheduled_tour_begins_in_this_arrival_hour-4.665139e-051.307335e-04
coef_previously_scheduled_tour_ends_in_this_departure_hour-2.485873e-05-6.743726e-06
coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours2.255476e-066.820090e-06
coef_rural_household_early_departure_interaction
coef_subsequent_2plus_work_departure_tours_shift_effects-8.338787e-03-3.661524e-05
coef_subsequent_2plus_work_duration_tours_shift_effects7.065869e-042.967934e-05
coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs-2.124468e-043.755607e-05
coef_subsequent_tour_must_start_after_previous_tour_ends
coef_tours_by_student_duration_lt_8_hrs-7.862668e-165.453713e-05
coef_tours_by_worker_duration_lt_8_hrs-7.862668e-165.453713e-05
coef_university_student_departure_shift_effects-1.306805e-025.979971e-05
nit64nfev177njev64status0message'Optimization terminated successfully'successTrueelapsed_time0:00:08.813504method'slsqp'n_cases2124iteration_number64logloss3.954718440601935" + "nit55nfev69njev55status0message'Optimization terminated successfully'successTrueelapsed_time0:00:40.125911method'slsqp'n_cases22637iteration_number55loglike-89082.99255547294" ], "text/plain": [ - "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.326676\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction 0.298901\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -0.038388\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -0.012605\n", - "┃ coef_arrival_constants_am_peak -2.527138\n", - "┃ ... \n", - "┃ coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs 5.443390\n", - "┃ coef_subsequent_tour_must_start_after_previous_tour_ends -25.000000\n", - "┃ coef_tours_by_student_duration_lt_8_hrs 20.927636\n", - "┃ coef_tours_by_worker_duration_lt_8_hrs 19.258236\n", - "┃ coef_university_student_departure_shift_effects 0.076480\n", + "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.346120\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -0.097167\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.103758\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -0.534895\n", + "┃ coef_arrival_constants_am_peak -1.859817\n", + "┃ ... \n", + "┃ coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs 2.609918\n", + "┃ coef_subsequent_tour_must_start_after_previous_tour_ends -100.000000\n", + "┃ coef_tours_by_student_duration_lt_8_hrs 2.619822\n", + "┃ coef_tours_by_worker_duration_lt_8_hrs 0.950422\n", + "┃ coef_university_student_departure_shift_effects 0.045387\n", "┃ Length: 63, dtype: float64\n", - "┣ loglike: -8399.82196783851\n", - "┣ d_loglike: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 6.227709e-05\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -3.094051e-04\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 2.047044e-04\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction 1.044967e-04\n", - "┃ coef_arrival_constants_am_peak -2.094941e-05\n", + "┣ logloss: 3.971246101795334\n", + "┣ d_logloss: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -2.476569e-05\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction 2.401362e-05\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -3.242733e-07\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction 1.721606e-05\n", + "┃ coef_arrival_constants_am_peak 6.908123e-05\n", "┃ ... \n", - "┃ coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs -2.124468e-04\n", + "┃ coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs 3.755607e-05\n", "┃ coef_subsequent_tour_must_start_after_previous_tour_ends 0.000000e+00\n", - "┃ coef_tours_by_student_duration_lt_8_hrs -7.862668e-16\n", - "┃ coef_tours_by_worker_duration_lt_8_hrs -7.862668e-16\n", - "┃ coef_university_student_departure_shift_effects -1.306805e-02\n", + "┃ coef_tours_by_student_duration_lt_8_hrs 5.453713e-05\n", + "┃ coef_tours_by_worker_duration_lt_8_hrs 5.453713e-05\n", + "┃ coef_university_student_departure_shift_effects 5.979971e-05\n", "┃ Length: 63, dtype: float64\n", - "┣ nit: 64\n", - "┣ nfev: 177\n", - "┣ njev: 64\n", + "┣ nit: 55\n", + "┣ nfev: 69\n", + "┣ njev: 55\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=8, microseconds=813504)\n", + "┣ elapsed_time: datetime.timedelta(seconds=40, microseconds=125911)\n", "┣ method: 'slsqp'\n", - "┣ n_cases: 2124\n", - "┣ iteration_number: 64\n", - "┣ logloss: 3.954718440601935" + "┣ n_cases: 22637\n", + "┣ iteration_number: 55\n", + "┣ loglike: -89082.99255547294" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - 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Value Std Err t Stat Signif Null Value Constrained
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.327 0.208 1.57 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction 0.299 0.244 1.23 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction-0.0384 0.207-0.19 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction-0.0126 0.274-0.05 0.00
coef_arrival_constants_am_peak-2.53 0.539-4.68*** 0.00
coef_arrival_constants_early 0.00 NA NA 0.00fixed value
coef_arrival_constants_evening-0.0858 0.208-0.41 0.00
coef_arrival_constants_late-1.22 0.282-4.33*** 0.00
coef_arrival_constants_midday_1-0.490 0.186-2.64** 0.00
coef_arrival_constants_midday_2-0.383 0.132-2.90** 0.00
coef_arrival_constants_pm_peak_1 0.00 NA NA 0.00fixed value
coef_arrival_constants_pm_peak_2 0.202 0.120 1.69 0.00
coef_arrival_constants_pm_peak_3 0.573 0.133 4.30*** 0.00
coef_arrival_constants_pm_peak_4 0.706 0.159 4.44*** 0.00
coef_departure_constants_am_peak_1-0.642 0.0804-7.99*** 0.00
coef_departure_constants_am_peak_2 0.00 NA NA 0.00fixed value
coef_departure_constants_am_peak_3-0.323 0.0737-4.38*** 0.00
coef_departure_constants_am_peak_4-1.22 0.117-10.45*** 0.00
coef_departure_constants_early-0.889 0.134-6.62*** 0.00
coef_departure_constants_evening-1.99 0.528-3.77*** 0.00
coef_departure_constants_late-2.94 1.18-2.49* 0.00
coef_departure_constants_midday_1-1.83 0.208-8.78*** 0.00
coef_departure_constants_midday_2-1.88 0.266-7.06*** 0.00
coef_departure_constants_pm_peak-1.69 0.371-4.56*** 0.00
coef_destination_in_cbd_departure_shift_effects 0.0287 0.0226 1.27 0.00
coef_destination_in_cbd_duration_shift_effects 0.0967 0.0220 4.39*** 0.00
coef_destination_in_cbd_early_departure_interaction-0.420 0.196-2.14* 0.00
coef_destination_in_cbd_late_arrival_interaction-0.267 0.283-0.94 0.00
coef_duration_constants_0_to_2_hours-2.06 0.351-5.88*** 0.00
coef_duration_constants_10_hours 0.00 NA NA 0.00fixed value
coef_duration_constants_11_hours-0.358 0.0855-4.19*** 0.00
coef_duration_constants_12_to_13_hours-1.13 0.126-9.00*** 0.00
coef_duration_constants_14_to_18_hours-1.85 0.206-9.00*** 0.00
coef_duration_constants_3_to_4_hours-0.600 0.269-2.23* 0.00
coef_duration_constants_5_to_6_hours-0.344 0.216-1.59 0.00
coef_duration_constants_7_to_8_hours-0.0340 0.168-0.20 0.00
coef_duration_constants_9_hours 0.137 0.0802 1.71 0.00
coef_first_of_2plus_work_tours_departure_shift_effects-0.290 0.0438-6.61*** 0.00
coef_first_of_2plus_work_tours_duration_lt_8_hrs 1.92 0.473 4.05*** 0.00
coef_first_of_2plus_work_tours_duration_shift_effects-0.286 0.117-2.44* 0.00
coef_free_flow_round_trip_auto_time_shift_effects_departure 0.000599 0.00113 0.53 0.00
coef_free_flow_round_trip_auto_time_shift_effects_duration 0.00422 0.00113 3.74*** 0.00
coef_full_time_worker_10_to_12_departure_interaction-0.678 0.188-3.61*** 0.00
coef_full_time_worker_duration_lt_9_hours_interaction-1.36 0.151-8.96*** 0.00
coef_household_income_departure_shift_effects 0.000111 5.14e-05 2.17* 0.00
coef_household_income_early_departure_interaction-0.583 0.190-3.06** 0.00
coef_household_income_late_arrival_interaction 0.0300 0.261 0.11 0.00
coef_mode_choice_logsum 0.247 0.208 1.19 0.00
coef_non_working_adult_duration_shift_effects-0.121 7.38e-09-BIG*** 0.00
coef_part_time_worker_13_to_15_arrival_interaction 0.375 0.175 2.15* 0.00
coef_part_time_worker_departure_shift_effects 0.0951 0.0253 3.76*** 0.00
coef_previously_scheduled_tour_begins_in_this_arrival_hour-6.72 146.-0.05 0.00
coef_previously_scheduled_tour_ends_in_this_departure_hour 0.259 0.357 0.72 0.00
coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours-6.87 21.4-0.32 0.00
coef_rural_household_early_departure_interaction 0.404 1.82e-10 BIG*** 0.00
coef_rural_household_late_arrival_interaction-0.345 1.13e-10-BIG*** 0.00
coef_subsequent_2plus_work_departure_tours_shift_effects 0.0608 0.0688 0.88 0.00
coef_subsequent_2plus_work_duration_tours_shift_effects-0.0810 0.0775-1.05 0.00
coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs 5.44 1.37 3.97*** 0.00
coef_subsequent_tour_must_start_after_previous_tour_ends-100. NA NA 0.00fixed value
coef_tours_by_student_duration_lt_8_hrs 20.9 8.74e-05 BIG*** 0.00
coef_tours_by_worker_duration_lt_8_hrs 19.3 8.74e-05 BIG*** 0.00
coef_university_student_departure_shift_effects 0.0765 0.0340 2.25* 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.346 0.844 0.41 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction-0.0972 0.215-0.45 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.104 0.118 0.88 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction-0.535 0.816-0.66 0.00
coef_arrival_constants_am_peak-1.86 0.145-12.84*** 0.00
coef_arrival_constants_early 0.00 0.00 NA 0.00fixed value
coef_arrival_constants_evening 0.109 0.0645 1.68 0.00
coef_arrival_constants_late-0.888 0.0858-10.35*** 0.00
coef_arrival_constants_midday_1-0.478 0.0618-7.73*** 0.00
coef_arrival_constants_midday_2-0.405 0.0420-9.64*** 0.00
coef_arrival_constants_pm_peak_1 0.00 0.00 NA 0.00fixed value
coef_arrival_constants_pm_peak_2 0.320 0.0376 8.52*** 0.00
coef_arrival_constants_pm_peak_3 0.737 0.0414 17.81*** 0.00
coef_arrival_constants_pm_peak_4 0.834 0.0492 16.97*** 0.00
coef_departure_constants_am_peak_1-0.601 0.0240-25.03*** 0.00
coef_departure_constants_am_peak_2 0.00 0.00 NA 0.00fixed value
coef_departure_constants_am_peak_3-0.221 0.0223-9.89*** 0.00
coef_departure_constants_am_peak_4-1.21 0.0368-32.83*** 0.00
coef_departure_constants_early-0.931 0.0394-23.64*** 0.00
coef_departure_constants_evening-1.50 0.169-8.89*** 0.00
coef_departure_constants_late-2.89 0.523-5.53*** 0.00
coef_departure_constants_midday_1-1.68 0.0667-25.25*** 0.00
coef_departure_constants_midday_2-1.71 0.0823-20.76*** 0.00
coef_departure_constants_pm_peak-1.38 0.115-11.93*** 0.00
coef_destination_in_cbd_departure_shift_effects 0.0579 0.00940 6.16*** 0.00
coef_destination_in_cbd_duration_shift_effects 0.0702 0.00841 8.35*** 0.00
coef_destination_in_cbd_early_departure_interaction-0.365 0.0703-5.19*** 0.00
coef_destination_in_cbd_late_arrival_interaction-0.157 0.102-1.54 0.00
coef_duration_constants_0_to_2_hours-2.60 0.110-23.52*** 0.00
coef_duration_constants_10_hours 0.00 0.00 NA 0.00fixed value
coef_duration_constants_11_hours-0.317 0.0255-12.41*** 0.00
coef_duration_constants_12_to_13_hours-0.999 0.0368-27.14*** 0.00
coef_duration_constants_14_to_18_hours-1.64 0.0595-27.58*** 0.00
coef_duration_constants_3_to_4_hours-0.977 0.0830-11.76*** 0.00
coef_duration_constants_5_to_6_hours-0.786 0.0678-11.60*** 0.00
coef_duration_constants_7_to_8_hours-0.192 0.0520-3.69*** 0.00
coef_duration_constants_9_hours 0.0413 0.0245 1.69 0.00
coef_first_of_2plus_work_tours_departure_shift_effects-0.287 0.0162-17.76*** 0.00
coef_first_of_2plus_work_tours_duration_lt_8_hrs 2.16 0.150 14.40*** 0.00
coef_first_of_2plus_work_tours_duration_shift_effects-0.159 0.0411-3.88*** 0.00
coef_free_flow_round_trip_auto_time_shift_effects_departure-0.00116 0.000147-7.89*** 0.00
coef_free_flow_round_trip_auto_time_shift_effects_duration 0.00210 0.000113 18.52*** 0.00
coef_full_time_worker_10_to_12_departure_interaction-0.474 0.0596-7.95*** 0.00
coef_full_time_worker_duration_lt_9_hours_interaction-1.19 0.0487-24.43*** 0.00
coef_household_income_departure_shift_effects 0.000219 3.32e-05 6.61*** 0.00
coef_household_income_early_departure_interaction-0.462 0.0533-8.67*** 0.00
coef_household_income_late_arrival_interaction-0.560 0.0878-6.38*** 0.00
coef_mode_choice_logsum 0.666 0.0752 8.86*** 0.00
coef_non_working_adult_duration_shift_effects-0.121 NA NA 0.00
coef_part_time_worker_13_to_15_arrival_interaction 0.688 0.0550 12.51*** 0.00
coef_part_time_worker_departure_shift_effects 0.0588 0.00877 6.70*** 0.00
coef_previously_scheduled_tour_begins_in_this_arrival_hour-1.25 3.92-0.32 0.00
coef_previously_scheduled_tour_ends_in_this_departure_hour-0.564 0.810-0.70 0.00
coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours-18.7 7.36-2.54* 0.00
coef_rural_household_early_departure_interaction 0.404 NA NA 0.00
coef_rural_household_late_arrival_interaction-0.345 2.80e-14-BIG*** 0.00
coef_subsequent_2plus_work_departure_tours_shift_effects 0.0599 0.0282 2.12* 0.00
coef_subsequent_2plus_work_duration_tours_shift_effects-0.353 0.0325-10.85*** 0.00
coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs 2.61 0.544 4.80*** 0.00
coef_subsequent_tour_must_start_after_previous_tour_ends-100. 0.00 NA 0.00fixed value
coef_tours_by_student_duration_lt_8_hrs 2.62 0.456 5.75*** 0.00
coef_tours_by_worker_duration_lt_8_hrs 0.950 0.456 2.08* 0.00
coef_university_student_departure_shift_effects 0.0454 0.0124 3.66*** 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2702,7 +2891,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -2723,20 +2912,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -2755,7 +2933,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -2794,19 +2972,19 @@ " \n", " 1\n", " coef_free_flow_round_trip_auto_time_shift_effe...\n", - " 0.000599\n", + " -0.001158\n", " F\n", " \n", " \n", " 2\n", " coef_free_flow_round_trip_auto_time_shift_effe...\n", - " 0.004216\n", + " 0.002097\n", " F\n", " \n", " \n", " 3\n", " coef_part_time_worker_departure_shift_effects\n", - " 0.095094\n", + " 0.058805\n", " F\n", " \n", " \n", @@ -2824,7 +3002,7 @@ " \n", " 59\n", " coef_duration_constants_9_hours\n", - " 0.137251\n", + " 0.041325\n", " F\n", " \n", " \n", @@ -2836,19 +3014,19 @@ " \n", " 61\n", " coef_duration_constants_11_hours\n", - " -0.358479\n", + " -0.316543\n", " F\n", " \n", " \n", " 62\n", " coef_duration_constants_12_to_13_hours\n", - " -1.132000\n", + " -0.998700\n", " F\n", " \n", " \n", " 63\n", " coef_duration_constants_14_to_18_hours\n", - " -1.854875\n", + " -1.641305\n", " F\n", " \n", " \n", @@ -2859,21 +3037,21 @@ "text/plain": [ " coefficient_name value constrain\n", "0 coef_dummy 1.000000 T\n", - "1 coef_free_flow_round_trip_auto_time_shift_effe... 0.000599 F\n", - "2 coef_free_flow_round_trip_auto_time_shift_effe... 0.004216 F\n", - "3 coef_part_time_worker_departure_shift_effects 0.095094 F\n", + "1 coef_free_flow_round_trip_auto_time_shift_effe... -0.001158 F\n", + "2 coef_free_flow_round_trip_auto_time_shift_effe... 0.002097 F\n", + "3 coef_part_time_worker_departure_shift_effects 0.058805 F\n", "4 coef_non_working_adult_duration_shift_effects -0.120700 F\n", ".. ... ... ...\n", - "59 coef_duration_constants_9_hours 0.137251 F\n", + "59 coef_duration_constants_9_hours 0.041325 F\n", "60 coef_duration_constants_10_hours 0.000000 T\n", - "61 coef_duration_constants_11_hours -0.358479 F\n", - "62 coef_duration_constants_12_to_13_hours -1.132000 F\n", - "63 coef_duration_constants_14_to_18_hours -1.854875 F\n", + "61 coef_duration_constants_11_hours -0.316543 F\n", + "62 coef_duration_constants_12_to_13_hours -0.998700 F\n", + "63 coef_duration_constants_14_to_18_hours -1.641305 F\n", "\n", "[64 rows x 3 columns]" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -2890,7 +3068,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -2904,7 +3082,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/09_school_tour_scheduling.ipynb b/activitysim/examples/example_estimation/notebooks/09_school_tour_scheduling.ipynb index 397fe34962..d05cc6e6f9 100644 --- a/activitysim/examples/example_estimation/notebooks/09_school_tour_scheduling.ipynb +++ b/activitysim/examples/example_estimation/notebooks/09_school_tour_scheduling.ipynb @@ -34,27 +34,75 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +116,28 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_school/tour_scheduling_school_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_school/mandatory_tour_scheduling_school_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_school/mandatory_tour_scheduling_school_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/mandatory_tour_scheduling_school/mandatory_tour_scheduling_school_choosers_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"mandatory_tour_scheduling_school\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -673,28 +737,28 @@ " 23\n", " coef_adjacent_window_exists_before_this_depart...\n", " Adjacent window exists before this departure h...\n", - " @(df.tour_count>1) & (df.tour_num == 1) & _adj...\n", + " @(df.tour_count>1) * (df.tour_num == 1) * _adj...\n", " coef_adjacent_window_exists_before_this_depart...\n", " \n", " \n", " 24\n", " coef_adjacent_window_exists_after_this_arrival...\n", " Adjacent window exists after this arrival hour...\n", - " @(df.tour_count>1) & (df.tour_num == 1) & _adj...\n", + " @(df.tour_count>1) * (df.tour_num == 1) * _adj...\n", " coef_adjacent_window_exists_after_this_arrival...\n", " \n", " \n", " 25\n", " util_adjacent_window_exists_before_this_depart...\n", " Adjacent window exists before this departure h...\n", - " @(df.tour_num > 1) & _adjacent_window_before\n", + " @(df.tour_num > 1) * _adjacent_window_before\n", " coef_adjacent_window_exists_before_this_depart...\n", " \n", " \n", " 26\n", " util_adjacent_window_exists_after_this_arrival...\n", " Adjacent window exists after this arrival hour...\n", - " @(df.tour_num > 1) & _adjacent_window_after\n", + " @(df.tour_num > 1) * _adjacent_window_after\n", " coef_adjacent_window_exists_after_this_arrival...\n", " \n", " \n", @@ -713,7 +777,7 @@ " \n", " \n", " 29\n", - " util_departure_constants_am_peak_1 _6\n", + " util_departure_constants_am_peak_1_6\n", " Departure Constants -- AM peak 1 (6)\n", " start == 6\n", " coef_departure_constants_am_peak_1\n", @@ -940,7 +1004,7 @@ "26 util_adjacent_window_exists_after_this_arrival... \n", "27 util_remaining_work_school_tours_to_be_schedul... \n", "28 util_departure_constants_early_up_to_5 \n", - "29 util_departure_constants_am_peak_1 _6 \n", + "29 util_departure_constants_am_peak_1_6 \n", "30 util_departure_constants_am_peak_2_7 \n", "31 util_departure_constants_am_peak_3_8 \n", "32 util_departure_constants_am_peak_4_9 \n", @@ -1048,10 +1112,10 @@ "18 mode_choice_logsum \n", "21 @tt.previous_tour_ends(df.person_id, df.start) \n", "22 @tt.previous_tour_begins(df.person_id, df.end) \n", - "23 @(df.tour_count>1) & (df.tour_num == 1) & _adj... \n", - "24 @(df.tour_count>1) & (df.tour_num == 1) & _adj... \n", - "25 @(df.tour_num > 1) & _adjacent_window_before \n", - "26 @(df.tour_num > 1) & _adjacent_window_after \n", + "23 @(df.tour_count>1) * (df.tour_num == 1) * _adj... \n", + "24 @(df.tour_count>1) * (df.tour_num == 1) * _adj... \n", + "25 @(df.tour_num > 1) * _adjacent_window_before \n", + "26 @(df.tour_num > 1) * _adjacent_window_after \n", "27 @((df.tour_count>1) & (df.tour_num == 1)) * 1.... \n", "28 start < 6 \n", "29 start == 6 \n", @@ -1209,10 +1273,10 @@ " \n", " \n", " 0\n", - " 25820\n", - " 106\n", - " 121\n", - " 629\n", + " 78669\n", + " 136\n", + " 59\n", + " 1918\n", " school\n", " 1\n", " 1\n", @@ -1226,17 +1290,17 @@ " True\n", " True\n", " -1\n", - " 12\n", - " 131\n", + " 188\n", + " 186\n", " 5\n", " 5\n", " \n", " \n", " 1\n", - " 52265\n", - " 88\n", - " 86\n", - " 1274\n", + " 131846\n", + " 175\n", + " 59\n", + " 3215\n", " school\n", " 1\n", " 1\n", @@ -1250,17 +1314,17 @@ " True\n", " True\n", " -1\n", - " 10\n", - " 166\n", + " 229\n", + " 252\n", " 5\n", " 5\n", " \n", " \n", " 2\n", - " 1117937\n", - " 163\n", - " 163\n", - " 27266\n", + " 178873\n", + " 166\n", + " 45\n", + " 4362\n", " school\n", " 1\n", " 1\n", @@ -1274,17 +1338,17 @@ " True\n", " True\n", " -1\n", - " 12\n", - " 9\n", + " 342\n", + " 313\n", " 5\n", " 5\n", " \n", " \n", " 3\n", - " 1148523\n", - " 126\n", - " 56\n", - " 28012\n", + " 240250\n", + " 154\n", + " 64\n", + " 5859\n", " school\n", " 1\n", " 1\n", @@ -1298,17 +1362,17 @@ " True\n", " True\n", " -1\n", - " 5\n", - " 10\n", + " 490\n", + " 400\n", " 5\n", " 5\n", " \n", " \n", " 4\n", - " 1208547\n", - " 61\n", - " 61\n", - " 29476\n", + " 250131\n", + " 164\n", + " 74\n", + " 6100\n", " school\n", " 1\n", " 1\n", @@ -1322,8 +1386,8 @@ " True\n", " True\n", " -1\n", - " 13\n", - " 16\n", + " 545\n", + " 437\n", " 5\n", " 5\n", " \n", @@ -1352,59 +1416,59 @@ " ...\n", " \n", " \n", - " 604\n", - " 307969317\n", - " 170\n", - " 170\n", - " 7511446\n", + " 10347\n", + " 309517395\n", + " 78\n", + " 60\n", + " 7549204\n", " school\n", " 1\n", " 1\n", - " 2\n", - " 2\n", + " 1\n", + " 1\n", " mandatory\n", " ...\n", " False\n", - " work_and_school\n", - " True\n", - " True\n", + " school1\n", + " False\n", " True\n", - " 92\n", - " 91\n", - " 8\n", - " 7\n", - " 12\n", + " False\n", + " -1\n", + " 885\n", + " 907\n", + " 5\n", + " 5\n", " \n", " \n", - " 605\n", - " 308122616\n", - " 163\n", - " 164\n", - " 7515185\n", + " 10348\n", + " 309518789\n", + " 45\n", + " 64\n", + " 7549238\n", " school\n", " 1\n", " 1\n", - " 2\n", - " 2\n", + " 1\n", + " 1\n", " mandatory\n", " ...\n", " False\n", - " work_and_school\n", - " True\n", - " True\n", + " school1\n", + " False\n", " True\n", - " 16\n", - " 188\n", - " 142\n", - " 7\n", - " 14\n", + " False\n", + " -1\n", + " 1018\n", + " 910\n", + " 5\n", + " 5\n", " \n", " \n", - " 606\n", - " 308631508\n", - " 169\n", - " 169\n", - " 7527597\n", + " 10349\n", + " 309590785\n", + " 126\n", + " 46\n", + " 7550994\n", " school\n", " 1\n", " 1\n", @@ -1416,133 +1480,133 @@ " school1\n", " False\n", " True\n", - " True\n", + " False\n", " -1\n", - " 13\n", - " 188\n", + " 1091\n", + " 1124\n", " 5\n", " 5\n", " \n", " \n", - " 607\n", - " 309183983\n", - " 22\n", - " 20\n", - " 7541072\n", + " 10350\n", + " 309649169\n", + " 62\n", + " 28\n", + " 7552418\n", " school\n", - " 2\n", " 1\n", " 1\n", - " 2\n", + " 1\n", + " 1\n", " mandatory\n", " ...\n", " False\n", - " school2\n", + " school1\n", " False\n", " True\n", - " True\n", + " False\n", " -1\n", - " 107\n", - " 117\n", + " 1263\n", + " 1268\n", " 5\n", " 5\n", " \n", " \n", - " 608\n", - " 309183984\n", - " 180\n", - " 175\n", - " 7541072\n", + " 10351\n", + " 310038054\n", + " 38\n", + " 148\n", + " 7561903\n", " school\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", " mandatory\n", " ...\n", " False\n", - " school2\n", + " school1\n", " False\n", " True\n", " True\n", " -1\n", - " 107\n", - " 117\n", - " 6\n", - " 9\n", + " 1127\n", + " 1121\n", + " 5\n", + " 5\n", " \n", " \n", "\n", - "

609 rows × 40 columns

\n", + "

10352 rows × 40 columns

\n", "" ], "text/plain": [ - " tour_id model_choice override_choice person_id tour_type \\\n", - "0 25820 106 121 629 school \n", - "1 52265 88 86 1274 school \n", - "2 1117937 163 163 27266 school \n", - "3 1148523 126 56 28012 school \n", - "4 1208547 61 61 29476 school \n", - ".. ... ... ... ... ... \n", - "604 307969317 170 170 7511446 school \n", - "605 308122616 163 164 7515185 school \n", - "606 308631508 169 169 7527597 school \n", - "607 309183983 22 20 7541072 school \n", - "608 309183984 180 175 7541072 school \n", + " tour_id model_choice override_choice person_id tour_type \\\n", + "0 78669 136 59 1918 school \n", + "1 131846 175 59 3215 school \n", + "2 178873 166 45 4362 school \n", + "3 240250 154 64 5859 school \n", + "4 250131 164 74 6100 school \n", + "... ... ... ... ... ... \n", + "10347 309517395 78 60 7549204 school \n", + "10348 309518789 45 64 7549238 school \n", + "10349 309590785 126 46 7550994 school \n", + "10350 309649169 62 28 7552418 school \n", + "10351 310038054 38 148 7561903 school \n", "\n", - " tour_type_count tour_type_num tour_num tour_count tour_category ... \\\n", - "0 1 1 1 1 mandatory ... \n", - "1 1 1 1 1 mandatory ... \n", - "2 1 1 1 1 mandatory ... \n", - "3 1 1 1 1 mandatory ... \n", - "4 1 1 1 1 mandatory ... \n", - ".. ... ... ... ... ... ... \n", - "604 1 1 2 2 mandatory ... \n", - "605 1 1 2 2 mandatory ... \n", - "606 1 1 1 1 mandatory ... \n", - "607 2 1 1 2 mandatory ... \n", - "608 2 2 2 2 mandatory ... \n", + " tour_type_count tour_type_num tour_num tour_count tour_category \\\n", + "0 1 1 1 1 mandatory \n", + "1 1 1 1 1 mandatory \n", + "2 1 1 1 1 mandatory \n", + "3 1 1 1 1 mandatory \n", + "4 1 1 1 1 mandatory \n", + "... ... ... ... ... ... \n", + "10347 1 1 1 1 mandatory \n", + "10348 1 1 1 1 mandatory \n", + "10349 1 1 1 1 mandatory \n", + "10350 1 1 1 1 mandatory \n", + "10351 1 1 1 1 mandatory \n", "\n", - " home_is_rural mandatory_tour_frequency is_worker is_student \\\n", - "0 False school1 False True \n", - "1 False school1 False True \n", - "2 False school1 False True \n", - "3 False school1 False True \n", - "4 False school1 False True \n", - ".. ... ... ... ... \n", - "604 False work_and_school True True \n", - "605 False work_and_school True True \n", - "606 False school1 False True \n", - "607 False school2 False True \n", - "608 False school2 False True \n", + " ... home_is_rural mandatory_tour_frequency is_worker is_student \\\n", + "0 ... False school1 False True \n", + "1 ... False school1 False True \n", + "2 ... False school1 False True \n", + "3 ... False school1 False True \n", + "4 ... False school1 False True \n", + "... ... ... ... ... ... \n", + "10347 ... False school1 False True \n", + "10348 ... False school1 False True \n", + "10349 ... False school1 False True \n", + "10350 ... False school1 False True \n", + "10351 ... False school1 False True \n", "\n", - " is_university workplace_zone_id school_zone_id home_zone_id \\\n", - "0 True -1 12 131 \n", - "1 True -1 10 166 \n", - "2 True -1 12 9 \n", - "3 True -1 5 10 \n", - "4 True -1 13 16 \n", - ".. ... ... ... ... \n", - "604 True 92 91 8 \n", - "605 True 16 188 142 \n", - "606 True -1 13 188 \n", - "607 True -1 107 117 \n", - "608 True -1 107 117 \n", + " is_university workplace_zone_id school_zone_id home_zone_id \\\n", + "0 True -1 188 186 \n", + "1 True -1 229 252 \n", + "2 True -1 342 313 \n", + "3 True -1 490 400 \n", + "4 True -1 545 437 \n", + "... ... ... ... ... \n", + "10347 False -1 885 907 \n", + "10348 False -1 1018 910 \n", + "10349 False -1 1091 1124 \n", + "10350 False -1 1263 1268 \n", + "10351 True -1 1127 1121 \n", "\n", - " start_previous end_previous \n", - "0 5 5 \n", - "1 5 5 \n", - "2 5 5 \n", - "3 5 5 \n", - "4 5 5 \n", - ".. ... ... \n", - "604 7 12 \n", - "605 7 14 \n", - "606 5 5 \n", - "607 5 5 \n", - "608 6 9 \n", + " start_previous end_previous \n", + "0 5 5 \n", + "1 5 5 \n", + "2 5 5 \n", + "3 5 5 \n", + "4 5 5 \n", + "... ... ... \n", + "10347 5 5 \n", + "10348 5 5 \n", + "10349 5 5 \n", + "10350 5 5 \n", + "10351 5 5 \n", "\n", - "[609 rows x 40 columns]" + "[10352 rows x 40 columns]" ] }, "execution_count": 6, @@ -1588,148 +1652,148 @@ " \n", " \n", " tour_id\n", - " variable\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", + " start\n", + " end\n", + " duration\n", + " tdd\n", + " out_period\n", + " in_period\n", + " mode_choice_logsum\n", + " util_roundtrip_auto_time_to_work\n", + " util_ft_worker_departure\n", " ...\n", - " 180\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", + " util_arrival_constants_late_22_and_later\n", + " util_duration_constants_0_to_2_hours\n", + " util_duration_constants_3_to_4_hours\n", + " util_duration_constants_5_to_6_hours\n", + " util_duration_constants_7_to_8_hours\n", + " util_duration_constants_9_hours\n", + " util_duration_constants_10_hours\n", + " util_duration_constants_11_hours\n", + " util_duration_constants_12_to_13_hours\n", + " util_duration_constants_14_to_18_hours\n", " \n", " \n", " \n", " \n", " 0\n", - " 25820\n", - " duration\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", + " 78669\n", + " 5\n", " 5\n", - " 6\n", - " 7\n", - " ...\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", " 0\n", - " 1\n", - " 2\n", " 0\n", - " 1\n", + " EA\n", + " EA\n", + " -0.558633\n", + " 0.0\n", " 0\n", + " ...\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 1\n", - " 25820\n", - " end\n", + " 78669\n", " 5\n", " 6\n", - " 7\n", - " 8\n", - " 9\n", - " 10\n", - " 11\n", - " 12\n", + " 1\n", + " 1\n", + " EA\n", + " AM\n", + " -0.452321\n", + " 0.0\n", + " 0\n", " ...\n", - " 20\n", - " 21\n", - " 22\n", - " 23\n", - " 21\n", - " 22\n", - " 23\n", - " 22\n", - " 23\n", - " 23\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 2\n", - " 25820\n", - " in_period\n", + " 78669\n", + " 5\n", + " 7\n", + " 2\n", + " 2\n", " EA\n", " AM\n", - " AM\n", - " AM\n", - " AM\n", - " MD\n", - " MD\n", - " MD\n", + " -0.452321\n", + " 0.0\n", + " 0\n", " ...\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 3\n", - " 25820\n", - " mode_choice_logsum\n", - " -1.296359741907684\n", - " -1.3037984712857993\n", - " -1.3037984712857993\n", - " -1.3037984712857993\n", - " -1.3037984712857993\n", - " -1.3201068063134296\n", - " -1.3201068063134296\n", - " -1.3201068063134296\n", + " 78669\n", + " 5\n", + " 8\n", + " 3\n", + " 3\n", + " EA\n", + " AM\n", + " -0.452321\n", + " 0.0\n", + " 0\n", " ...\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", - " -1.5517476709880444\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 4\n", - " 25820\n", - " out_period\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", - " EA\n", + " 78669\n", + " 5\n", + " 9\n", + " 4\n", + " 4\n", " EA\n", + " AM\n", + " -0.452321\n", + " 0.0\n", + " 0\n", " ...\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", - " EV\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " ...\n", @@ -1756,68 +1820,68 @@ " ...\n", " \n", " \n", - " 37144\n", - " 309183984\n", - " util_subsequent_2plus_school_tours_duration\n", - " 0\n", + " 1912825\n", + " 310038054\n", + " 21\n", + " 22\n", " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 185\n", + " EV\n", + " EV\n", + " 1.881379\n", + " 0.0\n", " 0\n", " ...\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 0\n", - " 1\n", - " 2\n", - " 0\n", - " 1\n", - " 0\n", - " \n", - " \n", - " 37145\n", - " 309183984\n", - " util_subsequent_of_2plus_school_lt_6_hours\n", " True\n", " True\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " \n", + " \n", + " 1912826\n", + " 310038054\n", + " 21\n", + " 23\n", + " 2\n", + " 186\n", + " EV\n", + " EV\n", + " 1.881379\n", + " 0.0\n", " 0\n", " ...\n", " True\n", " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 37146\n", - " 309183984\n", - " util_subsequent_tour_must_start_after_previous...\n", - " True\n", - " True\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 1912827\n", + " 310038054\n", + " 22\n", + " 22\n", " 0\n", + " 187\n", + " EV\n", + " EV\n", + " 1.881379\n", + " 0.0\n", " 0\n", " ...\n", - " False\n", - " False\n", + " True\n", + " True\n", " False\n", " False\n", " False\n", @@ -1828,151 +1892,216 @@ " False\n", " \n", " \n", - " 37147\n", - " 309183984\n", - " util_univ_departure\n", - " 5\n", - " 5\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " ...\n", - " 20\n", - " 20\n", - " 20\n", - " 20\n", - " 21\n", - " 21\n", - " 21\n", - " 22\n", + " 1912828\n", + " 310038054\n", " 22\n", " 23\n", - " \n", - " \n", - " 37148\n", - " 309183984\n", - " util_univ_duration\n", - " 0\n", " 1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 188\n", + " EV\n", + " EV\n", + " 1.881379\n", + " 0.0\n", " 0\n", " ...\n", + " True\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " \n", + " \n", + " 1912829\n", + " 310038054\n", + " 23\n", + " 23\n", " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 0\n", - " 1\n", - " 2\n", - " 0\n", - " 1\n", + " 189\n", + " EV\n", + " EV\n", + " 1.881379\n", + " 0.0\n", " 0\n", + " ...\n", + " True\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", "\n", - "

37149 rows × 192 columns

\n", + "

1912830 rows × 63 columns

\n", "" ], "text/plain": [ - " tour_id variable \\\n", - "0 25820 duration \n", - "1 25820 end \n", - "2 25820 in_period \n", - "3 25820 mode_choice_logsum \n", - "4 25820 out_period \n", - "... ... ... \n", - "37144 309183984 util_subsequent_2plus_school_tours_duration \n", - "37145 309183984 util_subsequent_of_2plus_school_lt_6_hours \n", - "37146 309183984 util_subsequent_tour_must_start_after_previous... \n", - "37147 309183984 util_univ_departure \n", - "37148 309183984 util_univ_duration \n", + " tour_id start end duration tdd out_period in_period \\\n", + "0 78669 5 5 0 0 EA EA \n", + "1 78669 5 6 1 1 EA AM \n", + "2 78669 5 7 2 2 EA AM \n", + "3 78669 5 8 3 3 EA AM \n", + "4 78669 5 9 4 4 EA AM \n", + "... ... ... ... ... ... ... ... \n", + "1912825 310038054 21 22 1 185 EV EV \n", + "1912826 310038054 21 23 2 186 EV EV \n", + "1912827 310038054 22 22 0 187 EV EV \n", + "1912828 310038054 22 23 1 188 EV EV \n", + "1912829 310038054 23 23 0 189 EV EV \n", + "\n", + " mode_choice_logsum util_roundtrip_auto_time_to_work \\\n", + "0 -0.558633 0.0 \n", + "1 -0.452321 0.0 \n", + "2 -0.452321 0.0 \n", + "3 -0.452321 0.0 \n", + "4 -0.452321 0.0 \n", + "... ... ... \n", + "1912825 1.881379 0.0 \n", + "1912826 1.881379 0.0 \n", + "1912827 1.881379 0.0 \n", + "1912828 1.881379 0.0 \n", + "1912829 1.881379 0.0 \n", + "\n", + " util_ft_worker_departure ... \\\n", + "0 0 ... \n", + "1 0 ... \n", + "2 0 ... \n", + "3 0 ... \n", + "4 0 ... \n", + "... ... ... \n", + "1912825 0 ... \n", + "1912826 0 ... \n", + "1912827 0 ... \n", + "1912828 0 ... \n", + "1912829 0 ... \n", + "\n", + " util_arrival_constants_late_22_and_later \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "1912825 True \n", + "1912826 True \n", + "1912827 True \n", + "1912828 True \n", + "1912829 True \n", + "\n", + " util_duration_constants_0_to_2_hours \\\n", + "0 True \n", + "1 True \n", + "2 True \n", + "3 False \n", + "4 False \n", + "... ... \n", + "1912825 True \n", + "1912826 True \n", + "1912827 True \n", + "1912828 True \n", + "1912829 True \n", "\n", - " 0 1 2 \\\n", - "0 0 1 2 \n", - "1 5 6 7 \n", - "2 EA AM AM \n", - "3 -1.296359741907684 -1.3037984712857993 -1.3037984712857993 \n", - "4 EA EA EA \n", - "... ... ... ... \n", - "37144 0 1 0 \n", - "37145 True True 0 \n", - "37146 True True 0 \n", - "37147 5 5 0 \n", - "37148 0 1 0 \n", + " util_duration_constants_3_to_4_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 True \n", + "4 True \n", + "... ... \n", + "1912825 False \n", + "1912826 False \n", + "1912827 False \n", + "1912828 False \n", + "1912829 False \n", "\n", - " 3 4 5 \\\n", - "0 3 4 5 \n", - "1 8 9 10 \n", - "2 AM AM MD \n", - "3 -1.3037984712857993 -1.3037984712857993 -1.3201068063134296 \n", - "4 EA EA EA \n", - "... ... ... ... \n", - "37144 0 0 0 \n", - "37145 0 0 0 \n", - "37146 0 0 0 \n", - "37147 0 0 0 \n", - "37148 0 0 0 \n", + " util_duration_constants_5_to_6_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "1912825 False \n", + "1912826 False \n", + "1912827 False \n", + "1912828 False \n", + "1912829 False \n", "\n", - " 6 7 ... 180 \\\n", - "0 6 7 ... 0 \n", - "1 11 12 ... 20 \n", - "2 MD MD ... EV \n", - "3 -1.3201068063134296 -1.3201068063134296 ... -1.5517476709880444 \n", - "4 EA EA ... EV \n", - "... ... ... ... ... \n", - "37144 0 0 ... 0 \n", - "37145 0 0 ... True \n", - "37146 0 0 ... False \n", - "37147 0 0 ... 20 \n", - "37148 0 0 ... 0 \n", + " util_duration_constants_7_to_8_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "1912825 False \n", + "1912826 False \n", + "1912827 False \n", + "1912828 False \n", + "1912829 False \n", "\n", - " 181 182 183 \\\n", - "0 1 2 3 \n", - "1 21 22 23 \n", - "2 EV EV EV \n", - "3 -1.5517476709880444 -1.5517476709880444 -1.5517476709880444 \n", - "4 EV EV EV \n", - "... ... ... ... \n", - "37144 1 2 3 \n", - "37145 True True True \n", - "37146 False False False \n", - "37147 20 20 20 \n", - "37148 1 2 3 \n", + " util_duration_constants_9_hours util_duration_constants_10_hours \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "1912825 False False \n", + "1912826 False False \n", + "1912827 False False \n", + "1912828 False False \n", + "1912829 False False \n", "\n", - " 184 185 186 \\\n", - "0 0 1 2 \n", - "1 21 22 23 \n", - "2 EV EV EV \n", - "3 -1.5517476709880444 -1.5517476709880444 -1.5517476709880444 \n", - "4 EV EV EV \n", - "... ... ... ... \n", - "37144 0 1 2 \n", - "37145 True True True \n", - "37146 False False False \n", - "37147 21 21 21 \n", - "37148 0 1 2 \n", + " util_duration_constants_11_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "1912825 False \n", + "1912826 False \n", + "1912827 False \n", + "1912828 False \n", + "1912829 False \n", "\n", - " 187 188 189 \n", - "0 0 1 0 \n", - "1 22 23 23 \n", - "2 EV EV EV \n", - "3 -1.5517476709880444 -1.5517476709880444 -1.5517476709880444 \n", - "4 EV EV EV \n", - "... ... ... ... \n", - "37144 0 1 0 \n", - "37145 True True True \n", - "37146 False False False \n", - "37147 22 22 23 \n", - "37148 0 1 0 \n", + " util_duration_constants_12_to_13_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "1912825 False \n", + "1912826 False \n", + "1912827 False \n", + "1912828 False \n", + "1912829 False \n", "\n", - "[37149 rows x 192 columns]" + " util_duration_constants_14_to_18_hours \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "1912825 False \n", + "1912826 False \n", + "1912827 False \n", + "1912828 False \n", + "1912829 False \n", + "\n", + "[1912830 rows x 63 columns]" ] }, "execution_count": 7, @@ -2002,13 +2131,80 @@ "name": "stderr", "output_type": "stream", "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "problem: chosen_but_not_available has (46 issues)\n" ] }, + { + "data": { + "text/plain": [ + "(,\n", + " ┣ chosen_but_not_available: altid n example rows\n", + " ┃ 0 42 1 3775\n", + " ┃ 1 43 1 3607\n", + " ┃ 2 56 1 2492\n", + " ┃ 3 57 1 9754\n", + " ┃ 4 58 1 3045\n", + " ┃ 5 60 1 4646\n", + " ┃ 6 72 2 2490, 5457\n", + " ┃ 7 76 2 2863, 7566\n", + " ┃ 8 89 1 178\n", + " ┃ 9 90 2 8286, 9903\n", + " ┃ 10 100 1 4456\n", + " ┃ 11 102 3 15, 4847, 7101\n", + " ┃ 12 103 1 4586\n", + " ┃ 13 104 2 1063, 3558\n", + " ┃ 14 105 2 3258, 4140\n", + " ┃ 15 113 2 117, 499\n", + " ┃ 16 114 3 4784, 6830, 7852\n", + " ┃ 17 115 1 604\n", + " ┃ 18 116 5 588, 3916, 7212\n", + " ┃ 19 117 1 145\n", + " ┃ 20 118 4 5150, 5158, 6843\n", + " ┃ 21 120 1 5714\n", + " ┃ 22 125 2 2341, 6819\n", + " ┃ 23 127 2 3989, 5454\n", + " ┃ 24 128 4 1505, 2340, 4408\n", + " ┃ 25 129 1 8024\n", + " ┃ 26 130 2 5146, 9537\n", + " ┃ 27 136 2 932, 1303\n", + " ┃ 28 138 1 8854\n", + " ┃ 29 139 2 4901, 5428\n", + " ┃ 30 140 1 2116\n", + " ┃ 31 141 2 2305, 5424\n", + " ┃ 32 146 4 54, 2193, 2350\n", + " ┃ 33 147 2 7792, 9568\n", + " ┃ 34 156 2 7158, 7229\n", + " ┃ 35 157 1 9717\n", + " ┃ 36 158 1 7840\n", + " ┃ 37 159 2 111, 7231\n", + " ┃ 38 163 2 540, 7847\n", + " ┃ 39 164 2 188, 1819\n", + " ┃ 40 166 1 2035\n", + " ┃ 41 167 1 2976\n", + " ┃ 42 168 1 2143\n", + " ┃ 43 170 2 5247, 7868\n", + " ┃ 44 171 1 64\n", + " ┃ 45 183 1 9440)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.doctor(repair_ch_av=\"-\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "

Iteration 077 [Optimization terminated successfully]

" + "

Iteration 139 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -2020,7 +2216,7 @@ { "data": { "text/html": [ - "

Best LL = -2331.7420221780226

" + "

Best LL = -39483.50824582766

" ], "text/plain": [ "" @@ -2051,853 +2247,811 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " -0.414229\n", + " 0.232285\n", + " 0.232285\n", " -0.003049\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.414229\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction\n", - " -0.640186\n", + " -1.201676\n", + " -1.201676\n", " -0.527100\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.640186\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " 1.656920\n", + " -0.419250\n", + " -0.419250\n", " 0.089750\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 1.656920\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction\n", - " 0.225722\n", + " -0.760747\n", + " -0.760747\n", " -0.440000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.225722\n", " \n", " \n", " coef_all_adults_ft_worker_duration\n", - " 0.052336\n", + " 0.079713\n", + " 0.079713\n", " 0.109300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.052336\n", " \n", " \n", " coef_arrival_constants_am_peak\n", - " -2.239162\n", + " -2.595385\n", + " -2.595385\n", " -2.428718\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.239162\n", " \n", " \n", " coef_arrival_constants_early\n", - " -1.065980\n", + " -2.446580\n", + " -2.446580\n", " -2.428718\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.065980\n", " \n", " \n", " coef_arrival_constants_evening\n", - " -1.033329\n", + " -0.707272\n", + " -0.707272\n", " -0.870147\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.033329\n", " \n", " \n", " coef_arrival_constants_late\n", - " -1.315910\n", + " -1.639520\n", + " -1.639520\n", " -1.752000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.315910\n", " \n", " \n", " coef_arrival_constants_midday_1\n", - " -1.302330\n", + " -1.267988\n", + " -1.267988\n", " -1.237909\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.302330\n", " \n", " \n", " coef_arrival_constants_midday_2\n", - " -0.554673\n", + " -0.523484\n", + " -0.523484\n", " -0.539769\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.554673\n", " \n", " \n", " coef_arrival_constants_pm_peak_1\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_arrival_constants_pm_peak_2\n", - " -0.441387\n", + " -0.360268\n", + " -0.360268\n", " -0.389169\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.441387\n", " \n", " \n", " coef_arrival_constants_pm_peak_3\n", - " -0.281247\n", + " -0.096426\n", + " -0.096426\n", " -0.198120\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.281247\n", " \n", " \n", " coef_arrival_constants_pm_peak_4\n", - " -0.370368\n", + " -0.127905\n", + " -0.127905\n", " -0.253625\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.370368\n", " \n", " \n", " coef_departure_constants_am_peak_1\n", - " -1.882694\n", + " -1.606835\n", + " -1.606835\n", " -1.617644\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.882694\n", " \n", " \n", " coef_departure_constants_am_peak_2\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_departure_constants_am_peak_3\n", - " -0.183985\n", + " -0.100582\n", + " -0.100582\n", " -0.073827\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.183985\n", " \n", " \n", " coef_departure_constants_am_peak_4\n", - " -2.016403\n", + " -2.142432\n", + " -2.142432\n", " -2.080571\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.016403\n", " \n", " \n", " coef_departure_constants_early\n", - " -3.636770\n", + " -3.850945\n", + " -3.850945\n", " -3.820662\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -3.636770\n", " \n", " \n", " coef_departure_constants_evening\n", - " -5.258795\n", + " -5.546826\n", + " -5.546826\n", " -5.230288\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -5.258795\n", " \n", " \n", " coef_departure_constants_late\n", - " -21.447622\n", + " -10.890722\n", + " -10.890722\n", " -11.886047\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -21.447622\n", " \n", " \n", " coef_departure_constants_midday_1\n", - " -3.279864\n", + " -3.124481\n", + " -3.124481\n", " -2.985739\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -3.279864\n", " \n", " \n", " coef_departure_constants_midday_2\n", - " -3.750548\n", + " -3.861525\n", + " -3.861525\n", " -3.628435\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -3.750548\n", " \n", " \n", " coef_departure_constants_pm_peak\n", - " -3.093167\n", + " -3.281532\n", + " -3.281532\n", " -3.102505\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -3.093167\n", " \n", " \n", " coef_duration_constants_0_to_2_hours\n", - " -1.372110\n", + " -1.318085\n", + " -1.318085\n", " -1.409956\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.372110\n", " \n", " \n", " coef_duration_constants_10_hours\n", - " -0.876448\n", + " -0.914359\n", + " -0.914359\n", " -0.904789\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.876448\n", " \n", " \n", " coef_duration_constants_11_hours\n", - " -1.127264\n", + " -1.643692\n", + " -1.643692\n", " -1.521163\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.127264\n", " \n", " \n", " coef_duration_constants_12_to_13_hours\n", - " -2.668346\n", + " -2.528269\n", + " -2.528269\n", " -2.418489\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.668346\n", " \n", " \n", " coef_duration_constants_14_to_18_hours\n", - " -2.675974\n", + " -2.541672\n", + " -2.541672\n", " -2.503137\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.675974\n", " \n", " \n", " coef_duration_constants_3_to_4_hours\n", - " -0.552200\n", + " -0.680239\n", + " -0.680239\n", " -0.745893\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.552200\n", " \n", " \n", " coef_duration_constants_5_to_6_hours\n", - " -0.532627\n", + " -0.551126\n", + " -0.551126\n", " -0.567637\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.532627\n", " \n", " \n", " coef_duration_constants_7_to_8_hours\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_duration_constants_9_hours\n", - " -0.487609\n", + " -0.664353\n", + " -0.664353\n", " -0.650807\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.487609\n", " \n", " \n", " coef_first_of_2plus_school_lt_6_hours\n", - " 0.169055\n", + " 1.768123\n", + " 1.768123\n", " 1.487000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.169055\n", " \n", " \n", " coef_first_of_2plus_school_tours_departure\n", - " -0.333830\n", + " -0.277161\n", + " -0.277161\n", " -0.300200\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.333830\n", " \n", " \n", " coef_first_of_2plus_school_tours_duration\n", - " -0.042558\n", + " -0.077846\n", + " -0.077846\n", " -0.159300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.042558\n", " \n", " \n", " coef_ft_worker_departure\n", " 0.397100\n", " 0.397100\n", - " 0.0\n", + " 0.397100\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.397100\n", " \n", " \n", " coef_ft_worker_duration\n", " -0.190800\n", " -0.190800\n", - " 0.0\n", + " -0.190800\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.190800\n", " \n", " \n", " coef_hh_income_early_departure\n", - " -13.757852\n", + " -0.643531\n", + " -0.643531\n", " -0.883700\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -13.757852\n", " \n", " \n", " coef_hh_income_late_arrival\n", - " 0.101726\n", + " -0.123912\n", + " -0.123912\n", " -0.353300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.101726\n", " \n", " \n", " coef_mode_choice_logsum\n", - " 0.056410\n", + " 1.403530\n", + " 1.403530\n", " 2.127000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.056410\n", " \n", " \n", " coef_non_worker_departure\n", " 0.553900\n", " 0.553900\n", - " 0.0\n", + " 0.553900\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.553900\n", " \n", " \n", " coef_previous_tour_begins_this_arrival_hour\n", - " -10.370632\n", + " 1.748525\n", + " 1.748525\n", " -1.102000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -10.370632\n", " \n", " \n", " coef_previous_tour_ends_this_departure_hour\n", - " 0.298551\n", + " -1.454921\n", + " -1.454921\n", " -0.599500\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.298551\n", " \n", " \n", " coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours\n", - " -25.000000\n", + " -16.666935\n", + " -16.666935\n", " -16.670000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -25.000000\n", " \n", " \n", " coef_roundtrip_auto_time_to_work\n", - " 0.005137\n", + " 0.002946\n", + " 0.002946\n", " 0.003195\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.005137\n", " \n", " \n", " coef_school_plus_work_tours_by_student_lt_6_hours\n", - " -2.483313\n", + " 1.805060\n", + " 1.805060\n", " 1.730000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.483313\n", " \n", " \n", " coef_school_plus_work_tours_by_worker_lt_6_hours\n", - " -2.071313\n", + " 2.217060\n", + " 2.217060\n", " 2.142000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.071313\n", " \n", " \n", " coef_student_driver_duration\n", - " -0.003958\n", + " 0.021414\n", + " 0.021414\n", " 0.034640\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.003958\n", " \n", " \n", " coef_subsequent_2plus_school_tours_duration\n", - " -0.188299\n", + " -0.309377\n", + " -0.309377\n", " -0.233800\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.188299\n", " \n", " \n", " coef_subsequent_of_2plus_school_lt_6_hours\n", - " 25.000000\n", + " 1.522961\n", + " 1.522961\n", " 2.142000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 25.000000\n", " \n", " \n", " coef_subsequent_tour_must_start_after_previous_tour_ends\n", " -100.000000\n", " -100.000000\n", + " -100.000000\n", + " -100.0\n", + " -100.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " -100.000000\n", " \n", " \n", " coef_univ_departure\n", - " 0.234367\n", + " 0.281763\n", + " 0.281763\n", " 0.280000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.234367\n", " \n", " \n", " coef_univ_duration\n", - " -0.409132\n", + " -0.287541\n", + " -0.287541\n", " -0.290700\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.409132\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_adjacent_window_exists_after_this_arrival_... -0.414229 -0.003049 \n", - "coef_adjacent_window_exists_after_this_arrival_... -0.640186 -0.527100 \n", - "coef_adjacent_window_exists_before_this_departu... 1.656920 0.089750 \n", - "coef_adjacent_window_exists_before_this_departu... 0.225722 -0.440000 \n", - "coef_all_adults_ft_worker_duration 0.052336 0.109300 \n", - "coef_arrival_constants_am_peak -2.239162 -2.428718 \n", - "coef_arrival_constants_early -1.065980 -2.428718 \n", - "coef_arrival_constants_evening -1.033329 -0.870147 \n", - "coef_arrival_constants_late -1.315910 -1.752000 \n", - "coef_arrival_constants_midday_1 -1.302330 -1.237909 \n", - "coef_arrival_constants_midday_2 -0.554673 -0.539769 \n", + " value best \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 0.232285 0.232285 \n", + "coef_adjacent_window_exists_after_this_arrival_... -1.201676 -1.201676 \n", + "coef_adjacent_window_exists_before_this_departu... -0.419250 -0.419250 \n", + "coef_adjacent_window_exists_before_this_departu... -0.760747 -0.760747 \n", + "coef_all_adults_ft_worker_duration 0.079713 0.079713 \n", + "coef_arrival_constants_am_peak -2.595385 -2.595385 \n", + "coef_arrival_constants_early -2.446580 -2.446580 \n", + "coef_arrival_constants_evening -0.707272 -0.707272 \n", + "coef_arrival_constants_late -1.639520 -1.639520 \n", + "coef_arrival_constants_midday_1 -1.267988 -1.267988 \n", + "coef_arrival_constants_midday_2 -0.523484 -0.523484 \n", "coef_arrival_constants_pm_peak_1 0.000000 0.000000 \n", - "coef_arrival_constants_pm_peak_2 -0.441387 -0.389169 \n", - "coef_arrival_constants_pm_peak_3 -0.281247 -0.198120 \n", - "coef_arrival_constants_pm_peak_4 -0.370368 -0.253625 \n", - "coef_departure_constants_am_peak_1 -1.882694 -1.617644 \n", + "coef_arrival_constants_pm_peak_2 -0.360268 -0.360268 \n", + "coef_arrival_constants_pm_peak_3 -0.096426 -0.096426 \n", + "coef_arrival_constants_pm_peak_4 -0.127905 -0.127905 \n", + "coef_departure_constants_am_peak_1 -1.606835 -1.606835 \n", "coef_departure_constants_am_peak_2 0.000000 0.000000 \n", - "coef_departure_constants_am_peak_3 -0.183985 -0.073827 \n", - "coef_departure_constants_am_peak_4 -2.016403 -2.080571 \n", - "coef_departure_constants_early -3.636770 -3.820662 \n", - "coef_departure_constants_evening -5.258795 -5.230288 \n", - "coef_departure_constants_late -21.447622 -11.886047 \n", - "coef_departure_constants_midday_1 -3.279864 -2.985739 \n", - "coef_departure_constants_midday_2 -3.750548 -3.628435 \n", - "coef_departure_constants_pm_peak -3.093167 -3.102505 \n", - "coef_duration_constants_0_to_2_hours -1.372110 -1.409956 \n", - "coef_duration_constants_10_hours -0.876448 -0.904789 \n", - "coef_duration_constants_11_hours -1.127264 -1.521163 \n", - "coef_duration_constants_12_to_13_hours -2.668346 -2.418489 \n", - "coef_duration_constants_14_to_18_hours -2.675974 -2.503137 \n", - "coef_duration_constants_3_to_4_hours -0.552200 -0.745893 \n", - "coef_duration_constants_5_to_6_hours -0.532627 -0.567637 \n", + "coef_departure_constants_am_peak_3 -0.100582 -0.100582 \n", + "coef_departure_constants_am_peak_4 -2.142432 -2.142432 \n", + "coef_departure_constants_early -3.850945 -3.850945 \n", + "coef_departure_constants_evening -5.546826 -5.546826 \n", + "coef_departure_constants_late -10.890722 -10.890722 \n", + "coef_departure_constants_midday_1 -3.124481 -3.124481 \n", + "coef_departure_constants_midday_2 -3.861525 -3.861525 \n", + "coef_departure_constants_pm_peak -3.281532 -3.281532 \n", + "coef_duration_constants_0_to_2_hours -1.318085 -1.318085 \n", + "coef_duration_constants_10_hours -0.914359 -0.914359 \n", + "coef_duration_constants_11_hours -1.643692 -1.643692 \n", + "coef_duration_constants_12_to_13_hours -2.528269 -2.528269 \n", + "coef_duration_constants_14_to_18_hours -2.541672 -2.541672 \n", + "coef_duration_constants_3_to_4_hours -0.680239 -0.680239 \n", + "coef_duration_constants_5_to_6_hours -0.551126 -0.551126 \n", "coef_duration_constants_7_to_8_hours 0.000000 0.000000 \n", - "coef_duration_constants_9_hours -0.487609 -0.650807 \n", - "coef_first_of_2plus_school_lt_6_hours 0.169055 1.487000 \n", - "coef_first_of_2plus_school_tours_departure -0.333830 -0.300200 \n", - "coef_first_of_2plus_school_tours_duration -0.042558 -0.159300 \n", + "coef_duration_constants_9_hours -0.664353 -0.664353 \n", + "coef_first_of_2plus_school_lt_6_hours 1.768123 1.768123 \n", + "coef_first_of_2plus_school_tours_departure -0.277161 -0.277161 \n", + "coef_first_of_2plus_school_tours_duration -0.077846 -0.077846 \n", "coef_ft_worker_departure 0.397100 0.397100 \n", "coef_ft_worker_duration -0.190800 -0.190800 \n", - "coef_hh_income_early_departure -13.757852 -0.883700 \n", - "coef_hh_income_late_arrival 0.101726 -0.353300 \n", - "coef_mode_choice_logsum 0.056410 2.127000 \n", + "coef_hh_income_early_departure -0.643531 -0.643531 \n", + "coef_hh_income_late_arrival -0.123912 -0.123912 \n", + "coef_mode_choice_logsum 1.403530 1.403530 \n", "coef_non_worker_departure 0.553900 0.553900 \n", - "coef_previous_tour_begins_this_arrival_hour -10.370632 -1.102000 \n", - "coef_previous_tour_ends_this_departure_hour 0.298551 -0.599500 \n", - "coef_remaining_work_school_tours_to_be_schedule... -25.000000 -16.670000 \n", - "coef_roundtrip_auto_time_to_work 0.005137 0.003195 \n", - "coef_school_plus_work_tours_by_student_lt_6_hours -2.483313 1.730000 \n", - "coef_school_plus_work_tours_by_worker_lt_6_hours -2.071313 2.142000 \n", - "coef_student_driver_duration -0.003958 0.034640 \n", - "coef_subsequent_2plus_school_tours_duration -0.188299 -0.233800 \n", - "coef_subsequent_of_2plus_school_lt_6_hours 25.000000 2.142000 \n", + "coef_previous_tour_begins_this_arrival_hour 1.748525 1.748525 \n", + "coef_previous_tour_ends_this_departure_hour -1.454921 -1.454921 \n", + "coef_remaining_work_school_tours_to_be_schedule... -16.666935 -16.666935 \n", + "coef_roundtrip_auto_time_to_work 0.002946 0.002946 \n", + "coef_school_plus_work_tours_by_student_lt_6_hours 1.805060 1.805060 \n", + "coef_school_plus_work_tours_by_worker_lt_6_hours 2.217060 2.217060 \n", + "coef_student_driver_duration 0.021414 0.021414 \n", + "coef_subsequent_2plus_school_tours_duration -0.309377 -0.309377 \n", + "coef_subsequent_of_2plus_school_lt_6_hours 1.522961 1.522961 \n", "coef_subsequent_tour_must_start_after_previous_... -100.000000 -100.000000 \n", - "coef_univ_departure 0.234367 0.280000 \n", - "coef_univ_duration -0.409132 -0.290700 \n", + "coef_univ_departure 0.281763 0.281763 \n", + "coef_univ_duration -0.287541 -0.287541 \n", "\n", - " nullvalue minimum \\\n", - "coef_adjacent_window_exists_after_this_arrival_... 0.0 -25.0 \n", - "coef_adjacent_window_exists_after_this_arrival_... 0.0 -25.0 \n", - "coef_adjacent_window_exists_before_this_departu... 0.0 -25.0 \n", - "coef_adjacent_window_exists_before_this_departu... 0.0 -25.0 \n", - "coef_all_adults_ft_worker_duration 0.0 -25.0 \n", - "coef_arrival_constants_am_peak 0.0 -25.0 \n", - "coef_arrival_constants_early 0.0 -25.0 \n", - "coef_arrival_constants_evening 0.0 -25.0 \n", - "coef_arrival_constants_late 0.0 -25.0 \n", - "coef_arrival_constants_midday_1 0.0 -25.0 \n", - "coef_arrival_constants_midday_2 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_1 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_2 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_3 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_4 0.0 -25.0 \n", - "coef_departure_constants_am_peak_1 0.0 -25.0 \n", - "coef_departure_constants_am_peak_2 0.0 -25.0 \n", - "coef_departure_constants_am_peak_3 0.0 -25.0 \n", - "coef_departure_constants_am_peak_4 0.0 -25.0 \n", - "coef_departure_constants_early 0.0 -25.0 \n", - "coef_departure_constants_evening 0.0 -25.0 \n", - "coef_departure_constants_late 0.0 -25.0 \n", - "coef_departure_constants_midday_1 0.0 -25.0 \n", - "coef_departure_constants_midday_2 0.0 -25.0 \n", - "coef_departure_constants_pm_peak 0.0 -25.0 \n", - "coef_duration_constants_0_to_2_hours 0.0 -25.0 \n", - "coef_duration_constants_10_hours 0.0 -25.0 \n", - "coef_duration_constants_11_hours 0.0 -25.0 \n", - "coef_duration_constants_12_to_13_hours 0.0 -25.0 \n", - "coef_duration_constants_14_to_18_hours 0.0 -25.0 \n", - "coef_duration_constants_3_to_4_hours 0.0 -25.0 \n", - "coef_duration_constants_5_to_6_hours 0.0 -25.0 \n", - "coef_duration_constants_7_to_8_hours 0.0 -25.0 \n", - "coef_duration_constants_9_hours 0.0 -25.0 \n", - "coef_first_of_2plus_school_lt_6_hours 0.0 -25.0 \n", - "coef_first_of_2plus_school_tours_departure 0.0 -25.0 \n", - "coef_first_of_2plus_school_tours_duration 0.0 -25.0 \n", - "coef_ft_worker_departure 0.0 -25.0 \n", - "coef_ft_worker_duration 0.0 -25.0 \n", - "coef_hh_income_early_departure 0.0 -25.0 \n", - "coef_hh_income_late_arrival 0.0 -25.0 \n", - "coef_mode_choice_logsum 0.0 -25.0 \n", - "coef_non_worker_departure 0.0 -25.0 \n", - "coef_previous_tour_begins_this_arrival_hour 0.0 -25.0 \n", - "coef_previous_tour_ends_this_departure_hour 0.0 -25.0 \n", - "coef_remaining_work_school_tours_to_be_schedule... 0.0 -25.0 \n", - "coef_roundtrip_auto_time_to_work 0.0 -25.0 \n", - "coef_school_plus_work_tours_by_student_lt_6_hours 0.0 -25.0 \n", - "coef_school_plus_work_tours_by_worker_lt_6_hours 0.0 -25.0 \n", - "coef_student_driver_duration 0.0 -25.0 \n", - "coef_subsequent_2plus_school_tours_duration 0.0 -25.0 \n", - "coef_subsequent_of_2plus_school_lt_6_hours 0.0 -25.0 \n", - "coef_subsequent_tour_must_start_after_previous_... 0.0 -25.0 \n", - "coef_univ_departure 0.0 -25.0 \n", - "coef_univ_duration 0.0 -25.0 \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... -0.003049 -25.0 \n", + "coef_adjacent_window_exists_after_this_arrival_... -0.527100 -25.0 \n", + "coef_adjacent_window_exists_before_this_departu... 0.089750 -25.0 \n", + "coef_adjacent_window_exists_before_this_departu... -0.440000 -25.0 \n", + "coef_all_adults_ft_worker_duration 0.109300 -25.0 \n", + "coef_arrival_constants_am_peak -2.428718 -25.0 \n", + "coef_arrival_constants_early -2.428718 -25.0 \n", + "coef_arrival_constants_evening -0.870147 -25.0 \n", + "coef_arrival_constants_late -1.752000 -25.0 \n", + "coef_arrival_constants_midday_1 -1.237909 -25.0 \n", + "coef_arrival_constants_midday_2 -0.539769 -25.0 \n", + "coef_arrival_constants_pm_peak_1 0.000000 0.0 \n", + "coef_arrival_constants_pm_peak_2 -0.389169 -25.0 \n", + "coef_arrival_constants_pm_peak_3 -0.198120 -25.0 \n", + "coef_arrival_constants_pm_peak_4 -0.253625 -25.0 \n", + "coef_departure_constants_am_peak_1 -1.617644 -25.0 \n", + "coef_departure_constants_am_peak_2 0.000000 0.0 \n", + "coef_departure_constants_am_peak_3 -0.073827 -25.0 \n", + "coef_departure_constants_am_peak_4 -2.080571 -25.0 \n", + "coef_departure_constants_early -3.820662 -25.0 \n", + "coef_departure_constants_evening -5.230288 -25.0 \n", + "coef_departure_constants_late -11.886047 -25.0 \n", + "coef_departure_constants_midday_1 -2.985739 -25.0 \n", + "coef_departure_constants_midday_2 -3.628435 -25.0 \n", + "coef_departure_constants_pm_peak -3.102505 -25.0 \n", + "coef_duration_constants_0_to_2_hours -1.409956 -25.0 \n", + "coef_duration_constants_10_hours -0.904789 -25.0 \n", + "coef_duration_constants_11_hours -1.521163 -25.0 \n", + "coef_duration_constants_12_to_13_hours -2.418489 -25.0 \n", + "coef_duration_constants_14_to_18_hours -2.503137 -25.0 \n", + "coef_duration_constants_3_to_4_hours -0.745893 -25.0 \n", + "coef_duration_constants_5_to_6_hours -0.567637 -25.0 \n", + "coef_duration_constants_7_to_8_hours 0.000000 0.0 \n", + "coef_duration_constants_9_hours -0.650807 -25.0 \n", + "coef_first_of_2plus_school_lt_6_hours 1.487000 -25.0 \n", + "coef_first_of_2plus_school_tours_departure -0.300200 -25.0 \n", + "coef_first_of_2plus_school_tours_duration -0.159300 -25.0 \n", + "coef_ft_worker_departure 0.397100 -25.0 \n", + "coef_ft_worker_duration -0.190800 -25.0 \n", + "coef_hh_income_early_departure -0.883700 -25.0 \n", + "coef_hh_income_late_arrival -0.353300 -25.0 \n", + "coef_mode_choice_logsum 2.127000 -25.0 \n", + "coef_non_worker_departure 0.553900 -25.0 \n", + "coef_previous_tour_begins_this_arrival_hour -1.102000 -25.0 \n", + "coef_previous_tour_ends_this_departure_hour -0.599500 -25.0 \n", + "coef_remaining_work_school_tours_to_be_schedule... -16.670000 -25.0 \n", + "coef_roundtrip_auto_time_to_work 0.003195 -25.0 \n", + "coef_school_plus_work_tours_by_student_lt_6_hours 1.730000 -25.0 \n", + "coef_school_plus_work_tours_by_worker_lt_6_hours 2.142000 -25.0 \n", + "coef_student_driver_duration 0.034640 -25.0 \n", + "coef_subsequent_2plus_school_tours_duration -0.233800 -25.0 \n", + "coef_subsequent_of_2plus_school_lt_6_hours 2.142000 -25.0 \n", + "coef_subsequent_tour_must_start_after_previous_... -100.000000 -100.0 \n", + "coef_univ_departure 0.280000 -25.0 \n", + "coef_univ_duration -0.290700 -25.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_adjacent_window_exists_after_this_arrival_... 25.0 0 \n", - "coef_adjacent_window_exists_after_this_arrival_... 25.0 0 \n", - "coef_adjacent_window_exists_before_this_departu... 25.0 0 \n", - "coef_adjacent_window_exists_before_this_departu... 25.0 0 \n", - "coef_all_adults_ft_worker_duration 25.0 0 \n", - "coef_arrival_constants_am_peak 25.0 0 \n", - "coef_arrival_constants_early 25.0 0 \n", - "coef_arrival_constants_evening 25.0 0 \n", - "coef_arrival_constants_late 25.0 0 \n", - "coef_arrival_constants_midday_1 25.0 0 \n", - "coef_arrival_constants_midday_2 25.0 0 \n", - "coef_arrival_constants_pm_peak_1 25.0 1 \n", - "coef_arrival_constants_pm_peak_2 25.0 0 \n", - "coef_arrival_constants_pm_peak_3 25.0 0 \n", - "coef_arrival_constants_pm_peak_4 25.0 0 \n", - "coef_departure_constants_am_peak_1 25.0 0 \n", - "coef_departure_constants_am_peak_2 25.0 1 \n", - "coef_departure_constants_am_peak_3 25.0 0 \n", - "coef_departure_constants_am_peak_4 25.0 0 \n", - "coef_departure_constants_early 25.0 0 \n", - "coef_departure_constants_evening 25.0 0 \n", - "coef_departure_constants_late 25.0 0 \n", - "coef_departure_constants_midday_1 25.0 0 \n", - "coef_departure_constants_midday_2 25.0 0 \n", - "coef_departure_constants_pm_peak 25.0 0 \n", - "coef_duration_constants_0_to_2_hours 25.0 0 \n", - "coef_duration_constants_10_hours 25.0 0 \n", - "coef_duration_constants_11_hours 25.0 0 \n", - "coef_duration_constants_12_to_13_hours 25.0 0 \n", - "coef_duration_constants_14_to_18_hours 25.0 0 \n", - "coef_duration_constants_3_to_4_hours 25.0 0 \n", - "coef_duration_constants_5_to_6_hours 25.0 0 \n", - "coef_duration_constants_7_to_8_hours 25.0 1 \n", - "coef_duration_constants_9_hours 25.0 0 \n", - "coef_first_of_2plus_school_lt_6_hours 25.0 0 \n", - "coef_first_of_2plus_school_tours_departure 25.0 0 \n", - "coef_first_of_2plus_school_tours_duration 25.0 0 \n", - "coef_ft_worker_departure 25.0 0 \n", - "coef_ft_worker_duration 25.0 0 \n", - "coef_hh_income_early_departure 25.0 0 \n", - "coef_hh_income_late_arrival 25.0 0 \n", - "coef_mode_choice_logsum 25.0 0 \n", - "coef_non_worker_departure 25.0 0 \n", - "coef_previous_tour_begins_this_arrival_hour 25.0 0 \n", - "coef_previous_tour_ends_this_departure_hour 25.0 0 \n", - "coef_remaining_work_school_tours_to_be_schedule... 25.0 0 \n", - "coef_roundtrip_auto_time_to_work 25.0 0 \n", - "coef_school_plus_work_tours_by_student_lt_6_hours 25.0 0 \n", - "coef_school_plus_work_tours_by_worker_lt_6_hours 25.0 0 \n", - "coef_student_driver_duration 25.0 0 \n", - "coef_subsequent_2plus_school_tours_duration 25.0 0 \n", - "coef_subsequent_of_2plus_school_lt_6_hours 25.0 0 \n", - "coef_subsequent_tour_must_start_after_previous_... 25.0 1 \n", - "coef_univ_departure 25.0 0 \n", - "coef_univ_duration 25.0 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 25.0 0.0 \n", + "coef_adjacent_window_exists_after_this_arrival_... 25.0 0.0 \n", + "coef_adjacent_window_exists_before_this_departu... 25.0 0.0 \n", + "coef_adjacent_window_exists_before_this_departu... 25.0 0.0 \n", + "coef_all_adults_ft_worker_duration 25.0 0.0 \n", + "coef_arrival_constants_am_peak 25.0 0.0 \n", + "coef_arrival_constants_early 25.0 0.0 \n", + "coef_arrival_constants_evening 25.0 0.0 \n", + "coef_arrival_constants_late 25.0 0.0 \n", + "coef_arrival_constants_midday_1 25.0 0.0 \n", + "coef_arrival_constants_midday_2 25.0 0.0 \n", + "coef_arrival_constants_pm_peak_1 0.0 0.0 \n", + "coef_arrival_constants_pm_peak_2 25.0 0.0 \n", + "coef_arrival_constants_pm_peak_3 25.0 0.0 \n", + "coef_arrival_constants_pm_peak_4 25.0 0.0 \n", + "coef_departure_constants_am_peak_1 25.0 0.0 \n", + "coef_departure_constants_am_peak_2 0.0 0.0 \n", + "coef_departure_constants_am_peak_3 25.0 0.0 \n", + "coef_departure_constants_am_peak_4 25.0 0.0 \n", + "coef_departure_constants_early 25.0 0.0 \n", + "coef_departure_constants_evening 25.0 0.0 \n", + "coef_departure_constants_late 25.0 0.0 \n", + "coef_departure_constants_midday_1 25.0 0.0 \n", + "coef_departure_constants_midday_2 25.0 0.0 \n", + "coef_departure_constants_pm_peak 25.0 0.0 \n", + "coef_duration_constants_0_to_2_hours 25.0 0.0 \n", + "coef_duration_constants_10_hours 25.0 0.0 \n", + "coef_duration_constants_11_hours 25.0 0.0 \n", + "coef_duration_constants_12_to_13_hours 25.0 0.0 \n", + "coef_duration_constants_14_to_18_hours 25.0 0.0 \n", + "coef_duration_constants_3_to_4_hours 25.0 0.0 \n", + "coef_duration_constants_5_to_6_hours 25.0 0.0 \n", + "coef_duration_constants_7_to_8_hours 0.0 0.0 \n", + "coef_duration_constants_9_hours 25.0 0.0 \n", + "coef_first_of_2plus_school_lt_6_hours 25.0 0.0 \n", + "coef_first_of_2plus_school_tours_departure 25.0 0.0 \n", + "coef_first_of_2plus_school_tours_duration 25.0 0.0 \n", + "coef_ft_worker_departure 25.0 0.0 \n", + "coef_ft_worker_duration 25.0 0.0 \n", + "coef_hh_income_early_departure 25.0 0.0 \n", + "coef_hh_income_late_arrival 25.0 0.0 \n", + "coef_mode_choice_logsum 25.0 0.0 \n", + "coef_non_worker_departure 25.0 0.0 \n", + "coef_previous_tour_begins_this_arrival_hour 25.0 0.0 \n", + "coef_previous_tour_ends_this_departure_hour 25.0 0.0 \n", + "coef_remaining_work_school_tours_to_be_schedule... 25.0 0.0 \n", + "coef_roundtrip_auto_time_to_work 25.0 0.0 \n", + "coef_school_plus_work_tours_by_student_lt_6_hours 25.0 0.0 \n", + "coef_school_plus_work_tours_by_worker_lt_6_hours 25.0 0.0 \n", + "coef_student_driver_duration 25.0 0.0 \n", + "coef_subsequent_2plus_school_tours_duration 25.0 0.0 \n", + "coef_subsequent_of_2plus_school_lt_6_hours 25.0 0.0 \n", + "coef_subsequent_tour_must_start_after_previous_... -100.0 0.0 \n", + "coef_univ_departure 25.0 0.0 \n", + "coef_univ_duration 25.0 0.0 \n", "\n", - " best \n", - "coef_adjacent_window_exists_after_this_arrival_... -0.414229 \n", - "coef_adjacent_window_exists_after_this_arrival_... -0.640186 \n", - "coef_adjacent_window_exists_before_this_departu... 1.656920 \n", - "coef_adjacent_window_exists_before_this_departu... 0.225722 \n", - "coef_all_adults_ft_worker_duration 0.052336 \n", - "coef_arrival_constants_am_peak -2.239162 \n", - "coef_arrival_constants_early -1.065980 \n", - "coef_arrival_constants_evening -1.033329 \n", - "coef_arrival_constants_late -1.315910 \n", - "coef_arrival_constants_midday_1 -1.302330 \n", - "coef_arrival_constants_midday_2 -0.554673 \n", - "coef_arrival_constants_pm_peak_1 0.000000 \n", - "coef_arrival_constants_pm_peak_2 -0.441387 \n", - "coef_arrival_constants_pm_peak_3 -0.281247 \n", - "coef_arrival_constants_pm_peak_4 -0.370368 \n", - "coef_departure_constants_am_peak_1 -1.882694 \n", - "coef_departure_constants_am_peak_2 0.000000 \n", - "coef_departure_constants_am_peak_3 -0.183985 \n", - "coef_departure_constants_am_peak_4 -2.016403 \n", - "coef_departure_constants_early -3.636770 \n", - "coef_departure_constants_evening -5.258795 \n", - "coef_departure_constants_late -21.447622 \n", - "coef_departure_constants_midday_1 -3.279864 \n", - "coef_departure_constants_midday_2 -3.750548 \n", - "coef_departure_constants_pm_peak -3.093167 \n", - "coef_duration_constants_0_to_2_hours -1.372110 \n", - "coef_duration_constants_10_hours -0.876448 \n", - "coef_duration_constants_11_hours -1.127264 \n", - "coef_duration_constants_12_to_13_hours -2.668346 \n", - "coef_duration_constants_14_to_18_hours -2.675974 \n", - "coef_duration_constants_3_to_4_hours -0.552200 \n", - "coef_duration_constants_5_to_6_hours -0.532627 \n", - "coef_duration_constants_7_to_8_hours 0.000000 \n", - "coef_duration_constants_9_hours -0.487609 \n", - "coef_first_of_2plus_school_lt_6_hours 0.169055 \n", - "coef_first_of_2plus_school_tours_departure -0.333830 \n", - "coef_first_of_2plus_school_tours_duration -0.042558 \n", - "coef_ft_worker_departure 0.397100 \n", - "coef_ft_worker_duration -0.190800 \n", - "coef_hh_income_early_departure -13.757852 \n", - "coef_hh_income_late_arrival 0.101726 \n", - "coef_mode_choice_logsum 0.056410 \n", - "coef_non_worker_departure 0.553900 \n", - "coef_previous_tour_begins_this_arrival_hour -10.370632 \n", - "coef_previous_tour_ends_this_departure_hour 0.298551 \n", - "coef_remaining_work_school_tours_to_be_schedule... -25.000000 \n", - "coef_roundtrip_auto_time_to_work 0.005137 \n", - "coef_school_plus_work_tours_by_student_lt_6_hours -2.483313 \n", - "coef_school_plus_work_tours_by_worker_lt_6_hours -2.071313 \n", - "coef_student_driver_duration -0.003958 \n", - "coef_subsequent_2plus_school_tours_duration -0.188299 \n", - "coef_subsequent_of_2plus_school_lt_6_hours 25.000000 \n", - "coef_subsequent_tour_must_start_after_previous_... -100.000000 \n", - "coef_univ_departure 0.234367 \n", - "coef_univ_duration -0.409132 " + " holdfast \n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 0 \n", + "coef_adjacent_window_exists_after_this_arrival_... 0 \n", + "coef_adjacent_window_exists_before_this_departu... 0 \n", + "coef_adjacent_window_exists_before_this_departu... 0 \n", + "coef_all_adults_ft_worker_duration 0 \n", + "coef_arrival_constants_am_peak 0 \n", + "coef_arrival_constants_early 0 \n", + "coef_arrival_constants_evening 0 \n", + "coef_arrival_constants_late 0 \n", + "coef_arrival_constants_midday_1 0 \n", + "coef_arrival_constants_midday_2 0 \n", + "coef_arrival_constants_pm_peak_1 1 \n", + "coef_arrival_constants_pm_peak_2 0 \n", + "coef_arrival_constants_pm_peak_3 0 \n", + "coef_arrival_constants_pm_peak_4 0 \n", + "coef_departure_constants_am_peak_1 0 \n", + "coef_departure_constants_am_peak_2 1 \n", + "coef_departure_constants_am_peak_3 0 \n", + "coef_departure_constants_am_peak_4 0 \n", + "coef_departure_constants_early 0 \n", + "coef_departure_constants_evening 0 \n", + "coef_departure_constants_late 0 \n", + "coef_departure_constants_midday_1 0 \n", + "coef_departure_constants_midday_2 0 \n", + "coef_departure_constants_pm_peak 0 \n", + "coef_duration_constants_0_to_2_hours 0 \n", + "coef_duration_constants_10_hours 0 \n", + "coef_duration_constants_11_hours 0 \n", + "coef_duration_constants_12_to_13_hours 0 \n", + "coef_duration_constants_14_to_18_hours 0 \n", + "coef_duration_constants_3_to_4_hours 0 \n", + "coef_duration_constants_5_to_6_hours 0 \n", + "coef_duration_constants_7_to_8_hours 1 \n", + "coef_duration_constants_9_hours 0 \n", + "coef_first_of_2plus_school_lt_6_hours 0 \n", + "coef_first_of_2plus_school_tours_departure 0 \n", + "coef_first_of_2plus_school_tours_duration 0 \n", + "coef_ft_worker_departure 0 \n", + "coef_ft_worker_duration 0 \n", + "coef_hh_income_early_departure 0 \n", + "coef_hh_income_late_arrival 0 \n", + "coef_mode_choice_logsum 0 \n", + "coef_non_worker_departure 0 \n", + "coef_previous_tour_begins_this_arrival_hour 0 \n", + "coef_previous_tour_ends_this_departure_hour 0 \n", + "coef_remaining_work_school_tours_to_be_schedule... 0 \n", + "coef_roundtrip_auto_time_to_work 0 \n", + "coef_school_plus_work_tours_by_student_lt_6_hours 0 \n", + "coef_school_plus_work_tours_by_worker_lt_6_hours 0 \n", + "coef_student_driver_duration 0 \n", + "coef_subsequent_2plus_school_tours_duration 0 \n", + "coef_subsequent_of_2plus_school_lt_6_hours 0 \n", + "coef_subsequent_tour_must_start_after_previous_... 1 \n", + "coef_univ_departure 0 \n", + "coef_univ_duration 0 " ] }, "metadata": {}, @@ -2907,10 +3061,8 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate()\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.3990405079720497e-24 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -2926,47 +3078,47 @@ " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " -0.414229\n", + " 0.232285\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction\n", - " -0.640186\n", + " -1.201676\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " 1.656920\n", + " -0.419250\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction\n", - " 0.225722\n", + " -0.760747\n", " \n", " \n", " coef_all_adults_ft_worker_duration\n", - " 0.052336\n", + " 0.079713\n", " \n", " \n", " coef_arrival_constants_am_peak\n", - " -2.239162\n", + " -2.595385\n", " \n", " \n", " coef_arrival_constants_early\n", - " -1.065980\n", + " -2.446580\n", " \n", " \n", " coef_arrival_constants_evening\n", - " -1.033329\n", + " -0.707272\n", " \n", " \n", " coef_arrival_constants_late\n", - " -1.315910\n", + " -1.639520\n", " \n", " \n", " coef_arrival_constants_midday_1\n", - " -1.302330\n", + " -1.267988\n", " \n", " \n", " coef_arrival_constants_midday_2\n", - " -0.554673\n", + " -0.523484\n", " \n", " \n", " coef_arrival_constants_pm_peak_1\n", @@ -2974,19 +3126,19 @@ " \n", " \n", " coef_arrival_constants_pm_peak_2\n", - " -0.441387\n", + " -0.360268\n", " \n", " \n", " coef_arrival_constants_pm_peak_3\n", - " -0.281247\n", + " -0.096426\n", " \n", " \n", " coef_arrival_constants_pm_peak_4\n", - " -0.370368\n", + " -0.127905\n", " \n", " \n", " coef_departure_constants_am_peak_1\n", - " -1.882694\n", + " -1.606835\n", " \n", " \n", " coef_departure_constants_am_peak_2\n", @@ -2994,63 +3146,63 @@ " \n", " \n", " coef_departure_constants_am_peak_3\n", - " -0.183985\n", + " -0.100582\n", " \n", " \n", " coef_departure_constants_am_peak_4\n", - " -2.016403\n", + " -2.142432\n", " \n", " \n", " coef_departure_constants_early\n", - " -3.636770\n", + " -3.850945\n", " \n", " \n", " coef_departure_constants_evening\n", - " -5.258795\n", + " -5.546826\n", " \n", " \n", " coef_departure_constants_late\n", - " -21.447622\n", + " -10.890722\n", " \n", " \n", " coef_departure_constants_midday_1\n", - " -3.279864\n", + " -3.124481\n", " \n", " \n", " coef_departure_constants_midday_2\n", - " -3.750548\n", + " -3.861525\n", " \n", " \n", " coef_departure_constants_pm_peak\n", - " -3.093167\n", + " -3.281532\n", " \n", " \n", " coef_duration_constants_0_to_2_hours\n", - " -1.372110\n", + " -1.318085\n", " \n", " \n", " coef_duration_constants_10_hours\n", - " -0.876448\n", + " -0.914359\n", " \n", " \n", " coef_duration_constants_11_hours\n", - " -1.127264\n", + " -1.643692\n", " \n", " \n", " coef_duration_constants_12_to_13_hours\n", - " -2.668346\n", + " -2.528269\n", " \n", " \n", " coef_duration_constants_14_to_18_hours\n", - " -2.675974\n", + " -2.541672\n", " \n", " \n", " coef_duration_constants_3_to_4_hours\n", - " -0.552200\n", + " -0.680239\n", " \n", " \n", " coef_duration_constants_5_to_6_hours\n", - " -0.532627\n", + " -0.551126\n", " \n", " \n", " coef_duration_constants_7_to_8_hours\n", @@ -3058,19 +3210,19 @@ " \n", " \n", " coef_duration_constants_9_hours\n", - " -0.487609\n", + " -0.664353\n", " \n", " \n", " coef_first_of_2plus_school_lt_6_hours\n", - " 0.169055\n", + " 1.768123\n", " \n", " \n", " coef_first_of_2plus_school_tours_departure\n", - " -0.333830\n", + " -0.277161\n", " \n", " \n", " coef_first_of_2plus_school_tours_duration\n", - " -0.042558\n", + " -0.077846\n", " \n", " \n", " coef_ft_worker_departure\n", @@ -3082,15 +3234,15 @@ " \n", " \n", " coef_hh_income_early_departure\n", - " -13.757852\n", + " -0.643531\n", " \n", " \n", " coef_hh_income_late_arrival\n", - " 0.101726\n", + " -0.123912\n", " \n", " \n", " coef_mode_choice_logsum\n", - " 0.056410\n", + " 1.403530\n", " \n", " \n", " coef_non_worker_departure\n", @@ -3098,54 +3250,54 @@ " \n", " \n", " coef_previous_tour_begins_this_arrival_hour\n", - " -10.370632\n", + " 1.748525\n", " \n", " \n", " coef_previous_tour_ends_this_departure_hour\n", - " 0.298551\n", + " -1.454921\n", " \n", " \n", " coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours\n", - " -25.000000\n", + " -16.666935\n", " \n", " \n", " coef_roundtrip_auto_time_to_work\n", - " 0.005137\n", + " 0.002946\n", " \n", " \n", " coef_school_plus_work_tours_by_student_lt_6_hours\n", - " -2.483313\n", + " 1.805060\n", " \n", " \n", " coef_school_plus_work_tours_by_worker_lt_6_hours\n", - " -2.071313\n", + " 2.217060\n", " \n", " \n", " coef_student_driver_duration\n", - " -0.003958\n", + " 0.021414\n", " \n", " \n", " coef_subsequent_2plus_school_tours_duration\n", - " -0.188299\n", + " -0.309377\n", " \n", " \n", " coef_subsequent_of_2plus_school_lt_6_hours\n", - " 25.000000\n", + " 1.522961\n", " \n", " \n", " coef_subsequent_tour_must_start_after_previous_tour_ends\n", - " -25.000000\n", + " -100.000000\n", " \n", " \n", " coef_univ_departure\n", - " 0.234367\n", + " 0.281763\n", " \n", " \n", " coef_univ_duration\n", - " -0.409132\n", + " -0.287541\n", " \n", " \n", - "loglike-2331.7420221780226d_loglike\n", + "
logloss3.8441737168559693d_logloss\n", " \n", " \n", " \n", @@ -3155,47 +3307,47 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3203,19 +3355,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3223,63 +3375,63 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3287,19 +3439,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3311,15 +3463,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3327,39 +3479,39 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3367,149 +3519,149 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction-1.569000e-041.678936e-05
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction2.469873e-05-4.551660e-05
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction-1.376329e-041.846962e-05
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction-1.199309e-04-1.145647e-04
coef_all_adults_ft_worker_duration-2.382589e-04-8.371558e-05
coef_arrival_constants_am_peak1.105861e-042.196925e-04
coef_arrival_constants_early-7.161434e-05-5.719292e-06
coef_arrival_constants_evening-3.213301e-04-8.835678e-05
coef_arrival_constants_late-3.685726e-051.013090e-04
coef_arrival_constants_midday_12.405910e-047.833025e-05
coef_arrival_constants_midday_23.974766e-04-1.965074e-04
coef_arrival_constants_pm_peak_1
coef_arrival_constants_pm_peak_2-3.784033e-04-9.685367e-05
coef_arrival_constants_pm_peak_3-2.802883e-04-9.087295e-05
coef_arrival_constants_pm_peak_41.590313e-042.740145e-05
coef_departure_constants_am_peak_13.893384e-051.513237e-04
coef_departure_constants_am_peak_2
coef_departure_constants_am_peak_3-1.076955e-04-8.937296e-06
coef_departure_constants_am_peak_4-1.023082e-04-8.822207e-05
coef_departure_constants_early-1.964617e-056.575707e-05
coef_departure_constants_evening9.016158e-05-4.373200e-05
coef_departure_constants_late-6.465407e-077.294452e-05
coef_departure_constants_midday_1-1.539484e-05-7.045255e-05
coef_departure_constants_midday_27.340411e-059.783210e-05
coef_departure_constants_pm_peak-3.023675e-041.854008e-04
coef_duration_constants_0_to_2_hours-1.095813e-04-1.017742e-05
coef_duration_constants_10_hours-4.107214e-05-1.010013e-04
coef_duration_constants_11_hours1.853670e-043.540305e-05
coef_duration_constants_12_to_13_hours4.561763e-053.622247e-06
coef_duration_constants_14_to_18_hours1.362278e-041.457916e-04
coef_duration_constants_3_to_4_hours3.779389e-06-1.500782e-04
coef_duration_constants_5_to_6_hours1.389604e-042.054373e-04
coef_duration_constants_7_to_8_hours
coef_duration_constants_9_hours-4.131255e-041.173908e-04
coef_first_of_2plus_school_lt_6_hours2.433801e-056.625071e-05
coef_first_of_2plus_school_tours_departure-1.088730e-044.904242e-05
coef_first_of_2plus_school_tours_duration-1.351925e-045.848347e-05
coef_ft_worker_departure
coef_hh_income_early_departure-1.184539e-06-5.079513e-05
coef_hh_income_late_arrival8.674395e-05-7.624692e-05
coef_mode_choice_logsum7.755139e-04-2.256830e-05
coef_non_worker_departure
coef_previous_tour_begins_this_arrival_hour-7.619359e-07-8.260618e-06
coef_previous_tour_ends_this_departure_hour-2.114195e-05-4.098700e-05
coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours-1.863986e-023.894311e-07
coef_roundtrip_auto_time_to_work-2.056667e-035.433902e-05
coef_school_plus_work_tours_by_student_lt_6_hours2.636310e-068.778429e-06
coef_school_plus_work_tours_by_worker_lt_6_hours2.636310e-068.778429e-06
coef_student_driver_duration4.065449e-04-5.008566e-05
coef_subsequent_2plus_school_tours_duration6.239050e-055.111004e-05
coef_subsequent_of_2plus_school_lt_6_hours2.696315e-069.902870e-05
coef_subsequent_tour_must_start_after_previous_tour_ends
coef_univ_departure-2.420842e-03-7.874285e-05
coef_univ_duration7.174716e-04-1.251813e-05
nit77nfev141njev77status0message'Optimization terminated successfully'successTrueelapsed_time0:00:02.917567method'slsqp'n_cases609iteration_number77logloss3.8288046341182636" + "nit139nfev142njev139status0message'Optimization terminated successfully'successTrueelapsed_time0:00:41.638335method'slsqp'n_cases10352iteration_number139loglike-39483.50824582766" ], "text/plain": [ - "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -0.414229\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -0.640186\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 1.656920\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction 0.225722\n", - "┃ coef_all_adults_ft_worker_duration 0.052336\n", - "┃ coef_arrival_constants_am_peak -2.239162\n", - "┃ coef_arrival_constants_early -1.065980\n", - "┃ coef_arrival_constants_evening -1.033329\n", - "┃ coef_arrival_constants_late -1.315910\n", - "┃ coef_arrival_constants_midday_1 -1.302330\n", - "┃ coef_arrival_constants_midday_2 -0.554673\n", - "┃ coef_arrival_constants_pm_peak_1 0.000000\n", - "┃ coef_arrival_constants_pm_peak_2 -0.441387\n", - "┃ coef_arrival_constants_pm_peak_3 -0.281247\n", - "┃ coef_arrival_constants_pm_peak_4 -0.370368\n", - "┃ coef_departure_constants_am_peak_1 -1.882694\n", - "┃ coef_departure_constants_am_peak_2 0.000000\n", - "┃ coef_departure_constants_am_peak_3 -0.183985\n", - "┃ coef_departure_constants_am_peak_4 -2.016403\n", - "┃ coef_departure_constants_early -3.636770\n", - "┃ coef_departure_constants_evening -5.258795\n", - "┃ coef_departure_constants_late -21.447622\n", - "┃ coef_departure_constants_midday_1 -3.279864\n", - "┃ coef_departure_constants_midday_2 -3.750548\n", - "┃ coef_departure_constants_pm_peak -3.093167\n", - "┃ coef_duration_constants_0_to_2_hours -1.372110\n", - "┃ coef_duration_constants_10_hours -0.876448\n", - "┃ coef_duration_constants_11_hours -1.127264\n", - "┃ coef_duration_constants_12_to_13_hours -2.668346\n", - "┃ coef_duration_constants_14_to_18_hours -2.675974\n", - "┃ coef_duration_constants_3_to_4_hours -0.552200\n", - "┃ coef_duration_constants_5_to_6_hours -0.532627\n", - "┃ coef_duration_constants_7_to_8_hours 0.000000\n", - "┃ coef_duration_constants_9_hours -0.487609\n", - "┃ coef_first_of_2plus_school_lt_6_hours 0.169055\n", - "┃ coef_first_of_2plus_school_tours_departure -0.333830\n", - "┃ coef_first_of_2plus_school_tours_duration -0.042558\n", - "┃ coef_ft_worker_departure 0.397100\n", - "┃ coef_ft_worker_duration -0.190800\n", - "┃ coef_hh_income_early_departure -13.757852\n", - "┃ coef_hh_income_late_arrival 0.101726\n", - "┃ coef_mode_choice_logsum 0.056410\n", - "┃ coef_non_worker_departure 0.553900\n", - "┃ coef_previous_tour_begins_this_arrival_hour -10.370632\n", - "┃ coef_previous_tour_ends_this_departure_hour 0.298551\n", - "┃ coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours -25.000000\n", - "┃ coef_roundtrip_auto_time_to_work 0.005137\n", - "┃ coef_school_plus_work_tours_by_student_lt_6_hours -2.483313\n", - "┃ coef_school_plus_work_tours_by_worker_lt_6_hours -2.071313\n", - "┃ coef_student_driver_duration -0.003958\n", - "┃ coef_subsequent_2plus_school_tours_duration -0.188299\n", - "┃ coef_subsequent_of_2plus_school_lt_6_hours 25.000000\n", - "┃ coef_subsequent_tour_must_start_after_previous_tour_ends -25.000000\n", - "┃ coef_univ_departure 0.234367\n", - "┃ coef_univ_duration -0.409132\n", + "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.232285\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -1.201676\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -0.419250\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -0.760747\n", + "┃ coef_all_adults_ft_worker_duration 0.079713\n", + "┃ coef_arrival_constants_am_peak -2.595385\n", + "┃ coef_arrival_constants_early -2.446580\n", + "┃ coef_arrival_constants_evening -0.707272\n", + "┃ coef_arrival_constants_late -1.639520\n", + "┃ coef_arrival_constants_midday_1 -1.267988\n", + "┃ coef_arrival_constants_midday_2 -0.523484\n", + "┃ coef_arrival_constants_pm_peak_1 0.000000\n", + "┃ coef_arrival_constants_pm_peak_2 -0.360268\n", + "┃ coef_arrival_constants_pm_peak_3 -0.096426\n", + "┃ coef_arrival_constants_pm_peak_4 -0.127905\n", + "┃ coef_departure_constants_am_peak_1 -1.606835\n", + "┃ coef_departure_constants_am_peak_2 0.000000\n", + "┃ coef_departure_constants_am_peak_3 -0.100582\n", + "┃ coef_departure_constants_am_peak_4 -2.142432\n", + "┃ coef_departure_constants_early -3.850945\n", + "┃ coef_departure_constants_evening -5.546826\n", + "┃ coef_departure_constants_late -10.890722\n", + "┃ coef_departure_constants_midday_1 -3.124481\n", + "┃ coef_departure_constants_midday_2 -3.861525\n", + "┃ coef_departure_constants_pm_peak -3.281532\n", + "┃ coef_duration_constants_0_to_2_hours -1.318085\n", + "┃ coef_duration_constants_10_hours -0.914359\n", + "┃ coef_duration_constants_11_hours -1.643692\n", + "┃ coef_duration_constants_12_to_13_hours -2.528269\n", + "┃ coef_duration_constants_14_to_18_hours -2.541672\n", + "┃ coef_duration_constants_3_to_4_hours -0.680239\n", + "┃ coef_duration_constants_5_to_6_hours -0.551126\n", + "┃ coef_duration_constants_7_to_8_hours 0.000000\n", + "┃ coef_duration_constants_9_hours -0.664353\n", + "┃ coef_first_of_2plus_school_lt_6_hours 1.768123\n", + "┃ coef_first_of_2plus_school_tours_departure -0.277161\n", + "┃ coef_first_of_2plus_school_tours_duration -0.077846\n", + "┃ coef_ft_worker_departure 0.397100\n", + "┃ coef_ft_worker_duration -0.190800\n", + "┃ coef_hh_income_early_departure -0.643531\n", + "┃ coef_hh_income_late_arrival -0.123912\n", + "┃ coef_mode_choice_logsum 1.403530\n", + "┃ coef_non_worker_departure 0.553900\n", + "┃ coef_previous_tour_begins_this_arrival_hour 1.748525\n", + "┃ coef_previous_tour_ends_this_departure_hour -1.454921\n", + "┃ coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours -16.666935\n", + "┃ coef_roundtrip_auto_time_to_work 0.002946\n", + "┃ coef_school_plus_work_tours_by_student_lt_6_hours 1.805060\n", + "┃ coef_school_plus_work_tours_by_worker_lt_6_hours 2.217060\n", + "┃ coef_student_driver_duration 0.021414\n", + "┃ coef_subsequent_2plus_school_tours_duration -0.309377\n", + "┃ coef_subsequent_of_2plus_school_lt_6_hours 1.522961\n", + "┃ coef_subsequent_tour_must_start_after_previous_tour_ends -100.000000\n", + "┃ coef_univ_departure 0.281763\n", + "┃ coef_univ_duration -0.287541\n", "┃ dtype: float64\n", - "┣ loglike: -2331.7420221780226\n", - "┣ d_loglike: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -1.569000e-04\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction 2.469873e-05\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -1.376329e-04\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -1.199309e-04\n", - "┃ coef_all_adults_ft_worker_duration -2.382589e-04\n", - "┃ coef_arrival_constants_am_peak 1.105861e-04\n", - "┃ coef_arrival_constants_early -7.161434e-05\n", - "┃ coef_arrival_constants_evening -3.213301e-04\n", - "┃ coef_arrival_constants_late -3.685726e-05\n", - "┃ coef_arrival_constants_midday_1 2.405910e-04\n", - "┃ coef_arrival_constants_midday_2 3.974766e-04\n", + "┣ logloss: 3.8441737168559693\n", + "┣ d_logloss: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 1.678936e-05\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -4.551660e-05\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 1.846962e-05\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -1.145647e-04\n", + "┃ coef_all_adults_ft_worker_duration -8.371558e-05\n", + "┃ coef_arrival_constants_am_peak 2.196925e-04\n", + "┃ coef_arrival_constants_early -5.719292e-06\n", + "┃ coef_arrival_constants_evening -8.835678e-05\n", + "┃ coef_arrival_constants_late 1.013090e-04\n", + "┃ coef_arrival_constants_midday_1 7.833025e-05\n", + "┃ coef_arrival_constants_midday_2 -1.965074e-04\n", "┃ coef_arrival_constants_pm_peak_1 0.000000e+00\n", - "┃ coef_arrival_constants_pm_peak_2 -3.784033e-04\n", - "┃ coef_arrival_constants_pm_peak_3 -2.802883e-04\n", - "┃ coef_arrival_constants_pm_peak_4 1.590313e-04\n", - "┃ coef_departure_constants_am_peak_1 3.893384e-05\n", + "┃ coef_arrival_constants_pm_peak_2 -9.685367e-05\n", + "┃ coef_arrival_constants_pm_peak_3 -9.087295e-05\n", + "┃ coef_arrival_constants_pm_peak_4 2.740145e-05\n", + "┃ coef_departure_constants_am_peak_1 1.513237e-04\n", "┃ coef_departure_constants_am_peak_2 0.000000e+00\n", - "┃ coef_departure_constants_am_peak_3 -1.076955e-04\n", - "┃ coef_departure_constants_am_peak_4 -1.023082e-04\n", - "┃ coef_departure_constants_early -1.964617e-05\n", - "┃ coef_departure_constants_evening 9.016158e-05\n", - "┃ coef_departure_constants_late -6.465407e-07\n", - "┃ coef_departure_constants_midday_1 -1.539484e-05\n", - "┃ coef_departure_constants_midday_2 7.340411e-05\n", - "┃ coef_departure_constants_pm_peak -3.023675e-04\n", - "┃ coef_duration_constants_0_to_2_hours -1.095813e-04\n", - "┃ coef_duration_constants_10_hours -4.107214e-05\n", - "┃ coef_duration_constants_11_hours 1.853670e-04\n", - "┃ coef_duration_constants_12_to_13_hours 4.561763e-05\n", - "┃ coef_duration_constants_14_to_18_hours 1.362278e-04\n", - "┃ coef_duration_constants_3_to_4_hours 3.779389e-06\n", - "┃ coef_duration_constants_5_to_6_hours 1.389604e-04\n", + "┃ coef_departure_constants_am_peak_3 -8.937296e-06\n", + "┃ coef_departure_constants_am_peak_4 -8.822207e-05\n", + "┃ coef_departure_constants_early 6.575707e-05\n", + "┃ coef_departure_constants_evening -4.373200e-05\n", + "┃ coef_departure_constants_late 7.294452e-05\n", + "┃ coef_departure_constants_midday_1 -7.045255e-05\n", + "┃ coef_departure_constants_midday_2 9.783210e-05\n", + "┃ coef_departure_constants_pm_peak 1.854008e-04\n", + "┃ coef_duration_constants_0_to_2_hours -1.017742e-05\n", + "┃ coef_duration_constants_10_hours -1.010013e-04\n", + "┃ coef_duration_constants_11_hours 3.540305e-05\n", + "┃ coef_duration_constants_12_to_13_hours 3.622247e-06\n", + "┃ coef_duration_constants_14_to_18_hours 1.457916e-04\n", + "┃ coef_duration_constants_3_to_4_hours -1.500782e-04\n", + "┃ coef_duration_constants_5_to_6_hours 2.054373e-04\n", "┃ coef_duration_constants_7_to_8_hours 0.000000e+00\n", - "┃ coef_duration_constants_9_hours -4.131255e-04\n", - "┃ coef_first_of_2plus_school_lt_6_hours 2.433801e-05\n", - "┃ coef_first_of_2plus_school_tours_departure -1.088730e-04\n", - "┃ coef_first_of_2plus_school_tours_duration -1.351925e-04\n", + "┃ coef_duration_constants_9_hours 1.173908e-04\n", + "┃ coef_first_of_2plus_school_lt_6_hours 6.625071e-05\n", + "┃ coef_first_of_2plus_school_tours_departure 4.904242e-05\n", + "┃ coef_first_of_2plus_school_tours_duration 5.848347e-05\n", "┃ coef_ft_worker_departure 0.000000e+00\n", "┃ coef_ft_worker_duration 0.000000e+00\n", - "┃ coef_hh_income_early_departure -1.184539e-06\n", - "┃ coef_hh_income_late_arrival 8.674395e-05\n", - "┃ coef_mode_choice_logsum 7.755139e-04\n", + "┃ coef_hh_income_early_departure -5.079513e-05\n", + "┃ coef_hh_income_late_arrival -7.624692e-05\n", + "┃ coef_mode_choice_logsum -2.256830e-05\n", "┃ coef_non_worker_departure 0.000000e+00\n", - "┃ coef_previous_tour_begins_this_arrival_hour -7.619359e-07\n", - "┃ coef_previous_tour_ends_this_departure_hour -2.114195e-05\n", - "┃ coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours -1.863986e-02\n", - "┃ coef_roundtrip_auto_time_to_work -2.056667e-03\n", - "┃ coef_school_plus_work_tours_by_student_lt_6_hours 2.636310e-06\n", - "┃ coef_school_plus_work_tours_by_worker_lt_6_hours 2.636310e-06\n", - "┃ coef_student_driver_duration 4.065449e-04\n", - "┃ coef_subsequent_2plus_school_tours_duration 6.239050e-05\n", - "┃ coef_subsequent_of_2plus_school_lt_6_hours 2.696315e-06\n", + "┃ coef_previous_tour_begins_this_arrival_hour -8.260618e-06\n", + "┃ coef_previous_tour_ends_this_departure_hour -4.098700e-05\n", + "┃ coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours 3.894311e-07\n", + "┃ coef_roundtrip_auto_time_to_work 5.433902e-05\n", + "┃ coef_school_plus_work_tours_by_student_lt_6_hours 8.778429e-06\n", + "┃ coef_school_plus_work_tours_by_worker_lt_6_hours 8.778429e-06\n", + "┃ coef_student_driver_duration -5.008566e-05\n", + "┃ coef_subsequent_2plus_school_tours_duration 5.111004e-05\n", + "┃ coef_subsequent_of_2plus_school_lt_6_hours 9.902870e-05\n", "┃ coef_subsequent_tour_must_start_after_previous_tour_ends 0.000000e+00\n", - "┃ coef_univ_departure -2.420842e-03\n", - "┃ coef_univ_duration 7.174716e-04\n", + "┃ coef_univ_departure -7.874285e-05\n", + "┃ coef_univ_duration -1.251813e-05\n", "┃ dtype: float64\n", - "┣ nit: 77\n", - "┣ nfev: 141\n", - "┣ njev: 77\n", + "┣ nit: 139\n", + "┣ nfev: 142\n", + "┣ njev: 139\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=2, microseconds=917567)\n", + "┣ elapsed_time: datetime.timedelta(seconds=41, microseconds=638335)\n", "┣ method: 'slsqp'\n", - "┣ n_cases: 609\n", - "┣ iteration_number: 77\n", - "┣ logloss: 3.8288046341182636" + "┣ n_cases: 10352\n", + "┣ iteration_number: 139\n", + "┣ loglike: -39483.50824582766" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.estimate()" + "model.estimate(maxiter=999)" ] }, { @@ -3521,579 +3673,550 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction-0.414 0.645-0.64 NA 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction-0.640 0.383-1.67 NA 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 1.66 0.794 2.09* NA 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction 0.226 0.377 0.60 NA 0.00
coef_all_adults_ft_worker_duration 0.0523 0.179 0.29 NA 0.00
coef_arrival_constants_am_peak-2.24 0.467-4.79*** NA 0.00
coef_arrival_constants_early-1.07 0.889-1.20 NA 0.00
coef_arrival_constants_evening-1.03 0.346-2.99** NA 0.00
coef_arrival_constants_late-1.32 0.510-2.58** NA 0.00
coef_arrival_constants_midday_1-1.30 0.285-4.57*** NA 0.00
coef_arrival_constants_midday_2-0.555 0.161-3.45*** NA 0.00
coef_arrival_constants_pm_peak_1 0.00 NA NA NA 0.00fixed value
coef_arrival_constants_pm_peak_2-0.441 0.167-2.64** NA 0.00
coef_arrival_constants_pm_peak_3-0.281 0.223-1.26 NA 0.00
coef_arrival_constants_pm_peak_4-0.370 0.274-1.35 NA 0.00
coef_departure_constants_am_peak_1-1.88 0.214-8.81*** NA 0.00
coef_departure_constants_am_peak_2 0.00 NA NA NA 0.00fixed value
coef_departure_constants_am_peak_3-0.184 0.114-1.61 NA 0.00
coef_departure_constants_am_peak_4-2.02 0.224-9.01*** NA 0.00
coef_departure_constants_early-3.64 0.600-6.07*** NA 0.00
coef_departure_constants_evening-5.26 0.830-6.34*** NA 0.00
coef_departure_constants_late-21.4 1.78e+03-0.01 NA 0.00
coef_departure_constants_midday_1-3.28 0.291-11.28*** NA 0.00
coef_departure_constants_midday_2-3.75 0.433-8.67*** NA 0.00
coef_departure_constants_pm_peak-3.09 0.579-5.34*** NA 0.00
coef_duration_constants_0_to_2_hours-1.37 0.393-3.49*** NA 0.00
coef_duration_constants_10_hours-0.876 0.271-3.24** NA 0.00
coef_duration_constants_11_hours-1.13 0.347-3.24** NA 0.00
coef_duration_constants_12_to_13_hours-2.67 0.530-5.04*** NA 0.00
coef_duration_constants_14_to_18_hours-2.68 0.614-4.36*** NA 0.00
coef_duration_constants_3_to_4_hours-0.552 0.277-2.00* NA 0.00
coef_duration_constants_5_to_6_hours-0.533 0.179-2.97** NA 0.00
coef_duration_constants_7_to_8_hours 0.00 NA NA NA 0.00fixed value
coef_duration_constants_9_hours-0.488 0.199-2.45* NA 0.00
coef_first_of_2plus_school_lt_6_hours 0.169 1.14 0.15 NA 0.00
coef_first_of_2plus_school_tours_departure-0.334 0.170-1.96* NA 0.00
coef_first_of_2plus_school_tours_duration-0.0426 0.214-0.20 NA 0.00
coef_ft_worker_departure 0.397 0.0143 27.72*** NA 0.00
coef_ft_worker_duration-0.191 0.00454-42.02*** NA 0.00
coef_hh_income_early_departure-13.8 934.-0.01 NA 0.00
coef_hh_income_late_arrival 0.102 0.762 0.13 NA 0.00
coef_mode_choice_logsum 0.0564 0.267 0.21 NA 0.00
coef_non_worker_departure 0.554 0.000124 BIG*** NA 0.00
coef_previous_tour_begins_this_arrival_hour-10.4 1.62e+03-0.01 NA 0.00
coef_previous_tour_ends_this_departure_hour 0.299 0.613 0.49 NA 0.00
coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours-25.0 NA NA[] 1.05 0.00coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours ≥ -25.0
coef_roundtrip_auto_time_to_work 0.00514 0.00516 1.00 NA 0.00
coef_school_plus_work_tours_by_student_lt_6_hours-2.48 314.-0.01 NA 0.00
coef_school_plus_work_tours_by_worker_lt_6_hours-2.07 314.-0.01 NA 0.00
coef_student_driver_duration-0.00396 0.0373-0.11 NA 0.00
coef_subsequent_2plus_school_tours_duration-0.188 0.149-1.27 NA 0.00
coef_subsequent_of_2plus_school_lt_6_hours 25.0 NA NA[***] 220.75 0.00coef_subsequent_of_2plus_school_lt_6_hours ≤ 25.0
coef_subsequent_tour_must_start_after_previous_tour_ends-100. NA NA NA 0.00fixed value
coef_univ_departure 0.234 0.0383 6.13*** NA 0.00
coef_univ_duration-0.409 0.0544-7.52*** NA 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.232 2.43 0.10 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction-1.20 0.265-4.54*** 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction-0.419 0.705-0.59 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction-0.761 0.722-1.05 0.00
coef_all_adults_ft_worker_duration 0.0797 0.0336 2.37* 0.00
coef_arrival_constants_am_peak-2.60 0.128-20.35*** 0.00
coef_arrival_constants_early-2.45 0.378-6.47*** 0.00
coef_arrival_constants_evening-0.707 0.0872-8.11*** 0.00
coef_arrival_constants_late-1.64 0.137-11.98*** 0.00
coef_arrival_constants_midday_1-1.27 0.0711-17.84*** 0.00
coef_arrival_constants_midday_2-0.523 0.0391-13.41*** 0.00
coef_arrival_constants_pm_peak_1 0.00 0.00 NA 0.00fixed value
coef_arrival_constants_pm_peak_2-0.360 0.0391-9.21*** 0.00
coef_arrival_constants_pm_peak_3-0.0964 0.0543-1.78 0.00
coef_arrival_constants_pm_peak_4-0.128 0.0672-1.90 0.00
coef_departure_constants_am_peak_1-1.61 0.0470-34.22*** 0.00
coef_departure_constants_am_peak_2 0.00 0.00 NA 0.00fixed value
coef_departure_constants_am_peak_3-0.101 0.0275-3.66*** 0.00
coef_departure_constants_am_peak_4-2.14 0.0570-37.57*** 0.00
coef_departure_constants_early-3.85 0.173-22.28*** 0.00
coef_departure_constants_evening-5.55 0.220-25.26*** 0.00
coef_departure_constants_late-10.9 2.02-5.39*** 0.00
coef_departure_constants_midday_1-3.12 0.0717-43.56*** 0.00
coef_departure_constants_midday_2-3.86 0.112-34.58*** 0.00
coef_departure_constants_pm_peak-3.28 0.148-22.16*** 0.00
coef_duration_constants_0_to_2_hours-1.32 0.0986-13.37*** 0.00
coef_duration_constants_10_hours-0.914 0.0627-14.59*** 0.00
coef_duration_constants_11_hours-1.64 0.0876-18.76*** 0.00
coef_duration_constants_12_to_13_hours-2.53 0.112-22.51*** 0.00
coef_duration_constants_14_to_18_hours-2.54 0.143-17.80*** 0.00
coef_duration_constants_3_to_4_hours-0.680 0.0695-9.79*** 0.00
coef_duration_constants_5_to_6_hours-0.551 0.0434-12.71*** 0.00
coef_duration_constants_7_to_8_hours 0.00 0.00 NA 0.00fixed value
coef_duration_constants_9_hours-0.664 0.0471-14.10*** 0.00
coef_first_of_2plus_school_lt_6_hours 1.77 0.267 6.63*** 0.00
coef_first_of_2plus_school_tours_departure-0.277 0.0289-9.59*** 0.00
coef_first_of_2plus_school_tours_duration-0.0778 0.0892-0.87 0.00
coef_ft_worker_departure 0.397 NA NA 0.00
coef_ft_worker_duration-0.191 NA NA 0.00
coef_hh_income_early_departure-0.644 0.381-1.69 0.00
coef_hh_income_late_arrival-0.124 0.206-0.60 0.00
coef_mode_choice_logsum 1.40 0.125 11.19*** 0.00
coef_non_worker_departure 0.554 2.69e-13 BIG*** 0.00
coef_previous_tour_begins_this_arrival_hour 1.75 0.946 1.85 0.00
coef_previous_tour_ends_this_departure_hour-1.45 0.690-2.11* 0.00
coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours-16.7 18.9-0.88 0.00
coef_roundtrip_auto_time_to_work 0.00295 0.000437 6.74*** 0.00
coef_school_plus_work_tours_by_student_lt_6_hours 1.81 1.68 1.07 0.00
coef_school_plus_work_tours_by_worker_lt_6_hours 2.22 1.68 1.32 0.00
coef_student_driver_duration 0.0214 0.00880 2.43* 0.00
coef_subsequent_2plus_school_tours_duration-0.309 0.0368-8.41*** 0.00
coef_subsequent_of_2plus_school_lt_6_hours 1.52 0.493 3.09** 0.00
coef_subsequent_tour_must_start_after_previous_tour_ends-100. 0.00 NA 0.00fixed value
coef_univ_departure 0.282 0.0112 25.08*** 0.00
coef_univ_duration-0.288 0.0153-18.82*** 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -4114,7 +4237,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -4135,20 +4258,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -4167,7 +4279,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -4206,7 +4318,7 @@ " \n", " 1\n", " coef_roundtrip_auto_time_to_work\n", - " 0.005137\n", + " 0.002946\n", " F\n", " \n", " \n", @@ -4230,25 +4342,25 @@ " \n", " 5\n", " coef_univ_departure\n", - " 0.234367\n", + " 0.281763\n", " F\n", " \n", " \n", " 6\n", " coef_univ_duration\n", - " -0.409132\n", + " -0.287541\n", " F\n", " \n", " \n", " 7\n", " coef_student_driver_duration\n", - " -0.003958\n", + " 0.021414\n", " F\n", " \n", " \n", " 8\n", " coef_all_adults_ft_worker_duration\n", - " 0.052336\n", + " 0.079713\n", " F\n", " \n", " \n", @@ -4260,115 +4372,115 @@ " \n", " 10\n", " coef_first_of_2plus_school_tours_departure\n", - " -0.333830\n", + " -0.277161\n", " F\n", " \n", " \n", " 11\n", " coef_first_of_2plus_school_tours_duration\n", - " -0.042558\n", + " -0.077846\n", " F\n", " \n", " \n", " 12\n", " coef_subsequent_2plus_school_tours_duration\n", - " -0.188299\n", + " -0.309377\n", " F\n", " \n", " \n", " 13\n", " coef_hh_income_early_departure\n", - " -13.757852\n", + " -0.643531\n", " F\n", " \n", " \n", " 14\n", " coef_hh_income_late_arrival\n", - " 0.101726\n", + " -0.123912\n", " F\n", " \n", " \n", " 15\n", " coef_first_of_2plus_school_lt_6_hours\n", - " 0.169055\n", + " 1.768123\n", " F\n", " \n", " \n", " 16\n", " coef_subsequent_of_2plus_school_lt_6_hours\n", - " 25.000000\n", + " 1.522961\n", " F\n", " \n", " \n", " 17\n", " coef_school_plus_work_tours_by_student_lt_6_hours\n", - " -2.483313\n", + " 1.805060\n", " F\n", " \n", " \n", " 18\n", " coef_school_plus_work_tours_by_worker_lt_6_hours\n", - " -2.071313\n", + " 2.217060\n", " F\n", " \n", " \n", " 19\n", " coef_mode_choice_logsum\n", - " 0.056410\n", + " 1.403530\n", " F\n", " \n", " \n", " 20\n", " coef_previous_tour_ends_this_departure_hour\n", - " 0.298551\n", + " -1.454921\n", " F\n", " \n", " \n", " 21\n", " coef_previous_tour_begins_this_arrival_hour\n", - " -10.370632\n", + " 1.748525\n", " F\n", " \n", " \n", " 22\n", " coef_adjacent_window_exists_before_this_depart...\n", - " 1.656920\n", + " -0.419250\n", " F\n", " \n", " \n", " 23\n", " coef_adjacent_window_exists_after_this_arrival...\n", - " -0.414229\n", + " 0.232285\n", " F\n", " \n", " \n", " 24\n", " coef_adjacent_window_exists_before_this_depart...\n", - " 0.225722\n", + " -0.760747\n", " F\n", " \n", " \n", " 25\n", " coef_adjacent_window_exists_after_this_arrival...\n", - " -0.640186\n", + " -1.201676\n", " F\n", " \n", " \n", " 26\n", " coef_remaining_work_school_tours_to_be_schedul...\n", - " -25.000000\n", + " -16.666935\n", " F\n", " \n", " \n", " 27\n", " coef_departure_constants_early\n", - " -3.636770\n", + " -3.850945\n", " F\n", " \n", " \n", " 28\n", " coef_departure_constants_am_peak_1\n", - " -1.882694\n", + " -1.606835\n", " F\n", " \n", " \n", @@ -4380,67 +4492,67 @@ " \n", " 30\n", " coef_departure_constants_am_peak_3\n", - " -0.183985\n", + " -0.100582\n", " F\n", " \n", " \n", " 31\n", " coef_departure_constants_am_peak_4\n", - " -2.016403\n", + " -2.142432\n", " F\n", " \n", " \n", " 32\n", " coef_departure_constants_midday_1\n", - " -3.279864\n", + " -3.124481\n", " F\n", " \n", " \n", " 33\n", " coef_departure_constants_midday_2\n", - " -3.750548\n", + " -3.861525\n", " F\n", " \n", " \n", " 34\n", " coef_departure_constants_pm_peak\n", - " -3.093167\n", + " -3.281532\n", " F\n", " \n", " \n", " 35\n", " coef_departure_constants_evening\n", - " -5.258795\n", + " -5.546826\n", " F\n", " \n", " \n", " 36\n", " coef_departure_constants_late\n", - " -21.447622\n", + " -10.890722\n", " F\n", " \n", " \n", " 37\n", " coef_arrival_constants_early\n", - " -1.065980\n", + " -2.446580\n", " F\n", " \n", " \n", " 38\n", " coef_arrival_constants_am_peak\n", - " -2.239162\n", + " -2.595385\n", " F\n", " \n", " \n", " 39\n", " coef_arrival_constants_midday_1\n", - " -1.302330\n", + " -1.267988\n", " F\n", " \n", " \n", " 40\n", " coef_arrival_constants_midday_2\n", - " -0.554673\n", + " -0.523484\n", " F\n", " \n", " \n", @@ -4452,49 +4564,49 @@ " \n", " 42\n", " coef_arrival_constants_pm_peak_2\n", - " -0.441387\n", + " -0.360268\n", " F\n", " \n", " \n", " 43\n", " coef_arrival_constants_pm_peak_3\n", - " -0.281247\n", + " -0.096426\n", " F\n", " \n", " \n", " 44\n", " coef_arrival_constants_pm_peak_4\n", - " -0.370368\n", + " -0.127905\n", " F\n", " \n", " \n", " 45\n", " coef_arrival_constants_evening\n", - " -1.033329\n", + " -0.707272\n", " F\n", " \n", " \n", " 46\n", " coef_arrival_constants_late\n", - " -1.315910\n", + " -1.639520\n", " F\n", " \n", " \n", " 47\n", " coef_duration_constants_0_to_2_hours\n", - " -1.372110\n", + " -1.318085\n", " F\n", " \n", " \n", " 48\n", " coef_duration_constants_3_to_4_hours\n", - " -0.552200\n", + " -0.680239\n", " F\n", " \n", " \n", " 49\n", " coef_duration_constants_5_to_6_hours\n", - " -0.532627\n", + " -0.551126\n", " F\n", " \n", " \n", @@ -4506,31 +4618,31 @@ " \n", " 51\n", " coef_duration_constants_9_hours\n", - " -0.487609\n", + " -0.664353\n", " F\n", " \n", " \n", " 52\n", " coef_duration_constants_10_hours\n", - " -0.876448\n", + " -0.914359\n", " F\n", " \n", " \n", " 53\n", " coef_duration_constants_11_hours\n", - " -1.127264\n", + " -1.643692\n", " F\n", " \n", " \n", " 54\n", " coef_duration_constants_12_to_13_hours\n", - " -2.668346\n", + " -2.528269\n", " F\n", " \n", " \n", " 55\n", " coef_duration_constants_14_to_18_hours\n", - " -2.675974\n", + " -2.541672\n", " F\n", " \n", " \n", @@ -4540,64 +4652,64 @@ "text/plain": [ " coefficient_name value constrain\n", "0 coef_dummy 1.000000 T\n", - "1 coef_roundtrip_auto_time_to_work 0.005137 F\n", + "1 coef_roundtrip_auto_time_to_work 0.002946 F\n", "2 coef_ft_worker_departure 0.397100 F\n", "3 coef_ft_worker_duration -0.190800 F\n", "4 coef_non_worker_departure 0.553900 F\n", - "5 coef_univ_departure 0.234367 F\n", - "6 coef_univ_duration -0.409132 F\n", - "7 coef_student_driver_duration -0.003958 F\n", - "8 coef_all_adults_ft_worker_duration 0.052336 F\n", + "5 coef_univ_departure 0.281763 F\n", + "6 coef_univ_duration -0.287541 F\n", + "7 coef_student_driver_duration 0.021414 F\n", + "8 coef_all_adults_ft_worker_duration 0.079713 F\n", "9 coef_subsequent_tour_must_start_after_previous... -100.000000 T\n", - "10 coef_first_of_2plus_school_tours_departure -0.333830 F\n", - "11 coef_first_of_2plus_school_tours_duration -0.042558 F\n", - "12 coef_subsequent_2plus_school_tours_duration -0.188299 F\n", - "13 coef_hh_income_early_departure -13.757852 F\n", - "14 coef_hh_income_late_arrival 0.101726 F\n", - "15 coef_first_of_2plus_school_lt_6_hours 0.169055 F\n", - "16 coef_subsequent_of_2plus_school_lt_6_hours 25.000000 F\n", - "17 coef_school_plus_work_tours_by_student_lt_6_hours -2.483313 F\n", - "18 coef_school_plus_work_tours_by_worker_lt_6_hours -2.071313 F\n", - "19 coef_mode_choice_logsum 0.056410 F\n", - "20 coef_previous_tour_ends_this_departure_hour 0.298551 F\n", - "21 coef_previous_tour_begins_this_arrival_hour -10.370632 F\n", - "22 coef_adjacent_window_exists_before_this_depart... 1.656920 F\n", - "23 coef_adjacent_window_exists_after_this_arrival... -0.414229 F\n", - "24 coef_adjacent_window_exists_before_this_depart... 0.225722 F\n", - "25 coef_adjacent_window_exists_after_this_arrival... -0.640186 F\n", - "26 coef_remaining_work_school_tours_to_be_schedul... -25.000000 F\n", - "27 coef_departure_constants_early -3.636770 F\n", - "28 coef_departure_constants_am_peak_1 -1.882694 F\n", + "10 coef_first_of_2plus_school_tours_departure -0.277161 F\n", + "11 coef_first_of_2plus_school_tours_duration -0.077846 F\n", + "12 coef_subsequent_2plus_school_tours_duration -0.309377 F\n", + "13 coef_hh_income_early_departure -0.643531 F\n", + "14 coef_hh_income_late_arrival -0.123912 F\n", + "15 coef_first_of_2plus_school_lt_6_hours 1.768123 F\n", + "16 coef_subsequent_of_2plus_school_lt_6_hours 1.522961 F\n", + "17 coef_school_plus_work_tours_by_student_lt_6_hours 1.805060 F\n", + "18 coef_school_plus_work_tours_by_worker_lt_6_hours 2.217060 F\n", + "19 coef_mode_choice_logsum 1.403530 F\n", + "20 coef_previous_tour_ends_this_departure_hour -1.454921 F\n", + "21 coef_previous_tour_begins_this_arrival_hour 1.748525 F\n", + "22 coef_adjacent_window_exists_before_this_depart... -0.419250 F\n", + "23 coef_adjacent_window_exists_after_this_arrival... 0.232285 F\n", + "24 coef_adjacent_window_exists_before_this_depart... -0.760747 F\n", + "25 coef_adjacent_window_exists_after_this_arrival... -1.201676 F\n", + "26 coef_remaining_work_school_tours_to_be_schedul... -16.666935 F\n", + "27 coef_departure_constants_early -3.850945 F\n", + "28 coef_departure_constants_am_peak_1 -1.606835 F\n", "29 coef_departure_constants_am_peak_2 0.000000 T\n", - "30 coef_departure_constants_am_peak_3 -0.183985 F\n", - "31 coef_departure_constants_am_peak_4 -2.016403 F\n", - "32 coef_departure_constants_midday_1 -3.279864 F\n", - "33 coef_departure_constants_midday_2 -3.750548 F\n", - "34 coef_departure_constants_pm_peak -3.093167 F\n", - "35 coef_departure_constants_evening -5.258795 F\n", - "36 coef_departure_constants_late -21.447622 F\n", - "37 coef_arrival_constants_early -1.065980 F\n", - "38 coef_arrival_constants_am_peak -2.239162 F\n", - "39 coef_arrival_constants_midday_1 -1.302330 F\n", - "40 coef_arrival_constants_midday_2 -0.554673 F\n", + "30 coef_departure_constants_am_peak_3 -0.100582 F\n", + "31 coef_departure_constants_am_peak_4 -2.142432 F\n", + "32 coef_departure_constants_midday_1 -3.124481 F\n", + "33 coef_departure_constants_midday_2 -3.861525 F\n", + "34 coef_departure_constants_pm_peak -3.281532 F\n", + "35 coef_departure_constants_evening -5.546826 F\n", + "36 coef_departure_constants_late -10.890722 F\n", + "37 coef_arrival_constants_early -2.446580 F\n", + "38 coef_arrival_constants_am_peak -2.595385 F\n", + "39 coef_arrival_constants_midday_1 -1.267988 F\n", + "40 coef_arrival_constants_midday_2 -0.523484 F\n", "41 coef_arrival_constants_pm_peak_1 0.000000 T\n", - "42 coef_arrival_constants_pm_peak_2 -0.441387 F\n", - "43 coef_arrival_constants_pm_peak_3 -0.281247 F\n", - "44 coef_arrival_constants_pm_peak_4 -0.370368 F\n", - "45 coef_arrival_constants_evening -1.033329 F\n", - "46 coef_arrival_constants_late -1.315910 F\n", - "47 coef_duration_constants_0_to_2_hours -1.372110 F\n", - "48 coef_duration_constants_3_to_4_hours -0.552200 F\n", - "49 coef_duration_constants_5_to_6_hours -0.532627 F\n", + "42 coef_arrival_constants_pm_peak_2 -0.360268 F\n", + "43 coef_arrival_constants_pm_peak_3 -0.096426 F\n", + "44 coef_arrival_constants_pm_peak_4 -0.127905 F\n", + "45 coef_arrival_constants_evening -0.707272 F\n", + "46 coef_arrival_constants_late -1.639520 F\n", + "47 coef_duration_constants_0_to_2_hours -1.318085 F\n", + "48 coef_duration_constants_3_to_4_hours -0.680239 F\n", + "49 coef_duration_constants_5_to_6_hours -0.551126 F\n", "50 coef_duration_constants_7_to_8_hours 0.000000 T\n", - "51 coef_duration_constants_9_hours -0.487609 F\n", - "52 coef_duration_constants_10_hours -0.876448 F\n", - "53 coef_duration_constants_11_hours -1.127264 F\n", - "54 coef_duration_constants_12_to_13_hours -2.668346 F\n", - "55 coef_duration_constants_14_to_18_hours -2.675974 F" + "51 coef_duration_constants_9_hours -0.664353 F\n", + "52 coef_duration_constants_10_hours -0.914359 F\n", + "53 coef_duration_constants_11_hours -1.643692 F\n", + "54 coef_duration_constants_12_to_13_hours -2.528269 F\n", + "55 coef_duration_constants_14_to_18_hours -2.541672 F" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4614,7 +4726,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -4628,7 +4740,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/10_joint_tour_freq.ipynb b/activitysim/examples/example_estimation/notebooks/10_joint_tour_freq.ipynb index a8d306f95a..4fd045c4dd 100644 --- a/activitysim/examples/example_estimation/notebooks/10_joint_tour_freq.ipynb +++ b/activitysim/examples/example_estimation/notebooks/10_joint_tour_freq.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +115,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/joint_tour_frequency/joint_tour_frequency_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/joint_tour_frequency/joint_tour_frequency_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_frequency/joint_tour_frequency_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"joint_tour_frequency\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -766,7 +828,7 @@ " \n", " \n", " \n", - " 189748\n", + " 190236\n", " 0_tours\n", " 0_tours\n", " 1.0\n", @@ -778,20 +840,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " False\n", " False\n", " 0\n", - " 2.996\n", - " 0.0\n", - " 0.000\n", - " 6.041458\n", + " 2.996094\n", + " 0.000000\n", + " 0.000000\n", + " 5.661266\n", " 1\n", " \n", " \n", - " 189758\n", - " 1_Eat\n", + " 190592\n", + " 1_Disc\n", " 1_Eat\n", " 1.0\n", " 0.0\n", @@ -802,19 +864,19 @@ " 0.0\n", " 0.0\n", " ...\n", - " 2.0\n", + " 0.0\n", " 0.0\n", " False\n", " False\n", " 0\n", - " 2.303\n", - " 0.0\n", - " 0.000\n", - " 6.035642\n", + " 2.996094\n", + " 0.000000\n", + " 0.000000\n", + " 5.384032\n", " 4\n", " \n", " \n", - " 189833\n", + " 190998\n", " 0_tours\n", " 0_tours\n", " 1.0\n", @@ -826,21 +888,21 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " False\n", " False\n", " 0\n", - " 2.996\n", - " 0.0\n", - " 0.000\n", - " 5.601202\n", + " 2.564453\n", + " 0.000000\n", + " 0.000000\n", + " 4.529309\n", " 1\n", " \n", " \n", - " 190134\n", - " 0_tours\n", + " 191185\n", " 0_tours\n", + " 1_Shop\n", " 1.0\n", " 0.0\n", " 0.0\n", @@ -855,16 +917,16 @@ " False\n", " False\n", " 0\n", - " 2.996\n", - " 0.0\n", - " 0.000\n", - " 5.941369\n", - " 1\n", + " 2.996094\n", + " 0.000000\n", + " 0.000000\n", + " 4.015752\n", + " 2\n", " \n", " \n", - " 190153\n", - " 0_tours\n", + " 191276\n", " 0_tours\n", + " 1_Disc\n", " 1.0\n", " 0.0\n", " 0.0\n", @@ -879,11 +941,11 @@ " False\n", " False\n", " 0\n", - " 2.996\n", - " 0.0\n", - " 0.000\n", - " 5.876868\n", - " 1\n", + " 2.996094\n", + " 0.000000\n", + " 0.000000\n", + " 4.422027\n", + " 6\n", " \n", " \n", " ...\n", @@ -910,38 +972,38 @@ " ...\n", " \n", " \n", - " 2749929\n", + " 2759076\n", " 0_tours\n", " 0_tours\n", " 1.0\n", + " 2.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", " 1.0\n", + " 2.0\n", " False\n", " True\n", " 0\n", - " 2.303\n", - " 0.0\n", - " 2.484\n", - " 3.937425\n", + " 2.996094\n", + " 1.946289\n", + " 2.564453\n", + " 6.481015\n", " 1\n", " \n", " \n", - " 2749970\n", + " 2759264\n", " 0_tours\n", " 0_tours\n", " 1.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -951,16 +1013,16 @@ " False\n", " True\n", " 0\n", - " 2.996\n", - " 0.0\n", - " 0.000\n", - " 5.523326\n", + " 2.996094\n", + " 0.000000\n", + " 0.000000\n", + " 6.343830\n", " 1\n", " \n", " \n", - " 2750003\n", - " 0_tours\n", + " 2759425\n", " 0_tours\n", + " 1_Disc\n", " 1.0\n", " 0.0\n", " 0.0\n", @@ -970,20 +1032,20 @@ " 0.0\n", " 0.0\n", " ...\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " False\n", " True\n", " 0\n", - " 2.080\n", - " 0.0\n", - " 0.000\n", - " 6.289115\n", - " 1\n", + " 2.996094\n", + " 0.000000\n", + " 2.996094\n", + " 4.507081\n", + " 6\n", " \n", " \n", - " 2758909\n", - " 0_tours\n", + " 2760422\n", + " 1_Shop\n", " 0_tours\n", " 1.0\n", " 0.0\n", @@ -999,14 +1061,14 @@ " False\n", " True\n", " 0\n", - " 2.484\n", - " 0.0\n", - " 0.000\n", - " 6.677761\n", + " 2.197266\n", + " 0.000000\n", + " 0.000000\n", + " 4.345421\n", " 1\n", " \n", " \n", - " 2759348\n", + " 2760425\n", " 0_tours\n", " 0_tours\n", " 1.0\n", @@ -1023,229 +1085,229 @@ " False\n", " True\n", " 0\n", - " 2.996\n", - " 0.0\n", - " 0.000\n", - " 5.760961\n", + " 2.080078\n", + " 0.000000\n", + " 0.000000\n", + " 3.586974\n", " 1\n", " \n", " \n", "\n", - "

1028 rows × 130 columns

\n", + "

13210 rows × 130 columns

\n", "" ], "text/plain": [ " model_choice override_choice \\\n", "household_id \n", - "189748 0_tours 0_tours \n", - "189758 1_Eat 1_Eat \n", - "189833 0_tours 0_tours \n", - "190134 0_tours 0_tours \n", - "190153 0_tours 0_tours \n", + "190236 0_tours 0_tours \n", + "190592 1_Disc 1_Eat \n", + "190998 0_tours 0_tours \n", + "191185 0_tours 1_Shop \n", + "191276 0_tours 1_Disc \n", "... ... ... \n", - "2749929 0_tours 0_tours \n", - "2749970 0_tours 0_tours \n", - "2750003 0_tours 0_tours \n", - "2758909 0_tours 0_tours \n", - "2759348 0_tours 0_tours \n", + "2759076 0_tours 0_tours \n", + "2759264 0_tours 0_tours \n", + "2759425 0_tours 1_Disc \n", + "2760422 1_Shop 0_tours \n", + "2760425 0_tours 0_tours \n", "\n", " util_alternative_specific_constants \\\n", "household_id \n", - "189748 1.0 \n", - "189758 1.0 \n", - "189833 1.0 \n", - "190134 1.0 \n", - "190153 1.0 \n", + "190236 1.0 \n", + "190592 1.0 \n", + "190998 1.0 \n", + "191185 1.0 \n", + "191276 1.0 \n", "... ... \n", - "2749929 1.0 \n", - "2749970 1.0 \n", - "2750003 1.0 \n", - "2758909 1.0 \n", - "2759348 1.0 \n", + "2759076 1.0 \n", + "2759264 1.0 \n", + "2759425 1.0 \n", + "2760422 1.0 \n", + "2760425 1.0 \n", "\n", " util_fullTimeHomeMaxThree_zero_tours \\\n", "household_id \n", - "189748 0.0 \n", - "189758 0.0 \n", - "189833 0.0 \n", - "190134 0.0 \n", - "190153 0.0 \n", + "190236 0.0 \n", + "190592 0.0 \n", + "190998 0.0 \n", + "191185 0.0 \n", + "191276 0.0 \n", "... ... \n", - "2749929 0.0 \n", - "2749970 0.0 \n", - "2750003 0.0 \n", - "2758909 0.0 \n", - "2759348 0.0 \n", + "2759076 2.0 \n", + "2759264 1.0 \n", + "2759425 0.0 \n", + "2760422 0.0 \n", + "2760425 0.0 \n", "\n", " util_partTimeHomeMaxThree_zero_tours \\\n", "household_id \n", - "189748 0.0 \n", - "189758 0.0 \n", - "189833 0.0 \n", - "190134 0.0 \n", - "190153 0.0 \n", + "190236 0.0 \n", + "190592 0.0 \n", + "190998 0.0 \n", + "191185 0.0 \n", + "191276 0.0 \n", "... ... \n", - "2749929 0.0 \n", - "2749970 0.0 \n", - "2750003 0.0 \n", - "2758909 0.0 \n", - "2759348 0.0 \n", + "2759076 0.0 \n", + "2759264 0.0 \n", + "2759425 0.0 \n", + "2760422 0.0 \n", + "2760425 0.0 \n", "\n", " util_nonWorkerHomeMaxThree_zero_tours \\\n", "household_id \n", - "189748 0.0 \n", - "189758 0.0 \n", - "189833 0.0 \n", - "190134 0.0 \n", - "190153 0.0 \n", + "190236 0.0 \n", + "190592 0.0 \n", + "190998 0.0 \n", + "191185 0.0 \n", + "191276 0.0 \n", "... ... \n", - "2749929 0.0 \n", - "2749970 0.0 \n", - "2750003 0.0 \n", - "2758909 0.0 \n", - "2759348 0.0 \n", + "2759076 0.0 \n", + "2759264 0.0 \n", + "2759425 0.0 \n", + "2760422 0.0 \n", + "2760425 0.0 \n", "\n", " util_retireeHomeMaxThree_zero_tours \\\n", "household_id \n", - "189748 0.0 \n", - "189758 0.0 \n", - "189833 0.0 \n", - "190134 0.0 \n", - "190153 0.0 \n", + "190236 0.0 \n", + "190592 0.0 \n", + "190998 0.0 \n", + "191185 0.0 \n", + "191276 0.0 \n", "... ... \n", - "2749929 0.0 \n", - "2749970 1.0 \n", - "2750003 0.0 \n", - "2758909 0.0 \n", - "2759348 0.0 \n", + "2759076 1.0 \n", + "2759264 0.0 \n", + "2759425 0.0 \n", + "2760422 0.0 \n", + "2760425 0.0 \n", "\n", " util_universityHomeMaxThree_univ_and_driving_zero_tours \\\n", "household_id \n", - "189748 0.0 \n", - "189758 0.0 \n", - "189833 0.0 \n", - "190134 0.0 \n", - "190153 0.0 \n", + "190236 0.0 \n", + "190592 0.0 \n", + "190998 0.0 \n", + "191185 0.0 \n", + "191276 0.0 \n", "... ... \n", - "2749929 0.0 \n", - "2749970 0.0 \n", - "2750003 0.0 \n", - "2758909 0.0 \n", - "2759348 0.0 \n", + "2759076 0.0 \n", + "2759264 0.0 \n", + "2759425 0.0 \n", + "2760422 0.0 \n", + "2760425 0.0 \n", "\n", " util_preDrivingHomeMaxThree_preschool_and_school_zero_tours \\\n", "household_id \n", - "189748 0.0 \n", - "189758 0.0 \n", - "189833 0.0 \n", - "190134 0.0 \n", - "190153 0.0 \n", + "190236 0.0 \n", + "190592 0.0 \n", + "190998 0.0 \n", + "191185 0.0 \n", + "191276 0.0 \n", "... ... \n", - "2749929 0.0 \n", - "2749970 0.0 \n", - "2750003 0.0 \n", - "2758909 0.0 \n", - "2759348 0.0 \n", + "2759076 0.0 \n", + "2759264 0.0 \n", + "2759425 0.0 \n", + "2760422 0.0 \n", + "2760425 0.0 \n", "\n", " util_fullTimeNonMandMaxThree_shopping ... \\\n", "household_id ... \n", - "189748 0.0 ... \n", - "189758 0.0 ... \n", - "189833 0.0 ... \n", - "190134 0.0 ... \n", - "190153 0.0 ... \n", + "190236 0.0 ... \n", + "190592 0.0 ... \n", + "190998 0.0 ... \n", + "191185 0.0 ... \n", + "191276 0.0 ... \n", "... ... ... \n", - "2749929 0.0 ... \n", - "2749970 0.0 ... \n", - "2750003 0.0 ... \n", - "2758909 0.0 ... \n", - "2759348 0.0 ... \n", + "2759076 0.0 ... \n", + "2759264 0.0 ... \n", + "2759425 0.0 ... \n", + "2760422 0.0 ... \n", + "2760425 0.0 ... \n", "\n", " cdap_mand_univ_driving_max3 cdap_mand_nondriving_child_max3 \\\n", "household_id \n", - "189748 0.0 0.0 \n", - "189758 2.0 0.0 \n", - "189833 0.0 0.0 \n", - "190134 0.0 0.0 \n", - "190153 0.0 0.0 \n", + "190236 1.0 0.0 \n", + "190592 0.0 0.0 \n", + "190998 1.0 0.0 \n", + "191185 0.0 0.0 \n", + "191276 0.0 0.0 \n", "... ... ... \n", - "2749929 0.0 1.0 \n", - "2749970 0.0 0.0 \n", - "2750003 0.0 0.0 \n", - "2758909 0.0 0.0 \n", - "2759348 0.0 0.0 \n", + "2759076 1.0 2.0 \n", + "2759264 0.0 0.0 \n", + "2759425 1.0 0.0 \n", + "2760422 0.0 0.0 \n", + "2760425 0.0 0.0 \n", "\n", " income_between_50_and_100 income_greater_than_100 \\\n", "household_id \n", - "189748 False False \n", - "189758 False False \n", - "189833 False False \n", - "190134 False False \n", - "190153 False False \n", + "190236 False False \n", + "190592 False False \n", + "190998 False False \n", + "191185 False False \n", + "191276 False False \n", "... ... ... \n", - "2749929 False True \n", - "2749970 False True \n", - "2750003 False True \n", - "2758909 False True \n", - "2759348 False True \n", + "2759076 False True \n", + "2759264 False True \n", + "2759425 False True \n", + "2760422 False True \n", + "2760425 False True \n", "\n", " income_missing log_time_window_overlap_adult \\\n", "household_id \n", - "189748 0 2.996 \n", - "189758 0 2.303 \n", - "189833 0 2.996 \n", - "190134 0 2.996 \n", - "190153 0 2.996 \n", + "190236 0 2.996094 \n", + "190592 0 2.996094 \n", + "190998 0 2.564453 \n", + "191185 0 2.996094 \n", + "191276 0 2.996094 \n", "... ... ... \n", - "2749929 0 2.303 \n", - "2749970 0 2.996 \n", - "2750003 0 2.080 \n", - "2758909 0 2.484 \n", - "2759348 0 2.996 \n", + "2759076 0 2.996094 \n", + "2759264 0 2.996094 \n", + "2759425 0 2.996094 \n", + "2760422 0 2.197266 \n", + "2760425 0 2.080078 \n", "\n", " log_time_window_overlap_child \\\n", "household_id \n", - "189748 0.0 \n", - "189758 0.0 \n", - "189833 0.0 \n", - "190134 0.0 \n", - "190153 0.0 \n", + "190236 0.000000 \n", + "190592 0.000000 \n", + "190998 0.000000 \n", + "191185 0.000000 \n", + "191276 0.000000 \n", "... ... \n", - "2749929 0.0 \n", - "2749970 0.0 \n", - "2750003 0.0 \n", - "2758909 0.0 \n", - "2759348 0.0 \n", + "2759076 1.946289 \n", + "2759264 0.000000 \n", + "2759425 0.000000 \n", + "2760422 0.000000 \n", + "2760425 0.000000 \n", "\n", " log_time_window_overlap_adult_child \\\n", "household_id \n", - "189748 0.000 \n", - "189758 0.000 \n", - "189833 0.000 \n", - "190134 0.000 \n", - "190153 0.000 \n", + "190236 0.000000 \n", + "190592 0.000000 \n", + "190998 0.000000 \n", + "191185 0.000000 \n", + "191276 0.000000 \n", "... ... \n", - "2749929 2.484 \n", - "2749970 0.000 \n", - "2750003 0.000 \n", - "2758909 0.000 \n", - "2759348 0.000 \n", + "2759076 2.564453 \n", + "2759264 0.000000 \n", + "2759425 2.996094 \n", + "2760422 0.000000 \n", + "2760425 0.000000 \n", "\n", " non_motorized_retail_accessibility override_choice_code \n", "household_id \n", - "189748 6.041458 1 \n", - "189758 6.035642 4 \n", - "189833 5.601202 1 \n", - "190134 5.941369 1 \n", - "190153 5.876868 1 \n", + "190236 5.661266 1 \n", + "190592 5.384032 4 \n", + "190998 4.529309 1 \n", + "191185 4.015752 2 \n", + "191276 4.422027 6 \n", "... ... ... \n", - "2749929 3.937425 1 \n", - "2749970 5.523326 1 \n", - "2750003 6.289115 1 \n", - "2758909 6.677761 1 \n", - "2759348 5.760961 1 \n", + "2759076 6.481015 1 \n", + "2759264 6.343830 1 \n", + "2759425 4.507081 6 \n", + "2760422 4.345421 1 \n", + "2760425 3.586974 1 \n", "\n", - "[1028 rows x 130 columns]" + "[13210 rows x 130 columns]" ] }, "execution_count": 6, @@ -1271,17 +1333,10 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 067 [Optimization terminated successfully]

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Iteration 061 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1293,7 +1348,7 @@ { "data": { "text/html": [ - "

Best LL = -391.31509615209995

" + "

Best LL = -5583.026333343983

" ], "text/plain": [ "" @@ -1324,70 +1379,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_asc_0_tours\n", " 0.000000\n", + " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_asc_1_Disc\n", - " -5.857843\n", + " -5.351766\n", + " -5.351766\n", " -5.4806\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -5.857843\n", " \n", " \n", " coef_asc_1_Eat\n", - " -5.140469\n", + " -6.074061\n", + " -6.074061\n", " -6.3757\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -5.140469\n", " \n", " \n", " coef_asc_1_Main\n", - " -4.843654\n", + " -5.816690\n", + " -5.816690\n", " -5.7389\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.843654\n", " \n", " \n", " coef_asc_1_Shop\n", - " -7.244895\n", + " -6.045190\n", + " -6.045190\n", " -6.0149\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -7.244895\n", " \n", " \n", " ...\n", @@ -1398,124 +1457,97 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coef_universityNonMandMaxThree_shopping\n", - " 1.134054\n", + " 0.857729\n", + " 0.857729\n", " 0.7648\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.134054\n", " \n", " \n", " coef_universityNonMandMaxThree_visiting\n", - " -18.048773\n", + " 0.282416\n", + " 0.282416\n", " 0.2809\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -18.048773\n", " \n", " \n", " coef_walkRetailAccessibility_eatout\n", - " -0.027363\n", + " 0.042572\n", + " 0.042572\n", " 0.0620\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.027363\n", " \n", " \n", " coef_zeroAutomobiles_disc\n", - " -0.539939\n", + " -0.953758\n", + " -0.953758\n", " -0.9090\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.539939\n", " \n", " \n", " coef_zeroAutomobiles_visiting\n", - " -18.311268\n", + " -0.900518\n", + " -0.900518\n", " -0.9800\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -18.311268\n", " \n", " \n", "\n", - "

76 rows × 8 columns

\n", + "

76 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue nullvalue \\\n", - "coef_asc_0_tours 0.000000 0.0000 0.0 \n", - "coef_asc_1_Disc -5.857843 -5.4806 0.0 \n", - "coef_asc_1_Eat -5.140469 -6.3757 0.0 \n", - "coef_asc_1_Main -4.843654 -5.7389 0.0 \n", - "coef_asc_1_Shop -7.244895 -6.0149 0.0 \n", - "... ... ... ... \n", - "coef_universityNonMandMaxThree_shopping 1.134054 0.7648 0.0 \n", - "coef_universityNonMandMaxThree_visiting -18.048773 0.2809 0.0 \n", - "coef_walkRetailAccessibility_eatout -0.027363 0.0620 0.0 \n", - "coef_zeroAutomobiles_disc -0.539939 -0.9090 0.0 \n", - "coef_zeroAutomobiles_visiting -18.311268 -0.9800 0.0 \n", + " value best initvalue \\\n", + "param_name \n", + "coef_asc_0_tours 0.000000 0.000000 0.0000 \n", + "coef_asc_1_Disc -5.351766 -5.351766 -5.4806 \n", + "coef_asc_1_Eat -6.074061 -6.074061 -6.3757 \n", + "coef_asc_1_Main -5.816690 -5.816690 -5.7389 \n", + "coef_asc_1_Shop -6.045190 -6.045190 -6.0149 \n", + "... ... ... ... \n", + "coef_universityNonMandMaxThree_shopping 0.857729 0.857729 0.7648 \n", + "coef_universityNonMandMaxThree_visiting 0.282416 0.282416 0.2809 \n", + "coef_walkRetailAccessibility_eatout 0.042572 0.042572 0.0620 \n", + "coef_zeroAutomobiles_disc -0.953758 -0.953758 -0.9090 \n", + "coef_zeroAutomobiles_visiting -0.900518 -0.900518 -0.9800 \n", "\n", - " minimum maximum holdfast note \\\n", - "coef_asc_0_tours NaN NaN 1 \n", - "coef_asc_1_Disc NaN NaN 0 \n", - "coef_asc_1_Eat NaN NaN 0 \n", - "coef_asc_1_Main NaN NaN 0 \n", - "coef_asc_1_Shop NaN NaN 0 \n", - "... ... ... ... ... \n", - "coef_universityNonMandMaxThree_shopping NaN NaN 0 \n", - "coef_universityNonMandMaxThree_visiting NaN NaN 0 \n", - "coef_walkRetailAccessibility_eatout NaN NaN 0 \n", - "coef_zeroAutomobiles_disc NaN NaN 0 \n", - "coef_zeroAutomobiles_visiting NaN NaN 0 \n", + " minimum maximum nullvalue holdfast \n", + "param_name \n", + "coef_asc_0_tours 0.0 0.0 0.0 1 \n", + "coef_asc_1_Disc -50.0 50.0 0.0 0 \n", + "coef_asc_1_Eat -50.0 50.0 0.0 0 \n", + "coef_asc_1_Main -50.0 50.0 0.0 0 \n", + "coef_asc_1_Shop -50.0 50.0 0.0 0 \n", + "... ... ... ... ... \n", + "coef_universityNonMandMaxThree_shopping -50.0 50.0 0.0 0 \n", + "coef_universityNonMandMaxThree_visiting -50.0 50.0 0.0 0 \n", + "coef_walkRetailAccessibility_eatout -50.0 50.0 0.0 0 \n", + "coef_zeroAutomobiles_disc -50.0 50.0 0.0 0 \n", + "coef_zeroAutomobiles_visiting -50.0 50.0 0.0 0 \n", "\n", - " best \n", - "coef_asc_0_tours 0.000000 \n", - "coef_asc_1_Disc -5.857843 \n", - "coef_asc_1_Eat -5.140469 \n", - "coef_asc_1_Main -4.843654 \n", - "coef_asc_1_Shop -7.244895 \n", - "... ... \n", - "coef_universityNonMandMaxThree_shopping 1.134054 \n", - "coef_universityNonMandMaxThree_visiting -18.048773 \n", - "coef_walkRetailAccessibility_eatout -0.027363 \n", - "coef_zeroAutomobiles_disc -0.539939 \n", - "coef_zeroAutomobiles_visiting -18.311268 \n", - "\n", - "[76 rows x 8 columns]" + "[76 rows x 7 columns]" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.4504151870310519e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" - ] - }, { "data": { "text/html": [ @@ -1529,310 +1561,310 @@ " \n", " \n", " coef_asc_0_tours\n", - " 4.128662e-14\n", + " 0.000000\n", " \n", " \n", " coef_asc_1_Disc\n", - " -5.857843e+00\n", + " -5.351766\n", " \n", " \n", " coef_asc_1_Eat\n", - " -5.140469e+00\n", + " -6.074061\n", " \n", " \n", " coef_asc_1_Main\n", - " -4.843654e+00\n", + " -5.816690\n", " \n", " \n", " coef_asc_1_Shop\n", - " -7.244895e+00\n", + " -6.045190\n", " \n", " \n", " coef_asc_1_Visit\n", - " -5.962149e+00\n", + " -5.839951\n", " \n", " \n", " coef_asc_2_tours\n", - " -1.537957e+01\n", + " -14.545006\n", " \n", " \n", " coef_drivingAgeStuMandMaxThree_disc\n", - " 2.642061e-01\n", + " 0.283573\n", " \n", " \n", " coef_drivingAgeStuMandMaxThree_maint\n", - " -2.049784e-01\n", + " -0.332304\n", " \n", " \n", " coef_fewerCarsThanDrivers_maint\n", - " -2.361602e-01\n", + " 0.768933\n", " \n", " \n", " coef_fewerCarsThanDrivers_shopping\n", - " 6.120417e-01\n", + " 0.173631\n", " \n", " \n", " coef_fullTimeHomeMaxThree_zero_tours\n", - " 2.001437e+00\n", + " 0.941096\n", " \n", " \n", " coef_fullTimeMandMaxThree_maint\n", - " -4.624882e-01\n", + " -0.397212\n", " \n", " \n", " coef_fullTimeMandMaxThree_shopping\n", - " 1.435394e-01\n", + " -0.402774\n", " \n", " \n", " coef_fullTimeNonMandMaxThree_disc\n", - " 2.181760e-01\n", + " -0.019740\n", " \n", " \n", " coef_fullTimeNonMandMaxThree_eatout\n", - " 3.257307e-01\n", + " 0.053619\n", " \n", " \n", " coef_fullTimeNonMandMaxThree_maint\n", - " 7.596262e-01\n", + " 0.114785\n", " \n", " \n", " coef_fullTimeNonMandMaxThree_shopping\n", - " -8.881290e-01\n", + " -0.253055\n", " \n", " \n", " coef_fullTimeNonMandMaxThree_visiting\n", - " 7.885926e-01\n", + " 0.750690\n", " \n", " \n", " coef_incomeBetween50And100_disc\n", - " 8.352921e-03\n", + " 0.132487\n", " \n", " \n", " coef_incomeBetween50And100_eatout\n", - " 1.031886e-01\n", + " 0.268954\n", " \n", " \n", " coef_incomeGreaterThan100_disc\n", - " 6.249345e-01\n", + " 0.366581\n", " \n", " \n", " coef_incomeGreaterThan100_eatout\n", - " 6.353587e-01\n", + " 0.493932\n", " \n", " \n", " coef_incomeMissing_dummy_always_zero_disc\n", - " 3.723000e-01\n", + " 0.372300\n", " \n", " \n", " coef_incomeMissing_dummy_always_zero_eatout\n", - " 2.780000e-01\n", + " 0.278000\n", " \n", " \n", " coef_logTimeWindowOverlapAdultChild_disc\n", - " 1.502560e-01\n", + " 0.303504\n", " \n", " \n", " coef_logTimeWindowOverlapAdultChild_eatout\n", - " -3.203806e-01\n", + " -0.094351\n", " \n", " \n", " coef_logTimeWindowOverlapAdultChild_maint\n", - " 2.695922e-01\n", + " 0.398958\n", " \n", " \n", " coef_logTimeWindowOverlapAdultChild_shopping\n", - " -4.153246e-01\n", + " 0.017970\n", " \n", " \n", " coef_logTimeWindowOverlapAdult_disc\n", - " 5.596579e-01\n", + " 0.411850\n", " \n", " \n", " coef_logTimeWindowOverlapAdult_eatout\n", - " 5.606143e-01\n", + " 0.467283\n", " \n", " \n", " coef_logTimeWindowOverlapAdult_maint\n", - " 4.376387e-01\n", + " 0.359818\n", " \n", " \n", " coef_logTimeWindowOverlapAdult_shopping\n", - " 8.640588e-01\n", + " 0.862228\n", " \n", " \n", " coef_logTimeWindowOverlapChild_disc\n", - " -1.693893e-01\n", + " 0.150068\n", " \n", " \n", " coef_logTimeWindowOverlapChild_maint\n", - " -1.037855e-02\n", + " 0.295107\n", " \n", " \n", " coef_logTimeWindowOverlapChild_shopping\n", - " 1.036654e+00\n", + " 0.255944\n", " \n", " \n", " coef_moreCarsThanWorkers_eatout\n", - " 7.948089e-02\n", + " 0.294865\n", " \n", " \n", " coef_moreCarsThanWorkers_shopping\n", - " -6.278022e-01\n", + " -0.551877\n", " \n", " \n", " coef_nonWorkerHomeMaxThree_zero_tours\n", - " -1.694121e-01\n", + " 0.379269\n", " \n", " \n", " coef_nonWorkerNonMandMaxThree_disc\n", - " -1.190124e-01\n", + " 0.120934\n", " \n", " \n", " coef_nonWorkerNonMandMaxThree_eatout\n", - " -3.544771e-01\n", + " 0.045074\n", " \n", " \n", " coef_nonWorkerNonMandMaxThree_maint\n", - " 3.725508e-01\n", + " 0.351215\n", " \n", " \n", " coef_nonWorkerNonMandMaxThree_shopping\n", - " 5.232632e-01\n", + " 0.422604\n", " \n", " \n", " coef_nonWorkerNonMandMaxThree_visiting\n", - " -3.598996e-01\n", + " 0.454085\n", " \n", " \n", " coef_partTimeHomeMaxThree_zero_tours\n", - " 1.700075e+00\n", + " 0.928683\n", " \n", " \n", " coef_partTimeNonMandMaxThree_disc\n", - " 2.264406e-01\n", + " 0.668232\n", " \n", " \n", " coef_partTimeNonMandMaxThree_eatout\n", - " -1.577062e+01\n", + " 0.381935\n", " \n", " \n", " coef_partTimeNonMandMaxThree_maint\n", - " 1.215592e-01\n", + " 0.071116\n", " \n", " \n", " coef_partTimeNonMandMaxThree_shopping\n", - " -1.914973e-01\n", + " -0.021127\n", " \n", " \n", " coef_partTimeNonMandMaxThree_visiting\n", - " 3.484080e-01\n", + " 0.417675\n", " \n", " \n", " coef_preDrivingAgeMandMaxThree_disc\n", - " 5.883044e-01\n", + " 0.133605\n", " \n", " \n", " coef_preDrivingAgeMandMaxThree_maint\n", - " 5.724992e-02\n", + " 0.065271\n", " \n", " \n", " coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours\n", - " -2.649396e-01\n", + " 0.086034\n", " \n", " \n", " coef_preDrivingNonMandMaxThree_disc\n", - " 4.697889e-01\n", + " 0.189119\n", " \n", " \n", " coef_preDrivingNonMandMaxThree_eatout\n", - " -1.367435e+01\n", + " 0.700510\n", " \n", " \n", " coef_preDrivingNonMandMaxThree_maint\n", - " -4.780032e-01\n", + " 0.163703\n", " \n", " \n", " coef_preDrivingNonMandMaxThree_shopping\n", - " 5.401680e-03\n", + " 0.448199\n", " \n", " \n", " coef_preDrivingNonMandMaxThree_visiting\n", - " -1.728326e+00\n", + " 0.241003\n", " \n", " \n", " coef_retireeHomeMaxThree_zero_tours\n", - " 3.072054e-01\n", + " 0.332620\n", " \n", " \n", " coef_retireeNonMandMaxThree_disc\n", - " 5.953927e-01\n", + " 0.596927\n", " \n", " \n", " coef_retireeNonMandMaxThree_eatout\n", - " 3.370647e-01\n", + " 0.444051\n", " \n", " \n", " coef_retireeNonMandMaxThree_maint\n", - " 9.306423e-01\n", + " 0.935537\n", " \n", " \n", " coef_retireeNonMandMaxThree_shopping\n", - " 9.523557e-01\n", + " 0.888533\n", " \n", " \n", " coef_retireeNonMandMaxThree_visiting\n", - " 1.743228e-01\n", + " 0.313616\n", " \n", " \n", " coef_timeWindowOverlapAdultChild_visiting\n", - " 1.404939e-01\n", + " 0.032122\n", " \n", " \n", " coef_timeWindowOverlapAdult_visiting\n", - " 1.103018e-01\n", + " 0.062728\n", " \n", " \n", " coef_timeWindowOverlapChild_visiting\n", - " -6.064464e-02\n", + " 0.037481\n", " \n", " \n", " coef_universityHomeMaxThree_univ_and_driving_zero_tours\n", - " -4.104066e-01\n", + " 0.115748\n", " \n", " \n", " coef_universityNonMandMaxThree_disc\n", - " -1.447281e-01\n", + " 0.478898\n", " \n", " \n", " coef_universityNonMandMaxThree_eatout\n", - " -1.408027e+01\n", + " 0.447854\n", " \n", " \n", " coef_universityNonMandMaxThree_maint\n", - " -4.377287e-01\n", + " -0.157965\n", " \n", " \n", " coef_universityNonMandMaxThree_shopping\n", - " 1.134054e+00\n", + " 0.857729\n", " \n", " \n", " coef_universityNonMandMaxThree_visiting\n", - " -1.804877e+01\n", + " 0.282416\n", " \n", " \n", " coef_walkRetailAccessibility_eatout\n", - " -2.736284e-02\n", + " 0.042572\n", " \n", " \n", " coef_zeroAutomobiles_disc\n", - " -5.399391e-01\n", + " -0.953758\n", " \n", " \n", " coef_zeroAutomobiles_visiting\n", - " -1.831127e+01\n", + " -0.900518\n", " \n", " \n", - "loglike-391.31509615209995d_loglike\n", + "
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coef_asc_0_tours0.000000e+000.000000
coef_asc_1_Disc1.523003e-040.000127
coef_asc_1_Eat6.759891e-050.000172
coef_asc_1_Main-2.935726e-04-0.000055
coef_asc_1_Shop-2.605190e-04-0.000097
coef_asc_1_Visit-1.539399e-04-0.000019
coef_asc_2_tours-7.190405e-050.000045
coef_drivingAgeStuMandMaxThree_disc-1.208006e-040.000034
coef_drivingAgeStuMandMaxThree_maint6.022683e-050.000009
coef_fewerCarsThanDrivers_maint-1.544212e-040.000025
coef_fewerCarsThanDrivers_shopping-5.384239e-050.000060
coef_fullTimeHomeMaxThree_zero_tours-2.514724e-040.000088
coef_fullTimeMandMaxThree_maint-2.064152e-04-0.000002
coef_fullTimeMandMaxThree_shopping-2.409727e-040.000014
coef_fullTimeNonMandMaxThree_disc1.867895e-040.000047
coef_fullTimeNonMandMaxThree_eatout-5.229114e-050.000034
coef_fullTimeNonMandMaxThree_maint-1.708359e-04-0.000036
coef_fullTimeNonMandMaxThree_shopping-8.986588e-050.000025
coef_fullTimeNonMandMaxThree_visiting-1.238448e-040.000019
coef_incomeBetween50And100_disc-3.214932e-040.000033
coef_incomeBetween50And100_eatout2.548977e-050.000004
coef_incomeGreaterThan100_disc1.527594e-050.000088
coef_incomeGreaterThan100_eatout-1.190833e-04-0.000003
coef_incomeMissing_dummy_always_zero_disc0.000000e+000.000000
coef_incomeMissing_dummy_always_zero_eatout0.000000e+000.000000
coef_logTimeWindowOverlapAdultChild_disc6.268423e-040.000322
coef_logTimeWindowOverlapAdultChild_eatout4.738068e-050.000270
coef_logTimeWindowOverlapAdultChild_maint-5.205643e-040.000069
coef_logTimeWindowOverlapAdultChild_shopping-7.016522e-040.000017
coef_logTimeWindowOverlapAdult_disc8.132405e-040.000280
coef_logTimeWindowOverlapAdult_eatout-5.191213e-050.000106
coef_logTimeWindowOverlapAdult_maint-7.074150e-04-0.000079
coef_logTimeWindowOverlapAdult_shopping-9.068285e-040.000016
coef_logTimeWindowOverlapChild_disc3.313056e-040.000050
coef_logTimeWindowOverlapChild_maint-3.411335e-04-0.000033
coef_logTimeWindowOverlapChild_shopping-6.088923e-040.000003
coef_moreCarsThanWorkers_eatout-1.693804e-040.000019
coef_moreCarsThanWorkers_shopping-7.535132e-05-0.000021
coef_nonWorkerHomeMaxThree_zero_tours1.546204e-04-0.000071
coef_nonWorkerNonMandMaxThree_disc2.251574e-04-0.000004
coef_nonWorkerNonMandMaxThree_eatout4.969366e-070.000084
coef_nonWorkerNonMandMaxThree_maint-8.922162e-050.000060
coef_nonWorkerNonMandMaxThree_shopping-2.254530e-04-0.000095
coef_nonWorkerNonMandMaxThree_visiting1.544757e-040.000032
coef_partTimeHomeMaxThree_zero_tours-5.144981e-050.000005
coef_partTimeNonMandMaxThree_disc1.814241e-040.000060
coef_partTimeNonMandMaxThree_eatout-3.175600e-07-0.000002
coef_partTimeNonMandMaxThree_maint-1.133846e-040.000030
coef_partTimeNonMandMaxThree_shopping1.063172e-040.000027
coef_partTimeNonMandMaxThree_visiting1.329104e-05-0.000037
coef_preDrivingAgeMandMaxThree_disc4.323394e-040.000164
coef_preDrivingAgeMandMaxThree_maint-1.660229e-040.000015
coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours2.715906e-040.000030
coef_preDrivingNonMandMaxThree_disc9.520881e-050.000037
coef_preDrivingNonMandMaxThree_eatout-7.286290e-070.000027
coef_preDrivingNonMandMaxThree_maint-8.707705e-05-0.000060
coef_preDrivingNonMandMaxThree_shopping-7.017908e-050.000009
coef_preDrivingNonMandMaxThree_visiting1.106056e-040.000014
coef_retireeHomeMaxThree_zero_tours-3.000796e-040.000050
coef_retireeNonMandMaxThree_disc3.955978e-050.000028
coef_retireeNonMandMaxThree_eatout-8.864118e-05-0.000012
coef_retireeNonMandMaxThree_maint1.123175e-05-0.000058
coef_retireeNonMandMaxThree_shopping-8.375532e-050.000009
coef_retireeNonMandMaxThree_visiting-7.291644e-050.000075
coef_timeWindowOverlapAdultChild_visiting1.533203e-03-0.000362
coef_timeWindowOverlapAdult_visiting-4.187359e-040.000189
coef_timeWindowOverlapChild_visiting1.535922e-03-0.000253
coef_universityHomeMaxThree_univ_and_driving_zero_tours-2.916835e-040.000140
coef_universityNonMandMaxThree_disc4.189163e-050.000095
coef_universityNonMandMaxThree_eatout-6.392369e-070.000004
coef_universityNonMandMaxThree_maint-2.743951e-05-0.000123
coef_universityNonMandMaxThree_shopping-1.669075e-040.000013
coef_universityNonMandMaxThree_visiting-3.279064e-08-0.000090
coef_walkRetailAccessibility_eatout-3.988777e-040.000218
coef_zeroAutomobiles_disc-5.720641e-05-0.000027
coef_zeroAutomobiles_visiting-5.437687e-080.000058
nit67nfev131njev67status0message'Optimization terminated successfully'successTrueelapsed_time0:00:03.730310method'SLSQP'n_cases1028iteration_number67logloss0.3806567083191634" + "nit61nfev63njev61status0message'Optimization terminated successfully'successTrueelapsed_time0:00:02.374665method'SLSQP'n_cases13210iteration_number61loglike-5583.026333343983" ], "text/plain": [ - "┣ x: coef_asc_0_tours 4.128662e-14\n", - "┃ coef_asc_1_Disc -5.857843e+00\n", - "┃ coef_asc_1_Eat -5.140469e+00\n", - "┃ coef_asc_1_Main -4.843654e+00\n", - "┃ coef_asc_1_Shop -7.244895e+00\n", - "┃ ... \n", - "┃ coef_universityNonMandMaxThree_shopping 1.134054e+00\n", - "┃ coef_universityNonMandMaxThree_visiting -1.804877e+01\n", - "┃ coef_walkRetailAccessibility_eatout -2.736284e-02\n", - "┃ coef_zeroAutomobiles_disc -5.399391e-01\n", - "┃ coef_zeroAutomobiles_visiting -1.831127e+01\n", + "┣ x: coef_asc_0_tours 0.000000\n", + "┃ coef_asc_1_Disc -5.351766\n", + "┃ coef_asc_1_Eat -6.074061\n", + "┃ coef_asc_1_Main -5.816690\n", + "┃ coef_asc_1_Shop -6.045190\n", + "┃ ... \n", + "┃ coef_universityNonMandMaxThree_shopping 0.857729\n", + "┃ coef_universityNonMandMaxThree_visiting 0.282416\n", + "┃ coef_walkRetailAccessibility_eatout 0.042572\n", + "┃ coef_zeroAutomobiles_disc -0.953758\n", + "┃ coef_zeroAutomobiles_visiting -0.900518\n", "┃ Length: 76, dtype: float64\n", - "┣ loglike: -391.31509615209995\n", - "┣ d_loglike: coef_asc_0_tours 0.000000e+00\n", - "┃ coef_asc_1_Disc 1.523003e-04\n", - "┃ coef_asc_1_Eat 6.759891e-05\n", - "┃ coef_asc_1_Main -2.935726e-04\n", - "┃ coef_asc_1_Shop -2.605190e-04\n", - "┃ ... \n", - "┃ coef_universityNonMandMaxThree_shopping -1.669075e-04\n", - "┃ coef_universityNonMandMaxThree_visiting -3.279064e-08\n", - "┃ coef_walkRetailAccessibility_eatout -3.988777e-04\n", - "┃ coef_zeroAutomobiles_disc -5.720641e-05\n", - "┃ coef_zeroAutomobiles_visiting -5.437687e-08\n", + "┣ logloss: 0.4226363613432236\n", + "┣ d_logloss: coef_asc_0_tours 0.000000\n", + "┃ coef_asc_1_Disc 0.000127\n", + "┃ coef_asc_1_Eat 0.000172\n", + "┃ coef_asc_1_Main -0.000055\n", + "┃ coef_asc_1_Shop -0.000097\n", + "┃ ... \n", + "┃ coef_universityNonMandMaxThree_shopping 0.000013\n", + "┃ coef_universityNonMandMaxThree_visiting -0.000090\n", + "┃ coef_walkRetailAccessibility_eatout 0.000218\n", + "┃ coef_zeroAutomobiles_disc -0.000027\n", + "┃ coef_zeroAutomobiles_visiting 0.000058\n", "┃ Length: 76, dtype: float64\n", - "┣ nit: 67\n", - "┣ nfev: 131\n", - "┣ njev: 67\n", + "┣ nit: 61\n", + "┣ nfev: 63\n", + "┣ njev: 61\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=3, microseconds=730310)\n", + "┣ elapsed_time: datetime.timedelta(seconds=2, microseconds=374665)\n", "┣ method: 'SLSQP'\n", - "┣ n_cases: 1028\n", - "┣ iteration_number: 67\n", - "┣ logloss: 0.3806567083191634" + "┣ n_cases: 13210\n", + "┣ iteration_number: 61\n", + "┣ loglike: -5583.026333343983" ] }, "execution_count": 7, @@ -2210,704 +2242,730 @@ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Null Value Constrained
coef_asc_0_tours 0.00 NA NA 0.00fixed value
coef_asc_1_Disc-5.86 1.09-5.39*** 0.00
coef_asc_1_Eat-5.14 2.07-2.48* 0.00
coef_asc_1_Main-4.84 0.973-4.98*** 0.00
coef_asc_1_Shop-7.24 1.18-6.14*** 0.00
coef_asc_1_Visit-5.96 0.749-7.96*** 0.00
coef_asc_2_tours-15.4 2.26-6.81*** 0.00
coef_drivingAgeStuMandMaxThree_disc 0.264 0.406 0.65 0.00
coef_drivingAgeStuMandMaxThree_maint-0.205 0.491-0.42 0.00
coef_fewerCarsThanDrivers_maint-0.236 0.439-0.54 0.00
coef_fewerCarsThanDrivers_shopping 0.612 0.596 1.03 0.00
coef_fullTimeHomeMaxThree_zero_tours 2.00 0.828 2.42* 0.00
coef_fullTimeMandMaxThree_maint-0.462 0.313-1.48 0.00
coef_fullTimeMandMaxThree_shopping 0.144 0.288 0.50 0.00
coef_fullTimeNonMandMaxThree_disc 0.218 0.569 0.38 0.00
coef_fullTimeNonMandMaxThree_eatout 0.326 0.669 0.49 0.00
coef_fullTimeNonMandMaxThree_maint 0.760 0.430 1.77 0.00
coef_fullTimeNonMandMaxThree_shopping-0.888 1.03-0.86 0.00
coef_fullTimeNonMandMaxThree_visiting 0.789 0.626 1.26 0.00
coef_incomeBetween50And100_disc 0.00835 0.621 0.01 0.00
coef_incomeBetween50And100_eatout 0.103 0.741 0.14 0.00
coef_incomeGreaterThan100_disc 0.625 0.562 1.11 0.00
coef_incomeGreaterThan100_eatout 0.635 0.684 0.93 0.00
coef_incomeMissing_dummy_always_zero_disc 0.372 0.000320 BIG*** 0.00
coef_incomeMissing_dummy_always_zero_eatout 0.278 0.000386 720.45*** 0.00
coef_logTimeWindowOverlapAdultChild_disc 0.150 0.322 0.47 0.00
coef_logTimeWindowOverlapAdultChild_eatout-0.320 0.337-0.95 0.00
coef_logTimeWindowOverlapAdultChild_maint 0.270 0.294 0.92 0.00
coef_logTimeWindowOverlapAdultChild_shopping-0.415 0.493-0.84 0.00
coef_logTimeWindowOverlapAdult_disc 0.560 0.501 1.12 0.00
coef_logTimeWindowOverlapAdult_eatout 0.561 0.692 0.81 0.00
coef_logTimeWindowOverlapAdult_maint 0.438 0.430 1.02 0.00
coef_logTimeWindowOverlapAdult_shopping 0.864 0.553 1.56 0.00
coef_logTimeWindowOverlapChild_disc-0.169 0.433-0.39 0.00
coef_logTimeWindowOverlapChild_maint-0.0104 0.373-0.03 0.00
coef_logTimeWindowOverlapChild_shopping 1.04 0.543 1.91 0.00
coef_moreCarsThanWorkers_eatout 0.0795 0.805 0.10 0.00
coef_moreCarsThanWorkers_shopping-0.628 0.690-0.91 0.00
coef_nonWorkerHomeMaxThree_zero_tours-0.169 0.391-0.43 0.00
coef_nonWorkerNonMandMaxThree_disc-0.119 0.447-0.27 0.00
coef_nonWorkerNonMandMaxThree_eatout-0.354 0.700-0.51 0.00
coef_nonWorkerNonMandMaxThree_maint 0.373 0.380 0.98 0.00
coef_nonWorkerNonMandMaxThree_shopping 0.523 0.427 1.23 0.00
coef_nonWorkerNonMandMaxThree_visiting-0.360 0.587-0.61 0.00
coef_partTimeHomeMaxThree_zero_tours 1.70 1.16 1.47 0.00
coef_partTimeNonMandMaxThree_disc 0.226 0.652 0.35 0.00
coef_partTimeNonMandMaxThree_eatout-15.8 2.42e+03-0.01 0.00
coef_partTimeNonMandMaxThree_maint 0.122 0.660 0.18 0.00
coef_partTimeNonMandMaxThree_shopping-0.191 0.769-0.25 0.00
coef_partTimeNonMandMaxThree_visiting 0.348 0.880 0.40 0.00
coef_preDrivingAgeMandMaxThree_disc 0.588 0.583 1.01 0.00
coef_preDrivingAgeMandMaxThree_maint 0.0572 0.514 0.11 0.00
coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours-0.265 0.450-0.59 0.00
coef_preDrivingNonMandMaxThree_disc 0.470 0.853 0.55 0.00
coef_preDrivingNonMandMaxThree_eatout-13.7 1.56e+03-0.01 0.00
coef_preDrivingNonMandMaxThree_maint-0.478 0.924-0.52 0.00
coef_preDrivingNonMandMaxThree_shopping 0.00540 0.540 0.01 0.00
coef_preDrivingNonMandMaxThree_visiting-1.73 1.27-1.36 0.00
coef_retireeHomeMaxThree_zero_tours 0.307 0.771 0.40 0.00
coef_retireeNonMandMaxThree_disc 0.595 0.430 1.38 0.00
coef_retireeNonMandMaxThree_eatout 0.337 0.553 0.61 0.00
coef_retireeNonMandMaxThree_maint 0.931 0.376 2.47* 0.00
coef_retireeNonMandMaxThree_shopping 0.952 0.504 1.89 0.00
coef_retireeNonMandMaxThree_visiting 0.174 0.583 0.30 0.00
coef_timeWindowOverlapAdultChild_visiting 0.140 0.0547 2.57* 0.00
coef_timeWindowOverlapAdult_visiting 0.110 0.0692 1.59 0.00
coef_timeWindowOverlapChild_visiting-0.0606 0.0845-0.72 0.00
coef_universityHomeMaxThree_univ_and_driving_zero_tours-0.410 0.677-0.61 0.00
coef_universityNonMandMaxThree_disc-0.145 1.02-0.14 0.00
coef_universityNonMandMaxThree_eatout-14.1 1.67e+03-0.01 0.00
coef_universityNonMandMaxThree_maint-0.438 0.999-0.44 0.00
coef_universityNonMandMaxThree_shopping 1.13 0.556 2.04* 0.00
coef_universityNonMandMaxThree_visiting-18.0 7.88e+03-0.00 0.00
coef_walkRetailAccessibility_eatout-0.0274 0.237-0.12 0.00
coef_zeroAutomobiles_disc-0.540 0.771-0.70 0.00
coef_zeroAutomobiles_visiting-18.3 6.06e+03-0.00 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_asc_0_tours 0.00 0.00 NA 0.00fixed value
coef_asc_1_Disc-5.35 0.217-24.68*** 0.00
coef_asc_1_Eat-6.07 0.382-15.88*** 0.00
coef_asc_1_Main-5.82 0.255-22.80*** 0.00
coef_asc_1_Shop-6.05 0.301-20.07*** 0.00
coef_asc_1_Visit-5.84 0.198-29.56*** 0.00
coef_asc_2_tours-14.5 0.456-31.88*** 0.00
coef_drivingAgeStuMandMaxThree_disc 0.284 0.0868 3.27** 0.00
coef_drivingAgeStuMandMaxThree_maint-0.332 0.148-2.24* 0.00
coef_fewerCarsThanDrivers_maint 0.769 0.142 5.42*** 0.00
coef_fewerCarsThanDrivers_shopping 0.174 0.141 1.23 0.00
coef_fullTimeHomeMaxThree_zero_tours 0.941 0.164 5.75*** 0.00
coef_fullTimeMandMaxThree_maint-0.397 0.0912-4.36*** 0.00
coef_fullTimeMandMaxThree_shopping-0.403 0.0941-4.28*** 0.00
coef_fullTimeNonMandMaxThree_disc-0.0197 0.143-0.14 0.00
coef_fullTimeNonMandMaxThree_eatout 0.0536 0.215 0.25 0.00
coef_fullTimeNonMandMaxThree_maint 0.115 0.177 0.65 0.00
coef_fullTimeNonMandMaxThree_shopping-0.253 0.198-1.28 0.00
coef_fullTimeNonMandMaxThree_visiting 0.751 0.171 4.38*** 0.00
coef_incomeBetween50And100_disc 0.132 0.128 1.04 0.00
coef_incomeBetween50And100_eatout 0.269 0.199 1.35 0.00
coef_incomeGreaterThan100_disc 0.367 0.125 2.94** 0.00
coef_incomeGreaterThan100_eatout 0.494 0.197 2.51* 0.00
coef_incomeMissing_dummy_always_zero_disc 0.372 7.15e-16 BIG*** 0.00
coef_incomeMissing_dummy_always_zero_eatout 0.278 9.58e-16 BIG*** 0.00
coef_logTimeWindowOverlapAdultChild_disc 0.304 0.0645 4.70*** 0.00
coef_logTimeWindowOverlapAdultChild_eatout-0.0944 0.0788-1.20 0.00
coef_logTimeWindowOverlapAdultChild_maint 0.399 0.0847 4.71*** 0.00
coef_logTimeWindowOverlapAdultChild_shopping 0.0180 0.0764 0.24 0.00
coef_logTimeWindowOverlapAdult_disc 0.412 0.101 4.09*** 0.00
coef_logTimeWindowOverlapAdult_eatout 0.467 0.167 2.80** 0.00
coef_logTimeWindowOverlapAdult_maint 0.360 0.117 3.07** 0.00
coef_logTimeWindowOverlapAdult_shopping 0.862 0.139 6.18*** 0.00
coef_logTimeWindowOverlapChild_disc 0.150 0.0771 1.95 0.00
coef_logTimeWindowOverlapChild_maint 0.295 0.0952 3.10** 0.00
coef_logTimeWindowOverlapChild_shopping 0.256 0.0873 2.93** 0.00
coef_moreCarsThanWorkers_eatout 0.295 0.188 1.57 0.00
coef_moreCarsThanWorkers_shopping-0.552 0.165-3.35*** 0.00
coef_nonWorkerHomeMaxThree_zero_tours 0.379 0.110 3.46*** 0.00
coef_nonWorkerNonMandMaxThree_disc 0.121 0.0878 1.38 0.00
coef_nonWorkerNonMandMaxThree_eatout 0.0451 0.150 0.30 0.00
coef_nonWorkerNonMandMaxThree_maint 0.351 0.104 3.37*** 0.00
coef_nonWorkerNonMandMaxThree_shopping 0.423 0.110 3.85*** 0.00
coef_nonWorkerNonMandMaxThree_visiting 0.454 0.139 3.26** 0.00
coef_partTimeHomeMaxThree_zero_tours 0.929 0.250 3.71*** 0.00
coef_partTimeNonMandMaxThree_disc 0.668 0.123 5.42*** 0.00
coef_partTimeNonMandMaxThree_eatout 0.382 0.210 1.82 0.00
coef_partTimeNonMandMaxThree_maint 0.0711 0.193 0.37 0.00
coef_partTimeNonMandMaxThree_shopping-0.0211 0.183-0.12 0.00
coef_partTimeNonMandMaxThree_visiting 0.418 0.214 1.95 0.00
coef_preDrivingAgeMandMaxThree_disc 0.134 0.0986 1.36 0.00
coef_preDrivingAgeMandMaxThree_maint 0.0653 0.118 0.56 0.00
coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours 0.0860 0.102 0.85 0.00
coef_preDrivingNonMandMaxThree_disc 0.189 0.160 1.19 0.00
coef_preDrivingNonMandMaxThree_eatout 0.701 0.195 3.60*** 0.00
coef_preDrivingNonMandMaxThree_maint 0.164 0.190 0.86 0.00
coef_preDrivingNonMandMaxThree_shopping 0.448 0.152 2.95** 0.00
coef_preDrivingNonMandMaxThree_visiting 0.241 0.196 1.23 0.00
coef_retireeHomeMaxThree_zero_tours 0.333 0.175 1.90 0.00
coef_retireeNonMandMaxThree_disc 0.597 0.107 5.56*** 0.00
coef_retireeNonMandMaxThree_eatout 0.444 0.160 2.77** 0.00
coef_retireeNonMandMaxThree_maint 0.936 0.123 7.59*** 0.00
coef_retireeNonMandMaxThree_shopping 0.889 0.123 7.22*** 0.00
coef_retireeNonMandMaxThree_visiting 0.314 0.190 1.65 0.00
coef_timeWindowOverlapAdultChild_visiting 0.0321 0.0152 2.12* 0.00
coef_timeWindowOverlapAdult_visiting 0.0627 0.0194 3.24** 0.00
coef_timeWindowOverlapChild_visiting 0.0375 0.0181 2.08* 0.00
coef_universityHomeMaxThree_univ_and_driving_zero_tours 0.116 0.210 0.55 0.00
coef_universityNonMandMaxThree_disc 0.479 0.144 3.33*** 0.00
coef_universityNonMandMaxThree_eatout 0.448 0.244 1.84 0.00
coef_universityNonMandMaxThree_maint-0.158 0.284-0.56 0.00
coef_universityNonMandMaxThree_shopping 0.858 0.157 5.46*** 0.00
coef_universityNonMandMaxThree_visiting 0.282 0.256 1.10 0.00
coef_walkRetailAccessibility_eatout 0.0426 0.0475 0.90 0.00
coef_zeroAutomobiles_disc-0.954 0.441-2.16* 0.00
coef_zeroAutomobiles_visiting-0.901 0.678-1.33 0.00
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -2954,18 +3012,7 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -3023,25 +3070,25 @@ " \n", " 1\n", " coef_asc_1_Shop\n", - " -7.244895\n", + " -6.045190\n", " F\n", " \n", " \n", " 2\n", " coef_asc_1_Main\n", - " -4.843654\n", + " -5.816690\n", " F\n", " \n", " \n", " 3\n", " coef_asc_1_Eat\n", - " -5.140469\n", + " -6.074061\n", " F\n", " \n", " \n", " 4\n", " coef_asc_1_Visit\n", - " -5.962149\n", + " -5.839951\n", " F\n", " \n", " \n", @@ -3053,19 +3100,19 @@ " \n", " 71\n", " coef_logTimeWindowOverlapAdultChild_disc\n", - " 0.150256\n", + " 0.303504\n", " F\n", " \n", " \n", " 72\n", " coef_incomeBetween50And100_disc\n", - " 0.008353\n", + " 0.132487\n", " F\n", " \n", " \n", " 73\n", " coef_incomeGreaterThan100_disc\n", - " 0.624935\n", + " 0.366581\n", " F\n", " \n", " \n", @@ -3077,7 +3124,7 @@ " \n", " 75\n", " coef_zeroAutomobiles_disc\n", - " -0.539939\n", + " -0.953758\n", " F\n", " \n", " \n", @@ -3088,16 +3135,16 @@ "text/plain": [ " coefficient_name value constrain\n", "0 coef_asc_0_tours 0.000000 T\n", - "1 coef_asc_1_Shop -7.244895 F\n", - "2 coef_asc_1_Main -4.843654 F\n", - "3 coef_asc_1_Eat -5.140469 F\n", - "4 coef_asc_1_Visit -5.962149 F\n", + "1 coef_asc_1_Shop -6.045190 F\n", + "2 coef_asc_1_Main -5.816690 F\n", + "3 coef_asc_1_Eat -6.074061 F\n", + "4 coef_asc_1_Visit -5.839951 F\n", ".. ... ... ...\n", - "71 coef_logTimeWindowOverlapAdultChild_disc 0.150256 F\n", - "72 coef_incomeBetween50And100_disc 0.008353 F\n", - "73 coef_incomeGreaterThan100_disc 0.624935 F\n", + "71 coef_logTimeWindowOverlapAdultChild_disc 0.303504 F\n", + "72 coef_incomeBetween50And100_disc 0.132487 F\n", + "73 coef_incomeGreaterThan100_disc 0.366581 F\n", "74 coef_incomeMissing_dummy_always_zero_disc 0.372300 F\n", - "75 coef_zeroAutomobiles_disc -0.539939 F\n", + "75 coef_zeroAutomobiles_disc -0.953758 F\n", "\n", "[76 rows x 3 columns]" ] @@ -3119,7 +3166,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -3133,7 +3180,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/11_joint_tour_composition.ipynb b/activitysim/examples/example_estimation/notebooks/11_joint_tour_composition.ipynb index 89c68c52f7..99bd4cc7cd 100644 --- a/activitysim/examples/example_estimation/notebooks/11_joint_tour_composition.ipynb +++ b/activitysim/examples/example_estimation/notebooks/11_joint_tour_composition.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +115,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/joint_tour_composition/joint_tour_composition_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/joint_tour_composition/joint_tour_composition_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_composition/joint_tour_composition_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"joint_tour_composition\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -798,8 +860,8 @@ " \n", " \n", " \n", - " 189758\n", - " 7785298\n", + " 190592\n", + " 7853686\n", " adults\n", " adults\n", " 1.0\n", @@ -807,23 +869,23 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 2.0\n", " ...\n", - " 0.000\n", + " 0.000000\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 2.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 201016\n", - " 8708454\n", + " 191185\n", + " 7902318\n", " adults\n", " adults\n", " 1.0\n", @@ -831,23 +893,23 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 2.0\n", " ...\n", - " 0.000\n", + " 0.000000\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 2.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 213291\n", - " 9715006\n", + " 191276\n", + " 7909776\n", " adults\n", " adults\n", " 1.0\n", @@ -855,14 +917,14 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 2.0\n", " ...\n", - " 0.000\n", + " 0.000000\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 2.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -870,8 +932,8 @@ " 1\n", " \n", " \n", - " 226902\n", - " 10831112\n", + " 192239\n", + " 7988742\n", " adults\n", " adults\n", " 1.0\n", @@ -879,43 +941,43 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 1.0\n", " ...\n", - " 0.000\n", + " 0.000000\n", " 0.0\n", " 0.0\n", - " 2.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 337259\n", - " 20334787\n", - " mixed\n", - " mixed\n", + " 193643\n", + " 8103874\n", + " adults\n", + " adults\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 1.0\n", " ...\n", - " 2.197\n", - " 1.0\n", + " 0.000000\n", + " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", " 1.0\n", " 0.0\n", " 0.0\n", - " False\n", - " 3\n", + " 0.0\n", + " True\n", + " 1\n", " \n", " \n", " ...\n", @@ -942,8 +1004,8 @@ " ...\n", " \n", " \n", - " 2628704\n", - " 283676518\n", + " 2753390\n", + " 303771147\n", " mixed\n", " mixed\n", " 1.0\n", @@ -954,7 +1016,7 @@ " 0.0\n", " 0.0\n", " ...\n", - " 2.398\n", + " 2.708984\n", " 3.0\n", " 0.0\n", " 0.0\n", @@ -962,252 +1024,252 @@ " 2.0\n", " 0.0\n", " 0.0\n", - " False\n", + " True\n", " 3\n", " \n", " \n", - " 2678969\n", - " 295260168\n", - " adults\n", - " adults\n", + " 2754817\n", + " 304144212\n", + " mixed\n", + " mixed\n", " 1.0\n", " 0.0\n", " 0.0\n", " 3.0\n", - " 1.0\n", " 0.0\n", - " 3.0\n", + " 0.0\n", + " 2.0\n", " ...\n", - " 2.080\n", + " 2.996094\n", " 3.0\n", - " 1.0\n", " 0.0\n", - " 3.0\n", - " 1.0\n", + " 0.0\n", + " 2.0\n", + " 2.0\n", " 0.0\n", " 0.0\n", " False\n", - " 1\n", + " 3\n", " \n", " \n", - " 2704338\n", - " 297646485\n", - " adults\n", - " adults\n", + " 2756049\n", + " 304463637\n", + " mixed\n", + " mixed\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 3.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", + " 2.0\n", " 0.0\n", " ...\n", - " 0.000\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 2.484375\n", + " 3.0\n", " 0.0\n", + " 2.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " True\n", - " 1\n", + " 3\n", " \n", " \n", - " 2718585\n", - " 298814741\n", - " adults\n", - " adults\n", + " 2758936\n", + " 305125582\n", + " children\n", + " mixed\n", " 1.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 2.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 0.000\n", - " 0.0\n", + " 2.564453\n", + " 2.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 3.0\n", " 0.0\n", " False\n", - " 1\n", + " 3\n", " \n", " \n", - " 2744529\n", - " 301810980\n", - " adults\n", - " adults\n", + " 2759425\n", + " 305246245\n", + " mixed\n", + " mixed\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 2.0\n", - " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " ...\n", - " 0.000\n", + " 2.996094\n", + " 1.0\n", " 2.0\n", + " 1.0\n", " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " False\n", - " 1\n", + " 3\n", " \n", " \n", "\n", - "

91 rows × 87 columns

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1277 rows × 87 columns

\n", "" ], "text/plain": [ " tour_id model_choice override_choice util_asc \\\n", "household_id \n", - "189758 7785298 adults adults 1.0 \n", - "201016 8708454 adults adults 1.0 \n", - "213291 9715006 adults adults 1.0 \n", - "226902 10831112 adults adults 1.0 \n", - "337259 20334787 mixed mixed 1.0 \n", + "190592 7853686 adults adults 1.0 \n", + "191185 7902318 adults adults 1.0 \n", + "191276 7909776 adults adults 1.0 \n", + "192239 7988742 adults adults 1.0 \n", + "193643 8103874 adults adults 1.0 \n", "... ... ... ... ... \n", - "2628704 283676518 mixed mixed 1.0 \n", - "2678969 295260168 adults adults 1.0 \n", - "2704338 297646485 adults adults 1.0 \n", - "2718585 298814741 adults adults 1.0 \n", - "2744529 301810980 adults adults 1.0 \n", + "2753390 303771147 mixed mixed 1.0 \n", + "2754817 304144212 mixed mixed 1.0 \n", + "2756049 304463637 mixed mixed 1.0 \n", + "2758936 305125582 children mixed 1.0 \n", + "2759425 305246245 mixed mixed 1.0 \n", "\n", " util_tour_purpose_is_eating_out \\\n", "household_id \n", - 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"189758 0.0 2.0 0.0 \n", - "201016 0.0 2.0 0.0 \n", - "213291 0.0 2.0 0.0 \n", - "226902 0.0 2.0 0.0 \n", - "337259 0.0 0.0 1.0 \n", + "190592 0.0 0.0 2.0 \n", + "191185 0.0 0.0 2.0 \n", + "191276 0.0 0.0 2.0 \n", + "192239 0.0 0.0 1.0 \n", + "193643 0.0 0.0 1.0 \n", "... ... ... ... \n", - "2628704 0.0 0.0 0.0 \n", - "2678969 1.0 0.0 3.0 \n", - "2704338 0.0 0.0 0.0 \n", - "2718585 1.0 0.0 0.0 \n", - "2744529 0.0 0.0 0.0 \n", + "2753390 0.0 0.0 0.0 \n", + "2754817 0.0 0.0 2.0 \n", + "2756049 0.0 2.0 0.0 \n", + "2758936 1.0 0.0 0.0 \n", + "2759425 2.0 1.0 0.0 \n", "\n", " num_preschool_max3 num_school_max3 num_driving_max3 \\\n", "household_id \n", - "189758 0.0 0.0 0.0 \n", - "201016 0.0 0.0 0.0 \n", - "213291 0.0 0.0 0.0 \n", - "226902 0.0 0.0 0.0 \n", - "337259 1.0 0.0 0.0 \n", + "190592 0.0 0.0 0.0 \n", + "191185 0.0 0.0 0.0 \n", + "191276 0.0 0.0 0.0 \n", + "192239 0.0 0.0 0.0 \n", + "193643 0.0 0.0 0.0 \n", "... ... ... ... \n", - "2628704 2.0 0.0 0.0 \n", - "2678969 1.0 0.0 0.0 \n", - 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"req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 033 [Optimization terminated successfully]

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Iteration 095 [Optimization terminated successfully]

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Best LL = -9.786597596036862e-07

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Best LL = -263.2609116263491

" ], "text/plain": [ "" @@ -1286,493 +1341,475 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_asc_children\n", - " 121.627746\n", + " 6.744041\n", + " 6.744041\n", " 5.3517\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 121.627746\n", " \n", " \n", " coef_asc_mixed\n", - " -88.249613\n", + " 6.886657\n", + " 6.886657\n", " 5.6290\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -88.249613\n", " \n", " \n", " coef_household_has_more_cars_than_workers_adults\n", - " -40.894264\n", + " 3.138920\n", + " 3.138920\n", " 1.3860\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -40.894264\n", " \n", " \n", " coef_household_has_more_cars_than_workers_mixed\n", - " 153.815203\n", + " 0.384250\n", + " 0.384250\n", " 0.7510\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 153.815203\n", " \n", " \n", " coef_household_in_suburban_area_adults\n", - " 0.510500\n", + " -1.123570\n", + " -1.123570\n", " 0.5105\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.510500\n", " \n", " \n", " coef_household_in_suburban_area_mixed\n", - " 0.128300\n", + " 0.258299\n", + " 0.258299\n", " 0.1283\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.128300\n", " \n", " \n", " coef_household_in_urban_area\n", - " -21.823334\n", + " -0.441828\n", + " -0.441828\n", " 0.5741\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -21.823334\n", " \n", " \n", " coef_log_max_overlap_of_adults_time_windows\n", - " -36.285976\n", + " 1.288269\n", + " 1.288269\n", " 1.1920\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - 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" NaN\n", - " NaN\n", " 0\n", - " \n", - " -161.727385\n", " \n", " \n", " coef_number_of_children_too_young_for_school_children\n", - " -110.048280\n", + " 0.661222\n", + " 0.661222\n", " 0.7306\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -110.048280\n", " \n", " \n", " coef_number_of_children_too_young_for_school_mixed\n", - " 9.539326\n", + " 1.065525\n", + " 1.065525\n", " 0.7906\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 9.539326\n", " \n", " \n", " coef_number_of_driving_age_children_children\n", - " -172.311937\n", + " 0.646806\n", + " 0.646806\n", " -0.2667\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -172.311937\n", " \n", " \n", " coef_number_of_driving_age_children_mixed\n", - " 184.761407\n", + " 0.494992\n", + " 0.494992\n", " -0.9399\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 184.761407\n", " \n", " \n", " coef_number_of_full_time_workers_adults\n", - " 185.490311\n", + " 1.638383\n", + " 1.638383\n", " 1.0240\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 185.490311\n", " \n", " \n", " coef_number_of_full_time_workers_mixed\n", - " 50.943193\n", + " 0.106573\n", + " 0.106573\n", " 0.3624\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 50.943193\n", " \n", " \n", " coef_number_of_non_workers_adults\n", - " 40.041010\n", + " 0.344889\n", + " 0.344889\n", " 0.6263\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 40.041010\n", " \n", " \n", " coef_number_of_non_workers_mixed\n", - " -96.438666\n", + " -0.067851\n", + " -0.067851\n", " -0.3724\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -96.438666\n", " \n", " \n", " coef_number_of_part_time_workers_adults\n", - " 47.088158\n", + " 0.094715\n", + " 0.094715\n", " 0.5412\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 47.088158\n", " \n", " \n", " coef_number_of_part_time_workers_mixed\n", - " 58.958712\n", + " 0.182067\n", + " 0.182067\n", " 0.3164\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 58.958712\n", " \n", " \n", " coef_number_of_pre_driving_age_children_children\n", - " 12.678858\n", + " 0.256545\n", + " 0.256545\n", " 0.7306\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 12.678858\n", " \n", " \n", " coef_number_of_pre_driving_age_children_mixed\n", - " 158.960267\n", + " 0.311384\n", + " 0.311384\n", " 0.3532\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 158.960267\n", " \n", " \n", " coef_number_of_university_students\n", - " -5.007830\n", + " 0.067485\n", + " 0.067485\n", " 0.8245\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -5.007830\n", " \n", " \n", " coef_tour_purpose_is_discretionary_adults\n", " 0.764800\n", + " 0.764800\n", " 0.7648\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.764800\n", " \n", " \n", " coef_tour_purpose_is_discretionary_children\n", " 0.510100\n", + " 0.510100\n", " 0.5101\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.510100\n", " \n", " \n", " coef_tour_purpose_is_eating_out_children\n", " -0.967800\n", + " -0.967800\n", " -0.9678\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.967800\n", " \n", " \n", " coef_tour_purpose_is_eating_out_mixed\n", " -0.802700\n", + " -0.802700\n", " -0.8027\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.802700\n", " \n", " \n", " coef_unavailable\n", " -999.000000\n", + " -999.000000\n", " -999.0000\n", + " -999.0\n", + " -999.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_asc_children 121.627746 5.3517 \n", - "coef_asc_mixed -88.249613 5.6290 \n", - "coef_household_has_more_cars_than_workers_adults -40.894264 1.3860 \n", - "coef_household_has_more_cars_than_workers_mixed 153.815203 0.7510 \n", - "coef_household_in_suburban_area_adults 0.510500 0.5105 \n", - "coef_household_in_suburban_area_mixed 0.128300 0.1283 \n", - "coef_household_in_urban_area -21.823334 0.5741 \n", - "coef_log_max_overlap_of_adults_time_windows -36.285976 1.1920 \n", - "coef_log_max_overlap_of_childrens_time_windows 283.696667 1.8410 \n", - "coef_log_max_overlap_of_time_windows 196.728573 1.9580 \n", - "coef_low_income_households_adults -16.894954 1.2480 \n", - "coef_low_income_households_mixed -3.143593 0.5755 \n", - "coef_medium_income_households -161.727385 0.8369 \n", - "coef_number_of_children_too_young_for_school_ch... -110.048280 0.7306 \n", - "coef_number_of_children_too_young_for_school_mixed 9.539326 0.7906 \n", - "coef_number_of_driving_age_children_children -172.311937 -0.2667 \n", - "coef_number_of_driving_age_children_mixed 184.761407 -0.9399 \n", - "coef_number_of_full_time_workers_adults 185.490311 1.0240 \n", - "coef_number_of_full_time_workers_mixed 50.943193 0.3624 \n", - "coef_number_of_non_workers_adults 40.041010 0.6263 \n", - "coef_number_of_non_workers_mixed -96.438666 -0.3724 \n", - "coef_number_of_part_time_workers_adults 47.088158 0.5412 \n", - "coef_number_of_part_time_workers_mixed 58.958712 0.3164 \n", - "coef_number_of_pre_driving_age_children_children 12.678858 0.7306 \n", - "coef_number_of_pre_driving_age_children_mixed 158.960267 0.3532 \n", - "coef_number_of_university_students -5.007830 0.8245 \n", - "coef_tour_purpose_is_discretionary_adults 0.764800 0.7648 \n", - "coef_tour_purpose_is_discretionary_children 0.510100 0.5101 \n", - "coef_tour_purpose_is_eating_out_children -0.967800 -0.9678 \n", - "coef_tour_purpose_is_eating_out_mixed -0.802700 -0.8027 \n", - "coef_unavailable -999.000000 -999.0000 \n", + " value best \\\n", + "param_name \n", + "coef_asc_children 6.744041 6.744041 \n", + "coef_asc_mixed 6.886657 6.886657 \n", + "coef_household_has_more_cars_than_workers_adults 3.138920 3.138920 \n", + "coef_household_has_more_cars_than_workers_mixed 0.384250 0.384250 \n", + "coef_household_in_suburban_area_adults -1.123570 -1.123570 \n", + "coef_household_in_suburban_area_mixed 0.258299 0.258299 \n", + "coef_household_in_urban_area -0.441828 -0.441828 \n", + "coef_log_max_overlap_of_adults_time_windows 1.288269 1.288269 \n", + "coef_log_max_overlap_of_childrens_time_windows 1.783892 1.783892 \n", + "coef_log_max_overlap_of_time_windows 1.505521 1.505521 \n", + "coef_low_income_households_adults 2.520875 2.520875 \n", + "coef_low_income_households_mixed 0.683195 0.683195 \n", + "coef_medium_income_households 1.853225 1.853225 \n", + "coef_number_of_children_too_young_for_school_ch... 0.661222 0.661222 \n", + "coef_number_of_children_too_young_for_school_mixed 1.065525 1.065525 \n", + "coef_number_of_driving_age_children_children 0.646806 0.646806 \n", + "coef_number_of_driving_age_children_mixed 0.494992 0.494992 \n", + "coef_number_of_full_time_workers_adults 1.638383 1.638383 \n", + "coef_number_of_full_time_workers_mixed 0.106573 0.106573 \n", + "coef_number_of_non_workers_adults 0.344889 0.344889 \n", + "coef_number_of_non_workers_mixed -0.067851 -0.067851 \n", + "coef_number_of_part_time_workers_adults 0.094715 0.094715 \n", + "coef_number_of_part_time_workers_mixed 0.182067 0.182067 \n", + "coef_number_of_pre_driving_age_children_children 0.256545 0.256545 \n", + "coef_number_of_pre_driving_age_children_mixed 0.311384 0.311384 \n", + "coef_number_of_university_students 0.067485 0.067485 \n", + "coef_tour_purpose_is_discretionary_adults 0.764800 0.764800 \n", + "coef_tour_purpose_is_discretionary_children 0.510100 0.510100 \n", + "coef_tour_purpose_is_eating_out_children -0.967800 -0.967800 \n", + "coef_tour_purpose_is_eating_out_mixed -0.802700 -0.802700 \n", + "coef_unavailable -999.000000 -999.000000 \n", "\n", - " nullvalue minimum \\\n", - "coef_asc_children 0.0 NaN \n", - "coef_asc_mixed 0.0 NaN \n", - "coef_household_has_more_cars_than_workers_adults 0.0 NaN \n", - "coef_household_has_more_cars_than_workers_mixed 0.0 NaN \n", - "coef_household_in_suburban_area_adults 0.0 NaN \n", - "coef_household_in_suburban_area_mixed 0.0 NaN \n", - "coef_household_in_urban_area 0.0 NaN \n", - "coef_log_max_overlap_of_adults_time_windows 0.0 NaN \n", - "coef_log_max_overlap_of_childrens_time_windows 0.0 NaN \n", - "coef_log_max_overlap_of_time_windows 0.0 NaN \n", - "coef_low_income_households_adults 0.0 NaN \n", - "coef_low_income_households_mixed 0.0 NaN \n", - "coef_medium_income_households 0.0 NaN \n", - "coef_number_of_children_too_young_for_school_ch... 0.0 NaN \n", - "coef_number_of_children_too_young_for_school_mixed 0.0 NaN \n", - "coef_number_of_driving_age_children_children 0.0 NaN \n", - "coef_number_of_driving_age_children_mixed 0.0 NaN \n", - "coef_number_of_full_time_workers_adults 0.0 NaN \n", - "coef_number_of_full_time_workers_mixed 0.0 NaN \n", - "coef_number_of_non_workers_adults 0.0 NaN \n", - "coef_number_of_non_workers_mixed 0.0 NaN \n", - "coef_number_of_part_time_workers_adults 0.0 NaN \n", - "coef_number_of_part_time_workers_mixed 0.0 NaN \n", - "coef_number_of_pre_driving_age_children_children 0.0 NaN \n", - "coef_number_of_pre_driving_age_children_mixed 0.0 NaN \n", - "coef_number_of_university_students 0.0 NaN \n", - "coef_tour_purpose_is_discretionary_adults 0.0 NaN \n", - "coef_tour_purpose_is_discretionary_children 0.0 NaN \n", - "coef_tour_purpose_is_eating_out_children 0.0 NaN \n", - "coef_tour_purpose_is_eating_out_mixed 0.0 NaN \n", - "coef_unavailable 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_asc_children 5.3517 -50.0 \n", + "coef_asc_mixed 5.6290 -50.0 \n", + "coef_household_has_more_cars_than_workers_adults 1.3860 -50.0 \n", + "coef_household_has_more_cars_than_workers_mixed 0.7510 -50.0 \n", + "coef_household_in_suburban_area_adults 0.5105 -50.0 \n", + "coef_household_in_suburban_area_mixed 0.1283 -50.0 \n", + "coef_household_in_urban_area 0.5741 -50.0 \n", + "coef_log_max_overlap_of_adults_time_windows 1.1920 -50.0 \n", + "coef_log_max_overlap_of_childrens_time_windows 1.8410 -50.0 \n", + "coef_log_max_overlap_of_time_windows 1.9580 -50.0 \n", + "coef_low_income_households_adults 1.2480 -50.0 \n", + "coef_low_income_households_mixed 0.5755 -50.0 \n", + "coef_medium_income_households 0.8369 -50.0 \n", + "coef_number_of_children_too_young_for_school_ch... 0.7306 -50.0 \n", + "coef_number_of_children_too_young_for_school_mixed 0.7906 -50.0 \n", + "coef_number_of_driving_age_children_children -0.2667 -50.0 \n", + "coef_number_of_driving_age_children_mixed -0.9399 -50.0 \n", + "coef_number_of_full_time_workers_adults 1.0240 -50.0 \n", + "coef_number_of_full_time_workers_mixed 0.3624 -50.0 \n", + "coef_number_of_non_workers_adults 0.6263 -50.0 \n", + "coef_number_of_non_workers_mixed -0.3724 -50.0 \n", + "coef_number_of_part_time_workers_adults 0.5412 -50.0 \n", + "coef_number_of_part_time_workers_mixed 0.3164 -50.0 \n", + "coef_number_of_pre_driving_age_children_children 0.7306 -50.0 \n", + "coef_number_of_pre_driving_age_children_mixed 0.3532 -50.0 \n", + "coef_number_of_university_students 0.8245 -50.0 \n", + "coef_tour_purpose_is_discretionary_adults 0.7648 -50.0 \n", + "coef_tour_purpose_is_discretionary_children 0.5101 -50.0 \n", + "coef_tour_purpose_is_eating_out_children -0.9678 -50.0 \n", + "coef_tour_purpose_is_eating_out_mixed -0.8027 -50.0 \n", + "coef_unavailable -999.0000 -999.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_asc_children NaN 0 \n", - "coef_asc_mixed NaN 0 \n", - "coef_household_has_more_cars_than_workers_adults NaN 0 \n", - "coef_household_has_more_cars_than_workers_mixed NaN 0 \n", - "coef_household_in_suburban_area_adults NaN 0 \n", - "coef_household_in_suburban_area_mixed NaN 0 \n", - "coef_household_in_urban_area NaN 0 \n", - "coef_log_max_overlap_of_adults_time_windows NaN 0 \n", - "coef_log_max_overlap_of_childrens_time_windows NaN 0 \n", - "coef_log_max_overlap_of_time_windows NaN 0 \n", - "coef_low_income_households_adults NaN 0 \n", - "coef_low_income_households_mixed NaN 0 \n", - "coef_medium_income_households NaN 0 \n", - "coef_number_of_children_too_young_for_school_ch... NaN 0 \n", - "coef_number_of_children_too_young_for_school_mixed NaN 0 \n", - "coef_number_of_driving_age_children_children NaN 0 \n", - "coef_number_of_driving_age_children_mixed NaN 0 \n", - "coef_number_of_full_time_workers_adults NaN 0 \n", - "coef_number_of_full_time_workers_mixed NaN 0 \n", - "coef_number_of_non_workers_adults NaN 0 \n", - "coef_number_of_non_workers_mixed NaN 0 \n", - "coef_number_of_part_time_workers_adults NaN 0 \n", - "coef_number_of_part_time_workers_mixed NaN 0 \n", - "coef_number_of_pre_driving_age_children_children NaN 0 \n", - "coef_number_of_pre_driving_age_children_mixed NaN 0 \n", - "coef_number_of_university_students NaN 0 \n", - "coef_tour_purpose_is_discretionary_adults NaN 0 \n", - "coef_tour_purpose_is_discretionary_children NaN 0 \n", - "coef_tour_purpose_is_eating_out_children NaN 0 \n", - "coef_tour_purpose_is_eating_out_mixed NaN 0 \n", - "coef_unavailable NaN 1 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_asc_children 50.0 0.0 \n", + "coef_asc_mixed 50.0 0.0 \n", + "coef_household_has_more_cars_than_workers_adults 50.0 0.0 \n", + "coef_household_has_more_cars_than_workers_mixed 50.0 0.0 \n", + "coef_household_in_suburban_area_adults 50.0 0.0 \n", + "coef_household_in_suburban_area_mixed 50.0 0.0 \n", + "coef_household_in_urban_area 50.0 0.0 \n", + "coef_log_max_overlap_of_adults_time_windows 50.0 0.0 \n", + "coef_log_max_overlap_of_childrens_time_windows 50.0 0.0 \n", + "coef_log_max_overlap_of_time_windows 50.0 0.0 \n", + "coef_low_income_households_adults 50.0 0.0 \n", + "coef_low_income_households_mixed 50.0 0.0 \n", + "coef_medium_income_households 50.0 0.0 \n", + "coef_number_of_children_too_young_for_school_ch... 50.0 0.0 \n", + "coef_number_of_children_too_young_for_school_mixed 50.0 0.0 \n", + "coef_number_of_driving_age_children_children 50.0 0.0 \n", + "coef_number_of_driving_age_children_mixed 50.0 0.0 \n", + "coef_number_of_full_time_workers_adults 50.0 0.0 \n", + "coef_number_of_full_time_workers_mixed 50.0 0.0 \n", + "coef_number_of_non_workers_adults 50.0 0.0 \n", + "coef_number_of_non_workers_mixed 50.0 0.0 \n", + "coef_number_of_part_time_workers_adults 50.0 0.0 \n", + "coef_number_of_part_time_workers_mixed 50.0 0.0 \n", + "coef_number_of_pre_driving_age_children_children 50.0 0.0 \n", + "coef_number_of_pre_driving_age_children_mixed 50.0 0.0 \n", + "coef_number_of_university_students 50.0 0.0 \n", + "coef_tour_purpose_is_discretionary_adults 50.0 0.0 \n", + "coef_tour_purpose_is_discretionary_children 50.0 0.0 \n", + "coef_tour_purpose_is_eating_out_children 50.0 0.0 \n", + "coef_tour_purpose_is_eating_out_mixed 50.0 0.0 \n", + "coef_unavailable -999.0 0.0 \n", "\n", - " best \n", - "coef_asc_children 121.627746 \n", - "coef_asc_mixed -88.249613 \n", - "coef_household_has_more_cars_than_workers_adults -40.894264 \n", - "coef_household_has_more_cars_than_workers_mixed 153.815203 \n", - "coef_household_in_suburban_area_adults 0.510500 \n", - "coef_household_in_suburban_area_mixed 0.128300 \n", - "coef_household_in_urban_area -21.823334 \n", - "coef_log_max_overlap_of_adults_time_windows -36.285976 \n", - "coef_log_max_overlap_of_childrens_time_windows 283.696667 \n", - "coef_log_max_overlap_of_time_windows 196.728573 \n", - "coef_low_income_households_adults -16.894954 \n", - "coef_low_income_households_mixed -3.143593 \n", - "coef_medium_income_households -161.727385 \n", - "coef_number_of_children_too_young_for_school_ch... -110.048280 \n", - "coef_number_of_children_too_young_for_school_mixed 9.539326 \n", - "coef_number_of_driving_age_children_children -172.311937 \n", - "coef_number_of_driving_age_children_mixed 184.761407 \n", - "coef_number_of_full_time_workers_adults 185.490311 \n", - "coef_number_of_full_time_workers_mixed 50.943193 \n", - "coef_number_of_non_workers_adults 40.041010 \n", - "coef_number_of_non_workers_mixed -96.438666 \n", - "coef_number_of_part_time_workers_adults 47.088158 \n", - "coef_number_of_part_time_workers_mixed 58.958712 \n", - "coef_number_of_pre_driving_age_children_children 12.678858 \n", - "coef_number_of_pre_driving_age_children_mixed 158.960267 \n", - "coef_number_of_university_students -5.007830 \n", - "coef_tour_purpose_is_discretionary_adults 0.764800 \n", - "coef_tour_purpose_is_discretionary_children 0.510100 \n", - "coef_tour_purpose_is_eating_out_children -0.967800 \n", - "coef_tour_purpose_is_eating_out_mixed -0.802700 \n", - "coef_unavailable -999.000000 " + " holdfast \n", + "param_name \n", + "coef_asc_children 0 \n", + "coef_asc_mixed 0 \n", + "coef_household_has_more_cars_than_workers_adults 0 \n", + "coef_household_has_more_cars_than_workers_mixed 0 \n", + "coef_household_in_suburban_area_adults 0 \n", + "coef_household_in_suburban_area_mixed 0 \n", + "coef_household_in_urban_area 0 \n", + "coef_log_max_overlap_of_adults_time_windows 0 \n", + "coef_log_max_overlap_of_childrens_time_windows 0 \n", + "coef_log_max_overlap_of_time_windows 0 \n", + "coef_low_income_households_adults 0 \n", + "coef_low_income_households_mixed 0 \n", + "coef_medium_income_households 0 \n", + "coef_number_of_children_too_young_for_school_ch... 0 \n", + "coef_number_of_children_too_young_for_school_mixed 0 \n", + "coef_number_of_driving_age_children_children 0 \n", + "coef_number_of_driving_age_children_mixed 0 \n", + "coef_number_of_full_time_workers_adults 0 \n", + "coef_number_of_full_time_workers_mixed 0 \n", + "coef_number_of_non_workers_adults 0 \n", + "coef_number_of_non_workers_mixed 0 \n", + "coef_number_of_part_time_workers_adults 0 \n", + "coef_number_of_part_time_workers_mixed 0 \n", + "coef_number_of_pre_driving_age_children_children 0 \n", + "coef_number_of_pre_driving_age_children_mixed 0 \n", + "coef_number_of_university_students 0 \n", + "coef_tour_purpose_is_discretionary_adults 0 \n", + "coef_tour_purpose_is_discretionary_children 0 \n", + "coef_tour_purpose_is_eating_out_children 0 \n", + "coef_tour_purpose_is_eating_out_mixed 0 \n", + "coef_unavailable 1 " ] }, "metadata": {}, @@ -1782,12 +1819,8 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: RuntimeWarning: invalid value encountered in sqrt\n", - " model.estimate(method='SLSQP')\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -1803,107 +1836,107 @@ " \n", " \n", " coef_asc_children\n", - " 121.627746\n", + " 6.744041\n", " \n", " \n", " coef_asc_mixed\n", - " -88.249613\n", + " 6.886657\n", " \n", " \n", " coef_household_has_more_cars_than_workers_adults\n", - " -40.894264\n", + " 3.138920\n", " \n", " \n", " coef_household_has_more_cars_than_workers_mixed\n", - " 153.815203\n", + " 0.384250\n", " \n", " \n", " coef_household_in_suburban_area_adults\n", - " 0.510500\n", + " -1.123570\n", " \n", " \n", " coef_household_in_suburban_area_mixed\n", - " 0.128300\n", + " 0.258299\n", " \n", " \n", " coef_household_in_urban_area\n", - " -21.823334\n", + " -0.441828\n", " \n", " \n", " coef_log_max_overlap_of_adults_time_windows\n", - " -36.285976\n", + " 1.288269\n", " \n", " \n", " coef_log_max_overlap_of_childrens_time_windows\n", - " 283.696667\n", + " 1.783892\n", " \n", " \n", " coef_log_max_overlap_of_time_windows\n", - " 196.728573\n", + " 1.505521\n", " \n", " \n", " coef_low_income_households_adults\n", - " -16.894954\n", + " 2.520875\n", " \n", " \n", " coef_low_income_households_mixed\n", - " -3.143593\n", + " 0.683195\n", " \n", " \n", " coef_medium_income_households\n", - " -161.727385\n", + " 1.853225\n", " \n", " \n", " coef_number_of_children_too_young_for_school_children\n", - " -110.048280\n", + " 0.661222\n", " \n", " \n", " coef_number_of_children_too_young_for_school_mixed\n", - " 9.539326\n", + " 1.065525\n", " \n", " \n", " coef_number_of_driving_age_children_children\n", - " -172.311937\n", + " 0.646806\n", " \n", " \n", " coef_number_of_driving_age_children_mixed\n", - " 184.761407\n", + " 0.494992\n", " \n", " \n", " coef_number_of_full_time_workers_adults\n", - " 185.490311\n", + " 1.638383\n", " \n", " \n", " coef_number_of_full_time_workers_mixed\n", - " 50.943193\n", + " 0.106573\n", " \n", " \n", " coef_number_of_non_workers_adults\n", - " 40.041010\n", + " 0.344889\n", " \n", " \n", " coef_number_of_non_workers_mixed\n", - " -96.438666\n", + " -0.067851\n", " \n", " \n", " coef_number_of_part_time_workers_adults\n", - " 47.088158\n", + " 0.094715\n", " \n", " \n", " coef_number_of_part_time_workers_mixed\n", - " 58.958712\n", + " 0.182067\n", " \n", " \n", " coef_number_of_pre_driving_age_children_children\n", - " 12.678858\n", + " 0.256545\n", " \n", " \n", " coef_number_of_pre_driving_age_children_mixed\n", - " 158.960267\n", + " 0.311384\n", " \n", " \n", " coef_number_of_university_students\n", - " -5.007830\n", + " 0.067485\n", " \n", " \n", " coef_tour_purpose_is_discretionary_adults\n", @@ -1926,7 +1959,7 @@ " -999.000000\n", " \n", " \n", - "loglike-9.786597596036862e-07d_loglike\n", + "
logloss0.2061557647817926d_logloss\n", " \n", " \n", " \n", @@ -1936,107 +1969,107 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2059,85 +2092,85 @@ " \n", " \n", " \n", - "
coef_asc_children-1.199240e-064.680292e-05
coef_asc_mixed1.199240e-061.506794e-04
coef_household_has_more_cars_than_workers_adults-7.697895e-122-1.303235e-04
coef_household_has_more_cars_than_workers_mixed2.677106e-094.087211e-05
coef_household_in_suburban_area_adults0.000000e+00-2.788428e-05
coef_household_in_suburban_area_mixed0.000000e+00-2.733559e-05
coef_household_in_urban_area-1.311061e-38-1.695981e-04
coef_log_max_overlap_of_adults_time_windows-2.551704e-387.673766e-05
coef_log_max_overlap_of_childrens_time_windows-2.523891e-063.533351e-05
coef_log_max_overlap_of_time_windows2.726540e-062.703275e-05
coef_low_income_households_adults-4.843014e-79-1.364236e-04
coef_low_income_households_mixed4.440892e-165.443801e-05
coef_medium_income_households-1.620456e-77-7.972989e-05
coef_number_of_children_too_young_for_school_children8.759917e-08-1.415851e-04
coef_number_of_children_too_young_for_school_mixed-8.759917e-08-1.265584e-04
coef_number_of_driving_age_children_children-2.272077e-16-7.515067e-05
coef_number_of_driving_age_children_mixed4.440892e-16-7.904237e-05
coef_number_of_full_time_workers_adults-3.933184e-381.573128e-04
coef_number_of_full_time_workers_mixed2.196009e-064.624742e-05
coef_number_of_non_workers_adults-4.909798e-77-2.862258e-05
coef_number_of_non_workers_mixed2.677106e-09-1.424617e-05
coef_number_of_part_time_workers_adults-1.620456e-77-2.202661e-04
coef_number_of_part_time_workers_mixed-8.759917e-082.445794e-07
coef_number_of_pre_driving_age_children_children-2.486079e-06-1.084978e-05
coef_number_of_pre_driving_age_children_mixed2.486079e-063.508229e-05
coef_number_of_university_students-2.474369e-317-5.764209e-05
coef_tour_purpose_is_discretionary_adults0.000000e+00
nit33nfev39njev33status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.266490method'SLSQP'n_cases91iteration_number33logloss1.075450285278776e-08" + "nit95nfev95njev95status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.247365method'SLSQP'n_cases1277iteration_number95loglike-263.2609116263491" ], "text/plain": [ - "┣ x: coef_asc_children 121.627746\n", - "┃ coef_asc_mixed -88.249613\n", - "┃ coef_household_has_more_cars_than_workers_adults -40.894264\n", - "┃ coef_household_has_more_cars_than_workers_mixed 153.815203\n", - "┃ coef_household_in_suburban_area_adults 0.510500\n", - "┃ coef_household_in_suburban_area_mixed 0.128300\n", - "┃ coef_household_in_urban_area -21.823334\n", - "┃ coef_log_max_overlap_of_adults_time_windows -36.285976\n", - "┃ coef_log_max_overlap_of_childrens_time_windows 283.696667\n", - "┃ coef_log_max_overlap_of_time_windows 196.728573\n", - "┃ coef_low_income_households_adults -16.894954\n", - "┃ coef_low_income_households_mixed -3.143593\n", - "┃ coef_medium_income_households -161.727385\n", - "┃ coef_number_of_children_too_young_for_school_children -110.048280\n", - "┃ coef_number_of_children_too_young_for_school_mixed 9.539326\n", - "┃ coef_number_of_driving_age_children_children -172.311937\n", - "┃ coef_number_of_driving_age_children_mixed 184.761407\n", - "┃ coef_number_of_full_time_workers_adults 185.490311\n", - "┃ coef_number_of_full_time_workers_mixed 50.943193\n", - "┃ coef_number_of_non_workers_adults 40.041010\n", - "┃ coef_number_of_non_workers_mixed -96.438666\n", - "┃ coef_number_of_part_time_workers_adults 47.088158\n", - "┃ coef_number_of_part_time_workers_mixed 58.958712\n", - "┃ coef_number_of_pre_driving_age_children_children 12.678858\n", - "┃ coef_number_of_pre_driving_age_children_mixed 158.960267\n", - "┃ coef_number_of_university_students -5.007830\n", + "┣ x: coef_asc_children 6.744041\n", + "┃ coef_asc_mixed 6.886657\n", + "┃ coef_household_has_more_cars_than_workers_adults 3.138920\n", + "┃ coef_household_has_more_cars_than_workers_mixed 0.384250\n", + "┃ coef_household_in_suburban_area_adults -1.123570\n", + "┃ coef_household_in_suburban_area_mixed 0.258299\n", + "┃ coef_household_in_urban_area -0.441828\n", + "┃ coef_log_max_overlap_of_adults_time_windows 1.288269\n", + "┃ coef_log_max_overlap_of_childrens_time_windows 1.783892\n", + "┃ coef_log_max_overlap_of_time_windows 1.505521\n", + "┃ coef_low_income_households_adults 2.520875\n", + "┃ coef_low_income_households_mixed 0.683195\n", + "┃ coef_medium_income_households 1.853225\n", + "┃ coef_number_of_children_too_young_for_school_children 0.661222\n", + "┃ coef_number_of_children_too_young_for_school_mixed 1.065525\n", + "┃ coef_number_of_driving_age_children_children 0.646806\n", + "┃ coef_number_of_driving_age_children_mixed 0.494992\n", + "┃ coef_number_of_full_time_workers_adults 1.638383\n", + "┃ coef_number_of_full_time_workers_mixed 0.106573\n", + "┃ coef_number_of_non_workers_adults 0.344889\n", + "┃ coef_number_of_non_workers_mixed -0.067851\n", + "┃ coef_number_of_part_time_workers_adults 0.094715\n", + "┃ coef_number_of_part_time_workers_mixed 0.182067\n", + "┃ coef_number_of_pre_driving_age_children_children 0.256545\n", + "┃ coef_number_of_pre_driving_age_children_mixed 0.311384\n", + "┃ coef_number_of_university_students 0.067485\n", "┃ coef_tour_purpose_is_discretionary_adults 0.764800\n", "┃ coef_tour_purpose_is_discretionary_children 0.510100\n", "┃ coef_tour_purpose_is_eating_out_children -0.967800\n", "┃ coef_tour_purpose_is_eating_out_mixed -0.802700\n", "┃ coef_unavailable -999.000000\n", "┃ dtype: float64\n", - "┣ loglike: -9.786597596036862e-07\n", - "┣ d_loglike: coef_asc_children -1.199240e-06\n", - "┃ coef_asc_mixed 1.199240e-06\n", - "┃ coef_household_has_more_cars_than_workers_adults -7.697895e-122\n", - "┃ coef_household_has_more_cars_than_workers_mixed 2.677106e-09\n", - "┃ coef_household_in_suburban_area_adults 0.000000e+00\n", - "┃ coef_household_in_suburban_area_mixed 0.000000e+00\n", - "┃ coef_household_in_urban_area -1.311061e-38\n", - "┃ coef_log_max_overlap_of_adults_time_windows -2.551704e-38\n", - "┃ coef_log_max_overlap_of_childrens_time_windows -2.523891e-06\n", - "┃ coef_log_max_overlap_of_time_windows 2.726540e-06\n", - "┃ coef_low_income_households_adults -4.843014e-79\n", - "┃ coef_low_income_households_mixed 4.440892e-16\n", - "┃ coef_medium_income_households -1.620456e-77\n", - "┃ coef_number_of_children_too_young_for_school_children 8.759917e-08\n", - "┃ coef_number_of_children_too_young_for_school_mixed -8.759917e-08\n", - "┃ coef_number_of_driving_age_children_children -2.272077e-16\n", - "┃ coef_number_of_driving_age_children_mixed 4.440892e-16\n", - "┃ coef_number_of_full_time_workers_adults -3.933184e-38\n", - "┃ coef_number_of_full_time_workers_mixed 2.196009e-06\n", - "┃ coef_number_of_non_workers_adults -4.909798e-77\n", - "┃ coef_number_of_non_workers_mixed 2.677106e-09\n", - "┃ coef_number_of_part_time_workers_adults -1.620456e-77\n", - "┃ coef_number_of_part_time_workers_mixed -8.759917e-08\n", - "┃ coef_number_of_pre_driving_age_children_children -2.486079e-06\n", - "┃ coef_number_of_pre_driving_age_children_mixed 2.486079e-06\n", - "┃ coef_number_of_university_students -2.474369e-317\n", - "┃ coef_tour_purpose_is_discretionary_adults 0.000000e+00\n", - "┃ coef_tour_purpose_is_discretionary_children 0.000000e+00\n", - "┃ coef_tour_purpose_is_eating_out_children 0.000000e+00\n", - "┃ coef_tour_purpose_is_eating_out_mixed 0.000000e+00\n", - "┃ coef_unavailable 0.000000e+00\n", + "┣ logloss: 0.2061557647817926\n", + "┣ d_logloss: coef_asc_children 4.680292e-05\n", + "┃ coef_asc_mixed 1.506794e-04\n", + "┃ coef_household_has_more_cars_than_workers_adults -1.303235e-04\n", + "┃ coef_household_has_more_cars_than_workers_mixed 4.087211e-05\n", + "┃ coef_household_in_suburban_area_adults -2.788428e-05\n", + "┃ coef_household_in_suburban_area_mixed -2.733559e-05\n", + "┃ coef_household_in_urban_area -1.695981e-04\n", + "┃ coef_log_max_overlap_of_adults_time_windows 7.673766e-05\n", + "┃ coef_log_max_overlap_of_childrens_time_windows 3.533351e-05\n", + "┃ coef_log_max_overlap_of_time_windows 2.703275e-05\n", + "┃ coef_low_income_households_adults -1.364236e-04\n", + "┃ coef_low_income_households_mixed 5.443801e-05\n", + "┃ coef_medium_income_households -7.972989e-05\n", + "┃ coef_number_of_children_too_young_for_school_children -1.415851e-04\n", + "┃ coef_number_of_children_too_young_for_school_mixed -1.265584e-04\n", + "┃ coef_number_of_driving_age_children_children -7.515067e-05\n", + "┃ coef_number_of_driving_age_children_mixed -7.904237e-05\n", + "┃ coef_number_of_full_time_workers_adults 1.573128e-04\n", + "┃ coef_number_of_full_time_workers_mixed 4.624742e-05\n", + "┃ coef_number_of_non_workers_adults -2.862258e-05\n", + "┃ coef_number_of_non_workers_mixed -1.424617e-05\n", + "┃ coef_number_of_part_time_workers_adults -2.202661e-04\n", + "┃ coef_number_of_part_time_workers_mixed 2.445794e-07\n", + "┃ coef_number_of_pre_driving_age_children_children -1.084978e-05\n", + "┃ coef_number_of_pre_driving_age_children_mixed 3.508229e-05\n", + "┃ coef_number_of_university_students -5.764209e-05\n", + "┃ coef_tour_purpose_is_discretionary_adults 0.000000e+00\n", + "┃ coef_tour_purpose_is_discretionary_children 0.000000e+00\n", + "┃ coef_tour_purpose_is_eating_out_children 0.000000e+00\n", + "┃ coef_tour_purpose_is_eating_out_mixed 0.000000e+00\n", + "┃ coef_unavailable 0.000000e+00\n", "┃ dtype: float64\n", - "┣ nit: 33\n", - "┣ nfev: 39\n", - "┣ njev: 33\n", + "┣ nit: 95\n", + "┣ nfev: 95\n", + "┣ njev: 95\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(microseconds=266490)\n", + "┣ elapsed_time: datetime.timedelta(microseconds=247365)\n", "┣ method: 'SLSQP'\n", - "┣ n_cases: 91\n", - "┣ iteration_number: 33\n", - "┣ logloss: 1.075450285278776e-08" + "┣ n_cases: 1277\n", + "┣ iteration_number: 95\n", + "┣ loglike: -263.2609116263491" ] }, "execution_count": 7, @@ -2172,330 +2205,325 @@ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_asc_children 122. NA NA[***] 306.42 0.00
coef_asc_mixed-88.2 2.20e+04-0.00 NA 0.00
coef_household_has_more_cars_than_workers_adults-40.9 NA NA[] 0.00 0.00
coef_household_has_more_cars_than_workers_mixed 154. 4.67e+04 0.00 NA 0.00
coef_household_in_suburban_area_adults 0.511 NA NA[] 0.00 0.00
coef_household_in_suburban_area_mixed 0.128 NA NA[] 0.00 0.00
coef_household_in_urban_area-21.8 NA NA[] 0.00 0.00
coef_log_max_overlap_of_adults_time_windows-36.3 NA NA[] 0.00 0.00
coef_log_max_overlap_of_childrens_time_windows 284. NA NA[***] BIG 0.00
coef_log_max_overlap_of_time_windows 197. 1.02e+04 0.02 NA 0.00
coef_low_income_households_adults-16.9 NA NA[] 0.00 0.00
coef_low_income_households_mixed-3.14 NA NA[] 0.00 0.00
coef_medium_income_households-162. NA NA[] 0.00 0.00
coef_number_of_children_too_young_for_school_children-110. 9.62e+03-0.01 NA 0.00
coef_number_of_children_too_young_for_school_mixed 9.54 4.19e+03 0.00 NA 0.00
coef_number_of_driving_age_children_children-172. 1.21e+05-0.00 NA 0.00
coef_number_of_driving_age_children_mixed 185. NA NA[***] 293.31 0.00
coef_number_of_full_time_workers_adults 185. NA NA[***] 216.30 0.00
coef_number_of_full_time_workers_mixed 50.9 3.72e+03 0.01 NA 0.00
coef_number_of_non_workers_adults 40.0 NA NA[] 0.00 0.00
coef_number_of_non_workers_mixed-96.4 2.02e+05-0.00 NA 0.00
coef_number_of_part_time_workers_adults 47.1 NA NA[] 0.00 0.00
coef_number_of_part_time_workers_mixed 59.0 4.19e+03 0.01 NA 0.00
coef_number_of_pre_driving_age_children_children 12.7 1.41e+04 0.00 NA 0.00
coef_number_of_pre_driving_age_children_mixed 159. 2.22e+04 0.01 NA 0.00
coef_number_of_university_students-5.01 0.00 NA[] 0.00 0.00
coef_tour_purpose_is_discretionary_adults 0.765 0.00 NA[] 0.00 0.00
coef_tour_purpose_is_discretionary_children 0.510 0.00 NA[] 0.00 0.00
coef_tour_purpose_is_eating_out_children-0.968 0.00 NA[] 0.00 0.00
coef_tour_purpose_is_eating_out_mixed-0.803 0.00 NA[] 0.00 0.00
coef_unavailable-999. NA NA NA 0.00fixed value
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_asc_children 6.74 NA NA 0.00
coef_asc_mixed 6.89 NA NA 0.00
coef_household_has_more_cars_than_workers_adults 3.14 1.19 2.64** 0.00
coef_household_has_more_cars_than_workers_mixed 0.384 0.275 1.40 0.00
coef_household_in_suburban_area_adults-1.12 NA NA 0.00
coef_household_in_suburban_area_mixed 0.258 0.272 0.95 0.00
coef_household_in_urban_area-0.442 NA NA 0.00
coef_log_max_overlap_of_adults_time_windows 1.29 0.921 1.40 0.00
coef_log_max_overlap_of_childrens_time_windows 1.78 0.379 4.71*** 0.00
coef_log_max_overlap_of_time_windows 1.51 0.376 4.00*** 0.00
coef_low_income_households_adults 2.52 1.01 2.48* 0.00
coef_low_income_households_mixed 0.683 0.345 1.98* 0.00
coef_medium_income_households 1.85 0.782 2.37* 0.00
coef_number_of_children_too_young_for_school_children 0.661 0.684 0.97 0.00
coef_number_of_children_too_young_for_school_mixed 1.07 0.653 1.63 0.00
coef_number_of_driving_age_children_children 0.647 0.901 0.72 0.00
coef_number_of_driving_age_children_mixed 0.495 0.870 0.57 0.00
coef_number_of_full_time_workers_adults 1.64 0.455 3.60*** 0.00
coef_number_of_full_time_workers_mixed 0.107 0.175 0.61 0.00
coef_number_of_non_workers_adults 0.345 0.455 0.76 0.00
coef_number_of_non_workers_mixed-0.0679 0.175-0.39 0.00
coef_number_of_part_time_workers_adults 0.0947 0.789 0.12 0.00
coef_number_of_part_time_workers_mixed 0.182 0.227 0.80 0.00
coef_number_of_pre_driving_age_children_children 0.257 0.503 0.51 0.00
coef_number_of_pre_driving_age_children_mixed 0.311 0.475 0.66 0.00
coef_number_of_university_students 0.0675 0.499 0.14 0.00
coef_tour_purpose_is_discretionary_adults 0.765 0.00 NA 0.00
coef_tour_purpose_is_discretionary_children 0.510 0.00 NA 0.00
coef_tour_purpose_is_eating_out_children-0.968 0.00 NA 0.00
coef_tour_purpose_is_eating_out_mixed-0.803 0.00 NA 0.00
coef_unavailable-999. 0.00 NA 0.00fixed value
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -2542,18 +2570,7 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -2611,13 +2628,13 @@ " \n", " 1\n", " coef_asc_children\n", - " 121.627746\n", + " 6.744041\n", " F\n", " \n", " \n", " 2\n", " coef_asc_mixed\n", - " -88.249613\n", + " 6.886657\n", " fF\n", " \n", " \n", @@ -2647,145 +2664,145 @@ " \n", " 7\n", " coef_number_of_full_time_workers_adults\n", - " 185.490311\n", + " 1.638383\n", " F\n", " \n", " \n", " 8\n", " coef_number_of_full_time_workers_mixed\n", - " 50.943193\n", + " 0.106573\n", " F\n", " \n", " \n", " 9\n", " coef_number_of_part_time_workers_adults\n", - " 47.088158\n", + " 0.094715\n", " F\n", " \n", " \n", " 10\n", " coef_number_of_part_time_workers_mixed\n", - " 58.958712\n", + " 0.182067\n", " F\n", " \n", " \n", " 11\n", " coef_number_of_university_students\n", - " -5.007830\n", + " 0.067485\n", " F\n", " \n", " \n", " 12\n", " coef_number_of_non_workers_adults\n", - " 40.041010\n", + " 0.344889\n", " F\n", " \n", " \n", " 13\n", " coef_number_of_non_workers_mixed\n", - " -96.438666\n", + " -0.067851\n", " F\n", " \n", " \n", " 14\n", " coef_number_of_children_too_young_for_school_c...\n", - " -110.048280\n", + " 0.661222\n", " F\n", " \n", " \n", " 15\n", " coef_number_of_children_too_young_for_school_m...\n", - " 9.539326\n", + " 1.065525\n", " F\n", " \n", " \n", " 16\n", " coef_number_of_pre_driving_age_children_children\n", - " 12.678858\n", + " 0.256545\n", " F\n", " \n", " \n", " 17\n", " coef_number_of_pre_driving_age_children_mixed\n", - " 158.960267\n", + " 0.311384\n", " F\n", " \n", " \n", " 18\n", " coef_number_of_driving_age_children_children\n", - " -172.311937\n", + " 0.646806\n", " F\n", " \n", " \n", " 19\n", " coef_number_of_driving_age_children_mixed\n", - " 184.761407\n", + " 0.494992\n", " F\n", " \n", " \n", " 20\n", " coef_low_income_households_adults\n", - " -16.894954\n", + " 2.520875\n", " F\n", " \n", " \n", " 21\n", " coef_low_income_households_mixed\n", - " -3.143593\n", + " 0.683195\n", " F\n", " \n", " \n", " 22\n", " coef_medium_income_households\n", - " -161.727385\n", + " 1.853225\n", " F\n", " \n", " \n", " 23\n", " coef_household_has_more_cars_than_workers_adults\n", - " -40.894264\n", + " 3.138920\n", " F\n", " \n", " \n", " 24\n", " coef_household_has_more_cars_than_workers_mixed\n", - " 153.815203\n", + " 0.384250\n", " F\n", " \n", " \n", " 25\n", " coef_household_in_urban_area\n", - " -21.823334\n", + " -0.441828\n", " F\n", " \n", " \n", " 26\n", " coef_household_in_suburban_area_adults\n", - " 0.510500\n", + " -1.123570\n", " F\n", " \n", " \n", " 27\n", " coef_household_in_suburban_area_mixed\n", - " 0.128300\n", + " 0.258299\n", " F\n", " \n", " \n", " 28\n", " coef_log_max_overlap_of_adults_time_windows\n", - " -36.285976\n", + " 1.288269\n", " F\n", " \n", " \n", " 29\n", " coef_log_max_overlap_of_childrens_time_windows\n", - " 283.696667\n", + " 1.783892\n", " F\n", " \n", " \n", " 30\n", " coef_log_max_overlap_of_time_windows\n", - " 196.728573\n", + " 1.505521\n", " F\n", " \n", " \n", @@ -2795,36 +2812,36 @@ "text/plain": [ " coefficient_name value constrain\n", "0 coef_unavailable -999.000000 T\n", - "1 coef_asc_children 121.627746 F\n", - "2 coef_asc_mixed -88.249613 fF\n", + "1 coef_asc_children 6.744041 F\n", + "2 coef_asc_mixed 6.886657 fF\n", "3 coef_tour_purpose_is_eating_out_children -0.967800 F\n", "4 coef_tour_purpose_is_eating_out_mixed -0.802700 F\n", "5 coef_tour_purpose_is_discretionary_adults 0.764800 F\n", "6 coef_tour_purpose_is_discretionary_children 0.510100 F\n", - "7 coef_number_of_full_time_workers_adults 185.490311 F\n", - "8 coef_number_of_full_time_workers_mixed 50.943193 F\n", - "9 coef_number_of_part_time_workers_adults 47.088158 F\n", - "10 coef_number_of_part_time_workers_mixed 58.958712 F\n", - "11 coef_number_of_university_students -5.007830 F\n", - "12 coef_number_of_non_workers_adults 40.041010 F\n", - "13 coef_number_of_non_workers_mixed -96.438666 F\n", - "14 coef_number_of_children_too_young_for_school_c... -110.048280 F\n", - "15 coef_number_of_children_too_young_for_school_m... 9.539326 F\n", - "16 coef_number_of_pre_driving_age_children_children 12.678858 F\n", - "17 coef_number_of_pre_driving_age_children_mixed 158.960267 F\n", - "18 coef_number_of_driving_age_children_children -172.311937 F\n", - "19 coef_number_of_driving_age_children_mixed 184.761407 F\n", - "20 coef_low_income_households_adults -16.894954 F\n", - "21 coef_low_income_households_mixed -3.143593 F\n", - "22 coef_medium_income_households -161.727385 F\n", - "23 coef_household_has_more_cars_than_workers_adults -40.894264 F\n", - "24 coef_household_has_more_cars_than_workers_mixed 153.815203 F\n", - "25 coef_household_in_urban_area -21.823334 F\n", - "26 coef_household_in_suburban_area_adults 0.510500 F\n", - "27 coef_household_in_suburban_area_mixed 0.128300 F\n", - "28 coef_log_max_overlap_of_adults_time_windows -36.285976 F\n", - "29 coef_log_max_overlap_of_childrens_time_windows 283.696667 F\n", - "30 coef_log_max_overlap_of_time_windows 196.728573 F" + "7 coef_number_of_full_time_workers_adults 1.638383 F\n", + "8 coef_number_of_full_time_workers_mixed 0.106573 F\n", + "9 coef_number_of_part_time_workers_adults 0.094715 F\n", + "10 coef_number_of_part_time_workers_mixed 0.182067 F\n", + "11 coef_number_of_university_students 0.067485 F\n", + "12 coef_number_of_non_workers_adults 0.344889 F\n", + "13 coef_number_of_non_workers_mixed -0.067851 F\n", + "14 coef_number_of_children_too_young_for_school_c... 0.661222 F\n", + "15 coef_number_of_children_too_young_for_school_m... 1.065525 F\n", + "16 coef_number_of_pre_driving_age_children_children 0.256545 F\n", + "17 coef_number_of_pre_driving_age_children_mixed 0.311384 F\n", + "18 coef_number_of_driving_age_children_children 0.646806 F\n", + "19 coef_number_of_driving_age_children_mixed 0.494992 F\n", + "20 coef_low_income_households_adults 2.520875 F\n", + "21 coef_low_income_households_mixed 0.683195 F\n", + "22 coef_medium_income_households 1.853225 F\n", + "23 coef_household_has_more_cars_than_workers_adults 3.138920 F\n", + "24 coef_household_has_more_cars_than_workers_mixed 0.384250 F\n", + "25 coef_household_in_urban_area -0.441828 F\n", + "26 coef_household_in_suburban_area_adults -1.123570 F\n", + "27 coef_household_in_suburban_area_mixed 0.258299 F\n", + "28 coef_log_max_overlap_of_adults_time_windows 1.288269 F\n", + "29 coef_log_max_overlap_of_childrens_time_windows 1.783892 F\n", + "30 coef_log_max_overlap_of_time_windows 1.505521 F" ] }, "execution_count": 11, @@ -2844,7 +2861,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -2858,7 +2875,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/12_joint_tour_participation.ipynb b/activitysim/examples/example_estimation/notebooks/12_joint_tour_participation.ipynb index 22eb78efae..6aa4c7d142 100644 --- a/activitysim/examples/example_estimation/notebooks/12_joint_tour_participation.ipynb +++ b/activitysim/examples/example_estimation/notebooks/12_joint_tour_participation.ipynb @@ -34,27 +34,75 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +116,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/joint_tour_participation/joint_tour_participation_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/joint_tour_participation/joint_tour_participation_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_participation/joint_tour_participation_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"joint_tour_participation\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -794,14 +857,14 @@ " \n", " \n", " \n", - " 778529801\n", + " 785368601\n", " 0\n", " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -812,20 +875,20 @@ " True\n", " False\n", " False\n", + " True\n", " False\n", - " False\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 778529802\n", + " 785368602\n", " 0\n", " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -836,20 +899,20 @@ " True\n", " False\n", " False\n", + " True\n", " False\n", - " False\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 870845401\n", + " 790231801\n", " 0\n", " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -860,20 +923,20 @@ " True\n", " False\n", " False\n", + " True\n", " False\n", - " False\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 870845402\n", + " 790231802\n", " 0\n", " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -884,20 +947,20 @@ " True\n", " False\n", " False\n", + " True\n", " False\n", - " False\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 971500601\n", + " 790977601\n", " 0\n", " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -910,7 +973,7 @@ " False\n", " False\n", " False\n", - " False\n", + " True\n", " 1\n", " \n", " \n", @@ -938,10 +1001,9 @@ " ...\n", " \n", " \n", - " 29881474102\n", - " 0\n", - " 0\n", - " 0.0\n", + " 30524624502\n", + " 1\n", + " 1\n", " 1.0\n", " 0.0\n", " 0.0\n", @@ -949,25 +1011,26 @@ " 0.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " ...\n", " False\n", " False\n", " False\n", - " True\n", " False\n", " False\n", + " True\n", " False\n", " True\n", " False\n", - " 1\n", + " 2\n", " \n", " \n", - " 30181098001\n", - " 1\n", - " 1\n", - " 0.0\n", + " 30524624503\n", + " 0\n", + " 0\n", " 0.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -977,21 +1040,21 @@ " False\n", " False\n", " False\n", - " True\n", " False\n", " False\n", + " True\n", " False\n", " True\n", " False\n", - " 2\n", + " 1\n", " \n", " \n", - " 30181098002\n", + " 30524624504\n", " 0\n", " 0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -1001,22 +1064,22 @@ " False\n", " False\n", " False\n", - " True\n", " False\n", " False\n", + " True\n", " False\n", " True\n", " False\n", " 1\n", " \n", " \n", - " 30181098003\n", + " 30524624505\n", " 0\n", " 0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -1025,16 +1088,16 @@ " False\n", " False\n", " False\n", - " True\n", " False\n", " False\n", + " True\n", " False\n", " True\n", " False\n", " 1\n", " \n", " \n", - " 30181098004\n", + " 30524624506\n", " 0\n", " 0\n", " 0.0\n", @@ -1044,14 +1107,14 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 0.0\n", + " 1.0\n", " ...\n", + " True\n", " False\n", " False\n", " False\n", - " True\n", - " False\n", " False\n", + " True\n", " False\n", " True\n", " False\n", @@ -1059,193 +1122,193 @@ " \n", " \n", "\n", - "

304 rows × 226 columns

\n", + "

4500 rows × 226 columns

\n", "" ], "text/plain": [ " model_choice override_choice \\\n", "participant_id \n", - "778529801 0 0 \n", - "778529802 0 0 \n", - "870845401 0 0 \n", - "870845402 0 0 \n", - "971500601 0 0 \n", + "785368601 0 0 \n", + "785368602 0 0 \n", + "790231801 0 0 \n", + "790231802 0 0 \n", + "790977601 0 0 \n", "... ... ... \n", - "29881474102 0 0 \n", - "30181098001 1 1 \n", - "30181098002 0 0 \n", - "30181098003 0 0 \n", - "30181098004 0 0 \n", + "30524624502 1 1 \n", + "30524624503 0 0 \n", + "30524624504 0 0 \n", + "30524624505 0 0 \n", + "30524624506 0 0 \n", "\n", " util_full_time_worker_mixed_party \\\n", "participant_id \n", - "778529801 0.0 \n", - "778529802 0.0 \n", - "870845401 0.0 \n", - "870845402 0.0 \n", - "971500601 0.0 \n", + "785368601 0.0 \n", + "785368602 0.0 \n", + "790231801 0.0 \n", + "790231802 0.0 \n", + "790977601 0.0 \n", "... ... \n", - "29881474102 0.0 \n", - "30181098001 0.0 \n", - "30181098002 0.0 \n", - "30181098003 0.0 \n", - "30181098004 0.0 \n", + "30524624502 1.0 \n", + "30524624503 0.0 \n", + "30524624504 0.0 \n", + "30524624505 0.0 \n", + "30524624506 0.0 \n", "\n", " util_part_time_worker_adults_only_party \\\n", "participant_id \n", - 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"29881474102 False False False \n", - "30181098001 False False False \n", - "30181098002 False False False \n", - "30181098003 False False False \n", - "30181098004 False False False \n", + "30524624502 False False False \n", + "30524624503 False False False \n", + "30524624504 False False False \n", + "30524624505 False False False \n", + "30524624506 True False False \n", "\n", " tour_composition_is_adults tour_composition_is_children \\\n", "participant_id \n", - "778529801 True False \n", - "778529802 True False \n", - "870845401 True False \n", - "870845402 True False \n", - "971500601 True False \n", + "785368601 True False \n", + "785368602 True False \n", + "790231801 True False \n", + "790231802 True False \n", + "790977601 True False \n", "... ... ... \n", - "29881474102 True False \n", - "30181098001 True False \n", - "30181098002 True False \n", - "30181098003 True False \n", - "30181098004 True False \n", + "30524624502 False False \n", + "30524624503 False False \n", + "30524624504 False False \n", + "30524624505 False False \n", + "30524624506 False False \n", "\n", " tour_composition_is_mixed home_is_suburban high_income \\\n", "participant_id \n", - 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"29881474102 False 1 \n", - "30181098001 False 2 \n", - "30181098002 False 1 \n", - "30181098003 False 1 \n", - "30181098004 False 1 \n", + "30524624502 False 2 \n", + "30524624503 False 1 \n", + "30524624504 False 1 \n", + "30524624505 False 1 \n", + "30524624506 False 1 \n", "\n", - "[304 rows x 226 columns]" + "[4500 rows x 226 columns]" ] }, "execution_count": 6, @@ -1271,17 +1334,10 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 112 [Optimization terminated successfully]

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Iteration 080 [Optimization terminated successfully]

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Best LL = -61.383086448632596

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Best LL = -1240.495238444391

" ], "text/plain": [ "" @@ -1324,913 +1380,867 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_adult_log_of_max_window_overlap_with_a_child_mixed\n", - " 4.595737\n", + " 2.125116\n", + " 2.125116\n", " 2.18900\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4.595737\n", " \n", " \n", " coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party\n", - " 1.692145\n", + " 1.280678\n", + " 1.280678\n", " 0.84360\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.692145\n", " \n", " \n", " coef_adult_more_automobiles_than_workers_adult_only_party\n", - " 0.311671\n", + " 0.121220\n", + " 0.121220\n", " -0.21330\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.311671\n", " \n", " \n", " coef_adult_more_automobiles_than_workers_mixed_party\n", - " -1.827740\n", + " -0.591966\n", + " -0.591966\n", " -0.60310\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.827740\n", " \n", " \n", " coef_adult_number_of_joint_tours_adult_only\n", - " -2.134054\n", + " -0.794091\n", + " -0.794091\n", " -0.32420\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.134054\n", " \n", " \n", " coef_adult_number_of_joint_tours_mixed\n", - " -28.572220\n", + " -1.417451\n", + " -1.417451\n", " -0.35840\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -28.572220\n", " \n", " \n", " coef_adult_number_of_other_adults_in_the_household_adults_only_party\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_adult_number_of_other_adults_in_the_household_mixed_party\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_child_log_of_max_window_overlap_with_a_child_child\n", - " 1.275585\n", + " 1.296000\n", + " 1.296000\n", " 1.29600\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.275585\n", " \n", " \n", " coef_child_log_of_max_window_overlap_with_an_adult_mixed\n", - " 1.539267\n", + " 1.538000\n", + " 1.538000\n", " 1.53800\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.539267\n", " \n", " \n", " coef_child_more_automobiles_than_workers_child_only_party\n", - " -0.394683\n", + " -0.421400\n", + " -0.421400\n", " -0.42140\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.394683\n", " \n", " \n", " coef_child_more_automobiles_than_workers_mixed_party\n", - " -1.551914\n", + " -0.366466\n", + " -0.366466\n", " -0.37760\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.551914\n", " \n", " \n", " coef_child_number_of_joint_tours_child_only\n", - " 4.977604\n", + " 0.866122\n", + " 0.866122\n", " 0.10470\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4.977604\n", " \n", " \n", " coef_child_number_of_joint_tours_mixed\n", - " 17.894892\n", + " -0.261747\n", + " -0.261747\n", " -0.50890\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 17.894892\n", " \n", " \n", " coef_child_number_of_other_children_in_the_household_child_only_party\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_child_number_of_other_children_in_the_household_mixed\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_child_too_young_for_school_children_only_party\n", - " -2.785631\n", + " -1.149596\n", + " -1.149596\n", " -2.78600\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.785631\n", " \n", " \n", " coef_child_too_young_for_school_mixed_party\n", - " 2.069934\n", + " 0.197836\n", + " 0.197836\n", " -1.89300\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.069934\n", " \n", " \n", " coef_child_too_young_for_school_specific_to_discretionary_joint_tours\n", - " 0.128371\n", + " 0.128400\n", + " 0.128400\n", " 0.12840\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.128371\n", " \n", " \n", " coef_child_too_young_for_school_specific_to_eating_out_joint_tours\n", - " 0.658655\n", + " 0.658900\n", + " 0.658900\n", " 0.65890\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.658655\n", " \n", " \n", " coef_driving_age_student_children_only_party\n", - " -1.822000\n", + " -2.120496\n", + " -2.120496\n", " -1.82200\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.822000\n", " \n", " \n", " coef_driving_age_student_mixed_party\n", - " -28.998969\n", + " 0.322986\n", + " 0.322986\n", " -1.35300\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -28.998969\n", " \n", " \n", " coef_driving_age_student_specific_to_discretionary_joint_tours\n", " -0.667500\n", + " -0.667500\n", " -0.66750\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.667500\n", " \n", " \n", " coef_driving_age_student_specific_to_eating_out_joint_tours\n", " 2.344000\n", + " 2.344000\n", " 2.34400\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.344000\n", " \n", " \n", " coef_dummy_for_high_income_for_adult_in_adult_party\n", - " 0.131783\n", + " -0.068202\n", + " -0.068202\n", " -0.16820\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.131783\n", " \n", " \n", " coef_dummy_for_high_income_for_adult_in_mixed_party\n", - " -0.378918\n", + " 0.112930\n", + " 0.112930\n", " -0.02613\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.378918\n", " \n", " \n", " coef_dummy_for_high_income_for_child_in_children_party\n", - " -0.561900\n", + " -0.211892\n", + " -0.211892\n", " -0.56190\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.561900\n", " \n", " \n", " coef_dummy_for_high_income_for_child_in_mixed_party\n", - " -0.513571\n", + " -0.013740\n", + " -0.013740\n", " -0.15280\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.513571\n", " \n", " \n", " coef_full_time_worker_mixed_party\n", - " -957.202749\n", + " -3.527365\n", + " -3.527365\n", " -3.56600\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -957.202749\n", " \n", " \n", " coef_full_time_worker_mixed_party_not\n", " 0.500000\n", + " 0.500000\n", " 0.50000\n", + " 0.5\n", + " 0.5\n", " 0.0\n", - " NaN\n", - " NaN\n", " 1\n", - " \n", - " 0.500000\n", " \n", " \n", " coef_full_time_worker_specific_to_discretionary_joint_tours\n", " 0.439200\n", + " 0.439200\n", " 0.43920\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.439200\n", " \n", " \n", " coef_full_time_worker_specific_to_discretionary_joint_tours_not\n", " 0.500000\n", + " 0.500000\n", " 0.50000\n", + " 0.5\n", + " 0.5\n", " 0.0\n", - " NaN\n", - " NaN\n", " 1\n", - " \n", - " 0.500000\n", " \n", " \n", " coef_full_time_worker_specific_to_eating_out_joint_tours\n", " 0.715700\n", + " 0.715700\n", " 0.71570\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.715700\n", " \n", " \n", " coef_full_time_worker_specific_to_eating_out_joint_tours_not\n", " 0.500000\n", + " 0.500000\n", " 0.50000\n", + " 0.5\n", + " 0.5\n", " 0.0\n", - " NaN\n", - " NaN\n", " 1\n", - " \n", - " 0.500000\n", " \n", " \n", " coef_household_in_suburban_area_adult_adult_only_party\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_household_in_suburban_area_adult_mixed_party\n", - " -0.060070\n", + " 1.239881\n", + " 1.239881\n", " -0.06007\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.060070\n", " \n", " \n", " coef_household_in_suburban_area_child_child_only_party\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_household_in_suburban_area_child_mixed_party\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_household_in_urban_area_adult_adult_only_party\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_household_in_urban_area_adult_mixed_party\n", - " 978.504490\n", + " 1.204039\n", + " 1.204039\n", " -0.13700\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 978.504490\n", " \n", " \n", " coef_household_in_urban_area_child_child_only_party\n", - " 6.082784\n", + " -0.145369\n", + " -0.145369\n", " 1.21000\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 6.082784\n", " \n", " \n", " coef_household_in_urban_area_child_mixed_party\n", - " -19.290030\n", + " -0.138857\n", + " -0.138857\n", " 0.62650\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -19.290030\n", " \n", " \n", " coef_non_worker_adults_only_party\n", - " -4.080384\n", + " -3.161945\n", + " -3.161945\n", " -3.16400\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.080384\n", " \n", " \n", " coef_non_worker_mixed_party\n", - " -125.288137\n", + " 1.059162\n", + " 1.059162\n", " 0.71520\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -125.288137\n", " \n", " \n", " coef_non_worker_specific_to_discretionary_joint_tours\n", " -0.183500\n", + " -0.183500\n", " -0.18350\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.183500\n", " \n", " \n", " coef_non_worker_specific_to_eating_out_joint_tours\n", " 0.161700\n", + " 0.161700\n", " 0.16170\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.161700\n", " \n", " \n", " coef_part_time_worker_adults_only_party\n", - " -3.248302\n", + " -3.631928\n", + " -3.631928\n", " -3.56600\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -3.248302\n", " \n", " \n", " coef_part_time_worker_adults_only_party_not\n", " 0.500000\n", + " 0.500000\n", " 0.50000\n", + " 0.5\n", + " 0.5\n", " 0.0\n", - " NaN\n", - " NaN\n", " 1\n", - " \n", - " 0.500000\n", " \n", " \n", " coef_part_time_worker_mixed_party\n", - " 2254.660944\n", + " -0.447984\n", + " -0.447984\n", " -0.36550\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2254.660944\n", " \n", " \n", " coef_part_time_worker_specific_to_discretionary_joint_tours\n", " 0.285000\n", + " 0.285000\n", " 0.28500\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.285000\n", " \n", " \n", " coef_part_time_worker_specific_to_eating_out_joint_tours\n", " 2.188000\n", + " 2.188000\n", " 2.18800\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.188000\n", " \n", " \n", " coef_pre_driving_age_student_children_only_party\n", - " 4.151085\n", + " 0.991812\n", + " 0.991812\n", " -0.72170\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4.151085\n", " \n", " \n", " coef_pre_driving_age_student_mixed_party\n", - " 2.007228\n", + " -0.073175\n", + " -0.073175\n", " -1.75200\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.007228\n", " \n", " \n", " coef_pre_driving_age_student_specific_to_discretionary_joint_tours\n", " 0.662600\n", + " 0.662600\n", " 0.66260\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.662600\n", " \n", " \n", " coef_pre_driving_age_student_specific_to_eating_out_joint_tours\n", " 1.391000\n", + " 1.391000\n", " 1.39100\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.391000\n", " \n", " \n", " coef_unavailable\n", " -999.000000\n", + " -999.000000\n", " -999.00000\n", + " -999.0\n", + " -999.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_university_student_mixed_party\n", - " -958.612314\n", + " -2.902637\n", + " -2.902637\n", " -3.04100\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -958.612314\n", " \n", " \n", " coef_university_student_specific_to_discretionary_joint_tours\n", " 0.000000\n", + " 0.000000\n", " 0.00000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_university_student_specific_to_eating_out_joint_tours\n", " -0.820000\n", + " -0.820000\n", " -0.82000\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.820000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_adult_log_of_max_window_overlap_with_a_chi... 4.595737 2.18900 \n", - "coef_adult_log_of_max_window_overlap_with_an_ad... 1.692145 0.84360 \n", - "coef_adult_more_automobiles_than_workers_adult_... 0.311671 -0.21330 \n", - "coef_adult_more_automobiles_than_workers_mixed_... -1.827740 -0.60310 \n", - "coef_adult_number_of_joint_tours_adult_only -2.134054 -0.32420 \n", - "coef_adult_number_of_joint_tours_mixed -28.572220 -0.35840 \n", - "coef_adult_number_of_other_adults_in_the_househ... 0.000000 0.00000 \n", - "coef_adult_number_of_other_adults_in_the_househ... 0.000000 0.00000 \n", - "coef_child_log_of_max_window_overlap_with_a_chi... 1.275585 1.29600 \n", - "coef_child_log_of_max_window_overlap_with_an_ad... 1.539267 1.53800 \n", - "coef_child_more_automobiles_than_workers_child_... -0.394683 -0.42140 \n", - "coef_child_more_automobiles_than_workers_mixed_... -1.551914 -0.37760 \n", - "coef_child_number_of_joint_tours_child_only 4.977604 0.10470 \n", - "coef_child_number_of_joint_tours_mixed 17.894892 -0.50890 \n", - "coef_child_number_of_other_children_in_the_hous... 0.000000 0.00000 \n", - "coef_child_number_of_other_children_in_the_hous... 0.000000 0.00000 \n", - "coef_child_too_young_for_school_children_only_p... -2.785631 -2.78600 \n", - "coef_child_too_young_for_school_mixed_party 2.069934 -1.89300 \n", - "coef_child_too_young_for_school_specific_to_dis... 0.128371 0.12840 \n", - "coef_child_too_young_for_school_specific_to_eat... 0.658655 0.65890 \n", - "coef_driving_age_student_children_only_party -1.822000 -1.82200 \n", - "coef_driving_age_student_mixed_party -28.998969 -1.35300 \n", - "coef_driving_age_student_specific_to_discretion... -0.667500 -0.66750 \n", - "coef_driving_age_student_specific_to_eating_out... 2.344000 2.34400 \n", - "coef_dummy_for_high_income_for_adult_in_adult_p... 0.131783 -0.16820 \n", - "coef_dummy_for_high_income_for_adult_in_mixed_p... -0.378918 -0.02613 \n", - "coef_dummy_for_high_income_for_child_in_childre... -0.561900 -0.56190 \n", - "coef_dummy_for_high_income_for_child_in_mixed_p... -0.513571 -0.15280 \n", - "coef_full_time_worker_mixed_party -957.202749 -3.56600 \n", - "coef_full_time_worker_mixed_party_not 0.500000 0.50000 \n", - "coef_full_time_worker_specific_to_discretionary... 0.439200 0.43920 \n", - "coef_full_time_worker_specific_to_discretionary... 0.500000 0.50000 \n", - "coef_full_time_worker_specific_to_eating_out_jo... 0.715700 0.71570 \n", - "coef_full_time_worker_specific_to_eating_out_jo... 0.500000 0.50000 \n", - "coef_household_in_suburban_area_adult_adult_onl... 0.000000 0.00000 \n", - "coef_household_in_suburban_area_adult_mixed_party -0.060070 -0.06007 \n", - "coef_household_in_suburban_area_child_child_onl... 0.000000 0.00000 \n", - "coef_household_in_suburban_area_child_mixed_party 0.000000 0.00000 \n", - "coef_household_in_urban_area_adult_adult_only_p... 0.000000 0.00000 \n", - "coef_household_in_urban_area_adult_mixed_party 978.504490 -0.13700 \n", - "coef_household_in_urban_area_child_child_only_p... 6.082784 1.21000 \n", - "coef_household_in_urban_area_child_mixed_party -19.290030 0.62650 \n", - "coef_non_worker_adults_only_party -4.080384 -3.16400 \n", - "coef_non_worker_mixed_party -125.288137 0.71520 \n", - "coef_non_worker_specific_to_discretionary_joint... -0.183500 -0.18350 \n", - "coef_non_worker_specific_to_eating_out_joint_tours 0.161700 0.16170 \n", - "coef_part_time_worker_adults_only_party -3.248302 -3.56600 \n", - "coef_part_time_worker_adults_only_party_not 0.500000 0.50000 \n", - "coef_part_time_worker_mixed_party 2254.660944 -0.36550 \n", - "coef_part_time_worker_specific_to_discretionary... 0.285000 0.28500 \n", - "coef_part_time_worker_specific_to_eating_out_jo... 2.188000 2.18800 \n", - "coef_pre_driving_age_student_children_only_party 4.151085 -0.72170 \n", - "coef_pre_driving_age_student_mixed_party 2.007228 -1.75200 \n", - "coef_pre_driving_age_student_specific_to_discre... 0.662600 0.66260 \n", - "coef_pre_driving_age_student_specific_to_eating... 1.391000 1.39100 \n", - "coef_unavailable -999.000000 -999.00000 \n", - "coef_university_student_mixed_party -958.612314 -3.04100 \n", - "coef_university_student_specific_to_discretiona... 0.000000 0.00000 \n", - "coef_university_student_specific_to_eating_out_... -0.820000 -0.82000 \n", + " value best \\\n", + "param_name \n", + "coef_adult_log_of_max_window_overlap_with_a_chi... 2.125116 2.125116 \n", + "coef_adult_log_of_max_window_overlap_with_an_ad... 1.280678 1.280678 \n", + "coef_adult_more_automobiles_than_workers_adult_... 0.121220 0.121220 \n", + "coef_adult_more_automobiles_than_workers_mixed_... -0.591966 -0.591966 \n", + "coef_adult_number_of_joint_tours_adult_only -0.794091 -0.794091 \n", + "coef_adult_number_of_joint_tours_mixed -1.417451 -1.417451 \n", + "coef_adult_number_of_other_adults_in_the_househ... 0.000000 0.000000 \n", + "coef_adult_number_of_other_adults_in_the_househ... 0.000000 0.000000 \n", + "coef_child_log_of_max_window_overlap_with_a_chi... 1.296000 1.296000 \n", + "coef_child_log_of_max_window_overlap_with_an_ad... 1.538000 1.538000 \n", + "coef_child_more_automobiles_than_workers_child_... -0.421400 -0.421400 \n", + "coef_child_more_automobiles_than_workers_mixed_... -0.366466 -0.366466 \n", + "coef_child_number_of_joint_tours_child_only 0.866122 0.866122 \n", + "coef_child_number_of_joint_tours_mixed -0.261747 -0.261747 \n", + "coef_child_number_of_other_children_in_the_hous... 0.000000 0.000000 \n", + "coef_child_number_of_other_children_in_the_hous... 0.000000 0.000000 \n", + "coef_child_too_young_for_school_children_only_p... -1.149596 -1.149596 \n", + "coef_child_too_young_for_school_mixed_party 0.197836 0.197836 \n", + "coef_child_too_young_for_school_specific_to_dis... 0.128400 0.128400 \n", + "coef_child_too_young_for_school_specific_to_eat... 0.658900 0.658900 \n", + "coef_driving_age_student_children_only_party -2.120496 -2.120496 \n", + "coef_driving_age_student_mixed_party 0.322986 0.322986 \n", + "coef_driving_age_student_specific_to_discretion... -0.667500 -0.667500 \n", + "coef_driving_age_student_specific_to_eating_out... 2.344000 2.344000 \n", + "coef_dummy_for_high_income_for_adult_in_adult_p... -0.068202 -0.068202 \n", + "coef_dummy_for_high_income_for_adult_in_mixed_p... 0.112930 0.112930 \n", + "coef_dummy_for_high_income_for_child_in_childre... -0.211892 -0.211892 \n", + "coef_dummy_for_high_income_for_child_in_mixed_p... -0.013740 -0.013740 \n", + "coef_full_time_worker_mixed_party -3.527365 -3.527365 \n", + "coef_full_time_worker_mixed_party_not 0.500000 0.500000 \n", + "coef_full_time_worker_specific_to_discretionary... 0.439200 0.439200 \n", + "coef_full_time_worker_specific_to_discretionary... 0.500000 0.500000 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 0.715700 0.715700 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 0.500000 0.500000 \n", + "coef_household_in_suburban_area_adult_adult_onl... 0.000000 0.000000 \n", + "coef_household_in_suburban_area_adult_mixed_party 1.239881 1.239881 \n", + "coef_household_in_suburban_area_child_child_onl... 0.000000 0.000000 \n", + "coef_household_in_suburban_area_child_mixed_party 0.000000 0.000000 \n", + "coef_household_in_urban_area_adult_adult_only_p... 0.000000 0.000000 \n", + "coef_household_in_urban_area_adult_mixed_party 1.204039 1.204039 \n", + "coef_household_in_urban_area_child_child_only_p... -0.145369 -0.145369 \n", + "coef_household_in_urban_area_child_mixed_party -0.138857 -0.138857 \n", + "coef_non_worker_adults_only_party -3.161945 -3.161945 \n", + "coef_non_worker_mixed_party 1.059162 1.059162 \n", + "coef_non_worker_specific_to_discretionary_joint... -0.183500 -0.183500 \n", + "coef_non_worker_specific_to_eating_out_joint_tours 0.161700 0.161700 \n", + "coef_part_time_worker_adults_only_party -3.631928 -3.631928 \n", + "coef_part_time_worker_adults_only_party_not 0.500000 0.500000 \n", + "coef_part_time_worker_mixed_party -0.447984 -0.447984 \n", + "coef_part_time_worker_specific_to_discretionary... 0.285000 0.285000 \n", + "coef_part_time_worker_specific_to_eating_out_jo... 2.188000 2.188000 \n", + "coef_pre_driving_age_student_children_only_party 0.991812 0.991812 \n", + "coef_pre_driving_age_student_mixed_party -0.073175 -0.073175 \n", + "coef_pre_driving_age_student_specific_to_discre... 0.662600 0.662600 \n", + "coef_pre_driving_age_student_specific_to_eating... 1.391000 1.391000 \n", + "coef_unavailable -999.000000 -999.000000 \n", + "coef_university_student_mixed_party -2.902637 -2.902637 \n", + "coef_university_student_specific_to_discretiona... 0.000000 0.000000 \n", + "coef_university_student_specific_to_eating_out_... -0.820000 -0.820000 \n", "\n", - " nullvalue minimum \\\n", - "coef_adult_log_of_max_window_overlap_with_a_chi... 0.0 NaN \n", - "coef_adult_log_of_max_window_overlap_with_an_ad... 0.0 NaN \n", - "coef_adult_more_automobiles_than_workers_adult_... 0.0 NaN \n", - "coef_adult_more_automobiles_than_workers_mixed_... 0.0 NaN \n", - "coef_adult_number_of_joint_tours_adult_only 0.0 NaN \n", - "coef_adult_number_of_joint_tours_mixed 0.0 NaN \n", - "coef_adult_number_of_other_adults_in_the_househ... 0.0 NaN \n", - "coef_adult_number_of_other_adults_in_the_househ... 0.0 NaN \n", - "coef_child_log_of_max_window_overlap_with_a_chi... 0.0 NaN \n", - "coef_child_log_of_max_window_overlap_with_an_ad... 0.0 NaN \n", - "coef_child_more_automobiles_than_workers_child_... 0.0 NaN \n", - "coef_child_more_automobiles_than_workers_mixed_... 0.0 NaN \n", - "coef_child_number_of_joint_tours_child_only 0.0 NaN \n", - "coef_child_number_of_joint_tours_mixed 0.0 NaN \n", - "coef_child_number_of_other_children_in_the_hous... 0.0 NaN \n", - "coef_child_number_of_other_children_in_the_hous... 0.0 NaN \n", - "coef_child_too_young_for_school_children_only_p... 0.0 NaN \n", - "coef_child_too_young_for_school_mixed_party 0.0 NaN \n", - "coef_child_too_young_for_school_specific_to_dis... 0.0 NaN \n", - "coef_child_too_young_for_school_specific_to_eat... 0.0 NaN \n", - "coef_driving_age_student_children_only_party 0.0 NaN \n", - "coef_driving_age_student_mixed_party 0.0 NaN \n", - "coef_driving_age_student_specific_to_discretion... 0.0 NaN \n", - "coef_driving_age_student_specific_to_eating_out... 0.0 NaN \n", - "coef_dummy_for_high_income_for_adult_in_adult_p... 0.0 NaN \n", - "coef_dummy_for_high_income_for_adult_in_mixed_p... 0.0 NaN \n", - "coef_dummy_for_high_income_for_child_in_childre... 0.0 NaN \n", - "coef_dummy_for_high_income_for_child_in_mixed_p... 0.0 NaN \n", - "coef_full_time_worker_mixed_party 0.0 NaN \n", - "coef_full_time_worker_mixed_party_not 0.0 NaN \n", - "coef_full_time_worker_specific_to_discretionary... 0.0 NaN \n", - "coef_full_time_worker_specific_to_discretionary... 0.0 NaN \n", - "coef_full_time_worker_specific_to_eating_out_jo... 0.0 NaN \n", - "coef_full_time_worker_specific_to_eating_out_jo... 0.0 NaN \n", - "coef_household_in_suburban_area_adult_adult_onl... 0.0 NaN \n", - "coef_household_in_suburban_area_adult_mixed_party 0.0 NaN \n", - "coef_household_in_suburban_area_child_child_onl... 0.0 NaN \n", - "coef_household_in_suburban_area_child_mixed_party 0.0 NaN \n", - "coef_household_in_urban_area_adult_adult_only_p... 0.0 NaN \n", - "coef_household_in_urban_area_adult_mixed_party 0.0 NaN \n", - "coef_household_in_urban_area_child_child_only_p... 0.0 NaN \n", - "coef_household_in_urban_area_child_mixed_party 0.0 NaN \n", - "coef_non_worker_adults_only_party 0.0 NaN \n", - "coef_non_worker_mixed_party 0.0 NaN \n", - "coef_non_worker_specific_to_discretionary_joint... 0.0 NaN \n", - "coef_non_worker_specific_to_eating_out_joint_tours 0.0 NaN \n", - "coef_part_time_worker_adults_only_party 0.0 NaN \n", - "coef_part_time_worker_adults_only_party_not 0.0 NaN \n", - "coef_part_time_worker_mixed_party 0.0 NaN \n", - "coef_part_time_worker_specific_to_discretionary... 0.0 NaN \n", - "coef_part_time_worker_specific_to_eating_out_jo... 0.0 NaN \n", - "coef_pre_driving_age_student_children_only_party 0.0 NaN \n", - "coef_pre_driving_age_student_mixed_party 0.0 NaN \n", - "coef_pre_driving_age_student_specific_to_discre... 0.0 NaN \n", - "coef_pre_driving_age_student_specific_to_eating... 0.0 NaN \n", - "coef_unavailable 0.0 NaN \n", - "coef_university_student_mixed_party 0.0 NaN \n", - "coef_university_student_specific_to_discretiona... 0.0 NaN \n", - "coef_university_student_specific_to_eating_out_... 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_adult_log_of_max_window_overlap_with_a_chi... 2.18900 -50.0 \n", + "coef_adult_log_of_max_window_overlap_with_an_ad... 0.84360 -50.0 \n", + "coef_adult_more_automobiles_than_workers_adult_... -0.21330 -50.0 \n", + "coef_adult_more_automobiles_than_workers_mixed_... -0.60310 -50.0 \n", + "coef_adult_number_of_joint_tours_adult_only -0.32420 -50.0 \n", + "coef_adult_number_of_joint_tours_mixed -0.35840 -50.0 \n", + "coef_adult_number_of_other_adults_in_the_househ... 0.00000 0.0 \n", + "coef_adult_number_of_other_adults_in_the_househ... 0.00000 0.0 \n", + "coef_child_log_of_max_window_overlap_with_a_chi... 1.29600 -50.0 \n", + "coef_child_log_of_max_window_overlap_with_an_ad... 1.53800 -50.0 \n", + "coef_child_more_automobiles_than_workers_child_... -0.42140 -50.0 \n", + "coef_child_more_automobiles_than_workers_mixed_... -0.37760 -50.0 \n", + "coef_child_number_of_joint_tours_child_only 0.10470 -50.0 \n", + "coef_child_number_of_joint_tours_mixed -0.50890 -50.0 \n", + "coef_child_number_of_other_children_in_the_hous... 0.00000 0.0 \n", + "coef_child_number_of_other_children_in_the_hous... 0.00000 0.0 \n", + "coef_child_too_young_for_school_children_only_p... -2.78600 -50.0 \n", + "coef_child_too_young_for_school_mixed_party -1.89300 -50.0 \n", + "coef_child_too_young_for_school_specific_to_dis... 0.12840 -50.0 \n", + "coef_child_too_young_for_school_specific_to_eat... 0.65890 -50.0 \n", + "coef_driving_age_student_children_only_party -1.82200 -50.0 \n", + "coef_driving_age_student_mixed_party -1.35300 -50.0 \n", + "coef_driving_age_student_specific_to_discretion... -0.66750 -50.0 \n", + "coef_driving_age_student_specific_to_eating_out... 2.34400 -50.0 \n", + "coef_dummy_for_high_income_for_adult_in_adult_p... -0.16820 -50.0 \n", + "coef_dummy_for_high_income_for_adult_in_mixed_p... -0.02613 -50.0 \n", + "coef_dummy_for_high_income_for_child_in_childre... -0.56190 -50.0 \n", + "coef_dummy_for_high_income_for_child_in_mixed_p... -0.15280 -50.0 \n", + "coef_full_time_worker_mixed_party -3.56600 -50.0 \n", + "coef_full_time_worker_mixed_party_not 0.50000 0.5 \n", + "coef_full_time_worker_specific_to_discretionary... 0.43920 -50.0 \n", + "coef_full_time_worker_specific_to_discretionary... 0.50000 0.5 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 0.71570 -50.0 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 0.50000 0.5 \n", + "coef_household_in_suburban_area_adult_adult_onl... 0.00000 0.0 \n", + "coef_household_in_suburban_area_adult_mixed_party -0.06007 -50.0 \n", + "coef_household_in_suburban_area_child_child_onl... 0.00000 0.0 \n", + "coef_household_in_suburban_area_child_mixed_party 0.00000 0.0 \n", + "coef_household_in_urban_area_adult_adult_only_p... 0.00000 0.0 \n", + "coef_household_in_urban_area_adult_mixed_party -0.13700 -50.0 \n", + "coef_household_in_urban_area_child_child_only_p... 1.21000 -50.0 \n", + "coef_household_in_urban_area_child_mixed_party 0.62650 -50.0 \n", + "coef_non_worker_adults_only_party -3.16400 -50.0 \n", + "coef_non_worker_mixed_party 0.71520 -50.0 \n", + "coef_non_worker_specific_to_discretionary_joint... -0.18350 -50.0 \n", + "coef_non_worker_specific_to_eating_out_joint_tours 0.16170 -50.0 \n", + "coef_part_time_worker_adults_only_party -3.56600 -50.0 \n", + "coef_part_time_worker_adults_only_party_not 0.50000 0.5 \n", + "coef_part_time_worker_mixed_party -0.36550 -50.0 \n", + "coef_part_time_worker_specific_to_discretionary... 0.28500 -50.0 \n", + "coef_part_time_worker_specific_to_eating_out_jo... 2.18800 -50.0 \n", + "coef_pre_driving_age_student_children_only_party -0.72170 -50.0 \n", + "coef_pre_driving_age_student_mixed_party -1.75200 -50.0 \n", + "coef_pre_driving_age_student_specific_to_discre... 0.66260 -50.0 \n", + "coef_pre_driving_age_student_specific_to_eating... 1.39100 -50.0 \n", + "coef_unavailable -999.00000 -999.0 \n", + "coef_university_student_mixed_party -3.04100 -50.0 \n", + "coef_university_student_specific_to_discretiona... 0.00000 0.0 \n", + "coef_university_student_specific_to_eating_out_... -0.82000 -50.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_adult_log_of_max_window_overlap_with_a_chi... NaN 0 \n", - "coef_adult_log_of_max_window_overlap_with_an_ad... NaN 0 \n", - "coef_adult_more_automobiles_than_workers_adult_... NaN 0 \n", - "coef_adult_more_automobiles_than_workers_mixed_... NaN 0 \n", - "coef_adult_number_of_joint_tours_adult_only NaN 0 \n", - "coef_adult_number_of_joint_tours_mixed NaN 0 \n", - "coef_adult_number_of_other_adults_in_the_househ... NaN 1 \n", - "coef_adult_number_of_other_adults_in_the_househ... NaN 1 \n", - "coef_child_log_of_max_window_overlap_with_a_chi... NaN 0 \n", - "coef_child_log_of_max_window_overlap_with_an_ad... NaN 0 \n", - "coef_child_more_automobiles_than_workers_child_... NaN 0 \n", - "coef_child_more_automobiles_than_workers_mixed_... NaN 0 \n", - "coef_child_number_of_joint_tours_child_only NaN 0 \n", - "coef_child_number_of_joint_tours_mixed NaN 0 \n", - "coef_child_number_of_other_children_in_the_hous... NaN 1 \n", - "coef_child_number_of_other_children_in_the_hous... NaN 1 \n", - "coef_child_too_young_for_school_children_only_p... NaN 0 \n", - "coef_child_too_young_for_school_mixed_party NaN 0 \n", - "coef_child_too_young_for_school_specific_to_dis... NaN 0 \n", - "coef_child_too_young_for_school_specific_to_eat... NaN 0 \n", - "coef_driving_age_student_children_only_party NaN 0 \n", - "coef_driving_age_student_mixed_party NaN 0 \n", - "coef_driving_age_student_specific_to_discretion... NaN 0 \n", - "coef_driving_age_student_specific_to_eating_out... NaN 0 \n", - "coef_dummy_for_high_income_for_adult_in_adult_p... NaN 0 \n", - "coef_dummy_for_high_income_for_adult_in_mixed_p... NaN 0 \n", - "coef_dummy_for_high_income_for_child_in_childre... NaN 0 \n", - "coef_dummy_for_high_income_for_child_in_mixed_p... NaN 0 \n", - "coef_full_time_worker_mixed_party NaN 0 \n", - "coef_full_time_worker_mixed_party_not NaN 1 \n", - "coef_full_time_worker_specific_to_discretionary... NaN 0 \n", - "coef_full_time_worker_specific_to_discretionary... NaN 1 \n", - "coef_full_time_worker_specific_to_eating_out_jo... NaN 0 \n", - "coef_full_time_worker_specific_to_eating_out_jo... NaN 1 \n", - "coef_household_in_suburban_area_adult_adult_onl... NaN 1 \n", - "coef_household_in_suburban_area_adult_mixed_party NaN 0 \n", - "coef_household_in_suburban_area_child_child_onl... NaN 1 \n", - "coef_household_in_suburban_area_child_mixed_party NaN 1 \n", - "coef_household_in_urban_area_adult_adult_only_p... NaN 1 \n", - "coef_household_in_urban_area_adult_mixed_party NaN 0 \n", - "coef_household_in_urban_area_child_child_only_p... NaN 0 \n", - "coef_household_in_urban_area_child_mixed_party NaN 0 \n", - "coef_non_worker_adults_only_party NaN 0 \n", - "coef_non_worker_mixed_party NaN 0 \n", - "coef_non_worker_specific_to_discretionary_joint... NaN 0 \n", - "coef_non_worker_specific_to_eating_out_joint_tours NaN 0 \n", - "coef_part_time_worker_adults_only_party NaN 0 \n", - "coef_part_time_worker_adults_only_party_not NaN 1 \n", - "coef_part_time_worker_mixed_party NaN 0 \n", - "coef_part_time_worker_specific_to_discretionary... NaN 0 \n", - "coef_part_time_worker_specific_to_eating_out_jo... NaN 0 \n", - "coef_pre_driving_age_student_children_only_party NaN 0 \n", - "coef_pre_driving_age_student_mixed_party NaN 0 \n", - "coef_pre_driving_age_student_specific_to_discre... NaN 0 \n", - "coef_pre_driving_age_student_specific_to_eating... NaN 0 \n", - "coef_unavailable NaN 1 \n", - "coef_university_student_mixed_party NaN 0 \n", - "coef_university_student_specific_to_discretiona... NaN 1 \n", - "coef_university_student_specific_to_eating_out_... NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_adult_log_of_max_window_overlap_with_a_chi... 50.0 0.0 \n", + "coef_adult_log_of_max_window_overlap_with_an_ad... 50.0 0.0 \n", + "coef_adult_more_automobiles_than_workers_adult_... 50.0 0.0 \n", + "coef_adult_more_automobiles_than_workers_mixed_... 50.0 0.0 \n", + "coef_adult_number_of_joint_tours_adult_only 50.0 0.0 \n", + "coef_adult_number_of_joint_tours_mixed 50.0 0.0 \n", + "coef_adult_number_of_other_adults_in_the_househ... 0.0 0.0 \n", + "coef_adult_number_of_other_adults_in_the_househ... 0.0 0.0 \n", + "coef_child_log_of_max_window_overlap_with_a_chi... 50.0 0.0 \n", + "coef_child_log_of_max_window_overlap_with_an_ad... 50.0 0.0 \n", + "coef_child_more_automobiles_than_workers_child_... 50.0 0.0 \n", + "coef_child_more_automobiles_than_workers_mixed_... 50.0 0.0 \n", + "coef_child_number_of_joint_tours_child_only 50.0 0.0 \n", + "coef_child_number_of_joint_tours_mixed 50.0 0.0 \n", + "coef_child_number_of_other_children_in_the_hous... 0.0 0.0 \n", + "coef_child_number_of_other_children_in_the_hous... 0.0 0.0 \n", + "coef_child_too_young_for_school_children_only_p... 50.0 0.0 \n", + "coef_child_too_young_for_school_mixed_party 50.0 0.0 \n", + "coef_child_too_young_for_school_specific_to_dis... 50.0 0.0 \n", + "coef_child_too_young_for_school_specific_to_eat... 50.0 0.0 \n", + "coef_driving_age_student_children_only_party 50.0 0.0 \n", + "coef_driving_age_student_mixed_party 50.0 0.0 \n", + "coef_driving_age_student_specific_to_discretion... 50.0 0.0 \n", + "coef_driving_age_student_specific_to_eating_out... 50.0 0.0 \n", + "coef_dummy_for_high_income_for_adult_in_adult_p... 50.0 0.0 \n", + "coef_dummy_for_high_income_for_adult_in_mixed_p... 50.0 0.0 \n", + "coef_dummy_for_high_income_for_child_in_childre... 50.0 0.0 \n", + "coef_dummy_for_high_income_for_child_in_mixed_p... 50.0 0.0 \n", + "coef_full_time_worker_mixed_party 50.0 0.0 \n", + "coef_full_time_worker_mixed_party_not 0.5 0.0 \n", + "coef_full_time_worker_specific_to_discretionary... 50.0 0.0 \n", + "coef_full_time_worker_specific_to_discretionary... 0.5 0.0 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 50.0 0.0 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 0.5 0.0 \n", + "coef_household_in_suburban_area_adult_adult_onl... 0.0 0.0 \n", + "coef_household_in_suburban_area_adult_mixed_party 50.0 0.0 \n", + "coef_household_in_suburban_area_child_child_onl... 0.0 0.0 \n", + "coef_household_in_suburban_area_child_mixed_party 0.0 0.0 \n", + "coef_household_in_urban_area_adult_adult_only_p... 0.0 0.0 \n", + "coef_household_in_urban_area_adult_mixed_party 50.0 0.0 \n", + "coef_household_in_urban_area_child_child_only_p... 50.0 0.0 \n", + "coef_household_in_urban_area_child_mixed_party 50.0 0.0 \n", + "coef_non_worker_adults_only_party 50.0 0.0 \n", + "coef_non_worker_mixed_party 50.0 0.0 \n", + "coef_non_worker_specific_to_discretionary_joint... 50.0 0.0 \n", + "coef_non_worker_specific_to_eating_out_joint_tours 50.0 0.0 \n", + "coef_part_time_worker_adults_only_party 50.0 0.0 \n", + "coef_part_time_worker_adults_only_party_not 0.5 0.0 \n", + "coef_part_time_worker_mixed_party 50.0 0.0 \n", + "coef_part_time_worker_specific_to_discretionary... 50.0 0.0 \n", + "coef_part_time_worker_specific_to_eating_out_jo... 50.0 0.0 \n", + "coef_pre_driving_age_student_children_only_party 50.0 0.0 \n", + "coef_pre_driving_age_student_mixed_party 50.0 0.0 \n", + "coef_pre_driving_age_student_specific_to_discre... 50.0 0.0 \n", + "coef_pre_driving_age_student_specific_to_eating... 50.0 0.0 \n", + "coef_unavailable -999.0 0.0 \n", + "coef_university_student_mixed_party 50.0 0.0 \n", + "coef_university_student_specific_to_discretiona... 0.0 0.0 \n", + "coef_university_student_specific_to_eating_out_... 50.0 0.0 \n", "\n", - " best \n", - "coef_adult_log_of_max_window_overlap_with_a_chi... 4.595737 \n", - "coef_adult_log_of_max_window_overlap_with_an_ad... 1.692145 \n", - "coef_adult_more_automobiles_than_workers_adult_... 0.311671 \n", - "coef_adult_more_automobiles_than_workers_mixed_... -1.827740 \n", - "coef_adult_number_of_joint_tours_adult_only -2.134054 \n", - "coef_adult_number_of_joint_tours_mixed -28.572220 \n", - "coef_adult_number_of_other_adults_in_the_househ... 0.000000 \n", - "coef_adult_number_of_other_adults_in_the_househ... 0.000000 \n", - "coef_child_log_of_max_window_overlap_with_a_chi... 1.275585 \n", - "coef_child_log_of_max_window_overlap_with_an_ad... 1.539267 \n", - "coef_child_more_automobiles_than_workers_child_... -0.394683 \n", - "coef_child_more_automobiles_than_workers_mixed_... -1.551914 \n", - "coef_child_number_of_joint_tours_child_only 4.977604 \n", - "coef_child_number_of_joint_tours_mixed 17.894892 \n", - "coef_child_number_of_other_children_in_the_hous... 0.000000 \n", - "coef_child_number_of_other_children_in_the_hous... 0.000000 \n", - "coef_child_too_young_for_school_children_only_p... -2.785631 \n", - "coef_child_too_young_for_school_mixed_party 2.069934 \n", - "coef_child_too_young_for_school_specific_to_dis... 0.128371 \n", - "coef_child_too_young_for_school_specific_to_eat... 0.658655 \n", - "coef_driving_age_student_children_only_party -1.822000 \n", - "coef_driving_age_student_mixed_party -28.998969 \n", - "coef_driving_age_student_specific_to_discretion... -0.667500 \n", - "coef_driving_age_student_specific_to_eating_out... 2.344000 \n", - "coef_dummy_for_high_income_for_adult_in_adult_p... 0.131783 \n", - "coef_dummy_for_high_income_for_adult_in_mixed_p... -0.378918 \n", - "coef_dummy_for_high_income_for_child_in_childre... -0.561900 \n", - "coef_dummy_for_high_income_for_child_in_mixed_p... -0.513571 \n", - "coef_full_time_worker_mixed_party -957.202749 \n", - "coef_full_time_worker_mixed_party_not 0.500000 \n", - "coef_full_time_worker_specific_to_discretionary... 0.439200 \n", - "coef_full_time_worker_specific_to_discretionary... 0.500000 \n", - "coef_full_time_worker_specific_to_eating_out_jo... 0.715700 \n", - "coef_full_time_worker_specific_to_eating_out_jo... 0.500000 \n", - "coef_household_in_suburban_area_adult_adult_onl... 0.000000 \n", - "coef_household_in_suburban_area_adult_mixed_party -0.060070 \n", - "coef_household_in_suburban_area_child_child_onl... 0.000000 \n", - "coef_household_in_suburban_area_child_mixed_party 0.000000 \n", - "coef_household_in_urban_area_adult_adult_only_p... 0.000000 \n", - "coef_household_in_urban_area_adult_mixed_party 978.504490 \n", - "coef_household_in_urban_area_child_child_only_p... 6.082784 \n", - "coef_household_in_urban_area_child_mixed_party -19.290030 \n", - "coef_non_worker_adults_only_party -4.080384 \n", - "coef_non_worker_mixed_party -125.288137 \n", - "coef_non_worker_specific_to_discretionary_joint... -0.183500 \n", - "coef_non_worker_specific_to_eating_out_joint_tours 0.161700 \n", - "coef_part_time_worker_adults_only_party -3.248302 \n", - "coef_part_time_worker_adults_only_party_not 0.500000 \n", - "coef_part_time_worker_mixed_party 2254.660944 \n", - "coef_part_time_worker_specific_to_discretionary... 0.285000 \n", - "coef_part_time_worker_specific_to_eating_out_jo... 2.188000 \n", - "coef_pre_driving_age_student_children_only_party 4.151085 \n", - "coef_pre_driving_age_student_mixed_party 2.007228 \n", - "coef_pre_driving_age_student_specific_to_discre... 0.662600 \n", - "coef_pre_driving_age_student_specific_to_eating... 1.391000 \n", - "coef_unavailable -999.000000 \n", - "coef_university_student_mixed_party -958.612314 \n", - "coef_university_student_specific_to_discretiona... 0.000000 \n", - "coef_university_student_specific_to_eating_out_... -0.820000 " + " holdfast \n", + "param_name \n", + "coef_adult_log_of_max_window_overlap_with_a_chi... 0 \n", + "coef_adult_log_of_max_window_overlap_with_an_ad... 0 \n", + "coef_adult_more_automobiles_than_workers_adult_... 0 \n", + "coef_adult_more_automobiles_than_workers_mixed_... 0 \n", + "coef_adult_number_of_joint_tours_adult_only 0 \n", + "coef_adult_number_of_joint_tours_mixed 0 \n", + "coef_adult_number_of_other_adults_in_the_househ... 1 \n", + "coef_adult_number_of_other_adults_in_the_househ... 1 \n", + "coef_child_log_of_max_window_overlap_with_a_chi... 0 \n", + "coef_child_log_of_max_window_overlap_with_an_ad... 0 \n", + "coef_child_more_automobiles_than_workers_child_... 0 \n", + "coef_child_more_automobiles_than_workers_mixed_... 0 \n", + "coef_child_number_of_joint_tours_child_only 0 \n", + "coef_child_number_of_joint_tours_mixed 0 \n", + "coef_child_number_of_other_children_in_the_hous... 1 \n", + "coef_child_number_of_other_children_in_the_hous... 1 \n", + "coef_child_too_young_for_school_children_only_p... 0 \n", + "coef_child_too_young_for_school_mixed_party 0 \n", + "coef_child_too_young_for_school_specific_to_dis... 0 \n", + "coef_child_too_young_for_school_specific_to_eat... 0 \n", + "coef_driving_age_student_children_only_party 0 \n", + "coef_driving_age_student_mixed_party 0 \n", + "coef_driving_age_student_specific_to_discretion... 0 \n", + "coef_driving_age_student_specific_to_eating_out... 0 \n", + "coef_dummy_for_high_income_for_adult_in_adult_p... 0 \n", + "coef_dummy_for_high_income_for_adult_in_mixed_p... 0 \n", + "coef_dummy_for_high_income_for_child_in_childre... 0 \n", + "coef_dummy_for_high_income_for_child_in_mixed_p... 0 \n", + "coef_full_time_worker_mixed_party 0 \n", + "coef_full_time_worker_mixed_party_not 1 \n", + "coef_full_time_worker_specific_to_discretionary... 0 \n", + "coef_full_time_worker_specific_to_discretionary... 1 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 0 \n", + "coef_full_time_worker_specific_to_eating_out_jo... 1 \n", + "coef_household_in_suburban_area_adult_adult_onl... 1 \n", + "coef_household_in_suburban_area_adult_mixed_party 0 \n", + "coef_household_in_suburban_area_child_child_onl... 1 \n", + "coef_household_in_suburban_area_child_mixed_party 1 \n", + "coef_household_in_urban_area_adult_adult_only_p... 1 \n", + "coef_household_in_urban_area_adult_mixed_party 0 \n", + "coef_household_in_urban_area_child_child_only_p... 0 \n", + "coef_household_in_urban_area_child_mixed_party 0 \n", + "coef_non_worker_adults_only_party 0 \n", + "coef_non_worker_mixed_party 0 \n", + "coef_non_worker_specific_to_discretionary_joint... 0 \n", + "coef_non_worker_specific_to_eating_out_joint_tours 0 \n", + "coef_part_time_worker_adults_only_party 0 \n", + "coef_part_time_worker_adults_only_party_not 1 \n", + "coef_part_time_worker_mixed_party 0 \n", + "coef_part_time_worker_specific_to_discretionary... 0 \n", + "coef_part_time_worker_specific_to_eating_out_jo... 0 \n", + "coef_pre_driving_age_student_children_only_party 0 \n", + "coef_pre_driving_age_student_mixed_party 0 \n", + "coef_pre_driving_age_student_specific_to_discre... 0 \n", + "coef_pre_driving_age_student_specific_to_eating... 0 \n", + "coef_unavailable 1 \n", + "coef_university_student_mixed_party 0 \n", + "coef_university_student_specific_to_discretiona... 1 \n", + "coef_university_student_specific_to_eating_out_... 0 " ] }, "metadata": {}, @@ -2240,16 +2250,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate()\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: RuntimeWarning: invalid value encountered in sqrt\n", - " model.estimate()\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -2265,242 +2267,242 @@ " \n", " \n", " coef_adult_log_of_max_window_overlap_with_a_child_mixed\n", - " 4.595737e+00\n", + " 2.125116\n", " \n", " \n", " coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party\n", - " 1.692145e+00\n", + " 1.280678\n", " \n", " \n", " coef_adult_more_automobiles_than_workers_adult_only_party\n", - " 3.116710e-01\n", + " 0.121220\n", " \n", " \n", " coef_adult_more_automobiles_than_workers_mixed_party\n", - " -1.827740e+00\n", + " -0.591966\n", " \n", " \n", " coef_adult_number_of_joint_tours_adult_only\n", - " -2.134054e+00\n", + " -0.794091\n", " \n", " \n", " coef_adult_number_of_joint_tours_mixed\n", - " -2.857222e+01\n", + " -1.417451\n", " \n", " \n", " coef_adult_number_of_other_adults_in_the_household_adults_only_party\n", - " -1.648956e-17\n", + " 0.000000\n", " \n", " \n", " coef_adult_number_of_other_adults_in_the_household_mixed_party\n", - " 7.224396e-18\n", + " 0.000000\n", " \n", " \n", " coef_child_log_of_max_window_overlap_with_a_child_child\n", - " 1.275585e+00\n", + " 1.296000\n", " \n", " \n", " coef_child_log_of_max_window_overlap_with_an_adult_mixed\n", - " 1.539267e+00\n", + " 1.538000\n", " \n", " \n", " coef_child_more_automobiles_than_workers_child_only_party\n", - " -3.946825e-01\n", + " -0.421400\n", " \n", " \n", " coef_child_more_automobiles_than_workers_mixed_party\n", - " -1.551914e+00\n", + " -0.366466\n", " \n", " \n", " coef_child_number_of_joint_tours_child_only\n", - " 4.977604e+00\n", + " 0.866122\n", " \n", " \n", " coef_child_number_of_joint_tours_mixed\n", - " 1.789489e+01\n", + " -0.261747\n", " \n", " \n", " coef_child_number_of_other_children_in_the_household_child_only_party\n", - " -2.228152e-18\n", + " 0.000000\n", " \n", " \n", " coef_child_number_of_other_children_in_the_household_mixed\n", - " -3.324715e-17\n", + " 0.000000\n", " \n", " \n", " coef_child_too_young_for_school_children_only_party\n", - " -2.785631e+00\n", + " -1.149596\n", " \n", " \n", " coef_child_too_young_for_school_mixed_party\n", - " 2.069934e+00\n", + " 0.197836\n", " \n", " \n", " coef_child_too_young_for_school_specific_to_discretionary_joint_tours\n", - " 1.283705e-01\n", + " 0.128400\n", " \n", " \n", " coef_child_too_young_for_school_specific_to_eating_out_joint_tours\n", - " 6.586549e-01\n", + " 0.658900\n", " \n", " \n", " coef_driving_age_student_children_only_party\n", - " -1.822000e+00\n", + " -2.120496\n", " \n", " \n", " coef_driving_age_student_mixed_party\n", - " -2.899897e+01\n", + " 0.322986\n", " \n", " \n", " coef_driving_age_student_specific_to_discretionary_joint_tours\n", - " -6.675000e-01\n", + " -0.667500\n", " \n", " \n", " coef_driving_age_student_specific_to_eating_out_joint_tours\n", - " 2.344000e+00\n", + " 2.344000\n", " \n", " \n", " coef_dummy_for_high_income_for_adult_in_adult_party\n", - " 1.317830e-01\n", + " -0.068202\n", " \n", " \n", " coef_dummy_for_high_income_for_adult_in_mixed_party\n", - " -3.789182e-01\n", + " 0.112930\n", " \n", " \n", " coef_dummy_for_high_income_for_child_in_children_party\n", - " -5.619000e-01\n", + " -0.211892\n", " \n", " \n", " coef_dummy_for_high_income_for_child_in_mixed_party\n", - " -5.135707e-01\n", + " -0.013740\n", " \n", " \n", " coef_full_time_worker_mixed_party\n", - " -9.572027e+02\n", + " -3.527365\n", " \n", " \n", " coef_full_time_worker_mixed_party_not\n", - " 5.000000e-01\n", + " 0.500000\n", " \n", " \n", " coef_full_time_worker_specific_to_discretionary_joint_tours\n", - " 4.392000e-01\n", + " 0.439200\n", " \n", " \n", " coef_full_time_worker_specific_to_discretionary_joint_tours_not\n", - " 5.000000e-01\n", + " 0.500000\n", " \n", " \n", " coef_full_time_worker_specific_to_eating_out_joint_tours\n", - " 7.157000e-01\n", + " 0.715700\n", " \n", " \n", " coef_full_time_worker_specific_to_eating_out_joint_tours_not\n", - " 5.000000e-01\n", + " 0.500000\n", " \n", " \n", " coef_household_in_suburban_area_adult_adult_only_party\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coef_household_in_suburban_area_adult_mixed_party\n", - " -6.007000e-02\n", + " 1.239881\n", " \n", " \n", " coef_household_in_suburban_area_child_child_only_party\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coef_household_in_suburban_area_child_mixed_party\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coef_household_in_urban_area_adult_adult_only_party\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coef_household_in_urban_area_adult_mixed_party\n", - " 9.785045e+02\n", + " 1.204039\n", " \n", " \n", " coef_household_in_urban_area_child_child_only_party\n", - " 6.082784e+00\n", + " -0.145369\n", " \n", " \n", " coef_household_in_urban_area_child_mixed_party\n", - " -1.929003e+01\n", + " -0.138857\n", " \n", " \n", " coef_non_worker_adults_only_party\n", - " -4.080384e+00\n", + " -3.161945\n", " \n", " \n", " coef_non_worker_mixed_party\n", - " -1.252881e+02\n", + " 1.059162\n", " \n", " \n", " coef_non_worker_specific_to_discretionary_joint_tours\n", - " -1.835000e-01\n", + " -0.183500\n", " \n", " \n", " coef_non_worker_specific_to_eating_out_joint_tours\n", - " 1.617000e-01\n", + " 0.161700\n", " \n", " \n", " coef_part_time_worker_adults_only_party\n", - " -3.248302e+00\n", + " -3.631928\n", " \n", " \n", " coef_part_time_worker_adults_only_party_not\n", - " 5.000000e-01\n", + " 0.500000\n", " \n", " \n", " coef_part_time_worker_mixed_party\n", - " 2.254661e+03\n", + " -0.447984\n", " \n", " \n", " coef_part_time_worker_specific_to_discretionary_joint_tours\n", - " 2.850000e-01\n", + " 0.285000\n", " \n", " \n", " coef_part_time_worker_specific_to_eating_out_joint_tours\n", - " 2.188000e+00\n", + " 2.188000\n", " \n", " \n", " coef_pre_driving_age_student_children_only_party\n", - " 4.151085e+00\n", + " 0.991812\n", " \n", " \n", " coef_pre_driving_age_student_mixed_party\n", - " 2.007228e+00\n", + " -0.073175\n", " \n", " \n", " coef_pre_driving_age_student_specific_to_discretionary_joint_tours\n", - " 6.626000e-01\n", + " 0.662600\n", " \n", " \n", " coef_pre_driving_age_student_specific_to_eating_out_joint_tours\n", - " 1.391000e+00\n", + " 1.391000\n", " \n", " \n", " coef_unavailable\n", - " -9.990000e+02\n", + " -999.000000\n", " \n", " \n", " coef_university_student_mixed_party\n", - " -9.586123e+02\n", + " -2.902637\n", " \n", " \n", " coef_university_student_specific_to_discretionary_joint_tours\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coef_university_student_specific_to_eating_out_joint_tours\n", - " -8.200000e-01\n", + " -0.820000\n", " \n", " \n", - "loglike-61.383086448632596d_loglike\n", + "
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coef_adult_log_of_max_window_overlap_with_a_child_mixed-4.096171e-040.000012
coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party2.436129e-050.000013
coef_adult_more_automobiles_than_workers_adult_only_party5.345149e-05-0.000165
coef_adult_more_automobiles_than_workers_mixed_party1.722466e-04-0.000051
coef_adult_number_of_joint_tours_adult_only1.769263e-04-0.000006
coef_adult_number_of_joint_tours_mixed3.181344e-04-0.000088
coef_adult_number_of_other_adults_in_the_household_adults_only_party0.000000e+000.000000
coef_adult_number_of_other_adults_in_the_household_mixed_party0.000000e+000.000000
coef_child_log_of_max_window_overlap_with_a_child_child0.000000e+000.000000
coef_child_log_of_max_window_overlap_with_an_adult_mixed0.000000e+000.000000
coef_child_more_automobiles_than_workers_child_only_party0.000000e+000.000000
coef_child_more_automobiles_than_workers_mixed_party1.722466e-04-0.000051
coef_child_number_of_joint_tours_child_only7.427872e-07-0.000119
coef_child_number_of_joint_tours_mixed-7.098060e-05-0.000047
coef_child_number_of_other_children_in_the_household_child_only_party0.000000e+000.000000
coef_child_number_of_other_children_in_the_household_mixed0.000000e+000.000000
coef_child_too_young_for_school_children_only_party0.000000e+000.000066
coef_child_too_young_for_school_mixed_party-4.267128e-050.000136
coef_child_too_young_for_school_specific_to_discretionary_joint_tours0.000000e+000.000000
coef_child_too_young_for_school_specific_to_eating_out_joint_tours0.000000e+000.000000
coef_driving_age_student_children_only_party0.000000e+000.000265
coef_driving_age_student_mixed_party-7.460379e-06-0.000060
coef_driving_age_student_specific_to_discretionary_joint_tours0.000000e+000.000000
coef_driving_age_student_specific_to_eating_out_joint_tours0.000000e+000.000000
coef_dummy_for_high_income_for_adult_in_adult_party-2.777090e-040.000017
coef_dummy_for_high_income_for_adult_in_mixed_party7.481814e-050.000026
coef_dummy_for_high_income_for_child_in_children_party0.000000e+000.000114
coef_dummy_for_high_income_for_child_in_mixed_party7.481814e-050.000026
coef_full_time_worker_mixed_party1.834492e-05-0.000152
coef_full_time_worker_mixed_party_not0.000000e+000.000000
coef_full_time_worker_specific_to_discretionary_joint_tours0.000000e+000.000000
coef_full_time_worker_specific_to_discretionary_joint_tours_not0.000000e+000.000000
coef_full_time_worker_specific_to_eating_out_joint_tours0.000000e+000.000000
coef_full_time_worker_specific_to_eating_out_joint_tours_not0.000000e+000.000000
coef_household_in_suburban_area_adult_adult_only_party0.000000e+000.000000
coef_household_in_suburban_area_adult_mixed_party0.000000e+000.000250
coef_household_in_suburban_area_child_child_only_party0.000000e+000.000000
coef_household_in_suburban_area_child_mixed_party0.000000e+000.000000
coef_household_in_urban_area_adult_adult_only_party0.000000e+000.000000
coef_household_in_urban_area_adult_mixed_party3.181344e-04-0.000179
coef_household_in_urban_area_child_child_only_party7.427872e-070.000131
coef_household_in_urban_area_child_mixed_party-6.353745e-05-0.000081
coef_non_worker_adults_only_party3.691375e-040.000062
coef_non_worker_mixed_party-1.839038e-700.000002
coef_non_worker_specific_to_discretionary_joint_tours0.000000e+000.000000
coef_non_worker_specific_to_eating_out_joint_tours0.000000e+000.000000
coef_part_time_worker_adults_only_party5.695694e-050.000239
coef_part_time_worker_adults_only_party_not0.000000e+000.000000
coef_part_time_worker_mixed_party0.000000e+00-0.000099
coef_part_time_worker_specific_to_discretionary_joint_tours0.000000e+000.000000
coef_part_time_worker_specific_to_eating_out_joint_tours0.000000e+000.000000
coef_pre_driving_age_student_children_only_party7.427872e-07-0.000108
coef_pre_driving_age_student_mixed_party-1.340579e-05-0.000009
coef_pre_driving_age_student_specific_to_discretionary_joint_tours0.000000e+000.000000
coef_pre_driving_age_student_specific_to_eating_out_joint_tours0.000000e+000.000000
coef_unavailable0.000000e+000.000000
coef_university_student_mixed_party2.997895e-040.000080
coef_university_student_specific_to_discretionary_joint_tours0.000000e+000.000000
coef_university_student_specific_to_eating_out_joint_tours0.000000e+000.000000
nit13nfev22njev13status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.868469method'bhhh|slsqp'n_cases304iteration_number112logloss0.2019180475283967" + "nit80nfev81njev80status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.325635method'slsqp'n_cases4500iteration_number80loglike-1240.495238444391" ], "text/plain": [ - "┣ x: coef_adult_log_of_max_window_overlap_with_a_child_mixed 4.595737e+00\n", - "┃ coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party 1.692145e+00\n", - "┃ coef_adult_more_automobiles_than_workers_adult_only_party 3.116710e-01\n", - "┃ coef_adult_more_automobiles_than_workers_mixed_party -1.827740e+00\n", - "┃ coef_adult_number_of_joint_tours_adult_only -2.134054e+00\n", - "┃ coef_adult_number_of_joint_tours_mixed -2.857222e+01\n", - "┃ coef_adult_number_of_other_adults_in_the_household_adults_only_party -1.648956e-17\n", - "┃ coef_adult_number_of_other_adults_in_the_household_mixed_party 7.224396e-18\n", - "┃ coef_child_log_of_max_window_overlap_with_a_child_child 1.275585e+00\n", - "┃ coef_child_log_of_max_window_overlap_with_an_adult_mixed 1.539267e+00\n", - "┃ coef_child_more_automobiles_than_workers_child_only_party -3.946825e-01\n", - "┃ coef_child_more_automobiles_than_workers_mixed_party -1.551914e+00\n", - "┃ coef_child_number_of_joint_tours_child_only 4.977604e+00\n", - "┃ coef_child_number_of_joint_tours_mixed 1.789489e+01\n", - "┃ coef_child_number_of_other_children_in_the_household_child_only_party -2.228152e-18\n", - "┃ coef_child_number_of_other_children_in_the_household_mixed -3.324715e-17\n", - "┃ coef_child_too_young_for_school_children_only_party -2.785631e+00\n", - "┃ coef_child_too_young_for_school_mixed_party 2.069934e+00\n", - "┃ coef_child_too_young_for_school_specific_to_discretionary_joint_tours 1.283705e-01\n", - "┃ coef_child_too_young_for_school_specific_to_eating_out_joint_tours 6.586549e-01\n", - "┃ coef_driving_age_student_children_only_party -1.822000e+00\n", - "┃ coef_driving_age_student_mixed_party -2.899897e+01\n", - "┃ coef_driving_age_student_specific_to_discretionary_joint_tours -6.675000e-01\n", - "┃ coef_driving_age_student_specific_to_eating_out_joint_tours 2.344000e+00\n", - "┃ coef_dummy_for_high_income_for_adult_in_adult_party 1.317830e-01\n", - "┃ coef_dummy_for_high_income_for_adult_in_mixed_party -3.789182e-01\n", - "┃ coef_dummy_for_high_income_for_child_in_children_party -5.619000e-01\n", - "┃ coef_dummy_for_high_income_for_child_in_mixed_party -5.135707e-01\n", - "┃ coef_full_time_worker_mixed_party -9.572027e+02\n", - "┃ coef_full_time_worker_mixed_party_not 5.000000e-01\n", - "┃ coef_full_time_worker_specific_to_discretionary_joint_tours 4.392000e-01\n", - "┃ coef_full_time_worker_specific_to_discretionary_joint_tours_not 5.000000e-01\n", - "┃ coef_full_time_worker_specific_to_eating_out_joint_tours 7.157000e-01\n", - "┃ coef_full_time_worker_specific_to_eating_out_joint_tours_not 5.000000e-01\n", - "┃ coef_household_in_suburban_area_adult_adult_only_party 0.000000e+00\n", - "┃ coef_household_in_suburban_area_adult_mixed_party -6.007000e-02\n", - "┃ coef_household_in_suburban_area_child_child_only_party 0.000000e+00\n", - "┃ coef_household_in_suburban_area_child_mixed_party 0.000000e+00\n", - "┃ coef_household_in_urban_area_adult_adult_only_party 0.000000e+00\n", - "┃ coef_household_in_urban_area_adult_mixed_party 9.785045e+02\n", - "┃ coef_household_in_urban_area_child_child_only_party 6.082784e+00\n", - "┃ coef_household_in_urban_area_child_mixed_party -1.929003e+01\n", - "┃ coef_non_worker_adults_only_party -4.080384e+00\n", - "┃ coef_non_worker_mixed_party -1.252881e+02\n", - "┃ coef_non_worker_specific_to_discretionary_joint_tours -1.835000e-01\n", - "┃ coef_non_worker_specific_to_eating_out_joint_tours 1.617000e-01\n", - "┃ coef_part_time_worker_adults_only_party -3.248302e+00\n", - "┃ coef_part_time_worker_adults_only_party_not 5.000000e-01\n", - "┃ coef_part_time_worker_mixed_party 2.254661e+03\n", - "┃ coef_part_time_worker_specific_to_discretionary_joint_tours 2.850000e-01\n", - "┃ coef_part_time_worker_specific_to_eating_out_joint_tours 2.188000e+00\n", - "┃ coef_pre_driving_age_student_children_only_party 4.151085e+00\n", - "┃ coef_pre_driving_age_student_mixed_party 2.007228e+00\n", - "┃ coef_pre_driving_age_student_specific_to_discretionary_joint_tours 6.626000e-01\n", - "┃ coef_pre_driving_age_student_specific_to_eating_out_joint_tours 1.391000e+00\n", - "┃ coef_unavailable -9.990000e+02\n", - "┃ coef_university_student_mixed_party -9.586123e+02\n", - "┃ coef_university_student_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_university_student_specific_to_eating_out_joint_tours -8.200000e-01\n", + "┣ x: coef_adult_log_of_max_window_overlap_with_a_child_mixed 2.125116\n", + "┃ coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party 1.280678\n", + "┃ coef_adult_more_automobiles_than_workers_adult_only_party 0.121220\n", + "┃ coef_adult_more_automobiles_than_workers_mixed_party -0.591966\n", + "┃ coef_adult_number_of_joint_tours_adult_only -0.794091\n", + "┃ coef_adult_number_of_joint_tours_mixed -1.417451\n", + "┃ coef_adult_number_of_other_adults_in_the_household_adults_only_party 0.000000\n", + "┃ coef_adult_number_of_other_adults_in_the_household_mixed_party 0.000000\n", + "┃ coef_child_log_of_max_window_overlap_with_a_child_child 1.296000\n", + "┃ coef_child_log_of_max_window_overlap_with_an_adult_mixed 1.538000\n", + "┃ coef_child_more_automobiles_than_workers_child_only_party -0.421400\n", + "┃ coef_child_more_automobiles_than_workers_mixed_party -0.366466\n", + "┃ coef_child_number_of_joint_tours_child_only 0.866122\n", + "┃ coef_child_number_of_joint_tours_mixed -0.261747\n", + "┃ coef_child_number_of_other_children_in_the_household_child_only_party 0.000000\n", + "┃ coef_child_number_of_other_children_in_the_household_mixed 0.000000\n", + "┃ coef_child_too_young_for_school_children_only_party -1.149596\n", + "┃ coef_child_too_young_for_school_mixed_party 0.197836\n", + "┃ coef_child_too_young_for_school_specific_to_discretionary_joint_tours 0.128400\n", + "┃ coef_child_too_young_for_school_specific_to_eating_out_joint_tours 0.658900\n", + "┃ coef_driving_age_student_children_only_party -2.120496\n", + "┃ coef_driving_age_student_mixed_party 0.322986\n", + "┃ coef_driving_age_student_specific_to_discretionary_joint_tours -0.667500\n", + "┃ coef_driving_age_student_specific_to_eating_out_joint_tours 2.344000\n", + "┃ coef_dummy_for_high_income_for_adult_in_adult_party -0.068202\n", + "┃ coef_dummy_for_high_income_for_adult_in_mixed_party 0.112930\n", + "┃ coef_dummy_for_high_income_for_child_in_children_party -0.211892\n", + "┃ coef_dummy_for_high_income_for_child_in_mixed_party -0.013740\n", + "┃ coef_full_time_worker_mixed_party -3.527365\n", + "┃ coef_full_time_worker_mixed_party_not 0.500000\n", + "┃ coef_full_time_worker_specific_to_discretionary_joint_tours 0.439200\n", + "┃ coef_full_time_worker_specific_to_discretionary_joint_tours_not 0.500000\n", + "┃ coef_full_time_worker_specific_to_eating_out_joint_tours 0.715700\n", + "┃ coef_full_time_worker_specific_to_eating_out_joint_tours_not 0.500000\n", + "┃ coef_household_in_suburban_area_adult_adult_only_party 0.000000\n", + "┃ coef_household_in_suburban_area_adult_mixed_party 1.239881\n", + "┃ coef_household_in_suburban_area_child_child_only_party 0.000000\n", + "┃ coef_household_in_suburban_area_child_mixed_party 0.000000\n", + "┃ coef_household_in_urban_area_adult_adult_only_party 0.000000\n", + "┃ coef_household_in_urban_area_adult_mixed_party 1.204039\n", + "┃ coef_household_in_urban_area_child_child_only_party -0.145369\n", + "┃ coef_household_in_urban_area_child_mixed_party -0.138857\n", + "┃ coef_non_worker_adults_only_party -3.161945\n", + "┃ coef_non_worker_mixed_party 1.059162\n", + "┃ coef_non_worker_specific_to_discretionary_joint_tours -0.183500\n", + "┃ coef_non_worker_specific_to_eating_out_joint_tours 0.161700\n", + "┃ coef_part_time_worker_adults_only_party -3.631928\n", + "┃ coef_part_time_worker_adults_only_party_not 0.500000\n", + "┃ coef_part_time_worker_mixed_party -0.447984\n", + "┃ coef_part_time_worker_specific_to_discretionary_joint_tours 0.285000\n", + "┃ coef_part_time_worker_specific_to_eating_out_joint_tours 2.188000\n", + "┃ coef_pre_driving_age_student_children_only_party 0.991812\n", + "┃ coef_pre_driving_age_student_mixed_party -0.073175\n", + "┃ coef_pre_driving_age_student_specific_to_discretionary_joint_tours 0.662600\n", + "┃ coef_pre_driving_age_student_specific_to_eating_out_joint_tours 1.391000\n", + "┃ coef_unavailable -999.000000\n", + "┃ coef_university_student_mixed_party -2.902637\n", + "┃ coef_university_student_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_university_student_specific_to_eating_out_joint_tours -0.820000\n", "┃ dtype: float64\n", - "┣ loglike: -61.383086448632596\n", - "┣ d_loglike: coef_adult_log_of_max_window_overlap_with_a_child_mixed -4.096171e-04\n", - "┃ coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party 2.436129e-05\n", - "┃ coef_adult_more_automobiles_than_workers_adult_only_party 5.345149e-05\n", - "┃ coef_adult_more_automobiles_than_workers_mixed_party 1.722466e-04\n", - "┃ coef_adult_number_of_joint_tours_adult_only 1.769263e-04\n", - "┃ coef_adult_number_of_joint_tours_mixed 3.181344e-04\n", - "┃ coef_adult_number_of_other_adults_in_the_household_adults_only_party 0.000000e+00\n", - "┃ coef_adult_number_of_other_adults_in_the_household_mixed_party 0.000000e+00\n", - "┃ coef_child_log_of_max_window_overlap_with_a_child_child 0.000000e+00\n", - "┃ coef_child_log_of_max_window_overlap_with_an_adult_mixed 0.000000e+00\n", - "┃ coef_child_more_automobiles_than_workers_child_only_party 0.000000e+00\n", - "┃ coef_child_more_automobiles_than_workers_mixed_party 1.722466e-04\n", - "┃ coef_child_number_of_joint_tours_child_only 7.427872e-07\n", - "┃ coef_child_number_of_joint_tours_mixed -7.098060e-05\n", - "┃ coef_child_number_of_other_children_in_the_household_child_only_party 0.000000e+00\n", - "┃ coef_child_number_of_other_children_in_the_household_mixed 0.000000e+00\n", - "┃ coef_child_too_young_for_school_children_only_party 0.000000e+00\n", - "┃ coef_child_too_young_for_school_mixed_party -4.267128e-05\n", - "┃ coef_child_too_young_for_school_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_child_too_young_for_school_specific_to_eating_out_joint_tours 0.000000e+00\n", - "┃ coef_driving_age_student_children_only_party 0.000000e+00\n", - "┃ coef_driving_age_student_mixed_party -7.460379e-06\n", - "┃ coef_driving_age_student_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_driving_age_student_specific_to_eating_out_joint_tours 0.000000e+00\n", - "┃ coef_dummy_for_high_income_for_adult_in_adult_party -2.777090e-04\n", - "┃ coef_dummy_for_high_income_for_adult_in_mixed_party 7.481814e-05\n", - "┃ coef_dummy_for_high_income_for_child_in_children_party 0.000000e+00\n", - "┃ coef_dummy_for_high_income_for_child_in_mixed_party 7.481814e-05\n", - "┃ coef_full_time_worker_mixed_party 1.834492e-05\n", - "┃ coef_full_time_worker_mixed_party_not 0.000000e+00\n", - "┃ coef_full_time_worker_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_full_time_worker_specific_to_discretionary_joint_tours_not 0.000000e+00\n", - "┃ coef_full_time_worker_specific_to_eating_out_joint_tours 0.000000e+00\n", - "┃ coef_full_time_worker_specific_to_eating_out_joint_tours_not 0.000000e+00\n", - "┃ coef_household_in_suburban_area_adult_adult_only_party 0.000000e+00\n", - "┃ coef_household_in_suburban_area_adult_mixed_party 0.000000e+00\n", - "┃ coef_household_in_suburban_area_child_child_only_party 0.000000e+00\n", - "┃ coef_household_in_suburban_area_child_mixed_party 0.000000e+00\n", - "┃ coef_household_in_urban_area_adult_adult_only_party 0.000000e+00\n", - "┃ coef_household_in_urban_area_adult_mixed_party 3.181344e-04\n", - "┃ coef_household_in_urban_area_child_child_only_party 7.427872e-07\n", - "┃ coef_household_in_urban_area_child_mixed_party -6.353745e-05\n", - "┃ coef_non_worker_adults_only_party 3.691375e-04\n", - "┃ coef_non_worker_mixed_party -1.839038e-70\n", - "┃ coef_non_worker_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_non_worker_specific_to_eating_out_joint_tours 0.000000e+00\n", - "┃ coef_part_time_worker_adults_only_party 5.695694e-05\n", - "┃ coef_part_time_worker_adults_only_party_not 0.000000e+00\n", - "┃ coef_part_time_worker_mixed_party 0.000000e+00\n", - "┃ coef_part_time_worker_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_part_time_worker_specific_to_eating_out_joint_tours 0.000000e+00\n", - "┃ coef_pre_driving_age_student_children_only_party 7.427872e-07\n", - "┃ coef_pre_driving_age_student_mixed_party -1.340579e-05\n", - "┃ coef_pre_driving_age_student_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_pre_driving_age_student_specific_to_eating_out_joint_tours 0.000000e+00\n", - "┃ coef_unavailable 0.000000e+00\n", - "┃ coef_university_student_mixed_party 2.997895e-04\n", - "┃ coef_university_student_specific_to_discretionary_joint_tours 0.000000e+00\n", - "┃ coef_university_student_specific_to_eating_out_joint_tours 0.000000e+00\n", + "┣ logloss: 0.275665608543198\n", + "┣ d_logloss: coef_adult_log_of_max_window_overlap_with_a_child_mixed 0.000012\n", + "┃ coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party 0.000013\n", + "┃ coef_adult_more_automobiles_than_workers_adult_only_party -0.000165\n", + "┃ coef_adult_more_automobiles_than_workers_mixed_party -0.000051\n", + "┃ coef_adult_number_of_joint_tours_adult_only -0.000006\n", + "┃ coef_adult_number_of_joint_tours_mixed -0.000088\n", + "┃ coef_adult_number_of_other_adults_in_the_household_adults_only_party 0.000000\n", + "┃ coef_adult_number_of_other_adults_in_the_household_mixed_party 0.000000\n", + "┃ coef_child_log_of_max_window_overlap_with_a_child_child 0.000000\n", + "┃ coef_child_log_of_max_window_overlap_with_an_adult_mixed 0.000000\n", + "┃ coef_child_more_automobiles_than_workers_child_only_party 0.000000\n", + "┃ coef_child_more_automobiles_than_workers_mixed_party -0.000051\n", + "┃ coef_child_number_of_joint_tours_child_only -0.000119\n", + "┃ coef_child_number_of_joint_tours_mixed -0.000047\n", + "┃ coef_child_number_of_other_children_in_the_household_child_only_party 0.000000\n", + "┃ coef_child_number_of_other_children_in_the_household_mixed 0.000000\n", + "┃ coef_child_too_young_for_school_children_only_party 0.000066\n", + "┃ coef_child_too_young_for_school_mixed_party 0.000136\n", + "┃ coef_child_too_young_for_school_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_child_too_young_for_school_specific_to_eating_out_joint_tours 0.000000\n", + "┃ coef_driving_age_student_children_only_party 0.000265\n", + "┃ coef_driving_age_student_mixed_party -0.000060\n", + "┃ coef_driving_age_student_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_driving_age_student_specific_to_eating_out_joint_tours 0.000000\n", + "┃ coef_dummy_for_high_income_for_adult_in_adult_party 0.000017\n", + "┃ coef_dummy_for_high_income_for_adult_in_mixed_party 0.000026\n", + "┃ coef_dummy_for_high_income_for_child_in_children_party 0.000114\n", + "┃ coef_dummy_for_high_income_for_child_in_mixed_party 0.000026\n", + "┃ coef_full_time_worker_mixed_party -0.000152\n", + "┃ coef_full_time_worker_mixed_party_not 0.000000\n", + "┃ coef_full_time_worker_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_full_time_worker_specific_to_discretionary_joint_tours_not 0.000000\n", + "┃ coef_full_time_worker_specific_to_eating_out_joint_tours 0.000000\n", + "┃ coef_full_time_worker_specific_to_eating_out_joint_tours_not 0.000000\n", + "┃ coef_household_in_suburban_area_adult_adult_only_party 0.000000\n", + "┃ coef_household_in_suburban_area_adult_mixed_party 0.000250\n", + "┃ coef_household_in_suburban_area_child_child_only_party 0.000000\n", + "┃ coef_household_in_suburban_area_child_mixed_party 0.000000\n", + "┃ coef_household_in_urban_area_adult_adult_only_party 0.000000\n", + "┃ coef_household_in_urban_area_adult_mixed_party -0.000179\n", + "┃ coef_household_in_urban_area_child_child_only_party 0.000131\n", + "┃ coef_household_in_urban_area_child_mixed_party -0.000081\n", + "┃ coef_non_worker_adults_only_party 0.000062\n", + "┃ coef_non_worker_mixed_party 0.000002\n", + "┃ coef_non_worker_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_non_worker_specific_to_eating_out_joint_tours 0.000000\n", + "┃ coef_part_time_worker_adults_only_party 0.000239\n", + "┃ coef_part_time_worker_adults_only_party_not 0.000000\n", + "┃ coef_part_time_worker_mixed_party -0.000099\n", + "┃ coef_part_time_worker_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_part_time_worker_specific_to_eating_out_joint_tours 0.000000\n", + "┃ coef_pre_driving_age_student_children_only_party -0.000108\n", + "┃ coef_pre_driving_age_student_mixed_party -0.000009\n", + "┃ coef_pre_driving_age_student_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_pre_driving_age_student_specific_to_eating_out_joint_tours 0.000000\n", + "┃ coef_unavailable 0.000000\n", + "┃ coef_university_student_mixed_party 0.000080\n", + "┃ coef_university_student_specific_to_discretionary_joint_tours 0.000000\n", + "┃ coef_university_student_specific_to_eating_out_joint_tours 0.000000\n", "┃ dtype: float64\n", - "┣ nit: 13\n", - "┣ nfev: 22\n", - "┣ njev: 13\n", + "┣ nit: 80\n", + "┣ nfev: 81\n", + "┣ njev: 80\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(microseconds=868469)\n", - "┣ method: 'bhhh|slsqp'\n", - "┣ n_cases: 304\n", - "┣ iteration_number: 112\n", - "┣ logloss: 0.2019180475283967" + "┣ elapsed_time: datetime.timedelta(microseconds=325635)\n", + "┣ method: 'slsqp'\n", + "┣ n_cases: 4500\n", + "┣ iteration_number: 80\n", + "┣ loglike: -1240.495238444391" ] }, "execution_count": 7, @@ -2926,30 +2928,30 @@ " \n", " 1\n", " participate\n", - " 228.0\n", - " 304.0\n", + " 3317\n", + " 4500\n", " \n", " \n", " 2\n", " not_participate\n", - " 76.0\n", - " 304.0\n", + " 1183\n", + " 4500\n", " \n", " \n", " < Total All Alternatives >\n", " \n", - " 304.0\n", - " \n", + " 4500\n", + " <NA>\n", " \n", " \n", "\n", "" ], "text/plain": [ - " name chosen available\n", - "1 participate 228.0 304.0\n", - "2 not_participate 76.0 304.0\n", - "< Total All Alternatives > 304.0 " + " name chosen available\n", + "1 participate 3317 4500\n", + "2 not_participate 1183 4500\n", + "< Total All Alternatives > 4500 " ] }, "execution_count": 8, @@ -2958,7 +2960,7 @@ } ], "source": [ - "model.dataframes.choice_avail_summary()" + "model.choice_avail_summary()" ] }, { @@ -2976,610 +2978,577 @@ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_adult_log_of_max_window_overlap_with_a_child_mixed 4.60 1.53 3.00** NA 0.00
coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party 1.69 0.833 2.03* NA 0.00
coef_adult_more_automobiles_than_workers_adult_only_party 0.312 1.08 0.29 NA 0.00
coef_adult_more_automobiles_than_workers_mixed_party-1.83 0.672-2.72** NA 0.00
coef_adult_number_of_joint_tours_adult_only-2.13 2.00-1.07 NA 0.00
coef_adult_number_of_joint_tours_mixed-28.6 NA NA[***] 499.96 0.00
coef_adult_number_of_other_adults_in_the_household_adults_only_party 0.00 NA NA NA 0.00fixed value
coef_adult_number_of_other_adults_in_the_household_mixed_party 0.00 NA NA NA 0.00fixed value
coef_child_log_of_max_window_overlap_with_a_child_child 1.28 0.00450 283.43*** NA 0.00
coef_child_log_of_max_window_overlap_with_an_adult_mixed 1.54 0.00635 242.24*** NA 0.00
coef_child_more_automobiles_than_workers_child_only_party-0.395 0.0443-8.91*** NA 0.00
coef_child_more_automobiles_than_workers_mixed_party-1.55 0.672-2.31* NA 0.00
coef_child_number_of_joint_tours_child_only 4.98 388. 0.01 NA 0.00
coef_child_number_of_joint_tours_mixed 17.9 3.48e+03 0.01 NA 0.00
coef_child_number_of_other_children_in_the_household_child_only_party 0.00 NA NA NA 0.00fixed value
coef_child_number_of_other_children_in_the_household_mixed 0.00 NA NA NA 0.00fixed value
coef_child_too_young_for_school_children_only_party-2.79 0.000873-BIG*** NA 0.00
coef_child_too_young_for_school_mixed_party 2.07 1.97e+04 0.00 NA 0.00
coef_child_too_young_for_school_specific_to_discretionary_joint_tours 0.128 0.000270 475.53*** NA 0.00
coef_child_too_young_for_school_specific_to_eating_out_joint_tours 0.659 3.84e-10 BIG*** NA 0.00
coef_driving_age_student_children_only_party-1.82 1.84e-10-BIG*** NA 0.00
coef_driving_age_student_mixed_party-29.0 2.06e+04-0.00 NA 0.00
coef_driving_age_student_specific_to_discretionary_joint_tours-0.667 5.70e-13-BIG*** NA 0.00
coef_driving_age_student_specific_to_eating_out_joint_tours 2.34 0.00 NA[] 0.00 0.00
coef_dummy_for_high_income_for_adult_in_adult_party 0.132 0.897 0.15 NA 0.00
coef_dummy_for_high_income_for_adult_in_mixed_party-0.379 0.321-1.18 NA 0.00
coef_dummy_for_high_income_for_child_in_children_party-0.562 0.00 NA[] 0.00 0.00
coef_dummy_for_high_income_for_child_in_mixed_party-0.514 0.321-1.60 NA 0.00
coef_full_time_worker_mixed_party-957. NA NA[***] NA 0.00
coef_full_time_worker_mixed_party_not 0.500 NA NA NA 0.00fixed value
coef_full_time_worker_specific_to_discretionary_joint_tours 0.439 0.00 NA[] 0.00 0.00
coef_full_time_worker_specific_to_discretionary_joint_tours_not 0.500 NA NA NA 0.00fixed value
coef_full_time_worker_specific_to_eating_out_joint_tours 0.716 0.00 NA[] 0.00 0.00
coef_full_time_worker_specific_to_eating_out_joint_tours_not 0.500 NA NA NA 0.00fixed value
coef_household_in_suburban_area_adult_adult_only_party 0.00 NA NA NA 0.00fixed value
coef_household_in_suburban_area_adult_mixed_party-0.0601 0.00 NA[] 0.00 0.00
coef_household_in_suburban_area_child_child_only_party 0.00 NA NA NA 0.00fixed value
coef_household_in_suburban_area_child_mixed_party 0.00 NA NA NA 0.00fixed value
coef_household_in_urban_area_adult_adult_only_party 0.00 NA NA NA 0.00fixed value
coef_household_in_urban_area_adult_mixed_party 979. 3.97e+03 0.25 NA 0.00
coef_household_in_urban_area_child_child_only_party 6.08 388. 0.02 NA 0.00
coef_household_in_urban_area_child_mixed_party-19.3 1.94e+04-0.00 NA 0.00
coef_non_worker_adults_only_party-4.08 1.45-2.81** NA 0.00
coef_non_worker_mixed_party-125. 0.00 NA[] 0.00 0.00
coef_non_worker_specific_to_discretionary_joint_tours-0.183 0.00 NA[] 0.00 0.00
coef_non_worker_specific_to_eating_out_joint_tours 0.162 0.00 NA[] 0.00 0.00
coef_part_time_worker_adults_only_party-3.25 1.03-3.14** NA 0.00
coef_part_time_worker_adults_only_party_not 0.500 NA NA NA 0.00fixed value
coef_part_time_worker_mixed_party 2.25e+03 0.00 NA[] 0.00 0.00
coef_part_time_worker_specific_to_discretionary_joint_tours 0.285 0.00 NA[] 0.00 0.00
coef_part_time_worker_specific_to_eating_out_joint_tours 2.19 0.00 NA[] 0.00 0.00
coef_pre_driving_age_student_children_only_party 4.15 388. 0.01 NA 0.00
coef_pre_driving_age_student_mixed_party 2.01 1.97e+04 0.00 NA 0.00
coef_pre_driving_age_student_specific_to_discretionary_joint_tours 0.663 0.00 NA[] 0.00 0.00
coef_pre_driving_age_student_specific_to_eating_out_joint_tours 1.39 0.00 NA[] 0.00 0.00
coef_unavailable-999. NA NA NA 0.00fixed value
coef_university_student_mixed_party-959. NA NA[***] NA 0.00
coef_university_student_specific_to_discretionary_joint_tours 0.00 NA NA NA 0.00fixed value
coef_university_student_specific_to_eating_out_joint_tours-0.820 0.00 NA[] 0.00 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_adult_log_of_max_window_overlap_with_a_child_mixed 2.13 0.234 9.09*** 0.00
coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party 1.28 0.229 5.59*** 0.00
coef_adult_more_automobiles_than_workers_adult_only_party 0.121 0.233 0.52 0.00
coef_adult_more_automobiles_than_workers_mixed_party-0.592 0.0889-6.66*** 0.00
coef_adult_number_of_joint_tours_adult_only-0.794 0.488-1.63 0.00
coef_adult_number_of_joint_tours_mixed-1.42 0.525-2.70** 0.00
coef_adult_number_of_other_adults_in_the_household_adults_only_party 0.00 0.00 NA 0.00fixed value
coef_adult_number_of_other_adults_in_the_household_mixed_party 0.00 0.00 NA 0.00fixed value
coef_child_log_of_max_window_overlap_with_a_child_child 1.30 NA NA 0.00
coef_child_log_of_max_window_overlap_with_an_adult_mixed 1.54 NA NA 0.00
coef_child_more_automobiles_than_workers_child_only_party-0.421 6.40e-14-BIG*** 0.00
coef_child_more_automobiles_than_workers_mixed_party-0.366 0.0889-4.12*** 0.00
coef_child_number_of_joint_tours_child_only 0.866 1.10 0.79 0.00
coef_child_number_of_joint_tours_mixed-0.262 0.497-0.53 0.00
coef_child_number_of_other_children_in_the_household_child_only_party 0.00 0.00 NA 0.00fixed value
coef_child_number_of_other_children_in_the_household_mixed 0.00 0.00 NA 0.00fixed value
coef_child_too_young_for_school_children_only_party-1.15 1.19-0.97 0.00
coef_child_too_young_for_school_mixed_party 0.198 0.525 0.38 0.00
coef_child_too_young_for_school_specific_to_discretionary_joint_tours 0.128 NA NA 0.00
coef_child_too_young_for_school_specific_to_eating_out_joint_tours 0.659 1.08e-14 BIG*** 0.00
coef_driving_age_student_children_only_party-2.12 1.40-1.51 0.00
coef_driving_age_student_mixed_party 0.323 0.613 0.53 0.00
coef_driving_age_student_specific_to_discretionary_joint_tours-0.668 4.92e-15-BIG*** 0.00
coef_driving_age_student_specific_to_eating_out_joint_tours 2.34 NA NA 0.00
coef_dummy_for_high_income_for_adult_in_adult_party-0.0682 0.231-0.29 0.00
coef_dummy_for_high_income_for_adult_in_mixed_party 0.113 0.0599 1.89 0.00
coef_dummy_for_high_income_for_child_in_children_party-0.212 0.453-0.47 0.00
coef_dummy_for_high_income_for_child_in_mixed_party-0.0137 0.0599-0.23 0.00
coef_full_time_worker_mixed_party-3.53 0.837-4.21*** 0.00
coef_full_time_worker_mixed_party_not 0.500 0.00 NA 0.00fixed value
coef_full_time_worker_specific_to_discretionary_joint_tours 0.439 7.59e-16 BIG*** 0.00
coef_full_time_worker_specific_to_discretionary_joint_tours_not 0.500 0.00 NA 0.00fixed value
coef_full_time_worker_specific_to_eating_out_joint_tours 0.716 1.68e-16 BIG*** 0.00
coef_full_time_worker_specific_to_eating_out_joint_tours_not 0.500 0.00 NA 0.00fixed value
coef_household_in_suburban_area_adult_adult_only_party 0.00 0.00 NA 0.00fixed value
coef_household_in_suburban_area_adult_mixed_party 1.24 1.10 1.13 0.00
coef_household_in_suburban_area_child_child_only_party 0.00 0.00 NA 0.00fixed value
coef_household_in_suburban_area_child_mixed_party 0.00 0.00 NA 0.00fixed value
coef_household_in_urban_area_adult_adult_only_party 0.00 0.00 NA 0.00fixed value
coef_household_in_urban_area_adult_mixed_party 1.20 1.11 1.09 0.00
coef_household_in_urban_area_child_child_only_party-0.145 0.520-0.28 0.00
coef_household_in_urban_area_child_mixed_party-0.139 0.189-0.74 0.00
coef_non_worker_adults_only_party-3.16 0.310-10.20*** 0.00
coef_non_worker_mixed_party 1.06 1.05 1.01 0.00
coef_non_worker_specific_to_discretionary_joint_tours-0.184 0.00 NA 0.00
coef_non_worker_specific_to_eating_out_joint_tours 0.162 0.00 NA 0.00
coef_part_time_worker_adults_only_party-3.63 0.389-9.33*** 0.00
coef_part_time_worker_adults_only_party_not 0.500 0.00 NA 0.00fixed value
coef_part_time_worker_mixed_party-0.448 0.942-0.48 0.00
coef_part_time_worker_specific_to_discretionary_joint_tours 0.285 0.00 NA 0.00
coef_part_time_worker_specific_to_eating_out_joint_tours 2.19 0.00 NA 0.00
coef_pre_driving_age_student_children_only_party 0.992 1.18 0.84 0.00
coef_pre_driving_age_student_mixed_party-0.0732 0.527-0.14 0.00
coef_pre_driving_age_student_specific_to_discretionary_joint_tours 0.663 0.00 NA 0.00
coef_pre_driving_age_student_specific_to_eating_out_joint_tours 1.39 0.00 NA 0.00
coef_unavailable-999. 0.00 NA 0.00fixed value
coef_university_student_mixed_party-2.90 0.860-3.38*** 0.00
coef_university_student_specific_to_discretionary_joint_tours 0.00 0.00 NA 0.00fixed value
coef_university_student_specific_to_eating_out_joint_tours-0.820 0.00 NA 0.00
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -3626,18 +3595,7 @@ "cell_type": "code", "execution_count": 11, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -3695,7 +3653,7 @@ " \n", " 1\n", " coef_full_time_worker_mixed_party\n", - " -957.202749\n", + " -3.527365\n", " F\n", " \n", " \n", @@ -3707,7 +3665,7 @@ " \n", " 3\n", " coef_part_time_worker_adults_only_party\n", - " -3.248302\n", + " -3.631928\n", " F\n", " \n", " \n", @@ -3719,61 +3677,61 @@ " \n", " 5\n", " coef_part_time_worker_mixed_party\n", - " 2254.660944\n", + " -0.447984\n", " F\n", " \n", " \n", " 6\n", " coef_university_student_mixed_party\n", - " -958.612314\n", + " -2.902637\n", " F\n", " \n", " \n", " 7\n", " coef_non_worker_adults_only_party\n", - " -4.080384\n", + " -3.161945\n", " F\n", " \n", " \n", " 8\n", " coef_non_worker_mixed_party\n", - " -125.288137\n", + " 1.059162\n", " F\n", " \n", " \n", " 9\n", " coef_child_too_young_for_school_children_only_...\n", - " -2.785631\n", + " -1.149596\n", " F\n", " \n", " \n", " 10\n", " coef_child_too_young_for_school_mixed_party\n", - " 2.069934\n", + " 0.197836\n", " F\n", " \n", " \n", " 11\n", " coef_pre_driving_age_student_children_only_party\n", - " 4.151085\n", + " 0.991812\n", " F\n", " \n", " \n", " 12\n", " coef_pre_driving_age_student_mixed_party\n", - " 2.007228\n", + " -0.073175\n", " F\n", " \n", " \n", " 13\n", " coef_driving_age_student_children_only_party\n", - " -1.822000\n", + " -2.120496\n", " F\n", " \n", " \n", " 14\n", " coef_driving_age_student_mixed_party\n", - " -28.998969\n", + " 0.322986\n", " F\n", " \n", " \n", @@ -3839,13 +3797,13 @@ " \n", " 25\n", " coef_child_too_young_for_school_specific_to_ea...\n", - " 0.658655\n", + " 0.658900\n", " F\n", " \n", " \n", " 26\n", " coef_child_too_young_for_school_specific_to_di...\n", - " 0.128371\n", + " 0.128400\n", " F\n", " \n", " \n", @@ -3881,19 +3839,19 @@ " \n", " 32\n", " coef_household_in_urban_area_adult_mixed_party\n", - " 978.504490\n", + " 1.204039\n", " F\n", " \n", " \n", " 33\n", " coef_household_in_urban_area_child_child_only_...\n", - " 6.082784\n", + " -0.145369\n", " F\n", " \n", " \n", " 34\n", " coef_household_in_urban_area_child_mixed_party\n", - " -19.290030\n", + " -0.138857\n", " F\n", " \n", " \n", @@ -3905,7 +3863,7 @@ " \n", " 36\n", " coef_household_in_suburban_area_adult_mixed_party\n", - " -0.060070\n", + " 1.239881\n", " F\n", " \n", " \n", @@ -3923,73 +3881,73 @@ " \n", " 39\n", " coef_adult_more_automobiles_than_workers_adult...\n", - " 0.311671\n", + " 0.121220\n", " F\n", " \n", " \n", " 40\n", " coef_adult_more_automobiles_than_workers_mixed...\n", - " -1.827740\n", + " -0.591966\n", " F\n", " \n", " \n", " 41\n", " coef_child_more_automobiles_than_workers_child...\n", - " -0.394683\n", + " -0.421400\n", " F\n", " \n", " \n", " 42\n", " coef_child_more_automobiles_than_workers_mixed...\n", - " -1.551914\n", + " -0.366466\n", " F\n", " \n", " \n", " 43\n", " coef_dummy_for_high_income_for_adult_in_adult_...\n", - " 0.131783\n", + " -0.068202\n", " F\n", " \n", " \n", " 44\n", " coef_dummy_for_high_income_for_adult_in_mixed_...\n", - " -0.378918\n", + " 0.112930\n", " F\n", " \n", " \n", " 45\n", " coef_dummy_for_high_income_for_child_in_childr...\n", - " -0.561900\n", + " -0.211892\n", " F\n", " \n", " \n", " 46\n", " coef_dummy_for_high_income_for_child_in_mixed_...\n", - " -0.513571\n", + " -0.013740\n", " F\n", " \n", " \n", " 47\n", " coef_adult_number_of_joint_tours_adult_only\n", - " -2.134054\n", + " -0.794091\n", " F\n", " \n", " \n", " 48\n", " coef_adult_number_of_joint_tours_mixed\n", - " -28.572220\n", + " -1.417451\n", " F\n", " \n", " \n", " 49\n", " coef_child_number_of_joint_tours_child_only\n", - " 4.977604\n", + " 0.866122\n", " F\n", " \n", " \n", " 50\n", " coef_child_number_of_joint_tours_mixed\n", - " 17.894892\n", + " -0.261747\n", " F\n", " \n", " \n", @@ -4019,25 +3977,25 @@ " \n", " 55\n", " coef_adult_log_of_max_window_overlap_with_an_a...\n", - " 1.692145\n", + " 1.280678\n", " F\n", " \n", " \n", " 56\n", " coef_adult_log_of_max_window_overlap_with_a_ch...\n", - " 4.595737\n", + " 2.125116\n", " F\n", " \n", " \n", " 57\n", " coef_child_log_of_max_window_overlap_with_an_a...\n", - " 1.539267\n", + " 1.538000\n", " F\n", " \n", " \n", " 58\n", " coef_child_log_of_max_window_overlap_with_a_ch...\n", - " 1.275585\n", + " 1.296000\n", " F\n", " \n", " \n", @@ -4045,66 +4003,66 @@ "" ], "text/plain": [ - " coefficient_name value constrain\n", - "0 coef_unavailable -999.000000 T\n", - "1 coef_full_time_worker_mixed_party -957.202749 F\n", - "2 coef_full_time_worker_mixed_party_not 0.500000 T\n", - "3 coef_part_time_worker_adults_only_party -3.248302 F\n", - "4 coef_part_time_worker_adults_only_party_not 0.500000 T\n", - "5 coef_part_time_worker_mixed_party 2254.660944 F\n", - "6 coef_university_student_mixed_party -958.612314 F\n", - "7 coef_non_worker_adults_only_party -4.080384 F\n", - "8 coef_non_worker_mixed_party -125.288137 F\n", - "9 coef_child_too_young_for_school_children_only_... -2.785631 F\n", - "10 coef_child_too_young_for_school_mixed_party 2.069934 F\n", - "11 coef_pre_driving_age_student_children_only_party 4.151085 F\n", - "12 coef_pre_driving_age_student_mixed_party 2.007228 F\n", - "13 coef_driving_age_student_children_only_party -1.822000 F\n", - "14 coef_driving_age_student_mixed_party -28.998969 F\n", - "15 coef_full_time_worker_specific_to_eating_out_j... 0.715700 F\n", - "16 coef_full_time_worker_specific_to_eating_out_j... 0.500000 T\n", - "17 coef_full_time_worker_specific_to_discretionar... 0.439200 F\n", - "18 coef_full_time_worker_specific_to_discretionar... 0.500000 T\n", - "19 coef_part_time_worker_specific_to_eating_out_j... 2.188000 F\n", - "20 coef_part_time_worker_specific_to_discretionar... 0.285000 F\n", - "21 coef_university_student_specific_to_eating_out... -0.820000 F\n", - "22 coef_university_student_specific_to_discretion... 0.000000 T\n", - "23 coef_non_worker_specific_to_eating_out_joint_t... 0.161700 F\n", - "24 coef_non_worker_specific_to_discretionary_join... -0.183500 F\n", - "25 coef_child_too_young_for_school_specific_to_ea... 0.658655 F\n", - "26 coef_child_too_young_for_school_specific_to_di... 0.128371 F\n", - "27 coef_pre_driving_age_student_specific_to_eatin... 1.391000 F\n", - "28 coef_pre_driving_age_student_specific_to_discr... 0.662600 F\n", - "29 coef_driving_age_student_specific_to_eating_ou... 2.344000 F\n", - "30 coef_driving_age_student_specific_to_discretio... -0.667500 F\n", - "31 coef_household_in_urban_area_adult_adult_only_... 0.000000 T\n", - "32 coef_household_in_urban_area_adult_mixed_party 978.504490 F\n", - "33 coef_household_in_urban_area_child_child_only_... 6.082784 F\n", - "34 coef_household_in_urban_area_child_mixed_party -19.290030 F\n", - "35 coef_household_in_suburban_area_adult_adult_on... 0.000000 T\n", - "36 coef_household_in_suburban_area_adult_mixed_party -0.060070 F\n", - "37 coef_household_in_suburban_area_child_child_on... 0.000000 T\n", - "38 coef_household_in_suburban_area_child_mixed_party 0.000000 T\n", - "39 coef_adult_more_automobiles_than_workers_adult... 0.311671 F\n", - "40 coef_adult_more_automobiles_than_workers_mixed... -1.827740 F\n", - "41 coef_child_more_automobiles_than_workers_child... -0.394683 F\n", - "42 coef_child_more_automobiles_than_workers_mixed... -1.551914 F\n", - "43 coef_dummy_for_high_income_for_adult_in_adult_... 0.131783 F\n", - "44 coef_dummy_for_high_income_for_adult_in_mixed_... -0.378918 F\n", - "45 coef_dummy_for_high_income_for_child_in_childr... -0.561900 F\n", - "46 coef_dummy_for_high_income_for_child_in_mixed_... -0.513571 F\n", - "47 coef_adult_number_of_joint_tours_adult_only -2.134054 F\n", - "48 coef_adult_number_of_joint_tours_mixed -28.572220 F\n", - "49 coef_child_number_of_joint_tours_child_only 4.977604 F\n", - "50 coef_child_number_of_joint_tours_mixed 17.894892 F\n", - "51 coef_adult_number_of_other_adults_in_the_house... 0.000000 T\n", - "52 coef_adult_number_of_other_adults_in_the_house... 0.000000 T\n", - "53 coef_child_number_of_other_children_in_the_hou... 0.000000 T\n", - "54 coef_child_number_of_other_children_in_the_hou... 0.000000 T\n", - "55 coef_adult_log_of_max_window_overlap_with_an_a... 1.692145 F\n", - "56 coef_adult_log_of_max_window_overlap_with_a_ch... 4.595737 F\n", - "57 coef_child_log_of_max_window_overlap_with_an_a... 1.539267 F\n", - "58 coef_child_log_of_max_window_overlap_with_a_ch... 1.275585 F" + " coefficient_name value constrain\n", + "0 coef_unavailable -999.000000 T\n", + "1 coef_full_time_worker_mixed_party -3.527365 F\n", + "2 coef_full_time_worker_mixed_party_not 0.500000 T\n", + "3 coef_part_time_worker_adults_only_party -3.631928 F\n", + "4 coef_part_time_worker_adults_only_party_not 0.500000 T\n", + "5 coef_part_time_worker_mixed_party -0.447984 F\n", + "6 coef_university_student_mixed_party -2.902637 F\n", + "7 coef_non_worker_adults_only_party -3.161945 F\n", + "8 coef_non_worker_mixed_party 1.059162 F\n", + "9 coef_child_too_young_for_school_children_only_... -1.149596 F\n", + "10 coef_child_too_young_for_school_mixed_party 0.197836 F\n", + "11 coef_pre_driving_age_student_children_only_party 0.991812 F\n", + "12 coef_pre_driving_age_student_mixed_party -0.073175 F\n", + "13 coef_driving_age_student_children_only_party -2.120496 F\n", + "14 coef_driving_age_student_mixed_party 0.322986 F\n", + "15 coef_full_time_worker_specific_to_eating_out_j... 0.715700 F\n", + "16 coef_full_time_worker_specific_to_eating_out_j... 0.500000 T\n", + "17 coef_full_time_worker_specific_to_discretionar... 0.439200 F\n", + "18 coef_full_time_worker_specific_to_discretionar... 0.500000 T\n", + "19 coef_part_time_worker_specific_to_eating_out_j... 2.188000 F\n", + "20 coef_part_time_worker_specific_to_discretionar... 0.285000 F\n", + "21 coef_university_student_specific_to_eating_out... -0.820000 F\n", + "22 coef_university_student_specific_to_discretion... 0.000000 T\n", + "23 coef_non_worker_specific_to_eating_out_joint_t... 0.161700 F\n", + "24 coef_non_worker_specific_to_discretionary_join... -0.183500 F\n", + "25 coef_child_too_young_for_school_specific_to_ea... 0.658900 F\n", + "26 coef_child_too_young_for_school_specific_to_di... 0.128400 F\n", + "27 coef_pre_driving_age_student_specific_to_eatin... 1.391000 F\n", + "28 coef_pre_driving_age_student_specific_to_discr... 0.662600 F\n", + "29 coef_driving_age_student_specific_to_eating_ou... 2.344000 F\n", + "30 coef_driving_age_student_specific_to_discretio... -0.667500 F\n", + "31 coef_household_in_urban_area_adult_adult_only_... 0.000000 T\n", + "32 coef_household_in_urban_area_adult_mixed_party 1.204039 F\n", + "33 coef_household_in_urban_area_child_child_only_... -0.145369 F\n", + "34 coef_household_in_urban_area_child_mixed_party -0.138857 F\n", + "35 coef_household_in_suburban_area_adult_adult_on... 0.000000 T\n", + "36 coef_household_in_suburban_area_adult_mixed_party 1.239881 F\n", + "37 coef_household_in_suburban_area_child_child_on... 0.000000 T\n", + "38 coef_household_in_suburban_area_child_mixed_party 0.000000 T\n", + "39 coef_adult_more_automobiles_than_workers_adult... 0.121220 F\n", + "40 coef_adult_more_automobiles_than_workers_mixed... -0.591966 F\n", + "41 coef_child_more_automobiles_than_workers_child... -0.421400 F\n", + "42 coef_child_more_automobiles_than_workers_mixed... -0.366466 F\n", + "43 coef_dummy_for_high_income_for_adult_in_adult_... -0.068202 F\n", + "44 coef_dummy_for_high_income_for_adult_in_mixed_... 0.112930 F\n", + "45 coef_dummy_for_high_income_for_child_in_childr... -0.211892 F\n", + "46 coef_dummy_for_high_income_for_child_in_mixed_... -0.013740 F\n", + "47 coef_adult_number_of_joint_tours_adult_only -0.794091 F\n", + "48 coef_adult_number_of_joint_tours_mixed -1.417451 F\n", + "49 coef_child_number_of_joint_tours_child_only 0.866122 F\n", + "50 coef_child_number_of_joint_tours_mixed -0.261747 F\n", + "51 coef_adult_number_of_other_adults_in_the_house... 0.000000 T\n", + "52 coef_adult_number_of_other_adults_in_the_house... 0.000000 T\n", + "53 coef_child_number_of_other_children_in_the_hou... 0.000000 T\n", + "54 coef_child_number_of_other_children_in_the_hou... 0.000000 T\n", + "55 coef_adult_log_of_max_window_overlap_with_an_a... 1.280678 F\n", + "56 coef_adult_log_of_max_window_overlap_with_a_ch... 2.125116 F\n", + "57 coef_child_log_of_max_window_overlap_with_an_a... 1.538000 F\n", + "58 coef_child_log_of_max_window_overlap_with_a_ch... 1.296000 F" ] }, "execution_count": 12, @@ -4124,7 +4082,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -4138,7 +4096,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/13_joint_nonmand_tour_dest.ipynb b/activitysim/examples/example_estimation/notebooks/13_joint_nonmand_tour_dest.ipynb index 82fc07c165..20b9d7cac1 100644 --- a/activitysim/examples/example_estimation/notebooks/13_joint_nonmand_tour_dest.ipynb +++ b/activitysim/examples/example_estimation/notebooks/13_joint_nonmand_tour_dest.ipynb @@ -28,30 +28,74 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import larch # !conda install larch #for estimation\n", + "import larch as lx\n", "import pandas as pd\n", - "import numpy as np\n", - "import yaml \n", - "import larch.util.excel\n", - "import os" + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -65,19 +109,36 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], - "source": [ - "modelnames = (\"non_mandatory_tour_destination\", \"joint_tour_destination\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_destination/non_mandatory_tour_destination_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_destination/non_mandatory_tour_destination_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_destination/non_mandatory_tour_destination_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_destination/non_mandatory_tour_destination_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_destination/non_mandatory_tour_destination_landuse.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_destination/non_mandatory_tour_destination_size_terms.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_destination/non_mandatory_tour_destination_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_destination/joint_tour_destination_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_destination/joint_tour_destination_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_destination/joint_tour_destination_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_destination/joint_tour_destination_landuse.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_destination/joint_tour_destination_size_terms.csv\n" + ] + } + ], "source": [ + "modelnames = (\"non_mandatory_tour_destination\", \"joint_tour_destination\")\n", + "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelnames, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelnames,\n", + " edb_directory=\"output-est-mode/estimation_data_bundle/{name}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -89,26 +150,6 @@ "all models in the group." ] }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "larch.model.model_group.ModelGroup" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(model)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -127,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -272,7 +313,7 @@ "coef_othdiscr_dist_5_plus -0.1193 F" ] }, - "execution_count": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -290,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -315,148 +356,88 @@ " \n", " \n", " tour_id\n", - " variable\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " ...\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", - " 190\n", + " alt_dest\n", + " variable_label0000\n", + " variable_label0001\n", + " variable_label0002\n", + " variable_label0003\n", + " variable_label0004\n", + " variable_label0005\n", + " variable_label0006\n", + " variable_label0007\n", + " variable_label0008\n", " \n", " \n", " \n", " \n", " 0\n", - " 6812\n", - " variable_label0001\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", + " 1870\n", + " 8\n", " 1.0\n", + " 1.00\n", + " 2.06\n", + " 0.0\n", + " 0.0\n", + " 7.492867\n", + " False\n", + " -0.349936\n", + " 4.970833\n", " \n", " \n", " 1\n", - " 6812\n", - " variable_label0004\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 1870\n", + " 16\n", + " 1.0\n", + " 1.00\n", + " 2.74\n", " 0.0\n", " 0.0\n", + " 8.289751\n", + " False\n", + " -0.487843\n", + " 4.510890\n", " \n", " \n", " 2\n", - " 6812\n", - " variable_label0002\n", - " 1.3199999332427979\n", - " 1.0299999713897705\n", - " 0.8499999046325684\n", - " 1.190000057220459\n", - " 1.0299999713897705\n", - " 0.7799999713897705\n", - " 0.8399999141693115\n", - " 0.9900000095367432\n", - " ...\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 2.7800002098083496\n", - " 2.25\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", + " 1870\n", + " 17\n", + " 1.0\n", + " 1.00\n", + " 3.00\n", + " 0.1\n", + " 0.0\n", + " 7.651656\n", + " False\n", + " -0.537413\n", + " 5.289744\n", " \n", " \n", " 3\n", - " 6812\n", - " variable_label0003\n", - " 0.0\n", - " 0.0\n", - 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22410 rows × 192 columns

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"814 5.563462376541104 7.432773706720803 3.829901471825824 \n", - "815 False False False \n", - "816 5.61268490695845 3.809978548664433 7.98807577917251 \n", - "817 1.0 1.0 1.0 \n", - "818 -0.2398310223012045 -0.27770426926470554 -0.45370314739749773 \n", - "\n", - " 190 \n", - "0 1.0 \n", - "1 0.0 \n", - "2 1.9100000858306885 \n", - "3 0.0 \n", - "4 5.304200029706192 \n", - ".. ... \n", - "814 5.955267258796094 \n", - "815 False \n", - "816 5.735554926665741 \n", - "817 1.0 \n", - "818 -0.42464839165931 \n", - "\n", - "[819 rows x 192 columns]" + "[32631 rows x 11 columns]" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1143,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -1178,48 +901,48 @@ " \n", " \n", " 0\n", - " 6812\n", - " 72\n", - " 72.0\n", - " 166\n", - " eatout\n", - " 71\n", + " 1870\n", + " 70\n", + " 26\n", + " 45\n", + " othdiscr\n", + " 52\n", " \n", " \n", " 1\n", - " 8110\n", - " 62\n", - " 47.0\n", - " 197\n", - " shopping\n", - " 80\n", + " 20468\n", + " 138\n", + " 161\n", + " 499\n", + " escort\n", + " 124\n", " \n", " \n", " 2\n", - " 11013\n", - " 33\n", - " 32.0\n", - " 268\n", - " othdiscr\n", - " 91\n", + " 27055\n", + " 133\n", + " 185\n", + " 659\n", + " social\n", + " 132\n", " \n", " \n", " 3\n", - " 11016\n", - " 71\n", - " 71.0\n", - " 268\n", - " othmaint\n", - " 91\n", + " 38877\n", + " 115\n", + " 147\n", + " 948\n", + " escort\n", + " 148\n", " \n", " \n", " 4\n", - " 15403\n", - " 67\n", - " 67.0\n", - " 375\n", - " othmaint\n", - " 105\n", + " 38904\n", + " 107\n", + " 104\n", + " 948\n", + " social\n", + " 148\n", " \n", " \n", " ...\n", @@ -1231,86 +954,86 @@ " ...\n", " \n", " \n", - " 2485\n", - " 309760814\n", - " 154\n", - " 154.0\n", - " 7555141\n", + " 29664\n", + " 310197956\n", + " 1374\n", + " 1384\n", + " 7565803\n", " shopping\n", - " 85\n", + " 1386\n", " \n", " \n", - " 2486\n", - " 309760815\n", - " 41\n", - " 41.0\n", - " 7555141\n", - " shopping\n", - " 85\n", + " 29665\n", + " 310202376\n", + " 1402\n", + " 1402\n", + " 7565911\n", + " othdiscr\n", + " 1402\n", " \n", " \n", - " 2487\n", - " 309790009\n", - " 36\n", - " 36.0\n", - " 7555853\n", - " social\n", - " 115\n", + " 29666\n", + " 310202384\n", + " 1402\n", + " 1402\n", + " 7565911\n", + " shopping\n", + " 1402\n", " \n", " \n", - " 2488\n", - " 309796968\n", - " 94\n", - " 94.0\n", - " 7556023\n", - " othdiscr\n", - " 136\n", + " 29667\n", + " 310212634\n", + " 1405\n", + " 1422\n", + " 7566161\n", + " shopping\n", + " 1421\n", " \n", " \n", - " 2489\n", - " 309796969\n", - " 129\n", - " 128.0\n", - " 7556023\n", - " othdiscr\n", - " 136\n", + " 29668\n", + " 310220296\n", + " 1156\n", + " 1430\n", + " 7566348\n", + " othmaint\n", + " 1437\n", " \n", " \n", "\n", - "

2490 rows × 6 columns

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29669 rows × 6 columns

\n", "" ], "text/plain": [ - " tour_id model_choice override_choice person_id tour_type \\\n", - "0 6812 72 72.0 166 eatout \n", - "1 8110 62 47.0 197 shopping \n", - "2 11013 33 32.0 268 othdiscr \n", - "3 11016 71 71.0 268 othmaint \n", - "4 15403 67 67.0 375 othmaint \n", - "... ... ... ... ... ... \n", - "2485 309760814 154 154.0 7555141 shopping \n", - "2486 309760815 41 41.0 7555141 shopping \n", - "2487 309790009 36 36.0 7555853 social \n", - "2488 309796968 94 94.0 7556023 othdiscr \n", - "2489 309796969 129 128.0 7556023 othdiscr \n", + " tour_id model_choice override_choice person_id tour_type \\\n", + "0 1870 70 26 45 othdiscr \n", + "1 20468 138 161 499 escort \n", + "2 27055 133 185 659 social \n", + "3 38877 115 147 948 escort \n", + "4 38904 107 104 948 social \n", + "... ... ... ... ... ... \n", + "29664 310197956 1374 1384 7565803 shopping \n", + "29665 310202376 1402 1402 7565911 othdiscr \n", + "29666 310202384 1402 1402 7565911 shopping \n", + "29667 310212634 1405 1422 7566161 shopping \n", + "29668 310220296 1156 1430 7566348 othmaint \n", "\n", - " home_zone_id \n", - "0 71 \n", - "1 80 \n", - "2 91 \n", - "3 91 \n", - "4 105 \n", - "... ... \n", - "2485 85 \n", - "2486 85 \n", - "2487 115 \n", - "2488 136 \n", - "2489 136 \n", + " home_zone_id \n", + "0 52 \n", + "1 124 \n", + "2 132 \n", + "3 148 \n", + "4 148 \n", + "... ... \n", + "29664 1386 \n", + "29665 1402 \n", + "29666 1402 \n", + "29667 1421 \n", + "29668 1437 \n", "\n", - "[2490 rows x 6 columns]" + "[29669 rows x 6 columns]" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1321,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1356,48 +1079,48 @@ " \n", " \n", " 0\n", - " 7785298\n", - " 113\n", - " 103.0\n", - " 189885\n", + " 7853686\n", + " 131\n", + " 131\n", + " 191553\n", " eatout\n", - " 135\n", + " 217\n", " \n", " \n", " 1\n", - " 8708454\n", - " 103\n", - " 106.0\n", - " 212401\n", - " eatout\n", - " 8\n", + " 7902318\n", + " 321\n", + " 323\n", + " 192739\n", + " shopping\n", + " 322\n", " \n", " \n", " 2\n", - " 9715006\n", - " 188\n", - " 188.0\n", - " 236951\n", + " 7909776\n", + " 350\n", + " 320\n", + " 192921\n", " othdiscr\n", - " 183\n", + " 330\n", " \n", " \n", " 3\n", - " 10831112\n", - " 105\n", - " 105.0\n", - " 264173\n", - " shopping\n", - " 10\n", + " 7988742\n", + " 527\n", + " 637\n", + " 194847\n", + " othdiscr\n", + " 526\n", " \n", " \n", " 4\n", - " 20334787\n", - " 157\n", - " 157.0\n", - " 495970\n", - " othmaint\n", - " 140\n", + " 8103874\n", + " 591\n", + " 687\n", + " 197655\n", + " shopping\n", + " 683\n", " \n", " \n", " ...\n", @@ -1409,86 +1132,86 @@ " ...\n", " \n", " \n", - " 86\n", - " 283676518\n", - " 115\n", - " 115.0\n", - " 6918939\n", - " shopping\n", - " 121\n", + " 1272\n", + " 303771147\n", + " 484\n", + " 673\n", + " 7409052\n", + " othdiscr\n", + " 675\n", " \n", " \n", - " 87\n", - " 295260168\n", - " 7\n", - " 7.0\n", - " 7201469\n", + " 1273\n", + " 304144212\n", + " 796\n", + " 843\n", + " 7418154\n", " social\n", - " 114\n", + " 806\n", " \n", " \n", - " 88\n", - " 297646485\n", - " 90\n", - " 89.0\n", - " 7259670\n", + " 1274\n", + " 304463637\n", + " 9\n", + " 38\n", + " 7425942\n", " othdiscr\n", - " 25\n", + " 1043\n", " \n", " \n", - " 89\n", - " 298814741\n", - " 147\n", - " 147.0\n", - " 7288164\n", - " othmaint\n", - " 74\n", + " 1275\n", + " 305125582\n", + " 29\n", + " 98\n", + " 7442087\n", + " othdiscr\n", + " 50\n", " \n", " \n", - " 90\n", - " 301810980\n", - " 66\n", - " 66.0\n", - " 7361244\n", - " othmaint\n", - " 177\n", + " 1276\n", + " 305246245\n", + " 2\n", + " 149\n", + " 7445030\n", + " othdiscr\n", + " 149\n", " \n", " \n", "\n", - "

91 rows × 6 columns

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1277 rows × 6 columns

\n", "" ], "text/plain": [ - " tour_id model_choice override_choice person_id tour_type \\\n", - "0 7785298 113 103.0 189885 eatout \n", - "1 8708454 103 106.0 212401 eatout \n", - "2 9715006 188 188.0 236951 othdiscr \n", - "3 10831112 105 105.0 264173 shopping \n", - "4 20334787 157 157.0 495970 othmaint \n", - ".. ... ... ... ... ... \n", - "86 283676518 115 115.0 6918939 shopping \n", - "87 295260168 7 7.0 7201469 social \n", - "88 297646485 90 89.0 7259670 othdiscr \n", - "89 298814741 147 147.0 7288164 othmaint \n", - "90 301810980 66 66.0 7361244 othmaint \n", + " tour_id model_choice override_choice person_id tour_type \\\n", + "0 7853686 131 131 191553 eatout \n", + "1 7902318 321 323 192739 shopping \n", + "2 7909776 350 320 192921 othdiscr \n", + "3 7988742 527 637 194847 othdiscr \n", + "4 8103874 591 687 197655 shopping \n", + "... ... ... ... ... ... \n", + "1272 303771147 484 673 7409052 othdiscr \n", + "1273 304144212 796 843 7418154 social \n", + "1274 304463637 9 38 7425942 othdiscr \n", + "1275 305125582 29 98 7442087 othdiscr \n", + "1276 305246245 2 149 7445030 othdiscr \n", "\n", - " home_zone_id \n", - "0 135 \n", - "1 8 \n", - "2 183 \n", - "3 10 \n", - "4 140 \n", - ".. ... \n", - "86 121 \n", - "87 114 \n", - "88 25 \n", - "89 74 \n", - "90 177 \n", + " home_zone_id \n", + "0 217 \n", + "1 322 \n", + "2 330 \n", + "3 526 \n", + "4 683 \n", + "... ... \n", + "1272 675 \n", + "1273 806 \n", + "1274 1043 \n", + "1275 50 \n", + "1276 149 \n", "\n", - "[91 rows x 6 columns]" + "[1277 rows x 6 columns]" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1506,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1592,9 +1315,9 @@ " 7\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 3\n", " 5.89564\n", " 2.875000\n", @@ -1616,9 +1339,9 @@ " 19\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.84871\n", " 5.195214\n", @@ -1640,9 +1363,9 @@ " 38\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.53231\n", " 80.470405\n", @@ -1664,9 +1387,9 @@ " 20\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 2\n", " 5.64330\n", " 7.947368\n", @@ -1688,9 +1411,9 @@ " 86\n", " ...\n", " 0\n", - " 0.00000\n", + " 0.0\n", " 72.14684\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.52555\n", " 38.187500\n", @@ -1723,191 +1446,191 @@ " ...\n", " \n", " \n", - " 186\n", - " 4\n", - " 4\n", - " 1\n", - " 2779\n", - " 8062\n", - " 376.0\n", - " 172.0\n", - " 15.00000\n", - " 1760\n", - " 1178\n", + " 1450\n", + " 34\n", + " 34\n", + " 9\n", + " 2724\n", + " 6493\n", + " 1320.0\n", + " 630.0\n", + " 69.00000\n", + " 1046\n", + " 1013\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 4\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.04173\n", - " 14.860963\n", - " 9.411765\n", - " 5.762347\n", + " 1.12116\n", + " 3.896996\n", + " 1.496423\n", + " 1.081235\n", " False\n", " \n", " \n", - " 187\n", - 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" 3.413233\n", + " 1.17587\n", + " 3.419152\n", + " 3.312402\n", + " 1.682465\n", " False\n", " \n", " \n", - " 189\n", - " 4\n", - " 4\n", - " 1\n", - " 3546\n", - " 8476\n", - " 201.0\n", - " 72.0\n", - " 6.00000\n", - " 226\n", - " 1057\n", + " 1453\n", + " 34\n", + " 34\n", + " 9\n", + " 298\n", + " 779\n", + " 14195.0\n", + " 429.0\n", + " 4.00000\n", + " 922\n", + " 88\n", " ...\n", - " 2\n", - " 0.00000\n", - " 0.00000\n", + " 5\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.88773\n", - " 45.461538\n", - " 2.897436\n", - " 2.723836\n", + " 1.01972\n", + " 0.688222\n", + " 2.129330\n", + " 0.520115\n", " False\n", " \n", " \n", - " 190\n", - " 4\n", - " 4\n", - " 1\n", - " 968\n", - " 1647\n", - " 1381.0\n", - " 14.0\n", - " 28.00000\n", - " 1010\n", - " 114\n", + " 1454\n", + " 34\n", + " 34\n", + " 9\n", + " 1068\n", + " 2337\n", + " 10469.0\n", + " 1114.0\n", + " 27.00000\n", + " 607\n", + " 418\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 5\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - 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190 rows × 28 columns

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1454 rows × 28 columns

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" COLLPTE TOPOLOGY TERMINAL household_density \\\n", - "zone_id \n", - "1 0.00000 3 5.89564 2.875000 \n", - "2 0.00000 1 5.84871 5.195214 \n", - "3 0.00000 1 5.53231 80.470405 \n", - "4 0.00000 2 5.64330 7.947368 \n", - "5 0.00000 1 5.52555 38.187500 \n", - "... ... ... ... ... \n", - "186 0.00000 1 2.04173 14.860963 \n", - "187 0.00000 2 1.73676 11.841270 \n", - "188 11152.93652 1 2.28992 3.984127 \n", - "189 0.00000 1 2.88773 45.461538 \n", - "190 0.00000 1 2.60309 23.047619 \n", + " TOPOLOGY TERMINAL household_density employment_density \\\n", + "zone_id \n", + "1 3 5.89564 2.875000 1707.375000 \n", + "2 1 5.84871 5.195214 1631.374751 \n", + "3 1 5.53231 80.470405 736.891913 \n", + "4 2 5.64330 7.947368 1180.736842 \n", + "5 1 5.52555 38.187500 978.875000 \n", + "... ... ... ... ... \n", + "1450 1 1.12116 3.896996 1.496423 \n", + "1451 1 1.17116 4.777251 1.793839 \n", + "1452 1 1.17587 3.419152 3.312402 \n", + "1453 1 1.01972 0.688222 2.129330 \n", + "1454 1 0.95542 0.936021 0.531989 \n", "\n", - 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"execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -2141,7 +1864,7 @@ "7 1 " ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -2159,7 +1882,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -2270,7 +1993,7 @@ "othdiscr 0.252 0.212 0.272 0.165 0.000 0.098" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -2290,21 +2013,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 029 [Optimization terminated successfully]

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Iteration 046 [Optimization terminated successfully]

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Best LL = -13268.709190928732

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Best LL = -85747.12036028807

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" NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.579932\n", " \n", " \n", " coef_othmaint_dist_5_plus\n", - " -0.229655\n", + " -0.156745\n", + " -0.156745\n", " -0.109300\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.229655\n", " \n", " \n", " coef_shopping_dist_2_5\n", - " -0.629792\n", + " -0.704553\n", + " -0.704553\n", " -0.565500\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.629792\n", " \n", " \n", " coef_shopping_dist_5_plus\n", - " -0.130649\n", + " -0.234821\n", + " -0.234821\n", " -0.183200\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.130649\n", " \n", " \n", " coef_social_dist_2_5\n", - " -0.245175\n", + " -0.464176\n", + " -0.464176\n", " -0.348500\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.245175\n", " \n", " \n", " coef_social_dist_5_plus\n", - " -0.190239\n", + " -0.176446\n", + " -0.176446\n", " -0.130600\n", + " -25.000000\n", + " 25.000000\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.190239\n", " \n", " \n", " eatout_HEREMPN\n", - " -0.946877\n", + " -1.601448\n", + " -1.601448\n", " -1.354796\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -0.946877\n", " \n", " \n", " eatout_RETEMPN\n", " -0.298406\n", " -0.298406\n", + " -0.298406\n", + " -0.298406\n", + " -0.298406\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -0.298406\n", " \n", " \n", " escort_AGE0519\n", - " -1.171241\n", + " -0.765963\n", + " -0.765963\n", " -0.765718\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -1.171241\n", " \n", " \n", " escort_HEREMPN\n", - " -2.450057\n", + " -1.996310\n", + " -1.996310\n", " -1.937942\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -2.450057\n", " \n", " \n", " escort_HSENROLL\n", - " -3.304440\n", - " -1.795767\n", + " -1.872611\n", + " -1.872611\n", + " -1.795768\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -3.304440\n", " \n", " \n", " escort_RETEMPN\n", " -1.491655\n", " -1.491655\n", + " -1.491655\n", + " -1.491655\n", + " -1.491655\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -1.491655\n", " \n", " \n", " othdiscr_HEREMPN\n", - " -1.584964\n", + " -1.342128\n", + " -1.342128\n", " -1.301953\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -1.584964\n", " \n", " \n", " othdiscr_HSENROLL\n", - " -2.466750\n", + " -2.287886\n", + " -2.287886\n", " -2.322788\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -2.466750\n", " \n", " \n", " othdiscr_OTHEMPN\n", - " -2.370292\n", + " -1.905661\n", + " -1.905661\n", " -1.801810\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -2.370292\n", " \n", " \n", " othdiscr_RETEMPN\n", - " -1.238181\n", + " -1.550175\n", + " -1.550175\n", " -1.551169\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -1.238181\n", " \n", " \n", " othdiscr_TOTHH\n", " -1.378326\n", " -1.378326\n", + " -1.378326\n", + " -1.378326\n", + " -1.378326\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -1.378326\n", " \n", " \n", " othmaint_HEREMPN\n", - " -0.668952\n", + " -0.689025\n", + " -0.689025\n", " -0.657780\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -0.668952\n", " \n", " \n", " othmaint_RETEMPN\n", " -0.729811\n", " -0.729811\n", + " -0.729811\n", + " -0.729811\n", + " -0.729811\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -0.729811\n", " \n", " \n", " shopping_RETEMPN\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " social_HEREMPN\n", - " -0.302843\n", + " -0.831065\n", + " -0.831065\n", " -0.738145\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -0.302843\n", " \n", " \n", " social_RETEMPN\n", " -0.650088\n", " -0.650088\n", + " -0.650088\n", + " -0.650088\n", + " -0.650088\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -0.650088\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue nullvalue minimum \\\n", - "-999 -999.000000 -999.000000 -999.0 -999.0 \n", - "0 0.000000 0.000000 0.0 0.0 \n", - "1 1.000000 1.000000 1.0 1.0 \n", - "coef_eatout_dist_0_2 -0.767979 -0.560900 0.0 NaN \n", - "coef_eatout_dist_2_5 -0.226448 -0.319200 0.0 NaN \n", - "coef_eatout_dist_5_plus -0.188137 -0.123800 0.0 NaN \n", - "coef_escort_dist_0_2 0.226691 -0.149900 0.0 NaN \n", - "coef_escort_dist_2_5 -0.813494 -0.867100 0.0 NaN \n", - "coef_escort_dist_5_plus -0.255056 -0.213700 0.0 NaN \n", - "coef_mode_logsum 0.662937 0.675500 0.0 NaN \n", - "coef_othdiscr_dist_0_2 -0.203187 -0.167700 0.0 NaN \n", - "coef_othdiscr_dist_2_5 -0.550945 -0.495500 0.0 NaN \n", - "coef_othdiscr_dist_5_plus -0.000141 -0.119300 0.0 NaN \n", - "coef_othmaint_dist_2_5 -0.579932 -0.605500 0.0 NaN \n", - "coef_othmaint_dist_5_plus -0.229655 -0.109300 0.0 NaN \n", - "coef_shopping_dist_2_5 -0.629792 -0.565500 0.0 NaN \n", - "coef_shopping_dist_5_plus -0.130649 -0.183200 0.0 NaN \n", - "coef_social_dist_2_5 -0.245175 -0.348500 0.0 NaN \n", - "coef_social_dist_5_plus -0.190239 -0.130600 0.0 NaN \n", - "eatout_HEREMPN -0.946877 -1.354796 0.0 -6.0 \n", - "eatout_RETEMPN -0.298406 -0.298406 0.0 -6.0 \n", - "escort_AGE0519 -1.171241 -0.765718 0.0 -6.0 \n", - "escort_HEREMPN -2.450057 -1.937942 0.0 -6.0 \n", - "escort_HSENROLL -3.304440 -1.795767 0.0 -6.0 \n", - "escort_RETEMPN -1.491655 -1.491655 0.0 -6.0 \n", - "othdiscr_HEREMPN -1.584964 -1.301953 0.0 -6.0 \n", - "othdiscr_HSENROLL -2.466750 -2.322788 0.0 -6.0 \n", - "othdiscr_OTHEMPN -2.370292 -1.801810 0.0 -6.0 \n", - "othdiscr_RETEMPN -1.238181 -1.551169 0.0 -6.0 \n", - "othdiscr_TOTHH -1.378326 -1.378326 0.0 -6.0 \n", - "othmaint_HEREMPN -0.668952 -0.657780 0.0 -6.0 \n", - "othmaint_RETEMPN -0.729811 -0.729811 0.0 -6.0 \n", - "shopping_RETEMPN 0.000000 0.000000 0.0 -6.0 \n", - "social_HEREMPN -0.302843 -0.738145 0.0 -6.0 \n", - "social_RETEMPN -0.650088 -0.650088 0.0 -6.0 \n", + " value best initvalue minimum \\\n", + "param_name \n", + "-999 -999.000000 -999.000000 -999.000000 -999.000000 \n", + "0 0.000000 0.000000 0.000000 0.000000 \n", + "1 1.000000 1.000000 1.000000 1.000000 \n", + "coef_eatout_dist_0_2 -0.959646 -0.959646 -0.560900 -25.000000 \n", + "coef_eatout_dist_2_5 -0.427890 -0.427890 -0.319200 -25.000000 \n", + "coef_eatout_dist_5_plus -0.182199 -0.182199 -0.123800 -25.000000 \n", + "coef_escort_dist_0_2 -0.290871 -0.290871 -0.149900 -25.000000 \n", + "coef_escort_dist_2_5 -0.959038 -0.959038 -0.867100 -25.000000 \n", + "coef_escort_dist_5_plus -0.264682 -0.264682 -0.213700 -25.000000 \n", + "coef_mode_logsum 0.165346 0.165346 0.675500 -25.000000 \n", + "coef_othdiscr_dist_0_2 -0.424399 -0.424399 -0.167700 -25.000000 \n", + "coef_othdiscr_dist_2_5 -0.615669 -0.615669 -0.495500 -25.000000 \n", + "coef_othdiscr_dist_5_plus -0.171387 -0.171387 -0.119300 -25.000000 \n", + "coef_othmaint_dist_2_5 -0.796780 -0.796780 -0.605500 -25.000000 \n", + "coef_othmaint_dist_5_plus -0.156745 -0.156745 -0.109300 -25.000000 \n", + "coef_shopping_dist_2_5 -0.704553 -0.704553 -0.565500 -25.000000 \n", + "coef_shopping_dist_5_plus -0.234821 -0.234821 -0.183200 -25.000000 \n", + "coef_social_dist_2_5 -0.464176 -0.464176 -0.348500 -25.000000 \n", + "coef_social_dist_5_plus -0.176446 -0.176446 -0.130600 -25.000000 \n", + "eatout_HEREMPN -1.601448 -1.601448 -1.354796 -6.000000 \n", + "eatout_RETEMPN -0.298406 -0.298406 -0.298406 -0.298406 \n", + "escort_AGE0519 -0.765963 -0.765963 -0.765718 -6.000000 \n", + "escort_HEREMPN -1.996310 -1.996310 -1.937942 -6.000000 \n", + "escort_HSENROLL -1.872611 -1.872611 -1.795768 -6.000000 \n", + "escort_RETEMPN -1.491655 -1.491655 -1.491655 -1.491655 \n", + "othdiscr_HEREMPN -1.342128 -1.342128 -1.301953 -6.000000 \n", + "othdiscr_HSENROLL -2.287886 -2.287886 -2.322788 -6.000000 \n", + "othdiscr_OTHEMPN -1.905661 -1.905661 -1.801810 -6.000000 \n", + "othdiscr_RETEMPN -1.550175 -1.550175 -1.551169 -6.000000 \n", + "othdiscr_TOTHH -1.378326 -1.378326 -1.378326 -1.378326 \n", + "othmaint_HEREMPN -0.689025 -0.689025 -0.657780 -6.000000 \n", + "othmaint_RETEMPN -0.729811 -0.729811 -0.729811 -0.729811 \n", + "shopping_RETEMPN 0.000000 0.000000 0.000000 0.000000 \n", + "social_HEREMPN -0.831065 -0.831065 -0.738145 -6.000000 \n", + "social_RETEMPN -0.650088 -0.650088 -0.650088 -0.650088 \n", "\n", - " maximum holdfast note best \n", - "-999 -999.0 1 -999.000000 \n", - "0 0.0 1 0.000000 \n", - "1 1.0 1 1.000000 \n", - "coef_eatout_dist_0_2 NaN 0 -0.767979 \n", - "coef_eatout_dist_2_5 NaN 0 -0.226448 \n", - "coef_eatout_dist_5_plus NaN 0 -0.188137 \n", - "coef_escort_dist_0_2 NaN 0 0.226691 \n", - "coef_escort_dist_2_5 NaN 0 -0.813494 \n", - "coef_escort_dist_5_plus NaN 0 -0.255056 \n", - "coef_mode_logsum NaN 0 0.662937 \n", - "coef_othdiscr_dist_0_2 NaN 0 -0.203187 \n", - "coef_othdiscr_dist_2_5 NaN 0 -0.550945 \n", - "coef_othdiscr_dist_5_plus NaN 0 -0.000141 \n", - "coef_othmaint_dist_2_5 NaN 0 -0.579932 \n", - "coef_othmaint_dist_5_plus NaN 0 -0.229655 \n", - "coef_shopping_dist_2_5 NaN 0 -0.629792 \n", - "coef_shopping_dist_5_plus NaN 0 -0.130649 \n", - "coef_social_dist_2_5 NaN 0 -0.245175 \n", - "coef_social_dist_5_plus NaN 0 -0.190239 \n", - "eatout_HEREMPN 6.0 0 -0.946877 \n", - "eatout_RETEMPN 6.0 1 -0.298406 \n", - "escort_AGE0519 6.0 0 -1.171241 \n", - "escort_HEREMPN 6.0 0 -2.450057 \n", - "escort_HSENROLL 6.0 0 -3.304440 \n", - "escort_RETEMPN 6.0 1 -1.491655 \n", - "othdiscr_HEREMPN 6.0 0 -1.584964 \n", - "othdiscr_HSENROLL 6.0 0 -2.466750 \n", - "othdiscr_OTHEMPN 6.0 0 -2.370292 \n", - "othdiscr_RETEMPN 6.0 0 -1.238181 \n", - "othdiscr_TOTHH 6.0 1 -1.378326 \n", - "othmaint_HEREMPN 6.0 0 -0.668952 \n", - "othmaint_RETEMPN 6.0 1 -0.729811 \n", - "shopping_RETEMPN 6.0 1 0.000000 \n", - "social_HEREMPN 6.0 0 -0.302843 \n", - "social_RETEMPN 6.0 1 -0.650088 " + " maximum nullvalue holdfast \n", + "param_name \n", + "-999 -999.000000 0.0 1 \n", + "0 0.000000 0.0 1 \n", + "1 1.000000 0.0 1 \n", + "coef_eatout_dist_0_2 25.000000 0.0 0 \n", + "coef_eatout_dist_2_5 25.000000 0.0 0 \n", + "coef_eatout_dist_5_plus 25.000000 0.0 0 \n", + "coef_escort_dist_0_2 25.000000 0.0 0 \n", + "coef_escort_dist_2_5 25.000000 0.0 0 \n", + "coef_escort_dist_5_plus 25.000000 0.0 0 \n", + "coef_mode_logsum 25.000000 0.0 0 \n", + "coef_othdiscr_dist_0_2 25.000000 0.0 0 \n", + "coef_othdiscr_dist_2_5 25.000000 0.0 0 \n", + "coef_othdiscr_dist_5_plus 25.000000 0.0 0 \n", + "coef_othmaint_dist_2_5 25.000000 0.0 0 \n", + "coef_othmaint_dist_5_plus 25.000000 0.0 0 \n", + "coef_shopping_dist_2_5 25.000000 0.0 0 \n", + "coef_shopping_dist_5_plus 25.000000 0.0 0 \n", + "coef_social_dist_2_5 25.000000 0.0 0 \n", + "coef_social_dist_5_plus 25.000000 0.0 0 \n", + "eatout_HEREMPN 6.000000 0.0 0 \n", + "eatout_RETEMPN -0.298406 0.0 1 \n", + "escort_AGE0519 6.000000 0.0 0 \n", + "escort_HEREMPN 6.000000 0.0 0 \n", + "escort_HSENROLL 6.000000 0.0 0 \n", + "escort_RETEMPN -1.491655 0.0 1 \n", + "othdiscr_HEREMPN 6.000000 0.0 0 \n", + "othdiscr_HSENROLL 6.000000 0.0 0 \n", + "othdiscr_OTHEMPN 6.000000 0.0 0 \n", + "othdiscr_RETEMPN 6.000000 0.0 0 \n", + "othdiscr_TOTHH -1.378326 0.0 1 \n", + "othmaint_HEREMPN 6.000000 0.0 0 \n", + "othmaint_RETEMPN -0.729811 0.0 1 \n", + "shopping_RETEMPN 0.000000 0.0 1 \n", + "social_HEREMPN 6.000000 0.0 0 \n", + "social_RETEMPN -0.650088 0.0 1 " ] }, "metadata": {}, @@ -2850,71 +2541,71 @@ " \n", " \n", " coef_eatout_dist_0_2\n", - " -0.767979\n", + " -0.959646\n", " \n", " \n", " coef_eatout_dist_2_5\n", - " -0.226448\n", + " -0.427890\n", " \n", " \n", " coef_eatout_dist_5_plus\n", - " -0.188137\n", + " -0.182199\n", " \n", " \n", " coef_escort_dist_0_2\n", - " 0.226691\n", + " -0.290871\n", " \n", " \n", " coef_escort_dist_2_5\n", - " -0.813494\n", + " -0.959038\n", " \n", " \n", " coef_escort_dist_5_plus\n", - " -0.255056\n", + " -0.264682\n", " \n", " \n", " coef_mode_logsum\n", - " 0.662937\n", + " 0.165346\n", " \n", " \n", " coef_othdiscr_dist_0_2\n", - " -0.203187\n", + " -0.424399\n", " \n", " \n", " coef_othdiscr_dist_2_5\n", - " -0.550945\n", + " -0.615669\n", " \n", " \n", " coef_othdiscr_dist_5_plus\n", - " -0.000141\n", + " -0.171387\n", " \n", " \n", " coef_othmaint_dist_2_5\n", - " -0.579932\n", + " -0.796780\n", " \n", " \n", " coef_othmaint_dist_5_plus\n", - " -0.229655\n", + " -0.156745\n", " \n", " \n", " coef_shopping_dist_2_5\n", - " -0.629792\n", + " -0.704553\n", " \n", " \n", " coef_shopping_dist_5_plus\n", - " -0.130649\n", + " -0.234821\n", " \n", " \n", " coef_social_dist_2_5\n", - " -0.245175\n", + " -0.464176\n", " \n", " \n", " coef_social_dist_5_plus\n", - " -0.190239\n", + " -0.176446\n", " \n", " \n", " eatout_HEREMPN\n", - " -0.946877\n", + " -1.601448\n", " \n", " \n", " eatout_RETEMPN\n", @@ -2922,15 +2613,15 @@ " \n", " \n", " escort_AGE0519\n", - " -1.171241\n", + " -0.765963\n", " \n", " \n", " escort_HEREMPN\n", - " -2.450057\n", + " -1.996310\n", " \n", " \n", " escort_HSENROLL\n", - " -3.304440\n", + " -1.872611\n", " \n", " \n", " escort_RETEMPN\n", @@ -2938,19 +2629,19 @@ " \n", " \n", " othdiscr_HEREMPN\n", - " -1.584964\n", + " -1.342128\n", " \n", " \n", " othdiscr_HSENROLL\n", - " -2.466750\n", + " -2.287886\n", " \n", " \n", " othdiscr_OTHEMPN\n", - " -2.370292\n", + " -1.905661\n", " \n", " \n", " othdiscr_RETEMPN\n", - " -1.238181\n", + " -1.550175\n", " \n", " \n", " othdiscr_TOTHH\n", @@ -2958,7 +2649,7 @@ " \n", " \n", " othmaint_HEREMPN\n", - " -0.668952\n", + " -0.689025\n", " \n", " \n", " othmaint_RETEMPN\n", @@ -2970,14 +2661,14 @@ " \n", " \n", " social_HEREMPN\n", - " -0.302843\n", + " -0.831065\n", " \n", " \n", " social_RETEMPN\n", " -0.650088\n", " \n", " \n", - "loglike-13268.709190928732d_loglike\n", + "
logloss2.770862804895239d_logloss\n", " \n", " \n", " \n", @@ -2999,71 +2690,71 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3071,15 +2762,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3087,19 +2778,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3107,7 +2798,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3119,109 +2810,109 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - "
coef_eatout_dist_0_20.0003340.000068
coef_eatout_dist_2_50.0006060.000060
coef_eatout_dist_5_plus-0.0012540.000251
coef_escort_dist_0_20.0000040.000236
coef_escort_dist_2_5-0.001135-0.000043
coef_escort_dist_5_plus-0.0005190.000007
coef_mode_logsum0.000531-0.000027
coef_othdiscr_dist_0_2-0.001077-0.000193
coef_othdiscr_dist_2_5-0.001877-0.000067
coef_othdiscr_dist_5_plus-0.0013870.000015
coef_othmaint_dist_2_5-0.000112-0.000110
coef_othmaint_dist_5_plus-0.000787-0.000227
coef_shopping_dist_2_5-0.0002320.000147
coef_shopping_dist_5_plus-0.0007950.000482
coef_social_dist_2_5-0.000397-0.000027
coef_social_dist_5_plus0.000937-0.000062
eatout_HEREMPN-0.0002880.000078
eatout_RETEMPN
escort_AGE0519-0.000725-0.000137
escort_HEREMPN0.000389-0.000064
escort_HSENROLL-0.000033-0.000067
escort_RETEMPN
othdiscr_HEREMPN0.0013260.000065
othdiscr_HSENROLL0.0000250.000034
othdiscr_OTHEMPN0.000044-0.000049
othdiscr_RETEMPN-0.0002870.000009
othdiscr_TOTHH
othmaint_HEREMPN0.000035-0.000002
othmaint_RETEMPN
social_HEREMPN-0.000091-0.000084
social_RETEMPN0.000000
nit29nfev79njev29status0message'Optimization terminated successfully'successTrueelapsed_time0:00:07.087910method'SLSQP'n_cases2581iteration_number29logloss5.140917935268784" + "nit46nfev46njev46status0message'Optimization terminated successfully'successTrueelapsed_time0:00:06.714015method'slsqp'n_cases30946iteration_number46loglike-85747.12036028807" ], "text/plain": [ "┣ x: -999 -999.000000\n", "┃ 0 0.000000\n", "┃ 1 1.000000\n", - "┃ coef_eatout_dist_0_2 -0.767979\n", - "┃ coef_eatout_dist_2_5 -0.226448\n", - "┃ coef_eatout_dist_5_plus -0.188137\n", - "┃ coef_escort_dist_0_2 0.226691\n", - "┃ coef_escort_dist_2_5 -0.813494\n", - "┃ coef_escort_dist_5_plus -0.255056\n", - "┃ coef_mode_logsum 0.662937\n", - "┃ coef_othdiscr_dist_0_2 -0.203187\n", - "┃ coef_othdiscr_dist_2_5 -0.550945\n", - "┃ coef_othdiscr_dist_5_plus -0.000141\n", - "┃ coef_othmaint_dist_2_5 -0.579932\n", - "┃ coef_othmaint_dist_5_plus -0.229655\n", - "┃ coef_shopping_dist_2_5 -0.629792\n", - "┃ coef_shopping_dist_5_plus -0.130649\n", - "┃ coef_social_dist_2_5 -0.245175\n", - "┃ coef_social_dist_5_plus -0.190239\n", - "┃ eatout_HEREMPN -0.946877\n", + "┃ coef_eatout_dist_0_2 -0.959646\n", + "┃ coef_eatout_dist_2_5 -0.427890\n", + "┃ coef_eatout_dist_5_plus -0.182199\n", + "┃ coef_escort_dist_0_2 -0.290871\n", + "┃ coef_escort_dist_2_5 -0.959038\n", + "┃ coef_escort_dist_5_plus -0.264682\n", + "┃ coef_mode_logsum 0.165346\n", + "┃ coef_othdiscr_dist_0_2 -0.424399\n", + "┃ coef_othdiscr_dist_2_5 -0.615669\n", + "┃ coef_othdiscr_dist_5_plus -0.171387\n", + "┃ coef_othmaint_dist_2_5 -0.796780\n", + "┃ coef_othmaint_dist_5_plus -0.156745\n", + "┃ coef_shopping_dist_2_5 -0.704553\n", + "┃ coef_shopping_dist_5_plus -0.234821\n", + "┃ coef_social_dist_2_5 -0.464176\n", + "┃ coef_social_dist_5_plus -0.176446\n", + "┃ eatout_HEREMPN -1.601448\n", "┃ eatout_RETEMPN -0.298406\n", - "┃ escort_AGE0519 -1.171241\n", - "┃ escort_HEREMPN -2.450057\n", - "┃ escort_HSENROLL -3.304440\n", + "┃ escort_AGE0519 -0.765963\n", + "┃ escort_HEREMPN -1.996310\n", + "┃ escort_HSENROLL -1.872611\n", "┃ escort_RETEMPN -1.491655\n", - "┃ othdiscr_HEREMPN -1.584964\n", - "┃ othdiscr_HSENROLL -2.466750\n", - "┃ othdiscr_OTHEMPN -2.370292\n", - "┃ othdiscr_RETEMPN -1.238181\n", + "┃ othdiscr_HEREMPN -1.342128\n", + "┃ othdiscr_HSENROLL -2.287886\n", + "┃ othdiscr_OTHEMPN -1.905661\n", + "┃ othdiscr_RETEMPN -1.550175\n", "┃ othdiscr_TOTHH -1.378326\n", - "┃ othmaint_HEREMPN -0.668952\n", + "┃ othmaint_HEREMPN -0.689025\n", "┃ othmaint_RETEMPN -0.729811\n", "┃ shopping_RETEMPN 0.000000\n", - "┃ social_HEREMPN -0.302843\n", + "┃ social_HEREMPN -0.831065\n", "┃ social_RETEMPN -0.650088\n", "┃ dtype: float64\n", - "┣ loglike: -13268.709190928732\n", - "┣ d_loglike: -999 0.000000\n", + "┣ logloss: 2.770862804895239\n", + "┣ d_logloss: -999 0.000000\n", "┃ 0 0.000000\n", "┃ 1 0.000000\n", - "┃ coef_eatout_dist_0_2 0.000334\n", - "┃ coef_eatout_dist_2_5 0.000606\n", - "┃ coef_eatout_dist_5_plus -0.001254\n", - "┃ coef_escort_dist_0_2 0.000004\n", - "┃ coef_escort_dist_2_5 -0.001135\n", - "┃ coef_escort_dist_5_plus -0.000519\n", - "┃ coef_mode_logsum 0.000531\n", - "┃ coef_othdiscr_dist_0_2 -0.001077\n", - "┃ coef_othdiscr_dist_2_5 -0.001877\n", - "┃ coef_othdiscr_dist_5_plus -0.001387\n", - "┃ coef_othmaint_dist_2_5 -0.000112\n", - "┃ coef_othmaint_dist_5_plus -0.000787\n", - "┃ coef_shopping_dist_2_5 -0.000232\n", - "┃ coef_shopping_dist_5_plus -0.000795\n", - "┃ coef_social_dist_2_5 -0.000397\n", - "┃ coef_social_dist_5_plus 0.000937\n", - "┃ eatout_HEREMPN -0.000288\n", + "┃ coef_eatout_dist_0_2 0.000068\n", + "┃ coef_eatout_dist_2_5 0.000060\n", + "┃ coef_eatout_dist_5_plus 0.000251\n", + "┃ coef_escort_dist_0_2 0.000236\n", + "┃ coef_escort_dist_2_5 -0.000043\n", + "┃ coef_escort_dist_5_plus 0.000007\n", + "┃ coef_mode_logsum -0.000027\n", + "┃ coef_othdiscr_dist_0_2 -0.000193\n", + "┃ coef_othdiscr_dist_2_5 -0.000067\n", + "┃ coef_othdiscr_dist_5_plus 0.000015\n", + "┃ coef_othmaint_dist_2_5 -0.000110\n", + "┃ coef_othmaint_dist_5_plus -0.000227\n", + "┃ coef_shopping_dist_2_5 0.000147\n", + "┃ coef_shopping_dist_5_plus 0.000482\n", + "┃ coef_social_dist_2_5 -0.000027\n", + "┃ coef_social_dist_5_plus -0.000062\n", + "┃ eatout_HEREMPN 0.000078\n", "┃ eatout_RETEMPN 0.000000\n", - "┃ escort_AGE0519 -0.000725\n", - "┃ escort_HEREMPN 0.000389\n", - "┃ escort_HSENROLL -0.000033\n", + "┃ escort_AGE0519 -0.000137\n", + "┃ escort_HEREMPN -0.000064\n", + "┃ escort_HSENROLL -0.000067\n", "┃ escort_RETEMPN 0.000000\n", - "┃ othdiscr_HEREMPN 0.001326\n", - "┃ othdiscr_HSENROLL 0.000025\n", - "┃ othdiscr_OTHEMPN 0.000044\n", - "┃ othdiscr_RETEMPN -0.000287\n", + "┃ othdiscr_HEREMPN 0.000065\n", + "┃ othdiscr_HSENROLL 0.000034\n", + "┃ othdiscr_OTHEMPN -0.000049\n", + "┃ othdiscr_RETEMPN 0.000009\n", "┃ othdiscr_TOTHH 0.000000\n", - "┃ othmaint_HEREMPN 0.000035\n", + "┃ othmaint_HEREMPN -0.000002\n", "┃ othmaint_RETEMPN 0.000000\n", "┃ shopping_RETEMPN 0.000000\n", - "┃ social_HEREMPN -0.000091\n", + "┃ social_HEREMPN -0.000084\n", "┃ social_RETEMPN 0.000000\n", "┃ dtype: float64\n", - "┣ nit: 29\n", - "┣ nfev: 79\n", - "┣ njev: 29\n", + "┣ nit: 46\n", + "┣ nfev: 46\n", + "┣ njev: 46\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=7, microseconds=87910)\n", - "┣ method: 'SLSQP'\n", - "┣ n_cases: 2581\n", - "┣ iteration_number: 29\n", - "┣ logloss: 5.140917935268784" + "┣ elapsed_time: datetime.timedelta(seconds=6, microseconds=714015)\n", + "┣ method: 'slsqp'\n", + "┣ n_cases: 30946\n", + "┣ iteration_number: 46\n", + "┣ loglike: -85747.12036028807" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.estimate(method='SLSQP')" + "model.estimate(maxiter=900)" ] }, { @@ -3233,309 +2924,333 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Null Value
-999-999. 0.00 NA-999.00
0 0.00 0.00 NA 0.00
1 1.00 0.00 NA 1.00
coef_eatout_dist_0_2-0.768 0.138-5.58*** 0.00
coef_eatout_dist_2_5-0.226 0.0689-3.29** 0.00
coef_eatout_dist_5_plus-0.188 0.105-1.80 0.00
coef_escort_dist_0_2 0.227 0.197 1.15 0.00
coef_escort_dist_2_5-0.813 0.0600-13.57*** 0.00
coef_escort_dist_5_plus-0.255 0.0751-3.39*** 0.00
coef_mode_logsum 0.663 0.0562 11.80*** 0.00
coef_othdiscr_dist_0_2-0.203 0.127-1.61 0.00
coef_othdiscr_dist_2_5-0.551 0.0530-10.39*** 0.00
coef_othdiscr_dist_5_plus-0.000141 0.0704-0.00 0.00
coef_othmaint_dist_2_5-0.580 0.0552-10.51*** 0.00
coef_othmaint_dist_5_plus-0.230 0.0903-2.54* 0.00
coef_shopping_dist_2_5-0.630 0.0445-14.16*** 0.00
coef_shopping_dist_5_plus-0.131 0.0623-2.10* 0.00
coef_social_dist_2_5-0.245 0.0817-3.00** 0.00
coef_social_dist_5_plus-0.190 0.129-1.48 0.00
eatout_HEREMPN-0.947 0.265-3.57*** 0.00
eatout_RETEMPN-0.298 0.00 NA 0.00
escort_AGE0519-1.17 0.413-2.84** 0.00
escort_HEREMPN-2.45 0.502-4.88*** 0.00
escort_HSENROLL-3.30 1.25-2.64** 0.00
escort_RETEMPN-1.49 0.00 NA 0.00
othdiscr_HEREMPN-1.58 0.284-5.58*** 0.00
othdiscr_HSENROLL-2.47 0.962-2.56* 0.00
othdiscr_OTHEMPN-2.37 0.595-3.98*** 0.00
othdiscr_RETEMPN-1.24 0.836-1.48 0.00
othdiscr_TOTHH-1.38 0.00 NA 0.00
othmaint_HEREMPN-0.669 0.275-2.44* 0.00
othmaint_RETEMPN-0.730 0.00 NA 0.00
shopping_RETEMPN 0.00 0.00 NA 0.00
social_HEREMPN-0.303 0.458-0.66 0.00
social_RETEMPN-0.650 0.00 NA 0.00
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 ValueStd Errt StatSignifNull Value
Parameter     
-999-999. 0.00 NA 0.00
0 0.00 0.00 NA 0.00
1 1.00 0.00 NA 0.00
coef_eatout_dist_0_2-0.960 0.0437-21.97*** 0.00
coef_eatout_dist_2_5-0.428 0.0207-20.70*** 0.00
coef_eatout_dist_5_plus-0.182 0.00484-37.68*** 0.00
coef_escort_dist_0_2-0.291 0.0329-8.85*** 0.00
coef_escort_dist_2_5-0.959 0.0161-59.53*** 0.00
coef_escort_dist_5_plus-0.265 0.00711-37.23*** 0.00
coef_mode_logsum 0.165 0.0124 13.36*** 0.00
coef_othdiscr_dist_0_2-0.424 0.0382-11.12*** 0.00
coef_othdiscr_dist_2_5-0.616 0.0146-42.26*** 0.00
coef_othdiscr_dist_5_plus-0.171 0.00349-49.12*** 0.00
coef_othmaint_dist_2_5-0.797 0.0158-50.41*** 0.00
coef_othmaint_dist_5_plus-0.157 0.00396-39.57*** 0.00
coef_shopping_dist_2_5-0.705 0.0120-58.52*** 0.00
coef_shopping_dist_5_plus-0.235 0.00446-52.69*** 0.00
coef_social_dist_2_5-0.464 0.0245-18.97*** 0.00
coef_social_dist_5_plus-0.176 0.00583-30.29*** 0.00
eatout_HEREMPN-1.60 0.0888-18.04*** 0.00
eatout_RETEMPN-0.298 0.00 NA 0.00
escort_AGE0519-0.766 0.185-4.15*** 0.00
escort_HEREMPN-2.00 0.229-8.71*** 0.00
escort_HSENROLL-1.87 0.200-9.36*** 0.00
escort_RETEMPN-1.49 0.00 NA 0.00
othdiscr_HEREMPN-1.34 0.0798-16.83*** 0.00
othdiscr_HSENROLL-2.29 0.182-12.56*** 0.00
othdiscr_OTHEMPN-1.91 0.128-14.93*** 0.00
othdiscr_RETEMPN-1.55 0.240-6.46*** 0.00
othdiscr_TOTHH-1.38 0.00 NA 0.00
othmaint_HEREMPN-0.689 0.0969-7.11*** 0.00
othmaint_RETEMPN-0.730 0.00 NA 0.00
shopping_RETEMPN 0.00 0.00 NA 0.00
social_HEREMPN-0.831 0.133-6.25*** 0.00
social_RETEMPN-0.650 0.00 NA 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3556,7 +3271,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -3582,7 +3297,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -3591,7 +3306,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -3610,7 +3325,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -3767,14 +3482,14 @@ " escort\n", " non_mandatory\n", " 0.000000\n", - " 0.341950\n", + " 0.229722\n", " 0.000000\n", - " 0.131140\n", + " 0.138687\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.471104\n", - " 0.055806\n", + " 0.474643\n", + " 0.156948\n", " 0.000000\n", " 0.000000\n", " \n", @@ -3799,9 +3514,9 @@ " eatout\n", " non_mandatory\n", " 0.000000\n", - " 0.656666\n", + " 0.786346\n", " 0.000000\n", - " 0.343334\n", + " 0.213654\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3815,9 +3530,9 @@ " othmaint\n", " non_mandatory\n", " 0.000000\n", - " 0.484790\n", + " 0.489805\n", " 0.000000\n", - " 0.515210\n", + " 0.510195\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3831,9 +3546,9 @@ " social\n", " non_mandatory\n", " 0.000000\n", - " 0.414051\n", + " 0.545121\n", " 0.000000\n", - " 0.585949\n", + " 0.454879\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3846,15 +3561,15 @@ " 12\n", " othdiscr\n", " non_mandatory\n", - " 0.272379\n", - " 0.313357\n", + " 0.258275\n", + " 0.217495\n", " 0.000000\n", - " 0.221530\n", - " 0.101011\n", + " 0.267795\n", + " 0.152428\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.091723\n", + " 0.104008\n", " 0.000000\n", " 0.000000\n", " \n", @@ -4015,12 +3730,12 @@ "4 university school 0.000000 0.000000 0.000000 0.000000 \n", "5 gradeschool school 0.000000 0.000000 0.000000 0.000000 \n", "6 highschool school 0.000000 0.000000 0.000000 0.000000 \n", - "7 escort non_mandatory 0.000000 0.341950 0.000000 0.131140 \n", + "7 escort non_mandatory 0.000000 0.229722 0.000000 0.138687 \n", "8 shopping non_mandatory 0.000000 1.000000 0.000000 0.000000 \n", - "9 eatout non_mandatory 0.000000 0.656666 0.000000 0.343334 \n", - "10 othmaint non_mandatory 0.000000 0.484790 0.000000 0.515210 \n", - "11 social non_mandatory 0.000000 0.414051 0.000000 0.585949 \n", - "12 othdiscr non_mandatory 0.272379 0.313357 0.000000 0.221530 \n", + "9 eatout non_mandatory 0.000000 0.786346 0.000000 0.213654 \n", + "10 othmaint non_mandatory 0.000000 0.489805 0.000000 0.510195 \n", + "11 social non_mandatory 0.000000 0.545121 0.000000 0.454879 \n", + "12 othdiscr non_mandatory 0.258275 0.217495 0.000000 0.267795 \n", "13 atwork atwork 0.000000 0.742000 0.000000 0.258000 \n", "14 work trip 0.000000 0.166667 0.166667 0.166667 \n", "15 escort trip 0.001000 0.225000 0.000000 0.144000 \n", @@ -4039,12 +3754,12 @@ "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.592000 0.408000 \n", "5 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 \n", "6 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 \n", - "7 0.000000 0.000000 0.000000 0.471104 0.055806 0.000000 0.000000 \n", + "7 0.000000 0.000000 0.000000 0.474643 0.156948 0.000000 0.000000 \n", "8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "9 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "10 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "11 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "12 0.101011 0.000000 0.000000 0.000000 0.091723 0.000000 0.000000 \n", + "12 0.152428 0.000000 0.000000 0.000000 0.104008 0.000000 0.000000 \n", "13 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "14 0.166667 0.166667 0.166667 0.000000 0.000000 0.000000 0.000000 \n", "15 0.000000 0.000000 0.000000 0.464000 0.166000 0.000000 0.000000 \n", @@ -4056,7 +3771,7 @@ "21 0.000000 0.000000 0.000000 0.000000 0.000000 0.591409 0.407592 " ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -4085,7 +3800,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -4113,7 +3828,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -4146,19 +3861,19 @@ " \n", " 0\n", " coef_mode_logsum\n", - " 0.662937\n", + " 0.165346\n", " F\n", " \n", " \n", " 1\n", " coef_escort_dist_0_2\n", - " 0.226691\n", + " -0.290871\n", " F\n", " \n", " \n", " 2\n", " coef_eatout_dist_0_2\n", - " -0.767979\n", + " -0.959646\n", " F\n", " \n", " \n", @@ -4170,79 +3885,79 @@ " \n", " 4\n", " coef_othdiscr_dist_0_2\n", - " -0.203187\n", + " -0.424399\n", " F\n", " \n", " \n", " 5\n", " coef_escort_dist_2_5\n", - " -0.813494\n", + " -0.959038\n", " F\n", " \n", " \n", " 6\n", " coef_shopping_dist_2_5\n", - " -0.629792\n", + " -0.704553\n", " F\n", " \n", " \n", " 7\n", " coef_eatout_dist_2_5\n", - " -0.226448\n", + " -0.427890\n", " F\n", " \n", " \n", " 8\n", " coef_othmaint_dist_2_5\n", - " -0.579932\n", + " -0.796780\n", " F\n", " \n", " \n", " 9\n", " coef_social_dist_2_5\n", - " -0.245175\n", + " -0.464176\n", " F\n", " \n", " \n", " 10\n", " coef_othdiscr_dist_2_5\n", - " -0.550945\n", + " -0.615669\n", " F\n", " \n", " \n", " 11\n", " coef_escort_dist_5_plus\n", - " -0.255056\n", + " -0.264682\n", " F\n", " \n", " \n", " 12\n", " coef_shopping_dist_5_plus\n", - " -0.130649\n", + " -0.234821\n", " F\n", " \n", " \n", " 13\n", " coef_eatout_dist_5_plus\n", - " -0.188137\n", + " -0.182199\n", " F\n", " \n", " \n", " 14\n", " coef_othmaint_dist_5_plus\n", - " -0.229655\n", + " -0.156745\n", " F\n", " \n", " \n", " 15\n", " coef_social_dist_5_plus\n", - " -0.190239\n", + " -0.176446\n", " F\n", " \n", " \n", " 16\n", " coef_othdiscr_dist_5_plus\n", - " -0.000141\n", + " -0.171387\n", " F\n", " \n", " \n", @@ -4251,26 +3966,26 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coef_mode_logsum 0.662937 F\n", - "1 coef_escort_dist_0_2 0.226691 F\n", - "2 coef_eatout_dist_0_2 -0.767979 F\n", + "0 coef_mode_logsum 0.165346 F\n", + "1 coef_escort_dist_0_2 -0.290871 F\n", + "2 coef_eatout_dist_0_2 -0.959646 F\n", "3 coef_eatout_social_0_2 -0.560900 F\n", - "4 coef_othdiscr_dist_0_2 -0.203187 F\n", - "5 coef_escort_dist_2_5 -0.813494 F\n", - "6 coef_shopping_dist_2_5 -0.629792 F\n", - "7 coef_eatout_dist_2_5 -0.226448 F\n", - "8 coef_othmaint_dist_2_5 -0.579932 F\n", - "9 coef_social_dist_2_5 -0.245175 F\n", - "10 coef_othdiscr_dist_2_5 -0.550945 F\n", - "11 coef_escort_dist_5_plus -0.255056 F\n", - "12 coef_shopping_dist_5_plus -0.130649 F\n", - "13 coef_eatout_dist_5_plus -0.188137 F\n", - "14 coef_othmaint_dist_5_plus -0.229655 F\n", - "15 coef_social_dist_5_plus -0.190239 F\n", - "16 coef_othdiscr_dist_5_plus -0.000141 F" + "4 coef_othdiscr_dist_0_2 -0.424399 F\n", + "5 coef_escort_dist_2_5 -0.959038 F\n", + "6 coef_shopping_dist_2_5 -0.704553 F\n", + "7 coef_eatout_dist_2_5 -0.427890 F\n", + "8 coef_othmaint_dist_2_5 -0.796780 F\n", + "9 coef_social_dist_2_5 -0.464176 F\n", + "10 coef_othdiscr_dist_2_5 -0.615669 F\n", + "11 coef_escort_dist_5_plus -0.264682 F\n", + "12 coef_shopping_dist_5_plus -0.234821 F\n", + "13 coef_eatout_dist_5_plus -0.182199 F\n", + "14 coef_othmaint_dist_5_plus -0.156745 F\n", + "15 coef_social_dist_5_plus -0.176446 F\n", + "16 coef_othdiscr_dist_5_plus -0.171387 F" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -4281,7 +3996,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -4447,14 +4162,14 @@ " escort\n", " non_mandatory\n", " 0.000000\n", - " 0.341950\n", + " 0.229722\n", " 0.000000\n", - " 0.131140\n", + " 0.138687\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.471104\n", - " 0.055806\n", + " 0.474643\n", + " 0.156948\n", " 0.000000\n", " 0.000000\n", " \n", @@ -4481,9 +4196,9 @@ " eatout\n", " non_mandatory\n", " 0.000000\n", - " 0.656666\n", + " 0.786346\n", " 0.000000\n", - " 0.343334\n", + " 0.213654\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4498,9 +4213,9 @@ " othmaint\n", " non_mandatory\n", " 0.000000\n", - " 0.484790\n", + " 0.489805\n", " 0.000000\n", - " 0.515210\n", + " 0.510195\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4515,9 +4230,9 @@ " social\n", " non_mandatory\n", " 0.000000\n", - " 0.414051\n", + " 0.545121\n", " 0.000000\n", - " 0.585949\n", + " 0.454879\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4531,15 +4246,15 @@ " 12\n", " othdiscr\n", " non_mandatory\n", - " 0.272379\n", - " 0.313357\n", + " 0.258275\n", + " 0.217495\n", " 0.000000\n", - " 0.221530\n", - " 0.101011\n", + " 0.267795\n", + " 0.152428\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.091723\n", + " 0.104008\n", " 0.000000\n", " 0.000000\n", " \n", @@ -4709,12 +4424,12 @@ "4 4 university school 0.000000 0.000000 0.000000 \n", "5 5 gradeschool school 0.000000 0.000000 0.000000 \n", "6 6 highschool school 0.000000 0.000000 0.000000 \n", - "7 7 escort non_mandatory 0.000000 0.341950 0.000000 \n", + "7 7 escort non_mandatory 0.000000 0.229722 0.000000 \n", "8 8 shopping non_mandatory 0.000000 1.000000 0.000000 \n", - "9 9 eatout non_mandatory 0.000000 0.656666 0.000000 \n", - "10 10 othmaint non_mandatory 0.000000 0.484790 0.000000 \n", - "11 11 social non_mandatory 0.000000 0.414051 0.000000 \n", - "12 12 othdiscr non_mandatory 0.272379 0.313357 0.000000 \n", + "9 9 eatout non_mandatory 0.000000 0.786346 0.000000 \n", + "10 10 othmaint non_mandatory 0.000000 0.489805 0.000000 \n", + "11 11 social non_mandatory 0.000000 0.545121 0.000000 \n", + "12 12 othdiscr non_mandatory 0.258275 0.217495 0.000000 \n", "13 13 atwork atwork 0.000000 0.742000 0.000000 \n", "14 14 work trip 0.000000 0.166667 0.166667 \n", "15 15 escort trip 0.001000 0.225000 0.000000 \n", @@ -4733,12 +4448,12 @@ "4 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.592000 \n", "5 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 \n", "6 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 \n", - "7 0.131140 0.000000 0.000000 0.000000 0.471104 0.055806 0.000000 \n", + "7 0.138687 0.000000 0.000000 0.000000 0.474643 0.156948 0.000000 \n", "8 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "9 0.343334 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "10 0.515210 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "11 0.585949 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "12 0.221530 0.101011 0.000000 0.000000 0.000000 0.091723 0.000000 \n", + "9 0.213654 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "10 0.510195 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "11 0.454879 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "12 0.267795 0.152428 0.000000 0.000000 0.000000 0.104008 0.000000 \n", "13 0.258000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "14 0.166667 0.166667 0.166667 0.166667 0.000000 0.000000 0.000000 \n", "15 0.144000 0.000000 0.000000 0.000000 0.464000 0.166000 0.000000 \n", @@ -4774,7 +4489,7 @@ "21 0.407592 " ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -4791,7 +4506,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -4805,7 +4520,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb b/activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb index c2973c29b1..b05d68490e 100644 --- a/activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb +++ b/activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +115,28 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/joint_tour_scheduling/tour_scheduling_joint_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_scheduling/joint_tour_scheduling_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_scheduling/joint_tour_scheduling_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/joint_tour_scheduling/joint_tour_scheduling_choosers_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"joint_tour_scheduling\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -746,28 +809,28 @@ " 30\n", " util_adjacent_window_exists_before_this_depart...\n", " Adjacent window exists before this departure h...\n", - " @(df.tour_type_count>1) & (df.tour_type_num ==...\n", + " @(df.tour_type_count>1) * (df.tour_type_num ==...\n", " coef_adjacent_window_exists_before_this_depart...\n", " \n", " \n", " 31\n", " util_adjacent_window_exists_after_this_arrival...\n", " Adjacent window exists after this arrival hour...\n", - " @(df.tour_type_count>1) & (df.tour_type_num ==...\n", + " @(df.tour_type_count>1) * (df.tour_type_num ==...\n", " coef_adjacent_window_exists_after_this_arrival...\n", " \n", " \n", " 32\n", " util_adjacent_window_exists_before_this_depart...\n", " Adjacent window exists before this departure h...\n", - " @(df.tour_type_num > 1) & _adjacent_window_before\n", + " @(df.tour_type_num > 1) * _adjacent_window_before\n", " coef_adjacent_window_exists_before_this_depart...\n", " \n", " \n", " 33\n", " util_adjacent_window_exists_after_this_arrival...\n", " Adjacent window exists after this arrival hour...\n", - " @(df.tour_type_num > 1) & _adjacent_window_after\n", + " @(df.tour_type_num > 1) * _adjacent_window_after\n", " coef_adjacent_window_exists_after_this_arrival...\n", " \n", " \n", @@ -1115,10 +1178,10 @@ "25 adult & (num_children > 0) & ( end > 18 ) & ( ... \n", "28 @tt.previous_tour_ends(df.tour_id, df.start) \n", "29 @tt.previous_tour_begins(df.tour_id, df.end) \n", - "30 @(df.tour_type_count>1) & (df.tour_type_num ==... \n", - "31 @(df.tour_type_count>1) & (df.tour_type_num ==... \n", - "32 @(df.tour_type_num > 1) & _adjacent_window_before \n", - "33 @(df.tour_type_num > 1) & _adjacent_window_after \n", + "30 @(df.tour_type_count>1) * (df.tour_type_num ==... \n", + "31 @(df.tour_type_count>1) * (df.tour_type_num ==... \n", + "32 @(df.tour_type_num > 1) * _adjacent_window_before \n", + "33 @(df.tour_type_num > 1) * _adjacent_window_after \n", "34 (tour_type != 'escort') & (start < 6) \n", "35 (tour_type != 'escort') & (start == 6) \n", "36 (tour_type != 'escort') & (start == 7) \n", @@ -1262,14 +1325,14 @@ " tour_count\n", " tour_category\n", " ...\n", - " COLLPTE\n", - " TOPOLOGY\n", - " TERMINAL\n", - " household_density\n", - " employment_density\n", - " density_index\n", - " is_cbd\n", - " tour_id.1\n", + " auOpRetail\n", + " auOpTotal\n", + " trPkRetail\n", + " trPkTotal\n", + " trOpRetail\n", + " trOpTotal\n", + " nmRetail\n", + " nmTotal\n", " start_previous\n", " end_previous\n", " \n", @@ -1277,10 +1340,10 @@ " \n", " \n", " 0\n", - " 7785298\n", - " 146\n", - " 146\n", - " 189885\n", + " 7853686\n", + " 115\n", + " 104\n", + " 191553\n", " eatout\n", " 1\n", " 1\n", @@ -1288,47 +1351,47 @@ " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 2.94885\n", - " 23.215385\n", - " 11.646154\n", - " 7.755537\n", - " False\n", - " 7785298\n", + " 10.032432\n", + " 12.426053\n", + " 4.842958\n", + " 6.577576\n", + " 4.825284\n", + " 6.554185\n", + " 5.384032\n", + " 6.267720\n", " 5\n", " 5\n", " \n", " \n", " 1\n", - " 8708454\n", - " 145\n", - " 145\n", - " 212401\n", - " eatout\n", + " 7902318\n", + " 127\n", + " 91\n", + " 192739\n", + " shopping\n", " 1\n", " 1\n", " 1\n", " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 2\n", - " 4.64648\n", - " 196.395950\n", - " 178.779465\n", - " 93.587057\n", - " False\n", - " 8708454\n", + " 9.804714\n", + " 12.378032\n", + " 2.085722\n", + " 3.940109\n", + " 2.097132\n", + " 3.946235\n", + " 4.015752\n", + " 6.526846\n", " 5\n", " 5\n", " \n", " \n", " 2\n", - " 9715006\n", - " 115\n", - " 115\n", - " 236951\n", + " 7909776\n", + " 90\n", + " 105\n", + " 192921\n", " othdiscr\n", " 1\n", " 1\n", @@ -1336,62 +1399,62 @@ " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 2.08699\n", - " 16.952703\n", - " 7.608108\n", - " 5.251374\n", - " False\n", - " 9715006\n", + " 9.924635\n", + " 12.521312\n", + " 1.451325\n", + " 3.811134\n", + " 1.458624\n", + " 3.813040\n", + " 4.422027\n", + " 6.644415\n", " 5\n", " 5\n", " \n", " \n", " 3\n", - " 10831112\n", - " 115\n", - " 115\n", - " 264173\n", - " shopping\n", + " 7988742\n", + " 101\n", + " 101\n", + " 194847\n", + " othdiscr\n", " 1\n", " 1\n", " 1\n", " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 3\n", - " 4.73802\n", - " 117.769796\n", - " 246.205869\n", - " 79.663609\n", - " False\n", - " 10831112\n", + " 10.124314\n", + " 12.525120\n", + " 2.760744\n", + " 4.989736\n", + " 2.749302\n", + " 4.964188\n", + " 4.106787\n", + " 6.259962\n", " 5\n", " 5\n", " \n", " \n", " 4\n", - " 20334787\n", - " 37\n", - " 37\n", - " 495970\n", - " othmaint\n", + " 8103874\n", + " 74\n", + " 74\n", + " 197655\n", + " shopping\n", " 1\n", " 1\n", " 1\n", " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 2.17734\n", - " 17.968750\n", - " 4.109375\n", - " 3.344502\n", - " False\n", - " 20334787\n", + " 9.966499\n", + " 12.292832\n", + " 2.097899\n", + " 4.282017\n", + " 0.920538\n", + " 2.790691\n", + " 5.043413\n", + " 6.194205\n", " 5\n", " 5\n", " \n", @@ -1420,35 +1483,35 @@ " ...\n", " \n", " \n", - " 86\n", - " 283676518\n", - " 145\n", - " 145\n", - " 6918939\n", - " shopping\n", + " 1272\n", + " 303771147\n", + " 171\n", + " 171\n", + " 7409052\n", + " othdiscr\n", " 1\n", " 1\n", " 1\n", " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 2.18894\n", - " 20.746032\n", - " 9.793651\n", - " 6.652963\n", - " False\n", - " 283676518\n", + " 9.795165\n", + " 12.124129\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 4.666818\n", + " 6.381846\n", " 5\n", " 5\n", " \n", " \n", - " 87\n", - " 295260168\n", - " 158\n", - " 158\n", - " 7201469\n", + " 1273\n", + " 304144212\n", + " 77\n", + " 126\n", + " 7418154\n", " social\n", " 1\n", " 1\n", @@ -1456,23 +1519,23 @@ " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 3.19780\n", - " 25.035085\n", - " 34.002224\n", - " 14.418824\n", - " False\n", - " 295260168\n", + " 9.808362\n", + " 12.224503\n", + " 0.198489\n", + " 0.838468\n", + " 0.197857\n", + " 0.826233\n", + " 5.214767\n", + " 7.781673\n", " 5\n", " 5\n", " \n", " \n", - " 88\n", - " 297646485\n", - " 104\n", - " 104\n", - " 7259670\n", + " 1274\n", + " 304463637\n", + " 138\n", + " 127\n", + " 7425942\n", " othdiscr\n", " 1\n", " 1\n", @@ -1480,124 +1543,124 @@ " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 4.89113\n", - " 193.875000\n", - " 201.000000\n", - " 98.686610\n", - " False\n", - " 297646485\n", + " 9.674598\n", + " 12.273909\n", + " 2.314454\n", + " 5.062339\n", + " 2.170577\n", + " 4.928170\n", + " 3.927607\n", + " 5.937753\n", " 5\n", " 5\n", " \n", " \n", - " 89\n", - " 298814741\n", - " 74\n", - " 74\n", - " 7288164\n", - " othmaint\n", + " 1275\n", + " 305125582\n", + " 156\n", + " 177\n", + " 7442087\n", + " othdiscr\n", " 1\n", " 1\n", " 1\n", " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 4.29646\n", - " 57.195122\n", - " 95.975610\n", - " 35.838026\n", - " True\n", - " 298814741\n", + " 10.150831\n", + " 12.839956\n", + " 6.746949\n", + " 9.644580\n", + " 6.245710\n", + " 8.925880\n", + " 6.393693\n", + " 8.676755\n", " 5\n", " 5\n", " \n", " \n", - " 90\n", - " 301810980\n", + " 1276\n", + " 305246245\n", " 54\n", " 54\n", - " 7361244\n", - " othmaint\n", + " 7445030\n", + " othdiscr\n", " 1\n", " 1\n", " 1\n", " 1\n", " joint\n", " ...\n", - " 0.0\n", - " 1\n", - " 2.47787\n", - " 23.993197\n", - " 11.945578\n", - " 7.975024\n", - " False\n", - " 301810980\n", + " 10.154352\n", + " 12.772396\n", + " 5.492171\n", + " 8.363419\n", + " 5.092918\n", + " 7.921567\n", + " 4.507081\n", + " 7.061687\n", " 5\n", " 5\n", " \n", " \n", "\n", - "

91 rows × 151 columns

\n", + "

1277 rows × 150 columns

\n", "" ], "text/plain": [ - " tour_id model_choice override_choice person_id tour_type \\\n", - "0 7785298 146 146 189885 eatout \n", - "1 8708454 145 145 212401 eatout \n", - "2 9715006 115 115 236951 othdiscr \n", - "3 10831112 115 115 264173 shopping \n", - "4 20334787 37 37 495970 othmaint \n", - ".. ... ... ... ... ... \n", - "86 283676518 145 145 6918939 shopping \n", - "87 295260168 158 158 7201469 social \n", - "88 297646485 104 104 7259670 othdiscr \n", - "89 298814741 74 74 7288164 othmaint \n", - "90 301810980 54 54 7361244 othmaint \n", + " tour_id model_choice override_choice person_id tour_type \\\n", + "0 7853686 115 104 191553 eatout \n", + "1 7902318 127 91 192739 shopping \n", + "2 7909776 90 105 192921 othdiscr \n", + "3 7988742 101 101 194847 othdiscr \n", + "4 8103874 74 74 197655 shopping \n", + "... ... ... ... ... ... \n", + "1272 303771147 171 171 7409052 othdiscr \n", + "1273 304144212 77 126 7418154 social \n", + "1274 304463637 138 127 7425942 othdiscr \n", + "1275 305125582 156 177 7442087 othdiscr \n", + "1276 305246245 54 54 7445030 othdiscr \n", "\n", - " tour_type_count tour_type_num tour_num tour_count tour_category ... \\\n", - "0 1 1 1 1 joint ... \n", - "1 1 1 1 1 joint ... \n", - "2 1 1 1 1 joint ... \n", - "3 1 1 1 1 joint ... \n", - "4 1 1 1 1 joint ... \n", - ".. ... ... ... ... ... ... \n", - "86 1 1 1 1 joint ... \n", - "87 1 1 1 1 joint ... \n", - "88 1 1 1 1 joint ... \n", - "89 1 1 1 1 joint ... \n", - "90 1 1 1 1 joint ... \n", + " tour_type_count tour_type_num tour_num tour_count tour_category ... \\\n", + "0 1 1 1 1 joint ... \n", + "1 1 1 1 1 joint ... \n", + "2 1 1 1 1 joint ... \n", + "3 1 1 1 1 joint ... \n", + "4 1 1 1 1 joint ... \n", + "... ... ... ... ... ... ... \n", + "1272 1 1 1 1 joint ... \n", + "1273 1 1 1 1 joint ... \n", + "1274 1 1 1 1 joint ... \n", + "1275 1 1 1 1 joint ... \n", + "1276 1 1 1 1 joint ... \n", "\n", - " COLLPTE TOPOLOGY TERMINAL household_density employment_density \\\n", - "0 0.0 1 2.94885 23.215385 11.646154 \n", - "1 0.0 2 4.64648 196.395950 178.779465 \n", - "2 0.0 1 2.08699 16.952703 7.608108 \n", - "3 0.0 3 4.73802 117.769796 246.205869 \n", - "4 0.0 1 2.17734 17.968750 4.109375 \n", - ".. ... ... ... ... ... \n", - "86 0.0 1 2.18894 20.746032 9.793651 \n", - "87 0.0 1 3.19780 25.035085 34.002224 \n", - "88 0.0 1 4.89113 193.875000 201.000000 \n", - "89 0.0 1 4.29646 57.195122 95.975610 \n", - "90 0.0 1 2.47787 23.993197 11.945578 \n", + " auOpRetail auOpTotal trPkRetail trPkTotal trOpRetail trOpTotal \\\n", + "0 10.032432 12.426053 4.842958 6.577576 4.825284 6.554185 \n", + "1 9.804714 12.378032 2.085722 3.940109 2.097132 3.946235 \n", + "2 9.924635 12.521312 1.451325 3.811134 1.458624 3.813040 \n", + "3 10.124314 12.525120 2.760744 4.989736 2.749302 4.964188 \n", + "4 9.966499 12.292832 2.097899 4.282017 0.920538 2.790691 \n", + "... ... ... ... ... ... ... \n", + "1272 9.795165 12.124129 0.000000 0.000000 0.000000 0.000000 \n", + "1273 9.808362 12.224503 0.198489 0.838468 0.197857 0.826233 \n", + "1274 9.674598 12.273909 2.314454 5.062339 2.170577 4.928170 \n", + "1275 10.150831 12.839956 6.746949 9.644580 6.245710 8.925880 \n", + "1276 10.154352 12.772396 5.492171 8.363419 5.092918 7.921567 \n", "\n", - " density_index is_cbd tour_id.1 start_previous end_previous \n", - "0 7.755537 False 7785298 5 5 \n", - "1 93.587057 False 8708454 5 5 \n", - "2 5.251374 False 9715006 5 5 \n", - "3 79.663609 False 10831112 5 5 \n", - "4 3.344502 False 20334787 5 5 \n", - ".. ... ... ... ... ... \n", - "86 6.652963 False 283676518 5 5 \n", - "87 14.418824 False 295260168 5 5 \n", - "88 98.686610 False 297646485 5 5 \n", - "89 35.838026 True 298814741 5 5 \n", - "90 7.975024 False 301810980 5 5 \n", + " nmRetail nmTotal start_previous end_previous \n", + "0 5.384032 6.267720 5 5 \n", + "1 4.015752 6.526846 5 5 \n", + "2 4.422027 6.644415 5 5 \n", + "3 4.106787 6.259962 5 5 \n", + "4 5.043413 6.194205 5 5 \n", + "... ... ... ... ... \n", + "1272 4.666818 6.381846 5 5 \n", + "1273 5.214767 7.781673 5 5 \n", + "1274 3.927607 5.937753 5 5 \n", + "1275 6.393693 8.676755 5 5 \n", + "1276 4.507081 7.061687 5 5 \n", "\n", - "[91 rows x 151 columns]" + "[1277 rows x 150 columns]" ] }, "execution_count": 6, @@ -1643,135 +1706,135 @@ " \n", " \n", " tour_id\n", - " variable\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", + " start\n", + " end\n", + " duration\n", + " tdd\n", + " mode_choice_logsum\n", + " util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends\n", + " util_free_flow_round_trip_auto_time_shift_effects_duration\n", + " util_shopping_tour_departure_shift_effects\n", + " util_shopping_tour_duration_shift_effects\n", " ...\n", - " 180\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", + " util_arrival_constants_pm_peak_4\n", + " util_arrival_constants_evening\n", + " util_arrival_constants_late\n", + " util_duration_constants_0_to_1_hours\n", + " util_duration_constants_2_to_3_hours\n", + " util_duration_constants_4_to_5_hours\n", + " util_duration_constants_6_to_7_hours\n", + " util_duration_constants_8_to_10_hours\n", + " util_duration_constants_11_to_13_hours\n", + " util_duration_constants_14_to_18_hours\n", " \n", " \n", " \n", " \n", " 0\n", - " 7785298\n", - " duration\n", - " 0.0\n", - " 1.0\n", - " 2.0\n", + " 7853686\n", + " 5\n", + " 5\n", " 0\n", " 0\n", " 0\n", + " False\n", + " 0.000000\n", " 0\n", " 0\n", " ...\n", - " 0.0\n", - " 1.0\n", - " 2.0\n", - " 3.0\n", - " 0.0\n", - " 1.0\n", - " 2.0\n", - " 0.0\n", - " 1.0\n", - " 0.0\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 1\n", - " 7785298\n", - " end\n", - " 5.0\n", - " 6.0\n", - " 7.0\n", - " 0\n", - " 0\n", + " 7853686\n", + " 5\n", + " 6\n", + " 1\n", + " 1\n", " 0\n", + " False\n", + " 24.259998\n", " 0\n", " 0\n", " ...\n", - " 20.0\n", - " 21.0\n", - " 22.0\n", - " 23.0\n", - " 21.0\n", - " 22.0\n", - " 23.0\n", - " 22.0\n", - " 23.0\n", - " 23.0\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 2\n", - " 7785298\n", - " mode_choice_logsum\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0\n", - " 0\n", + " 7853686\n", + " 5\n", + " 7\n", + " 2\n", + " 2\n", " 0\n", + " False\n", + " 48.519997\n", " 0\n", " 0\n", " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 3\n", - " 7785298\n", - " start\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " 0\n", - " 0\n", + " 7853686\n", + " 5\n", + " 8\n", + " 3\n", + " 3\n", " 0\n", + " False\n", + " 72.779999\n", " 0\n", " 0\n", " ...\n", - " 20.0\n", - " 20.0\n", - " 20.0\n", - " 20.0\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " 22.0\n", - " 22.0\n", - " 23.0\n", - " \n", - " \n", - " 4\n", - " 7785298\n", - " util_adjacent_window_exists_after_this_arrival...\n", " False\n", " False\n", " False\n", + " False\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " \n", + " \n", + " 4\n", + " 7853686\n", + " 5\n", + " 9\n", + " 4\n", + " 4\n", " 0\n", - " 0\n", - " 0\n", + " False\n", + " 97.039993\n", " 0\n", " 0\n", " ...\n", @@ -1780,7 +1843,7 @@ " False\n", " False\n", " False\n", - " False\n", + " True\n", " False\n", " False\n", " False\n", @@ -1811,47 +1874,47 @@ " ...\n", " \n", " \n", - " 5728\n", - " 301810980\n", - " util_subsequent_of_2_plus_tours_for_same_purpo...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 107101\n", + " 305246245\n", + " 21\n", + " 22\n", + " 1\n", + " 185\n", " 0\n", + " False\n", + " 2.480000\n", " 0\n", " 0\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 5729\n", - " 301810980\n", - " util_subsequent_tour_must_start_after_previous...\n", + " False\n", + " False\n", + " True\n", + " True\n", " False\n", " False\n", " False\n", " False\n", + " False\n", + " False\n", + " \n", + " \n", + " 107102\n", + " 305246245\n", + " 21\n", + " 23\n", + " 2\n", + " 186\n", " 0\n", - " 0\n", + " False\n", + " 4.960000\n", " 0\n", " 0\n", " ...\n", " False\n", " False\n", + " True\n", " False\n", - " False\n", - " False\n", + " True\n", " False\n", " False\n", " False\n", @@ -1859,22 +1922,22 @@ " False\n", " \n", " \n", - " 5730\n", - " 301810980\n", - " util_university_student_arrive_after_22\n", - " False\n", - " False\n", - " False\n", - " False\n", + " 107103\n", + " 305246245\n", + " 22\n", + " 22\n", " 0\n", + " 187\n", " 0\n", + " False\n", + " 0.000000\n", " 0\n", " 0\n", " ...\n", " False\n", " False\n", - " False\n", - " False\n", + " True\n", + " True\n", " False\n", " False\n", " False\n", @@ -1883,99 +1946,229 @@ " False\n", " \n", " \n", - " 5731\n", - " 301810980\n", - " util_visit_tour_departure_shift_effects\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 107104\n", + " 305246245\n", + " 22\n", + " 23\n", + " 1\n", + " 188\n", " 0\n", + " False\n", + " 2.480000\n", " 0\n", " 0\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " True\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 5732\n", - " 301810980\n", - " util_visit_tour_duration_shift_effects\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 107105\n", + " 305246245\n", + " 23\n", + " 23\n", " 0\n", + " 189\n", " 0\n", + " False\n", + " 0.000000\n", " 0\n", " 0\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " True\n", + " True\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", "\n", - "

5733 rows × 192 columns

\n", + "

107106 rows × 65 columns

\n", "" ], "text/plain": [ - " tour_id variable 0 \\\n", - "0 7785298 duration 0.0 \n", - "1 7785298 end 5.0 \n", - "2 7785298 mode_choice_logsum 0.0 \n", - "3 7785298 start 5.0 \n", - "4 7785298 util_adjacent_window_exists_after_this_arrival... False \n", - "... ... ... ... \n", - "5728 301810980 util_subsequent_of_2_plus_tours_for_same_purpo... 0 \n", - "5729 301810980 util_subsequent_tour_must_start_after_previous... False \n", - "5730 301810980 util_university_student_arrive_after_22 False \n", - "5731 301810980 util_visit_tour_departure_shift_effects 0 \n", - "5732 301810980 util_visit_tour_duration_shift_effects 0 \n", + " tour_id start end duration tdd mode_choice_logsum \\\n", + "0 7853686 5 5 0 0 0 \n", + "1 7853686 5 6 1 1 0 \n", + "2 7853686 5 7 2 2 0 \n", + "3 7853686 5 8 3 3 0 \n", + "4 7853686 5 9 4 4 0 \n", + "... ... ... ... ... ... ... \n", + "107101 305246245 21 22 1 185 0 \n", + "107102 305246245 21 23 2 186 0 \n", + "107103 305246245 22 22 0 187 0 \n", + "107104 305246245 22 23 1 188 0 \n", + "107105 305246245 23 23 0 189 0 \n", + "\n", + " util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "107101 False \n", + "107102 False \n", + "107103 False \n", + "107104 False \n", + "107105 False \n", + "\n", + " util_free_flow_round_trip_auto_time_shift_effects_duration \\\n", + "0 0.000000 \n", + "1 24.259998 \n", + "2 48.519997 \n", + "3 72.779999 \n", + "4 97.039993 \n", + "... ... \n", + "107101 2.480000 \n", + "107102 4.960000 \n", + "107103 0.000000 \n", + "107104 2.480000 \n", + "107105 0.000000 \n", + "\n", + " util_shopping_tour_departure_shift_effects \\\n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "... ... \n", + "107101 0 \n", + "107102 0 \n", + "107103 0 \n", + "107104 0 \n", + "107105 0 \n", + "\n", + " util_shopping_tour_duration_shift_effects ... \\\n", + "0 0 ... \n", + "1 0 ... \n", + "2 0 ... \n", + "3 0 ... \n", + "4 0 ... \n", + "... ... ... \n", + "107101 0 ... \n", + "107102 0 ... \n", + "107103 0 ... \n", + "107104 0 ... \n", + "107105 0 ... \n", + "\n", + " util_arrival_constants_pm_peak_4 util_arrival_constants_evening \\\n", + "0 False False \n", + "1 False False \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "107101 False False \n", + "107102 False False \n", + "107103 False False \n", + "107104 False False \n", + "107105 False False \n", + "\n", + " util_arrival_constants_late util_duration_constants_0_to_1_hours \\\n", + "0 False True \n", + "1 False True \n", + "2 False False \n", + "3 False False \n", + "4 False False \n", + "... ... ... \n", + "107101 True True \n", + "107102 True False \n", + "107103 True True \n", + "107104 True True \n", + "107105 True True \n", + "\n", + " util_duration_constants_2_to_3_hours \\\n", + "0 False \n", + "1 False \n", + "2 True \n", + "3 True \n", + "4 False \n", + "... ... \n", + "107101 False \n", + "107102 True \n", + "107103 False \n", + "107104 False \n", + "107105 False \n", "\n", - " 1 2 3 4 5 6 7 ... 180 181 182 183 184 \\\n", - "0 1.0 2.0 0 0 0 0 0 ... 0.0 1.0 2.0 3.0 0.0 \n", - "1 6.0 7.0 0 0 0 0 0 ... 20.0 21.0 22.0 23.0 21.0 \n", - "2 0.0 0.0 0 0 0 0 0 ... 0.0 0.0 0.0 0.0 0.0 \n", - "3 5.0 5.0 0 0 0 0 0 ... 20.0 20.0 20.0 20.0 21.0 \n", - "4 False False 0 0 0 0 0 ... False False False False False \n", - "... ... ... ... .. .. .. .. ... ... ... ... ... ... \n", - "5728 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", - "5729 False False False 0 0 0 0 ... False False False False False \n", - "5730 False False False 0 0 0 0 ... False False False False False \n", - "5731 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", - "5732 0 0 0 0 0 0 0 ... 0 0 0 0 0 \n", + " util_duration_constants_4_to_5_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 True \n", + "... ... \n", + "107101 False \n", + "107102 False \n", + "107103 False \n", + "107104 False \n", + "107105 False \n", "\n", - " 185 186 187 188 189 \n", - "0 1.0 2.0 0.0 1.0 0.0 \n", - "1 22.0 23.0 22.0 23.0 23.0 \n", - "2 0.0 0.0 0.0 0.0 0.0 \n", - "3 21.0 21.0 22.0 22.0 23.0 \n", - "4 False False False False False \n", - "... ... ... ... ... ... \n", - "5728 0 0 0 0 0 \n", - "5729 False False False False False \n", - "5730 False False False False False \n", - "5731 0 0 0 0 0 \n", - "5732 0 0 0 0 0 \n", + " util_duration_constants_6_to_7_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "107101 False \n", + "107102 False \n", + "107103 False \n", + "107104 False \n", + "107105 False \n", "\n", - "[5733 rows x 192 columns]" + " util_duration_constants_8_to_10_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "107101 False \n", + "107102 False \n", + "107103 False \n", + "107104 False \n", + "107105 False \n", + "\n", + " util_duration_constants_11_to_13_hours \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "107101 False \n", + "107102 False \n", + "107103 False \n", + "107104 False \n", + "107105 False \n", + "\n", + " util_duration_constants_14_to_18_hours \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "107101 False \n", + "107102 False \n", + "107103 False \n", + "107104 False \n", + "107105 False \n", + "\n", + "[107106 rows x 65 columns]" ] }, "execution_count": 7, @@ -2005,13 +2198,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "problem: chosen_but_not_available has (2 issues)\n" ] }, + { + "data": { + "text/plain": [ + "(,\n", + " ┣ chosen_but_not_available: altid n example rows\n", + " ┃ 0 115 1 356\n", + " ┃ 1 149 2 732, 1183)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.doctor(repair_ch_av=\"-\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "

Iteration 067 [Optimization terminated successfully]

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Iteration 144 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -2023,7 +2239,7 @@ { "data": { "text/html": [ - "

Best LL = -173.82837615906058

" + "

Best LL = -2789.512559021124

" ], "text/plain": [ "" @@ -2054,913 +2270,867 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " -13.526580\n", + " 0.800851\n", + " 0.800851\n", " -0.025700\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -13.526580\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction\n", - " -14.169341\n", + " 0.494438\n", + " 0.494438\n", " -0.027340\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -14.169341\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " -13.495922\n", + " 0.008839\n", + " 0.008839\n", " 0.008442\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -13.495922\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction\n", - " -7.716247\n", + " -0.549692\n", + " -0.549692\n", " -0.059300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -7.716247\n", " \n", " \n", " coef_adult_with_children_in_hh_arrive_19_21\n", - " -0.002541\n", + " -0.078997\n", + " -0.078997\n", " 0.336000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.002541\n", " \n", " \n", " coef_arrival_constants_am_peak\n", - " 4.647673\n", + " -8.413713\n", + " -8.413713\n", " -8.728880\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 4.647673\n", " \n", " \n", " coef_arrival_constants_early\n", - " 24.793142\n", + " -6.758955\n", + " -6.758955\n", " -8.728880\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 24.793142\n", " \n", " \n", " coef_arrival_constants_evening\n", - " -4.291783\n", + " -2.675953\n", + " -2.675953\n", " -2.748940\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -4.291783\n", " \n", " \n", " coef_arrival_constants_late\n", - " -9.617038\n", + " -4.194942\n", + " -4.194942\n", " -4.242530\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -9.617038\n", " \n", " \n", " coef_arrival_constants_midday_1\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_arrival_constants_midday_2\n", - " 2.692519\n", + " 1.416723\n", + " 1.416723\n", " 1.408040\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 2.692519\n", " \n", " \n", " coef_arrival_constants_pm_peak_1\n", - " 1.849237\n", + " 0.964024\n", + " 0.964024\n", " 1.020360\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 1.849237\n", " \n", " \n", " coef_arrival_constants_pm_peak_2\n", - " 1.518410\n", + " 1.200835\n", + " 1.200835\n", " 1.068630\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 1.518410\n", " \n", " \n", " coef_arrival_constants_pm_peak_3\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_arrival_constants_pm_peak_4\n", - " -1.182768\n", + " -0.725959\n", + " -0.725959\n", " -0.596260\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.182768\n", " \n", " \n", " coef_departure_constants_am_peak_1\n", - " -25.000000\n", + " -13.990759\n", + " -13.990759\n", " -11.595050\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -25.000000\n", " \n", " \n", " coef_departure_constants_am_peak_2\n", - " -4.236849\n", + " -10.163788\n", + " -10.163788\n", " -9.005190\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -4.236849\n", " \n", " \n", " coef_departure_constants_am_peak_3\n", - " 0.806223\n", + " -2.799430\n", + " -2.799430\n", " -2.733150\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.806223\n", " \n", " \n", " coef_departure_constants_am_peak_4\n", - " 1.408142\n", + " 0.514393\n", + " 0.514393\n", " 0.266540\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 1.408142\n", " \n", " \n", " coef_departure_constants_early\n", - " -24.418086\n", + " -17.865880\n", + " -17.865880\n", " -14.477080\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -24.418086\n", " \n", " \n", " coef_departure_constants_evening\n", - " -14.557517\n", + " -19.574287\n", + " -19.574287\n", " -18.987370\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -14.557517\n", " \n", " \n", " coef_departure_constants_late\n", - " -20.421814\n", + " -21.444922\n", + " -21.444922\n", " -20.278070\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -20.421814\n", " \n", " \n", " coef_departure_constants_midday_1\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_departure_constants_midday_2\n", - " -4.501238\n", + " -1.886316\n", + " -1.886316\n", " -1.602600\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -4.501238\n", " \n", " \n", " coef_departure_constants_pm_peak\n", - " -12.638403\n", + " -18.238556\n", + " -18.238556\n", " -17.695980\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -12.638403\n", " \n", " \n", " coef_destination_in_cbd_duration_shift_effects\n", - " 0.572330\n", + " 0.130935\n", + " 0.130935\n", " 0.106700\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.572330\n", " \n", " \n", " coef_discretionary_tour_duration_lt_2_hours\n", - " -1.022488\n", + " -0.772425\n", + " -0.772425\n", " -0.697400\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.022488\n", " \n", " \n", " coef_duration_constants_0_to_1_hours\n", - " -2.750481\n", + " -2.021348\n", + " -2.021348\n", " -2.228260\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.750481\n", " \n", " \n", " coef_duration_constants_11_to_13_hours\n", - " -6.162737\n", + " -1.608105\n", + " -1.608105\n", " -0.815190\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -6.162737\n", " \n", " \n", " coef_duration_constants_14_to_18_hours\n", - " -2.800706\n", + " -3.067613\n", + " -3.067613\n", " -2.738440\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.800706\n", " \n", " \n", " coef_duration_constants_2_to_3_hours\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_duration_constants_4_to_5_hours\n", - " 0.301145\n", + " -0.519561\n", + " -0.519561\n", " -0.561740\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.301145\n", " \n", " \n", " coef_duration_constants_6_to_7_hours\n", - " 0.144072\n", + " -0.868309\n", + " -0.868309\n", " -0.655470\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.144072\n", " \n", " \n", " coef_duration_constants_8_to_10_hours\n", - " -10.570858\n", + " -1.020095\n", + " -1.020095\n", " -0.740620\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -10.570858\n", " \n", " \n", " coef_eat_out_tour_departure_shift_effects\n", - " -0.024649\n", + " 0.056578\n", + " 0.056578\n", " 0.075490\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.024649\n", " \n", " \n", " coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect\n", - " -1.329351\n", + " -0.506782\n", + " -0.506782\n", " -0.236400\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.329351\n", " \n", " \n", " coef_free_flow_round_trip_auto_time_shift_effects_duration\n", - " 0.017752\n", + " 0.003622\n", + " 0.003622\n", " 0.003195\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.017752\n", " \n", " \n", " coef_maintenance_tour_depart_before_7\n", - " 1.154584\n", + " -0.523693\n", + " -0.523693\n", " -0.882600\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 1.154584\n", " \n", " \n", " coef_maintenance_tour_departure_shift_effects\n", - " -0.159210\n", + " -0.135634\n", + " -0.135634\n", " -0.148900\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.159210\n", " \n", " \n", " coef_maintenance_tour_duration_shift_effects\n", - " -0.146459\n", + " -0.038428\n", + " -0.038428\n", " -0.083720\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.146459\n", " \n", " \n", " coef_number_of_joint_tours_departure_shift_effects\n", - " 1.356173\n", + " 0.128742\n", + " 0.128742\n", " 0.052080\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 1.356173\n", " \n", " \n", " coef_number_of_mandatory_tours_departure_shift_effects\n", - " 0.076326\n", + " -0.009788\n", + " -0.009788\n", " 0.046730\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.076326\n", " \n", " \n", " coef_school_child_age_16_plus_departure_shift_effects\n", + " 0.106395\n", + " 0.106395\n", " 0.072660\n", - " 0.072660\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.072660\n", " \n", " \n", " coef_school_child_age_16_plus_duration_shift_effects\n", + " 0.315781\n", + " 0.315781\n", " 0.209500\n", - " 0.209500\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.209500\n", " \n", " \n", " coef_school_child_age_under_16_departure_shift_effects\n", - " 0.239007\n", + " 0.015473\n", + " 0.015473\n", " 0.046570\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.239007\n", " \n", " \n", " coef_school_child_age_under_16_duration_shift_effects\n", - " -0.567863\n", + " 0.327527\n", + " 0.327527\n", " 0.327200\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.567863\n", " \n", " \n", " coef_school_child_under_16_arrive_after_22\n", - " -6.147237\n", + " -5.038827\n", + " -5.038827\n", " -1.180000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -6.147237\n", " \n", " \n", " coef_shopping_tour_arrive_after_22\n", - " -7.221041\n", + " -0.033268\n", + " -0.033268\n", " -0.602700\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -7.221041\n", " \n", " \n", " coef_shopping_tour_depart_before_8\n", - " 1.458488\n", + " -1.484109\n", + " -1.484109\n", " -1.037000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 1.458488\n", " \n", " \n", " coef_shopping_tour_departure_shift_effects\n", - " -0.045633\n", + " -0.099254\n", + " -0.099254\n", " -0.060150\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.045633\n", " \n", " \n", " coef_shopping_tour_duration_lt_2_hours\n", - " 0.189427\n", + " 0.699700\n", + " 0.699700\n", " 0.516800\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.189427\n", " \n", " \n", " coef_shopping_tour_duration_shift_effects\n", - " -0.359705\n", + " -0.101207\n", + " -0.101207\n", " -0.120800\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.359705\n", " \n", " \n", " coef_some_previously_scheduled_tour_begins_in_this_arrival_hour\n", - " 0.411979\n", + " -0.017681\n", + " -0.017681\n", " -0.399200\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.411979\n", " \n", " \n", " coef_some_previously_scheduled_tour_ends_in_this_departure_hour\n", - " 0.282029\n", + " -0.288048\n", + " -0.288048\n", " -0.456200\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.282029\n", " \n", " \n", " coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect\n", - " 0.191264\n", + " 0.062417\n", + " 0.062417\n", " -0.173100\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.191264\n", " \n", " \n", " coef_unavailable\n", " -999.000000\n", " -999.000000\n", + " -999.000000\n", + " -999.0\n", + " -999.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_university_student_arrive_after_22\n", - " -9.181095\n", + " 0.360948\n", + " 0.360948\n", " 0.546600\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -9.181095\n", " \n", " \n", " coef_visit_tour_departure_shift_effects\n", - " -0.109515\n", + " 0.136717\n", + " 0.136717\n", " 0.096880\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.109515\n", " \n", " \n", " coef_visit_tour_duration_shift_effects\n", - " -0.123814\n", + " 0.172436\n", + " 0.172436\n", " 0.163800\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.123814\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_adjacent_window_exists_after_this_arrival_... -13.526580 -0.025700 \n", - "coef_adjacent_window_exists_after_this_arrival_... -14.169341 -0.027340 \n", - "coef_adjacent_window_exists_before_this_departu... -13.495922 0.008442 \n", - "coef_adjacent_window_exists_before_this_departu... -7.716247 -0.059300 \n", - "coef_adult_with_children_in_hh_arrive_19_21 -0.002541 0.336000 \n", - "coef_arrival_constants_am_peak 4.647673 -8.728880 \n", - "coef_arrival_constants_early 24.793142 -8.728880 \n", - "coef_arrival_constants_evening -4.291783 -2.748940 \n", - "coef_arrival_constants_late -9.617038 -4.242530 \n", + " value best \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 0.800851 0.800851 \n", + "coef_adjacent_window_exists_after_this_arrival_... 0.494438 0.494438 \n", + "coef_adjacent_window_exists_before_this_departu... 0.008839 0.008839 \n", + "coef_adjacent_window_exists_before_this_departu... -0.549692 -0.549692 \n", + "coef_adult_with_children_in_hh_arrive_19_21 -0.078997 -0.078997 \n", + "coef_arrival_constants_am_peak -8.413713 -8.413713 \n", + "coef_arrival_constants_early -6.758955 -6.758955 \n", + "coef_arrival_constants_evening -2.675953 -2.675953 \n", + "coef_arrival_constants_late -4.194942 -4.194942 \n", "coef_arrival_constants_midday_1 0.000000 0.000000 \n", - "coef_arrival_constants_midday_2 2.692519 1.408040 \n", - "coef_arrival_constants_pm_peak_1 1.849237 1.020360 \n", - "coef_arrival_constants_pm_peak_2 1.518410 1.068630 \n", + "coef_arrival_constants_midday_2 1.416723 1.416723 \n", + "coef_arrival_constants_pm_peak_1 0.964024 0.964024 \n", + "coef_arrival_constants_pm_peak_2 1.200835 1.200835 \n", "coef_arrival_constants_pm_peak_3 0.000000 0.000000 \n", - "coef_arrival_constants_pm_peak_4 -1.182768 -0.596260 \n", - "coef_departure_constants_am_peak_1 -25.000000 -11.595050 \n", - "coef_departure_constants_am_peak_2 -4.236849 -9.005190 \n", - "coef_departure_constants_am_peak_3 0.806223 -2.733150 \n", - "coef_departure_constants_am_peak_4 1.408142 0.266540 \n", - "coef_departure_constants_early -24.418086 -14.477080 \n", - "coef_departure_constants_evening -14.557517 -18.987370 \n", - "coef_departure_constants_late -20.421814 -20.278070 \n", + "coef_arrival_constants_pm_peak_4 -0.725959 -0.725959 \n", + "coef_departure_constants_am_peak_1 -13.990759 -13.990759 \n", + "coef_departure_constants_am_peak_2 -10.163788 -10.163788 \n", + "coef_departure_constants_am_peak_3 -2.799430 -2.799430 \n", + "coef_departure_constants_am_peak_4 0.514393 0.514393 \n", + "coef_departure_constants_early -17.865880 -17.865880 \n", + "coef_departure_constants_evening -19.574287 -19.574287 \n", + "coef_departure_constants_late -21.444922 -21.444922 \n", "coef_departure_constants_midday_1 0.000000 0.000000 \n", - "coef_departure_constants_midday_2 -4.501238 -1.602600 \n", - "coef_departure_constants_pm_peak -12.638403 -17.695980 \n", - "coef_destination_in_cbd_duration_shift_effects 0.572330 0.106700 \n", - "coef_discretionary_tour_duration_lt_2_hours -1.022488 -0.697400 \n", - "coef_duration_constants_0_to_1_hours -2.750481 -2.228260 \n", - "coef_duration_constants_11_to_13_hours -6.162737 -0.815190 \n", - "coef_duration_constants_14_to_18_hours -2.800706 -2.738440 \n", + "coef_departure_constants_midday_2 -1.886316 -1.886316 \n", + "coef_departure_constants_pm_peak -18.238556 -18.238556 \n", + "coef_destination_in_cbd_duration_shift_effects 0.130935 0.130935 \n", + "coef_discretionary_tour_duration_lt_2_hours -0.772425 -0.772425 \n", + "coef_duration_constants_0_to_1_hours -2.021348 -2.021348 \n", + "coef_duration_constants_11_to_13_hours -1.608105 -1.608105 \n", + "coef_duration_constants_14_to_18_hours -3.067613 -3.067613 \n", "coef_duration_constants_2_to_3_hours 0.000000 0.000000 \n", - "coef_duration_constants_4_to_5_hours 0.301145 -0.561740 \n", - "coef_duration_constants_6_to_7_hours 0.144072 -0.655470 \n", - "coef_duration_constants_8_to_10_hours -10.570858 -0.740620 \n", - "coef_eat_out_tour_departure_shift_effects -0.024649 0.075490 \n", - "coef_first_of_2_plus_tours_for_same_purpose_dep... -1.329351 -0.236400 \n", - "coef_free_flow_round_trip_auto_time_shift_effec... 0.017752 0.003195 \n", - "coef_maintenance_tour_depart_before_7 1.154584 -0.882600 \n", - "coef_maintenance_tour_departure_shift_effects -0.159210 -0.148900 \n", - "coef_maintenance_tour_duration_shift_effects -0.146459 -0.083720 \n", - "coef_number_of_joint_tours_departure_shift_effects 1.356173 0.052080 \n", - "coef_number_of_mandatory_tours_departure_shift_... 0.076326 0.046730 \n", - "coef_school_child_age_16_plus_departure_shift_e... 0.072660 0.072660 \n", - "coef_school_child_age_16_plus_duration_shift_ef... 0.209500 0.209500 \n", - "coef_school_child_age_under_16_departure_shift_... 0.239007 0.046570 \n", - "coef_school_child_age_under_16_duration_shift_e... -0.567863 0.327200 \n", - "coef_school_child_under_16_arrive_after_22 -6.147237 -1.180000 \n", - "coef_shopping_tour_arrive_after_22 -7.221041 -0.602700 \n", - "coef_shopping_tour_depart_before_8 1.458488 -1.037000 \n", - "coef_shopping_tour_departure_shift_effects -0.045633 -0.060150 \n", - "coef_shopping_tour_duration_lt_2_hours 0.189427 0.516800 \n", - "coef_shopping_tour_duration_shift_effects -0.359705 -0.120800 \n", - "coef_some_previously_scheduled_tour_begins_in_t... 0.411979 -0.399200 \n", - "coef_some_previously_scheduled_tour_ends_in_thi... 0.282029 -0.456200 \n", - "coef_subsequent_of_2_plus_tours_for_same_purpos... 0.191264 -0.173100 \n", + "coef_duration_constants_4_to_5_hours -0.519561 -0.519561 \n", + "coef_duration_constants_6_to_7_hours -0.868309 -0.868309 \n", + "coef_duration_constants_8_to_10_hours -1.020095 -1.020095 \n", + "coef_eat_out_tour_departure_shift_effects 0.056578 0.056578 \n", + "coef_first_of_2_plus_tours_for_same_purpose_dep... -0.506782 -0.506782 \n", + "coef_free_flow_round_trip_auto_time_shift_effec... 0.003622 0.003622 \n", + "coef_maintenance_tour_depart_before_7 -0.523693 -0.523693 \n", + "coef_maintenance_tour_departure_shift_effects -0.135634 -0.135634 \n", + "coef_maintenance_tour_duration_shift_effects -0.038428 -0.038428 \n", + "coef_number_of_joint_tours_departure_shift_effects 0.128742 0.128742 \n", + "coef_number_of_mandatory_tours_departure_shift_... -0.009788 -0.009788 \n", + "coef_school_child_age_16_plus_departure_shift_e... 0.106395 0.106395 \n", + "coef_school_child_age_16_plus_duration_shift_ef... 0.315781 0.315781 \n", + "coef_school_child_age_under_16_departure_shift_... 0.015473 0.015473 \n", + "coef_school_child_age_under_16_duration_shift_e... 0.327527 0.327527 \n", + "coef_school_child_under_16_arrive_after_22 -5.038827 -5.038827 \n", + "coef_shopping_tour_arrive_after_22 -0.033268 -0.033268 \n", + "coef_shopping_tour_depart_before_8 -1.484109 -1.484109 \n", + "coef_shopping_tour_departure_shift_effects -0.099254 -0.099254 \n", + "coef_shopping_tour_duration_lt_2_hours 0.699700 0.699700 \n", + "coef_shopping_tour_duration_shift_effects -0.101207 -0.101207 \n", + "coef_some_previously_scheduled_tour_begins_in_t... -0.017681 -0.017681 \n", + "coef_some_previously_scheduled_tour_ends_in_thi... -0.288048 -0.288048 \n", + "coef_subsequent_of_2_plus_tours_for_same_purpos... 0.062417 0.062417 \n", "coef_unavailable -999.000000 -999.000000 \n", - "coef_university_student_arrive_after_22 -9.181095 0.546600 \n", - "coef_visit_tour_departure_shift_effects -0.109515 0.096880 \n", - "coef_visit_tour_duration_shift_effects -0.123814 0.163800 \n", + "coef_university_student_arrive_after_22 0.360948 0.360948 \n", + "coef_visit_tour_departure_shift_effects 0.136717 0.136717 \n", + "coef_visit_tour_duration_shift_effects 0.172436 0.172436 \n", "\n", - " nullvalue minimum \\\n", - "coef_adjacent_window_exists_after_this_arrival_... 0.0 -25.0 \n", - "coef_adjacent_window_exists_after_this_arrival_... 0.0 -25.0 \n", - "coef_adjacent_window_exists_before_this_departu... 0.0 -25.0 \n", - "coef_adjacent_window_exists_before_this_departu... 0.0 -25.0 \n", - "coef_adult_with_children_in_hh_arrive_19_21 0.0 -25.0 \n", - "coef_arrival_constants_am_peak 0.0 -25.0 \n", - "coef_arrival_constants_early 0.0 -25.0 \n", - "coef_arrival_constants_evening 0.0 -25.0 \n", - "coef_arrival_constants_late 0.0 -25.0 \n", - "coef_arrival_constants_midday_1 0.0 -25.0 \n", - "coef_arrival_constants_midday_2 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_1 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_2 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_3 0.0 -25.0 \n", - "coef_arrival_constants_pm_peak_4 0.0 -25.0 \n", - "coef_departure_constants_am_peak_1 0.0 -25.0 \n", - "coef_departure_constants_am_peak_2 0.0 -25.0 \n", - "coef_departure_constants_am_peak_3 0.0 -25.0 \n", - "coef_departure_constants_am_peak_4 0.0 -25.0 \n", - "coef_departure_constants_early 0.0 -25.0 \n", - "coef_departure_constants_evening 0.0 -25.0 \n", - "coef_departure_constants_late 0.0 -25.0 \n", - "coef_departure_constants_midday_1 0.0 -25.0 \n", - "coef_departure_constants_midday_2 0.0 -25.0 \n", - "coef_departure_constants_pm_peak 0.0 -25.0 \n", - "coef_destination_in_cbd_duration_shift_effects 0.0 -25.0 \n", - "coef_discretionary_tour_duration_lt_2_hours 0.0 -25.0 \n", - "coef_duration_constants_0_to_1_hours 0.0 -25.0 \n", - "coef_duration_constants_11_to_13_hours 0.0 -25.0 \n", - "coef_duration_constants_14_to_18_hours 0.0 -25.0 \n", - "coef_duration_constants_2_to_3_hours 0.0 -25.0 \n", - "coef_duration_constants_4_to_5_hours 0.0 -25.0 \n", - "coef_duration_constants_6_to_7_hours 0.0 -25.0 \n", - "coef_duration_constants_8_to_10_hours 0.0 -25.0 \n", - "coef_eat_out_tour_departure_shift_effects 0.0 -25.0 \n", - "coef_first_of_2_plus_tours_for_same_purpose_dep... 0.0 -25.0 \n", - "coef_free_flow_round_trip_auto_time_shift_effec... 0.0 -25.0 \n", - "coef_maintenance_tour_depart_before_7 0.0 -25.0 \n", - "coef_maintenance_tour_departure_shift_effects 0.0 -25.0 \n", - "coef_maintenance_tour_duration_shift_effects 0.0 -25.0 \n", - "coef_number_of_joint_tours_departure_shift_effects 0.0 -25.0 \n", - "coef_number_of_mandatory_tours_departure_shift_... 0.0 -25.0 \n", - "coef_school_child_age_16_plus_departure_shift_e... 0.0 -25.0 \n", - "coef_school_child_age_16_plus_duration_shift_ef... 0.0 -25.0 \n", - "coef_school_child_age_under_16_departure_shift_... 0.0 -25.0 \n", - "coef_school_child_age_under_16_duration_shift_e... 0.0 -25.0 \n", - "coef_school_child_under_16_arrive_after_22 0.0 -25.0 \n", - "coef_shopping_tour_arrive_after_22 0.0 -25.0 \n", - "coef_shopping_tour_depart_before_8 0.0 -25.0 \n", - "coef_shopping_tour_departure_shift_effects 0.0 -25.0 \n", - "coef_shopping_tour_duration_lt_2_hours 0.0 -25.0 \n", - "coef_shopping_tour_duration_shift_effects 0.0 -25.0 \n", - "coef_some_previously_scheduled_tour_begins_in_t... 0.0 -25.0 \n", - "coef_some_previously_scheduled_tour_ends_in_thi... 0.0 -25.0 \n", - "coef_subsequent_of_2_plus_tours_for_same_purpos... 0.0 -25.0 \n", - "coef_unavailable 0.0 -25.0 \n", - "coef_university_student_arrive_after_22 0.0 -25.0 \n", - "coef_visit_tour_departure_shift_effects 0.0 -25.0 \n", - "coef_visit_tour_duration_shift_effects 0.0 -25.0 \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... -0.025700 -25.0 \n", + "coef_adjacent_window_exists_after_this_arrival_... -0.027340 -25.0 \n", + "coef_adjacent_window_exists_before_this_departu... 0.008442 -25.0 \n", + "coef_adjacent_window_exists_before_this_departu... -0.059300 -25.0 \n", + "coef_adult_with_children_in_hh_arrive_19_21 0.336000 -25.0 \n", + "coef_arrival_constants_am_peak -8.728880 -25.0 \n", + "coef_arrival_constants_early -8.728880 -25.0 \n", + "coef_arrival_constants_evening -2.748940 -25.0 \n", + "coef_arrival_constants_late -4.242530 -25.0 \n", + "coef_arrival_constants_midday_1 0.000000 0.0 \n", + "coef_arrival_constants_midday_2 1.408040 -25.0 \n", + "coef_arrival_constants_pm_peak_1 1.020360 -25.0 \n", + "coef_arrival_constants_pm_peak_2 1.068630 -25.0 \n", + "coef_arrival_constants_pm_peak_3 0.000000 0.0 \n", + "coef_arrival_constants_pm_peak_4 -0.596260 -25.0 \n", + "coef_departure_constants_am_peak_1 -11.595050 -25.0 \n", + "coef_departure_constants_am_peak_2 -9.005190 -25.0 \n", + "coef_departure_constants_am_peak_3 -2.733150 -25.0 \n", + "coef_departure_constants_am_peak_4 0.266540 -25.0 \n", + "coef_departure_constants_early -14.477080 -25.0 \n", + "coef_departure_constants_evening -18.987370 -25.0 \n", + "coef_departure_constants_late -20.278070 -25.0 \n", + "coef_departure_constants_midday_1 0.000000 0.0 \n", + "coef_departure_constants_midday_2 -1.602600 -25.0 \n", + "coef_departure_constants_pm_peak -17.695980 -25.0 \n", + "coef_destination_in_cbd_duration_shift_effects 0.106700 -25.0 \n", + "coef_discretionary_tour_duration_lt_2_hours -0.697400 -25.0 \n", + "coef_duration_constants_0_to_1_hours -2.228260 -25.0 \n", + "coef_duration_constants_11_to_13_hours -0.815190 -25.0 \n", + "coef_duration_constants_14_to_18_hours -2.738440 -25.0 \n", + "coef_duration_constants_2_to_3_hours 0.000000 0.0 \n", + "coef_duration_constants_4_to_5_hours -0.561740 -25.0 \n", + "coef_duration_constants_6_to_7_hours -0.655470 -25.0 \n", + "coef_duration_constants_8_to_10_hours -0.740620 -25.0 \n", + "coef_eat_out_tour_departure_shift_effects 0.075490 -25.0 \n", + "coef_first_of_2_plus_tours_for_same_purpose_dep... -0.236400 -25.0 \n", + "coef_free_flow_round_trip_auto_time_shift_effec... 0.003195 -25.0 \n", + "coef_maintenance_tour_depart_before_7 -0.882600 -25.0 \n", + "coef_maintenance_tour_departure_shift_effects -0.148900 -25.0 \n", + "coef_maintenance_tour_duration_shift_effects -0.083720 -25.0 \n", + "coef_number_of_joint_tours_departure_shift_effects 0.052080 -25.0 \n", + "coef_number_of_mandatory_tours_departure_shift_... 0.046730 -25.0 \n", + "coef_school_child_age_16_plus_departure_shift_e... 0.072660 -25.0 \n", + "coef_school_child_age_16_plus_duration_shift_ef... 0.209500 -25.0 \n", + "coef_school_child_age_under_16_departure_shift_... 0.046570 -25.0 \n", + "coef_school_child_age_under_16_duration_shift_e... 0.327200 -25.0 \n", + "coef_school_child_under_16_arrive_after_22 -1.180000 -25.0 \n", + "coef_shopping_tour_arrive_after_22 -0.602700 -25.0 \n", + "coef_shopping_tour_depart_before_8 -1.037000 -25.0 \n", + "coef_shopping_tour_departure_shift_effects -0.060150 -25.0 \n", + "coef_shopping_tour_duration_lt_2_hours 0.516800 -25.0 \n", + "coef_shopping_tour_duration_shift_effects -0.120800 -25.0 \n", + "coef_some_previously_scheduled_tour_begins_in_t... -0.399200 -25.0 \n", + "coef_some_previously_scheduled_tour_ends_in_thi... -0.456200 -25.0 \n", + "coef_subsequent_of_2_plus_tours_for_same_purpos... -0.173100 -25.0 \n", + "coef_unavailable -999.000000 -999.0 \n", + "coef_university_student_arrive_after_22 0.546600 -25.0 \n", + "coef_visit_tour_departure_shift_effects 0.096880 -25.0 \n", + "coef_visit_tour_duration_shift_effects 0.163800 -25.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_adjacent_window_exists_after_this_arrival_... 25.0 0 \n", - "coef_adjacent_window_exists_after_this_arrival_... 25.0 0 \n", - "coef_adjacent_window_exists_before_this_departu... 25.0 0 \n", - "coef_adjacent_window_exists_before_this_departu... 25.0 0 \n", - "coef_adult_with_children_in_hh_arrive_19_21 25.0 0 \n", - "coef_arrival_constants_am_peak 25.0 0 \n", - "coef_arrival_constants_early 25.0 0 \n", - "coef_arrival_constants_evening 25.0 0 \n", - "coef_arrival_constants_late 25.0 0 \n", - "coef_arrival_constants_midday_1 25.0 1 \n", - "coef_arrival_constants_midday_2 25.0 0 \n", - "coef_arrival_constants_pm_peak_1 25.0 0 \n", - "coef_arrival_constants_pm_peak_2 25.0 0 \n", - "coef_arrival_constants_pm_peak_3 25.0 1 \n", - "coef_arrival_constants_pm_peak_4 25.0 0 \n", - "coef_departure_constants_am_peak_1 25.0 0 \n", - "coef_departure_constants_am_peak_2 25.0 0 \n", - "coef_departure_constants_am_peak_3 25.0 0 \n", - "coef_departure_constants_am_peak_4 25.0 0 \n", - "coef_departure_constants_early 25.0 0 \n", - "coef_departure_constants_evening 25.0 0 \n", - "coef_departure_constants_late 25.0 0 \n", - "coef_departure_constants_midday_1 25.0 1 \n", - "coef_departure_constants_midday_2 25.0 0 \n", - "coef_departure_constants_pm_peak 25.0 0 \n", - "coef_destination_in_cbd_duration_shift_effects 25.0 0 \n", - "coef_discretionary_tour_duration_lt_2_hours 25.0 0 \n", - "coef_duration_constants_0_to_1_hours 25.0 0 \n", - "coef_duration_constants_11_to_13_hours 25.0 0 \n", - "coef_duration_constants_14_to_18_hours 25.0 0 \n", - "coef_duration_constants_2_to_3_hours 25.0 1 \n", - "coef_duration_constants_4_to_5_hours 25.0 0 \n", - "coef_duration_constants_6_to_7_hours 25.0 0 \n", - "coef_duration_constants_8_to_10_hours 25.0 0 \n", - "coef_eat_out_tour_departure_shift_effects 25.0 0 \n", - "coef_first_of_2_plus_tours_for_same_purpose_dep... 25.0 0 \n", - "coef_free_flow_round_trip_auto_time_shift_effec... 25.0 0 \n", - "coef_maintenance_tour_depart_before_7 25.0 0 \n", - "coef_maintenance_tour_departure_shift_effects 25.0 0 \n", - "coef_maintenance_tour_duration_shift_effects 25.0 0 \n", - "coef_number_of_joint_tours_departure_shift_effects 25.0 0 \n", - "coef_number_of_mandatory_tours_departure_shift_... 25.0 0 \n", - "coef_school_child_age_16_plus_departure_shift_e... 25.0 0 \n", - "coef_school_child_age_16_plus_duration_shift_ef... 25.0 0 \n", - "coef_school_child_age_under_16_departure_shift_... 25.0 0 \n", - "coef_school_child_age_under_16_duration_shift_e... 25.0 0 \n", - "coef_school_child_under_16_arrive_after_22 25.0 0 \n", - "coef_shopping_tour_arrive_after_22 25.0 0 \n", - "coef_shopping_tour_depart_before_8 25.0 0 \n", - "coef_shopping_tour_departure_shift_effects 25.0 0 \n", - "coef_shopping_tour_duration_lt_2_hours 25.0 0 \n", - "coef_shopping_tour_duration_shift_effects 25.0 0 \n", - "coef_some_previously_scheduled_tour_begins_in_t... 25.0 0 \n", - "coef_some_previously_scheduled_tour_ends_in_thi... 25.0 0 \n", - "coef_subsequent_of_2_plus_tours_for_same_purpos... 25.0 0 \n", - "coef_unavailable 25.0 1 \n", - "coef_university_student_arrive_after_22 25.0 0 \n", - "coef_visit_tour_departure_shift_effects 25.0 0 \n", - "coef_visit_tour_duration_shift_effects 25.0 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 25.0 0.0 \n", + "coef_adjacent_window_exists_after_this_arrival_... 25.0 0.0 \n", + "coef_adjacent_window_exists_before_this_departu... 25.0 0.0 \n", + "coef_adjacent_window_exists_before_this_departu... 25.0 0.0 \n", + "coef_adult_with_children_in_hh_arrive_19_21 25.0 0.0 \n", + "coef_arrival_constants_am_peak 25.0 0.0 \n", + "coef_arrival_constants_early 25.0 0.0 \n", + "coef_arrival_constants_evening 25.0 0.0 \n", + "coef_arrival_constants_late 25.0 0.0 \n", + "coef_arrival_constants_midday_1 0.0 0.0 \n", + "coef_arrival_constants_midday_2 25.0 0.0 \n", + "coef_arrival_constants_pm_peak_1 25.0 0.0 \n", + "coef_arrival_constants_pm_peak_2 25.0 0.0 \n", + "coef_arrival_constants_pm_peak_3 0.0 0.0 \n", + "coef_arrival_constants_pm_peak_4 25.0 0.0 \n", + "coef_departure_constants_am_peak_1 25.0 0.0 \n", + "coef_departure_constants_am_peak_2 25.0 0.0 \n", + "coef_departure_constants_am_peak_3 25.0 0.0 \n", + "coef_departure_constants_am_peak_4 25.0 0.0 \n", + "coef_departure_constants_early 25.0 0.0 \n", + "coef_departure_constants_evening 25.0 0.0 \n", + "coef_departure_constants_late 25.0 0.0 \n", + "coef_departure_constants_midday_1 0.0 0.0 \n", + "coef_departure_constants_midday_2 25.0 0.0 \n", + "coef_departure_constants_pm_peak 25.0 0.0 \n", + "coef_destination_in_cbd_duration_shift_effects 25.0 0.0 \n", + "coef_discretionary_tour_duration_lt_2_hours 25.0 0.0 \n", + "coef_duration_constants_0_to_1_hours 25.0 0.0 \n", + "coef_duration_constants_11_to_13_hours 25.0 0.0 \n", + "coef_duration_constants_14_to_18_hours 25.0 0.0 \n", + "coef_duration_constants_2_to_3_hours 0.0 0.0 \n", + "coef_duration_constants_4_to_5_hours 25.0 0.0 \n", + "coef_duration_constants_6_to_7_hours 25.0 0.0 \n", + "coef_duration_constants_8_to_10_hours 25.0 0.0 \n", + "coef_eat_out_tour_departure_shift_effects 25.0 0.0 \n", + "coef_first_of_2_plus_tours_for_same_purpose_dep... 25.0 0.0 \n", + "coef_free_flow_round_trip_auto_time_shift_effec... 25.0 0.0 \n", + "coef_maintenance_tour_depart_before_7 25.0 0.0 \n", + "coef_maintenance_tour_departure_shift_effects 25.0 0.0 \n", + "coef_maintenance_tour_duration_shift_effects 25.0 0.0 \n", + "coef_number_of_joint_tours_departure_shift_effects 25.0 0.0 \n", + "coef_number_of_mandatory_tours_departure_shift_... 25.0 0.0 \n", + "coef_school_child_age_16_plus_departure_shift_e... 25.0 0.0 \n", + "coef_school_child_age_16_plus_duration_shift_ef... 25.0 0.0 \n", + "coef_school_child_age_under_16_departure_shift_... 25.0 0.0 \n", + "coef_school_child_age_under_16_duration_shift_e... 25.0 0.0 \n", + "coef_school_child_under_16_arrive_after_22 25.0 0.0 \n", + "coef_shopping_tour_arrive_after_22 25.0 0.0 \n", + "coef_shopping_tour_depart_before_8 25.0 0.0 \n", + "coef_shopping_tour_departure_shift_effects 25.0 0.0 \n", + "coef_shopping_tour_duration_lt_2_hours 25.0 0.0 \n", + "coef_shopping_tour_duration_shift_effects 25.0 0.0 \n", + "coef_some_previously_scheduled_tour_begins_in_t... 25.0 0.0 \n", + "coef_some_previously_scheduled_tour_ends_in_thi... 25.0 0.0 \n", + "coef_subsequent_of_2_plus_tours_for_same_purpos... 25.0 0.0 \n", + "coef_unavailable -999.0 0.0 \n", + "coef_university_student_arrive_after_22 25.0 0.0 \n", + "coef_visit_tour_departure_shift_effects 25.0 0.0 \n", + "coef_visit_tour_duration_shift_effects 25.0 0.0 \n", "\n", - " best \n", - "coef_adjacent_window_exists_after_this_arrival_... -13.526580 \n", - "coef_adjacent_window_exists_after_this_arrival_... -14.169341 \n", - "coef_adjacent_window_exists_before_this_departu... -13.495922 \n", - "coef_adjacent_window_exists_before_this_departu... -7.716247 \n", - "coef_adult_with_children_in_hh_arrive_19_21 -0.002541 \n", - "coef_arrival_constants_am_peak 4.647673 \n", - "coef_arrival_constants_early 24.793142 \n", - "coef_arrival_constants_evening -4.291783 \n", - "coef_arrival_constants_late -9.617038 \n", - "coef_arrival_constants_midday_1 0.000000 \n", - "coef_arrival_constants_midday_2 2.692519 \n", - "coef_arrival_constants_pm_peak_1 1.849237 \n", - "coef_arrival_constants_pm_peak_2 1.518410 \n", - "coef_arrival_constants_pm_peak_3 0.000000 \n", - "coef_arrival_constants_pm_peak_4 -1.182768 \n", - "coef_departure_constants_am_peak_1 -25.000000 \n", - "coef_departure_constants_am_peak_2 -4.236849 \n", - "coef_departure_constants_am_peak_3 0.806223 \n", - "coef_departure_constants_am_peak_4 1.408142 \n", - "coef_departure_constants_early -24.418086 \n", - "coef_departure_constants_evening -14.557517 \n", - "coef_departure_constants_late -20.421814 \n", - "coef_departure_constants_midday_1 0.000000 \n", - "coef_departure_constants_midday_2 -4.501238 \n", - "coef_departure_constants_pm_peak -12.638403 \n", - "coef_destination_in_cbd_duration_shift_effects 0.572330 \n", - "coef_discretionary_tour_duration_lt_2_hours -1.022488 \n", - "coef_duration_constants_0_to_1_hours -2.750481 \n", - "coef_duration_constants_11_to_13_hours -6.162737 \n", - "coef_duration_constants_14_to_18_hours -2.800706 \n", - "coef_duration_constants_2_to_3_hours 0.000000 \n", - "coef_duration_constants_4_to_5_hours 0.301145 \n", - "coef_duration_constants_6_to_7_hours 0.144072 \n", - "coef_duration_constants_8_to_10_hours -10.570858 \n", - "coef_eat_out_tour_departure_shift_effects -0.024649 \n", - "coef_first_of_2_plus_tours_for_same_purpose_dep... -1.329351 \n", - "coef_free_flow_round_trip_auto_time_shift_effec... 0.017752 \n", - "coef_maintenance_tour_depart_before_7 1.154584 \n", - "coef_maintenance_tour_departure_shift_effects -0.159210 \n", - "coef_maintenance_tour_duration_shift_effects -0.146459 \n", - "coef_number_of_joint_tours_departure_shift_effects 1.356173 \n", - "coef_number_of_mandatory_tours_departure_shift_... 0.076326 \n", - "coef_school_child_age_16_plus_departure_shift_e... 0.072660 \n", - "coef_school_child_age_16_plus_duration_shift_ef... 0.209500 \n", - "coef_school_child_age_under_16_departure_shift_... 0.239007 \n", - "coef_school_child_age_under_16_duration_shift_e... -0.567863 \n", - "coef_school_child_under_16_arrive_after_22 -6.147237 \n", - "coef_shopping_tour_arrive_after_22 -7.221041 \n", - "coef_shopping_tour_depart_before_8 1.458488 \n", - "coef_shopping_tour_departure_shift_effects -0.045633 \n", - "coef_shopping_tour_duration_lt_2_hours 0.189427 \n", - "coef_shopping_tour_duration_shift_effects -0.359705 \n", - "coef_some_previously_scheduled_tour_begins_in_t... 0.411979 \n", - "coef_some_previously_scheduled_tour_ends_in_thi... 0.282029 \n", - "coef_subsequent_of_2_plus_tours_for_same_purpos... 0.191264 \n", - "coef_unavailable -999.000000 \n", - "coef_university_student_arrive_after_22 -9.181095 \n", - "coef_visit_tour_departure_shift_effects -0.109515 \n", - "coef_visit_tour_duration_shift_effects -0.123814 " + " holdfast \n", + "param_name \n", + "coef_adjacent_window_exists_after_this_arrival_... 0 \n", + "coef_adjacent_window_exists_after_this_arrival_... 0 \n", + "coef_adjacent_window_exists_before_this_departu... 0 \n", + "coef_adjacent_window_exists_before_this_departu... 0 \n", + "coef_adult_with_children_in_hh_arrive_19_21 0 \n", + "coef_arrival_constants_am_peak 0 \n", + "coef_arrival_constants_early 0 \n", + "coef_arrival_constants_evening 0 \n", + "coef_arrival_constants_late 0 \n", + "coef_arrival_constants_midday_1 1 \n", + "coef_arrival_constants_midday_2 0 \n", + "coef_arrival_constants_pm_peak_1 0 \n", + "coef_arrival_constants_pm_peak_2 0 \n", + "coef_arrival_constants_pm_peak_3 1 \n", + "coef_arrival_constants_pm_peak_4 0 \n", + "coef_departure_constants_am_peak_1 0 \n", + "coef_departure_constants_am_peak_2 0 \n", + "coef_departure_constants_am_peak_3 0 \n", + "coef_departure_constants_am_peak_4 0 \n", + "coef_departure_constants_early 0 \n", + "coef_departure_constants_evening 0 \n", + "coef_departure_constants_late 0 \n", + "coef_departure_constants_midday_1 1 \n", + "coef_departure_constants_midday_2 0 \n", + "coef_departure_constants_pm_peak 0 \n", + "coef_destination_in_cbd_duration_shift_effects 0 \n", + "coef_discretionary_tour_duration_lt_2_hours 0 \n", + "coef_duration_constants_0_to_1_hours 0 \n", + "coef_duration_constants_11_to_13_hours 0 \n", + "coef_duration_constants_14_to_18_hours 0 \n", + "coef_duration_constants_2_to_3_hours 1 \n", + "coef_duration_constants_4_to_5_hours 0 \n", + "coef_duration_constants_6_to_7_hours 0 \n", + "coef_duration_constants_8_to_10_hours 0 \n", + "coef_eat_out_tour_departure_shift_effects 0 \n", + "coef_first_of_2_plus_tours_for_same_purpose_dep... 0 \n", + "coef_free_flow_round_trip_auto_time_shift_effec... 0 \n", + "coef_maintenance_tour_depart_before_7 0 \n", + "coef_maintenance_tour_departure_shift_effects 0 \n", + "coef_maintenance_tour_duration_shift_effects 0 \n", + "coef_number_of_joint_tours_departure_shift_effects 0 \n", + "coef_number_of_mandatory_tours_departure_shift_... 0 \n", + "coef_school_child_age_16_plus_departure_shift_e... 0 \n", + "coef_school_child_age_16_plus_duration_shift_ef... 0 \n", + "coef_school_child_age_under_16_departure_shift_... 0 \n", + "coef_school_child_age_under_16_duration_shift_e... 0 \n", + "coef_school_child_under_16_arrive_after_22 0 \n", + "coef_shopping_tour_arrive_after_22 0 \n", + "coef_shopping_tour_depart_before_8 0 \n", + "coef_shopping_tour_departure_shift_effects 0 \n", + "coef_shopping_tour_duration_lt_2_hours 0 \n", + "coef_shopping_tour_duration_shift_effects 0 \n", + "coef_some_previously_scheduled_tour_begins_in_t... 0 \n", + "coef_some_previously_scheduled_tour_ends_in_thi... 0 \n", + "coef_subsequent_of_2_plus_tours_for_same_purpos... 0 \n", + "coef_unavailable 1 \n", + "coef_university_student_arrive_after_22 0 \n", + "coef_visit_tour_departure_shift_effects 0 \n", + "coef_visit_tour_duration_shift_effects 0 " ] }, "metadata": {}, @@ -2970,10 +3140,8 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate()\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.472441734021349e-17 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -2989,39 +3157,39 @@ " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " -13.526580\n", + " 0.800851\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction\n", - " -14.169341\n", + " 0.494438\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " -13.495922\n", + " 0.008839\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction\n", - " -7.716247\n", + " -0.549692\n", " \n", " \n", " coef_adult_with_children_in_hh_arrive_19_21\n", - " -0.002541\n", + " -0.078997\n", " \n", " \n", " coef_arrival_constants_am_peak\n", - " 4.647673\n", + " -8.413713\n", " \n", " \n", " coef_arrival_constants_early\n", - " 24.793142\n", + " -6.758955\n", " \n", " \n", " coef_arrival_constants_evening\n", - " -4.291783\n", + " -2.675953\n", " \n", " \n", " coef_arrival_constants_late\n", - " -9.617038\n", + " -4.194942\n", " \n", " \n", " coef_arrival_constants_midday_1\n", @@ -3029,15 +3197,15 @@ " \n", " \n", " coef_arrival_constants_midday_2\n", - " 2.692519\n", + " 1.416723\n", " \n", " \n", " coef_arrival_constants_pm_peak_1\n", - " 1.849237\n", + " 0.964024\n", " \n", " \n", " coef_arrival_constants_pm_peak_2\n", - " 1.518410\n", + " 1.200835\n", " \n", " \n", " coef_arrival_constants_pm_peak_3\n", @@ -3045,35 +3213,35 @@ " \n", " \n", " coef_arrival_constants_pm_peak_4\n", - " -1.182768\n", + " -0.725959\n", " \n", " \n", " coef_departure_constants_am_peak_1\n", - " -25.000000\n", + " -13.990759\n", " \n", " \n", " coef_departure_constants_am_peak_2\n", - " -4.236849\n", + " -10.163788\n", " \n", " \n", " coef_departure_constants_am_peak_3\n", - " 0.806223\n", + " -2.799430\n", " \n", " \n", " coef_departure_constants_am_peak_4\n", - " 1.408142\n", + " 0.514393\n", " \n", " \n", " coef_departure_constants_early\n", - " -24.418086\n", + " -17.865880\n", " \n", " \n", " coef_departure_constants_evening\n", - " -14.557517\n", + " -19.574287\n", " \n", " \n", " coef_departure_constants_late\n", - " -20.421814\n", + " -21.444922\n", " \n", " \n", " coef_departure_constants_midday_1\n", @@ -3081,31 +3249,31 @@ " \n", " \n", " coef_departure_constants_midday_2\n", - " -4.501238\n", + " -1.886316\n", " \n", " \n", " coef_departure_constants_pm_peak\n", - " -12.638403\n", + " -18.238556\n", " \n", " \n", " coef_destination_in_cbd_duration_shift_effects\n", - " 0.572330\n", + " 0.130935\n", " \n", " \n", " coef_discretionary_tour_duration_lt_2_hours\n", - " -1.022488\n", + " -0.772425\n", " \n", " \n", " coef_duration_constants_0_to_1_hours\n", - " -2.750481\n", + " -2.021348\n", " \n", " \n", " coef_duration_constants_11_to_13_hours\n", - " -6.162737\n", + " -1.608105\n", " \n", " \n", " coef_duration_constants_14_to_18_hours\n", - " -2.800706\n", + " -3.067613\n", " \n", " \n", " coef_duration_constants_2_to_3_hours\n", @@ -3113,118 +3281,118 @@ " \n", " \n", " coef_duration_constants_4_to_5_hours\n", - " 0.301145\n", + " -0.519561\n", " \n", " \n", " coef_duration_constants_6_to_7_hours\n", - " 0.144072\n", + " -0.868309\n", " \n", " \n", " coef_duration_constants_8_to_10_hours\n", - " -10.570858\n", + " -1.020095\n", " \n", " \n", " coef_eat_out_tour_departure_shift_effects\n", - " -0.024649\n", + " 0.056578\n", " \n", " \n", " coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect\n", - " -1.329351\n", + " -0.506782\n", " \n", " \n", " coef_free_flow_round_trip_auto_time_shift_effects_duration\n", - " 0.017752\n", + " 0.003622\n", " \n", " \n", " coef_maintenance_tour_depart_before_7\n", - " 1.154584\n", + " -0.523693\n", " \n", " \n", " coef_maintenance_tour_departure_shift_effects\n", - " -0.159210\n", + " -0.135634\n", " \n", " \n", " coef_maintenance_tour_duration_shift_effects\n", - " -0.146459\n", + " -0.038428\n", " \n", " \n", " coef_number_of_joint_tours_departure_shift_effects\n", - " 1.356173\n", + " 0.128742\n", " \n", " \n", " coef_number_of_mandatory_tours_departure_shift_effects\n", - " 0.076326\n", + " -0.009788\n", " \n", " \n", " coef_school_child_age_16_plus_departure_shift_effects\n", - " 0.072660\n", + " 0.106395\n", " \n", " \n", " coef_school_child_age_16_plus_duration_shift_effects\n", - " 0.209500\n", + " 0.315781\n", " \n", " \n", " coef_school_child_age_under_16_departure_shift_effects\n", - " 0.239007\n", + " 0.015473\n", " \n", " \n", " coef_school_child_age_under_16_duration_shift_effects\n", - " -0.567863\n", + " 0.327527\n", " \n", " \n", " coef_school_child_under_16_arrive_after_22\n", - " -6.147237\n", + " -5.038827\n", " \n", " \n", " coef_shopping_tour_arrive_after_22\n", - " -7.221041\n", + " -0.033268\n", " \n", " \n", " coef_shopping_tour_depart_before_8\n", - " 1.458488\n", + " -1.484109\n", " \n", " \n", " coef_shopping_tour_departure_shift_effects\n", - " -0.045633\n", + " -0.099254\n", " \n", " \n", " coef_shopping_tour_duration_lt_2_hours\n", - " 0.189427\n", + " 0.699700\n", " \n", " \n", " coef_shopping_tour_duration_shift_effects\n", - " -0.359705\n", + " -0.101207\n", " \n", " \n", " coef_some_previously_scheduled_tour_begins_in_this_arrival_hour\n", - " 0.411979\n", + " -0.017681\n", " \n", " \n", " coef_some_previously_scheduled_tour_ends_in_this_departure_hour\n", - " 0.282029\n", + " -0.288048\n", " \n", " \n", " coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect\n", - " 0.191264\n", + " 0.062417\n", " \n", " \n", " coef_unavailable\n", - " -25.000000\n", + " -999.000000\n", " \n", " \n", " coef_university_student_arrive_after_22\n", - " -9.181095\n", + " 0.360948\n", " \n", " \n", " coef_visit_tour_departure_shift_effects\n", - " -0.109515\n", + " 0.136717\n", " \n", " \n", " coef_visit_tour_duration_shift_effects\n", - " -0.123814\n", + " 0.172436\n", " \n", " \n", - "loglike-173.82837615906058d_loglike\n", + "
logloss2.1895702975048073d_logloss\n", " \n", " \n", " \n", @@ -3234,39 +3402,39 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3274,15 +3442,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3290,35 +3458,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3326,31 +3494,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3358,99 +3526,99 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3458,161 +3626,161 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction-9.350492e-076.340297e-05
coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction-3.831848e-074.838604e-05
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction-1.411114e-062.000787e-08
coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction-8.562988e-066.385755e-05
coef_adult_with_children_in_hh_arrive_19_219.515373e-051.672535e-04
coef_arrival_constants_am_peak-5.959256e-054.015659e-05
coef_arrival_constants_early-6.500054e-05-7.121334e-05
coef_arrival_constants_evening2.456794e-041.588972e-05
coef_arrival_constants_late-2.788545e-041.960950e-04
coef_arrival_constants_midday_1
coef_arrival_constants_midday_2-2.921253e-05-7.425169e-06
coef_arrival_constants_pm_peak_1-8.553639e-05-8.554627e-05
coef_arrival_constants_pm_peak_22.958919e-05-2.178755e-05
coef_arrival_constants_pm_peak_3
coef_arrival_constants_pm_peak_4-6.027051e-063.187347e-05
coef_departure_constants_am_peak_1-9.035869e-057.176011e-06
coef_departure_constants_am_peak_2-3.843278e-071.002541e-04
coef_departure_constants_am_peak_3-1.038927e-045.286380e-05
coef_departure_constants_am_peak_42.089562e-055.196924e-06
coef_departure_constants_early2.533008e-05-4.734722e-05
coef_departure_constants_evening9.169928e-06-5.250786e-05
coef_departure_constants_late-5.558022e-044.394692e-05
coef_departure_constants_midday_1
coef_departure_constants_midday_22.692412e-05-1.886914e-04
coef_departure_constants_pm_peak1.942225e-043.759255e-05
coef_destination_in_cbd_duration_shift_effects3.441152e-041.613685e-05
coef_discretionary_tour_duration_lt_2_hours7.722617e-05-2.070609e-04
coef_duration_constants_0_to_1_hours-1.588601e-044.953135e-05
coef_duration_constants_11_to_13_hours-3.605513e-042.382282e-05
coef_duration_constants_14_to_18_hours-1.774351e-04-3.488047e-05
coef_duration_constants_2_to_3_hours
coef_duration_constants_4_to_5_hours2.266202e-05-7.768694e-07
coef_duration_constants_6_to_7_hours-4.640922e-05-1.838870e-05
coef_duration_constants_8_to_10_hours-2.554467e-05-2.938670e-05
coef_eat_out_tour_departure_shift_effects-3.267677e-042.917206e-05
coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect-1.021958e-059.739371e-05
coef_free_flow_round_trip_auto_time_shift_effects_duration8.421848e-03-1.212989e-05
coef_maintenance_tour_depart_before_7-1.129004e-048.298165e-06
coef_maintenance_tour_departure_shift_effects-2.093211e-031.059914e-04
coef_maintenance_tour_duration_shift_effects2.786266e-042.022297e-05
coef_number_of_joint_tours_departure_shift_effects-2.856908e-03-1.252624e-04
coef_number_of_mandatory_tours_departure_shift_effects-2.687845e-03-1.288240e-04
coef_school_child_age_16_plus_departure_shift_effects0.000000e+001.808366e-04
coef_school_child_age_16_plus_duration_shift_effects0.000000e+004.559202e-05
coef_school_child_age_under_16_departure_shift_effects-1.932195e-04-1.455769e-05
coef_school_child_age_under_16_duration_shift_effects1.479815e-04-4.476322e-05
coef_school_child_under_16_arrive_after_22-9.565965e-05-4.170945e-05
coef_shopping_tour_arrive_after_22-7.152259e-05-8.668123e-05
coef_shopping_tour_depart_before_8-3.306479e-056.613654e-05
coef_shopping_tour_departure_shift_effects-2.740920e-049.925783e-05
coef_shopping_tour_duration_lt_2_hours3.816925e-058.629531e-05
coef_shopping_tour_duration_shift_effects1.258312e-041.970358e-05
coef_some_previously_scheduled_tour_begins_in_this_arrival_hour3.226289e-056.069742e-05
coef_some_previously_scheduled_tour_ends_in_this_departure_hour3.161563e-043.334213e-05
coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect1.181360e-05-1.230984e-04
coef_unavailable
coef_university_student_arrive_after_22-2.193359e-06-5.714988e-05
coef_visit_tour_departure_shift_effects-4.986513e-04-1.378200e-04
coef_visit_tour_duration_shift_effects2.469947e-04-2.132350e-05
nit67nfev106njev67status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.587568method'slsqp'n_cases91iteration_number67logloss1.9102019358138524" + "nit144nfev148njev144status0message'Optimization terminated successfully'successTrueelapsed_time0:00:03.104941method'slsqp'n_cases1277iteration_number144loglike-2789.5125590211246" ], "text/plain": [ - "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -13.526580\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction -14.169341\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -13.495922\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction -7.716247\n", - "┃ coef_adult_with_children_in_hh_arrive_19_21 -0.002541\n", - "┃ coef_arrival_constants_am_peak 4.647673\n", - "┃ coef_arrival_constants_early 24.793142\n", - "┃ coef_arrival_constants_evening -4.291783\n", - "┃ coef_arrival_constants_late -9.617038\n", - "┃ coef_arrival_constants_midday_1 0.000000\n", - "┃ coef_arrival_constants_midday_2 2.692519\n", - "┃ coef_arrival_constants_pm_peak_1 1.849237\n", - "┃ coef_arrival_constants_pm_peak_2 1.518410\n", - "┃ coef_arrival_constants_pm_peak_3 0.000000\n", - "┃ coef_arrival_constants_pm_peak_4 -1.182768\n", - "┃ coef_departure_constants_am_peak_1 -25.000000\n", - "┃ coef_departure_constants_am_peak_2 -4.236849\n", - "┃ coef_departure_constants_am_peak_3 0.806223\n", - "┃ coef_departure_constants_am_peak_4 1.408142\n", - "┃ coef_departure_constants_early -24.418086\n", - "┃ coef_departure_constants_evening -14.557517\n", - "┃ coef_departure_constants_late -20.421814\n", - "┃ coef_departure_constants_midday_1 0.000000\n", - "┃ coef_departure_constants_midday_2 -4.501238\n", - "┃ coef_departure_constants_pm_peak -12.638403\n", - "┃ coef_destination_in_cbd_duration_shift_effects 0.572330\n", - "┃ coef_discretionary_tour_duration_lt_2_hours -1.022488\n", - "┃ coef_duration_constants_0_to_1_hours -2.750481\n", - "┃ coef_duration_constants_11_to_13_hours -6.162737\n", - "┃ coef_duration_constants_14_to_18_hours -2.800706\n", - "┃ coef_duration_constants_2_to_3_hours 0.000000\n", - "┃ coef_duration_constants_4_to_5_hours 0.301145\n", - "┃ coef_duration_constants_6_to_7_hours 0.144072\n", - "┃ coef_duration_constants_8_to_10_hours -10.570858\n", - "┃ coef_eat_out_tour_departure_shift_effects -0.024649\n", - "┃ coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect -1.329351\n", - "┃ coef_free_flow_round_trip_auto_time_shift_effects_duration 0.017752\n", - "┃ coef_maintenance_tour_depart_before_7 1.154584\n", - "┃ coef_maintenance_tour_departure_shift_effects -0.159210\n", - "┃ coef_maintenance_tour_duration_shift_effects -0.146459\n", - "┃ coef_number_of_joint_tours_departure_shift_effects 1.356173\n", - "┃ coef_number_of_mandatory_tours_departure_shift_effects 0.076326\n", - "┃ coef_school_child_age_16_plus_departure_shift_effects 0.072660\n", - "┃ coef_school_child_age_16_plus_duration_shift_effects 0.209500\n", - "┃ coef_school_child_age_under_16_departure_shift_effects 0.239007\n", - "┃ coef_school_child_age_under_16_duration_shift_effects -0.567863\n", - "┃ coef_school_child_under_16_arrive_after_22 -6.147237\n", - "┃ coef_shopping_tour_arrive_after_22 -7.221041\n", - "┃ coef_shopping_tour_depart_before_8 1.458488\n", - "┃ coef_shopping_tour_departure_shift_effects -0.045633\n", - "┃ coef_shopping_tour_duration_lt_2_hours 0.189427\n", - "┃ coef_shopping_tour_duration_shift_effects -0.359705\n", - "┃ coef_some_previously_scheduled_tour_begins_in_this_arrival_hour 0.411979\n", - "┃ coef_some_previously_scheduled_tour_ends_in_this_departure_hour 0.282029\n", - "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect 0.191264\n", - "┃ coef_unavailable -25.000000\n", - "┃ coef_university_student_arrive_after_22 -9.181095\n", - "┃ coef_visit_tour_departure_shift_effects -0.109515\n", - "┃ coef_visit_tour_duration_shift_effects -0.123814\n", + "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.800851\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction 0.494438\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.008839\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction -0.549692\n", + "┃ coef_adult_with_children_in_hh_arrive_19_21 -0.078997\n", + "┃ coef_arrival_constants_am_peak -8.413713\n", + "┃ coef_arrival_constants_early -6.758955\n", + "┃ coef_arrival_constants_evening -2.675953\n", + "┃ coef_arrival_constants_late -4.194942\n", + "┃ coef_arrival_constants_midday_1 0.000000\n", + "┃ coef_arrival_constants_midday_2 1.416723\n", + "┃ coef_arrival_constants_pm_peak_1 0.964024\n", + "┃ coef_arrival_constants_pm_peak_2 1.200835\n", + "┃ coef_arrival_constants_pm_peak_3 0.000000\n", + "┃ coef_arrival_constants_pm_peak_4 -0.725959\n", + "┃ coef_departure_constants_am_peak_1 -13.990759\n", + "┃ coef_departure_constants_am_peak_2 -10.163788\n", + "┃ coef_departure_constants_am_peak_3 -2.799430\n", + "┃ coef_departure_constants_am_peak_4 0.514393\n", + "┃ coef_departure_constants_early -17.865880\n", + "┃ coef_departure_constants_evening -19.574287\n", + "┃ coef_departure_constants_late -21.444922\n", + "┃ coef_departure_constants_midday_1 0.000000\n", + "┃ coef_departure_constants_midday_2 -1.886316\n", + "┃ coef_departure_constants_pm_peak -18.238556\n", + "┃ coef_destination_in_cbd_duration_shift_effects 0.130935\n", + "┃ coef_discretionary_tour_duration_lt_2_hours -0.772425\n", + "┃ coef_duration_constants_0_to_1_hours -2.021348\n", + "┃ coef_duration_constants_11_to_13_hours -1.608105\n", + "┃ coef_duration_constants_14_to_18_hours -3.067613\n", + "┃ coef_duration_constants_2_to_3_hours 0.000000\n", + "┃ coef_duration_constants_4_to_5_hours -0.519561\n", + "┃ coef_duration_constants_6_to_7_hours -0.868309\n", + "┃ coef_duration_constants_8_to_10_hours -1.020095\n", + "┃ coef_eat_out_tour_departure_shift_effects 0.056578\n", + "┃ coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect -0.506782\n", + "┃ coef_free_flow_round_trip_auto_time_shift_effects_duration 0.003622\n", + "┃ coef_maintenance_tour_depart_before_7 -0.523693\n", + "┃ coef_maintenance_tour_departure_shift_effects -0.135634\n", + "┃ coef_maintenance_tour_duration_shift_effects -0.038428\n", + "┃ coef_number_of_joint_tours_departure_shift_effects 0.128742\n", + "┃ coef_number_of_mandatory_tours_departure_shift_effects -0.009788\n", + "┃ coef_school_child_age_16_plus_departure_shift_effects 0.106395\n", + "┃ coef_school_child_age_16_plus_duration_shift_effects 0.315781\n", + "┃ coef_school_child_age_under_16_departure_shift_effects 0.015473\n", + "┃ coef_school_child_age_under_16_duration_shift_effects 0.327527\n", + "┃ coef_school_child_under_16_arrive_after_22 -5.038827\n", + "┃ coef_shopping_tour_arrive_after_22 -0.033268\n", + "┃ coef_shopping_tour_depart_before_8 -1.484109\n", + "┃ coef_shopping_tour_departure_shift_effects -0.099254\n", + "┃ coef_shopping_tour_duration_lt_2_hours 0.699700\n", + "┃ coef_shopping_tour_duration_shift_effects -0.101207\n", + "┃ coef_some_previously_scheduled_tour_begins_in_this_arrival_hour -0.017681\n", + "┃ coef_some_previously_scheduled_tour_ends_in_this_departure_hour -0.288048\n", + "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect 0.062417\n", + "┃ coef_unavailable -999.000000\n", + "┃ coef_university_student_arrive_after_22 0.360948\n", + "┃ coef_visit_tour_departure_shift_effects 0.136717\n", + "┃ coef_visit_tour_duration_shift_effects 0.172436\n", "┃ dtype: float64\n", - "┣ loglike: -173.82837615906058\n", - "┣ d_loglike: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -9.350492e-07\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction -3.831848e-07\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -1.411114e-06\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction -8.562988e-06\n", - "┃ coef_adult_with_children_in_hh_arrive_19_21 9.515373e-05\n", - "┃ coef_arrival_constants_am_peak -5.959256e-05\n", - "┃ coef_arrival_constants_early -6.500054e-05\n", - "┃ coef_arrival_constants_evening 2.456794e-04\n", - "┃ coef_arrival_constants_late -2.788545e-04\n", + "┣ logloss: 2.1895702975048073\n", + "┣ d_logloss: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 6.340297e-05\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction 4.838604e-05\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 2.000787e-08\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction 6.385755e-05\n", + "┃ coef_adult_with_children_in_hh_arrive_19_21 1.672535e-04\n", + "┃ coef_arrival_constants_am_peak 4.015659e-05\n", + "┃ coef_arrival_constants_early -7.121334e-05\n", + "┃ coef_arrival_constants_evening 1.588972e-05\n", + "┃ coef_arrival_constants_late 1.960950e-04\n", "┃ coef_arrival_constants_midday_1 0.000000e+00\n", - "┃ coef_arrival_constants_midday_2 -2.921253e-05\n", - "┃ coef_arrival_constants_pm_peak_1 -8.553639e-05\n", - "┃ coef_arrival_constants_pm_peak_2 2.958919e-05\n", + "┃ coef_arrival_constants_midday_2 -7.425169e-06\n", + "┃ coef_arrival_constants_pm_peak_1 -8.554627e-05\n", + "┃ coef_arrival_constants_pm_peak_2 -2.178755e-05\n", "┃ coef_arrival_constants_pm_peak_3 0.000000e+00\n", - "┃ coef_arrival_constants_pm_peak_4 -6.027051e-06\n", - "┃ coef_departure_constants_am_peak_1 -9.035869e-05\n", - "┃ coef_departure_constants_am_peak_2 -3.843278e-07\n", - "┃ coef_departure_constants_am_peak_3 -1.038927e-04\n", - "┃ coef_departure_constants_am_peak_4 2.089562e-05\n", - "┃ coef_departure_constants_early 2.533008e-05\n", - "┃ coef_departure_constants_evening 9.169928e-06\n", - "┃ coef_departure_constants_late -5.558022e-04\n", + "┃ coef_arrival_constants_pm_peak_4 3.187347e-05\n", + "┃ coef_departure_constants_am_peak_1 7.176011e-06\n", + "┃ coef_departure_constants_am_peak_2 1.002541e-04\n", + "┃ coef_departure_constants_am_peak_3 5.286380e-05\n", + "┃ coef_departure_constants_am_peak_4 5.196924e-06\n", + "┃ coef_departure_constants_early -4.734722e-05\n", + "┃ coef_departure_constants_evening -5.250786e-05\n", + "┃ coef_departure_constants_late 4.394692e-05\n", "┃ coef_departure_constants_midday_1 0.000000e+00\n", - "┃ coef_departure_constants_midday_2 2.692412e-05\n", - "┃ coef_departure_constants_pm_peak 1.942225e-04\n", - "┃ coef_destination_in_cbd_duration_shift_effects 3.441152e-04\n", - "┃ coef_discretionary_tour_duration_lt_2_hours 7.722617e-05\n", - "┃ coef_duration_constants_0_to_1_hours -1.588601e-04\n", - "┃ coef_duration_constants_11_to_13_hours -3.605513e-04\n", - "┃ coef_duration_constants_14_to_18_hours -1.774351e-04\n", + "┃ coef_departure_constants_midday_2 -1.886914e-04\n", + "┃ coef_departure_constants_pm_peak 3.759255e-05\n", + "┃ coef_destination_in_cbd_duration_shift_effects 1.613685e-05\n", + "┃ coef_discretionary_tour_duration_lt_2_hours -2.070609e-04\n", + "┃ coef_duration_constants_0_to_1_hours 4.953135e-05\n", + "┃ coef_duration_constants_11_to_13_hours 2.382282e-05\n", + "┃ coef_duration_constants_14_to_18_hours -3.488047e-05\n", "┃ coef_duration_constants_2_to_3_hours 0.000000e+00\n", - "┃ coef_duration_constants_4_to_5_hours 2.266202e-05\n", - "┃ coef_duration_constants_6_to_7_hours -4.640922e-05\n", - "┃ coef_duration_constants_8_to_10_hours -2.554467e-05\n", - "┃ coef_eat_out_tour_departure_shift_effects -3.267677e-04\n", - "┃ coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect -1.021958e-05\n", - "┃ coef_free_flow_round_trip_auto_time_shift_effects_duration 8.421848e-03\n", - "┃ coef_maintenance_tour_depart_before_7 -1.129004e-04\n", - "┃ coef_maintenance_tour_departure_shift_effects -2.093211e-03\n", - "┃ coef_maintenance_tour_duration_shift_effects 2.786266e-04\n", - "┃ coef_number_of_joint_tours_departure_shift_effects -2.856908e-03\n", - "┃ coef_number_of_mandatory_tours_departure_shift_effects -2.687845e-03\n", - "┃ coef_school_child_age_16_plus_departure_shift_effects 0.000000e+00\n", - "┃ coef_school_child_age_16_plus_duration_shift_effects 0.000000e+00\n", - "┃ coef_school_child_age_under_16_departure_shift_effects -1.932195e-04\n", - "┃ coef_school_child_age_under_16_duration_shift_effects 1.479815e-04\n", - "┃ coef_school_child_under_16_arrive_after_22 -9.565965e-05\n", - "┃ coef_shopping_tour_arrive_after_22 -7.152259e-05\n", - "┃ coef_shopping_tour_depart_before_8 -3.306479e-05\n", - "┃ coef_shopping_tour_departure_shift_effects -2.740920e-04\n", - "┃ coef_shopping_tour_duration_lt_2_hours 3.816925e-05\n", - "┃ coef_shopping_tour_duration_shift_effects 1.258312e-04\n", - "┃ coef_some_previously_scheduled_tour_begins_in_this_arrival_hour 3.226289e-05\n", - "┃ coef_some_previously_scheduled_tour_ends_in_this_departure_hour 3.161563e-04\n", - "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect 1.181360e-05\n", + "┃ coef_duration_constants_4_to_5_hours -7.768694e-07\n", + "┃ coef_duration_constants_6_to_7_hours -1.838870e-05\n", + "┃ coef_duration_constants_8_to_10_hours -2.938670e-05\n", + "┃ coef_eat_out_tour_departure_shift_effects 2.917206e-05\n", + "┃ coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect 9.739371e-05\n", + "┃ coef_free_flow_round_trip_auto_time_shift_effects_duration -1.212989e-05\n", + "┃ coef_maintenance_tour_depart_before_7 8.298165e-06\n", + "┃ coef_maintenance_tour_departure_shift_effects 1.059914e-04\n", + "┃ coef_maintenance_tour_duration_shift_effects 2.022297e-05\n", + "┃ coef_number_of_joint_tours_departure_shift_effects -1.252624e-04\n", + "┃ coef_number_of_mandatory_tours_departure_shift_effects -1.288240e-04\n", + "┃ coef_school_child_age_16_plus_departure_shift_effects 1.808366e-04\n", + "┃ coef_school_child_age_16_plus_duration_shift_effects 4.559202e-05\n", + "┃ coef_school_child_age_under_16_departure_shift_effects -1.455769e-05\n", + "┃ coef_school_child_age_under_16_duration_shift_effects -4.476322e-05\n", + "┃ coef_school_child_under_16_arrive_after_22 -4.170945e-05\n", + "┃ coef_shopping_tour_arrive_after_22 -8.668123e-05\n", + "┃ coef_shopping_tour_depart_before_8 6.613654e-05\n", + "┃ coef_shopping_tour_departure_shift_effects 9.925783e-05\n", + "┃ coef_shopping_tour_duration_lt_2_hours 8.629531e-05\n", + "┃ coef_shopping_tour_duration_shift_effects 1.970358e-05\n", + "┃ coef_some_previously_scheduled_tour_begins_in_this_arrival_hour 6.069742e-05\n", + "┃ coef_some_previously_scheduled_tour_ends_in_this_departure_hour 3.334213e-05\n", + "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect -1.230984e-04\n", "┃ coef_unavailable 0.000000e+00\n", - "┃ coef_university_student_arrive_after_22 -2.193359e-06\n", - "┃ coef_visit_tour_departure_shift_effects -4.986513e-04\n", - "┃ coef_visit_tour_duration_shift_effects 2.469947e-04\n", + "┃ coef_university_student_arrive_after_22 -5.714988e-05\n", + "┃ coef_visit_tour_departure_shift_effects -1.378200e-04\n", + "┃ coef_visit_tour_duration_shift_effects -2.132350e-05\n", "┃ dtype: float64\n", - "┣ nit: 67\n", - "┣ nfev: 106\n", - "┣ njev: 67\n", + "┣ nit: 144\n", + "┣ nfev: 148\n", + "┣ njev: 144\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(microseconds=587568)\n", + "┣ elapsed_time: datetime.timedelta(seconds=3, microseconds=104941)\n", "┣ method: 'slsqp'\n", - "┣ n_cases: 91\n", - "┣ iteration_number: 67\n", - "┣ logloss: 1.9102019358138524" + "┣ n_cases: 1277\n", + "┣ iteration_number: 144\n", + "┣ loglike: -2789.5125590211246" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction-13.5 1.03e+03-0.01 NA 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction-14.2 1.62e+03-0.01 NA 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction-13.5 842.-0.02 NA 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction-7.72 342.-0.02 NA 0.00
coef_adult_with_children_in_hh_arrive_19_21-0.00254 0.815-0.00 NA 0.00
coef_arrival_constants_am_peak 4.65 1.75 2.66** NA 0.00
coef_arrival_constants_early 24.8 1.71 14.53*** NA 0.00
coef_arrival_constants_evening-4.29 0.931-4.61*** NA 0.00
coef_arrival_constants_late-9.62 1.70-5.65*** NA 0.00
coef_arrival_constants_midday_1 0.00 NA NA NA 0.00fixed value
coef_arrival_constants_midday_2 2.69 0.752 3.58*** NA 0.00
coef_arrival_constants_pm_peak_1 1.85 0.647 2.86** NA 0.00
coef_arrival_constants_pm_peak_2 1.52 0.532 2.85** NA 0.00
coef_arrival_constants_pm_peak_3 0.00 NA NA NA 0.00fixed value
coef_arrival_constants_pm_peak_4-1.18 0.567-2.08* NA 0.00
coef_departure_constants_am_peak_1-25.0 NA NA[***] BIG 0.00coef_departure_constants_am_peak_1 ≥ -25.0
coef_departure_constants_am_peak_2-4.24 1.90-2.23* NA 0.00
coef_departure_constants_am_peak_3 0.806 1.29 0.62 NA 0.00
coef_departure_constants_am_peak_4 1.41 0.775 1.82 NA 0.00
coef_departure_constants_early-24.4 1.04-23.39*** NA 0.00
coef_departure_constants_evening-14.6 2.93-4.96*** NA 0.00
coef_departure_constants_late-20.4 42.5-0.48 NA 0.00
coef_departure_constants_midday_1 0.00 NA NA NA 0.00fixed value
coef_departure_constants_midday_2-4.50 1.17-3.85*** NA 0.00
coef_departure_constants_pm_peak-12.6 2.65-4.77*** NA 0.00
coef_destination_in_cbd_duration_shift_effects 0.572 0.222 2.58** NA 0.00
coef_discretionary_tour_duration_lt_2_hours-1.02 1.10-0.93 NA 0.00
coef_duration_constants_0_to_1_hours-2.75 0.678-4.05*** NA 0.00
coef_duration_constants_11_to_13_hours-6.16 52.7-0.12 NA 0.00
coef_duration_constants_14_to_18_hours-2.80 75.2-0.04 NA 0.00
coef_duration_constants_2_to_3_hours 0.00 NA NA NA 0.00fixed value
coef_duration_constants_4_to_5_hours 0.301 0.552 0.55 NA 0.00
coef_duration_constants_6_to_7_hours 0.144 1.03 0.14 NA 0.00
coef_duration_constants_8_to_10_hours-10.6 198.-0.05 NA 0.00
coef_eat_out_tour_departure_shift_effects-0.0246 0.146-0.17 NA 0.00
coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect-1.33 0.662-2.01* NA 0.00
coef_free_flow_round_trip_auto_time_shift_effects_duration 0.0178 0.00963 1.84 NA 0.00
coef_maintenance_tour_depart_before_7 1.15 1.69 0.68 NA 0.00
coef_maintenance_tour_departure_shift_effects-0.159 0.129-1.23 NA 0.00
coef_maintenance_tour_duration_shift_effects-0.146 0.234-0.62 NA 0.00
coef_number_of_joint_tours_departure_shift_effects 1.36 0.199 6.82*** NA 0.00
coef_number_of_mandatory_tours_departure_shift_effects 0.0763 0.0738 1.03 NA 0.00
coef_school_child_age_16_plus_departure_shift_effects 0.0727 6.26e-07 BIG*** NA 0.00
coef_school_child_age_16_plus_duration_shift_effects 0.209 2.94e-07 BIG*** NA 0.00
coef_school_child_age_under_16_departure_shift_effects 0.239 0.289 0.83 NA 0.00
coef_school_child_age_under_16_duration_shift_effects-0.568 0.535-1.06 NA 0.00
coef_school_child_under_16_arrive_after_22-6.15 102.-0.06 NA 0.00
coef_shopping_tour_arrive_after_22-7.22 118.-0.06 NA 0.00
coef_shopping_tour_depart_before_8 1.46 2.09 0.70 NA 0.00
coef_shopping_tour_departure_shift_effects-0.0456 0.177-0.26 NA 0.00
coef_shopping_tour_duration_lt_2_hours 0.189 1.14 0.17 NA 0.00
coef_shopping_tour_duration_shift_effects-0.360 0.344-1.04 NA 0.00
coef_some_previously_scheduled_tour_begins_in_this_arrival_hour 0.412 0.861 0.48 NA 0.00
coef_some_previously_scheduled_tour_ends_in_this_departure_hour 0.282 0.440 0.64 NA 0.00
coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect 0.191 0.745 0.26 NA 0.00
coef_unavailable-999. NA NA NA 0.00fixed value
coef_university_student_arrive_after_22-9.18 675.-0.01 NA 0.00
coef_visit_tour_departure_shift_effects-0.110 0.135-0.81 NA 0.00
coef_visit_tour_duration_shift_effects-0.124 0.317-0.39 NA 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.801 3.59 0.22 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction 0.494 4.24 0.12 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.00884 198. 0.00 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction-0.550 1.32-0.42 0.00
coef_adult_with_children_in_hh_arrive_19_21-0.0790 0.205-0.39 0.00
coef_arrival_constants_am_peak-8.41 19.7-0.43 0.00
coef_arrival_constants_early-6.76 19.7-0.34 0.00
coef_arrival_constants_evening-2.68 0.227-11.78*** 0.00
coef_arrival_constants_late-4.19 0.286-14.65*** 0.00
coef_arrival_constants_midday_1 0.00 0.00 NA 0.00fixed value
coef_arrival_constants_midday_2 1.42 0.133 10.62*** 0.00
coef_arrival_constants_pm_peak_1 0.964 0.146 6.59*** 0.00
coef_arrival_constants_pm_peak_2 1.20 0.131 9.17*** 0.00
coef_arrival_constants_pm_peak_3 0.00 0.00 NA 0.00fixed value
coef_arrival_constants_pm_peak_4-0.726 0.157-4.63*** 0.00
coef_departure_constants_am_peak_1-14.0 1.35-10.33*** 0.00
coef_departure_constants_am_peak_2-10.2 0.850-11.96*** 0.00
coef_departure_constants_am_peak_3-2.80 0.381-7.35*** 0.00
coef_departure_constants_am_peak_4 0.514 0.155 3.32*** 0.00
coef_departure_constants_early-17.9 1.63-10.95*** 0.00
coef_departure_constants_evening-19.6 19.7-0.99 0.00
coef_departure_constants_late-21.4 19.7-1.09 0.00
coef_departure_constants_midday_1 0.00 0.00 NA 0.00fixed value
coef_departure_constants_midday_2-1.89 0.244-7.72*** 0.00
coef_departure_constants_pm_peak-18.2 19.7-0.93 0.00
coef_destination_in_cbd_duration_shift_effects 0.131 0.0653 2.01* 0.00
coef_discretionary_tour_duration_lt_2_hours-0.772 0.233-3.32*** 0.00
coef_duration_constants_0_to_1_hours-2.02 0.171-11.83*** 0.00
coef_duration_constants_11_to_13_hours-1.61 0.861-1.87 0.00
coef_duration_constants_14_to_18_hours-3.07 4.80-0.64 0.00
coef_duration_constants_2_to_3_hours 0.00 0.00 NA 0.00fixed value
coef_duration_constants_4_to_5_hours-0.520 0.116-4.49*** 0.00
coef_duration_constants_6_to_7_hours-0.868 0.210-4.13*** 0.00
coef_duration_constants_8_to_10_hours-1.02 0.359-2.84** 0.00
coef_eat_out_tour_departure_shift_effects 0.0566 0.0437 1.29 0.00
coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect-0.507 0.511-0.99 0.00
coef_free_flow_round_trip_auto_time_shift_effects_duration 0.00362 0.00112 3.25** 0.00
coef_maintenance_tour_depart_before_7-0.524 0.819-0.64 0.00
coef_maintenance_tour_departure_shift_effects-0.136 0.0418-3.24** 0.00
coef_maintenance_tour_duration_shift_effects-0.0384 0.0575-0.67 0.00
coef_number_of_joint_tours_departure_shift_effects 0.129 0.0651 1.98* 0.00
coef_number_of_mandatory_tours_departure_shift_effects-0.00979 0.0303-0.32 0.00
coef_school_child_age_16_plus_departure_shift_effects 0.106 0.110 0.96 0.00
coef_school_child_age_16_plus_duration_shift_effects 0.316 0.131 2.40* 0.00
coef_school_child_age_under_16_departure_shift_effects 0.0155 0.0681 0.23 0.00
coef_school_child_age_under_16_duration_shift_effects 0.328 0.0792 4.13*** 0.00
coef_school_child_under_16_arrive_after_22-5.04 4.37-1.15 0.00
coef_shopping_tour_arrive_after_22-0.0333 0.576-0.06 0.00
coef_shopping_tour_depart_before_8-1.48 1.11-1.34 0.00
coef_shopping_tour_departure_shift_effects-0.0993 0.0762-1.30 0.00
coef_shopping_tour_duration_lt_2_hours 0.700 0.275 2.54* 0.00
coef_shopping_tour_duration_shift_effects-0.101 0.0665-1.52 0.00
coef_some_previously_scheduled_tour_begins_in_this_arrival_hour-0.0177 0.224-0.08 0.00
coef_some_previously_scheduled_tour_ends_in_this_departure_hour-0.288 0.121-2.38* 0.00
coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect 0.0624 0.340 0.18 0.00
coef_unavailable-999. 0.00 NA 0.00fixed value
coef_university_student_arrive_after_22 0.361 0.694 0.52 0.00
coef_visit_tour_departure_shift_effects 0.137 0.0492 2.78** 0.00
coef_visit_tour_duration_shift_effects 0.172 0.0622 2.77** 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -4257,7 +4392,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -4278,20 +4413,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -4310,7 +4434,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -4355,217 +4479,217 @@ " \n", " 2\n", " coef_free_flow_round_trip_auto_time_shift_effe...\n", - " 0.017752\n", + " 0.003622\n", " F\n", " \n", " \n", " 3\n", " coef_shopping_tour_departure_shift_effects\n", - " -0.045633\n", + " -0.099254\n", " F\n", " \n", " \n", " 4\n", " coef_shopping_tour_duration_shift_effects\n", - " -0.359705\n", + " -0.101207\n", " F\n", " \n", " \n", " 5\n", " coef_maintenance_tour_departure_shift_effects\n", - " -0.159210\n", + " -0.135634\n", " F\n", " \n", " \n", " 6\n", " coef_maintenance_tour_duration_shift_effects\n", - " -0.146459\n", + " -0.038428\n", " F\n", " \n", " \n", " 7\n", " coef_visit_tour_departure_shift_effects\n", - " -0.109515\n", + " 0.136717\n", " F\n", " \n", " \n", " 8\n", " coef_visit_tour_duration_shift_effects\n", - " -0.123814\n", + " 0.172436\n", " F\n", " \n", " \n", " 9\n", " coef_eat_out_tour_departure_shift_effects\n", - " -0.024649\n", + " 0.056578\n", " F\n", " \n", " \n", " 10\n", " coef_school_child_age_16_plus_departure_shift_...\n", - " 0.072660\n", + " 0.106395\n", " F\n", " \n", " \n", " 11\n", " coef_school_child_age_16_plus_duration_shift_e...\n", - " 0.209500\n", + " 0.315781\n", " F\n", " \n", " \n", " 12\n", " coef_school_child_age_under_16_departure_shift...\n", - " 0.239007\n", + " 0.015473\n", " F\n", " \n", " \n", " 13\n", " coef_school_child_age_under_16_duration_shift_...\n", - " -0.567863\n", + " 0.327527\n", " F\n", " \n", " \n", " 14\n", " coef_destination_in_cbd_duration_shift_effects\n", - " 0.572330\n", + " 0.130935\n", " F\n", " \n", " \n", " 15\n", " coef_number_of_mandatory_tours_departure_shift...\n", - " 0.076326\n", + " -0.009788\n", " F\n", " \n", " \n", " 16\n", " coef_number_of_joint_tours_departure_shift_eff...\n", - " 1.356173\n", + " 0.128742\n", " F\n", " \n", " \n", " 17\n", " coef_first_of_2_plus_tours_for_same_purpose_de...\n", - " -1.329351\n", + " -0.506782\n", " F\n", " \n", " \n", " 18\n", " coef_subsequent_of_2_plus_tours_for_same_purpo...\n", - " 0.191264\n", + " 0.062417\n", " F\n", " \n", " \n", " 19\n", " coef_maintenance_tour_depart_before_7\n", - " 1.154584\n", + " -0.523693\n", " F\n", " \n", " \n", " 20\n", " coef_shopping_tour_depart_before_8\n", - " 1.458488\n", + " -1.484109\n", " F\n", " \n", " \n", " 21\n", " coef_shopping_tour_arrive_after_22\n", - " -7.221041\n", + " -0.033268\n", " F\n", " \n", " \n", " 22\n", " coef_school_child_under_16_arrive_after_22\n", - " -6.147237\n", + " -5.038827\n", " F\n", " \n", " \n", " 23\n", " coef_university_student_arrive_after_22\n", - " -9.181095\n", + " 0.360948\n", " F\n", " \n", " \n", " 24\n", " coef_shopping_tour_duration_lt_2_hours\n", - " 0.189427\n", + " 0.699700\n", " F\n", " \n", " \n", " 25\n", " coef_discretionary_tour_duration_lt_2_hours\n", - " -1.022488\n", + " -0.772425\n", " F\n", " \n", " \n", " 26\n", " coef_adult_with_children_in_hh_arrive_19_21\n", - " -0.002541\n", + " -0.078997\n", " F\n", " \n", " \n", " 27\n", " coef_some_previously_scheduled_tour_ends_in_th...\n", - " 0.282029\n", + " -0.288048\n", " F\n", " \n", " \n", " 28\n", " coef_some_previously_scheduled_tour_begins_in_...\n", - " 0.411979\n", + " -0.017681\n", " F\n", " \n", " \n", " 29\n", " coef_adjacent_window_exists_before_this_depart...\n", - " -13.495922\n", + " 0.008839\n", " F\n", " \n", " \n", " 30\n", " coef_adjacent_window_exists_after_this_arrival...\n", - " -13.526580\n", + " 0.800851\n", " F\n", " \n", " \n", " 31\n", " coef_adjacent_window_exists_before_this_depart...\n", - " -7.716247\n", + " -0.549692\n", " F\n", " \n", " \n", " 32\n", " coef_adjacent_window_exists_after_this_arrival...\n", - " -14.169341\n", + " 0.494438\n", " F\n", " \n", " \n", " 33\n", " coef_departure_constants_early\n", - " -24.418086\n", + " -17.865880\n", " F\n", " \n", " \n", " 34\n", " coef_departure_constants_am_peak_1\n", - " -25.000000\n", + " -13.990759\n", " F\n", " \n", " \n", " 35\n", " coef_departure_constants_am_peak_2\n", - " -4.236849\n", + " -10.163788\n", " F\n", " \n", " \n", " 36\n", " coef_departure_constants_am_peak_3\n", - " 0.806223\n", + " -2.799430\n", " F\n", " \n", " \n", " 37\n", " coef_departure_constants_am_peak_4\n", - " 1.408142\n", + " 0.514393\n", " F\n", " \n", " \n", @@ -4577,37 +4701,37 @@ " \n", " 39\n", " coef_departure_constants_midday_2\n", - " -4.501238\n", + " -1.886316\n", " F\n", " \n", " \n", " 40\n", " coef_departure_constants_pm_peak\n", - " -12.638403\n", + " -18.238556\n", " F\n", " \n", " \n", " 41\n", " coef_departure_constants_evening\n", - " -14.557517\n", + " -19.574287\n", " F\n", " \n", " \n", " 42\n", " coef_departure_constants_late\n", - " -20.421814\n", + " -21.444922\n", " F\n", " \n", " \n", " 43\n", " coef_arrival_constants_early\n", - " 24.793142\n", + " -6.758955\n", " F\n", " \n", " \n", " 44\n", " coef_arrival_constants_am_peak\n", - " 4.647673\n", + " -8.413713\n", " F\n", " \n", " \n", @@ -4619,19 +4743,19 @@ " \n", " 46\n", " coef_arrival_constants_midday_2\n", - " 2.692519\n", + " 1.416723\n", " F\n", " \n", " \n", " 47\n", " coef_arrival_constants_pm_peak_1\n", - " 1.849237\n", + " 0.964024\n", " F\n", " \n", " \n", " 48\n", " coef_arrival_constants_pm_peak_2\n", - " 1.518410\n", + " 1.200835\n", " F\n", " \n", " \n", @@ -4643,25 +4767,25 @@ " \n", " 50\n", " coef_arrival_constants_pm_peak_4\n", - " -1.182768\n", + " -0.725959\n", " F\n", " \n", " \n", " 51\n", " coef_arrival_constants_evening\n", - " -4.291783\n", + " -2.675953\n", " F\n", " \n", " \n", " 52\n", " coef_arrival_constants_late\n", - " -9.617038\n", + " -4.194942\n", " F\n", " \n", " \n", " 53\n", " coef_duration_constants_0_to_1_hours\n", - " -2.750481\n", + " -2.021348\n", " F\n", " \n", " \n", @@ -4673,31 +4797,31 @@ " \n", " 55\n", " coef_duration_constants_4_to_5_hours\n", - " 0.301145\n", + " -0.519561\n", " F\n", " \n", " \n", " 56\n", " coef_duration_constants_6_to_7_hours\n", - " 0.144072\n", + " -0.868309\n", " F\n", " \n", " \n", " 57\n", " coef_duration_constants_8_to_10_hours\n", - " -10.570858\n", + " -1.020095\n", " F\n", " \n", " \n", " 58\n", " coef_duration_constants_11_to_13_hours\n", - " -6.162737\n", + " -1.608105\n", " F\n", " \n", " \n", " 59\n", " coef_duration_constants_14_to_18_hours\n", - " -2.800706\n", + " -3.067613\n", " F\n", " \n", " \n", @@ -4708,67 +4832,67 @@ " coefficient_name value constrain\n", "0 coef_dummy 1.000000 T\n", "1 coef_unavailable -999.000000 T\n", - "2 coef_free_flow_round_trip_auto_time_shift_effe... 0.017752 F\n", - "3 coef_shopping_tour_departure_shift_effects -0.045633 F\n", - "4 coef_shopping_tour_duration_shift_effects -0.359705 F\n", - "5 coef_maintenance_tour_departure_shift_effects -0.159210 F\n", - "6 coef_maintenance_tour_duration_shift_effects -0.146459 F\n", - "7 coef_visit_tour_departure_shift_effects -0.109515 F\n", - "8 coef_visit_tour_duration_shift_effects -0.123814 F\n", - "9 coef_eat_out_tour_departure_shift_effects -0.024649 F\n", - "10 coef_school_child_age_16_plus_departure_shift_... 0.072660 F\n", - "11 coef_school_child_age_16_plus_duration_shift_e... 0.209500 F\n", - "12 coef_school_child_age_under_16_departure_shift... 0.239007 F\n", - "13 coef_school_child_age_under_16_duration_shift_... -0.567863 F\n", - "14 coef_destination_in_cbd_duration_shift_effects 0.572330 F\n", - "15 coef_number_of_mandatory_tours_departure_shift... 0.076326 F\n", - "16 coef_number_of_joint_tours_departure_shift_eff... 1.356173 F\n", - "17 coef_first_of_2_plus_tours_for_same_purpose_de... -1.329351 F\n", - "18 coef_subsequent_of_2_plus_tours_for_same_purpo... 0.191264 F\n", - "19 coef_maintenance_tour_depart_before_7 1.154584 F\n", - "20 coef_shopping_tour_depart_before_8 1.458488 F\n", - "21 coef_shopping_tour_arrive_after_22 -7.221041 F\n", - "22 coef_school_child_under_16_arrive_after_22 -6.147237 F\n", - "23 coef_university_student_arrive_after_22 -9.181095 F\n", - "24 coef_shopping_tour_duration_lt_2_hours 0.189427 F\n", - "25 coef_discretionary_tour_duration_lt_2_hours -1.022488 F\n", - "26 coef_adult_with_children_in_hh_arrive_19_21 -0.002541 F\n", - "27 coef_some_previously_scheduled_tour_ends_in_th... 0.282029 F\n", - "28 coef_some_previously_scheduled_tour_begins_in_... 0.411979 F\n", - "29 coef_adjacent_window_exists_before_this_depart... -13.495922 F\n", - "30 coef_adjacent_window_exists_after_this_arrival... -13.526580 F\n", - "31 coef_adjacent_window_exists_before_this_depart... -7.716247 F\n", - "32 coef_adjacent_window_exists_after_this_arrival... -14.169341 F\n", - "33 coef_departure_constants_early -24.418086 F\n", - "34 coef_departure_constants_am_peak_1 -25.000000 F\n", - "35 coef_departure_constants_am_peak_2 -4.236849 F\n", - "36 coef_departure_constants_am_peak_3 0.806223 F\n", - "37 coef_departure_constants_am_peak_4 1.408142 F\n", + "2 coef_free_flow_round_trip_auto_time_shift_effe... 0.003622 F\n", + "3 coef_shopping_tour_departure_shift_effects -0.099254 F\n", + "4 coef_shopping_tour_duration_shift_effects -0.101207 F\n", + "5 coef_maintenance_tour_departure_shift_effects -0.135634 F\n", + "6 coef_maintenance_tour_duration_shift_effects -0.038428 F\n", + "7 coef_visit_tour_departure_shift_effects 0.136717 F\n", + "8 coef_visit_tour_duration_shift_effects 0.172436 F\n", + "9 coef_eat_out_tour_departure_shift_effects 0.056578 F\n", + "10 coef_school_child_age_16_plus_departure_shift_... 0.106395 F\n", + "11 coef_school_child_age_16_plus_duration_shift_e... 0.315781 F\n", + "12 coef_school_child_age_under_16_departure_shift... 0.015473 F\n", + "13 coef_school_child_age_under_16_duration_shift_... 0.327527 F\n", + "14 coef_destination_in_cbd_duration_shift_effects 0.130935 F\n", + "15 coef_number_of_mandatory_tours_departure_shift... -0.009788 F\n", + "16 coef_number_of_joint_tours_departure_shift_eff... 0.128742 F\n", + "17 coef_first_of_2_plus_tours_for_same_purpose_de... -0.506782 F\n", + "18 coef_subsequent_of_2_plus_tours_for_same_purpo... 0.062417 F\n", + "19 coef_maintenance_tour_depart_before_7 -0.523693 F\n", + "20 coef_shopping_tour_depart_before_8 -1.484109 F\n", + "21 coef_shopping_tour_arrive_after_22 -0.033268 F\n", + "22 coef_school_child_under_16_arrive_after_22 -5.038827 F\n", + "23 coef_university_student_arrive_after_22 0.360948 F\n", + "24 coef_shopping_tour_duration_lt_2_hours 0.699700 F\n", + "25 coef_discretionary_tour_duration_lt_2_hours -0.772425 F\n", + "26 coef_adult_with_children_in_hh_arrive_19_21 -0.078997 F\n", + "27 coef_some_previously_scheduled_tour_ends_in_th... -0.288048 F\n", + "28 coef_some_previously_scheduled_tour_begins_in_... -0.017681 F\n", + "29 coef_adjacent_window_exists_before_this_depart... 0.008839 F\n", + "30 coef_adjacent_window_exists_after_this_arrival... 0.800851 F\n", + "31 coef_adjacent_window_exists_before_this_depart... -0.549692 F\n", + "32 coef_adjacent_window_exists_after_this_arrival... 0.494438 F\n", + "33 coef_departure_constants_early -17.865880 F\n", + "34 coef_departure_constants_am_peak_1 -13.990759 F\n", + "35 coef_departure_constants_am_peak_2 -10.163788 F\n", + "36 coef_departure_constants_am_peak_3 -2.799430 F\n", + "37 coef_departure_constants_am_peak_4 0.514393 F\n", "38 coef_departure_constants_midday_1 0.000000 T\n", - "39 coef_departure_constants_midday_2 -4.501238 F\n", - "40 coef_departure_constants_pm_peak -12.638403 F\n", - "41 coef_departure_constants_evening -14.557517 F\n", - "42 coef_departure_constants_late -20.421814 F\n", - "43 coef_arrival_constants_early 24.793142 F\n", - "44 coef_arrival_constants_am_peak 4.647673 F\n", + "39 coef_departure_constants_midday_2 -1.886316 F\n", + "40 coef_departure_constants_pm_peak -18.238556 F\n", + "41 coef_departure_constants_evening -19.574287 F\n", + "42 coef_departure_constants_late -21.444922 F\n", + "43 coef_arrival_constants_early -6.758955 F\n", + "44 coef_arrival_constants_am_peak -8.413713 F\n", "45 coef_arrival_constants_midday_1 0.000000 T\n", - "46 coef_arrival_constants_midday_2 2.692519 F\n", - "47 coef_arrival_constants_pm_peak_1 1.849237 F\n", - "48 coef_arrival_constants_pm_peak_2 1.518410 F\n", + "46 coef_arrival_constants_midday_2 1.416723 F\n", + "47 coef_arrival_constants_pm_peak_1 0.964024 F\n", + "48 coef_arrival_constants_pm_peak_2 1.200835 F\n", "49 coef_arrival_constants_pm_peak_3 0.000000 T\n", - "50 coef_arrival_constants_pm_peak_4 -1.182768 F\n", - "51 coef_arrival_constants_evening -4.291783 F\n", - "52 coef_arrival_constants_late -9.617038 F\n", - "53 coef_duration_constants_0_to_1_hours -2.750481 F\n", + "50 coef_arrival_constants_pm_peak_4 -0.725959 F\n", + "51 coef_arrival_constants_evening -2.675953 F\n", + "52 coef_arrival_constants_late -4.194942 F\n", + "53 coef_duration_constants_0_to_1_hours -2.021348 F\n", "54 coef_duration_constants_2_to_3_hours 0.000000 T\n", - "55 coef_duration_constants_4_to_5_hours 0.301145 F\n", - "56 coef_duration_constants_6_to_7_hours 0.144072 F\n", - "57 coef_duration_constants_8_to_10_hours -10.570858 F\n", - "58 coef_duration_constants_11_to_13_hours -6.162737 F\n", - "59 coef_duration_constants_14_to_18_hours -2.800706 F" + "55 coef_duration_constants_4_to_5_hours -0.519561 F\n", + "56 coef_duration_constants_6_to_7_hours -0.868309 F\n", + "57 coef_duration_constants_8_to_10_hours -1.020095 F\n", + "58 coef_duration_constants_11_to_13_hours -1.608105 F\n", + "59 coef_duration_constants_14_to_18_hours -3.067613 F" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4785,7 +4909,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -4799,7 +4923,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb b/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb index 81092083fc..933656370f 100644 --- a/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb +++ b/activitysim/examples/example_estimation/notebooks/15_non_mand_tour_freq.ipynb @@ -35,10 +35,22 @@ "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, { "data": { "text/plain": [ - "'1.1.0'" + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" ] }, "execution_count": 1, @@ -47,27 +59,49 @@ } ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", + "import larch as lx\n", "import pandas as pd\n", - "import activitysim\n", - "activitysim.__version__" + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -81,12 +115,67 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading EDB for PTYPE_FULL segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_FULL/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_FULL/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_FULL/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "Loading EDB for PTYPE_PART segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_PART/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_PART/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_PART/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "Loading EDB for PTYPE_UNIVERSITY segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_UNIVERSITY/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_UNIVERSITY/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_UNIVERSITY/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "Loading EDB for PTYPE_NONWORK segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_NONWORK/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_NONWORK/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_NONWORK/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "Loading EDB for PTYPE_RETIRED segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_RETIRED/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_RETIRED/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_RETIRED/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "Loading EDB for PTYPE_DRIVING segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_DRIVING/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_DRIVING/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_DRIVING/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "Loading EDB for PTYPE_SCHOOL segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_SCHOOL/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_SCHOOL/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_SCHOOL/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "Loading EDB for PTYPE_PRESCHOOL segment\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_PRESCHOOL/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_PRESCHOOL/non_mandatory_tour_frequency_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_PRESCHOOL/non_mandatory_tour_frequency_interaction_expression_values.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_FULL/non_mandatory_tour_frequency_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/PTYPE_FULL/non_mandatory_tour_frequency_alternatives.csv\n", + "Creating larch model for PTYPE_FULL\n", + "Creating larch model for PTYPE_PART\n", + "Creating larch model for PTYPE_UNIVERSITY\n", + "Creating larch model for PTYPE_NONWORK\n", + "Creating larch model for PTYPE_RETIRED\n", + "Creating larch model for PTYPE_DRIVING\n", + "Creating larch model for PTYPE_SCHOOL\n", + "Creating larch model for PTYPE_PRESCHOOL\n" + ] + } + ], "source": [ "modelname = \"nonmand_tour_freq\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True, condense_parameters=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/non_mandatory_tour_frequency/\",\n", + " return_data=True,\n", + " condense_parameters=True,\n", + ")" ] }, { @@ -383,20 +472,20 @@ " \n", " \n", " 0\n", - " 72241\n", + " 72355\n", " 0\n", " 0\n", - " 72241\n", - " 56\n", - " 1\n", + " 72355\n", + " 52\n", " 1\n", + " 2\n", " 1\n", " 3\n", " 1\n", " ...\n", " False\n", " False\n", - " 0\n", + " 1\n", " 0\n", " 0\n", " 0\n", @@ -407,13 +496,13 @@ " \n", " \n", " 1\n", - " 72441\n", + " 72384\n", " 0\n", " 0\n", - " 72441\n", - " 49\n", - " 1\n", + " 72384\n", + " 28\n", " 1\n", + " 2\n", " 1\n", " 3\n", " 1\n", @@ -431,11 +520,11 @@ " \n", " \n", " 2\n", - " 73144\n", - " 0\n", + " 72407\n", + " 1\n", " 0\n", - " 73144\n", - " 31\n", + " 72407\n", + " 52\n", " 1\n", " 2\n", " 1\n", @@ -443,8 +532,8 @@ " 1\n", " ...\n", " False\n", - " True\n", - " -1\n", + " False\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -455,20 +544,20 @@ " \n", " \n", " 3\n", - " 73493\n", + " 72459\n", " 0\n", " 0\n", - " 73493\n", - " 31\n", + " 72459\n", + " 38\n", + " 1\n", " 1\n", - " 2\n", " 1\n", " 3\n", " 1\n", " ...\n", " False\n", - " False\n", - " 0\n", + " True\n", + " -1\n", " 0\n", " 0\n", " 0\n", @@ -479,11 +568,11 @@ " \n", " \n", " 4\n", - " 73706\n", - " 0\n", - " 0\n", - " 73706\n", - " 26\n", + " 72569\n", + " 1\n", + " 20\n", + " 72569\n", + " 49\n", " 1\n", " 1\n", " 1\n", @@ -491,14 +580,14 @@ " 1\n", " ...\n", " False\n", - " False\n", + " True\n", + " -1\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 1\n", " 0\n", " \n", " \n", @@ -526,14 +615,14 @@ " ...\n", " \n", " \n", - " 1759\n", - " 7512288\n", - " 5\n", - " 5\n", - " 2820953\n", - " 28\n", - " 1\n", + " 19354\n", + " 7538573\n", + " 0\n", + " 0\n", + " 2847238\n", + " 67\n", " 1\n", + " 2\n", " 1\n", " 3\n", " 1\n", @@ -546,18 +635,18 @@ " 0\n", " 0\n", " 0\n", - " 0\n", + " 1\n", " 0\n", " \n", " \n", - " 1760\n", - " 7512469\n", + " 19355\n", + " 7538990\n", " 1\n", + " 19\n", + " 2847655\n", + " 72\n", " 1\n", - " 2821134\n", - " 34\n", " 1\n", - " 2\n", " 1\n", " 3\n", " 1\n", @@ -570,16 +659,16 @@ " 0\n", " 0\n", " 0\n", - " 1\n", + " 0\n", " 0\n", " \n", " \n", - " 1761\n", - " 7513117\n", + " 19356\n", + " 7539071\n", " 0\n", " 0\n", - " 2821782\n", - " 16\n", + " 2847736\n", + " 70\n", " 1\n", " 1\n", " 1\n", @@ -587,8 +676,8 @@ " 1\n", " ...\n", " False\n", - " True\n", - " -1\n", + " False\n", + " 1\n", " 0\n", " 0\n", " 0\n", @@ -598,12 +687,12 @@ " 0\n", " \n", " \n", - " 1762\n", - " 7513996\n", + " 19357\n", + " 7539203\n", " 0\n", " 0\n", - " 2822661\n", - " 24\n", + " 2847868\n", + " 87\n", " 1\n", " 1\n", " 1\n", @@ -622,21 +711,21 @@ " 0\n", " \n", " \n", - " 1763\n", - " 7514404\n", + " 19358\n", + " 7539317\n", " 0\n", " 0\n", - " 2823069\n", - " 30\n", + " 2847982\n", + " 70\n", + " 1\n", " 1\n", - " 2\n", " 1\n", " 3\n", " 1\n", " ...\n", " False\n", - " True\n", - " -1\n", + " False\n", + " 0\n", " 0\n", " 0\n", " 0\n", @@ -647,76 +736,76 @@ " \n", " \n", "\n", - "

1764 rows × 142 columns

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+       "  * person_id                                                                    (person_id) int64 155kB ...\n",
+       "  * alt_id                                                                       (alt_id) int64 768B ...\n",
+       "Data variables: (12/353)\n",
+       "    index                                                                        (person_id, alt_id) int64 15MB ...\n",
+       "    util_escorting_tour                                                          (person_id, alt_id) int64 15MB ...\n",
+       "    util_discretionary_tour                                                      (person_id, alt_id) int64 15MB ...\n",
+       "    util_shopping_tour                                                           (person_id, alt_id) int64 15MB ...\n",
+       "    util_maintenance_tour                                                        (person_id, alt_id) int64 15MB ...\n",
+       "    util_visiting_or_social_tour                                                 (person_id, alt_id) int64 15MB ...\n",
+       "    ...                                                                           ...\n",
+       "    num_hh_joint_eatout_tours                                                    (person_id) int8 19kB ...\n",
+       "    num_hh_joint_maint_tours                                                     (person_id) int8 19kB ...\n",
+       "    num_hh_joint_social_tours                                                    (person_id) int8 19kB ...\n",
+       "    num_hh_joint_othdiscr_tours                                                  (person_id) int8 19kB ...\n",
+       "    has_mandatory_tour                                                           (person_id) int64 155kB ...\n",
+       "    has_joint_tour                                                               (person_id) int64 155kB ...\n",
+       "Attributes:\n",
+       "    _caseid_:  person_id\n",
+       "    _altid_:   alt_id
" + ], + "text/plain": [ + " Size: 2GB\n", + "Dimensions: (\n", + " person_id: 19359,\n", + " alt_id: 96)\n", + "Coordinates:\n", + " * person_id (person_id) int64 155kB ...\n", + " * alt_id (alt_id) int64 768B ...\n", + "Data variables: (12/353)\n", + " index (person_id, alt_id) int64 15MB ...\n", + " util_escorting_tour (person_id, alt_id) int64 15MB ...\n", + " util_discretionary_tour (person_id, alt_id) int64 15MB ...\n", + " util_shopping_tour (person_id, alt_id) int64 15MB ...\n", + " util_maintenance_tour (person_id, alt_id) int64 15MB ...\n", + " util_visiting_or_social_tour (person_id, alt_id) int64 15MB ...\n", + " ... ...\n", + " num_hh_joint_eatout_tours (person_id) int8 19kB ...\n", + " num_hh_joint_maint_tours (person_id) int8 19kB ...\n", + " num_hh_joint_social_tours (person_id) int8 19kB ...\n", + " num_hh_joint_othdiscr_tours (person_id) int8 19kB ...\n", + " has_mandatory_tour (person_id) int64 155kB ...\n", + " has_joint_tour (person_id) int64 155kB ...\n", + "Attributes:\n", + " _caseid_: person_id\n", + " _altid_: alt_id" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model['PTYPE_FULL'].datatree.root_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -768,27 +2747,29 @@ "source": [ "For future estimation work, parameters can be intelligently named and applied to match the model developer's desired structure (by using the same named parameter for multiple rows of the spec file). If this is done, the \"short cut\" should be disabled by setting `condense_parameters=False` in the loading step above.\n", "\n", - "Larch has a built-in estimation methods including BHHH, and also offers access to more advanced general purpose non-linear optimizers in the `scipy` package, including SLSQP, which allows for bounds and constraints on parameters. BHHH is the default and typically runs faster, but does not follow constraints on parameters." + "Larch has a built-in estimation methods including BHHH, and also offers access to more advanced general purpose non-linear optimizers in the `scipy` package, including SLSQP, which allows for bounds and constraints on parameters. BHHH is the default and typically runs faster, but does not follow constraints on parameters.\n", + "\n", + "For this model, we will sequentially estimate the models by person type and then save and clear the results, to avoid running out of memory, as the models are large and use a lot of resources. " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "Model PTYPE_FULL\n" ] }, { "data": { "text/html": [ - "

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" ], "text/plain": [ "" @@ -800,7 +2781,7 @@ { "data": { "text/html": [ - "

Best LL = -1831.0744991680774

" + "

Best LL = -19916.199393036724

" ], "text/plain": [ "" @@ -831,70 +2812,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", " -2.000000\n", + " -2.000000\n", " -2.0000\n", - " 0.0\n", " -2.0\n", " -2.0\n", + " 0.0\n", " 1\n", - " \n", - " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " 0.319037\n", + " -0.065728\n", + " -0.065728\n", " 0.0298\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.319037\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -1.012856\n", + " 0.047768\n", + " 0.047768\n", " 0.0097\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.012856\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -2.842643\n", + " 0.079776\n", + " 0.079776\n", " 0.1202\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.842643\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " 10.543979\n", + " 0.733799\n", + " 0.733799\n", " 0.7412\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 10.543979\n", " \n", " \n", " ...\n", @@ -905,122 +2890,120 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coef_walk_access_to_retail_and_discretionary\n", - " 0.160520\n", + " 0.060958\n", + " 0.060958\n", " 0.0567\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.160520\n", " \n", " \n", " coef_walk_access_to_retail_and_eating_out\n", - " 0.211289\n", + " 0.156964\n", + " 0.156964\n", " 0.1450\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.211289\n", " \n", " \n", " coef_walk_access_to_retail_and_escorting\n", - " -0.105285\n", + " 0.066031\n", + " 0.066031\n", " 0.0451\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.105285\n", " \n", " \n", " coef_walk_access_to_retail_and_shopping\n", - " 0.030182\n", + " 0.031695\n", + " 0.031695\n", " 0.0330\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.030182\n", " \n", " \n", " coef_zero_car_ownership_and_tour_frequency_is_5_plus\n", - " -0.227492\n", + " -0.239893\n", + " -0.239893\n", " -0.3486\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.227492\n", " \n", " \n", "\n", - "

72 rows × 8 columns

\n", + "

72 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant 0.319037 0.0298 \n", - "coef_1_plus_eating_out_tours_constant -1.012856 0.0097 \n", - "coef_1_plus_maintenance_tours_constant -2.842643 0.1202 \n", - "coef_1_plus_other_discretionary_tours_constant 10.543979 0.7412 \n", - "... ... ... \n", - "coef_walk_access_to_retail_and_discretionary 0.160520 0.0567 \n", - "coef_walk_access_to_retail_and_eating_out 0.211289 0.1450 \n", - "coef_walk_access_to_retail_and_escorting -0.105285 0.0451 \n", - "coef_walk_access_to_retail_and_shopping 0.030182 0.0330 \n", - "coef_zero_car_ownership_and_tour_frequency_is_5... -0.227492 -0.3486 \n", + " value best \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.000000 \n", + "coef_1_escort_tour_constant -0.065728 -0.065728 \n", + "coef_1_plus_eating_out_tours_constant 0.047768 0.047768 \n", + "coef_1_plus_maintenance_tours_constant 0.079776 0.079776 \n", + "coef_1_plus_other_discretionary_tours_constant 0.733799 0.733799 \n", + "... ... ... \n", + "coef_walk_access_to_retail_and_discretionary 0.060958 0.060958 \n", + "coef_walk_access_to_retail_and_eating_out 0.156964 0.156964 \n", + "coef_walk_access_to_retail_and_escorting 0.066031 0.066031 \n", + "coef_walk_access_to_retail_and_shopping 0.031695 0.031695 \n", + "coef_zero_car_ownership_and_tour_frequency_is_5... -0.239893 -0.239893 \n", "\n", - " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", - "coef_1_escort_tour_constant 0.0 NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0000 -2.0 \n", + "coef_1_escort_tour_constant 0.0298 -20.0 \n", + "coef_1_plus_eating_out_tours_constant 0.0097 -20.0 \n", + "coef_1_plus_maintenance_tours_constant 0.1202 -20.0 \n", + "coef_1_plus_other_discretionary_tours_constant 0.7412 -20.0 \n", "... ... ... \n", - "coef_walk_access_to_retail_and_discretionary 0.0 NaN \n", - "coef_walk_access_to_retail_and_eating_out 0.0 NaN \n", - "coef_walk_access_to_retail_and_escorting 0.0 NaN \n", - "coef_walk_access_to_retail_and_shopping 0.0 NaN \n", - "coef_zero_car_ownership_and_tour_frequency_is_5... 0.0 NaN \n", + "coef_walk_access_to_retail_and_discretionary 0.0567 -20.0 \n", + "coef_walk_access_to_retail_and_eating_out 0.1450 -20.0 \n", + "coef_walk_access_to_retail_and_escorting 0.0451 -20.0 \n", + "coef_walk_access_to_retail_and_shopping 0.0330 -20.0 \n", + "coef_zero_car_ownership_and_tour_frequency_is_5... -0.3486 -20.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour -2.0 1 \n", - "coef_1_escort_tour_constant NaN 0 \n", - "coef_1_plus_eating_out_tours_constant NaN 0 \n", - "coef_1_plus_maintenance_tours_constant NaN 0 \n", - "coef_1_plus_other_discretionary_tours_constant NaN 0 \n", - "... ... ... ... \n", - "coef_walk_access_to_retail_and_discretionary NaN 0 \n", - "coef_walk_access_to_retail_and_eating_out NaN 0 \n", - "coef_walk_access_to_retail_and_escorting NaN 0 \n", - "coef_walk_access_to_retail_and_shopping NaN 0 \n", - "coef_zero_car_ownership_and_tour_frequency_is_5... NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0 0.0 \n", + "coef_1_escort_tour_constant 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant 20.0 0.0 \n", + "... ... ... \n", + "coef_walk_access_to_retail_and_discretionary 20.0 0.0 \n", + "coef_walk_access_to_retail_and_eating_out 20.0 0.0 \n", + "coef_walk_access_to_retail_and_escorting 20.0 0.0 \n", + "coef_walk_access_to_retail_and_shopping 20.0 0.0 \n", + "coef_zero_car_ownership_and_tour_frequency_is_5... 20.0 0.0 \n", "\n", - " best \n", - "coef_0_auto_household_and_escorting_tour -2.000000 \n", - "coef_1_escort_tour_constant 0.319037 \n", - "coef_1_plus_eating_out_tours_constant -1.012856 \n", - "coef_1_plus_maintenance_tours_constant -2.842643 \n", - "coef_1_plus_other_discretionary_tours_constant 10.543979 \n", - "... ... \n", - "coef_walk_access_to_retail_and_discretionary 0.160520 \n", - "coef_walk_access_to_retail_and_eating_out 0.211289 \n", - "coef_walk_access_to_retail_and_escorting -0.105285 \n", - "coef_walk_access_to_retail_and_shopping 0.030182 \n", - "coef_zero_car_ownership_and_tour_frequency_is_5... -0.227492 \n", + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 1 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", + "... ... \n", + "coef_walk_access_to_retail_and_discretionary 0 \n", + "coef_walk_access_to_retail_and_eating_out 0 \n", + "coef_walk_access_to_retail_and_escorting 0 \n", + "coef_walk_access_to_retail_and_shopping 0 \n", + "coef_zero_car_ownership_and_tour_frequency_is_5... 0 \n", "\n", - "[72 rows x 8 columns]" + "[72 rows x 7 columns]" ] }, "metadata": {}, @@ -1030,24 +3013,717 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 5.456681257677148e-07 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 3 parameter estimators with negative variance\n", - "- coef_1_escort_tour_constant\n", - "- coef_2_plus_escort_tours_constant\n", - "- coef_urban_and_escorting_tour\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - " m.estimate(method='SLSQP')\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { "data": { "text/html": [ - "

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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.00 0.00 NA 0.00fixed value
coef_1_escort_tour_constant-0.0657 49.6-0.00 0.00
coef_1_plus_eating_out_tours_constant 0.0478 49.6 0.00 0.00
coef_1_plus_maintenance_tours_constant 0.0798 49.6 0.00 0.00
coef_1_plus_other_discretionary_tours_constant 0.734 49.6 0.01 0.00
coef_1_plus_shopping_tours_constant 0.360 49.6 0.01 0.00
coef_1_plus_visting_tours_constant-0.0190 49.6-0.00 0.00
coef_2_plus_escort_tours_constant 0.688 99.2 0.01 0.00
coef_at_home_pre_driving_school_kid_and_escorting_tour-0.471 0.160-2.94** 0.00
coef_at_home_pre_school_kid_and_discretionary_tour-0.223 0.186-1.20 0.00
coef_at_home_pre_school_kid_and_escorting_tour-1.53 0.227-6.75*** 0.00
coef_auto_access_to_retail_and_discretionary 0.0857 0.0630 1.36 0.00
coef_auto_access_to_retail_and_maintenance 0.0843 0.0587 1.44 0.00
coef_auto_access_to_retail_and_shopping 0.103 0.0604 1.71 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus 0.120 0.0470 2.55* 0.00
coef_female_and_escorting_tour 0.177 0.0645 2.74** 0.00
coef_female_and_tour_frequency_is_1-0.0844 0.0430-1.96* 0.00
coef_female_and_tour_frequency_is_2-0.0975 0.0857-1.14 0.00
coef_female_and_tour_frequency_is_5-0.446 0.180-2.47* 0.00
coef_high_income_group_and_discretionary_tour 0.213 0.0780 2.74** 0.00
coef_high_income_group_and_eating_out_tour 0.384 0.0963 3.99*** 0.00
coef_high_income_group_and_tour_frequency_is_1 0.355 0.114 3.12** 0.00
coef_high_income_group_and_tour_frequency_is_2 0.990 0.240 4.12*** 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 1.29 0.555 2.32* 0.00
coef_high_income_group_and_visiting_tour-0.228 0.122-1.87 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 1.28 0.0731 17.53*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_2 1.45 0.0918 15.83*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 1.43 0.142 10.06*** 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_1 0.376 0.110 3.42*** 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_2 0.794 0.235 3.38*** 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_5_plus 1.03 0.548 1.88 0.00
coef_number_of_joint_eating_out_tours-0.618 0.529-1.17 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_2-1.07 0.158-6.75*** 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus-2.50 0.307-8.15*** 0.00
coef_presence_of_driving_school_kid_and_discretionary_tour-0.364 0.128-2.84** 0.00
coef_presence_of_driving_school_kid_and_escorting_tour 0.429 0.0738 5.81*** 0.00
coef_presence_of_full_time_worker_and_discretionary_tour-0.601 0.0679-8.86*** 0.00
coef_presence_of_full_time_worker_and_eating_out_tour-0.280 0.0777-3.60*** 0.00
coef_presence_of_full_time_worker_and_maintenance_tour-0.264 0.0767-3.45*** 0.00
coef_presence_of_full_time_worker_and_shopping_tour-0.175 0.0618-2.83** 0.00
coef_presence_of_non_worker_and_discretionary_tour-0.484 0.0844-5.73*** 0.00
coef_presence_of_non_worker_and_eating_out_tour-0.172 0.0913-1.88 0.00
coef_presence_of_non_worker_and_escorting_tour-0.476 0.0747-6.37*** 0.00
coef_presence_of_non_worker_and_maintenance_tour-0.279 0.0939-2.97** 0.00
coef_presence_of_non_worker_and_shopping_tour-0.419 0.0783-5.35*** 0.00
coef_presence_of_part_time_worker_and_discretionary_tour-0.252 0.0806-3.13** 0.00
coef_presence_of_part_time_worker_and_maintenance_tour-0.0951 0.0856-1.11 0.00
coef_presence_of_part_time_worker_and_shopping_tour-0.150 0.0686-2.19* 0.00
coef_presence_of_pre_driving_school_kid_and_discretionary_tour-0.401 0.0837-4.79*** 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 1.38 0.0611 22.52*** 0.00
coef_presence_of_pre_school_kid_and_discretionary_tour-0.509 0.100-5.09*** 0.00
coef_presence_of_pre_school_kid_and_eating_out_tour-0.481 0.108-4.44*** 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 0.654 0.0634 10.32*** 0.00
coef_presence_of_pre_school_kid_and_shopping_tour-0.0802 0.0795-1.01 0.00
coef_presence_of_retiree_and_discretionary_tour-1.05 0.154-6.82*** 0.00
coef_presence_of_retiree_and_eating_out_tour-0.430 0.147-2.92** 0.00
coef_presence_of_retiree_and_escorting_tour-0.694 0.143-4.86*** 0.00
coef_presence_of_university_student_and_discretionary_tour-0.532 0.114-4.67*** 0.00
coef_total_number_of_tours_is_1-7.26 49.6-0.15 0.00
coef_total_number_of_tours_is_2-10.7 99.2-0.11 0.00
coef_total_number_of_tours_is_3-13.4 149.-0.09 0.00
coef_total_number_of_tours_is_4-16.5 198.-0.08 0.00
coef_total_number_of_tours_is_5-19.7 248.-0.08 0.00
coef_total_number_of_tours_is_6_plus-999. 0.00 NA 0.00fixed value
coef_transit_access_to_retail_and_tour_frequency_is_5_plus 0.0132 0.0114 1.15 0.00
coef_urban_and_discretionary_tour 0.00 0.00 NA 0.00fixed value
coef_urban_and_escorting_tour-0.486 0.0782-6.21*** 0.00
coef_walk_access_to_retail_and_discretionary 0.0610 0.0251 2.42* 0.00
coef_walk_access_to_retail_and_eating_out 0.157 0.0243 6.47*** 0.00
coef_walk_access_to_retail_and_escorting 0.0660 0.0200 3.30*** 0.00
coef_walk_access_to_retail_and_shopping 0.0317 0.0241 1.31 0.00
coef_zero_car_ownership_and_tour_frequency_is_5_plus-0.240 0.109-2.19* 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model PTYPE_PART\n" + ] + }, + { + "data": { + "text/html": [ + "

Iteration 060 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1059,7 +3735,7 @@ { "data": { "text/html": [ - "

Best LL = -856.8595081667797

" + "

Best LL = -9327.08229602112

" ], "text/plain": [ "" @@ -1090,793 +3766,755 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", " -2.000000\n", - " -2.0000\n", - " 0.0\n", + " -2.000000\n", + " -2.000000\n", " -2.0\n", " -2.0\n", + " 0.0\n", " 1\n", - " \n", - " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " 0.777927\n", - " 0.5272\n", + " 0.430423\n", + " 0.430423\n", + " 0.527200\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.777927\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " 1.665075\n", - " 0.6914\n", + " 0.732307\n", + " 0.732307\n", + " 0.691400\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.665075\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " 1.214734\n", - " 0.5533\n", + " 0.480847\n", + " 0.480847\n", + " 0.553300\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.214734\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " 1.783871\n", - " 0.7989\n", + " 0.900550\n", + " 0.900550\n", + " 0.798900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.783871\n", " \n", " \n", " coef_1_plus_shopping_tours_constant\n", - " 1.521613\n", - " 0.7569\n", + " 0.731723\n", + " 0.731723\n", + " 0.756900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.521613\n", " \n", " \n", " coef_1_plus_visting_tours_constant\n", - " 1.045482\n", - " 0.1405\n", + " 0.250360\n", + " 0.250360\n", + " 0.140500\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.045482\n", " \n", " \n", " coef_2_plus_escort_tours_constant\n", - " 1.882661\n", - " 1.5987\n", + " 1.457716\n", + " 1.457716\n", + " 1.598700\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.882661\n", " \n", " \n", " coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus\n", - " 0.049232\n", - " -0.5498\n", + " -0.400969\n", + " -0.400969\n", + " -0.549800\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.049232\n", " \n", " \n", " coef_female_and_discretionary_tour\n", - " 0.439252\n", - " 0.3072\n", + " 0.316191\n", + " 0.316191\n", + " 0.307200\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.439252\n", " \n", " \n", " coef_female_and_shopping_tour\n", - " 0.753133\n", - " 0.4524\n", + " 0.352178\n", + " 0.352178\n", + " 0.452400\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.753133\n", " \n", " \n", " coef_high_income_group_and_discretionary_tour\n", - " 0.236506\n", - " 0.2960\n", + " 0.245706\n", + " 0.245706\n", + " 0.296000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.236506\n", " \n", " \n", " coef_high_income_group_and_maintenance_tour\n", - " 1.385100\n", - " 0.6763\n", + " 0.828693\n", + " 0.828693\n", + " 0.676300\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.385100\n", " \n", " \n", " coef_high_income_group_and_shopping_tour\n", - " 0.819417\n", - " 0.7066\n", + " 0.846024\n", + " 0.846024\n", + " 0.706600\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.819417\n", " \n", " \n", " coef_high_income_group_and_tour_frequency_is_1\n", - " 1.145715\n", - " 0.8682\n", + " 0.809721\n", + " 0.809721\n", + " 0.868200\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.145715\n", " \n", " \n", " coef_high_income_group_and_tour_frequency_is_2\n", - " 1.347519\n", - " 1.5362\n", + " 1.223360\n", + " 1.223360\n", + " 1.536200\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.347519\n", " \n", " \n", " coef_high_income_group_and_tour_frequency_is_5_plus\n", - " 1.498903\n", - " 1.9331\n", + " 1.571734\n", + " 1.571734\n", + " 1.933100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.498903\n", " \n", " \n", " coef_high_income_group_and_visiting_tour\n", - " -0.517316\n", - " -0.6868\n", + " -0.714757\n", + " -0.714757\n", + " -0.686800\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.517316\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_1\n", - " 0.768519\n", - " 1.5748\n", + " 1.659306\n", + " 1.659306\n", + " 1.574800\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.768519\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_5_plus\n", - " 1.243995\n", - " 2.0026\n", + " 1.957087\n", + " 1.957087\n", + " 2.002600\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.243995\n", " \n", " \n", " coef_mediumhigh_income_group_and_shopping_tour\n", - " 0.605047\n", - " 0.4421\n", + " 0.573217\n", + " 0.573217\n", + " 0.442100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.605047\n", " \n", " \n", " coef_mediumlow_income_group_and_tour_frequency_is_1\n", - " 0.784377\n", - " 0.5981\n", + " 0.685180\n", + " 0.685180\n", + " 0.598100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.784377\n", " \n", " \n", " coef_mediumlow_income_group_and_tour_frequency_is_2\n", - " 0.776466\n", - " 0.9178\n", + " 0.592898\n", + " 0.592898\n", + " 0.917800\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.776466\n", " \n", " \n", " coef_mediumlow_income_group_and_tour_frequency_is_5_plus\n", - " 0.850898\n", - " 1.7539\n", + " 1.597675\n", + " 1.597675\n", + " 1.753900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.850898\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_4\n", - " -1.347657\n", - " -1.1986\n", + " -0.760824\n", + " -0.760824\n", + " -1.198600\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.347657\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_5_plus\n", " -999.000000\n", - " -999.0000\n", - " 0.0\n", + " -999.000000\n", + " -999.000000\n", " -999.0\n", " -999.0\n", + " 0.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_1\n", - " -0.989069\n", - " -0.2390\n", + " -0.358322\n", + " -0.358322\n", + " -0.239000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.989069\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_2\n", - " -2.829794\n", - " -1.8208\n", + " -1.622848\n", + " -1.622848\n", + " -1.820800\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.829794\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus\n", - " -2.785677\n", - " -2.5923\n", + " -2.631492\n", + " -2.631492\n", + " -2.592300\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.785677\n", " \n", " \n", " coef_presence_of_driving_school_kid_and_escorting_tour\n", - " 1.029447\n", - " 0.4164\n", + " 0.367517\n", + " 0.367517\n", + " 0.416400\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.029447\n", " \n", " \n", " coef_presence_of_full_time_worker_and_maintenance_tour\n", - " -0.484999\n", - " -0.3131\n", + " -0.254950\n", + " -0.254950\n", + " -0.313100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.484999\n", " \n", " \n", " coef_presence_of_non_worker_and_discretionary_tour\n", - " -0.556803\n", - " -1.0371\n", + " -0.828046\n", + " -0.828046\n", + " -1.037100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.556803\n", " \n", " \n", " coef_presence_of_non_worker_and_eating_out_tour\n", - " -0.231608\n", - " -0.6545\n", + " -0.660344\n", + " -0.660344\n", + " -0.654500\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.231608\n", " \n", " \n", " coef_presence_of_non_worker_and_escorting_tour\n", - " -0.617113\n", - " -0.5263\n", + " -0.530905\n", + " -0.530905\n", + " -0.526300\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.617113\n", " \n", " \n", " coef_presence_of_part_time_worker_and_maintenance_tour\n", - " -1.034909\n", - " -0.5621\n", + " -0.576071\n", + " -0.576071\n", + " -0.562100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.034909\n", " \n", " \n", " coef_presence_of_pre_driving_school_kid_and_escorting_tour\n", - " 1.808890\n", - " 1.5795\n", + " 1.756066\n", + " 1.756066\n", + " 1.579500\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.808890\n", " \n", " \n", " coef_presence_of_pre_school_kid_and_escorting_tour\n", - " 0.291702\n", - " 0.5414\n", + " 0.549515\n", + " 0.549515\n", + " 0.541400\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.291702\n", " \n", " \n", " coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1\n", - " 0.308611\n", - " -0.1559\n", + " -0.340060\n", + " -0.340060\n", + " -0.155900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.308611\n", " \n", " \n", " coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5\n", - " -0.530685\n", - " -0.5681\n", + " -0.650421\n", + " -0.650421\n", + " -0.568100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.530685\n", " \n", " \n", " coef_presence_of_retiree_and_eating_out_tour\n", - " 0.109209\n", - " -1.3890\n", + " -1.202708\n", + " -1.202708\n", + " -1.389000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.109209\n", " \n", " \n", " coef_presence_of_retiree_and_escorting_tour\n", - " -0.461150\n", - " -0.7516\n", + " -0.801304\n", + " -0.801304\n", + " -0.751600\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.461150\n", " \n", " \n", " coef_presence_of_university_student_and_eating_out_tour\n", - " -1.759323\n", - " -1.4318\n", + " -1.695822\n", + " -1.695822\n", + " -1.431800\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.759323\n", " \n", " \n", " coef_total_number_of_tours_is_1\n", - " -6.812615\n", - " -7.6391\n", + " -7.835780\n", + " -7.835780\n", + " -7.639100\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -6.812615\n", " \n", " \n", " coef_total_number_of_tours_is_2\n", - " -9.848060\n", - " -10.4557\n", + " -10.277923\n", + " -10.277923\n", + " -10.455700\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -9.848060\n", " \n", " \n", " coef_total_number_of_tours_is_3\n", - " -12.828277\n", - " -14.0176\n", + " -14.048815\n", + " -14.048815\n", + " -14.017600\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -12.828277\n", " \n", " \n", " coef_total_number_of_tours_is_4\n", - " -17.097027\n", - " -16.9717\n", + " -17.043997\n", + " -17.043997\n", + " -16.971701\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -17.097027\n", " \n", " \n", " coef_urban_and_discretionary_tour\n", " 0.000000\n", - " 0.0000\n", + " 0.000000\n", + " 0.000000\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_urban_and_escorting_tour\n", - " 0.425750\n", - " -0.3929\n", + " -0.503217\n", + " -0.503217\n", + " -0.392900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.425750\n", " \n", " \n", " coef_walk_access_to_retail_and_tour_frequency_is_1\n", - " 0.080140\n", - " 0.0899\n", + " 0.108136\n", + " 0.108136\n", + " 0.089900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.080140\n", " \n", " \n", " coef_walk_access_to_retail_and_tour_frequency_is_2\n", - " 0.103820\n", - " 0.1447\n", + " 0.128901\n", + " 0.128901\n", + " 0.144700\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.103820\n", " \n", " \n", " coef_walk_access_to_retail_and_tour_frequency_is_5_plus\n", - " 0.050631\n", - " 0.3479\n", + " 0.385133\n", + " 0.385133\n", + " 0.347900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.050631\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant 0.777927 0.5272 \n", - "coef_1_plus_eating_out_tours_constant 1.665075 0.6914 \n", - "coef_1_plus_maintenance_tours_constant 1.214734 0.5533 \n", - "coef_1_plus_other_discretionary_tours_constant 1.783871 0.7989 \n", - "coef_1_plus_shopping_tours_constant 1.521613 0.7569 \n", - "coef_1_plus_visting_tours_constant 1.045482 0.1405 \n", - "coef_2_plus_escort_tours_constant 1.882661 1.5987 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... 0.049232 -0.5498 \n", - "coef_female_and_discretionary_tour 0.439252 0.3072 \n", - "coef_female_and_shopping_tour 0.753133 0.4524 \n", - "coef_high_income_group_and_discretionary_tour 0.236506 0.2960 \n", - "coef_high_income_group_and_maintenance_tour 1.385100 0.6763 \n", - "coef_high_income_group_and_shopping_tour 0.819417 0.7066 \n", - "coef_high_income_group_and_tour_frequency_is_1 1.145715 0.8682 \n", - "coef_high_income_group_and_tour_frequency_is_2 1.347519 1.5362 \n", - "coef_high_income_group_and_tour_frequency_is_5_... 1.498903 1.9331 \n", - "coef_high_income_group_and_visiting_tour -0.517316 -0.6868 \n", - "coef_logged_maximum_residual_window_tour_freque... 0.768519 1.5748 \n", - "coef_logged_maximum_residual_window_tour_freque... 1.243995 2.0026 \n", - "coef_mediumhigh_income_group_and_shopping_tour 0.605047 0.4421 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.784377 0.5981 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.776466 0.9178 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.850898 1.7539 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_4 -1.347657 -1.1986 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.0000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -0.989069 -0.2390 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -2.829794 -1.8208 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -2.785677 -2.5923 \n", - "coef_presence_of_driving_school_kid_and_escorti... 1.029447 0.4164 \n", - "coef_presence_of_full_time_worker_and_maintenan... -0.484999 -0.3131 \n", - "coef_presence_of_non_worker_and_discretionary_tour -0.556803 -1.0371 \n", - "coef_presence_of_non_worker_and_eating_out_tour -0.231608 -0.6545 \n", - "coef_presence_of_non_worker_and_escorting_tour -0.617113 -0.5263 \n", - "coef_presence_of_part_time_worker_and_maintenan... -1.034909 -0.5621 \n", - "coef_presence_of_pre_driving_school_kid_and_esc... 1.808890 1.5795 \n", - "coef_presence_of_pre_school_kid_and_escorting_tour 0.291702 0.5414 \n", - "coef_presence_of_preschool_kid_in_household_and... 0.308611 -0.1559 \n", - "coef_presence_of_preschool_kid_in_household_and... -0.530685 -0.5681 \n", - "coef_presence_of_retiree_and_eating_out_tour 0.109209 -1.3890 \n", - "coef_presence_of_retiree_and_escorting_tour -0.461150 -0.7516 \n", - "coef_presence_of_university_student_and_eating_... -1.759323 -1.4318 \n", - "coef_total_number_of_tours_is_1 -6.812615 -7.6391 \n", - "coef_total_number_of_tours_is_2 -9.848060 -10.4557 \n", - "coef_total_number_of_tours_is_3 -12.828277 -14.0176 \n", - "coef_total_number_of_tours_is_4 -17.097027 -16.9717 \n", - "coef_urban_and_discretionary_tour 0.000000 0.0000 \n", - "coef_urban_and_escorting_tour 0.425750 -0.3929 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 0.080140 0.0899 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 0.103820 0.1447 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... 0.050631 0.3479 \n", + " value best \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.000000 \n", + "coef_1_escort_tour_constant 0.430423 0.430423 \n", + "coef_1_plus_eating_out_tours_constant 0.732307 0.732307 \n", + "coef_1_plus_maintenance_tours_constant 0.480847 0.480847 \n", + "coef_1_plus_other_discretionary_tours_constant 0.900550 0.900550 \n", + "coef_1_plus_shopping_tours_constant 0.731723 0.731723 \n", + "coef_1_plus_visting_tours_constant 0.250360 0.250360 \n", + "coef_2_plus_escort_tours_constant 1.457716 1.457716 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... -0.400969 -0.400969 \n", + "coef_female_and_discretionary_tour 0.316191 0.316191 \n", + "coef_female_and_shopping_tour 0.352178 0.352178 \n", + "coef_high_income_group_and_discretionary_tour 0.245706 0.245706 \n", + "coef_high_income_group_and_maintenance_tour 0.828693 0.828693 \n", + "coef_high_income_group_and_shopping_tour 0.846024 0.846024 \n", + "coef_high_income_group_and_tour_frequency_is_1 0.809721 0.809721 \n", + "coef_high_income_group_and_tour_frequency_is_2 1.223360 1.223360 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 1.571734 1.571734 \n", + "coef_high_income_group_and_visiting_tour -0.714757 -0.714757 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.659306 1.659306 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.957087 1.957087 \n", + "coef_mediumhigh_income_group_and_shopping_tour 0.573217 0.573217 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0.685180 0.685180 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0.592898 0.592898 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 1.597675 1.597675 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_4 -0.760824 -0.760824 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.000000 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -0.358322 -0.358322 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -1.622848 -1.622848 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -2.631492 -2.631492 \n", + "coef_presence_of_driving_school_kid_and_escorti... 0.367517 0.367517 \n", + "coef_presence_of_full_time_worker_and_maintenan... -0.254950 -0.254950 \n", + "coef_presence_of_non_worker_and_discretionary_tour -0.828046 -0.828046 \n", + "coef_presence_of_non_worker_and_eating_out_tour -0.660344 -0.660344 \n", + "coef_presence_of_non_worker_and_escorting_tour -0.530905 -0.530905 \n", + "coef_presence_of_part_time_worker_and_maintenan... -0.576071 -0.576071 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 1.756066 1.756066 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 0.549515 0.549515 \n", + "coef_presence_of_preschool_kid_in_household_and... -0.340060 -0.340060 \n", + "coef_presence_of_preschool_kid_in_household_and... -0.650421 -0.650421 \n", + "coef_presence_of_retiree_and_eating_out_tour -1.202708 -1.202708 \n", + "coef_presence_of_retiree_and_escorting_tour -0.801304 -0.801304 \n", + "coef_presence_of_university_student_and_eating_... -1.695822 -1.695822 \n", + "coef_total_number_of_tours_is_1 -7.835780 -7.835780 \n", + "coef_total_number_of_tours_is_2 -10.277923 -10.277923 \n", + "coef_total_number_of_tours_is_3 -14.048815 -14.048815 \n", + "coef_total_number_of_tours_is_4 -17.043997 -17.043997 \n", + "coef_urban_and_discretionary_tour 0.000000 0.000000 \n", + "coef_urban_and_escorting_tour -0.503217 -0.503217 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 0.108136 0.108136 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 0.128901 0.128901 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0.385133 0.385133 \n", "\n", - " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", - "coef_1_escort_tour_constant 0.0 NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN \n", - "coef_1_plus_shopping_tours_constant 0.0 NaN \n", - "coef_1_plus_visting_tours_constant 0.0 NaN \n", - "coef_2_plus_escort_tours_constant 0.0 NaN \n", - "coef_car_shortage_vs_workers_and_tour_frequency... 0.0 NaN \n", - "coef_female_and_discretionary_tour 0.0 NaN \n", - "coef_female_and_shopping_tour 0.0 NaN \n", - "coef_high_income_group_and_discretionary_tour 0.0 NaN \n", - "coef_high_income_group_and_maintenance_tour 0.0 NaN \n", - "coef_high_income_group_and_shopping_tour 0.0 NaN \n", - "coef_high_income_group_and_tour_frequency_is_1 0.0 NaN \n", - "coef_high_income_group_and_tour_frequency_is_2 0.0 NaN \n", - "coef_high_income_group_and_tour_frequency_is_5_... 0.0 NaN \n", - "coef_high_income_group_and_visiting_tour 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_mediumhigh_income_group_and_shopping_tour 0.0 NaN \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.0 NaN \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.0 NaN \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_is_4 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_presence_of_driving_school_kid_and_escorti... 0.0 NaN \n", - "coef_presence_of_full_time_worker_and_maintenan... 0.0 NaN \n", - "coef_presence_of_non_worker_and_discretionary_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_eating_out_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_escorting_tour 0.0 NaN \n", - "coef_presence_of_part_time_worker_and_maintenan... 0.0 NaN \n", - "coef_presence_of_pre_driving_school_kid_and_esc... 0.0 NaN \n", - "coef_presence_of_pre_school_kid_and_escorting_tour 0.0 NaN \n", - "coef_presence_of_preschool_kid_in_household_and... 0.0 NaN \n", - "coef_presence_of_preschool_kid_in_household_and... 0.0 NaN \n", - "coef_presence_of_retiree_and_eating_out_tour 0.0 NaN \n", - "coef_presence_of_retiree_and_escorting_tour 0.0 NaN \n", - "coef_presence_of_university_student_and_eating_... 0.0 NaN \n", - "coef_total_number_of_tours_is_1 0.0 NaN \n", - "coef_total_number_of_tours_is_2 0.0 NaN \n", - "coef_total_number_of_tours_is_3 0.0 NaN \n", - "coef_total_number_of_tours_is_4 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 0.0 \n", - "coef_urban_and_escorting_tour 0.0 NaN \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 0.0 NaN \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 0.0 NaN \n", - "coef_walk_access_to_retail_and_tour_frequency_i... 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.0 \n", + "coef_1_escort_tour_constant 0.527200 -20.0 \n", + "coef_1_plus_eating_out_tours_constant 0.691400 -20.0 \n", + "coef_1_plus_maintenance_tours_constant 0.553300 -20.0 \n", + "coef_1_plus_other_discretionary_tours_constant 0.798900 -20.0 \n", + "coef_1_plus_shopping_tours_constant 0.756900 -20.0 \n", + "coef_1_plus_visting_tours_constant 0.140500 -20.0 \n", + "coef_2_plus_escort_tours_constant 1.598700 -20.0 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... -0.549800 -20.0 \n", + "coef_female_and_discretionary_tour 0.307200 -20.0 \n", + "coef_female_and_shopping_tour 0.452400 -20.0 \n", + "coef_high_income_group_and_discretionary_tour 0.296000 -20.0 \n", + "coef_high_income_group_and_maintenance_tour 0.676300 -20.0 \n", + "coef_high_income_group_and_shopping_tour 0.706600 -20.0 \n", + "coef_high_income_group_and_tour_frequency_is_1 0.868200 -20.0 \n", + "coef_high_income_group_and_tour_frequency_is_2 1.536200 -20.0 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 1.933100 -20.0 \n", + "coef_high_income_group_and_visiting_tour -0.686800 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.574800 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 2.002600 -20.0 \n", + "coef_mediumhigh_income_group_and_shopping_tour 0.442100 -20.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0.598100 -20.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0.917800 -20.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 1.753900 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_4 -1.198600 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -0.239000 -20.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -1.820800 -20.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -2.592300 -20.0 \n", + "coef_presence_of_driving_school_kid_and_escorti... 0.416400 -20.0 \n", + "coef_presence_of_full_time_worker_and_maintenan... -0.313100 -20.0 \n", + "coef_presence_of_non_worker_and_discretionary_tour -1.037100 -20.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour -0.654500 -20.0 \n", + "coef_presence_of_non_worker_and_escorting_tour -0.526300 -20.0 \n", + "coef_presence_of_part_time_worker_and_maintenan... -0.562100 -20.0 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 1.579500 -20.0 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 0.541400 -20.0 \n", + "coef_presence_of_preschool_kid_in_household_and... -0.155900 -20.0 \n", + "coef_presence_of_preschool_kid_in_household_and... -0.568100 -20.0 \n", + "coef_presence_of_retiree_and_eating_out_tour -1.389000 -20.0 \n", + "coef_presence_of_retiree_and_escorting_tour -0.751600 -20.0 \n", + "coef_presence_of_university_student_and_eating_... -1.431800 -20.0 \n", + "coef_total_number_of_tours_is_1 -7.639100 -20.0 \n", + "coef_total_number_of_tours_is_2 -10.455700 -20.0 \n", + "coef_total_number_of_tours_is_3 -14.017600 -20.0 \n", + "coef_total_number_of_tours_is_4 -16.971701 -20.0 \n", + "coef_urban_and_discretionary_tour 0.000000 0.0 \n", + "coef_urban_and_escorting_tour -0.392900 -20.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 0.089900 -20.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 0.144700 -20.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0.347900 -20.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour -2.0 1 \n", - "coef_1_escort_tour_constant NaN 0 \n", - "coef_1_plus_eating_out_tours_constant NaN 0 \n", - "coef_1_plus_maintenance_tours_constant NaN 0 \n", - "coef_1_plus_other_discretionary_tours_constant NaN 0 \n", - "coef_1_plus_shopping_tours_constant NaN 0 \n", - "coef_1_plus_visting_tours_constant NaN 0 \n", - "coef_2_plus_escort_tours_constant NaN 0 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... NaN 0 \n", - "coef_female_and_discretionary_tour NaN 0 \n", - "coef_female_and_shopping_tour NaN 0 \n", - "coef_high_income_group_and_discretionary_tour NaN 0 \n", - "coef_high_income_group_and_maintenance_tour NaN 0 \n", - "coef_high_income_group_and_shopping_tour NaN 0 \n", - "coef_high_income_group_and_tour_frequency_is_1 NaN 0 \n", - "coef_high_income_group_and_tour_frequency_is_2 NaN 0 \n", - "coef_high_income_group_and_tour_frequency_is_5_... NaN 0 \n", - "coef_high_income_group_and_visiting_tour NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_mediumhigh_income_group_and_shopping_tour NaN 0 \n", - "coef_mediumlow_income_group_and_tour_frequency_... NaN 0 \n", - "coef_mediumlow_income_group_and_tour_frequency_... NaN 0 \n", - "coef_mediumlow_income_group_and_tour_frequency_... NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_4 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_presence_of_driving_school_kid_and_escorti... NaN 0 \n", - "coef_presence_of_full_time_worker_and_maintenan... NaN 0 \n", - "coef_presence_of_non_worker_and_discretionary_tour NaN 0 \n", - "coef_presence_of_non_worker_and_eating_out_tour NaN 0 \n", - "coef_presence_of_non_worker_and_escorting_tour NaN 0 \n", - "coef_presence_of_part_time_worker_and_maintenan... NaN 0 \n", - "coef_presence_of_pre_driving_school_kid_and_esc... NaN 0 \n", - "coef_presence_of_pre_school_kid_and_escorting_tour NaN 0 \n", - "coef_presence_of_preschool_kid_in_household_and... NaN 0 \n", - "coef_presence_of_preschool_kid_in_household_and... NaN 0 \n", - "coef_presence_of_retiree_and_eating_out_tour NaN 0 \n", - "coef_presence_of_retiree_and_escorting_tour NaN 0 \n", - "coef_presence_of_university_student_and_eating_... NaN 0 \n", - "coef_total_number_of_tours_is_1 NaN 0 \n", - "coef_total_number_of_tours_is_2 NaN 0 \n", - "coef_total_number_of_tours_is_3 NaN 0 \n", - "coef_total_number_of_tours_is_4 NaN 0 \n", - "coef_urban_and_discretionary_tour 0.0 1 \n", - "coef_urban_and_escorting_tour NaN 0 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 NaN 0 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 NaN 0 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0 0.0 \n", + "coef_1_escort_tour_constant 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant 20.0 0.0 \n", + "coef_1_plus_shopping_tours_constant 20.0 0.0 \n", + "coef_1_plus_visting_tours_constant 20.0 0.0 \n", + "coef_2_plus_escort_tours_constant 20.0 0.0 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... 20.0 0.0 \n", + "coef_female_and_discretionary_tour 20.0 0.0 \n", + "coef_female_and_shopping_tour 20.0 0.0 \n", + "coef_high_income_group_and_discretionary_tour 20.0 0.0 \n", + "coef_high_income_group_and_maintenance_tour 20.0 0.0 \n", + "coef_high_income_group_and_shopping_tour 20.0 0.0 \n", + "coef_high_income_group_and_tour_frequency_is_1 20.0 0.0 \n", + "coef_high_income_group_and_tour_frequency_is_2 20.0 0.0 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 20.0 0.0 \n", + "coef_high_income_group_and_visiting_tour 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_mediumhigh_income_group_and_shopping_tour 20.0 0.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 20.0 0.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 20.0 0.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_4 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_presence_of_driving_school_kid_and_escorti... 20.0 0.0 \n", + "coef_presence_of_full_time_worker_and_maintenan... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_discretionary_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_escorting_tour 20.0 0.0 \n", + "coef_presence_of_part_time_worker_and_maintenan... 20.0 0.0 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 20.0 0.0 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 20.0 0.0 \n", + "coef_presence_of_preschool_kid_in_household_and... 20.0 0.0 \n", + "coef_presence_of_preschool_kid_in_household_and... 20.0 0.0 \n", + "coef_presence_of_retiree_and_eating_out_tour 20.0 0.0 \n", + "coef_presence_of_retiree_and_escorting_tour 20.0 0.0 \n", + "coef_presence_of_university_student_and_eating_... 20.0 0.0 \n", + "coef_total_number_of_tours_is_1 20.0 0.0 \n", + "coef_total_number_of_tours_is_2 20.0 0.0 \n", + "coef_total_number_of_tours_is_3 20.0 0.0 \n", + "coef_total_number_of_tours_is_4 20.0 0.0 \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", + "coef_urban_and_escorting_tour 20.0 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 20.0 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 20.0 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 20.0 0.0 \n", "\n", - " best \n", - "coef_0_auto_household_and_escorting_tour -2.000000 \n", - "coef_1_escort_tour_constant 0.777927 \n", - "coef_1_plus_eating_out_tours_constant 1.665075 \n", - "coef_1_plus_maintenance_tours_constant 1.214734 \n", - "coef_1_plus_other_discretionary_tours_constant 1.783871 \n", - "coef_1_plus_shopping_tours_constant 1.521613 \n", - "coef_1_plus_visting_tours_constant 1.045482 \n", - "coef_2_plus_escort_tours_constant 1.882661 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... 0.049232 \n", - "coef_female_and_discretionary_tour 0.439252 \n", - "coef_female_and_shopping_tour 0.753133 \n", - "coef_high_income_group_and_discretionary_tour 0.236506 \n", - "coef_high_income_group_and_maintenance_tour 1.385100 \n", - "coef_high_income_group_and_shopping_tour 0.819417 \n", - "coef_high_income_group_and_tour_frequency_is_1 1.145715 \n", - "coef_high_income_group_and_tour_frequency_is_2 1.347519 \n", - "coef_high_income_group_and_tour_frequency_is_5_... 1.498903 \n", - "coef_high_income_group_and_visiting_tour -0.517316 \n", - "coef_logged_maximum_residual_window_tour_freque... 0.768519 \n", - "coef_logged_maximum_residual_window_tour_freque... 1.243995 \n", - "coef_mediumhigh_income_group_and_shopping_tour 0.605047 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.784377 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.776466 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.850898 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_4 -1.347657 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -0.989069 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -2.829794 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -2.785677 \n", - "coef_presence_of_driving_school_kid_and_escorti... 1.029447 \n", - "coef_presence_of_full_time_worker_and_maintenan... -0.484999 \n", - "coef_presence_of_non_worker_and_discretionary_tour -0.556803 \n", - "coef_presence_of_non_worker_and_eating_out_tour -0.231608 \n", - "coef_presence_of_non_worker_and_escorting_tour -0.617113 \n", - "coef_presence_of_part_time_worker_and_maintenan... -1.034909 \n", - "coef_presence_of_pre_driving_school_kid_and_esc... 1.808890 \n", - "coef_presence_of_pre_school_kid_and_escorting_tour 0.291702 \n", - "coef_presence_of_preschool_kid_in_household_and... 0.308611 \n", - "coef_presence_of_preschool_kid_in_household_and... -0.530685 \n", - "coef_presence_of_retiree_and_eating_out_tour 0.109209 \n", - "coef_presence_of_retiree_and_escorting_tour -0.461150 \n", - "coef_presence_of_university_student_and_eating_... -1.759323 \n", - "coef_total_number_of_tours_is_1 -6.812615 \n", - "coef_total_number_of_tours_is_2 -9.848060 \n", - "coef_total_number_of_tours_is_3 -12.828277 \n", - "coef_total_number_of_tours_is_4 -17.097027 \n", - "coef_urban_and_discretionary_tour 0.000000 \n", - "coef_urban_and_escorting_tour 0.425750 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 0.080140 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 0.103820 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... 0.050631 " + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 1 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", + "coef_1_plus_shopping_tours_constant 0 \n", + "coef_1_plus_visting_tours_constant 0 \n", + "coef_2_plus_escort_tours_constant 0 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... 0 \n", + "coef_female_and_discretionary_tour 0 \n", + "coef_female_and_shopping_tour 0 \n", + "coef_high_income_group_and_discretionary_tour 0 \n", + "coef_high_income_group_and_maintenance_tour 0 \n", + "coef_high_income_group_and_shopping_tour 0 \n", + "coef_high_income_group_and_tour_frequency_is_1 0 \n", + "coef_high_income_group_and_tour_frequency_is_2 0 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 0 \n", + "coef_high_income_group_and_visiting_tour 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_mediumhigh_income_group_and_shopping_tour 0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_4 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 1 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_presence_of_driving_school_kid_and_escorti... 0 \n", + "coef_presence_of_full_time_worker_and_maintenan... 0 \n", + "coef_presence_of_non_worker_and_discretionary_tour 0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 0 \n", + "coef_presence_of_non_worker_and_escorting_tour 0 \n", + "coef_presence_of_part_time_worker_and_maintenan... 0 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 0 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 0 \n", + "coef_presence_of_preschool_kid_in_household_and... 0 \n", + "coef_presence_of_preschool_kid_in_household_and... 0 \n", + "coef_presence_of_retiree_and_eating_out_tour 0 \n", + "coef_presence_of_retiree_and_escorting_tour 0 \n", + "coef_presence_of_university_student_and_eating_... 0 \n", + "coef_total_number_of_tours_is_1 0 \n", + "coef_total_number_of_tours_is_2 0 \n", + "coef_total_number_of_tours_is_3 0 \n", + "coef_total_number_of_tours_is_4 0 \n", + "coef_urban_and_discretionary_tour 1 \n", + "coef_urban_and_escorting_tour 0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0 " ] }, "metadata": {}, @@ -1886,23 +4524,528 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.818666298527715e-07 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 2 parameter estimators with negative variance\n", - "- coef_1_escort_tour_constant\n", - "- coef_2_plus_escort_tours_constant\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - " m.estimate(method='SLSQP')\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { "data": { "text/html": [ - 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Iteration 076 [Optimization terminated successfully]

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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.00 0.00 NA 0.00fixed value
coef_1_escort_tour_constant 0.430 NA NA 0.00
coef_1_plus_eating_out_tours_constant 0.732 NA NA 0.00
coef_1_plus_maintenance_tours_constant 0.481 NA NA 0.00
coef_1_plus_other_discretionary_tours_constant 0.901 NA NA 0.00
coef_1_plus_shopping_tours_constant 0.732 NA NA 0.00
coef_1_plus_visting_tours_constant 0.250 NA NA 0.00
coef_2_plus_escort_tours_constant 1.46 NA NA 0.00
coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus-0.401 0.0883-4.54*** 0.00
coef_female_and_discretionary_tour 0.316 0.0881 3.59*** 0.00
coef_female_and_shopping_tour 0.352 0.0792 4.45*** 0.00
coef_high_income_group_and_discretionary_tour 0.246 0.163 1.50 0.00
coef_high_income_group_and_maintenance_tour 0.829 0.195 4.25*** 0.00
coef_high_income_group_and_shopping_tour 0.846 0.170 4.98*** 0.00
coef_high_income_group_and_tour_frequency_is_1 0.810 0.162 5.00*** 0.00
coef_high_income_group_and_tour_frequency_is_2 1.22 0.228 5.37*** 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 1.57 0.350 4.49*** 0.00
coef_high_income_group_and_visiting_tour-0.715 0.204-3.50*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 1.66 0.131 12.64*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 1.96 0.141 13.90*** 0.00
coef_mediumhigh_income_group_and_shopping_tour 0.573 0.164 3.49*** 0.00
coef_mediumlow_income_group_and_tour_frequency_is_1 0.685 0.171 4.01*** 0.00
coef_mediumlow_income_group_and_tour_frequency_is_2 0.593 0.242 2.45* 0.00
coef_mediumlow_income_group_and_tour_frequency_is_5_plus 1.60 0.362 4.41*** 0.00
coef_number_of_joint_tours_and_tour_frequency_is_4-0.761 0.227-3.36*** 0.00
coef_number_of_joint_tours_and_tour_frequency_is_5_plus-999. 0.00 NA 0.00fixed value
coef_number_of_mandatory_tours_and_tour_frequency_is_1-0.358 0.150-2.39* 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_2-1.62 0.237-6.86*** 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus-2.63 0.260-10.11*** 0.00
coef_presence_of_driving_school_kid_and_escorting_tour 0.368 0.0942 3.90*** 0.00
coef_presence_of_full_time_worker_and_maintenance_tour-0.255 0.0976-2.61** 0.00
coef_presence_of_non_worker_and_discretionary_tour-0.828 0.122-6.79*** 0.00
coef_presence_of_non_worker_and_eating_out_tour-0.660 0.155-4.26*** 0.00
coef_presence_of_non_worker_and_escorting_tour-0.531 0.0850-6.24*** 0.00
coef_presence_of_part_time_worker_and_maintenance_tour-0.576 0.109-5.30*** 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 1.76 0.0737 23.84*** 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 0.550 0.0912 6.02*** 0.00
coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1-0.340 0.112-3.02** 0.00
coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5-0.650 0.151-4.31*** 0.00
coef_presence_of_retiree_and_eating_out_tour-1.20 0.245-4.90*** 0.00
coef_presence_of_retiree_and_escorting_tour-0.801 0.136-5.90*** 0.00
coef_presence_of_university_student_and_eating_out_tour-1.70 0.334-5.08*** 0.00
coef_total_number_of_tours_is_1-7.84 NA NA 0.00
coef_total_number_of_tours_is_2-10.3 NA NA 0.00
coef_total_number_of_tours_is_3-14.0 NA NA 0.00
coef_total_number_of_tours_is_4-17.0 NA NA 0.00
coef_urban_and_discretionary_tour 0.00 0.00 NA 0.00fixed value
coef_urban_and_escorting_tour-0.503 0.0816-6.17*** 0.00
coef_walk_access_to_retail_and_tour_frequency_is_1 0.108 0.0253 4.28*** 0.00
coef_walk_access_to_retail_and_tour_frequency_is_2 0.129 0.0303 4.25*** 0.00
coef_walk_access_to_retail_and_tour_frequency_is_5_plus 0.385 0.0468 8.23*** 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model PTYPE_UNIVERSITY\n" + ] + }, + { + "data": { + "text/html": [ + "

Iteration 074 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1914,7 +5057,7 @@ { "data": { "text/html": [ - "

Best LL = -324.8584172756779

" + "

Best LL = -3512.6973954855457

" ], "text/plain": [ "" @@ -1945,70 +5088,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", " -2.000000\n", + " -2.000000\n", " -2.0000\n", - " 0.0\n", " -2.0\n", " -2.0\n", + " 0.0\n", " 1\n", - " \n", - " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " 1.851506\n", + " 1.761998\n", + " 1.761998\n", " 1.7028\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.851506\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -13.970901\n", + " 2.214614\n", + " 2.214614\n", " 2.0723\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -13.970901\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -0.338031\n", + " 0.085206\n", + " 0.085206\n", " 0.3348\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.338031\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " -1.473992\n", + " 1.308003\n", + " 1.308003\n", " 1.3389\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.473992\n", " \n", " \n", " ...\n", @@ -2019,109 +5166,106 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coef_urban_and_shopping_tour\n", - " 0.711573\n", + " 0.637185\n", + " 0.637185\n", " 0.5330\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.711573\n", " \n", " \n", " coef_urban_and_tour_frequency_is_1\n", - " -3.537340\n", + " -0.924987\n", + " -0.924987\n", " -1.1648\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -3.537340\n", " \n", " \n", " coef_urban_and_tour_frequency_is_2\n", - " -4.729195\n", + " -2.118276\n", + " -2.118276\n", " -2.3177\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.729195\n", " \n", " \n", " coef_urban_and_tour_frequency_is_5_plus\n", - " -6.020888\n", + " -2.502952\n", + " -2.502952\n", " -2.5027\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -6.020888\n", " \n", " \n", " coef_walk_access_to_retail_and_shopping\n", - " 0.102379\n", + " 0.072376\n", + " 0.072376\n", " 0.0972\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.102379\n", " \n", " \n", "\n", - "

70 rows × 8 columns

\n", + "

70 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant 1.851506 1.7028 \n", - "coef_1_plus_eating_out_tours_constant -13.970901 2.0723 \n", - "coef_1_plus_maintenance_tours_constant -0.338031 0.3348 \n", - "coef_1_plus_other_discretionary_tours_constant -1.473992 1.3389 \n", - "... ... ... \n", - "coef_urban_and_shopping_tour 0.711573 0.5330 \n", - "coef_urban_and_tour_frequency_is_1 -3.537340 -1.1648 \n", - "coef_urban_and_tour_frequency_is_2 -4.729195 -2.3177 \n", - "coef_urban_and_tour_frequency_is_5_plus -6.020888 -2.5027 \n", - "coef_walk_access_to_retail_and_shopping 0.102379 0.0972 \n", + " value best initvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.000000 -2.0000 \n", + "coef_1_escort_tour_constant 1.761998 1.761998 1.7028 \n", + "coef_1_plus_eating_out_tours_constant 2.214614 2.214614 2.0723 \n", + "coef_1_plus_maintenance_tours_constant 0.085206 0.085206 0.3348 \n", + "coef_1_plus_other_discretionary_tours_constant 1.308003 1.308003 1.3389 \n", + "... ... ... ... \n", + "coef_urban_and_shopping_tour 0.637185 0.637185 0.5330 \n", + "coef_urban_and_tour_frequency_is_1 -0.924987 -0.924987 -1.1648 \n", + "coef_urban_and_tour_frequency_is_2 -2.118276 -2.118276 -2.3177 \n", + "coef_urban_and_tour_frequency_is_5_plus -2.502952 -2.502952 -2.5027 \n", + "coef_walk_access_to_retail_and_shopping 0.072376 0.072376 0.0972 \n", "\n", - " nullvalue minimum maximum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 -2.0 -2.0 \n", - "coef_1_escort_tour_constant 0.0 NaN NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN NaN \n", - "... ... ... ... \n", - "coef_urban_and_shopping_tour 0.0 NaN NaN \n", - "coef_urban_and_tour_frequency_is_1 0.0 NaN NaN \n", - "coef_urban_and_tour_frequency_is_2 0.0 NaN NaN \n", - "coef_urban_and_tour_frequency_is_5_plus 0.0 NaN NaN \n", - "coef_walk_access_to_retail_and_shopping 0.0 NaN NaN \n", + " minimum maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0 -2.0 0.0 \n", + "coef_1_escort_tour_constant -20.0 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant -20.0 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant -20.0 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant -20.0 20.0 0.0 \n", + "... ... ... ... \n", + "coef_urban_and_shopping_tour -20.0 20.0 0.0 \n", + "coef_urban_and_tour_frequency_is_1 -20.0 20.0 0.0 \n", + "coef_urban_and_tour_frequency_is_2 -20.0 20.0 0.0 \n", + "coef_urban_and_tour_frequency_is_5_plus -20.0 20.0 0.0 \n", + "coef_walk_access_to_retail_and_shopping -20.0 20.0 0.0 \n", "\n", - " holdfast note best \n", - "coef_0_auto_household_and_escorting_tour 1 -2.000000 \n", - "coef_1_escort_tour_constant 0 1.851506 \n", - "coef_1_plus_eating_out_tours_constant 0 -13.970901 \n", - "coef_1_plus_maintenance_tours_constant 0 -0.338031 \n", - "coef_1_plus_other_discretionary_tours_constant 0 -1.473992 \n", - "... ... ... ... \n", - "coef_urban_and_shopping_tour 0 0.711573 \n", - "coef_urban_and_tour_frequency_is_1 0 -3.537340 \n", - "coef_urban_and_tour_frequency_is_2 0 -4.729195 \n", - "coef_urban_and_tour_frequency_is_5_plus 0 -6.020888 \n", - "coef_walk_access_to_retail_and_shopping 0 0.102379 \n", + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 1 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", + "... ... \n", + "coef_urban_and_shopping_tour 0 \n", + "coef_urban_and_tour_frequency_is_1 0 \n", + "coef_urban_and_tour_frequency_is_2 0 \n", + "coef_urban_and_tour_frequency_is_5_plus 0 \n", + "coef_walk_access_to_retail_and_shopping 0 \n", "\n", - "[70 rows x 8 columns]" + "[70 rows x 7 columns]" ] }, "metadata": {}, @@ -2131,26 +5275,699 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.8717456834857003e-13 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 13 parameter estimators with negative variance\n", - "- coef_1_plus_eating_out_tours_constant\n", - "- coef_1_plus_shopping_tours_constant\n", - "- coef_1_plus_visting_tours_constant\n", - "- coef_logged_maximum_residual_window_tour_frequency_is_0\n", - "- and 9 more\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - " m.estimate(method='SLSQP')\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { "data": { "text/html": [ - "

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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.00 0.00 NA 0.00fixed value
coef_1_escort_tour_constant 1.76 185. 0.01 0.00
coef_1_plus_eating_out_tours_constant 2.21 185. 0.01 0.00
coef_1_plus_maintenance_tours_constant 0.0852 185. 0.00 0.00
coef_1_plus_other_discretionary_tours_constant 1.31 185. 0.01 0.00
coef_1_plus_shopping_tours_constant 1.84 185. 0.01 0.00
coef_1_plus_visting_tours_constant 1.17 185. 0.01 0.00
coef_2_plus_escort_tours_constant 3.01 371. 0.01 0.00
coef_auto_access_to_retail_and_discretionary 0.0900 0.138 0.65 0.00
coef_auto_access_to_retail_and_eating_out 0.0731 0.147 0.50 0.00
coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus-0.790 0.187-4.23*** 0.00
coef_female_and_discretionary_tour-0.393 0.156-2.52* 0.00
coef_female_and_eatingout_tour-0.727 0.176-4.14*** 0.00
coef_female_and_tour_frequency_is_1-0.0110 0.151-0.07 0.00
coef_female_and_tour_frequency_is_2 0.140 0.188 0.74 0.00
coef_female_and_tour_frequency_is_5 1.41 0.250 5.62*** 0.00
coef_high_income_group_and_eating_out_tour-0.130 0.215-0.60 0.00
coef_high_income_group_and_shopping_tour 0.648 0.180 3.60*** 0.00
coef_high_income_group_and_tour_frequency_is_1 0.192 0.180 1.07 0.00
coef_high_income_group_and_tour_frequency_is_2 0.658 0.215 3.07** 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 0.540 0.282 1.92 0.00
coef_high_income_group_and_visiting_tour-0.536 0.215-2.50* 0.00
coef_logged_maximum_residual_window_tour_frequency_is_0 1.14 NA NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 1.53 NA NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_1-0.0200 0.180-0.11 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_2 0.0675 0.226 0.30 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_5_plus 0.530 0.268 1.98* 0.00
coef_number_of_joint_discretionary_tours 1.21 0.353 3.43*** 0.00
coef_number_of_joint_shopping_tours-0.600 0.791-0.76 0.00
coef_number_of_joint_tours_and_tour_frequency_is_2-0.543 0.347-1.57 0.00
coef_number_of_joint_tours_and_tour_frequency_is_3-0.800 0.423-1.89 0.00
coef_number_of_joint_tours_and_tour_frequency_is_5_plus-999. 0.00 NA 0.00fixed value
coef_number_of_mandatory_tours_and_tour_frequency_is_1-0.0978 0.170-0.58 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_2-0.496 0.194-2.55* 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_3-1.55 0.250-6.21*** 0.00
coef_presence_of_full_time_worker_and_discretionary_tour-0.576 0.145-3.98*** 0.00
coef_presence_of_full_time_worker_and_eating_out_tour-0.770 0.198-3.89*** 0.00
coef_presence_of_full_time_worker_and_shopping_tour-0.732 0.158-4.63*** 0.00
coef_presence_of_non_worker_and_discretionary_tour 1.12 0.179 6.23*** 0.00
coef_presence_of_non_worker_and_tour_frequency_is_1-0.653 0.180-3.64*** 0.00
coef_presence_of_non_worker_and_tour_frequency_is_5-1.46 0.214-6.82*** 0.00
coef_presence_of_part_time_worker_and_eating_out_tour-2.81 0.388-7.24*** 0.00
coef_presence_of_part_time_worker_and_escorting_tour-1.59 0.172-9.21*** 0.00
coef_presence_of_part_time_worker_and_shopping_tour-0.486 0.156-3.11** 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 0.992 0.114 8.69*** 0.00
coef_presence_of_pre_driving_school_kid_and_maintenance_tour 0.880 0.221 3.98*** 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 1.88 0.216 8.71*** 0.00
coef_presence_of_pre_school_kid_and_maintenance_tour 0.753 0.317 2.38* 0.00
coef_presence_of_pre_school_kid_and_shopping_tour 1.06 0.245 4.32*** 0.00
coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1-1.00 0.269-3.71*** 0.00
coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2-1.76 0.389-4.53*** 0.00
coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5-2.20 0.521-4.23*** 0.00
coef_presence_of_university_student_and_discretionary_tour-0.735 0.169-4.35*** 0.00
coef_presence_of_university_student_and_eating_out_tour-0.545 0.227-2.41* 0.00
coef_total_number_of_tours_is_1-6.46 185.-0.03 0.00
coef_total_number_of_tours_is_2-9.07 371.-0.02 0.00
coef_total_number_of_tours_is_3-12.0 556.-0.02 0.00
coef_total_number_of_tours_is_4-15.8 741.-0.02 0.00
coef_transit_access_to_retail_and_discretionary 0.00 0.00 NA 0.00fixed value
coef_transit_access_to_retail_and_maintenance 0.100 0.0621 1.61 0.00
coef_transit_access_to_retail_and_tour_frequency_is_5_plus 0.0272 0.0421 0.65 0.00
coef_urban_and_discretionary_tour 1.25 0.258 4.83*** 0.00
coef_urban_and_eatingout_tour 0.594 0.274 2.17* 0.00
coef_urban_and_escorting_tour 0.778 0.233 3.35*** 0.00
coef_urban_and_maintenance_tour 0.853 0.339 2.52* 0.00
coef_urban_and_shopping_tour 0.637 0.262 2.43* 0.00
coef_urban_and_tour_frequency_is_1-0.925 0.280-3.30*** 0.00
coef_urban_and_tour_frequency_is_2-2.12 0.455-4.66*** 0.00
coef_urban_and_tour_frequency_is_5_plus-2.50 0.659-3.80*** 0.00
coef_walk_access_to_retail_and_shopping 0.0724 0.0523 1.38 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model PTYPE_NONWORK\n" + ] + }, + { + "data": { + "text/html": [ + "

Iteration 152 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -2162,7 +5979,7 @@ { "data": { "text/html": [ - "

Best LL = -1038.9605658891858

" + "

Best LL = -15004.401530353549

" ], "text/plain": [ "" @@ -2193,70 +6010,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", " -2.000000\n", + " -2.000000\n", " -2.0000\n", - " 0.0\n", " -2.0\n", " -2.0\n", + " 0.0\n", " 1\n", - " \n", - " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " -4.317862\n", + " -0.539501\n", + " -0.539501\n", " -0.0629\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.317862\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -4.459267\n", + " -0.520038\n", + " -0.520038\n", " -0.1429\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.459267\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -9.609591\n", + " -0.627098\n", + " -0.627098\n", " -0.0653\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -9.609591\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " -4.488178\n", + " -0.154697\n", + " -0.154697\n", " 0.3334\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.488178\n", " \n", " \n", " ...\n", @@ -2267,122 +6088,120 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coef_walk_access_to_retail_and_discretionary\n", - " 0.214072\n", + " 0.089664\n", + " 0.089664\n", " 0.0772\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.214072\n", " \n", " \n", " coef_walk_access_to_retail_and_shopping\n", - " 0.039849\n", + " 0.080837\n", + " 0.080837\n", " 0.0598\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.039849\n", " \n", " \n", " coef_walk_access_to_retail_and_tour_frequency_is_1\n", - " -0.507092\n", + " 0.126334\n", + " 0.126334\n", " 0.0713\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.507092\n", " \n", " \n", " coef_walk_access_to_retail_and_tour_frequency_is_2\n", - " -0.403984\n", + " 0.186292\n", + " 0.186292\n", " 0.1256\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.403984\n", " \n", " \n", " coef_walk_access_to_retail_and_tour_frequency_is_5_plus\n", - " -0.260693\n", + " 0.163397\n", + " 0.163397\n", " 0.1508\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.260693\n", " \n", " \n", "\n", - "

77 rows × 8 columns

\n", + "

77 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant -4.317862 -0.0629 \n", - "coef_1_plus_eating_out_tours_constant -4.459267 -0.1429 \n", - "coef_1_plus_maintenance_tours_constant -9.609591 -0.0653 \n", - "coef_1_plus_other_discretionary_tours_constant -4.488178 0.3334 \n", - "... ... ... \n", - "coef_walk_access_to_retail_and_discretionary 0.214072 0.0772 \n", - "coef_walk_access_to_retail_and_shopping 0.039849 0.0598 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 -0.507092 0.0713 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 -0.403984 0.1256 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... -0.260693 0.1508 \n", + " value best \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.000000 \n", + "coef_1_escort_tour_constant -0.539501 -0.539501 \n", + "coef_1_plus_eating_out_tours_constant -0.520038 -0.520038 \n", + "coef_1_plus_maintenance_tours_constant -0.627098 -0.627098 \n", + "coef_1_plus_other_discretionary_tours_constant -0.154697 -0.154697 \n", + "... ... ... \n", + "coef_walk_access_to_retail_and_discretionary 0.089664 0.089664 \n", + "coef_walk_access_to_retail_and_shopping 0.080837 0.080837 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 0.126334 0.126334 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 0.186292 0.186292 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0.163397 0.163397 \n", "\n", - " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", - "coef_1_escort_tour_constant 0.0 NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0000 -2.0 \n", + "coef_1_escort_tour_constant -0.0629 -20.0 \n", + "coef_1_plus_eating_out_tours_constant -0.1429 -20.0 \n", + "coef_1_plus_maintenance_tours_constant -0.0653 -20.0 \n", + "coef_1_plus_other_discretionary_tours_constant 0.3334 -20.0 \n", "... ... ... \n", - "coef_walk_access_to_retail_and_discretionary 0.0 NaN \n", - "coef_walk_access_to_retail_and_shopping 0.0 NaN \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 0.0 NaN \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 0.0 NaN \n", - "coef_walk_access_to_retail_and_tour_frequency_i... 0.0 NaN \n", + "coef_walk_access_to_retail_and_discretionary 0.0772 -20.0 \n", + "coef_walk_access_to_retail_and_shopping 0.0598 -20.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 0.0713 -20.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 0.1256 -20.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0.1508 -20.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour -2.0 1 \n", - "coef_1_escort_tour_constant NaN 0 \n", - "coef_1_plus_eating_out_tours_constant NaN 0 \n", - "coef_1_plus_maintenance_tours_constant NaN 0 \n", - "coef_1_plus_other_discretionary_tours_constant NaN 0 \n", - "... ... ... ... \n", - "coef_walk_access_to_retail_and_discretionary NaN 0 \n", - "coef_walk_access_to_retail_and_shopping NaN 0 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 NaN 0 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 NaN 0 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0 0.0 \n", + "coef_1_escort_tour_constant 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant 20.0 0.0 \n", + "... ... ... \n", + "coef_walk_access_to_retail_and_discretionary 20.0 0.0 \n", + "coef_walk_access_to_retail_and_shopping 20.0 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 20.0 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 20.0 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 20.0 0.0 \n", "\n", - " best \n", - "coef_0_auto_household_and_escorting_tour -2.000000 \n", - "coef_1_escort_tour_constant -4.317862 \n", - "coef_1_plus_eating_out_tours_constant -4.459267 \n", - "coef_1_plus_maintenance_tours_constant -9.609591 \n", - "coef_1_plus_other_discretionary_tours_constant -4.488178 \n", + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 1 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", "... ... \n", - "coef_walk_access_to_retail_and_discretionary 0.214072 \n", - "coef_walk_access_to_retail_and_shopping 0.039849 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_1 -0.507092 \n", - "coef_walk_access_to_retail_and_tour_frequency_is_2 -0.403984 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... -0.260693 \n", + "coef_walk_access_to_retail_and_discretionary 0 \n", + "coef_walk_access_to_retail_and_shopping 0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_1 0 \n", + "coef_walk_access_to_retail_and_tour_frequency_is_2 0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0 \n", "\n", - "[77 rows x 8 columns]" + "[77 rows x 7 columns]" ] }, "metadata": {}, @@ -2392,26 +6211,762 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.673077519570165e-16 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 21 parameter estimators with negative variance\n", - "- coef_1_escort_tour_constant\n", - "- coef_1_plus_eating_out_tours_constant\n", - "- coef_1_plus_maintenance_tours_constant\n", - "- coef_1_plus_other_discretionary_tours_constant\n", - "- and 17 more\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - " m.estimate(method='SLSQP')\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { "data": { "text/html": [ - "

Iteration 074 [Optimization terminated successfully]

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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.00 0.00 NA 0.00fixed value
coef_1_escort_tour_constant-0.540 102.-0.01 0.00
coef_1_plus_eating_out_tours_constant-0.520 102.-0.01 0.00
coef_1_plus_maintenance_tours_constant-0.627 102.-0.01 0.00
coef_1_plus_other_discretionary_tours_constant-0.155 102.-0.00 0.00
coef_1_plus_shopping_tours_constant-0.0544 102.-0.00 0.00
coef_1_plus_visting_tours_constant-0.452 102.-0.00 0.00
coef_2_plus_escort_tours_constant-0.0628 203.-0.00 0.00
coef_at_home_pre_driving_school_kid_and_escorting_tour-1.18 0.154-7.67*** 0.00
coef_at_home_pre_school_kid_and_eating_out_tour-0.368 0.216-1.70 0.00
coef_at_home_pre_school_kid_and_escorting_tour-0.332 0.105-3.18** 0.00
coef_auto_access_to_retail_and_maintenance 0.108 0.0554 1.94 0.00
coef_car_shortage_vs_workers_and_tour_frequency_is_1-0.160 0.414-0.39 0.00
coef_car_shortage_vs_workers_and_tour_frequency_is_2-1.15 0.428-2.70** 0.00
coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus-1.52 0.484-3.14** 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus 0.878 0.240 3.65*** 0.00
coef_female_and_maintenance_tour-0.350 0.0674-5.19*** 0.00
coef_female_and_tour_frequency_is_1 0.509 0.192 2.65** 0.00
coef_female_and_tour_frequency_is_2 0.461 0.198 2.33* 0.00
coef_female_and_tour_frequency_is_3 0.637 0.220 2.89** 0.00
coef_female_and_tour_frequency_is_5 0.996 0.304 3.28** 0.00
coef_high_income_group_and_discretionary_tour 0.831 0.107 7.78*** 0.00
coef_high_income_group_and_eating_out_tour 0.589 0.153 3.86*** 0.00
coef_high_income_group_and_shopping_tour 0.913 0.0932 9.79*** 0.00
coef_high_income_group_and_tour_frequency_is_2 1.24 0.312 3.99*** 0.00
coef_high_income_group_and_tour_frequency_is_3 1.94 0.340 5.71*** 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 2.71 0.384 7.04*** 0.00
coef_high_income_group_and_visiting_tour-1.12 0.169-6.63*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 3.58 0.342 10.47*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 2.85 0.387 7.37*** 0.00
coef_mediumhigh_income_group_and_discretionary_tour 0.511 0.103 4.96*** 0.00
coef_mediumhigh_income_group_and_eating_out_tour 0.441 0.147 3.00** 0.00
coef_mediumhigh_income_group_and_shopping_tour 0.942 0.0888 10.61*** 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_1 0.690 0.282 2.44* 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_2 0.681 0.291 2.34* 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_5_plus 0.625 0.314 1.99* 0.00
coef_mediumhigh_income_group_and_visiting_tour-0.429 0.112-3.83*** 0.00
coef_mediumlow_income_group_and_discretionary_tour 0.141 0.101 1.40 0.00
coef_mediumlow_income_group_and_eating_out_tour 0.125 0.142 0.88 0.00
coef_mediumlow_income_group_and_shopping_tour 0.698 0.0888 7.86*** 0.00
coef_mediumlow_income_group_and_tour_frequency_is_1 0.934 0.288 3.24** 0.00
coef_mediumlow_income_group_and_tour_frequency_is_5_plus 1.11 0.296 3.77*** 0.00
coef_number_of_joint_eating_out_tours-0.263 0.580-0.45 0.00
coef_number_of_joint_shopping_tours-0.612 0.233-2.62** 0.00
coef_number_of_joint_tours_and_tour_frequency_is_1-3.09 1.93-1.60 0.00
coef_number_of_joint_tours_and_tour_frequency_is_2-1.35 1.65-0.81 0.00
coef_number_of_joint_tours_and_tour_frequency_is_3-1.20 1.65-0.73 0.00
coef_number_of_joint_tours_and_tour_frequency_is_5_plus-1.39 1.67-0.83 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_1-0.677 1.06e-09-BIG*** 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_3-1.05 1.80e-09-BIG*** 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus-999. 0.00 NA 0.00fixed value
coef_presence_of_full_time_worker_and_eating_out_tour-0.429 0.109-3.93*** 0.00
coef_presence_of_full_time_worker_and_escorting_tour 0.436 0.0527 8.26*** 0.00
coef_presence_of_non_worker_and_eating_out_tour-0.413 0.107-3.86*** 0.00
coef_presence_of_non_worker_and_tour_frequency_is_1-0.162 0.185-0.87 0.00
coef_presence_of_non_worker_and_tour_frequency_is_2-0.633 0.192-3.30*** 0.00
coef_presence_of_non_worker_and_tour_frequency_is_5-1.03 0.212-4.86*** 0.00
coef_presence_of_part_time_worker_and_discretionary_tour-0.381 0.0565-6.74*** 0.00
coef_presence_of_part_time_worker_and_escorting_tour-0.642 0.0637-10.08*** 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 1.35 0.0589 22.99*** 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 0.820 0.0530 15.45*** 0.00
coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1 0.0317 0.189 0.17 0.00
coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5 0.573 0.200 2.86** 0.00
coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5-0.739 0.186-3.98*** 0.00
coef_presence_of_retiree_and_eating_out_tour-0.778 0.186-4.19*** 0.00
coef_presence_of_retiree_and_tour_frequency_is_1-0.223 0.223-1.00 0.00
coef_presence_of_retiree_and_tour_frequency_is_5-0.225 0.230-0.98 0.00
coef_total_number_of_tours_is_1-10.6 102.-0.10 0.00
coef_total_number_of_tours_is_2-13.0 203.-0.06 0.00
coef_total_number_of_tours_is_3-15.2 305.-0.05 0.00
coef_total_number_of_tours_is_4-17.7 407.-0.04 0.00
coef_urban_and_discretionary_tour 0.00 0.00 NA 0.00fixed value
coef_walk_access_to_retail_and_discretionary 0.0897 0.0225 3.98*** 0.00
coef_walk_access_to_retail_and_shopping 0.0808 0.0199 4.07*** 0.00
coef_walk_access_to_retail_and_tour_frequency_is_1 0.126 0.0653 1.93 0.00
coef_walk_access_to_retail_and_tour_frequency_is_2 0.186 0.0679 2.74** 0.00
coef_walk_access_to_retail_and_tour_frequency_is_5_plus 0.163 0.0735 2.22* 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model PTYPE_RETIRED\n" + ] + }, + { + "data": { + "text/html": [ + "

Iteration 076 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -2423,7 +6978,7 @@ { "data": { "text/html": [ - "

Best LL = -787.4000431560225

" + "

Best LL = -7936.229778686792

" ], "text/plain": [ "" @@ -2454,823 +7009,783 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", " -2.000000\n", + " -2.000000\n", " -2.0000\n", - " 0.0\n", " -2.0\n", " -2.0\n", + " 0.0\n", " 1\n", - " \n", - " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " -2.730596\n", + " -0.420225\n", + " -0.420225\n", " -0.3992\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.730596\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -2.583305\n", + " -0.040375\n", + " -0.040375\n", " 0.0245\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.583305\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -2.299219\n", + " 0.008956\n", + " 0.008956\n", " 0.1046\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.299219\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " -2.243196\n", + " 0.373522\n", + " 0.373522\n", " 0.4282\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.243196\n", " \n", " \n", " coef_1_plus_shopping_tours_constant\n", - " -1.916643\n", + " 0.512414\n", + " 0.512414\n", " 0.5947\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.916643\n", " \n", " \n", " coef_1_plus_visting_tours_constant\n", - " -2.344018\n", + " 0.079465\n", + " 0.079465\n", " 0.2789\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.344018\n", " \n", " \n", " coef_2_plus_escort_tours_constant\n", - " -4.935937\n", + " 0.307462\n", + " 0.307462\n", " 0.5175\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.935937\n", " \n", " \n", " coef_car_surplus_vs_workers_and_tour_frequency_is_1\n", - " 3.094754\n", + " 1.243116\n", + " 1.243116\n", " 0.7965\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 3.094754\n", " \n", " \n", " coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus\n", - " 3.475854\n", + " 2.463002\n", + " 2.463002\n", " 2.1302\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 3.475854\n", " \n", " \n", " coef_female_and_discretionary_tour\n", - " 0.541528\n", + " 0.503760\n", + " 0.503760\n", " 0.4954\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.541528\n", " \n", " \n", " coef_female_and_maintenance_tour\n", - " 0.708328\n", + " 0.726465\n", + " 0.726465\n", " 0.7424\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.708328\n", " \n", " \n", " coef_female_and_shopping_tour\n", - " 0.609316\n", + " 0.951849\n", + " 0.951849\n", " 0.9688\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.609316\n", " \n", " \n", " coef_female_and_tour_frequency_is_1\n", - " -2.519155\n", + " -0.637595\n", + " -0.637595\n", " -0.9348\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.519155\n", " \n", " \n", " coef_female_and_tour_frequency_is_2\n", - " -2.727169\n", + " -0.922030\n", + " -0.922030\n", " -1.3028\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.727169\n", " \n", " \n", " coef_female_and_tour_frequency_is_5\n", - " -4.160203\n", + " -1.995909\n", + " -1.995909\n", " -2.2660\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.160203\n", " \n", " \n", " coef_high_income_group_and_discretionary_tour\n", - " 0.925884\n", + " 0.912572\n", + " 0.912572\n", " 1.0095\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.925884\n", " \n", " \n", " coef_high_income_group_and_eating_out_tour\n", - " 1.624493\n", + " 1.333766\n", + " 1.333766\n", " 1.4842\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.624493\n", " \n", " \n", " coef_high_income_group_and_maintenance_tour\n", - " 1.006105\n", + " 1.269533\n", + " 1.269533\n", " 1.3795\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.006105\n", " \n", " \n", " coef_high_income_group_and_shopping_tour\n", - " 1.105171\n", + " 0.999823\n", + " 0.999823\n", " 1.0949\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.105171\n", " \n", " \n", " coef_high_income_group_and_visiting_tour\n", - " -0.656664\n", + " -0.187117\n", + " -0.187117\n", " -0.5137\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.656664\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_1\n", - " 2.966557\n", + " 2.036712\n", + " 2.036712\n", " 1.8357\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.966557\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_2\n", - " 5.581416\n", + " 2.413288\n", + " 2.413288\n", " 2.2707\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 5.581416\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_5_plus\n", - " 31.909531\n", + " 4.720353\n", + " 4.720353\n", " 4.4023\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 31.909531\n", " \n", " \n", " coef_mediumhigh_income_group_and_eating_out_tour\n", - " 0.964279\n", + " 0.944127\n", + " 0.944127\n", " 1.1810\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.964279\n", " \n", " \n", " coef_mediumhigh_income_group_and_maintenance_tour\n", - " 0.465351\n", + " 0.688701\n", + " 0.688701\n", " 0.7648\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.465351\n", " \n", " \n", " coef_mediumhigh_income_group_and_visiting_tour\n", - " -0.478169\n", + " -0.370854\n", + " -0.370854\n", " -0.4368\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.478169\n", " \n", " \n", " coef_mediumlow_income_group_and_eating_out_tour\n", - " 0.877486\n", + " 0.937822\n", + " 0.937822\n", " 0.9769\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.877486\n", " \n", " \n", " coef_number_of_joint_shopping_tours\n", - " -0.290998\n", + " -1.313333\n", + " -1.313333\n", " -0.8072\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.290998\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_2\n", - " -6.327143\n", + " -1.010634\n", + " -1.010634\n", " -0.9500\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -6.327143\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_3\n", - " -85.627389\n", + " -7.212476\n", + " -7.212476\n", " -7.1430\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -85.627389\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_5_plus\n", " -999.000000\n", + " -999.000000\n", " -999.0000\n", - " 0.0\n", " -999.0\n", " -999.0\n", + " 0.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_3\n", " -5.019600\n", + " -5.019600\n", " -5.0196\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -5.019600\n", " \n", " \n", " coef_presence_of_full_time_worker_and_discretionary_tour\n", - " -0.331984\n", + " -0.657085\n", + " -0.657085\n", " -0.4835\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.331984\n", " \n", " \n", " coef_presence_of_full_time_worker_and_shopping_tour\n", - " -0.207325\n", + " -0.421774\n", + " -0.421774\n", " -0.3609\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.207325\n", " \n", " \n", " coef_presence_of_non_worker_and_discretionary_tour\n", - " -0.084104\n", + " -0.575662\n", + " -0.575662\n", " -0.5603\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.084104\n", " \n", " \n", " coef_presence_of_non_worker_and_eating_out_tour\n", - " -0.701536\n", + " -0.905311\n", + " -0.905311\n", " -0.7880\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.701536\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_1\n", - " 1.190681\n", + " -0.017018\n", + " -0.017018\n", " 0.2240\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.190681\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_2\n", - " 1.374195\n", + " 0.067997\n", + " 0.067997\n", " 0.2436\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.374195\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_3\n", - " 2.156822\n", + " 0.258114\n", + " 0.258114\n", " 0.6200\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 2.156822\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_5\n", - " 3.694303\n", + " 3.203479\n", + " 3.203479\n", " 3.3742\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 3.694303\n", " \n", " \n", " coef_presence_of_pre_driving_school_kid_and_escorting_tour\n", - " 1.682880\n", + " 1.424066\n", + " 1.424066\n", " 1.4903\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.682880\n", " \n", " \n", " coef_presence_of_pre_school_kid_and_escorting_tour\n", - " 0.641813\n", + " 0.393961\n", + " 0.393961\n", " 0.5027\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.641813\n", " \n", " \n", " coef_presence_of_retiree_and_eating_out_tour\n", - " -0.944680\n", + " -0.793588\n", + " -0.793588\n", " -0.9282\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.944680\n", " \n", " \n", " coef_presence_of_retiree_and_tour_frequency_is_1\n", - " 0.321424\n", + " -0.721457\n", + " -0.721457\n", " -0.4458\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.321424\n", " \n", " \n", " coef_presence_of_retiree_and_tour_frequency_is_5\n", - " -0.051242\n", + " -0.751932\n", + " -0.751932\n", " -0.5315\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.051242\n", " \n", " \n", " coef_total_number_of_tours_is_1\n", - " -14.532345\n", + " -8.702153\n", + " -8.702153\n", " -8.5684\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -14.532345\n", " \n", " \n", " coef_total_number_of_tours_is_2\n", - " -15.267480\n", + " -12.664662\n", + " -12.664662\n", " -12.7416\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -15.267480\n", " \n", " \n", " coef_total_number_of_tours_is_3\n", - " -15.029318\n", + " -14.734677\n", + " -14.734677\n", " -15.0978\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -15.029318\n", " \n", " \n", " coef_total_number_of_tours_is_4\n", - " -14.905140\n", + " -19.432621\n", + " -19.432621\n", " -19.5439\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -14.905140\n", " \n", " \n", " coef_total_number_of_tours_is_5\n", - " -27.549766\n", + " -19.999982\n", + " -19.999982\n", " -20.7897\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -27.549766\n", " \n", " \n", " coef_urban_and_discretionary_tour\n", " 0.000000\n", + " 0.000000\n", " 0.0000\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_walk_access_to_retail_and_tour_frequency_is_5_plus\n", - " 0.691082\n", + " -0.010167\n", + " -0.010167\n", " 0.0616\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.691082\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant -2.730596 -0.3992 \n", - "coef_1_plus_eating_out_tours_constant -2.583305 0.0245 \n", - "coef_1_plus_maintenance_tours_constant -2.299219 0.1046 \n", - "coef_1_plus_other_discretionary_tours_constant -2.243196 0.4282 \n", - "coef_1_plus_shopping_tours_constant -1.916643 0.5947 \n", - "coef_1_plus_visting_tours_constant -2.344018 0.2789 \n", - "coef_2_plus_escort_tours_constant -4.935937 0.5175 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 3.094754 0.7965 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 3.475854 2.1302 \n", - "coef_female_and_discretionary_tour 0.541528 0.4954 \n", - "coef_female_and_maintenance_tour 0.708328 0.7424 \n", - "coef_female_and_shopping_tour 0.609316 0.9688 \n", - "coef_female_and_tour_frequency_is_1 -2.519155 -0.9348 \n", - "coef_female_and_tour_frequency_is_2 -2.727169 -1.3028 \n", - "coef_female_and_tour_frequency_is_5 -4.160203 -2.2660 \n", - "coef_high_income_group_and_discretionary_tour 0.925884 1.0095 \n", - "coef_high_income_group_and_eating_out_tour 1.624493 1.4842 \n", - "coef_high_income_group_and_maintenance_tour 1.006105 1.3795 \n", - "coef_high_income_group_and_shopping_tour 1.105171 1.0949 \n", - "coef_high_income_group_and_visiting_tour -0.656664 -0.5137 \n", - "coef_logged_maximum_residual_window_tour_freque... 2.966557 1.8357 \n", - "coef_logged_maximum_residual_window_tour_freque... 5.581416 2.2707 \n", - "coef_logged_maximum_residual_window_tour_freque... 31.909531 4.4023 \n", - "coef_mediumhigh_income_group_and_eating_out_tour 0.964279 1.1810 \n", - "coef_mediumhigh_income_group_and_maintenance_tour 0.465351 0.7648 \n", - "coef_mediumhigh_income_group_and_visiting_tour -0.478169 -0.4368 \n", - "coef_mediumlow_income_group_and_eating_out_tour 0.877486 0.9769 \n", - "coef_number_of_joint_shopping_tours -0.290998 -0.8072 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -6.327143 -0.9500 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 -85.627389 -7.1430 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.0000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -5.019600 -5.0196 \n", - "coef_presence_of_full_time_worker_and_discretio... -0.331984 -0.4835 \n", - "coef_presence_of_full_time_worker_and_shopping_... -0.207325 -0.3609 \n", - "coef_presence_of_non_worker_and_discretionary_tour -0.084104 -0.5603 \n", - "coef_presence_of_non_worker_and_eating_out_tour -0.701536 -0.7880 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 1.190681 0.2240 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 1.374195 0.2436 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 2.156822 0.6200 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 3.694303 3.3742 \n", - "coef_presence_of_pre_driving_school_kid_and_esc... 1.682880 1.4903 \n", - "coef_presence_of_pre_school_kid_and_escorting_tour 0.641813 0.5027 \n", - "coef_presence_of_retiree_and_eating_out_tour -0.944680 -0.9282 \n", - "coef_presence_of_retiree_and_tour_frequency_is_1 0.321424 -0.4458 \n", - "coef_presence_of_retiree_and_tour_frequency_is_5 -0.051242 -0.5315 \n", - "coef_total_number_of_tours_is_1 -14.532345 -8.5684 \n", - "coef_total_number_of_tours_is_2 -15.267480 -12.7416 \n", - "coef_total_number_of_tours_is_3 -15.029318 -15.0978 \n", - "coef_total_number_of_tours_is_4 -14.905140 -19.5439 \n", - "coef_total_number_of_tours_is_5 -27.549766 -20.7897 \n", - "coef_urban_and_discretionary_tour 0.000000 0.0000 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... 0.691082 0.0616 \n", + " value best \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.000000 \n", + "coef_1_escort_tour_constant -0.420225 -0.420225 \n", + "coef_1_plus_eating_out_tours_constant -0.040375 -0.040375 \n", + "coef_1_plus_maintenance_tours_constant 0.008956 0.008956 \n", + "coef_1_plus_other_discretionary_tours_constant 0.373522 0.373522 \n", + "coef_1_plus_shopping_tours_constant 0.512414 0.512414 \n", + "coef_1_plus_visting_tours_constant 0.079465 0.079465 \n", + "coef_2_plus_escort_tours_constant 0.307462 0.307462 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 1.243116 1.243116 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 2.463002 2.463002 \n", + "coef_female_and_discretionary_tour 0.503760 0.503760 \n", + "coef_female_and_maintenance_tour 0.726465 0.726465 \n", + "coef_female_and_shopping_tour 0.951849 0.951849 \n", + "coef_female_and_tour_frequency_is_1 -0.637595 -0.637595 \n", + "coef_female_and_tour_frequency_is_2 -0.922030 -0.922030 \n", + "coef_female_and_tour_frequency_is_5 -1.995909 -1.995909 \n", + "coef_high_income_group_and_discretionary_tour 0.912572 0.912572 \n", + "coef_high_income_group_and_eating_out_tour 1.333766 1.333766 \n", + "coef_high_income_group_and_maintenance_tour 1.269533 1.269533 \n", + "coef_high_income_group_and_shopping_tour 0.999823 0.999823 \n", + "coef_high_income_group_and_visiting_tour -0.187117 -0.187117 \n", + "coef_logged_maximum_residual_window_tour_freque... 2.036712 2.036712 \n", + "coef_logged_maximum_residual_window_tour_freque... 2.413288 2.413288 \n", + "coef_logged_maximum_residual_window_tour_freque... 4.720353 4.720353 \n", + "coef_mediumhigh_income_group_and_eating_out_tour 0.944127 0.944127 \n", + "coef_mediumhigh_income_group_and_maintenance_tour 0.688701 0.688701 \n", + "coef_mediumhigh_income_group_and_visiting_tour -0.370854 -0.370854 \n", + "coef_mediumlow_income_group_and_eating_out_tour 0.937822 0.937822 \n", + "coef_number_of_joint_shopping_tours -1.313333 -1.313333 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -1.010634 -1.010634 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 -7.212476 -7.212476 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.000000 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -5.019600 -5.019600 \n", + "coef_presence_of_full_time_worker_and_discretio... -0.657085 -0.657085 \n", + "coef_presence_of_full_time_worker_and_shopping_... -0.421774 -0.421774 \n", + "coef_presence_of_non_worker_and_discretionary_tour -0.575662 -0.575662 \n", + "coef_presence_of_non_worker_and_eating_out_tour -0.905311 -0.905311 \n", + "coef_presence_of_non_worker_and_tour_frequency_... -0.017018 -0.017018 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0.067997 0.067997 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0.258114 0.258114 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 3.203479 3.203479 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 1.424066 1.424066 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 0.393961 0.393961 \n", + "coef_presence_of_retiree_and_eating_out_tour -0.793588 -0.793588 \n", + "coef_presence_of_retiree_and_tour_frequency_is_1 -0.721457 -0.721457 \n", + "coef_presence_of_retiree_and_tour_frequency_is_5 -0.751932 -0.751932 \n", + "coef_total_number_of_tours_is_1 -8.702153 -8.702153 \n", + "coef_total_number_of_tours_is_2 -12.664662 -12.664662 \n", + "coef_total_number_of_tours_is_3 -14.734677 -14.734677 \n", + "coef_total_number_of_tours_is_4 -19.432621 -19.432621 \n", + "coef_total_number_of_tours_is_5 -19.999982 -19.999982 \n", + "coef_urban_and_discretionary_tour 0.000000 0.000000 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... -0.010167 -0.010167 \n", "\n", - " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", - "coef_1_escort_tour_constant 0.0 NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN \n", - "coef_1_plus_shopping_tours_constant 0.0 NaN \n", - "coef_1_plus_visting_tours_constant 0.0 NaN \n", - "coef_2_plus_escort_tours_constant 0.0 NaN \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 0.0 NaN \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 0.0 NaN \n", - "coef_female_and_discretionary_tour 0.0 NaN \n", - "coef_female_and_maintenance_tour 0.0 NaN \n", - "coef_female_and_shopping_tour 0.0 NaN \n", - "coef_female_and_tour_frequency_is_1 0.0 NaN \n", - "coef_female_and_tour_frequency_is_2 0.0 NaN \n", - "coef_female_and_tour_frequency_is_5 0.0 NaN \n", - "coef_high_income_group_and_discretionary_tour 0.0 NaN \n", - "coef_high_income_group_and_eating_out_tour 0.0 NaN \n", - "coef_high_income_group_and_maintenance_tour 0.0 NaN \n", - "coef_high_income_group_and_shopping_tour 0.0 NaN \n", - "coef_high_income_group_and_visiting_tour 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_mediumhigh_income_group_and_eating_out_tour 0.0 NaN \n", - "coef_mediumhigh_income_group_and_maintenance_tour 0.0 NaN \n", - "coef_mediumhigh_income_group_and_visiting_tour 0.0 NaN \n", - "coef_mediumlow_income_group_and_eating_out_tour 0.0 NaN \n", - "coef_number_of_joint_shopping_tours 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_presence_of_full_time_worker_and_discretio... 0.0 NaN \n", - "coef_presence_of_full_time_worker_and_shopping_... 0.0 NaN \n", - "coef_presence_of_non_worker_and_discretionary_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_eating_out_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_tour_frequency_... 0.0 NaN \n", - "coef_presence_of_non_worker_and_tour_frequency_... 0.0 NaN \n", - "coef_presence_of_non_worker_and_tour_frequency_... 0.0 NaN \n", - "coef_presence_of_non_worker_and_tour_frequency_... 0.0 NaN \n", - "coef_presence_of_pre_driving_school_kid_and_esc... 0.0 NaN \n", - "coef_presence_of_pre_school_kid_and_escorting_tour 0.0 NaN \n", - "coef_presence_of_retiree_and_eating_out_tour 0.0 NaN \n", - "coef_presence_of_retiree_and_tour_frequency_is_1 0.0 NaN \n", - "coef_presence_of_retiree_and_tour_frequency_is_5 0.0 NaN \n", - "coef_total_number_of_tours_is_1 0.0 NaN \n", - "coef_total_number_of_tours_is_2 0.0 NaN \n", - "coef_total_number_of_tours_is_3 0.0 NaN \n", - "coef_total_number_of_tours_is_4 0.0 NaN \n", - "coef_total_number_of_tours_is_5 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 0.0 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0000 -2.0 \n", + "coef_1_escort_tour_constant -0.3992 -20.0 \n", + "coef_1_plus_eating_out_tours_constant 0.0245 -20.0 \n", + "coef_1_plus_maintenance_tours_constant 0.1046 -20.0 \n", + "coef_1_plus_other_discretionary_tours_constant 0.4282 -20.0 \n", + "coef_1_plus_shopping_tours_constant 0.5947 -20.0 \n", + "coef_1_plus_visting_tours_constant 0.2789 -20.0 \n", + "coef_2_plus_escort_tours_constant 0.5175 -20.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 0.7965 -20.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 2.1302 -20.0 \n", + "coef_female_and_discretionary_tour 0.4954 -20.0 \n", + "coef_female_and_maintenance_tour 0.7424 -20.0 \n", + "coef_female_and_shopping_tour 0.9688 -20.0 \n", + "coef_female_and_tour_frequency_is_1 -0.9348 -20.0 \n", + "coef_female_and_tour_frequency_is_2 -1.3028 -20.0 \n", + "coef_female_and_tour_frequency_is_5 -2.2660 -20.0 \n", + "coef_high_income_group_and_discretionary_tour 1.0095 -20.0 \n", + "coef_high_income_group_and_eating_out_tour 1.4842 -20.0 \n", + "coef_high_income_group_and_maintenance_tour 1.3795 -20.0 \n", + "coef_high_income_group_and_shopping_tour 1.0949 -20.0 \n", + "coef_high_income_group_and_visiting_tour -0.5137 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.8357 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 2.2707 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 4.4023 -20.0 \n", + "coef_mediumhigh_income_group_and_eating_out_tour 1.1810 -20.0 \n", + "coef_mediumhigh_income_group_and_maintenance_tour 0.7648 -20.0 \n", + "coef_mediumhigh_income_group_and_visiting_tour -0.4368 -20.0 \n", + "coef_mediumlow_income_group_and_eating_out_tour 0.9769 -20.0 \n", + "coef_number_of_joint_shopping_tours -0.8072 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -0.9500 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 -7.1430 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0000 -999.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -5.0196 -20.0 \n", + "coef_presence_of_full_time_worker_and_discretio... -0.4835 -20.0 \n", + "coef_presence_of_full_time_worker_and_shopping_... -0.3609 -20.0 \n", + "coef_presence_of_non_worker_and_discretionary_tour -0.5603 -20.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour -0.7880 -20.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0.2240 -20.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0.2436 -20.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0.6200 -20.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 3.3742 -20.0 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 1.4903 -20.0 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 0.5027 -20.0 \n", + "coef_presence_of_retiree_and_eating_out_tour -0.9282 -20.0 \n", + "coef_presence_of_retiree_and_tour_frequency_is_1 -0.4458 -20.0 \n", + "coef_presence_of_retiree_and_tour_frequency_is_5 -0.5315 -20.0 \n", + "coef_total_number_of_tours_is_1 -8.5684 -20.0 \n", + "coef_total_number_of_tours_is_2 -12.7416 -20.0 \n", + "coef_total_number_of_tours_is_3 -15.0978 -20.0 \n", + "coef_total_number_of_tours_is_4 -19.5439 -20.0 \n", + "coef_total_number_of_tours_is_5 -20.7897 -20.0 \n", + "coef_urban_and_discretionary_tour 0.0000 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0.0616 -20.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour -2.0 1 \n", - "coef_1_escort_tour_constant NaN 0 \n", - "coef_1_plus_eating_out_tours_constant NaN 0 \n", - "coef_1_plus_maintenance_tours_constant NaN 0 \n", - "coef_1_plus_other_discretionary_tours_constant NaN 0 \n", - "coef_1_plus_shopping_tours_constant NaN 0 \n", - "coef_1_plus_visting_tours_constant NaN 0 \n", - "coef_2_plus_escort_tours_constant NaN 0 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... NaN 0 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... NaN 0 \n", - "coef_female_and_discretionary_tour NaN 0 \n", - "coef_female_and_maintenance_tour NaN 0 \n", - "coef_female_and_shopping_tour NaN 0 \n", - "coef_female_and_tour_frequency_is_1 NaN 0 \n", - "coef_female_and_tour_frequency_is_2 NaN 0 \n", - "coef_female_and_tour_frequency_is_5 NaN 0 \n", - "coef_high_income_group_and_discretionary_tour NaN 0 \n", - "coef_high_income_group_and_eating_out_tour NaN 0 \n", - "coef_high_income_group_and_maintenance_tour NaN 0 \n", - "coef_high_income_group_and_shopping_tour NaN 0 \n", - "coef_high_income_group_and_visiting_tour NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_mediumhigh_income_group_and_eating_out_tour NaN 0 \n", - "coef_mediumhigh_income_group_and_maintenance_tour NaN 0 \n", - "coef_mediumhigh_income_group_and_visiting_tour NaN 0 \n", - "coef_mediumlow_income_group_and_eating_out_tour NaN 0 \n", - "coef_number_of_joint_shopping_tours NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_presence_of_full_time_worker_and_discretio... NaN 0 \n", - "coef_presence_of_full_time_worker_and_shopping_... NaN 0 \n", - "coef_presence_of_non_worker_and_discretionary_tour NaN 0 \n", - "coef_presence_of_non_worker_and_eating_out_tour NaN 0 \n", - "coef_presence_of_non_worker_and_tour_frequency_... NaN 0 \n", - "coef_presence_of_non_worker_and_tour_frequency_... NaN 0 \n", - "coef_presence_of_non_worker_and_tour_frequency_... NaN 0 \n", - "coef_presence_of_non_worker_and_tour_frequency_... NaN 0 \n", - "coef_presence_of_pre_driving_school_kid_and_esc... NaN 0 \n", - "coef_presence_of_pre_school_kid_and_escorting_tour NaN 0 \n", - "coef_presence_of_retiree_and_eating_out_tour NaN 0 \n", - "coef_presence_of_retiree_and_tour_frequency_is_1 NaN 0 \n", - "coef_presence_of_retiree_and_tour_frequency_is_5 NaN 0 \n", - "coef_total_number_of_tours_is_1 NaN 0 \n", - "coef_total_number_of_tours_is_2 NaN 0 \n", - "coef_total_number_of_tours_is_3 NaN 0 \n", - "coef_total_number_of_tours_is_4 NaN 0 \n", - "coef_total_number_of_tours_is_5 NaN 0 \n", - "coef_urban_and_discretionary_tour 0.0 1 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0 0.0 \n", + "coef_1_escort_tour_constant 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant 20.0 0.0 \n", + "coef_1_plus_shopping_tours_constant 20.0 0.0 \n", + "coef_1_plus_visting_tours_constant 20.0 0.0 \n", + "coef_2_plus_escort_tours_constant 20.0 0.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 20.0 0.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 20.0 0.0 \n", + "coef_female_and_discretionary_tour 20.0 0.0 \n", + "coef_female_and_maintenance_tour 20.0 0.0 \n", + "coef_female_and_shopping_tour 20.0 0.0 \n", + "coef_female_and_tour_frequency_is_1 20.0 0.0 \n", + "coef_female_and_tour_frequency_is_2 20.0 0.0 \n", + "coef_female_and_tour_frequency_is_5 20.0 0.0 \n", + "coef_high_income_group_and_discretionary_tour 20.0 0.0 \n", + "coef_high_income_group_and_eating_out_tour 20.0 0.0 \n", + "coef_high_income_group_and_maintenance_tour 20.0 0.0 \n", + "coef_high_income_group_and_shopping_tour 20.0 0.0 \n", + "coef_high_income_group_and_visiting_tour 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_mediumhigh_income_group_and_eating_out_tour 20.0 0.0 \n", + "coef_mediumhigh_income_group_and_maintenance_tour 20.0 0.0 \n", + "coef_mediumhigh_income_group_and_visiting_tour 20.0 0.0 \n", + "coef_mediumlow_income_group_and_eating_out_tour 20.0 0.0 \n", + "coef_number_of_joint_shopping_tours 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_presence_of_full_time_worker_and_discretio... 20.0 0.0 \n", + "coef_presence_of_full_time_worker_and_shopping_... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_discretionary_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 20.0 0.0 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 20.0 0.0 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 20.0 0.0 \n", + "coef_presence_of_retiree_and_eating_out_tour 20.0 0.0 \n", + "coef_presence_of_retiree_and_tour_frequency_is_1 20.0 0.0 \n", + "coef_presence_of_retiree_and_tour_frequency_is_5 20.0 0.0 \n", + "coef_total_number_of_tours_is_1 20.0 0.0 \n", + "coef_total_number_of_tours_is_2 20.0 0.0 \n", + "coef_total_number_of_tours_is_3 20.0 0.0 \n", + "coef_total_number_of_tours_is_4 20.0 0.0 \n", + "coef_total_number_of_tours_is_5 20.0 0.0 \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 20.0 0.0 \n", "\n", - " best \n", - "coef_0_auto_household_and_escorting_tour -2.000000 \n", - "coef_1_escort_tour_constant -2.730596 \n", - "coef_1_plus_eating_out_tours_constant -2.583305 \n", - "coef_1_plus_maintenance_tours_constant -2.299219 \n", - "coef_1_plus_other_discretionary_tours_constant -2.243196 \n", - "coef_1_plus_shopping_tours_constant -1.916643 \n", - "coef_1_plus_visting_tours_constant -2.344018 \n", - "coef_2_plus_escort_tours_constant -4.935937 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 3.094754 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 3.475854 \n", - "coef_female_and_discretionary_tour 0.541528 \n", - "coef_female_and_maintenance_tour 0.708328 \n", - "coef_female_and_shopping_tour 0.609316 \n", - "coef_female_and_tour_frequency_is_1 -2.519155 \n", - "coef_female_and_tour_frequency_is_2 -2.727169 \n", - "coef_female_and_tour_frequency_is_5 -4.160203 \n", - "coef_high_income_group_and_discretionary_tour 0.925884 \n", - "coef_high_income_group_and_eating_out_tour 1.624493 \n", - "coef_high_income_group_and_maintenance_tour 1.006105 \n", - "coef_high_income_group_and_shopping_tour 1.105171 \n", - "coef_high_income_group_and_visiting_tour -0.656664 \n", - "coef_logged_maximum_residual_window_tour_freque... 2.966557 \n", - "coef_logged_maximum_residual_window_tour_freque... 5.581416 \n", - "coef_logged_maximum_residual_window_tour_freque... 31.909531 \n", - "coef_mediumhigh_income_group_and_eating_out_tour 0.964279 \n", - "coef_mediumhigh_income_group_and_maintenance_tour 0.465351 \n", - "coef_mediumhigh_income_group_and_visiting_tour -0.478169 \n", - "coef_mediumlow_income_group_and_eating_out_tour 0.877486 \n", - "coef_number_of_joint_shopping_tours -0.290998 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -6.327143 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 -85.627389 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -5.019600 \n", - "coef_presence_of_full_time_worker_and_discretio... -0.331984 \n", - "coef_presence_of_full_time_worker_and_shopping_... -0.207325 \n", - "coef_presence_of_non_worker_and_discretionary_tour -0.084104 \n", - "coef_presence_of_non_worker_and_eating_out_tour -0.701536 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 1.190681 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 1.374195 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 2.156822 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 3.694303 \n", - "coef_presence_of_pre_driving_school_kid_and_esc... 1.682880 \n", - "coef_presence_of_pre_school_kid_and_escorting_tour 0.641813 \n", - "coef_presence_of_retiree_and_eating_out_tour -0.944680 \n", - "coef_presence_of_retiree_and_tour_frequency_is_1 0.321424 \n", - "coef_presence_of_retiree_and_tour_frequency_is_5 -0.051242 \n", - "coef_total_number_of_tours_is_1 -14.532345 \n", - "coef_total_number_of_tours_is_2 -15.267480 \n", - "coef_total_number_of_tours_is_3 -15.029318 \n", - "coef_total_number_of_tours_is_4 -14.905140 \n", - "coef_total_number_of_tours_is_5 -27.549766 \n", - "coef_urban_and_discretionary_tour 0.000000 \n", - "coef_walk_access_to_retail_and_tour_frequency_i... 0.691082 " + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 1 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", + "coef_1_plus_shopping_tours_constant 0 \n", + "coef_1_plus_visting_tours_constant 0 \n", + "coef_2_plus_escort_tours_constant 0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 0 \n", + "coef_female_and_discretionary_tour 0 \n", + "coef_female_and_maintenance_tour 0 \n", + "coef_female_and_shopping_tour 0 \n", + "coef_female_and_tour_frequency_is_1 0 \n", + "coef_female_and_tour_frequency_is_2 0 \n", + "coef_female_and_tour_frequency_is_5 0 \n", + "coef_high_income_group_and_discretionary_tour 0 \n", + "coef_high_income_group_and_eating_out_tour 0 \n", + "coef_high_income_group_and_maintenance_tour 0 \n", + "coef_high_income_group_and_shopping_tour 0 \n", + "coef_high_income_group_and_visiting_tour 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_mediumhigh_income_group_and_eating_out_tour 0 \n", + "coef_mediumhigh_income_group_and_maintenance_tour 0 \n", + "coef_mediumhigh_income_group_and_visiting_tour 0 \n", + "coef_mediumlow_income_group_and_eating_out_tour 0 \n", + "coef_number_of_joint_shopping_tours 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 1 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_presence_of_full_time_worker_and_discretio... 0 \n", + "coef_presence_of_full_time_worker_and_shopping_... 0 \n", + "coef_presence_of_non_worker_and_discretionary_tour 0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0 \n", + "coef_presence_of_pre_driving_school_kid_and_esc... 0 \n", + "coef_presence_of_pre_school_kid_and_escorting_tour 0 \n", + "coef_presence_of_retiree_and_eating_out_tour 0 \n", + "coef_presence_of_retiree_and_tour_frequency_is_1 0 \n", + "coef_presence_of_retiree_and_tour_frequency_is_5 0 \n", + "coef_total_number_of_tours_is_1 0 \n", + "coef_total_number_of_tours_is_2 0 \n", + "coef_total_number_of_tours_is_3 0 \n", + "coef_total_number_of_tours_is_4 0 \n", + "coef_total_number_of_tours_is_5 0 \n", + "coef_urban_and_discretionary_tour 1 \n", + "coef_walk_access_to_retail_and_tour_frequency_i... 0 " ] }, "metadata": {}, @@ -3280,26 +7795,546 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.7375419460797773e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 11 parameter estimators with negative variance\n", - "- coef_1_escort_tour_constant\n", - "- coef_1_plus_eating_out_tours_constant\n", - "- coef_1_plus_maintenance_tours_constant\n", - "- coef_1_plus_other_discretionary_tours_constant\n", - "- and 7 more\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - 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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.00 0.00 NA 0.00fixed value
coef_1_escort_tour_constant-0.420 NA NA 0.00
coef_1_plus_eating_out_tours_constant-0.0404 NA NA 0.00
coef_1_plus_maintenance_tours_constant 0.00896 NA NA 0.00
coef_1_plus_other_discretionary_tours_constant 0.374 NA NA 0.00
coef_1_plus_shopping_tours_constant 0.512 NA NA 0.00
coef_1_plus_visting_tours_constant 0.0795 NA NA 0.00
coef_2_plus_escort_tours_constant 0.307 NA NA 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_1 1.24 0.325 3.82*** 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus 2.46 0.352 7.00*** 0.00
coef_female_and_discretionary_tour 0.504 0.0998 5.05*** 0.00
coef_female_and_maintenance_tour 0.726 0.101 7.19*** 0.00
coef_female_and_shopping_tour 0.952 0.0924 10.30*** 0.00
coef_female_and_tour_frequency_is_1-0.638 0.212-3.00** 0.00
coef_female_and_tour_frequency_is_2-0.922 0.243-3.79*** 0.00
coef_female_and_tour_frequency_is_5-2.00 0.299-6.67*** 0.00
coef_high_income_group_and_discretionary_tour 0.913 0.110 8.32*** 0.00
coef_high_income_group_and_eating_out_tour 1.33 0.218 6.13*** 0.00
coef_high_income_group_and_maintenance_tour 1.27 0.137 9.27*** 0.00
coef_high_income_group_and_shopping_tour 1.00 0.102 9.82*** 0.00
coef_high_income_group_and_visiting_tour-0.187 0.184-1.02 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 2.04 0.397 5.13*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_2 2.41 0.555 4.35*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 4.72 1.20 3.94*** 0.00
coef_mediumhigh_income_group_and_eating_out_tour 0.944 0.193 4.88*** 0.00
coef_mediumhigh_income_group_and_maintenance_tour 0.689 0.112 6.16*** 0.00
coef_mediumhigh_income_group_and_visiting_tour-0.371 0.155-2.40* 0.00
coef_mediumlow_income_group_and_eating_out_tour 0.938 0.162 5.79*** 0.00
coef_number_of_joint_shopping_tours-1.31 0.343-3.83*** 0.00
coef_number_of_joint_tours_and_tour_frequency_is_2-1.01 1.37-0.74 0.00
coef_number_of_joint_tours_and_tour_frequency_is_3-7.21 3.36-2.15* 0.00
coef_number_of_joint_tours_and_tour_frequency_is_5_plus-999. 0.00 NA 0.00fixed value
coef_number_of_mandatory_tours_and_tour_frequency_is_3-5.02 1.36e-09-BIG*** 0.00
coef_presence_of_full_time_worker_and_discretionary_tour-0.657 0.104-6.31*** 0.00
coef_presence_of_full_time_worker_and_shopping_tour-0.422 0.0891-4.73*** 0.00
coef_presence_of_non_worker_and_discretionary_tour-0.576 0.128-4.50*** 0.00
coef_presence_of_non_worker_and_eating_out_tour-0.905 0.113-8.00*** 0.00
coef_presence_of_non_worker_and_tour_frequency_is_1-0.0170 0.239-0.07 0.00
coef_presence_of_non_worker_and_tour_frequency_is_2 0.0680 0.262 0.26 0.00
coef_presence_of_non_worker_and_tour_frequency_is_3 0.258 0.330 0.78 0.00
coef_presence_of_non_worker_and_tour_frequency_is_5 3.20 0.707 4.53*** 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 1.42 0.127 11.20*** 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 0.394 0.177 2.22* 0.00
coef_presence_of_retiree_and_eating_out_tour-0.794 0.140-5.65*** 0.00
coef_presence_of_retiree_and_tour_frequency_is_1-0.721 0.238-3.03** 0.00
coef_presence_of_retiree_and_tour_frequency_is_5-0.752 0.248-3.03** 0.00
coef_total_number_of_tours_is_1-8.70 NA NA 0.00
coef_total_number_of_tours_is_2-12.7 NA NA 0.00
coef_total_number_of_tours_is_3-14.7 NA NA 0.00
coef_total_number_of_tours_is_4-19.4 NA NA 0.00
coef_total_number_of_tours_is_5-20.0 NA NA 0.00
coef_urban_and_discretionary_tour 0.00 0.00 NA 0.00fixed value
coef_walk_access_to_retail_and_tour_frequency_is_5_plus-0.0102 0.0639-0.16 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model PTYPE_DRIVING\n" ] }, { "data": { "text/html": [ - "

Iteration 078 [Optimization terminated successfully]

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Iteration 111 [Optimization terminated successfully]

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Best LL = -13.427352167831913

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Best LL = -1410.7703396627119

" ], "text/plain": [ "" @@ -3342,673 +8377,643 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", - " -289.819438\n", + " -2.355904\n", + " -2.355904\n", " -2.0000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -289.819438\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " -6434.961317\n", + " -0.591707\n", + " -0.591707\n", " -0.4934\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -6434.961317\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -3885.411913\n", + " -0.530705\n", + " -0.530705\n", " -0.0242\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -3885.411913\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -1195.807014\n", + " -0.478676\n", + " -0.478676\n", " -0.4344\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1195.807014\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " -2406.806121\n", + " 0.075144\n", + " 0.075144\n", " -0.2602\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2406.806121\n", " \n", " \n", " coef_1_plus_shopping_tours_constant\n", - " -2389.305945\n", + " 0.278338\n", + " 0.278338\n", " 0.5320\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2389.305945\n", " \n", " \n", " coef_1_plus_visting_tours_constant\n", - " -2389.305996\n", + " 0.074734\n", + " 0.074734\n", " 0.2367\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2389.305996\n", " \n", " \n", " coef_2_plus_escort_tours_constant\n", - " -5667.685401\n", + " 0.538812\n", + " 0.538812\n", " 1.4155\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -5667.685401\n", " \n", " \n", " coef_auto_access_to_retail_and_tour_frequency_is_5_plus\n", - " -44.188373\n", + " 0.120695\n", + " 0.120695\n", " 0.1004\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -44.188373\n", " \n", " \n", " coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus\n", - " -17232.832071\n", + " -0.895829\n", + " -0.895829\n", " -0.6369\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -17232.832071\n", " \n", " \n", " coef_car_surplus_vs_workers_and_tour_frequency_is_1\n", - " -4035.619542\n", + " 0.384556\n", + " 0.384556\n", " 0.2902\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4035.619542\n", " \n", " \n", " coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus\n", - " 15225.992542\n", + " 1.915045\n", + " 1.915045\n", " 2.0352\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 15225.992542\n", " \n", " \n", " coef_high_income_group_and_discretionary_tour\n", - " 4481.084393\n", + " 1.973678\n", + " 1.973678\n", " 2.3270\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4481.084393\n", " \n", " \n", " coef_high_income_group_and_eating_out_tour\n", - " -3157.273834\n", + " 1.115882\n", + " 1.115882\n", " 0.4916\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -3157.273834\n", " \n", " \n", " coef_high_income_group_and_maintenance_tour\n", - " 4461.484862\n", + " 0.535657\n", + " 0.535657\n", " 0.3982\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4461.484862\n", " \n", " \n", " coef_high_income_group_and_shopping_tour\n", - " 4461.390803\n", + " 0.297949\n", + " 0.297949\n", " 0.2443\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4461.390803\n", " \n", " \n", " coef_high_income_group_and_visiting_tour\n", - " 4461.390852\n", + " 0.657170\n", + " 0.657170\n", " 0.2858\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4461.390852\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_1\n", - " 16.594329\n", + " 1.842413\n", + " 1.842413\n", " 1.3298\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 16.594329\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_2\n", - " -19742.937757\n", + " 2.746659\n", + " 2.746659\n", " 1.3759\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -19742.937757\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_5_plus\n", - " -11491.665678\n", + " 3.207837\n", + " 3.207837\n", " 3.2808\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -11491.665678\n", " \n", " \n", " coef_mediumhigh_income_group_and_discretionary_tour\n", - " 572.847509\n", + " 0.785646\n", + " 0.785646\n", " 1.4050\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 572.847509\n", " \n", " \n", " coef_mediumlow_income_group_and_discretionary_tour\n", - " -4429.942211\n", + " 0.715806\n", + " 0.715806\n", " 0.9169\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4429.942211\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_1\n", - " -4180.353968\n", + " -0.076456\n", + " -0.076456\n", " -0.2162\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4180.353968\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_2\n", - " -1606.502880\n", + " -2.349128\n", + " -2.349128\n", " -0.3587\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1606.502880\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_3\n", - " -109.819329\n", + " -4.951681\n", + " -4.951681\n", " -4.2701\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -109.819329\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_5_plus\n", " -999.000000\n", + " -999.000000\n", " -999.0000\n", - " 0.0\n", " -999.0\n", " -999.0\n", + " 0.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_1\n", - " -16877.023970\n", + " -0.717100\n", + " -0.717100\n", " -0.2340\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -16877.023970\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_2\n", - " 12620.764595\n", + " -2.894440\n", + " -2.894440\n", " -0.9231\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 12620.764595\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_3\n", - " -4861.997912\n", + " -6.487708\n", + " -6.487708\n", " -6.5835\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4861.997912\n", " \n", " \n", " coef_presence_of_driving_school_kid_and_discretionary_tour\n", - " -578.872073\n", + " -0.825813\n", + " -0.825813\n", " -0.9202\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -578.872073\n", " \n", " \n", " coef_presence_of_driving_school_kid_and_eating_out_tour\n", - " 6.381728\n", + " -0.605776\n", + " -0.605776\n", " -0.6377\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 6.381728\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_2\n", - " 878.008827\n", + " -0.268732\n", + " -0.268732\n", " -0.6571\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 878.008827\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_5\n", - " -1944.922644\n", + " -3.177667\n", + " -3.177667\n", " -1.4044\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1944.922644\n", " \n", " \n", " coef_presence_of_pre_driving_school_kid_and_eating_out_tour\n", - " -164.906255\n", + " -1.449331\n", + " -1.449331\n", " -1.5698\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -164.906255\n", " \n", " \n", " coef_presence_of_pre_school_kid_and_eating_out_tour\n", - " 313.476309\n", + " -1.804988\n", + " -1.804988\n", " -0.2987\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 313.476309\n", " \n", " \n", " coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1\n", - " 4033.566871\n", + " -0.395397\n", + " -0.395397\n", " -0.3219\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 4033.566871\n", " \n", " \n", " coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5\n", - " 10149.331976\n", + " -1.985303\n", + " -1.985303\n", " -1.0874\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 10149.331976\n", " \n", " \n", " coef_presence_of_university_student_and_discretionary_tour\n", - " 3906.378403\n", + " -1.663216\n", + " -1.663216\n", " -1.2834\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 3906.378403\n", " \n", " \n", " coef_total_number_of_tours_is_1\n", - " 15200.662337\n", + " -7.917385\n", + " -7.917385\n", " -7.1506\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 15200.662337\n", " \n", " \n", " coef_total_number_of_tours_is_2\n", - " -1638.556186\n", + " -11.926451\n", + " -11.926451\n", " -11.1214\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1638.556186\n", " \n", " \n", " coef_total_number_of_tours_is_3\n", - " -14010.608324\n", + " -13.210287\n", + " -13.210287\n", " -13.1750\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -14010.608324\n", " \n", " \n", " coef_urban_and_discretionary_tour\n", " 0.000000\n", + " 0.000000\n", " 0.0000\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_urban_and_maintenance_tour\n", - " -1194.333214\n", + " 1.131129\n", + " 1.131129\n", " 1.0394\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1194.333214\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -289.819438 -2.0000 \n", - "coef_1_escort_tour_constant -6434.961317 -0.4934 \n", - "coef_1_plus_eating_out_tours_constant -3885.411913 -0.0242 \n", - "coef_1_plus_maintenance_tours_constant -1195.807014 -0.4344 \n", - "coef_1_plus_other_discretionary_tours_constant -2406.806121 -0.2602 \n", - "coef_1_plus_shopping_tours_constant -2389.305945 0.5320 \n", - "coef_1_plus_visting_tours_constant -2389.305996 0.2367 \n", - "coef_2_plus_escort_tours_constant -5667.685401 1.4155 \n", - "coef_auto_access_to_retail_and_tour_frequency_i... -44.188373 0.1004 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... -17232.832071 -0.6369 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... -4035.619542 0.2902 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 15225.992542 2.0352 \n", - "coef_high_income_group_and_discretionary_tour 4481.084393 2.3270 \n", - "coef_high_income_group_and_eating_out_tour -3157.273834 0.4916 \n", - "coef_high_income_group_and_maintenance_tour 4461.484862 0.3982 \n", - "coef_high_income_group_and_shopping_tour 4461.390803 0.2443 \n", - "coef_high_income_group_and_visiting_tour 4461.390852 0.2858 \n", - "coef_logged_maximum_residual_window_tour_freque... 16.594329 1.3298 \n", - "coef_logged_maximum_residual_window_tour_freque... -19742.937757 1.3759 \n", - "coef_logged_maximum_residual_window_tour_freque... -11491.665678 3.2808 \n", - "coef_mediumhigh_income_group_and_discretionary_... 572.847509 1.4050 \n", - "coef_mediumlow_income_group_and_discretionary_tour -4429.942211 0.9169 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_1 -4180.353968 -0.2162 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -1606.502880 -0.3587 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 -109.819329 -4.2701 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.0000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -16877.023970 -0.2340 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 12620.764595 -0.9231 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -4861.997912 -6.5835 \n", - "coef_presence_of_driving_school_kid_and_discret... -578.872073 -0.9202 \n", - "coef_presence_of_driving_school_kid_and_eating_... 6.381728 -0.6377 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 878.008827 -0.6571 \n", - "coef_presence_of_non_worker_and_tour_frequency_... -1944.922644 -1.4044 \n", - "coef_presence_of_pre_driving_school_kid_and_eat... -164.906255 -1.5698 \n", - "coef_presence_of_pre_school_kid_and_eating_out_... 313.476309 -0.2987 \n", - "coef_presence_of_predriving_school_kid_in_house... 4033.566871 -0.3219 \n", - "coef_presence_of_predriving_school_kid_in_house... 10149.331976 -1.0874 \n", - "coef_presence_of_university_student_and_discret... 3906.378403 -1.2834 \n", - "coef_total_number_of_tours_is_1 15200.662337 -7.1506 \n", - "coef_total_number_of_tours_is_2 -1638.556186 -11.1214 \n", - "coef_total_number_of_tours_is_3 -14010.608324 -13.1750 \n", - "coef_urban_and_discretionary_tour 0.000000 0.0000 \n", - "coef_urban_and_maintenance_tour -1194.333214 1.0394 \n", + " value best \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.355904 -2.355904 \n", + "coef_1_escort_tour_constant -0.591707 -0.591707 \n", + "coef_1_plus_eating_out_tours_constant -0.530705 -0.530705 \n", + "coef_1_plus_maintenance_tours_constant -0.478676 -0.478676 \n", + "coef_1_plus_other_discretionary_tours_constant 0.075144 0.075144 \n", + "coef_1_plus_shopping_tours_constant 0.278338 0.278338 \n", + "coef_1_plus_visting_tours_constant 0.074734 0.074734 \n", + "coef_2_plus_escort_tours_constant 0.538812 0.538812 \n", + "coef_auto_access_to_retail_and_tour_frequency_i... 0.120695 0.120695 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... -0.895829 -0.895829 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 0.384556 0.384556 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 1.915045 1.915045 \n", + "coef_high_income_group_and_discretionary_tour 1.973678 1.973678 \n", + "coef_high_income_group_and_eating_out_tour 1.115882 1.115882 \n", + "coef_high_income_group_and_maintenance_tour 0.535657 0.535657 \n", + "coef_high_income_group_and_shopping_tour 0.297949 0.297949 \n", + "coef_high_income_group_and_visiting_tour 0.657170 0.657170 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.842413 1.842413 \n", + "coef_logged_maximum_residual_window_tour_freque... 2.746659 2.746659 \n", + "coef_logged_maximum_residual_window_tour_freque... 3.207837 3.207837 \n", + "coef_mediumhigh_income_group_and_discretionary_... 0.785646 0.785646 \n", + "coef_mediumlow_income_group_and_discretionary_tour 0.715806 0.715806 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_1 -0.076456 -0.076456 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -2.349128 -2.349128 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 -4.951681 -4.951681 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.000000 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -0.717100 -0.717100 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -2.894440 -2.894440 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -6.487708 -6.487708 \n", + "coef_presence_of_driving_school_kid_and_discret... -0.825813 -0.825813 \n", + "coef_presence_of_driving_school_kid_and_eating_... -0.605776 -0.605776 \n", + "coef_presence_of_non_worker_and_tour_frequency_... -0.268732 -0.268732 \n", + "coef_presence_of_non_worker_and_tour_frequency_... -3.177667 -3.177667 \n", + "coef_presence_of_pre_driving_school_kid_and_eat... -1.449331 -1.449331 \n", + "coef_presence_of_pre_school_kid_and_eating_out_... -1.804988 -1.804988 \n", + "coef_presence_of_predriving_school_kid_in_house... -0.395397 -0.395397 \n", + "coef_presence_of_predriving_school_kid_in_house... -1.985303 -1.985303 \n", + "coef_presence_of_university_student_and_discret... -1.663216 -1.663216 \n", + "coef_total_number_of_tours_is_1 -7.917385 -7.917385 \n", + "coef_total_number_of_tours_is_2 -11.926451 -11.926451 \n", + "coef_total_number_of_tours_is_3 -13.210287 -13.210287 \n", + "coef_urban_and_discretionary_tour 0.000000 0.000000 \n", + "coef_urban_and_maintenance_tour 1.131129 1.131129 \n", "\n", - " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 NaN \n", - "coef_1_escort_tour_constant 0.0 NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN \n", - "coef_1_plus_shopping_tours_constant 0.0 NaN \n", - "coef_1_plus_visting_tours_constant 0.0 NaN \n", - "coef_2_plus_escort_tours_constant 0.0 NaN \n", - "coef_auto_access_to_retail_and_tour_frequency_i... 0.0 NaN \n", - "coef_car_shortage_vs_workers_and_tour_frequency... 0.0 NaN \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 0.0 NaN \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 0.0 NaN \n", - "coef_high_income_group_and_discretionary_tour 0.0 NaN \n", - "coef_high_income_group_and_eating_out_tour 0.0 NaN \n", - "coef_high_income_group_and_maintenance_tour 0.0 NaN \n", - "coef_high_income_group_and_shopping_tour 0.0 NaN \n", - "coef_high_income_group_and_visiting_tour 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_mediumhigh_income_group_and_discretionary_... 0.0 NaN \n", - "coef_mediumlow_income_group_and_discretionary_tour 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_is_1 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_presence_of_driving_school_kid_and_discret... 0.0 NaN \n", - "coef_presence_of_driving_school_kid_and_eating_... 0.0 NaN \n", - "coef_presence_of_non_worker_and_tour_frequency_... 0.0 NaN \n", - "coef_presence_of_non_worker_and_tour_frequency_... 0.0 NaN \n", - "coef_presence_of_pre_driving_school_kid_and_eat... 0.0 NaN \n", - "coef_presence_of_pre_school_kid_and_eating_out_... 0.0 NaN \n", - "coef_presence_of_predriving_school_kid_in_house... 0.0 NaN \n", - "coef_presence_of_predriving_school_kid_in_house... 0.0 NaN \n", - "coef_presence_of_university_student_and_discret... 0.0 NaN \n", - "coef_total_number_of_tours_is_1 0.0 NaN \n", - "coef_total_number_of_tours_is_2 0.0 NaN \n", - "coef_total_number_of_tours_is_3 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 0.0 \n", - "coef_urban_and_maintenance_tour 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0000 -20.0 \n", + "coef_1_escort_tour_constant -0.4934 -20.0 \n", + "coef_1_plus_eating_out_tours_constant -0.0242 -20.0 \n", + "coef_1_plus_maintenance_tours_constant -0.4344 -20.0 \n", + "coef_1_plus_other_discretionary_tours_constant -0.2602 -20.0 \n", + "coef_1_plus_shopping_tours_constant 0.5320 -20.0 \n", + "coef_1_plus_visting_tours_constant 0.2367 -20.0 \n", + "coef_2_plus_escort_tours_constant 1.4155 -20.0 \n", + "coef_auto_access_to_retail_and_tour_frequency_i... 0.1004 -20.0 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... -0.6369 -20.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 0.2902 -20.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 2.0352 -20.0 \n", + "coef_high_income_group_and_discretionary_tour 2.3270 -20.0 \n", + "coef_high_income_group_and_eating_out_tour 0.4916 -20.0 \n", + "coef_high_income_group_and_maintenance_tour 0.3982 -20.0 \n", + "coef_high_income_group_and_shopping_tour 0.2443 -20.0 \n", + "coef_high_income_group_and_visiting_tour 0.2858 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.3298 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.3759 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 3.2808 -20.0 \n", + "coef_mediumhigh_income_group_and_discretionary_... 1.4050 -20.0 \n", + "coef_mediumlow_income_group_and_discretionary_tour 0.9169 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_1 -0.2162 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -0.3587 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 -4.2701 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0000 -999.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -0.2340 -20.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -0.9231 -20.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -6.5835 -20.0 \n", + "coef_presence_of_driving_school_kid_and_discret... -0.9202 -20.0 \n", + "coef_presence_of_driving_school_kid_and_eating_... -0.6377 -20.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... -0.6571 -20.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... -1.4044 -20.0 \n", + "coef_presence_of_pre_driving_school_kid_and_eat... -1.5698 -20.0 \n", + "coef_presence_of_pre_school_kid_and_eating_out_... -0.2987 -20.0 \n", + "coef_presence_of_predriving_school_kid_in_house... -0.3219 -20.0 \n", + "coef_presence_of_predriving_school_kid_in_house... -1.0874 -20.0 \n", + "coef_presence_of_university_student_and_discret... -1.2834 -20.0 \n", + "coef_total_number_of_tours_is_1 -7.1506 -20.0 \n", + "coef_total_number_of_tours_is_2 -11.1214 -20.0 \n", + "coef_total_number_of_tours_is_3 -13.1750 -20.0 \n", + "coef_urban_and_discretionary_tour 0.0000 0.0 \n", + "coef_urban_and_maintenance_tour 1.0394 -20.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour NaN 0 \n", - "coef_1_escort_tour_constant NaN 0 \n", - "coef_1_plus_eating_out_tours_constant NaN 0 \n", - "coef_1_plus_maintenance_tours_constant NaN 0 \n", - "coef_1_plus_other_discretionary_tours_constant NaN 0 \n", - "coef_1_plus_shopping_tours_constant NaN 0 \n", - "coef_1_plus_visting_tours_constant NaN 0 \n", - "coef_2_plus_escort_tours_constant NaN 0 \n", - "coef_auto_access_to_retail_and_tour_frequency_i... NaN 0 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... NaN 0 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... NaN 0 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... NaN 0 \n", - "coef_high_income_group_and_discretionary_tour NaN 0 \n", - "coef_high_income_group_and_eating_out_tour NaN 0 \n", - "coef_high_income_group_and_maintenance_tour NaN 0 \n", - "coef_high_income_group_and_shopping_tour NaN 0 \n", - "coef_high_income_group_and_visiting_tour NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_mediumhigh_income_group_and_discretionary_... NaN 0 \n", - "coef_mediumlow_income_group_and_discretionary_tour NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_1 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_presence_of_driving_school_kid_and_discret... NaN 0 \n", - "coef_presence_of_driving_school_kid_and_eating_... NaN 0 \n", - "coef_presence_of_non_worker_and_tour_frequency_... NaN 0 \n", - "coef_presence_of_non_worker_and_tour_frequency_... NaN 0 \n", - "coef_presence_of_pre_driving_school_kid_and_eat... NaN 0 \n", - "coef_presence_of_pre_school_kid_and_eating_out_... NaN 0 \n", - "coef_presence_of_predriving_school_kid_in_house... NaN 0 \n", - "coef_presence_of_predriving_school_kid_in_house... NaN 0 \n", - "coef_presence_of_university_student_and_discret... NaN 0 \n", - "coef_total_number_of_tours_is_1 NaN 0 \n", - "coef_total_number_of_tours_is_2 NaN 0 \n", - "coef_total_number_of_tours_is_3 NaN 0 \n", - "coef_urban_and_discretionary_tour 0.0 1 \n", - "coef_urban_and_maintenance_tour NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 20.0 0.0 \n", + "coef_1_escort_tour_constant 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant 20.0 0.0 \n", + "coef_1_plus_shopping_tours_constant 20.0 0.0 \n", + "coef_1_plus_visting_tours_constant 20.0 0.0 \n", + "coef_2_plus_escort_tours_constant 20.0 0.0 \n", + "coef_auto_access_to_retail_and_tour_frequency_i... 20.0 0.0 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... 20.0 0.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 20.0 0.0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 20.0 0.0 \n", + "coef_high_income_group_and_discretionary_tour 20.0 0.0 \n", + "coef_high_income_group_and_eating_out_tour 20.0 0.0 \n", + "coef_high_income_group_and_maintenance_tour 20.0 0.0 \n", + "coef_high_income_group_and_shopping_tour 20.0 0.0 \n", + "coef_high_income_group_and_visiting_tour 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_mediumhigh_income_group_and_discretionary_... 20.0 0.0 \n", + "coef_mediumlow_income_group_and_discretionary_tour 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_1 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_presence_of_driving_school_kid_and_discret... 20.0 0.0 \n", + "coef_presence_of_driving_school_kid_and_eating_... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 20.0 0.0 \n", + "coef_presence_of_pre_driving_school_kid_and_eat... 20.0 0.0 \n", + "coef_presence_of_pre_school_kid_and_eating_out_... 20.0 0.0 \n", + "coef_presence_of_predriving_school_kid_in_house... 20.0 0.0 \n", + "coef_presence_of_predriving_school_kid_in_house... 20.0 0.0 \n", + "coef_presence_of_university_student_and_discret... 20.0 0.0 \n", + "coef_total_number_of_tours_is_1 20.0 0.0 \n", + "coef_total_number_of_tours_is_2 20.0 0.0 \n", + "coef_total_number_of_tours_is_3 20.0 0.0 \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", + "coef_urban_and_maintenance_tour 20.0 0.0 \n", "\n", - " best \n", - "coef_0_auto_household_and_escorting_tour -289.819438 \n", - "coef_1_escort_tour_constant -6434.961317 \n", - "coef_1_plus_eating_out_tours_constant -3885.411913 \n", - "coef_1_plus_maintenance_tours_constant -1195.807014 \n", - "coef_1_plus_other_discretionary_tours_constant -2406.806121 \n", - "coef_1_plus_shopping_tours_constant -2389.305945 \n", - "coef_1_plus_visting_tours_constant -2389.305996 \n", - "coef_2_plus_escort_tours_constant -5667.685401 \n", - "coef_auto_access_to_retail_and_tour_frequency_i... -44.188373 \n", - "coef_car_shortage_vs_workers_and_tour_frequency... -17232.832071 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... -4035.619542 \n", - "coef_car_surplus_vs_workers_and_tour_frequency_... 15225.992542 \n", - "coef_high_income_group_and_discretionary_tour 4481.084393 \n", - "coef_high_income_group_and_eating_out_tour -3157.273834 \n", - "coef_high_income_group_and_maintenance_tour 4461.484862 \n", - "coef_high_income_group_and_shopping_tour 4461.390803 \n", - "coef_high_income_group_and_visiting_tour 4461.390852 \n", - "coef_logged_maximum_residual_window_tour_freque... 16.594329 \n", - "coef_logged_maximum_residual_window_tour_freque... -19742.937757 \n", - "coef_logged_maximum_residual_window_tour_freque... -11491.665678 \n", - "coef_mediumhigh_income_group_and_discretionary_... 572.847509 \n", - "coef_mediumlow_income_group_and_discretionary_tour -4429.942211 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_1 -4180.353968 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -1606.502880 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_3 -109.819329 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -16877.023970 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 12620.764595 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -4861.997912 \n", - "coef_presence_of_driving_school_kid_and_discret... -578.872073 \n", - "coef_presence_of_driving_school_kid_and_eating_... 6.381728 \n", - "coef_presence_of_non_worker_and_tour_frequency_... 878.008827 \n", - "coef_presence_of_non_worker_and_tour_frequency_... -1944.922644 \n", - "coef_presence_of_pre_driving_school_kid_and_eat... -164.906255 \n", - "coef_presence_of_pre_school_kid_and_eating_out_... 313.476309 \n", - "coef_presence_of_predriving_school_kid_in_house... 4033.566871 \n", - "coef_presence_of_predriving_school_kid_in_house... 10149.331976 \n", - "coef_presence_of_university_student_and_discret... 3906.378403 \n", - "coef_total_number_of_tours_is_1 15200.662337 \n", - "coef_total_number_of_tours_is_2 -1638.556186 \n", - "coef_total_number_of_tours_is_3 -14010.608324 \n", - "coef_urban_and_discretionary_tour 0.000000 \n", - "coef_urban_and_maintenance_tour -1194.333214 " + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 0 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", + "coef_1_plus_shopping_tours_constant 0 \n", + "coef_1_plus_visting_tours_constant 0 \n", + "coef_2_plus_escort_tours_constant 0 \n", + "coef_auto_access_to_retail_and_tour_frequency_i... 0 \n", + "coef_car_shortage_vs_workers_and_tour_frequency... 0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 0 \n", + "coef_car_surplus_vs_workers_and_tour_frequency_... 0 \n", + "coef_high_income_group_and_discretionary_tour 0 \n", + "coef_high_income_group_and_eating_out_tour 0 \n", + "coef_high_income_group_and_maintenance_tour 0 \n", + "coef_high_income_group_and_shopping_tour 0 \n", + "coef_high_income_group_and_visiting_tour 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_mediumhigh_income_group_and_discretionary_... 0 \n", + "coef_mediumlow_income_group_and_discretionary_tour 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_1 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_3 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 1 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_presence_of_driving_school_kid_and_discret... 0 \n", + "coef_presence_of_driving_school_kid_and_eating_... 0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0 \n", + "coef_presence_of_pre_driving_school_kid_and_eat... 0 \n", + "coef_presence_of_pre_school_kid_and_eating_out_... 0 \n", + "coef_presence_of_predriving_school_kid_in_house... 0 \n", + "coef_presence_of_predriving_school_kid_in_house... 0 \n", + "coef_presence_of_university_student_and_discret... 0 \n", + "coef_total_number_of_tours_is_1 0 \n", + "coef_total_number_of_tours_is_2 0 \n", + "coef_total_number_of_tours_is_3 0 \n", + "coef_urban_and_discretionary_tour 1 \n", + "coef_urban_and_maintenance_tour 0 " ] }, "metadata": {}, @@ -4018,26 +9023,456 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 0.0 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 14 parameter estimators with negative variance\n", - "- coef_1_plus_maintenance_tours_constant\n", - "- coef_1_plus_other_discretionary_tours_constant\n", - "- coef_1_plus_shopping_tours_constant\n", - "- coef_1_plus_visting_tours_constant\n", - "- and 10 more\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - 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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.36 3.76-0.63 0.00
coef_1_escort_tour_constant-0.592 NA NA 0.00
coef_1_plus_eating_out_tours_constant-0.531 NA NA 0.00
coef_1_plus_maintenance_tours_constant-0.479 NA NA 0.00
coef_1_plus_other_discretionary_tours_constant 0.0751 NA NA 0.00
coef_1_plus_shopping_tours_constant 0.278 NA NA 0.00
coef_1_plus_visting_tours_constant 0.0747 NA NA 0.00
coef_2_plus_escort_tours_constant 0.539 NA NA 0.00
coef_auto_access_to_retail_and_tour_frequency_is_5_plus 0.121 0.110 1.10 0.00
coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus-0.896 0.225-3.98*** 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_1 0.385 0.160 2.40* 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus 1.92 0.421 4.55*** 0.00
coef_high_income_group_and_discretionary_tour 1.97 0.433 4.56*** 0.00
coef_high_income_group_and_eating_out_tour 1.12 0.412 2.71** 0.00
coef_high_income_group_and_maintenance_tour 0.536 0.283 1.89 0.00
coef_high_income_group_and_shopping_tour 0.298 0.256 1.17 0.00
coef_high_income_group_and_visiting_tour 0.657 0.266 2.47* 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 1.84 0.280 6.58*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_2 2.75 0.544 5.05*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 3.21 3.57 0.90 0.00
coef_mediumhigh_income_group_and_discretionary_tour 0.786 0.459 1.71 0.00
coef_mediumlow_income_group_and_discretionary_tour 0.716 0.472 1.52 0.00
coef_number_of_joint_tours_and_tour_frequency_is_1-0.0765 0.227-0.34 0.00
coef_number_of_joint_tours_and_tour_frequency_is_2-2.35 1.41-1.66 0.00
coef_number_of_joint_tours_and_tour_frequency_is_3-4.95 10.5-0.47 0.00
coef_number_of_joint_tours_and_tour_frequency_is_5_plus-999. 0.00 NA 0.00fixed value
coef_number_of_mandatory_tours_and_tour_frequency_is_1-0.717 0.256-2.80** 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_2-2.89 1.19-2.44* 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_3-6.49 8.76-0.74 0.00
coef_presence_of_driving_school_kid_and_discretionary_tour-0.826 0.330-2.50* 0.00
coef_presence_of_driving_school_kid_and_eating_out_tour-0.606 0.545-1.11 0.00
coef_presence_of_non_worker_and_tour_frequency_is_2-0.269 0.382-0.70 0.00
coef_presence_of_non_worker_and_tour_frequency_is_5-3.18 2.64-1.21 0.00
coef_presence_of_pre_driving_school_kid_and_eating_out_tour-1.45 0.500-2.90** 0.00
coef_presence_of_pre_school_kid_and_eating_out_tour-1.80 1.09-1.65 0.00
coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1-0.395 0.149-2.65** 0.00
coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5-1.99 0.501-3.96*** 0.00
coef_presence_of_university_student_and_discretionary_tour-1.66 0.377-4.42*** 0.00
coef_total_number_of_tours_is_1-7.92 NA NA 0.00
coef_total_number_of_tours_is_2-11.9 NA NA 0.00
coef_total_number_of_tours_is_3-13.2 NA NA 0.00
coef_urban_and_discretionary_tour 0.00 0.00 NA 0.00fixed value
coef_urban_and_maintenance_tour 1.13 0.259 4.36*** 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model PTYPE_SCHOOL\n" ] }, { "data": { "text/html": [ - "

Iteration 037 [Optimization terminated successfully]

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Iteration 055 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -4049,7 +9484,7 @@ { "data": { "text/html": [ - "

Best LL = -256.78783276592117

" + "

Best LL = -5118.870584262372

" ], "text/plain": [ "" @@ -4080,538 +9515,517 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", " -2.000000\n", + " -2.000000\n", " -2.0000\n", - " 0.0\n", " -2.0\n", " -2.0\n", + " 0.0\n", " 1\n", - " \n", - " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " -1.572081\n", + " -0.710427\n", + " -0.710427\n", " -0.7551\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.572081\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " -2.222210\n", + " 1.267594\n", + " 1.267594\n", " 1.1145\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.222210\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " -1.497135\n", + " -0.580534\n", + " -0.580534\n", " -0.5060\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.497135\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " 0.090818\n", + " 0.614538\n", + " 0.614538\n", " 0.4634\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.090818\n", " \n", " \n", " coef_1_plus_shopping_tours_constant\n", - " 1.474688\n", + " 1.037830\n", + " 1.037830\n", " 0.4783\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.474688\n", " \n", " \n", " coef_1_plus_visting_tours_constant\n", - " -1.792608\n", + " -0.445905\n", + " -0.445905\n", " -0.4006\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.792608\n", " \n", " \n", " coef_2_plus_escort_tours_constant\n", - " -2.059623\n", + " -0.076053\n", + " -0.076053\n", " -0.0086\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -2.059623\n", " \n", " \n", " coef_auto_access_to_retail_and_escorting\n", - " 0.555108\n", + " 0.063236\n", + " 0.063236\n", " 0.0629\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.555108\n", " \n", " \n", " coef_high_income_group_and_eating_out_tour\n", - " -1.361980\n", + " -0.810212\n", + " -0.810212\n", " -0.7010\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.361980\n", " \n", " \n", " coef_high_income_group_and_shopping_tour\n", - " -3.736670\n", + " -0.945693\n", + " -0.945693\n", " -0.6506\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -3.736670\n", " \n", " \n", " coef_high_income_group_and_tour_frequency_is_5_plus\n", - " 16.017262\n", + " 2.304674\n", + " 2.304674\n", " 2.0175\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 16.017262\n", " \n", " \n", " coef_logged_maximum_residual_window_tour_frequency_is_5_plus\n", - " 1.526025\n", + " 1.353636\n", + " 1.353636\n", " 1.5603\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.526025\n", " \n", " \n", " coef_mediumhigh_income_group_and_tour_frequency_is_5_plus\n", - " 15.096814\n", + " 1.811428\n", + " 1.811428\n", " 1.5197\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 15.096814\n", " \n", " \n", " coef_mediumlow_income_group_and_tour_frequency_is_5_plus\n", - " 14.227759\n", + " 1.143302\n", + " 1.143302\n", " 1.0873\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 14.227759\n", " \n", " \n", " coef_number_of_joint_maintenance_tours\n", " -1.347600\n", + " -1.347600\n", " -1.3476\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.347600\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_2\n", - " -1.197115\n", + " -0.540183\n", + " -0.540183\n", " -0.6149\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.197115\n", " \n", " \n", " coef_number_of_joint_tours_and_tour_frequency_is_5_plus\n", " -999.000000\n", + " -999.000000\n", " -999.0000\n", - " 0.0\n", " -999.0\n", " -999.0\n", + " 0.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_1\n", - " -9.889679\n", + " -1.256737\n", + " -1.256737\n", " -1.0331\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -9.889679\n", " \n", " \n", " coef_number_of_mandatory_tours_and_tour_frequency_is_3\n", - " -10.675107\n", + " -3.162364\n", + " -3.162364\n", " -2.7445\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -10.675107\n", " \n", " \n", " coef_presence_of_full_time_worker_and_discretionary_tour\n", - " 0.167217\n", + " 0.646457\n", + " 0.646457\n", " 0.7526\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.167217\n", " \n", " \n", " coef_presence_of_non_worker_and_eating_out_tour\n", - " -1.295662\n", + " -1.533575\n", + " -1.533575\n", " -1.3074\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.295662\n", " \n", " \n", " coef_presence_of_non_worker_and_shopping_tour\n", - " -1.353670\n", + " -0.760303\n", + " -0.760303\n", " -0.6450\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.353670\n", " \n", " \n", " coef_presence_of_non_worker_and_tour_frequency_is_5\n", - " -0.434686\n", + " 0.353557\n", + " 0.353557\n", " 0.2177\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.434686\n", " \n", " \n", " coef_presence_of_part_time_worker_and_discretionary_tour\n", - " -0.342155\n", + " 0.436183\n", + " 0.436183\n", " 0.3721\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.342155\n", " \n", " \n", " coef_presence_of_pre_driving_school_kid_and_shopping_tour\n", - " 1.415379\n", + " 0.745895\n", + " 0.745895\n", " 0.9365\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.415379\n", " \n", " \n", " coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5\n", - " 0.290181\n", + " -0.086497\n", + " -0.086497\n", " -0.2264\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.290181\n", " \n", " \n", " coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5\n", - " 0.328944\n", + " -0.357691\n", + " -0.357691\n", " -0.4439\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.328944\n", " \n", " \n", " coef_total_number_of_tours_is_1\n", - " -11.291182\n", + " -7.203758\n", + " -7.203758\n", " -7.4863\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -11.291182\n", " \n", " \n", " coef_total_number_of_tours_is_2\n", - " -13.228339\n", + " -10.489540\n", + " -10.489540\n", " -10.7180\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -13.228339\n", " \n", " \n", " coef_total_number_of_tours_is_3\n", - " -14.184905\n", + " -13.816990\n", + " -13.816990\n", " -13.7884\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -14.184905\n", " \n", " \n", " coef_urban_and_discretionary_tour\n", " 0.000000\n", + " 0.000000\n", " 0.0000\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_urban_and_escorting_tour\n", - " -4.483827\n", + " 0.728596\n", + " 0.728596\n", " 0.4352\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.483827\n", " \n", " \n", " coef_walk_access_to_retail_and_eating_out\n", - " 0.400103\n", + " 0.099568\n", + " 0.099568\n", " 0.0738\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.400103\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant -1.572081 -0.7551 \n", - "coef_1_plus_eating_out_tours_constant -2.222210 1.1145 \n", - "coef_1_plus_maintenance_tours_constant -1.497135 -0.5060 \n", - "coef_1_plus_other_discretionary_tours_constant 0.090818 0.4634 \n", - "coef_1_plus_shopping_tours_constant 1.474688 0.4783 \n", - "coef_1_plus_visting_tours_constant -1.792608 -0.4006 \n", - "coef_2_plus_escort_tours_constant -2.059623 -0.0086 \n", - "coef_auto_access_to_retail_and_escorting 0.555108 0.0629 \n", - "coef_high_income_group_and_eating_out_tour -1.361980 -0.7010 \n", - "coef_high_income_group_and_shopping_tour -3.736670 -0.6506 \n", - "coef_high_income_group_and_tour_frequency_is_5_... 16.017262 2.0175 \n", - "coef_logged_maximum_residual_window_tour_freque... 1.526025 1.5603 \n", - "coef_mediumhigh_income_group_and_tour_frequency... 15.096814 1.5197 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 14.227759 1.0873 \n", - "coef_number_of_joint_maintenance_tours -1.347600 -1.3476 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -1.197115 -0.6149 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.0000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -9.889679 -1.0331 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -10.675107 -2.7445 \n", - "coef_presence_of_full_time_worker_and_discretio... 0.167217 0.7526 \n", - "coef_presence_of_non_worker_and_eating_out_tour -1.295662 -1.3074 \n", - "coef_presence_of_non_worker_and_shopping_tour -1.353670 -0.6450 \n", - "coef_presence_of_non_worker_and_tour_frequency_... -0.434686 0.2177 \n", - "coef_presence_of_part_time_worker_and_discretio... -0.342155 0.3721 \n", - "coef_presence_of_pre_driving_school_kid_and_sho... 1.415379 0.9365 \n", - "coef_presence_of_predriving_school_kid_in_house... 0.290181 -0.2264 \n", - "coef_presence_of_preschool_kid_in_household_and... 0.328944 -0.4439 \n", - "coef_total_number_of_tours_is_1 -11.291182 -7.4863 \n", - "coef_total_number_of_tours_is_2 -13.228339 -10.7180 \n", - "coef_total_number_of_tours_is_3 -14.184905 -13.7884 \n", - "coef_urban_and_discretionary_tour 0.000000 0.0000 \n", - "coef_urban_and_escorting_tour -4.483827 0.4352 \n", - "coef_walk_access_to_retail_and_eating_out 0.400103 0.0738 \n", + " value best \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.000000 \n", + "coef_1_escort_tour_constant -0.710427 -0.710427 \n", + "coef_1_plus_eating_out_tours_constant 1.267594 1.267594 \n", + "coef_1_plus_maintenance_tours_constant -0.580534 -0.580534 \n", + "coef_1_plus_other_discretionary_tours_constant 0.614538 0.614538 \n", + "coef_1_plus_shopping_tours_constant 1.037830 1.037830 \n", + "coef_1_plus_visting_tours_constant -0.445905 -0.445905 \n", + "coef_2_plus_escort_tours_constant -0.076053 -0.076053 \n", + "coef_auto_access_to_retail_and_escorting 0.063236 0.063236 \n", + "coef_high_income_group_and_eating_out_tour -0.810212 -0.810212 \n", + "coef_high_income_group_and_shopping_tour -0.945693 -0.945693 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 2.304674 2.304674 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.353636 1.353636 \n", + "coef_mediumhigh_income_group_and_tour_frequency... 1.811428 1.811428 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 1.143302 1.143302 \n", + "coef_number_of_joint_maintenance_tours -1.347600 -1.347600 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -0.540183 -0.540183 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 -999.000000 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -1.256737 -1.256737 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -3.162364 -3.162364 \n", + "coef_presence_of_full_time_worker_and_discretio... 0.646457 0.646457 \n", + "coef_presence_of_non_worker_and_eating_out_tour -1.533575 -1.533575 \n", + "coef_presence_of_non_worker_and_shopping_tour -0.760303 -0.760303 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0.353557 0.353557 \n", + "coef_presence_of_part_time_worker_and_discretio... 0.436183 0.436183 \n", + "coef_presence_of_pre_driving_school_kid_and_sho... 0.745895 0.745895 \n", + "coef_presence_of_predriving_school_kid_in_house... -0.086497 -0.086497 \n", + "coef_presence_of_preschool_kid_in_household_and... -0.357691 -0.357691 \n", + "coef_total_number_of_tours_is_1 -7.203758 -7.203758 \n", + "coef_total_number_of_tours_is_2 -10.489540 -10.489540 \n", + "coef_total_number_of_tours_is_3 -13.816990 -13.816990 \n", + "coef_urban_and_discretionary_tour 0.000000 0.000000 \n", + "coef_urban_and_escorting_tour 0.728596 0.728596 \n", + "coef_walk_access_to_retail_and_eating_out 0.099568 0.099568 \n", "\n", - " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", - "coef_1_escort_tour_constant 0.0 NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN \n", - "coef_1_plus_shopping_tours_constant 0.0 NaN \n", - "coef_1_plus_visting_tours_constant 0.0 NaN \n", - "coef_2_plus_escort_tours_constant 0.0 NaN \n", - "coef_auto_access_to_retail_and_escorting 0.0 NaN \n", - "coef_high_income_group_and_eating_out_tour 0.0 NaN \n", - "coef_high_income_group_and_shopping_tour 0.0 NaN \n", - "coef_high_income_group_and_tour_frequency_is_5_... 0.0 NaN \n", - "coef_logged_maximum_residual_window_tour_freque... 0.0 NaN \n", - "coef_mediumhigh_income_group_and_tour_frequency... 0.0 NaN \n", - "coef_mediumlow_income_group_and_tour_frequency_... 0.0 NaN \n", - "coef_number_of_joint_maintenance_tours 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 0.0 NaN \n", - "coef_number_of_joint_tours_and_tour_frequency_i... 0.0 -999.0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_number_of_mandatory_tours_and_tour_frequen... 0.0 NaN \n", - "coef_presence_of_full_time_worker_and_discretio... 0.0 NaN \n", - "coef_presence_of_non_worker_and_eating_out_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_shopping_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_tour_frequency_... 0.0 NaN \n", - "coef_presence_of_part_time_worker_and_discretio... 0.0 NaN \n", - "coef_presence_of_pre_driving_school_kid_and_sho... 0.0 NaN \n", - "coef_presence_of_predriving_school_kid_in_house... 0.0 NaN \n", - "coef_presence_of_preschool_kid_in_household_and... 0.0 NaN \n", - "coef_total_number_of_tours_is_1 0.0 NaN \n", - "coef_total_number_of_tours_is_2 0.0 NaN \n", - "coef_total_number_of_tours_is_3 0.0 NaN \n", - "coef_urban_and_discretionary_tour 0.0 0.0 \n", - "coef_urban_and_escorting_tour 0.0 NaN \n", - "coef_walk_access_to_retail_and_eating_out 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0000 -2.0 \n", + "coef_1_escort_tour_constant -0.7551 -20.0 \n", + "coef_1_plus_eating_out_tours_constant 1.1145 -20.0 \n", + "coef_1_plus_maintenance_tours_constant -0.5060 -20.0 \n", + "coef_1_plus_other_discretionary_tours_constant 0.4634 -20.0 \n", + "coef_1_plus_shopping_tours_constant 0.4783 -20.0 \n", + "coef_1_plus_visting_tours_constant -0.4006 -20.0 \n", + "coef_2_plus_escort_tours_constant -0.0086 -20.0 \n", + "coef_auto_access_to_retail_and_escorting 0.0629 -20.0 \n", + "coef_high_income_group_and_eating_out_tour -0.7010 -20.0 \n", + "coef_high_income_group_and_shopping_tour -0.6506 -20.0 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 2.0175 -20.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 1.5603 -20.0 \n", + "coef_mediumhigh_income_group_and_tour_frequency... 1.5197 -20.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 1.0873 -20.0 \n", + "coef_number_of_joint_maintenance_tours -1.3476 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 -0.6149 -20.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0000 -999.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -1.0331 -20.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... -2.7445 -20.0 \n", + "coef_presence_of_full_time_worker_and_discretio... 0.7526 -20.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour -1.3074 -20.0 \n", + "coef_presence_of_non_worker_and_shopping_tour -0.6450 -20.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0.2177 -20.0 \n", + "coef_presence_of_part_time_worker_and_discretio... 0.3721 -20.0 \n", + "coef_presence_of_pre_driving_school_kid_and_sho... 0.9365 -20.0 \n", + "coef_presence_of_predriving_school_kid_in_house... -0.2264 -20.0 \n", + "coef_presence_of_preschool_kid_in_household_and... -0.4439 -20.0 \n", + "coef_total_number_of_tours_is_1 -7.4863 -20.0 \n", + "coef_total_number_of_tours_is_2 -10.7180 -20.0 \n", + "coef_total_number_of_tours_is_3 -13.7884 -20.0 \n", + "coef_urban_and_discretionary_tour 0.0000 0.0 \n", + "coef_urban_and_escorting_tour 0.4352 -20.0 \n", + "coef_walk_access_to_retail_and_eating_out 0.0738 -20.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour -2.0 1 \n", - "coef_1_escort_tour_constant NaN 0 \n", - "coef_1_plus_eating_out_tours_constant NaN 0 \n", - "coef_1_plus_maintenance_tours_constant NaN 0 \n", - "coef_1_plus_other_discretionary_tours_constant NaN 0 \n", - "coef_1_plus_shopping_tours_constant NaN 0 \n", - "coef_1_plus_visting_tours_constant NaN 0 \n", - "coef_2_plus_escort_tours_constant NaN 0 \n", - "coef_auto_access_to_retail_and_escorting NaN 0 \n", - "coef_high_income_group_and_eating_out_tour NaN 0 \n", - "coef_high_income_group_and_shopping_tour NaN 0 \n", - "coef_high_income_group_and_tour_frequency_is_5_... NaN 0 \n", - "coef_logged_maximum_residual_window_tour_freque... NaN 0 \n", - "coef_mediumhigh_income_group_and_tour_frequency... NaN 0 \n", - "coef_mediumlow_income_group_and_tour_frequency_... NaN 0 \n", - "coef_number_of_joint_maintenance_tours NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 NaN 0 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 1 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... NaN 0 \n", - "coef_presence_of_full_time_worker_and_discretio... NaN 0 \n", - "coef_presence_of_non_worker_and_eating_out_tour NaN 0 \n", - "coef_presence_of_non_worker_and_shopping_tour NaN 0 \n", - "coef_presence_of_non_worker_and_tour_frequency_... NaN 0 \n", - "coef_presence_of_part_time_worker_and_discretio... NaN 0 \n", - "coef_presence_of_pre_driving_school_kid_and_sho... NaN 0 \n", - "coef_presence_of_predriving_school_kid_in_house... NaN 0 \n", - "coef_presence_of_preschool_kid_in_household_and... NaN 0 \n", - "coef_total_number_of_tours_is_1 NaN 0 \n", - "coef_total_number_of_tours_is_2 NaN 0 \n", - "coef_total_number_of_tours_is_3 NaN 0 \n", - "coef_urban_and_discretionary_tour 0.0 1 \n", - "coef_urban_and_escorting_tour NaN 0 \n", - "coef_walk_access_to_retail_and_eating_out NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0 0.0 \n", + "coef_1_escort_tour_constant 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant 20.0 0.0 \n", + "coef_1_plus_shopping_tours_constant 20.0 0.0 \n", + "coef_1_plus_visting_tours_constant 20.0 0.0 \n", + "coef_2_plus_escort_tours_constant 20.0 0.0 \n", + "coef_auto_access_to_retail_and_escorting 20.0 0.0 \n", + "coef_high_income_group_and_eating_out_tour 20.0 0.0 \n", + "coef_high_income_group_and_shopping_tour 20.0 0.0 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 20.0 0.0 \n", + "coef_logged_maximum_residual_window_tour_freque... 20.0 0.0 \n", + "coef_mediumhigh_income_group_and_tour_frequency... 20.0 0.0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 20.0 0.0 \n", + "coef_number_of_joint_maintenance_tours 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 20.0 0.0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... -999.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 20.0 0.0 \n", + "coef_presence_of_full_time_worker_and_discretio... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_shopping_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 20.0 0.0 \n", + "coef_presence_of_part_time_worker_and_discretio... 20.0 0.0 \n", + "coef_presence_of_pre_driving_school_kid_and_sho... 20.0 0.0 \n", + "coef_presence_of_predriving_school_kid_in_house... 20.0 0.0 \n", + "coef_presence_of_preschool_kid_in_household_and... 20.0 0.0 \n", + "coef_total_number_of_tours_is_1 20.0 0.0 \n", + "coef_total_number_of_tours_is_2 20.0 0.0 \n", + "coef_total_number_of_tours_is_3 20.0 0.0 \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", + "coef_urban_and_escorting_tour 20.0 0.0 \n", + "coef_walk_access_to_retail_and_eating_out 20.0 0.0 \n", "\n", - " best \n", - "coef_0_auto_household_and_escorting_tour -2.000000 \n", - "coef_1_escort_tour_constant -1.572081 \n", - "coef_1_plus_eating_out_tours_constant -2.222210 \n", - "coef_1_plus_maintenance_tours_constant -1.497135 \n", - "coef_1_plus_other_discretionary_tours_constant 0.090818 \n", - "coef_1_plus_shopping_tours_constant 1.474688 \n", - "coef_1_plus_visting_tours_constant -1.792608 \n", - "coef_2_plus_escort_tours_constant -2.059623 \n", - "coef_auto_access_to_retail_and_escorting 0.555108 \n", - "coef_high_income_group_and_eating_out_tour -1.361980 \n", - "coef_high_income_group_and_shopping_tour -3.736670 \n", - "coef_high_income_group_and_tour_frequency_is_5_... 16.017262 \n", - "coef_logged_maximum_residual_window_tour_freque... 1.526025 \n", - "coef_mediumhigh_income_group_and_tour_frequency... 15.096814 \n", - "coef_mediumlow_income_group_and_tour_frequency_... 14.227759 \n", - "coef_number_of_joint_maintenance_tours -1.347600 \n", - "coef_number_of_joint_tours_and_tour_frequency_is_2 -1.197115 \n", - "coef_number_of_joint_tours_and_tour_frequency_i... -999.000000 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -9.889679 \n", - "coef_number_of_mandatory_tours_and_tour_frequen... -10.675107 \n", - "coef_presence_of_full_time_worker_and_discretio... 0.167217 \n", - "coef_presence_of_non_worker_and_eating_out_tour -1.295662 \n", - "coef_presence_of_non_worker_and_shopping_tour -1.353670 \n", - "coef_presence_of_non_worker_and_tour_frequency_... -0.434686 \n", - "coef_presence_of_part_time_worker_and_discretio... -0.342155 \n", - "coef_presence_of_pre_driving_school_kid_and_sho... 1.415379 \n", - "coef_presence_of_predriving_school_kid_in_house... 0.290181 \n", - "coef_presence_of_preschool_kid_in_household_and... 0.328944 \n", - "coef_total_number_of_tours_is_1 -11.291182 \n", - "coef_total_number_of_tours_is_2 -13.228339 \n", - "coef_total_number_of_tours_is_3 -14.184905 \n", - "coef_urban_and_discretionary_tour 0.000000 \n", - "coef_urban_and_escorting_tour -4.483827 \n", - "coef_walk_access_to_retail_and_eating_out 0.400103 " + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 1 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", + "coef_1_plus_shopping_tours_constant 0 \n", + "coef_1_plus_visting_tours_constant 0 \n", + "coef_2_plus_escort_tours_constant 0 \n", + "coef_auto_access_to_retail_and_escorting 0 \n", + "coef_high_income_group_and_eating_out_tour 0 \n", + "coef_high_income_group_and_shopping_tour 0 \n", + "coef_high_income_group_and_tour_frequency_is_5_... 0 \n", + "coef_logged_maximum_residual_window_tour_freque... 0 \n", + "coef_mediumhigh_income_group_and_tour_frequency... 0 \n", + "coef_mediumlow_income_group_and_tour_frequency_... 0 \n", + "coef_number_of_joint_maintenance_tours 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_is_2 0 \n", + "coef_number_of_joint_tours_and_tour_frequency_i... 1 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_number_of_mandatory_tours_and_tour_frequen... 0 \n", + "coef_presence_of_full_time_worker_and_discretio... 0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 0 \n", + "coef_presence_of_non_worker_and_shopping_tour 0 \n", + "coef_presence_of_non_worker_and_tour_frequency_... 0 \n", + "coef_presence_of_part_time_worker_and_discretio... 0 \n", + "coef_presence_of_pre_driving_school_kid_and_sho... 0 \n", + "coef_presence_of_predriving_school_kid_in_house... 0 \n", + "coef_presence_of_preschool_kid_in_household_and... 0 \n", + "coef_total_number_of_tours_is_1 0 \n", + "coef_total_number_of_tours_is_2 0 \n", + "coef_total_number_of_tours_is_3 0 \n", + "coef_urban_and_discretionary_tour 1 \n", + "coef_urban_and_escorting_tour 0 \n", + "coef_walk_access_to_retail_and_eating_out 0 " ] }, "metadata": {}, @@ -4621,24 +10035,375 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.064162929131762e-17 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 3 parameter estimators with negative variance\n", - "- coef_1_escort_tour_constant\n", - "- coef_2_plus_escort_tours_constant\n", - "- coef_urban_and_escorting_tour\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - " m.estimate(method='SLSQP')\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.00 0.00 NA 0.00fixed value
coef_1_escort_tour_constant-0.710 191.-0.00 0.00
coef_1_plus_eating_out_tours_constant 1.27 191. 0.01 0.00
coef_1_plus_maintenance_tours_constant-0.581 191.-0.00 0.00
coef_1_plus_other_discretionary_tours_constant 0.615 191. 0.00 0.00
coef_1_plus_shopping_tours_constant 1.04 191. 0.01 0.00
coef_1_plus_visting_tours_constant-0.446 191.-0.00 0.00
coef_2_plus_escort_tours_constant-0.0761 382.-0.00 0.00
coef_auto_access_to_retail_and_escorting 0.0632 0.112 0.56 0.00
coef_high_income_group_and_eating_out_tour-0.810 0.242-3.35*** 0.00
coef_high_income_group_and_shopping_tour-0.946 0.239-3.95*** 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 2.30 0.217 10.62*** 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 1.35 0.147 9.23*** 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_5_plus 1.81 0.217 8.33*** 0.00
coef_mediumlow_income_group_and_tour_frequency_is_5_plus 1.14 0.225 5.08*** 0.00
coef_number_of_joint_maintenance_tours-1.35 NA NA 0.00
coef_number_of_joint_tours_and_tour_frequency_is_2-0.540 0.138-3.92*** 0.00
coef_number_of_joint_tours_and_tour_frequency_is_5_plus-999. 0.00 NA 0.00fixed value
coef_number_of_mandatory_tours_and_tour_frequency_is_1-1.26 0.206-6.10*** 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_3-3.16 0.312-10.14*** 0.00
coef_presence_of_full_time_worker_and_discretionary_tour 0.646 0.148 4.36*** 0.00
coef_presence_of_non_worker_and_eating_out_tour-1.53 0.168-9.11*** 0.00
coef_presence_of_non_worker_and_shopping_tour-0.760 0.151-5.05*** 0.00
coef_presence_of_non_worker_and_tour_frequency_is_5 0.354 0.0883 4.01*** 0.00
coef_presence_of_part_time_worker_and_discretionary_tour 0.436 0.0926 4.71*** 0.00
coef_presence_of_pre_driving_school_kid_and_shopping_tour 0.746 0.150 4.98*** 0.00
coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5-0.0865 0.0791-1.09 0.00
coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5-0.358 0.0910-3.93*** 0.00
coef_total_number_of_tours_is_1-7.20 191.-0.04 0.00
coef_total_number_of_tours_is_2-10.5 382.-0.03 0.00
coef_total_number_of_tours_is_3-13.8 574.-0.02 0.00
coef_urban_and_discretionary_tour 0.00 0.00 NA 0.00fixed value
coef_urban_and_escorting_tour 0.729 0.147 4.97*** 0.00
coef_walk_access_to_retail_and_eating_out 0.0996 0.0402 2.47* 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model PTYPE_PRESCHOOL\n" ] }, { "data": { "text/html": [ - "

Iteration 030 [Optimization terminated successfully]

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Iteration 074 [Optimization terminated successfully]

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Best LL = -225.00971520122772

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Best LL = -3515.750312068413

" ], "text/plain": [ "" @@ -4681,403 +10446,391 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_0_auto_household_and_escorting_tour\n", " -2.000000\n", - " -2.0000\n", - " 0.0\n", + " -2.000000\n", + " -2.000000\n", " -2.0\n", " -2.0\n", + " 0.0\n", " 1\n", - " \n", - " -2.000000\n", " \n", " \n", " coef_1_escort_tour_constant\n", - " 0.179651\n", - " 0.3622\n", + " 0.274841\n", + " 0.274841\n", + " 0.362200\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.179651\n", " \n", " \n", " coef_1_plus_eating_out_tours_constant\n", - " 0.204827\n", - " 0.9612\n", + " -0.253387\n", + " -0.253387\n", + " 0.961200\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.204827\n", " \n", " \n", " coef_1_plus_maintenance_tours_constant\n", - " 0.073169\n", - " 0.6788\n", + " 0.316648\n", + " 0.316648\n", + " 0.678800\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.073169\n", " \n", " \n", " coef_1_plus_other_discretionary_tours_constant\n", - " 1.133662\n", - " 1.4935\n", + " 1.168896\n", + " 1.168896\n", + " 1.493500\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.133662\n", " \n", " \n", " coef_1_plus_shopping_tours_constant\n", - " 0.785367\n", - " 1.6919\n", + " 1.139680\n", + " 1.139680\n", + " 1.691900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.785367\n", " \n", " \n", " coef_1_plus_visting_tours_constant\n", - " -0.011286\n", - " 0.4424\n", + " 0.059049\n", + " 0.059049\n", + " 0.442400\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.011286\n", " \n", " \n", " coef_2_plus_escort_tours_constant\n", - " 1.744429\n", - " 2.2219\n", + " 1.941139\n", + " 1.941139\n", + " 2.221900\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.744429\n", " \n", " \n", " coef_discretionary_tour\n", - " 0.543162\n", - " 0.9030\n", + " 0.578396\n", + " 0.578396\n", + " 0.903000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.543162\n", " \n", " \n", " coef_escorting_tour\n", - " 1.353509\n", - " 2.4910\n", + " 1.842119\n", + " 1.842119\n", + " 2.491000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.353509\n", " \n", " \n", " coef_maintenance_tour\n", - " 0.416369\n", - " 1.0220\n", + " 0.659848\n", + " 0.659848\n", + " 1.022000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.416369\n", " \n", " \n", " coef_presence_of_full_time_worker_and_escorting_tour\n", - " -0.613492\n", - " -0.8930\n", + " -0.928522\n", + " -0.928522\n", + " -0.893000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.613492\n", " \n", " \n", " coef_presence_of_non_worker_and_discretionary_tour\n", - " 0.013879\n", - " 0.7910\n", + " 0.693868\n", + " 0.693868\n", + " 0.791000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.013879\n", " \n", " \n", " coef_presence_of_non_worker_and_eating_out_tour\n", - " 1.115052\n", - " 1.1570\n", + " 1.549677\n", + " 1.549677\n", + " 1.157000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.115052\n", " \n", " \n", " coef_presence_of_non_worker_and_escorting_tour\n", - " 0.890993\n", - " 0.8900\n", + " 0.960167\n", + " 0.960167\n", + " 0.890000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.890993\n", " \n", " \n", " coef_presence_of_non_worker_and_shopping_tour\n", - " 0.231053\n", - " 0.8080\n", + " 0.669579\n", + " 0.669579\n", + " 0.808000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.231053\n", " \n", " \n", " coef_presence_of_part_time_worker_and_eating_out_tour\n", - " 1.392675\n", - " 1.0370\n", + " 1.135686\n", + " 1.135686\n", + " 1.037000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.392675\n", " \n", " \n", " coef_presence_of_part_time_worker_and_shopping_tour\n", - " 1.044044\n", - " 1.1550\n", + " 1.013348\n", + " 1.013348\n", + " 1.155000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.044044\n", " \n", " \n", " coef_total_number_of_tours_is_1\n", - " -4.436747\n", - " -5.7590\n", + " -4.911268\n", + " -4.911268\n", + " -5.759000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -4.436747\n", " \n", " \n", " coef_total_number_of_tours_is_2\n", - " -9.039558\n", - " -11.5170\n", + " -9.965345\n", + " -9.965345\n", + " -11.517000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -9.039558\n", " \n", " \n", " coef_total_number_of_tours_is_3\n", - " -12.714429\n", - " -17.2760\n", + " -15.118161\n", + " -15.118161\n", + " -17.275999\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -12.714429\n", " \n", " \n", " coef_total_number_of_tours_is_4\n", - " -29.080351\n", - " -23.0350\n", + " -19.999999\n", + " -19.999999\n", + " -23.035000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -29.080351\n", " \n", " \n", " coef_total_number_of_tours_is_6_plus\n", " -999.000000\n", - " -999.0000\n", - " 0.0\n", + " -999.000000\n", + " -999.000000\n", " -999.0\n", " -999.0\n", + " 0.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_urban_and_discretionary_tour\n", " 0.000000\n", - " 0.0000\n", + " 0.000000\n", + " 0.000000\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_visiting_or_social_tour\n", - " 0.315314\n", - " 0.7690\n", + " 0.385649\n", + " 0.385649\n", + " 0.769000\n", + " -20.0\n", + " 20.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.315314\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_0_auto_household_and_escorting_tour -2.000000 -2.0000 \n", - "coef_1_escort_tour_constant 0.179651 0.3622 \n", - "coef_1_plus_eating_out_tours_constant 0.204827 0.9612 \n", - "coef_1_plus_maintenance_tours_constant 0.073169 0.6788 \n", - "coef_1_plus_other_discretionary_tours_constant 1.133662 1.4935 \n", - "coef_1_plus_shopping_tours_constant 0.785367 1.6919 \n", - "coef_1_plus_visting_tours_constant -0.011286 0.4424 \n", - "coef_2_plus_escort_tours_constant 1.744429 2.2219 \n", - "coef_discretionary_tour 0.543162 0.9030 \n", - "coef_escorting_tour 1.353509 2.4910 \n", - "coef_maintenance_tour 0.416369 1.0220 \n", - "coef_presence_of_full_time_worker_and_escorting... -0.613492 -0.8930 \n", - "coef_presence_of_non_worker_and_discretionary_tour 0.013879 0.7910 \n", - "coef_presence_of_non_worker_and_eating_out_tour 1.115052 1.1570 \n", - "coef_presence_of_non_worker_and_escorting_tour 0.890993 0.8900 \n", - "coef_presence_of_non_worker_and_shopping_tour 0.231053 0.8080 \n", - "coef_presence_of_part_time_worker_and_eating_ou... 1.392675 1.0370 \n", - "coef_presence_of_part_time_worker_and_shopping_... 1.044044 1.1550 \n", - "coef_total_number_of_tours_is_1 -4.436747 -5.7590 \n", - "coef_total_number_of_tours_is_2 -9.039558 -11.5170 \n", - "coef_total_number_of_tours_is_3 -12.714429 -17.2760 \n", - "coef_total_number_of_tours_is_4 -29.080351 -23.0350 \n", - "coef_total_number_of_tours_is_6_plus -999.000000 -999.0000 \n", - "coef_urban_and_discretionary_tour 0.000000 0.0000 \n", - "coef_visiting_or_social_tour 0.315314 0.7690 \n", + " value best \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.000000 \n", + "coef_1_escort_tour_constant 0.274841 0.274841 \n", + "coef_1_plus_eating_out_tours_constant -0.253387 -0.253387 \n", + "coef_1_plus_maintenance_tours_constant 0.316648 0.316648 \n", + "coef_1_plus_other_discretionary_tours_constant 1.168896 1.168896 \n", + "coef_1_plus_shopping_tours_constant 1.139680 1.139680 \n", + "coef_1_plus_visting_tours_constant 0.059049 0.059049 \n", + "coef_2_plus_escort_tours_constant 1.941139 1.941139 \n", + "coef_discretionary_tour 0.578396 0.578396 \n", + "coef_escorting_tour 1.842119 1.842119 \n", + "coef_maintenance_tour 0.659848 0.659848 \n", + "coef_presence_of_full_time_worker_and_escorting... -0.928522 -0.928522 \n", + "coef_presence_of_non_worker_and_discretionary_tour 0.693868 0.693868 \n", + "coef_presence_of_non_worker_and_eating_out_tour 1.549677 1.549677 \n", + "coef_presence_of_non_worker_and_escorting_tour 0.960167 0.960167 \n", + "coef_presence_of_non_worker_and_shopping_tour 0.669579 0.669579 \n", + "coef_presence_of_part_time_worker_and_eating_ou... 1.135686 1.135686 \n", + "coef_presence_of_part_time_worker_and_shopping_... 1.013348 1.013348 \n", + "coef_total_number_of_tours_is_1 -4.911268 -4.911268 \n", + "coef_total_number_of_tours_is_2 -9.965345 -9.965345 \n", + "coef_total_number_of_tours_is_3 -15.118161 -15.118161 \n", + "coef_total_number_of_tours_is_4 -19.999999 -19.999999 \n", + "coef_total_number_of_tours_is_6_plus -999.000000 -999.000000 \n", + "coef_urban_and_discretionary_tour 0.000000 0.000000 \n", + "coef_visiting_or_social_tour 0.385649 0.385649 \n", "\n", - " nullvalue minimum \\\n", - "coef_0_auto_household_and_escorting_tour 0.0 -2.0 \n", - "coef_1_escort_tour_constant 0.0 NaN \n", - "coef_1_plus_eating_out_tours_constant 0.0 NaN \n", - "coef_1_plus_maintenance_tours_constant 0.0 NaN \n", - "coef_1_plus_other_discretionary_tours_constant 0.0 NaN \n", - "coef_1_plus_shopping_tours_constant 0.0 NaN \n", - "coef_1_plus_visting_tours_constant 0.0 NaN \n", - "coef_2_plus_escort_tours_constant 0.0 NaN \n", - "coef_discretionary_tour 0.0 NaN \n", - "coef_escorting_tour 0.0 NaN \n", - "coef_maintenance_tour 0.0 NaN \n", - "coef_presence_of_full_time_worker_and_escorting... 0.0 NaN \n", - "coef_presence_of_non_worker_and_discretionary_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_eating_out_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_escorting_tour 0.0 NaN \n", - "coef_presence_of_non_worker_and_shopping_tour 0.0 NaN \n", - "coef_presence_of_part_time_worker_and_eating_ou... 0.0 NaN \n", - "coef_presence_of_part_time_worker_and_shopping_... 0.0 NaN \n", - "coef_total_number_of_tours_is_1 0.0 NaN \n", - "coef_total_number_of_tours_is_2 0.0 NaN \n", - "coef_total_number_of_tours_is_3 0.0 NaN \n", - "coef_total_number_of_tours_is_4 0.0 NaN \n", - "coef_total_number_of_tours_is_6_plus 0.0 -999.0 \n", - "coef_urban_and_discretionary_tour 0.0 0.0 \n", - "coef_visiting_or_social_tour 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.000000 -2.0 \n", + "coef_1_escort_tour_constant 0.362200 -20.0 \n", + "coef_1_plus_eating_out_tours_constant 0.961200 -20.0 \n", + "coef_1_plus_maintenance_tours_constant 0.678800 -20.0 \n", + "coef_1_plus_other_discretionary_tours_constant 1.493500 -20.0 \n", + "coef_1_plus_shopping_tours_constant 1.691900 -20.0 \n", + "coef_1_plus_visting_tours_constant 0.442400 -20.0 \n", + "coef_2_plus_escort_tours_constant 2.221900 -20.0 \n", + "coef_discretionary_tour 0.903000 -20.0 \n", + "coef_escorting_tour 2.491000 -20.0 \n", + "coef_maintenance_tour 1.022000 -20.0 \n", + "coef_presence_of_full_time_worker_and_escorting... -0.893000 -20.0 \n", + "coef_presence_of_non_worker_and_discretionary_tour 0.791000 -20.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 1.157000 -20.0 \n", + "coef_presence_of_non_worker_and_escorting_tour 0.890000 -20.0 \n", + "coef_presence_of_non_worker_and_shopping_tour 0.808000 -20.0 \n", + "coef_presence_of_part_time_worker_and_eating_ou... 1.037000 -20.0 \n", + "coef_presence_of_part_time_worker_and_shopping_... 1.155000 -20.0 \n", + "coef_total_number_of_tours_is_1 -5.759000 -20.0 \n", + "coef_total_number_of_tours_is_2 -11.517000 -20.0 \n", + "coef_total_number_of_tours_is_3 -17.275999 -20.0 \n", + "coef_total_number_of_tours_is_4 -23.035000 -20.0 \n", + "coef_total_number_of_tours_is_6_plus -999.000000 -999.0 \n", + "coef_urban_and_discretionary_tour 0.000000 0.0 \n", + "coef_visiting_or_social_tour 0.769000 -20.0 \n", "\n", - " maximum holdfast note \\\n", - "coef_0_auto_household_and_escorting_tour -2.0 1 \n", - "coef_1_escort_tour_constant NaN 0 \n", - "coef_1_plus_eating_out_tours_constant NaN 0 \n", - "coef_1_plus_maintenance_tours_constant NaN 0 \n", - "coef_1_plus_other_discretionary_tours_constant NaN 0 \n", - "coef_1_plus_shopping_tours_constant NaN 0 \n", - "coef_1_plus_visting_tours_constant NaN 0 \n", - "coef_2_plus_escort_tours_constant NaN 0 \n", - "coef_discretionary_tour NaN 0 \n", - "coef_escorting_tour NaN 0 \n", - "coef_maintenance_tour NaN 0 \n", - "coef_presence_of_full_time_worker_and_escorting... NaN 0 \n", - "coef_presence_of_non_worker_and_discretionary_tour NaN 0 \n", - "coef_presence_of_non_worker_and_eating_out_tour NaN 0 \n", - "coef_presence_of_non_worker_and_escorting_tour NaN 0 \n", - "coef_presence_of_non_worker_and_shopping_tour NaN 0 \n", - "coef_presence_of_part_time_worker_and_eating_ou... NaN 0 \n", - "coef_presence_of_part_time_worker_and_shopping_... NaN 0 \n", - "coef_total_number_of_tours_is_1 NaN 0 \n", - "coef_total_number_of_tours_is_2 NaN 0 \n", - "coef_total_number_of_tours_is_3 NaN 0 \n", - "coef_total_number_of_tours_is_4 NaN 0 \n", - "coef_total_number_of_tours_is_6_plus -999.0 1 \n", - "coef_urban_and_discretionary_tour 0.0 1 \n", - "coef_visiting_or_social_tour NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour -2.0 0.0 \n", + "coef_1_escort_tour_constant 20.0 0.0 \n", + "coef_1_plus_eating_out_tours_constant 20.0 0.0 \n", + "coef_1_plus_maintenance_tours_constant 20.0 0.0 \n", + "coef_1_plus_other_discretionary_tours_constant 20.0 0.0 \n", + "coef_1_plus_shopping_tours_constant 20.0 0.0 \n", + "coef_1_plus_visting_tours_constant 20.0 0.0 \n", + "coef_2_plus_escort_tours_constant 20.0 0.0 \n", + "coef_discretionary_tour 20.0 0.0 \n", + "coef_escorting_tour 20.0 0.0 \n", + "coef_maintenance_tour 20.0 0.0 \n", + "coef_presence_of_full_time_worker_and_escorting... 20.0 0.0 \n", + "coef_presence_of_non_worker_and_discretionary_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_escorting_tour 20.0 0.0 \n", + "coef_presence_of_non_worker_and_shopping_tour 20.0 0.0 \n", + "coef_presence_of_part_time_worker_and_eating_ou... 20.0 0.0 \n", + "coef_presence_of_part_time_worker_and_shopping_... 20.0 0.0 \n", + "coef_total_number_of_tours_is_1 20.0 0.0 \n", + "coef_total_number_of_tours_is_2 20.0 0.0 \n", + "coef_total_number_of_tours_is_3 20.0 0.0 \n", + "coef_total_number_of_tours_is_4 20.0 0.0 \n", + "coef_total_number_of_tours_is_6_plus -999.0 0.0 \n", + "coef_urban_and_discretionary_tour 0.0 0.0 \n", + "coef_visiting_or_social_tour 20.0 0.0 \n", "\n", - " best \n", - "coef_0_auto_household_and_escorting_tour -2.000000 \n", - "coef_1_escort_tour_constant 0.179651 \n", - "coef_1_plus_eating_out_tours_constant 0.204827 \n", - "coef_1_plus_maintenance_tours_constant 0.073169 \n", - "coef_1_plus_other_discretionary_tours_constant 1.133662 \n", - "coef_1_plus_shopping_tours_constant 0.785367 \n", - "coef_1_plus_visting_tours_constant -0.011286 \n", - "coef_2_plus_escort_tours_constant 1.744429 \n", - "coef_discretionary_tour 0.543162 \n", - "coef_escorting_tour 1.353509 \n", - "coef_maintenance_tour 0.416369 \n", - "coef_presence_of_full_time_worker_and_escorting... -0.613492 \n", - "coef_presence_of_non_worker_and_discretionary_tour 0.013879 \n", - "coef_presence_of_non_worker_and_eating_out_tour 1.115052 \n", - "coef_presence_of_non_worker_and_escorting_tour 0.890993 \n", - "coef_presence_of_non_worker_and_shopping_tour 0.231053 \n", - "coef_presence_of_part_time_worker_and_eating_ou... 1.392675 \n", - "coef_presence_of_part_time_worker_and_shopping_... 1.044044 \n", - "coef_total_number_of_tours_is_1 -4.436747 \n", - "coef_total_number_of_tours_is_2 -9.039558 \n", - "coef_total_number_of_tours_is_3 -12.714429 \n", - "coef_total_number_of_tours_is_4 -29.080351 \n", - "coef_total_number_of_tours_is_6_plus -999.000000 \n", - "coef_urban_and_discretionary_tour 0.000000 \n", - "coef_visiting_or_social_tour 0.315314 " + " holdfast \n", + "param_name \n", + "coef_0_auto_household_and_escorting_tour 1 \n", + "coef_1_escort_tour_constant 0 \n", + "coef_1_plus_eating_out_tours_constant 0 \n", + "coef_1_plus_maintenance_tours_constant 0 \n", + "coef_1_plus_other_discretionary_tours_constant 0 \n", + "coef_1_plus_shopping_tours_constant 0 \n", + "coef_1_plus_visting_tours_constant 0 \n", + "coef_2_plus_escort_tours_constant 0 \n", + "coef_discretionary_tour 0 \n", + "coef_escorting_tour 0 \n", + "coef_maintenance_tour 0 \n", + "coef_presence_of_full_time_worker_and_escorting... 0 \n", + "coef_presence_of_non_worker_and_discretionary_tour 0 \n", + "coef_presence_of_non_worker_and_eating_out_tour 0 \n", + "coef_presence_of_non_worker_and_escorting_tour 0 \n", + "coef_presence_of_non_worker_and_shopping_tour 0 \n", + "coef_presence_of_part_time_worker_and_eating_ou... 0 \n", + "coef_presence_of_part_time_worker_and_shopping_... 0 \n", + "coef_total_number_of_tours_is_1 0 \n", + "coef_total_number_of_tours_is_2 0 \n", + "coef_total_number_of_tours_is_3 0 \n", + "coef_total_number_of_tours_is_4 0 \n", + "coef_total_number_of_tours_is_6_plus 1 \n", + "coef_urban_and_discretionary_tour 1 \n", + "coef_visiting_or_social_tour 0 " ] }, "metadata": {}, @@ -5087,873 +10840,307 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " m.estimate(method='SLSQP')\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/linalg/__init__.py:18: UserWarning: minimum eig 2.8528068249273466e-08 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: PossibleOverspecification: WARNING: Model seems to have 14 parameter estimators with negative variance\n", - "- coef_1_escort_tour_constant\n", - "- coef_1_plus_eating_out_tours_constant\n", - "- coef_1_plus_maintenance_tours_constant\n", - "- coef_1_plus_shopping_tours_constant\n", - "- and 10 more\n", - " m.estimate(method='SLSQP')\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_48972/89088409.py:2: RuntimeWarning: invalid value encountered in sqrt\n", - " m.estimate(method='SLSQP')\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] - } - ], - "source": [ - "for k, m in model.items():\n", - " m.estimate(method='SLSQP')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Estimated coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ "\n", - "\n", + "
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 ValueStd Errt StatSignifLike RatioNull ValueConstrainedValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_0_auto_household_and_escorting_tour-2.00 NA NA NA 0.00fixed value
coef_1_escort_tour_constant 0.319 NA NA[*] 2.36 0.00
coef_1_plus_eating_out_tours_constant-1.01 124.-0.01 NA 0.00
coef_1_plus_maintenance_tours_constant-2.84 124.-0.02 NA 0.00
coef_1_plus_other_discretionary_tours_constant 10.5 123. 0.09 NA 0.00
coef_1_plus_shopping_tours_constant 7.18 124. 0.06 NA 0.00
coef_1_plus_visting_tours_constant-0.321 124.-0.00 NA 0.00
coef_2_plus_escort_tours_constant 0.607 NA NA[] 0.97 0.00
coef_at_home_pre_driving_school_kid_and_escorting_tour-0.926 1.05-0.89 NA 0.00
coef_at_home_pre_school_kid_and_discretionary_tour-0.656 0.743-0.88 NA 0.00
coef_at_home_pre_school_kid_and_escorting_tour-0.793 0.821-0.97 NA 0.00
coef_auto_access_to_retail_and_discretionary-1.05 0.957-1.10 NA 0.00
coef_auto_access_to_retail_and_maintenance 0.279 0.886 0.31 NA 0.00
coef_auto_access_to_retail_and_shopping-0.631 0.855-0.74 NA 0.00
coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus 0.168 0.233 0.72 NA 0.00
coef_female_and_escorting_tour 0.601 0.266 2.26* NA 0.00
coef_female_and_tour_frequency_is_1-0.316 0.137-2.31* NA 0.00
coef_female_and_tour_frequency_is_2-0.583 0.277-2.11* NA 0.00
coef_female_and_tour_frequency_is_5 0.0227 0.671 0.03 NA 0.00
coef_high_income_group_and_discretionary_tour-0.0604 0.241-0.25 NA 0.00
coef_high_income_group_and_eating_out_tour 0.726 0.282 2.58** NA 0.00
coef_high_income_group_and_tour_frequency_is_1 1.14 0.408 2.79** NA 0.00
coef_high_income_group_and_tour_frequency_is_2 2.22 0.869 2.55* NA 0.00
coef_high_income_group_and_tour_frequency_is_5_plus 0.105 1.42 0.07 NA 0.00
coef_high_income_group_and_visiting_tour-0.863 0.350-2.46* NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_1 1.34 0.232 5.76*** NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_2 1.43 0.289 4.94*** NA 0.00
coef_logged_maximum_residual_window_tour_frequency_is_5_plus 0.640 1.09 0.59 NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_1 1.09 0.394 2.77** NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_2 1.81 0.850 2.13* NA 0.00
coef_mediumhigh_income_group_and_tour_frequency_is_5_plus 1.04 1.25 0.83 NA 0.00
coef_number_of_joint_eating_out_tours-9.98 147.-0.07 NA 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_2-1.43 0.489-2.93** NA 0.00
coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus-1.64 2.28-0.72 NA 0.00
coef_presence_of_driving_school_kid_and_discretionary_tour 0.384 0.452 0.85 NA 0.00
coef_presence_of_driving_school_kid_and_escorting_tour-0.211 0.529-0.40 NA 0.00
coef_presence_of_full_time_worker_and_discretionary_tour-0.783 0.222-3.53*** NA 0.00
coef_presence_of_full_time_worker_and_eating_out_tour-0.748 0.221-3.38*** NA 0.00
coef_presence_of_full_time_worker_and_maintenance_tour-0.342 0.271-1.27 NA 0.00
coef_presence_of_full_time_worker_and_shopping_tour-0.631 0.190-3.32*** NA 0.00
coef_presence_of_non_worker_and_discretionary_tour-1.27 0.384-3.30*** NA 0.00
coef_presence_of_non_worker_and_eating_out_tour-0.445 0.327-1.36 NA 0.00
coef_presence_of_non_worker_and_escorting_tour-0.854 0.404-2.11* NA 0.00
coef_presence_of_non_worker_and_maintenance_tour-0.209 0.364-0.58 NA 0.00
coef_presence_of_non_worker_and_shopping_tour-0.844 0.301-2.80** NA 0.00
coef_presence_of_part_time_worker_and_discretionary_tour-0.0412 0.265-0.16 NA 0.00
coef_presence_of_part_time_worker_and_maintenance_tour-0.183 0.328-0.56 NA 0.00
coef_presence_of_part_time_worker_and_shopping_tour-0.149 0.224-0.66 NA 0.00
coef_presence_of_pre_driving_school_kid_and_discretionary_tour-0.126 0.314-0.40 NA 0.00
coef_presence_of_pre_driving_school_kid_and_escorting_tour 1.40 0.268 5.23*** NA 0.00
coef_presence_of_pre_school_kid_and_discretionary_tour-0.0171 0.349-0.05 NA 0.00
coef_presence_of_pre_school_kid_and_eating_out_tour-0.845 0.421-2.01* NA 0.00
coef_presence_of_pre_school_kid_and_escorting_tour 0.748 0.309 2.42* NA 0.00
coef_presence_of_pre_school_kid_and_shopping_tour-0.00614 0.294-0.02 NA 0.00
coef_presence_of_retiree_and_discretionary_tour-0.597 0.461-1.29 NA 0.00
coef_presence_of_retiree_and_eating_out_tour-1.29 0.618-2.08* NA 0.00
coef_presence_of_retiree_and_escorting_tour-12.9 250.-0.05 NA 0.00
coef_presence_of_university_student_and_discretionary_tour-0.407 0.376-1.08 NA 0.00
coef_total_number_of_tours_is_1-7.52 124.-0.06 NA 0.00
coef_total_number_of_tours_is_2-10.1 247.-0.04 NA 0.00
coef_total_number_of_tours_is_3-11.1 371.-0.03 NA 0.00
coef_total_number_of_tours_is_4-12.8 494.-0.03 NA 0.00
coef_total_number_of_tours_is_5-21.8 623.-0.03 NA 0.00
coef_total_number_of_tours_is_6_plus-999. NA NA NA 0.00fixed value
coef_transit_access_to_retail_and_tour_frequency_is_5_plus 0.0745 0.114 0.65 NA 0.00
coef_urban_and_discretionary_tour 0.00 NA NA NA 0.00fixed value
coef_urban_and_escorting_tour-0.409 NA NA[***] 6.54 0.00
coef_walk_access_to_retail_and_discretionary 0.161 0.169 0.95 NA 0.00
coef_walk_access_to_retail_and_eating_out 0.211 0.128 1.65 NA 0.00
coef_walk_access_to_retail_and_escorting-0.105 0.147-0.72 NA 0.00
coef_walk_access_to_retail_and_shopping 0.0302 0.146 0.21 NA 0.00
coef_zero_car_ownership_and_tour_frequency_is_5_plus-0.227 0.175-1.30 NA 0.00coef_0_auto_household_and_escorting_tour-2.00 0.00 NA 0.00fixed value
coef_1_escort_tour_constant 0.275 211. 0.00 0.00
coef_1_plus_eating_out_tours_constant-0.253 0.215-1.18 0.00
coef_1_plus_maintenance_tours_constant 0.317 9.89e+13 0.00 0.00
coef_1_plus_other_discretionary_tours_constant 1.17 2.63e+06 0.00 0.00
coef_1_plus_shopping_tours_constant 1.14 0.0702 16.23*** 0.00
coef_1_plus_visting_tours_constant 0.0590 NA NA 0.00
coef_2_plus_escort_tours_constant 1.94 421. 0.00 0.00
coef_discretionary_tour 0.578 NA NA 0.00
coef_escorting_tour 1.84 212. 0.01 0.00
coef_maintenance_tour 0.660 9.89e+13 0.00 0.00
coef_presence_of_full_time_worker_and_escorting_tour-0.929 0.105-8.82*** 0.00
coef_presence_of_non_worker_and_discretionary_tour 0.694 0.129 5.36*** 0.00
coef_presence_of_non_worker_and_eating_out_tour 1.55 0.222 6.97*** 0.00
coef_presence_of_non_worker_and_escorting_tour 0.960 0.127 7.56*** 0.00
coef_presence_of_non_worker_and_shopping_tour 0.670 0.151 4.44*** 0.00
coef_presence_of_part_time_worker_and_eating_out_tour 1.14 0.195 5.81*** 0.00
coef_presence_of_part_time_worker_and_shopping_tour 1.01 0.142 7.12*** 0.00
coef_total_number_of_tours_is_1-4.91 NA NA 0.00
coef_total_number_of_tours_is_2-9.97 NA NA 0.00
coef_total_number_of_tours_is_3-15.1 0.239-63.27*** 0.00
coef_total_number_of_tours_is_4-20.0 0.00 NA 0.00coef_total_number_of_tours_is_4 ≥ -20.0
coef_total_number_of_tours_is_6_plus-999. 0.00 NA 0.00fixed value
coef_urban_and_discretionary_tour 0.00 0.00 NA 0.00fixed value
coef_visiting_or_social_tour 0.386 NA NA 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], - "source": [ - "model['PTYPE_FULL'].parameter_summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "TojXWivZsx7M" - }, - "source": [ - "# Output Estimation Results" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], "source": [ "from activitysim.estimation.larch import update_coefficients\n", + "\n", "for k, m in model.items():\n", + " print(f\"Model {k}\")\n", + " m.set_cap(20)\n", + " m.estimate(method='SLSQP', maxiter=900)\n", + "\n", + " display(m.parameter_summary())\n", + "\n", " result_dir = data.edb_directory/k/\"estimated\"\n", " update_coefficients(\n", " m, data.coefficients[k], result_dir,\n", " output_file=f\"{modelname}_{k}_coefficients_revised.csv\",\n", " relabel_coef=data.relabel_coef.get(k),\n", - " );" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write the model estimation report, including coefficient t-statistic and log likelihood" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n", - "/Users/jeffnewman/LocalGit/asim-larch/activitysim-larch/conda-environments/AL-ENV/lib/python3.9/site-packages/larch/util/excel.py:523: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n" - ] - } - ], - "source": [ - "for k, m in model.items():\n", - " result_dir = data.edb_directory/k/\"estimated\"\n", + " )\n", + "\n", " m.to_xlsx(\n", " result_dir/f\"{modelname}_{k}_model_estimation.xlsx\", \n", " data_statistics=False,\n", - " )" + " )\n", + "\n", + " m.release_memory()\n" ] }, { @@ -5967,7 +11154,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -6036,25 +11223,25 @@ " \n", " 205\n", " coef_1_plus_maintenance_tours_constant\n", - " -2.842643\n", + " 0.079776\n", " F\n", " \n", " \n", " 206\n", " coef_1_plus_eating_out_tours_constant\n", - " -1.012856\n", + " 0.047768\n", " F\n", " \n", " \n", " 207\n", " coef_1_plus_visting_tours_constant\n", - " -0.320820\n", + " -0.019022\n", " F\n", " \n", " \n", " 208\n", " coef_1_plus_other_discretionary_tours_constant\n", - " 10.543979\n", + " 0.733799\n", " F\n", " \n", " \n", @@ -6069,23 +11256,23 @@ "" ], "text/plain": [ - " coefficient_name value constrain\n", - "0 coef_escorting_tour 0.000000 T\n", - "1 coef_discretionary_tour 0.000000 T\n", - "2 coef_shopping_tour 0.000000 T\n", - "3 coef_maintenance_tour 0.000000 T\n", - "4 coef_visiting_or_social_tour 0.000000 T\n", - ".. ... ... ...\n", - "205 coef_1_plus_maintenance_tours_constant -2.842643 F\n", - "206 coef_1_plus_eating_out_tours_constant -1.012856 F\n", - "207 coef_1_plus_visting_tours_constant -0.320820 F\n", - "208 coef_1_plus_other_discretionary_tours_constant 10.543979 F\n", - "209 coef_0_auto_household_and_escorting_tour -2.000000 T\n", + " coefficient_name value constrain\n", + "0 coef_escorting_tour 0.000000 T\n", + "1 coef_discretionary_tour 0.000000 T\n", + "2 coef_shopping_tour 0.000000 T\n", + "3 coef_maintenance_tour 0.000000 T\n", + "4 coef_visiting_or_social_tour 0.000000 T\n", + ".. ... ... ...\n", + "205 coef_1_plus_maintenance_tours_constant 0.079776 F\n", + "206 coef_1_plus_eating_out_tours_constant 0.047768 F\n", + "207 coef_1_plus_visting_tours_constant -0.019022 F\n", + "208 coef_1_plus_other_discretionary_tours_constant 0.733799 F\n", + "209 coef_0_auto_household_and_escorting_tour -2.000000 T\n", "\n", "[210 rows x 3 columns]" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -6103,7 +11290,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -6117,7 +11304,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.15" }, "toc": { "base_numbering": 1, @@ -6135,4 +11322,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} \ No newline at end of file +} diff --git a/activitysim/examples/example_estimation/notebooks/16_nonmand_tour_scheduling.ipynb b/activitysim/examples/example_estimation/notebooks/16_nonmand_tour_scheduling.ipynb index 3d7a8214f7..522ff71ca3 100644 --- a/activitysim/examples/example_estimation/notebooks/16_nonmand_tour_scheduling.ipynb +++ b/activitysim/examples/example_estimation/notebooks/16_nonmand_tour_scheduling.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +115,28 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_scheduling/tour_scheduling_nonmandatory_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_scheduling/non_mandatory_tour_scheduling_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_scheduling/non_mandatory_tour_scheduling_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/non_mandatory_tour_scheduling/non_mandatory_tour_scheduling_choosers_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"non_mandatory_tour_scheduling\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -256,38 +319,38 @@ " \n", " \n", " \n", - " 0\n", + " 5\n", " util_subsequent_tour_must_start_after_previous...\n", " Subsequent tour must start after previous tour...\n", " (start < end_previous) & (tour_type_num > 1)\n", " coef_subsequent_tour_must_start_after_previous...\n", " \n", " \n", - " 1\n", + " 6\n", " util_free_flow_round_trip_auto_time_shift_effe...\n", " Free-flow round trip auto time shift effects -...\n", " roundtrip_auto_time_to_work * duration\n", " coef_free_flow_round_trip_auto_time_shift_effe...\n", " \n", " \n", - " 2\n", + " 7\n", " util_shopping_tour_departure_shift_effects\n", " Shopping tour - departure shift effects\n", - " (tour_type == 'shopping') * start\n", + " @(_is_shopping) * df.start\n", " coef_shopping_tour_departure_shift_effects\n", " \n", " \n", - " 3\n", + " 8\n", " util_shopping_tour_duration_shift_effects\n", " Shopping tour - duration shift effects\n", - " (tour_type == 'shopping') * duration\n", + " @(_is_shopping) * df.duration\n", " coef_shopping_tour_duration_shift_effects\n", " \n", " \n", - " 4\n", + " 9\n", " util_maintenance_tour_departure_shift_effects\n", " Maintenance tour - departure shift effects\n", - " (tour_type == 'othmaint') * start\n", + " @(_is_othmaint) * df.start\n", " coef_maintenance_tour_departure_shift_effects\n", " \n", " \n", @@ -298,38 +361,38 @@ " ...\n", " \n", " \n", - " 86\n", + " 91\n", " util_escort_tour_duration_constants_4_to_5_hours\n", " Escort Tour Duration Constants -- 4 to 5 hours\n", - " (tour_type == 'escort') & (duration > 3) & (du...\n", + " @(_is_escort) * (df.duration > 3) * (df.durati...\n", " coef_escort_tour_duration_constants_4_to_5_hours\n", " \n", " \n", - " 87\n", + " 92\n", " util_escort_tour_duration_constants_6_to_7_hours\n", " Escort Tour Duration Constants -- 6 to 7 hours\n", - " (tour_type == 'escort') & (duration > 5) & (du...\n", + " @(_is_escort) * (df.duration > 5) * (df.durati...\n", " coef_escort_tour_duration_constants_6_to_7_hours\n", " \n", " \n", - " 88\n", + " 93\n", " util_escort_tour_duration_constants_8_to_10_hours\n", " Escort Tour Duration Constants -- 8 to 10 hours\n", - " (tour_type == 'escort') & (duration > 7) & (du...\n", + " @(_is_escort) * (df.duration > 7) * (df.durati...\n", " coef_escort_tour_duration_constants_8_to_10_hours\n", " \n", " \n", - " 89\n", + " 94\n", " util_escort_tour_duration_constants_11_to_13_h...\n", " Escort Tour Duration Constants -- 11 to 13 hours\n", - " (tour_type == 'escort') & (duration > 10) & (d...\n", + " @(_is_escort) * (df.duration > 10) * (df.durat...\n", " coef_escort_tour_duration_constants_11_to_13_h...\n", " \n", " \n", - " 90\n", + " 95\n", " util_escort_tour_duration_constants_14_to_18_h...\n", " Escort Tour Duration Constants -- 14 to 18 hours\n", - " (tour_type == 'escort') & (duration > 13) & (d...\n", + " @(_is_escort) * (df.duration > 13) * (df.durat...\n", " coef_escort_tour_duration_constants_14_to_18_h...\n", " \n", " \n", @@ -339,56 +402,56 @@ ], "text/plain": [ " Label \\\n", - "0 util_subsequent_tour_must_start_after_previous... \n", - "1 util_free_flow_round_trip_auto_time_shift_effe... \n", - "2 util_shopping_tour_departure_shift_effects \n", - "3 util_shopping_tour_duration_shift_effects \n", - "4 util_maintenance_tour_departure_shift_effects \n", + "5 util_subsequent_tour_must_start_after_previous... \n", + "6 util_free_flow_round_trip_auto_time_shift_effe... \n", + "7 util_shopping_tour_departure_shift_effects \n", + "8 util_shopping_tour_duration_shift_effects \n", + "9 util_maintenance_tour_departure_shift_effects \n", ".. ... \n", - "86 util_escort_tour_duration_constants_4_to_5_hours \n", - "87 util_escort_tour_duration_constants_6_to_7_hours \n", - "88 util_escort_tour_duration_constants_8_to_10_hours \n", - "89 util_escort_tour_duration_constants_11_to_13_h... \n", - "90 util_escort_tour_duration_constants_14_to_18_h... \n", + "91 util_escort_tour_duration_constants_4_to_5_hours \n", + "92 util_escort_tour_duration_constants_6_to_7_hours \n", + "93 util_escort_tour_duration_constants_8_to_10_hours \n", + "94 util_escort_tour_duration_constants_11_to_13_h... \n", + "95 util_escort_tour_duration_constants_14_to_18_h... \n", "\n", " Description \\\n", - "0 Subsequent tour must start after previous tour... \n", - "1 Free-flow round trip auto time shift effects -... \n", - "2 Shopping tour - departure shift effects \n", - "3 Shopping tour - duration shift effects \n", - "4 Maintenance tour - departure shift effects \n", + "5 Subsequent tour must start after previous tour... \n", + "6 Free-flow round trip auto time shift effects -... \n", + "7 Shopping tour - departure shift effects \n", + "8 Shopping tour - duration shift effects \n", + "9 Maintenance tour - departure shift effects \n", ".. ... \n", - "86 Escort Tour Duration Constants -- 4 to 5 hours \n", - "87 Escort Tour Duration Constants -- 6 to 7 hours \n", - "88 Escort Tour Duration Constants -- 8 to 10 hours \n", - "89 Escort Tour Duration Constants -- 11 to 13 hours \n", - "90 Escort Tour Duration Constants -- 14 to 18 hours \n", + "91 Escort Tour Duration Constants -- 4 to 5 hours \n", + "92 Escort Tour Duration Constants -- 6 to 7 hours \n", + "93 Escort Tour Duration Constants -- 8 to 10 hours \n", + "94 Escort Tour Duration Constants -- 11 to 13 hours \n", + "95 Escort Tour Duration Constants -- 14 to 18 hours \n", "\n", " Expression \\\n", - "0 (start < end_previous) & (tour_type_num > 1) \n", - "1 roundtrip_auto_time_to_work * duration \n", - "2 (tour_type == 'shopping') * start \n", - "3 (tour_type == 'shopping') * duration \n", - "4 (tour_type == 'othmaint') * start \n", + "5 (start < end_previous) & (tour_type_num > 1) \n", + "6 roundtrip_auto_time_to_work * duration \n", + "7 @(_is_shopping) * df.start \n", + "8 @(_is_shopping) * df.duration \n", + "9 @(_is_othmaint) * df.start \n", ".. ... \n", - "86 (tour_type == 'escort') & (duration > 3) & (du... \n", - "87 (tour_type == 'escort') & (duration > 5) & (du... \n", - "88 (tour_type == 'escort') & (duration > 7) & (du... \n", - "89 (tour_type == 'escort') & (duration > 10) & (d... \n", - "90 (tour_type == 'escort') & (duration > 13) & (d... \n", + "91 @(_is_escort) * (df.duration > 3) * (df.durati... \n", + "92 @(_is_escort) * (df.duration > 5) * (df.durati... \n", + "93 @(_is_escort) * (df.duration > 7) * (df.durati... \n", + "94 @(_is_escort) * (df.duration > 10) * (df.durat... \n", + "95 @(_is_escort) * (df.duration > 13) * (df.durat... \n", "\n", " Coefficient \n", - "0 coef_subsequent_tour_must_start_after_previous... \n", - "1 coef_free_flow_round_trip_auto_time_shift_effe... \n", - "2 coef_shopping_tour_departure_shift_effects \n", - "3 coef_shopping_tour_duration_shift_effects \n", - "4 coef_maintenance_tour_departure_shift_effects \n", + "5 coef_subsequent_tour_must_start_after_previous... \n", + "6 coef_free_flow_round_trip_auto_time_shift_effe... \n", + "7 coef_shopping_tour_departure_shift_effects \n", + "8 coef_shopping_tour_duration_shift_effects \n", + "9 coef_maintenance_tour_departure_shift_effects \n", ".. ... \n", - "86 coef_escort_tour_duration_constants_4_to_5_hours \n", - "87 coef_escort_tour_duration_constants_6_to_7_hours \n", - "88 coef_escort_tour_duration_constants_8_to_10_hours \n", - "89 coef_escort_tour_duration_constants_11_to_13_h... \n", - "90 coef_escort_tour_duration_constants_14_to_18_h... \n", + "91 coef_escort_tour_duration_constants_4_to_5_hours \n", + "92 coef_escort_tour_duration_constants_6_to_7_hours \n", + "93 coef_escort_tour_duration_constants_8_to_10_hours \n", + "94 coef_escort_tour_duration_constants_11_to_13_h... \n", + "95 coef_escort_tour_duration_constants_14_to_18_h... \n", "\n", "[89 rows x 4 columns]" ] @@ -461,11 +524,11 @@ " \n", " \n", " 0\n", - " 6812\n", - " 118\n", - " 118\n", - " 166\n", - " eatout\n", + " 1870\n", + " 73\n", + " 137\n", + " 45\n", + " othdiscr\n", " 1\n", " 1\n", " 1\n", @@ -485,11 +548,11 @@ " \n", " \n", " 1\n", - " 8110\n", - " 112\n", - " 112\n", - " 197\n", - " shopping\n", + " 20468\n", + " 99\n", + " 124\n", + " 499\n", + " escort\n", " 1\n", " 1\n", " 1\n", @@ -501,23 +564,23 @@ " 0\n", " 0.0\n", " 0\n", - " 0\n", " 1\n", + " 0\n", " True\n", " 5\n", " 5\n", " \n", " \n", " 2\n", - " 11013\n", - " 169\n", + " 27055\n", + " 146\n", " 169\n", - " 268\n", - " othdiscr\n", + " 659\n", + " social\n", + " 1\n", + " 1\n", " 1\n", " 1\n", - " 2\n", - " 2\n", " non_mandatory\n", " ...\n", " 0\n", @@ -526,18 +589,18 @@ " 0.0\n", " 0\n", " 0\n", - " 2\n", + " 1\n", " True\n", - " 12\n", - " 15\n", + " 5\n", + " 5\n", " \n", " \n", " 3\n", - " 11016\n", - " 115\n", - " 115\n", - " 268\n", - " othmaint\n", + " 38877\n", + " 37\n", + " 125\n", + " 948\n", + " escort\n", " 1\n", " 1\n", " 1\n", @@ -549,23 +612,23 @@ " 0\n", " 0.0\n", " 0\n", - " 0\n", - " 2\n", + " 1\n", + " 1\n", " True\n", " 5\n", " 5\n", " \n", " \n", " 4\n", - " 15403\n", - " 99\n", - " 99\n", - " 375\n", - " othmaint\n", - " 1\n", - " 1\n", + " 38904\n", + " 95\n", + " 172\n", + " 948\n", + " social\n", " 1\n", " 1\n", + " 2\n", + " 2\n", " non_mandatory\n", " ...\n", " 0\n", @@ -573,11 +636,11 @@ " 0\n", " 0.0\n", " 0\n", - " 0\n", + " 1\n", " 1\n", " True\n", - " 5\n", - " 5\n", + " 7\n", + " 7\n", " \n", " \n", " ...\n", @@ -604,16 +667,16 @@ " ...\n", " \n", " \n", - " 2485\n", - " 309760814\n", - " 71\n", - " 71\n", - " 7555141\n", + " 29664\n", + " 310197956\n", + " 125\n", + " 127\n", + " 7565803\n", " shopping\n", - " 2\n", " 1\n", " 1\n", - " 3\n", + " 1\n", + " 1\n", " non_mandatory\n", " ...\n", " 0\n", @@ -622,22 +685,22 @@ " 0.0\n", " 0\n", " 0\n", - " 3\n", + " 1\n", " True\n", " 5\n", " 5\n", " \n", " \n", - " 2486\n", - " 309760815\n", - " 137\n", - " 137\n", - " 7555141\n", - " shopping\n", - " 2\n", + " 29665\n", + " 310202376\n", + " 126\n", + " 5\n", + " 7565911\n", + " othdiscr\n", + " 1\n", + " 1\n", " 2\n", " 2\n", - " 3\n", " non_mandatory\n", " ...\n", " 0\n", @@ -646,22 +709,22 @@ " 0.0\n", " 0\n", " 0\n", - " 3\n", + " 2\n", " True\n", - " 9\n", - " 10\n", + " 15\n", + " 19\n", " \n", " \n", - " 2487\n", - " 309790009\n", - " 109\n", - " 109\n", - " 7555853\n", - " social\n", - " 1\n", + " 29666\n", + " 310202384\n", + " 149\n", + " 129\n", + " 7565911\n", + " shopping\n", " 1\n", " 1\n", " 1\n", + " 2\n", " non_mandatory\n", " ...\n", " 0\n", @@ -670,22 +733,22 @@ " 0.0\n", " 0\n", " 0\n", - " 1\n", + " 2\n", " True\n", " 5\n", " 5\n", " \n", " \n", - " 2488\n", - " 309796968\n", - " 146\n", + " 29667\n", + " 310212634\n", + " 86\n", " 146\n", - " 7556023\n", - " othdiscr\n", - " 2\n", + " 7566161\n", + " shopping\n", + " 1\n", + " 1\n", " 1\n", " 1\n", - " 2\n", " non_mandatory\n", " ...\n", " 0\n", @@ -694,22 +757,22 @@ " 0.0\n", " 0\n", " 0\n", - " 2\n", + " 1\n", " True\n", " 5\n", " 5\n", " \n", " \n", - " 2489\n", - " 309796969\n", - " 180\n", - " 180\n", - " 7556023\n", - " othdiscr\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", + " 29668\n", + " 310220296\n", + " 74\n", + " 126\n", + " 7566348\n", + " othmaint\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", " non_mandatory\n", " ...\n", " 0\n", @@ -718,83 +781,83 @@ " 0.0\n", " 0\n", " 0\n", - " 2\n", + " 1\n", " True\n", - " 15\n", - " 16\n", + " 5\n", + " 5\n", " \n", " \n", "\n", - "

2490 rows × 31 columns

\n", + "

29669 rows × 31 columns

\n", "" ], "text/plain": [ - " tour_id model_choice override_choice person_id tour_type \\\n", - "0 6812 118 118 166 eatout \n", - "1 8110 112 112 197 shopping \n", - "2 11013 169 169 268 othdiscr \n", - "3 11016 115 115 268 othmaint \n", - "4 15403 99 99 375 othmaint \n", - "... ... ... ... ... ... \n", - "2485 309760814 71 71 7555141 shopping \n", - "2486 309760815 137 137 7555141 shopping \n", - "2487 309790009 109 109 7555853 social \n", - "2488 309796968 146 146 7556023 othdiscr \n", - "2489 309796969 180 180 7556023 othdiscr \n", + " tour_id model_choice override_choice person_id tour_type \\\n", + "0 1870 73 137 45 othdiscr \n", + "1 20468 99 124 499 escort \n", + "2 27055 146 169 659 social \n", + "3 38877 37 125 948 escort \n", + "4 38904 95 172 948 social \n", + "... ... ... ... ... ... \n", + "29664 310197956 125 127 7565803 shopping \n", + "29665 310202376 126 5 7565911 othdiscr \n", + "29666 310202384 149 129 7565911 shopping \n", + "29667 310212634 86 146 7566161 shopping \n", + "29668 310220296 74 126 7566348 othmaint \n", "\n", - " tour_type_count tour_type_num tour_num tour_count tour_category \\\n", - "0 1 1 1 1 non_mandatory \n", - "1 1 1 1 1 non_mandatory \n", - "2 1 1 2 2 non_mandatory \n", - "3 1 1 1 2 non_mandatory \n", - "4 1 1 1 1 non_mandatory \n", - "... ... ... ... ... ... \n", - "2485 2 1 1 3 non_mandatory \n", - "2486 2 2 2 3 non_mandatory \n", - "2487 1 1 1 1 non_mandatory \n", - "2488 2 1 1 2 non_mandatory \n", - "2489 2 2 2 2 non_mandatory \n", + " tour_type_count tour_type_num tour_num tour_count tour_category \\\n", + "0 1 1 1 1 non_mandatory \n", + "1 1 1 1 1 non_mandatory \n", + "2 1 1 1 1 non_mandatory \n", + "3 1 1 1 2 non_mandatory \n", + "4 1 1 2 2 non_mandatory \n", + "... ... ... ... ... ... \n", + "29664 1 1 1 1 non_mandatory \n", + "29665 1 1 2 2 non_mandatory \n", + "29666 1 1 1 2 non_mandatory \n", + "29667 1 1 1 1 non_mandatory \n", + "29668 1 1 1 1 non_mandatory \n", "\n", - " ... num_person_joint_tours ptype num_children \\\n", - "0 ... 0 4 0 \n", - "1 ... 0 4 0 \n", - "2 ... 0 4 0 \n", - "3 ... 0 4 0 \n", - "4 ... 0 4 0 \n", - "... ... ... ... ... \n", - "2485 ... 0 5 0 \n", - "2486 ... 0 5 0 \n", - "2487 ... 0 5 0 \n", - "2488 ... 0 5 0 \n", - "2489 ... 0 5 0 \n", + " ... num_person_joint_tours ptype num_children \\\n", + "0 ... 0 4 0 \n", + "1 ... 0 4 0 \n", + "2 ... 0 4 0 \n", + "3 ... 0 4 0 \n", + "4 ... 0 4 0 \n", + "... ... ... ... ... \n", + "29664 ... 0 5 0 \n", + "29665 ... 0 5 0 \n", + "29666 ... 0 5 0 \n", + "29667 ... 0 5 0 \n", + "29668 ... 0 5 0 \n", "\n", - " roundtrip_auto_time_to_work num_mand num_escort_tours \\\n", - "0 0.0 0 0 \n", - "1 0.0 0 0 \n", - "2 0.0 0 0 \n", - "3 0.0 0 0 \n", - "4 0.0 0 0 \n", - "... ... ... ... \n", - "2485 0.0 0 0 \n", - "2486 0.0 0 0 \n", - "2487 0.0 0 0 \n", - "2488 0.0 0 0 \n", - "2489 0.0 0 0 \n", + " roundtrip_auto_time_to_work num_mand num_escort_tours \\\n", + "0 0.0 0 0 \n", + "1 0.0 0 1 \n", + "2 0.0 0 0 \n", + "3 0.0 0 1 \n", + "4 0.0 0 1 \n", + "... ... ... ... \n", + "29664 0.0 0 0 \n", + "29665 0.0 0 0 \n", + "29666 0.0 0 0 \n", + "29667 0.0 0 0 \n", + "29668 0.0 0 0 \n", "\n", - " num_non_escort_tours adult start_previous end_previous \n", - "0 1 True 5 5 \n", - "1 1 True 5 5 \n", - "2 2 True 12 15 \n", - "3 2 True 5 5 \n", - "4 1 True 5 5 \n", - "... ... ... ... ... \n", - "2485 3 True 5 5 \n", - "2486 3 True 9 10 \n", - "2487 1 True 5 5 \n", - "2488 2 True 5 5 \n", - "2489 2 True 15 16 \n", + " num_non_escort_tours adult start_previous end_previous \n", + "0 1 True 5 5 \n", + "1 0 True 5 5 \n", + "2 1 True 5 5 \n", + "3 1 True 5 5 \n", + "4 1 True 7 7 \n", + "... ... ... ... ... \n", + "29664 1 True 5 5 \n", + "29665 2 True 15 19 \n", + "29666 2 True 5 5 \n", + "29667 1 True 5 5 \n", + "29668 1 True 5 5 \n", "\n", - "[2490 rows x 31 columns]" + "[29669 rows x 31 columns]" ] }, "execution_count": 6, @@ -840,129 +903,114 @@ " \n", " \n", " tour_id\n", - " variable\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", + " start\n", + " end\n", + " duration\n", + " tdd\n", + " mode_choice_logsum\n", + " util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends\n", + " util_free_flow_round_trip_auto_time_shift_effects_duration\n", + " util_shopping_tour_departure_shift_effects\n", + " util_shopping_tour_duration_shift_effects\n", " ...\n", - " 180\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", + " util_escort_tour_arrival_constants_pm_peak_4\n", + " util_escort_tour_arrival_constants_evening\n", + " util_escort_tour_arrival_constants_late\n", + " util_escort_tour_duration_constants_0_to_1_hours\n", + " util_escort_tour_duration_constants_2_to_3_hours\n", + " util_escort_tour_duration_constants_4_to_5_hours\n", + " util_escort_tour_duration_constants_6_to_7_hours\n", + " util_escort_tour_duration_constants_8_to_10_hours\n", + " util_escort_tour_duration_constants_11_to_13_hours\n", + " util_escort_tour_duration_constants_14_to_18_hours\n", " \n", " \n", " \n", " \n", " 0\n", - " 6812\n", - " duration\n", + " 1870\n", + " 5\n", + " 5\n", + " 0\n", + " 0\n", + " 0\n", + " False\n", " 0.0\n", - " 1.0\n", - " 2.0\n", - " 3.0\n", - " 4.0\n", - " 5.0\n", - " 6.0\n", - " 7.0\n", + " 0\n", + " 0\n", " ...\n", - " 0.0\n", - " 1.0\n", - " 2.0\n", - " 3.0\n", - " 0.0\n", - " 1.0\n", - " 2.0\n", - " 0.0\n", - " 1.0\n", - " 0.0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 1\n", - " 6812\n", - " end\n", - " 5.0\n", - " 6.0\n", - " 7.0\n", - " 8.0\n", - " 9.0\n", - " 10.0\n", - " 11.0\n", - " 12.0\n", - " ...\n", - " 20.0\n", - " 21.0\n", - " 22.0\n", - " 23.0\n", - " 21.0\n", - " 22.0\n", - " 23.0\n", - " 22.0\n", - " 23.0\n", - " 23.0\n", + " 1870\n", + " 5\n", + " 6\n", + " 1\n", + " 1\n", + " 0\n", + " False\n", + " 0.0\n", + " 0\n", + " 0\n", + " ...\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 2\n", - " 6812\n", - " mode_choice_logsum\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 1870\n", + " 5\n", + " 7\n", + " 2\n", + " 2\n", + " 0\n", + " False\n", " 0.0\n", + " 0\n", + " 0\n", " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 3\n", - " 6812\n", - " start\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " 5.0\n", - " ...\n", - " 20.0\n", - " 20.0\n", - " 20.0\n", - " 20.0\n", - " 21.0\n", - " 21.0\n", - " 21.0\n", - " 22.0\n", - " 22.0\n", - " 23.0\n", - " \n", - " \n", - " 4\n", - " 6812\n", - " util_adjacent_window_exists_after_this_arrival...\n", + " 1870\n", + " 5\n", + " 8\n", + " 3\n", + " 3\n", + " 0\n", + " False\n", + " 0.0\n", + " 0\n", + " 0\n", + " ...\n", " False\n", " False\n", " False\n", @@ -971,6 +1019,21 @@ " False\n", " False\n", " False\n", + " False\n", + " False\n", + " \n", + " \n", + " 4\n", + " 1870\n", + " 5\n", + " 9\n", + " 4\n", + " 4\n", + " 0\n", + " False\n", + " 0.0\n", + " 0\n", + " 0\n", " ...\n", " False\n", " False\n", @@ -1008,41 +1071,17 @@ " ...\n", " \n", " \n", - " 231565\n", - " 309796969\n", - " util_subsequent_of_2_plus_tours_for_same_purpo...\n", - " 0\n", + " 3632342\n", + " 310220296\n", + " 21\n", + " 22\n", " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " ...\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", + " 185\n", " 0\n", - " 1\n", - " 2\n", + " False\n", + " 0.0\n", " 0\n", - " 1\n", " 0\n", - " \n", - " \n", - " 231566\n", - " 309796969\n", - " util_subsequent_tour_must_start_after_previous...\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", - " True\n", " ...\n", " False\n", " False\n", @@ -1056,9 +1095,21 @@ " False\n", " \n", " \n", - " 231567\n", - " 309796969\n", - " util_university_student_arrive_after_22\n", + " 3632343\n", + " 310220296\n", + " 21\n", + " 23\n", + " 2\n", + " 186\n", + " 0\n", + " False\n", + " 0.0\n", + " 0\n", + " 0\n", + " ...\n", + " False\n", + " False\n", + " False\n", " False\n", " False\n", " False\n", @@ -1066,7 +1117,19 @@ " False\n", " False\n", " False\n", + " \n", + " \n", + " 3632344\n", + " 310220296\n", + " 22\n", + " 22\n", + " 0\n", + " 187\n", + " 0\n", " False\n", + " 0.0\n", + " 0\n", + " 0\n", " ...\n", " False\n", " False\n", @@ -1080,99 +1143,255 @@ " False\n", " \n", " \n", - " 231568\n", - " 309796969\n", - " util_visit_tour_departure_shift_effects_start\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 3632345\n", + " 310220296\n", + " 22\n", + " 23\n", + " 1\n", + " 188\n", " 0\n", + " False\n", + " 0.0\n", " 0\n", " 0\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 231569\n", - " 309796969\n", - " util_visit_tour_duration_shift_effects_duration\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 3632346\n", + " 310220296\n", + " 23\n", + " 23\n", " 0\n", + " 189\n", " 0\n", + " False\n", + " 0.0\n", " 0\n", " 0\n", " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", "\n", - "

231570 rows × 192 columns

\n", + "

3632347 rows × 95 columns

\n", "" ], "text/plain": [ - " tour_id variable 0 \\\n", - "0 6812 duration 0.0 \n", - "1 6812 end 5.0 \n", - "2 6812 mode_choice_logsum 0.0 \n", - "3 6812 start 5.0 \n", - "4 6812 util_adjacent_window_exists_after_this_arrival... False \n", - "... ... ... ... \n", - "231565 309796969 util_subsequent_of_2_plus_tours_for_same_purpo... 0 \n", - "231566 309796969 util_subsequent_tour_must_start_after_previous... True \n", - "231567 309796969 util_university_student_arrive_after_22 False \n", - "231568 309796969 util_visit_tour_departure_shift_effects_start 0 \n", - "231569 309796969 util_visit_tour_duration_shift_effects_duration 0 \n", + " tour_id start end duration tdd mode_choice_logsum \\\n", + "0 1870 5 5 0 0 0 \n", + "1 1870 5 6 1 1 0 \n", + "2 1870 5 7 2 2 0 \n", + "3 1870 5 8 3 3 0 \n", + "4 1870 5 9 4 4 0 \n", + "... ... ... ... ... ... ... \n", + "3632342 310220296 21 22 1 185 0 \n", + "3632343 310220296 21 23 2 186 0 \n", + "3632344 310220296 22 22 0 187 0 \n", + "3632345 310220296 22 23 1 188 0 \n", + "3632346 310220296 23 23 0 189 0 \n", + "\n", + " util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "3632342 False \n", + "3632343 False \n", + "3632344 False \n", + "3632345 False \n", + "3632346 False \n", "\n", - " 1 2 3 4 5 6 7 ... 180 181 \\\n", - "0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 ... 0.0 1.0 \n", - "1 6.0 7.0 8.0 9.0 10.0 11.0 12.0 ... 20.0 21.0 \n", - "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 \n", - "3 5.0 5.0 5.0 5.0 5.0 5.0 5.0 ... 20.0 20.0 \n", - "4 False False False False False False False ... 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False False \n", - "231568 0 0 0 0 0 0 0 ... 0 0 \n", - "231569 0 0 0 0 0 0 0 ... 0 0 \n", + " util_free_flow_round_trip_auto_time_shift_effects_duration \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "3632342 0.0 \n", + "3632343 0.0 \n", + "3632344 0.0 \n", + "3632345 0.0 \n", + "3632346 0.0 \n", "\n", - " 182 183 184 185 186 187 188 189 \n", - "0 2.0 3.0 0.0 1.0 2.0 0.0 1.0 0.0 \n", - "1 22.0 23.0 21.0 22.0 23.0 22.0 23.0 23.0 \n", - "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 20.0 20.0 21.0 21.0 21.0 22.0 22.0 23.0 \n", - "4 False False False False False False False False \n", - "... ... ... ... ... ... ... ... ... \n", - "231565 2 3 0 1 2 0 1 0 \n", - "231566 False False False False False False False False \n", - "231567 False False False False False False False False \n", - "231568 0 0 0 0 0 0 0 0 \n", - "231569 0 0 0 0 0 0 0 0 \n", + " util_shopping_tour_departure_shift_effects \\\n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "... ... \n", + "3632342 0 \n", + "3632343 0 \n", + "3632344 0 \n", + "3632345 0 \n", + "3632346 0 \n", "\n", - 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"req_data does not request avail_ca or avail_co but it is set and being provided\n" + "problem: chosen_but_not_available has (149 issues)\n" ] }, + { + "data": { + "text/plain": [ + "(,\n", + " ┣ chosen_but_not_available: altid n example rows\n", + " ┃ 0 2 1 26680\n", + " ┃ 1 7 1 15549\n", + " ┃ 2 8 1 2727\n", + " ┃ 3 12 1 18739\n", + " ┃ 4 20 5 9872, 15764, 22362\n", + " ┃ .. ... .. ...\n", + " ┃ 144 183 12 4208, 14096, 15246\n", + " ┃ 145 184 5 1829, 8653, 13517\n", + " ┃ 146 185 10 626, 5650, 9208\n", + " ┃ 147 186 9 4563, 8308, 8746\n", + " ┃ 148 187 7 8216, 13933, 19491\n", + " ┃ \n", + " ┃ [149 rows x 3 columns])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.doctor(repair_ch_av=\"-\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ - "

Iteration 079 [Optimization terminated successfully]

" + "

Iteration 121 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1220,7 +1473,7 @@ { "data": { "text/html": [ - "

Best LL = -9499.39971608494

" + "

Best LL = -103688.04725680027

" ], "text/plain": [ "" @@ -1251,70 +1504,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " -0.127954\n", + " 0.126593\n", + " 0.126593\n", " -0.025700\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.127954\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction\n", - " 0.093954\n", + " -0.610258\n", + " -0.610258\n", " -0.027340\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.093954\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " 0.253749\n", + " 0.113506\n", + " 0.113506\n", " 0.008442\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.253749\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction\n", - " -0.198343\n", + " 0.386575\n", + " 0.386575\n", " -0.059300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.198343\n", " \n", " \n", " coef_adult_with_children_in_hh_arrive_19_21\n", - " 0.309424\n", + " 0.281164\n", + " 0.281164\n", " 0.336000\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.309424\n", " \n", " \n", " ...\n", @@ -1325,135 +1582,125 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect\n", - " -0.115107\n", + " -0.281272\n", + " -0.281272\n", " -0.173100\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.115107\n", " \n", " \n", " coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends\n", " -999.000000\n", " -999.000000\n", + " -999.000000\n", + " -999.0\n", + " -999.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " coef_university_student_arrive_after_22\n", - " -0.537299\n", + " 0.609651\n", + " 0.609651\n", " 0.546600\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.537299\n", " \n", " \n", " coef_visit_tour_departure_shift_effects\n", - " 0.169598\n", + " 0.080406\n", + " 0.080406\n", " 0.096880\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.169598\n", " \n", " \n", " coef_visit_tour_duration_shift_effects\n", - " 0.155783\n", + " 0.140532\n", + " 0.140532\n", " 0.163800\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.155783\n", " \n", " \n", "\n", - "

89 rows × 8 columns

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89 rows × 7 columns

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"[89 rows x 8 columns]" + "[89 rows x 7 columns]" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeffnewman/opt/anaconda3/envs/garage38/lib/python3.8/site-packages/scipy/optimize/optimize.py:282: RuntimeWarning: Values in x were outside bounds during a minimize step, clipping to bounds\n", - " warnings.warn(\"Values in x were outside bounds during a \"\n" - ] - }, { "data": { "text/html": [ @@ -1467,31 +1714,31 @@ " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction\n", - " -0.127954\n", + " 0.126593\n", " \n", " \n", " coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction\n", - " 0.093954\n", + " -0.610258\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction\n", - " 0.253749\n", + " 0.113506\n", " \n", " \n", " coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction\n", - " -0.198343\n", + " 0.386575\n", " \n", " \n", " coef_adult_with_children_in_hh_arrive_19_21\n", - " 0.309424\n", + " 0.281164\n", " \n", " \n", " coef_arrival_constants_am_peak\n", - " 0.423382\n", + " -1.621626\n", " \n", " \n", " coef_arrival_constants_early\n", - " 2.912542\n", + " 0.006167\n", " \n", " \n", " coef_arrival_constants_evening\n", @@ -1499,67 +1746,67 @@ " \n", " \n", " coef_arrival_constants_late\n", - " -1.191037\n", + " -0.817500\n", " \n", " \n", " coef_arrival_constants_midday_1\n", - " 1.483808\n", + " 0.050461\n", " \n", " \n", " coef_arrival_constants_midday_2\n", - " 1.574014\n", + " 0.512472\n", " \n", " \n", " coef_arrival_constants_pm_peak_1\n", - " 1.409632\n", + " 0.637774\n", " \n", " \n", " coef_arrival_constants_pm_peak_2\n", - " 1.601585\n", + " 0.623529\n", " \n", " \n", " coef_arrival_constants_pm_peak_3\n", - " 1.110412\n", + " 0.299165\n", " \n", " \n", " coef_arrival_constants_pm_peak_4\n", - " 0.718971\n", + " 0.085991\n", " \n", " \n", " coef_departure_constants_am_peak_1\n", - " -0.462981\n", + " -0.564262\n", " \n", " \n", " coef_departure_constants_am_peak_2\n", - " 1.259376\n", + " 0.617882\n", " \n", " \n", " coef_departure_constants_am_peak_3\n", - " 1.496731\n", + " 1.077085\n", " \n", " \n", " coef_departure_constants_am_peak_4\n", - " 1.574361\n", + " 0.967759\n", " \n", " \n", " coef_departure_constants_early\n", - " -1.029511\n", + " -1.393229\n", " \n", " \n", " coef_departure_constants_evening\n", - " -1.905774\n", + " -1.688698\n", " \n", " \n", " coef_departure_constants_late\n", - " -5.863097\n", + " -4.308508\n", " \n", " \n", " coef_departure_constants_midday_1\n", - " 1.406032\n", + " 0.803175\n", " \n", " \n", " coef_departure_constants_midday_2\n", - " 0.672976\n", + " 0.328097\n", " \n", " \n", " coef_departure_constants_pm_peak\n", @@ -1567,11 +1814,11 @@ " \n", " \n", " coef_destination_in_cbd_duration_shift_effects\n", - " 0.116120\n", + " 0.110221\n", " \n", " \n", " coef_discretionary_tour_duration_lt_2_hours\n", - " -0.263935\n", + " -0.712606\n", " \n", " \n", " coef_duration_constants_0_to_1_hours\n", @@ -1579,31 +1826,31 @@ " \n", " \n", " coef_duration_constants_11_to_13_hours\n", - " -0.877324\n", + " -1.002178\n", " \n", " \n", " coef_duration_constants_14_to_18_hours\n", - " -2.821298\n", + " -1.203201\n", " \n", " \n", " coef_duration_constants_2_to_3_hours\n", - " 0.435394\n", + " -0.064944\n", " \n", " \n", " coef_duration_constants_4_to_5_hours\n", - " -0.223601\n", + " -0.717940\n", " \n", " \n", " coef_duration_constants_6_to_7_hours\n", - " -0.584059\n", + " -1.110132\n", " \n", " \n", " coef_duration_constants_8_to_10_hours\n", - " -0.527906\n", + " -0.954018\n", " \n", " \n", " coef_eat_out_tour_departure_shift_effects\n", - " 0.105657\n", + " 0.065095\n", " \n", " \n", " coef_escort_tour_arrival_constants_am_peak\n", @@ -1615,11 +1862,11 @@ " \n", " \n", " coef_escort_tour_arrival_constants_evening\n", - " -0.878853\n", + " -0.305413\n", " \n", " \n", " coef_escort_tour_arrival_constants_late\n", - " -1.254415\n", + " -0.704974\n", " \n", " \n", " coef_escort_tour_arrival_constants_midday_1\n", @@ -1647,35 +1894,35 @@ " \n", " \n", " coef_escort_tour_departure_constants_am_peak_1\n", - " 0.704309\n", + " -0.874537\n", " \n", " \n", " coef_escort_tour_departure_constants_am_peak_2\n", - " 2.276345\n", + " 1.002187\n", " \n", " \n", " coef_escort_tour_departure_constants_am_peak_3\n", - " 2.300896\n", + " 1.431663\n", " \n", " \n", " coef_escort_tour_departure_constants_am_peak_4\n", - " 0.493137\n", + " -0.076284\n", " \n", " \n", " coef_escort_tour_departure_constants_early\n", - " -0.493910\n", + " -1.237400\n", " \n", " \n", " coef_escort_tour_departure_constants_evening\n", - " -4.802269\n", + " -3.664017\n", " \n", " \n", " coef_escort_tour_departure_constants_late\n", - " -6.857486\n", + " -7.398170\n", " \n", " \n", " coef_escort_tour_departure_constants_midday_1\n", - " 0.746447\n", + " 0.185749\n", " \n", " \n", " coef_escort_tour_departure_constants_midday_2\n", @@ -1683,7 +1930,7 @@ " \n", " \n", " coef_escort_tour_departure_constants_pm_peak\n", - " -1.823360\n", + " -1.171787\n", " \n", " \n", " coef_escort_tour_duration_constants_0_to_1_hours\n", @@ -1691,138 +1938,138 @@ " \n", " \n", " coef_escort_tour_duration_constants_11_to_13_hours\n", - " -6.393148\n", + " -2.821199\n", " \n", " \n", " coef_escort_tour_duration_constants_14_to_18_hours\n", - " -9.419793\n", + " -2.493843\n", " \n", " \n", " coef_escort_tour_duration_constants_2_to_3_hours\n", - " -2.595592\n", + " -2.026673\n", " \n", " \n", " coef_escort_tour_duration_constants_4_to_5_hours\n", - " -3.522889\n", + " -2.941739\n", " \n", " \n", " coef_escort_tour_duration_constants_6_to_7_hours\n", - " -3.979277\n", + " -3.103903\n", " \n", " \n", " coef_escort_tour_duration_constants_8_to_10_hours\n", - " -5.158316\n", + " -3.097454\n", " \n", " \n", " coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect\n", - " -0.285201\n", + " -0.272458\n", " \n", " \n", " coef_free_flow_round_trip_auto_time_shift_effects_duration\n", - " 0.004992\n", + " 0.004326\n", " \n", " \n", " coef_maintenance_tour_depart_before_7\n", - " 0.041997\n", + " -0.819334\n", " \n", " \n", " coef_maintenance_tour_departure_shift_effects\n", - " -0.036636\n", + " -0.140265\n", " \n", " \n", " coef_maintenance_tour_duration_shift_effects\n", - " -0.115944\n", + " -0.068132\n", " \n", " \n", " coef_number_of_escort_tours_departure_shift_effects\n", - " 0.115214\n", + " 0.055565\n", " \n", " \n", " coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects\n", - " 0.104375\n", + " 0.054223\n", " \n", " \n", " coef_number_of_joint_tours_departure_shift_effects\n", - " 0.121920\n", + " 0.029269\n", " \n", " \n", " coef_number_of_mandatory_tours_departure_shift_effects\n", - " 0.170817\n", + " 0.031273\n", " \n", " \n", " coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours\n", - " 5.643619\n", + " -13.624709\n", " \n", " \n", " coef_school_child_age_16_plus_departure_shift_effects\n", - " 0.000283\n", + " 0.086050\n", " \n", " \n", " coef_school_child_age_16_plus_duration_shift_effects\n", - " 0.306122\n", + " 0.250586\n", " \n", " \n", " coef_school_child_age_under_16_departure_shift_effects\n", - " 0.110796\n", + " 0.053201\n", " \n", " \n", " coef_school_child_age_under_16_duration_shift_effects\n", - " 0.338212\n", + " 0.312477\n", " \n", " \n", " coef_school_child_under_16_arrive_after_22\n", - " -0.402410\n", + " -1.096083\n", " \n", " \n", " coef_shopping_tour_arrive_after_22\n", - " -2.545226\n", + " -0.583411\n", " \n", " \n", " coef_shopping_tour_depart_before_8\n", - " -0.787100\n", + " -1.172034\n", " \n", " \n", " coef_shopping_tour_departure_shift_effects\n", - " 0.015067\n", + " -0.061028\n", " \n", " \n", " coef_shopping_tour_duration_lt_2_hours\n", - " 0.943420\n", + " 0.405721\n", " \n", " \n", " coef_shopping_tour_duration_shift_effects\n", - " -0.075331\n", + " -0.113223\n", " \n", " \n", " coef_some_previously_scheduled_tour_begins_in_this_arrival_hour\n", - " -0.054385\n", + " -0.260255\n", " \n", " \n", " coef_some_previously_scheduled_tour_ends_in_this_departure_hour\n", - " -0.345727\n", + " -0.317129\n", " \n", " \n", " coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect\n", - " -0.115107\n", + " -0.281272\n", " \n", " \n", " coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends\n", - " -25.000000\n", + " -999.000000\n", " \n", " \n", " coef_university_student_arrive_after_22\n", - " -0.537299\n", + " 0.609651\n", " \n", " \n", " coef_visit_tour_departure_shift_effects\n", - " 0.169598\n", + " 0.080406\n", " \n", " \n", " coef_visit_tour_duration_shift_effects\n", - " 0.155783\n", + " 0.140532\n", " \n", " \n", - "loglike-9499.39971608494d_loglike\n", + "
logloss3.8088398507438663d_logloss\n", " \n", " \n", " \n", @@ -1832,409 +2079,409 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction0.0001143.438973e-05
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction-0.0003853.037850e-05
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction0.000097-6.894273e-05
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction-0.0002052.343581e-05
coef_adult_with_children_in_hh_arrive_19_210.0002946.653629e-05
coef_arrival_constants_am_peak0.0000379.439405e-07
coef_arrival_constants_early0.000111-8.901383e-06
coef_arrival_constants_evening0.0000000.000000e+00
coef_arrival_constants_late0.000194-6.165299e-05
coef_arrival_constants_midday_1-0.001155-3.871814e-05
coef_arrival_constants_midday_2-0.0007143.424832e-05
coef_arrival_constants_pm_peak_1-0.0000882.789318e-05
coef_arrival_constants_pm_peak_2-0.0003175.620495e-05
coef_arrival_constants_pm_peak_3-0.0000917.993874e-05
coef_arrival_constants_pm_peak_40.0007294.315887e-05
coef_departure_constants_am_peak_10.000352-8.495800e-05
coef_departure_constants_am_peak_2-0.000454-1.392003e-05
coef_departure_constants_am_peak_30.0002356.548727e-06
coef_departure_constants_am_peak_4-0.0003863.101496e-05
coef_departure_constants_early-0.000155-4.598593e-05
coef_departure_constants_evening0.0008867.934172e-05
coef_departure_constants_late0.000080-3.695844e-05
coef_departure_constants_midday_1-0.000777-5.887180e-05
coef_departure_constants_midday_20.0000792.120332e-05
coef_departure_constants_pm_peak0.0000000.000000e+00
coef_destination_in_cbd_duration_shift_effects0.002269-5.090224e-04
coef_discretionary_tour_duration_lt_2_hours-0.0004347.164386e-05
coef_duration_constants_0_to_1_hours0.0000000.000000e+00
coef_duration_constants_11_to_13_hours-0.000100-7.457357e-05
coef_duration_constants_14_to_18_hours-0.000201-6.196472e-06
coef_duration_constants_2_to_3_hours0.0007523.359402e-06
coef_duration_constants_4_to_5_hours0.000035-1.152395e-04
coef_duration_constants_6_to_7_hours0.0002578.557322e-05
coef_duration_constants_8_to_10_hours0.0000551.199127e-04
coef_eat_out_tour_departure_shift_effects-0.0008649.487469e-05
coef_escort_tour_arrival_constants_am_peak0.0000000.000000e+00
coef_escort_tour_arrival_constants_early0.0000000.000000e+00
coef_escort_tour_arrival_constants_evening0.000050-2.421029e-06
coef_escort_tour_arrival_constants_late-0.000165-2.218474e-05
coef_escort_tour_arrival_constants_midday_10.0000000.000000e+00
coef_escort_tour_arrival_constants_midday_20.0000000.000000e+00
coef_escort_tour_arrival_constants_pm_peak_10.0000000.000000e+00
coef_escort_tour_arrival_constants_pm_peak_20.0000000.000000e+00
coef_escort_tour_arrival_constants_pm_peak_30.0000000.000000e+00
coef_escort_tour_arrival_constants_pm_peak_40.0000000.000000e+00
coef_escort_tour_departure_constants_am_peak_10.000085-4.267869e-05
coef_escort_tour_departure_constants_am_peak_2-0.0000066.901890e-05
coef_escort_tour_departure_constants_am_peak_30.0001894.201443e-05
coef_escort_tour_departure_constants_am_peak_4-0.0003224.405139e-05
coef_escort_tour_departure_constants_early-0.0001185.739071e-05
coef_escort_tour_departure_constants_evening0.000116-1.563838e-05
coef_escort_tour_departure_constants_late-0.0000661.808622e-04
coef_escort_tour_departure_constants_midday_10.000073-5.212731e-06
coef_escort_tour_departure_constants_midday_20.0000000.000000e+00
coef_escort_tour_departure_constants_pm_peak0.000128-3.144269e-05
coef_escort_tour_duration_constants_0_to_1_hours0.0000000.000000e+00
coef_escort_tour_duration_constants_11_to_13_hours0.0004584.885945e-05
coef_escort_tour_duration_constants_14_to_18_hours-0.000124-6.674129e-06
coef_escort_tour_duration_constants_2_to_3_hours-0.0002188.004432e-05
coef_escort_tour_duration_constants_4_to_5_hours0.0000737.200311e-06
coef_escort_tour_duration_constants_6_to_7_hours0.0000929.728923e-06
coef_escort_tour_duration_constants_8_to_10_hours0.0000133.329902e-06
coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect0.0083783.750465e-05
coef_free_flow_round_trip_auto_time_shift_effects_duration-0.0392021.926232e-04
coef_maintenance_tour_depart_before_70.000197-7.804297e-06
coef_maintenance_tour_departure_shift_effects-0.0042501.482773e-04
coef_maintenance_tour_duration_shift_effects0.0011253.493923e-04
coef_number_of_escort_tours_departure_shift_effects0.0076461.304873e-05
coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects0.015415-1.378737e-05
coef_number_of_joint_tours_departure_shift_effects0.000633-9.589696e-05
coef_number_of_mandatory_tours_departure_shift_effects0.003775-4.122044e-05
coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours0.0000442.048882e-05
coef_school_child_age_16_plus_departure_shift_effects-0.000324-1.242593e-05
coef_school_child_age_16_plus_duration_shift_effects0.000284-1.367621e-05
coef_school_child_age_under_16_departure_shift_effects0.000075-4.089013e-05
coef_school_child_age_under_16_duration_shift_effects0.001635-2.174936e-05
coef_school_child_under_16_arrive_after_22-0.0001867.030245e-07
coef_shopping_tour_arrive_after_22-0.0000441.001740e-06
coef_shopping_tour_depart_before_8-0.0003601.716270e-05
coef_shopping_tour_departure_shift_effects0.0175473.564338e-05
coef_shopping_tour_duration_lt_2_hours-0.000129-4.343344e-05
coef_shopping_tour_duration_shift_effects0.0037181.135557e-04
coef_some_previously_scheduled_tour_begins_in_this_arrival_hour-0.0000754.711918e-06
coef_some_previously_scheduled_tour_ends_in_this_departure_hour-0.000476-5.226511e-05
coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect0.0005837.117498e-05
coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends0.0000000.000000e+00
coef_university_student_arrive_after_22-0.000256-8.028019e-06
coef_visit_tour_departure_shift_effects-0.000785-8.601128e-05
coef_visit_tour_duration_shift_effects-0.001139-1.064345e-04
nit79nfev236njev79status0message'Optimization terminated successfully'successTrueelapsed_time0:00:18.828322method'slsqp'n_cases2490iteration_number79logloss3.8150199662991726" + "nit121nfev130njev121status0message'Optimization terminated successfully'successTrueelapsed_time0:01:35.575519method'slsqp'n_cases29669iteration_number121loglike-103688.04725680027" ], "text/plain": [ - "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -0.127954\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction 0.093954\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.253749\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -0.198343\n", - "┃ coef_adult_with_children_in_hh_arrive_19_21 0.309424\n", - "┃ ... \n", - "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect -0.115107\n", - "┃ coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends -25.000000\n", - "┃ coef_university_student_arrive_after_22 -0.537299\n", - "┃ coef_visit_tour_departure_shift_effects 0.169598\n", - "┃ coef_visit_tour_duration_shift_effects 0.155783\n", + "┣ x: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.126593\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -0.610258\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.113506\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction 0.386575\n", + "┃ coef_adult_with_children_in_hh_arrive_19_21 0.281164\n", + "┃ ... \n", + "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect -0.281272\n", + "┃ coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends -999.000000\n", + "┃ coef_university_student_arrive_after_22 0.609651\n", + "┃ coef_visit_tour_departure_shift_effects 0.080406\n", + "┃ coef_visit_tour_duration_shift_effects 0.140532\n", "┃ Length: 89, dtype: float64\n", - "┣ loglike: -9499.39971608494\n", - "┣ d_loglike: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.000114\n", - "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -0.000385\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.000097\n", - "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -0.000205\n", - "┃ coef_adult_with_children_in_hh_arrive_19_21 0.000294\n", + "┣ logloss: 3.8088398507438663\n", + "┣ d_logloss: coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.000034\n", + "┃ coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction 0.000030\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -0.000069\n", + "┃ coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction 0.000023\n", + "┃ coef_adult_with_children_in_hh_arrive_19_21 0.000067\n", "┃ ... \n", - "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect 0.000583\n", + "┃ coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect 0.000071\n", "┃ coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends 0.000000\n", - "┃ coef_university_student_arrive_after_22 -0.000256\n", - "┃ coef_visit_tour_departure_shift_effects -0.000785\n", - "┃ coef_visit_tour_duration_shift_effects -0.001139\n", + "┃ coef_university_student_arrive_after_22 -0.000008\n", + "┃ coef_visit_tour_departure_shift_effects -0.000086\n", + "┃ coef_visit_tour_duration_shift_effects -0.000106\n", "┃ Length: 89, dtype: float64\n", - "┣ nit: 79\n", - "┣ nfev: 236\n", - "┣ njev: 79\n", + "┣ nit: 121\n", + "┣ nfev: 130\n", + "┣ njev: 121\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=18, microseconds=828322)\n", + "┣ elapsed_time: datetime.timedelta(seconds=95, microseconds=575519)\n", "┣ method: 'slsqp'\n", - "┣ n_cases: 2490\n", - "┣ iteration_number: 79\n", - "┣ logloss: 3.8150199662991726" + "┣ n_cases: 29669\n", + "┣ iteration_number: 121\n", + "┣ loglike: -103688.04725680027" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.estimate()" + "model.estimate(maxiter=900)" ] }, { @@ -2246,830 +2493,856 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Null Value Constrained
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction-0.128 0.141-0.91 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction 0.0940 0.137 0.69 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.254 0.148 1.72 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction-0.198 0.140-1.42 0.00
coef_adult_with_children_in_hh_arrive_19_21 0.309 0.136 2.27* 0.00
coef_arrival_constants_am_peak 0.423 0.303 1.40 0.00
coef_arrival_constants_early 2.91 0.429 6.78*** 0.00
coef_arrival_constants_evening 0.00 NA NA 0.00fixed value
coef_arrival_constants_late-1.19 0.133-8.96*** 0.00
coef_arrival_constants_midday_1 1.48 0.220 6.75*** 0.00
coef_arrival_constants_midday_2 1.57 0.174 9.05*** 0.00
coef_arrival_constants_pm_peak_1 1.41 0.160 8.80*** 0.00
coef_arrival_constants_pm_peak_2 1.60 0.132 12.14*** 0.00
coef_arrival_constants_pm_peak_3 1.11 0.118 9.40*** 0.00
coef_arrival_constants_pm_peak_4 0.719 0.108 6.69*** 0.00
coef_departure_constants_am_peak_1-0.463 0.384-1.21 0.00
coef_departure_constants_am_peak_2 1.26 0.276 4.56*** 0.00
coef_departure_constants_am_peak_3 1.50 0.246 6.10*** 0.00
coef_departure_constants_am_peak_4 1.57 0.223 7.06*** 0.00
coef_departure_constants_early-1.03 0.420-2.45* 0.00
coef_departure_constants_evening-1.91 0.136-14.03*** 0.00
coef_departure_constants_late-5.86 0.728-8.05*** 0.00
coef_departure_constants_midday_1 1.41 0.171 8.23*** 0.00
coef_departure_constants_midday_2 0.673 0.113 5.98*** 0.00
coef_departure_constants_pm_peak 0.00 NA NA 0.00fixed value
coef_destination_in_cbd_duration_shift_effects 0.116 0.0186 6.26*** 0.00
coef_discretionary_tour_duration_lt_2_hours-0.264 0.136-1.93 0.00
coef_duration_constants_0_to_1_hours 0.00 NA NA 0.00fixed value
coef_duration_constants_11_to_13_hours-0.877 0.352-2.49* 0.00
coef_duration_constants_14_to_18_hours-2.82 0.771-3.66*** 0.00
coef_duration_constants_2_to_3_hours 0.435 0.0972 4.48*** 0.00
coef_duration_constants_4_to_5_hours-0.224 0.139-1.60 0.00
coef_duration_constants_6_to_7_hours-0.584 0.192-3.04** 0.00
coef_duration_constants_8_to_10_hours-0.528 0.249-2.12* 0.00
coef_eat_out_tour_departure_shift_effects 0.106 0.0224 4.71*** 0.00
coef_escort_tour_arrival_constants_am_peak 0.00 NA NA 0.00fixed value
coef_escort_tour_arrival_constants_early 0.00 NA NA 0.00fixed value
coef_escort_tour_arrival_constants_evening-0.879 0.304-2.89** 0.00
coef_escort_tour_arrival_constants_late-1.25 0.579-2.17* 0.00
coef_escort_tour_arrival_constants_midday_1 0.00 NA NA 0.00fixed value
coef_escort_tour_arrival_constants_midday_2 0.00 NA NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_1 0.00 NA NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_2 0.00 NA NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_3 0.00 NA NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_4 0.00 NA NA 0.00fixed value
coef_escort_tour_departure_constants_am_peak_1 0.704 0.317 2.22* 0.00
coef_escort_tour_departure_constants_am_peak_2 2.28 0.192 11.84*** 0.00
coef_escort_tour_departure_constants_am_peak_3 2.30 0.187 12.28*** 0.00
coef_escort_tour_departure_constants_am_peak_4 0.493 0.316 1.56 0.00
coef_escort_tour_departure_constants_early-0.494 0.566-0.87 0.00
coef_escort_tour_departure_constants_evening-4.80 0.503-9.55*** 0.00
coef_escort_tour_departure_constants_late-6.86 1.10-6.23*** 0.00
coef_escort_tour_departure_constants_midday_1 0.746 0.165 4.53*** 0.00
coef_escort_tour_departure_constants_midday_2 0.00 NA NA 0.00fixed value
coef_escort_tour_departure_constants_pm_peak-1.82 0.210-8.67*** 0.00
coef_escort_tour_duration_constants_0_to_1_hours 0.00 NA NA 0.00fixed value
coef_escort_tour_duration_constants_11_to_13_hours-6.39 0.808-7.92*** 0.00
coef_escort_tour_duration_constants_14_to_18_hours-9.42 1.63-5.78*** 0.00
coef_escort_tour_duration_constants_2_to_3_hours-2.60 0.212-12.27*** 0.00
coef_escort_tour_duration_constants_4_to_5_hours-3.52 0.319-11.05*** 0.00
coef_escort_tour_duration_constants_6_to_7_hours-3.98 0.379-10.49*** 0.00
coef_escort_tour_duration_constants_8_to_10_hours-5.16 0.492-10.47*** 0.00
coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect-0.285 0.0225-12.67*** 0.00
coef_free_flow_round_trip_auto_time_shift_effects_duration 0.00499 0.000948 5.27*** 0.00
coef_maintenance_tour_depart_before_7 0.0420 0.359 0.12 0.00
coef_maintenance_tour_departure_shift_effects-0.0366 0.0193-1.90 0.00
coef_maintenance_tour_duration_shift_effects-0.116 0.0298-3.89*** 0.00
coef_number_of_escort_tours_departure_shift_effects 0.115 0.00964 11.95*** 0.00
coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects 0.104 0.00667 15.64*** 0.00
coef_number_of_joint_tours_departure_shift_effects 0.122 0.0296 4.12*** 0.00
coef_number_of_mandatory_tours_departure_shift_effects 0.171 0.0125 13.63*** 0.00
coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours 5.64 0.804 7.02*** 0.00
coef_school_child_age_16_plus_departure_shift_effects 0.000283 0.112 0.00 0.00
coef_school_child_age_16_plus_duration_shift_effects 0.306 0.0952 3.22** 0.00
coef_school_child_age_under_16_departure_shift_effects 0.111 0.0367 3.02** 0.00
coef_school_child_age_under_16_duration_shift_effects 0.338 0.0418 8.10*** 0.00
coef_school_child_under_16_arrive_after_22-0.402 0.480-0.84 0.00
coef_shopping_tour_arrive_after_22-2.55 1.01-2.52* 0.00
coef_shopping_tour_depart_before_8-0.787 0.254-3.10** 0.00
coef_shopping_tour_departure_shift_effects 0.0151 0.0182 0.83 0.00
coef_shopping_tour_duration_lt_2_hours 0.943 0.144 6.55*** 0.00
coef_shopping_tour_duration_shift_effects-0.0753 0.0296-2.55* 0.00
coef_some_previously_scheduled_tour_begins_in_this_arrival_hour-0.0544 0.0946-0.58 0.00
coef_some_previously_scheduled_tour_ends_in_this_departure_hour-0.346 0.0763-4.53*** 0.00
coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect-0.115 0.0570-2.02* 0.00
coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends-999. NA NA 0.00fixed value
coef_university_student_arrive_after_22-0.537 0.853-0.63 0.00
coef_visit_tour_departure_shift_effects 0.170 0.0312 5.44*** 0.00
coef_visit_tour_duration_shift_effects 0.156 0.0369 4.22*** 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction 0.127 0.0994 1.27 0.00
coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction-0.610 0.0968-6.30*** 0.00
coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction 0.114 0.0850 1.34 0.00
coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction 0.387 0.0868 4.45*** 0.00
coef_adult_with_children_in_hh_arrive_19_21 0.281 0.0376 7.47*** 0.00
coef_arrival_constants_am_peak-1.62 0.104-15.66*** 0.00
coef_arrival_constants_early 0.00617 0.146 0.04 0.00
coef_arrival_constants_evening 0.00 0.00 NA 0.00fixed value
coef_arrival_constants_late-0.817 0.0418-19.58*** 0.00
coef_arrival_constants_midday_1 0.0505 0.0730 0.69 0.00
coef_arrival_constants_midday_2 0.512 0.0562 9.12*** 0.00
coef_arrival_constants_pm_peak_1 0.638 0.0500 12.74*** 0.00
coef_arrival_constants_pm_peak_2 0.624 0.0421 14.82*** 0.00
coef_arrival_constants_pm_peak_3 0.299 0.0377 7.95*** 0.00
coef_arrival_constants_pm_peak_4 0.0860 0.0339 2.53* 0.00
coef_departure_constants_am_peak_1-0.564 0.115-4.91*** 0.00
coef_departure_constants_am_peak_2 0.618 0.0862 7.17*** 0.00
coef_departure_constants_am_peak_3 1.08 0.0750 14.36*** 0.00
coef_departure_constants_am_peak_4 0.968 0.0684 14.16*** 0.00
coef_departure_constants_early-1.39 0.133-10.46*** 0.00
coef_departure_constants_evening-1.69 0.0406-41.61*** 0.00
coef_departure_constants_late-4.31 0.172-25.06*** 0.00
coef_departure_constants_midday_1 0.803 0.0524 15.33*** 0.00
coef_departure_constants_midday_2 0.328 0.0342 9.58*** 0.00
coef_departure_constants_pm_peak 0.00 0.00 NA 0.00fixed value
coef_destination_in_cbd_duration_shift_effects 0.110 0.00846 13.02*** 0.00
coef_discretionary_tour_duration_lt_2_hours-0.713 0.0444-16.04*** 0.00
coef_duration_constants_0_to_1_hours 0.00 0.00 NA 0.00fixed value
coef_duration_constants_11_to_13_hours-1.00 0.115-8.70*** 0.00
coef_duration_constants_14_to_18_hours-1.20 0.192-6.28*** 0.00
coef_duration_constants_2_to_3_hours-0.0649 0.0325-2.00* 0.00
coef_duration_constants_4_to_5_hours-0.718 0.0457-15.71*** 0.00
coef_duration_constants_6_to_7_hours-1.11 0.0630-17.62*** 0.00
coef_duration_constants_8_to_10_hours-0.954 0.0817-11.68*** 0.00
coef_eat_out_tour_departure_shift_effects 0.0651 0.00756 8.61*** 0.00
coef_escort_tour_arrival_constants_am_peak 0.00 0.00 NA 0.00fixed value
coef_escort_tour_arrival_constants_early 0.00 0.00 NA 0.00fixed value
coef_escort_tour_arrival_constants_evening-0.305 0.0678-4.51*** 0.00
coef_escort_tour_arrival_constants_late-0.705 0.132-5.34*** 0.00
coef_escort_tour_arrival_constants_midday_1 0.00 0.00 NA 0.00fixed value
coef_escort_tour_arrival_constants_midday_2 0.00 0.00 NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_1 0.00 0.00 NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_2 0.00 0.00 NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_3 0.00 0.00 NA 0.00fixed value
coef_escort_tour_arrival_constants_pm_peak_4 0.00 0.00 NA 0.00fixed value
coef_escort_tour_departure_constants_am_peak_1-0.875 0.0968-9.04*** 0.00
coef_escort_tour_departure_constants_am_peak_2 1.00 0.0627 15.99*** 0.00
coef_escort_tour_departure_constants_am_peak_3 1.43 0.0560 25.58*** 0.00
coef_escort_tour_departure_constants_am_peak_4-0.0763 0.0774-0.99 0.00
coef_escort_tour_departure_constants_early-1.24 0.114-10.89*** 0.00
coef_escort_tour_departure_constants_evening-3.66 0.115-31.78*** 0.00
coef_escort_tour_departure_constants_late-7.40 0.594-12.45*** 0.00
coef_escort_tour_departure_constants_midday_1 0.186 0.0464 4.00*** 0.00
coef_escort_tour_departure_constants_midday_2 0.00 0.00 NA 0.00fixed value
coef_escort_tour_departure_constants_pm_peak-1.17 0.0525-22.33*** 0.00
coef_escort_tour_duration_constants_0_to_1_hours 0.00 0.00 NA 0.00fixed value
coef_escort_tour_duration_constants_11_to_13_hours-2.82 0.160-17.67*** 0.00
coef_escort_tour_duration_constants_14_to_18_hours-2.49 0.263-9.48*** 0.00
coef_escort_tour_duration_constants_2_to_3_hours-2.03 0.0487-41.65*** 0.00
coef_escort_tour_duration_constants_4_to_5_hours-2.94 0.0862-34.13*** 0.00
coef_escort_tour_duration_constants_6_to_7_hours-3.10 0.108-28.63*** 0.00
coef_escort_tour_duration_constants_8_to_10_hours-3.10 0.116-26.68*** 0.00
coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect-0.272 0.00757-36.01*** 0.00
coef_free_flow_round_trip_auto_time_shift_effects_duration 0.00433 0.000116 37.30*** 0.00
coef_maintenance_tour_depart_before_7-0.819 0.121-6.78*** 0.00
coef_maintenance_tour_departure_shift_effects-0.140 0.00739-18.98*** 0.00
coef_maintenance_tour_duration_shift_effects-0.0681 0.00931-7.32*** 0.00
coef_number_of_escort_tours_departure_shift_effects 0.0556 0.00378 14.70*** 0.00
coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects 0.0542 0.00268 20.25*** 0.00
coef_number_of_joint_tours_departure_shift_effects 0.0293 0.00871 3.36*** 0.00
coef_number_of_mandatory_tours_departure_shift_effects 0.0313 0.00474 6.60*** 0.00
coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours-13.6 0.746-18.26*** 0.00
coef_school_child_age_16_plus_departure_shift_effects 0.0860 0.0191 4.51*** 0.00
coef_school_child_age_16_plus_duration_shift_effects 0.251 0.0205 12.25*** 0.00
coef_school_child_age_under_16_departure_shift_effects 0.0532 0.00949 5.60*** 0.00
coef_school_child_age_under_16_duration_shift_effects 0.312 0.0106 29.41*** 0.00
coef_school_child_under_16_arrive_after_22-1.10 0.170-6.44*** 0.00
coef_shopping_tour_arrive_after_22-0.583 0.148-3.95*** 0.00
coef_shopping_tour_depart_before_8-1.17 0.0899-13.03*** 0.00
coef_shopping_tour_departure_shift_effects-0.0610 0.00644-9.48*** 0.00
coef_shopping_tour_duration_lt_2_hours 0.406 0.0459 8.83*** 0.00
coef_shopping_tour_duration_shift_effects-0.113 0.00985-11.49*** 0.00
coef_some_previously_scheduled_tour_begins_in_this_arrival_hour-0.260 0.0337-7.71*** 0.00
coef_some_previously_scheduled_tour_ends_in_this_departure_hour-0.317 0.0252-12.58*** 0.00
coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect-0.281 0.0193-14.55*** 0.00
coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends-999. 0.00 NA 0.00fixed value
coef_university_student_arrive_after_22 0.610 0.171 3.57*** 0.00
coef_visit_tour_departure_shift_effects 0.0804 0.00979 8.21*** 0.00
coef_visit_tour_duration_shift_effects 0.141 0.0115 12.20*** 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -3090,7 +3363,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -3111,20 +3384,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -3143,7 +3405,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -3188,19 +3450,19 @@ " \n", " 2\n", " coef_free_flow_round_trip_auto_time_shift_effe...\n", - " 0.004992\n", + " 0.004326\n", " F\n", " \n", " \n", " 3\n", " coef_shopping_tour_departure_shift_effects\n", - " 0.015067\n", + " -0.061028\n", " F\n", " \n", " \n", " 4\n", " coef_shopping_tour_duration_shift_effects\n", - " -0.075331\n", + " -0.113223\n", " F\n", " \n", " \n", @@ -3212,31 +3474,31 @@ " \n", " 85\n", " coef_escort_tour_duration_constants_4_to_5_hours\n", - " -3.522889\n", + " -2.941739\n", " F\n", " \n", " \n", " 86\n", " coef_escort_tour_duration_constants_6_to_7_hours\n", - " -3.979277\n", + " -3.103903\n", " F\n", " \n", " \n", " 87\n", " coef_escort_tour_duration_constants_8_to_10_hours\n", - " -5.158316\n", + " -3.097454\n", " F\n", " \n", " \n", " 88\n", " coef_escort_tour_duration_constants_11_to_13_h...\n", - " -6.393148\n", + " -2.821199\n", " F\n", " \n", " \n", " 89\n", " coef_escort_tour_duration_constants_14_to_18_h...\n", - " -9.419793\n", + " -2.493843\n", " F\n", " \n", " \n", @@ -3248,20 +3510,20 @@ " coefficient_name value constrain\n", "0 coef_dummy 1.000000 T\n", "1 coef_subsequent_tour_must_start_after_previous... -999.000000 T\n", - "2 coef_free_flow_round_trip_auto_time_shift_effe... 0.004992 F\n", - "3 coef_shopping_tour_departure_shift_effects 0.015067 F\n", - "4 coef_shopping_tour_duration_shift_effects -0.075331 F\n", + "2 coef_free_flow_round_trip_auto_time_shift_effe... 0.004326 F\n", + "3 coef_shopping_tour_departure_shift_effects -0.061028 F\n", + "4 coef_shopping_tour_duration_shift_effects -0.113223 F\n", ".. ... ... ...\n", - "85 coef_escort_tour_duration_constants_4_to_5_hours -3.522889 F\n", - "86 coef_escort_tour_duration_constants_6_to_7_hours -3.979277 F\n", - "87 coef_escort_tour_duration_constants_8_to_10_hours -5.158316 F\n", - "88 coef_escort_tour_duration_constants_11_to_13_h... -6.393148 F\n", - "89 coef_escort_tour_duration_constants_14_to_18_h... -9.419793 F\n", + "85 coef_escort_tour_duration_constants_4_to_5_hours -2.941739 F\n", + "86 coef_escort_tour_duration_constants_6_to_7_hours -3.103903 F\n", + "87 coef_escort_tour_duration_constants_8_to_10_hours -3.097454 F\n", + "88 coef_escort_tour_duration_constants_11_to_13_h... -2.821199 F\n", + "89 coef_escort_tour_duration_constants_14_to_18_h... -2.493843 F\n", "\n", "[90 rows x 3 columns]" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -3278,7 +3540,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -3292,7 +3554,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/17_tour_mode_choice.ipynb b/activitysim/examples/example_estimation/notebooks/17_tour_mode_choice.ipynb index e502d50ff8..535c1bedb7 100644 --- a/activitysim/examples/example_estimation/notebooks/17_tour_mode_choice.ipynb +++ b/activitysim/examples/example_estimation/notebooks/17_tour_mode_choice.ipynb @@ -31,30 +31,128 @@ "height": 34 }, "colab_type": "code", + "execution": { + "iopub.execute_input": "2025-06-26T02:18:53.757761Z", + "iopub.status.busy": "2025-06-26T02:18:53.757352Z", + "iopub.status.idle": "2025-06-26T02:18:55.343119Z", + "shell.execute_reply": "2025-06-26T02:18:55.342813Z", + "shell.execute_reply.started": "2025-06-26T02:18:53.757721Z" + }, "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.41',\n", + " 'sharrow': '2.13.1.dev2+g065e1fe',\n", + " 'numpy': '1.24.4',\n", + " 'pandas': '2.3.0',\n", + " 'xarray': '2025.4.0',\n", + " 'numba': '0.60.0',\n", + " 'jax': 'not installed'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:18:55.343775Z", + "iopub.status.busy": "2025-06-26T02:18:55.343576Z", + "iopub.status.idle": "2025-06-26T02:18:55.594289Z", + "shell.execute_reply": "2025-06-26T02:18:55.593986Z", + "shell.execute_reply.started": "2025-06-26T02:18:55.343766Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from est_mode_setup import prepare, backup\n", + "\n", + "prepare()" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "In this demo notebook, we will (later) edit some model files. But for demo purposes, we want to\n", + "make sure we are starting from the \"original\" files, so we'll check that now. For actual \n", + "applications, this step would not be necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:18:55.594903Z", + "iopub.status.busy": "2025-06-26T02:18:55.594763Z", + "iopub.status.idle": "2025-06-26T02:18:55.597557Z", + "shell.execute_reply": "2025-06-26T02:18:55.597315Z", + "shell.execute_reply.started": "2025-06-26T02:18:55.594892Z" + } + }, "outputs": [], "source": [ - "os.chdir('test')" + "backup(\"output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_coefficients.csv\")\n", + "backup(\"output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_coefficients_template.csv\")\n", + "backup(\"output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_SPEC.csv\")" ] }, { @@ -66,14 +164,38 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:18:55.599005Z", + "iopub.status.busy": "2025-06-26T02:18:55.598789Z", + "iopub.status.idle": "2025-06-26T02:19:10.986768Z", + "shell.execute_reply": "2025-06-26T02:19:10.986372Z", + "shell.execute_reply.started": "2025-06-26T02:18:55.598996Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_coefficients_template.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"tour_mode_choice\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -86,17 +208,48 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:19:10.987338Z", + "iopub.status.busy": "2025-06-26T02:19:10.987239Z", + "iopub.status.idle": "2025-06-26T02:19:12.247678Z", + "shell.execute_reply": "2025-06-26T02:19:12.247318Z", + "shell.execute_reply.started": "2025-06-26T02:19:10.987328Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_mode_choice/tour_mode_choice_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_mode_choice/atwork_subtour_mode_choice_coefficients_template.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/atwork_subtour_mode_choice/atwork_subtour_mode_choice_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_mode_choice/atwork_subtour_mode_choice_values_combined.parquet\n" + ] + } + ], "source": [ - "model2, data2 = component_model(\"atwork_subtour_mode_choice\", return_data=True)" + "model2, data2 = component_model(\n", + " \"atwork_subtour_mode_choice\", \n", + " edb_directory=\"output-est-mode/estimation_data_bundle/atwork_subtour_mode_choice\",\n", + " return_data=True,\n", + " )" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:19:12.248184Z", + "iopub.status.busy": "2025-06-26T02:19:12.248080Z", + "iopub.status.idle": "2025-06-26T02:19:14.272297Z", + "shell.execute_reply": "2025-06-26T02:19:14.271908Z", + "shell.execute_reply.started": "2025-06-26T02:19:12.248174Z" + } + }, "outputs": [], "source": [ "model.extend(model2)" @@ -120,8 +273,16 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:19:14.272784Z", + "iopub.status.busy": "2025-06-26T02:19:14.272698Z", + "iopub.status.idle": "2025-06-26T02:19:14.277447Z", + "shell.execute_reply": "2025-06-26T02:19:14.277183Z", + "shell.execute_reply.started": "2025-06-26T02:19:14.272775Z" + } + }, "outputs": [ { "data": { @@ -185,16 +346,6 @@ " ...\n", " \n", " \n", - " walk_transit_CBD_ASC_atwork\n", - " 0.564\n", - " F\n", - " \n", - " \n", - " drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social\n", - " 0.525\n", - " F\n", - " \n", - " \n", " drive_transit_CBD_ASC_school_univ\n", " 0.672\n", " F\n", @@ -209,9 +360,19 @@ " 0.564\n", " F\n", " \n", + " \n", + " coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork\n", + " 0.000\n", + " F\n", + " \n", + " \n", + " coef_test_school_univ\n", + " 0.000\n", + " F\n", + " \n", " \n", "\n", - "

307 rows × 2 columns

\n", + "

309 rows × 2 columns

\n", "" ], "text/plain": [ @@ -223,16 +384,16 @@ "coef_nest_AUTO_DRIVEALONE 0.350 T\n", "coef_nest_AUTO_SHAREDRIDE2 0.350 T\n", "... ... ...\n", - "walk_transit_CBD_ASC_atwork 0.564 F\n", - "drive_transit_CBD_ASC_eatout_escort_othdiscr_ot... 0.525 F\n", "drive_transit_CBD_ASC_school_univ 0.672 F\n", "drive_transit_CBD_ASC_work 1.100 F\n", "drive_transit_CBD_ASC_atwork 0.564 F\n", + "coef_test_eatout_escort_othdiscr_othmaint_shopp... 0.000 F\n", + "coef_test_school_univ 0.000 F\n", "\n", - "[307 rows x 2 columns]" + "[309 rows x 2 columns]" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -250,8 +411,16 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:19:14.277824Z", + "iopub.status.busy": "2025-06-26T02:19:14.277744Z", + "iopub.status.idle": "2025-06-26T02:19:14.284481Z", + "shell.execute_reply": "2025-06-26T02:19:14.284213Z", + "shell.execute_reply.started": "2025-06-26T02:19:14.277816Z" + } + }, "outputs": [ { "data": { @@ -443,30 +612,6 @@ " ...\n", " \n", " \n", - " 310\n", - " util_Drive_to_Transit_dest_CBD\n", - " Drive to Transit dest CBD\n", - " @df.destination_in_cbd\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " drive_transit_CBD_ASC\n", - " drive_transit_CBD_ASC\n", - " drive_transit_CBD_ASC\n", - " drive_transit_CBD_ASC\n", - " drive_transit_CBD_ASC\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", " 311\n", " util_Drive_to_Transit_distance_penalty\n", " Drive to Transit - distance penalty\n", @@ -562,9 +707,33 @@ " NaN\n", " NaN\n", " \n", + " \n", + " 315\n", + " util_test\n", + " Drive alone not available for escort tours\n", + " @1\n", + " coef_test\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " ...\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " \n", " \n", "\n", - "

315 rows × 24 columns

\n", + "

316 rows × 24 columns

\n", "" ], "text/plain": [ @@ -575,11 +744,11 @@ "3 util_DRIVEALONEFREE_Unavailable_for_joint_tours \n", "4 util_DRIVEALONEFREE_Unavailable_if_didnt_drive... \n", ".. ... \n", - "310 util_Drive_to_Transit_dest_CBD \n", "311 util_Drive_to_Transit_distance_penalty \n", "312 util_Walk_not_available_for_long_distances \n", "313 util_Bike_not_available_for_long_distances \n", "314 util_Drive_alone_not_available_for_escort_tours \n", + "315 util_test \n", "\n", " Description \\\n", "0 DRIVEALONEFREE - Unavailable \n", @@ -588,11 +757,11 @@ "3 DRIVEALONEFREE - Unavailable for joint tours \n", "4 DRIVEALONEFREE - Unavailable if didn't drive t... \n", ".. ... \n", - "310 Drive to Transit dest CBD \n", "311 Drive to Transit - distance penalty \n", "312 Walk not available for long distances \n", "313 Bike not available for long distances \n", "314 Drive alone not available for escort tours \n", + "315 Drive alone not available for escort tours \n", "\n", " Expression DRIVEALONEFREE \\\n", "0 sov_available == False -999 \n", @@ -601,11 +770,11 @@ "3 is_joint == True -999 \n", "4 is_atwork_subtour & ~work_tour_is_SOV -999 \n", ".. ... ... \n", - "310 @df.destination_in_cbd NaN \n", "311 @drvtrn_distpen_0_multiplier * (1-od_skims['DI... NaN \n", "312 @od_skims.max('DISTWALK') > 3 NaN \n", "313 @od_skims.max('DISTBIKE') > 8 NaN \n", "314 is_escort -999 \n", + "315 @1 coef_test \n", "\n", " DRIVEALONEPAY SHARED2FREE SHARED2PAY SHARED3FREE SHARED3PAY WALK ... \\\n", "0 NaN NaN NaN NaN NaN NaN ... \n", @@ -614,37 +783,24 @@ "3 NaN NaN NaN NaN NaN NaN ... \n", "4 NaN NaN NaN NaN NaN NaN ... \n", ".. ... ... ... ... ... ... ... \n", - "310 NaN NaN NaN NaN NaN NaN ... \n", "311 NaN NaN NaN NaN NaN NaN ... \n", "312 NaN NaN NaN NaN NaN -999 ... \n", "313 NaN NaN NaN NaN NaN NaN ... \n", "314 -999 NaN NaN NaN NaN NaN ... \n", + "315 NaN NaN NaN NaN NaN NaN ... \n", "\n", - " WALK_HVY WALK_COM DRIVE_LOC DRIVE_LRF \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "310 NaN NaN drive_transit_CBD_ASC drive_transit_CBD_ASC \n", - "311 NaN NaN coef_ivt coef_ivt \n", - "312 NaN NaN NaN NaN \n", - "313 NaN NaN NaN NaN \n", - "314 NaN NaN NaN NaN \n", - "\n", - " DRIVE_EXP DRIVE_HVY DRIVE_COM TAXI \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - ".. ... ... ... ... \n", - "310 drive_transit_CBD_ASC drive_transit_CBD_ASC drive_transit_CBD_ASC NaN \n", - "311 coef_ivt coef_ivt coef_ivt NaN \n", - "312 NaN NaN NaN NaN \n", - "313 NaN NaN NaN NaN \n", - "314 NaN NaN NaN NaN \n", + " WALK_HVY WALK_COM DRIVE_LOC DRIVE_LRF DRIVE_EXP DRIVE_HVY DRIVE_COM TAXI \\\n", + "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN NaN \n", + ".. ... ... ... ... ... ... ... ... \n", + "311 NaN NaN coef_ivt coef_ivt coef_ivt coef_ivt coef_ivt NaN \n", + "312 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "313 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "314 NaN NaN NaN NaN NaN NaN NaN NaN \n", + "315 NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " TNC_SINGLE TNC_SHARED \n", "0 NaN NaN \n", @@ -653,16 +809,16 @@ "3 NaN NaN \n", "4 NaN NaN \n", ".. ... ... \n", - "310 NaN NaN \n", "311 NaN NaN \n", "312 NaN NaN \n", "313 NaN NaN \n", "314 NaN NaN \n", + "315 NaN NaN \n", "\n", - "[315 rows x 24 columns]" + "[316 rows x 24 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -680,8 +836,16 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:19:14.284943Z", + "iopub.status.busy": "2025-06-26T02:19:14.284856Z", + "iopub.status.idle": "2025-06-26T02:19:14.298921Z", + "shell.execute_reply": "2025-06-26T02:19:14.298594Z", + "shell.execute_reply.started": "2025-06-26T02:19:14.284935Z" + } + }, "outputs": [ { "data": { @@ -715,6 +879,7 @@ " util_DRIVEALONEFREE_Terminal_time\n", " util_DRIVEALONEFREE_Operating_cost\n", " ...\n", + " walk_lrf_available\n", " walk_ferry_available\n", " drive_local_available\n", " drive_commuter_available\n", @@ -723,7 +888,6 @@ " drive_lrf_available\n", " drive_ferry_available\n", " destination_in_cbd\n", - " tour_id.1\n", " override_choice_code\n", " \n", " \n", @@ -753,91 +917,92 @@ " \n", " \n", " \n", - " 6812\n", - " WALK\n", - " WALK\n", + " 1870\n", + " SHARED3FREE\n", + " SHARED3FREE\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 5.480000\n", - " 15.45016\n", - " 2.156258\n", + " 0.0\n", + " 20.830002\n", + " 21.26572\n", + " 6.293324\n", " ...\n", " False\n", " False\n", + " True\n", + " True\n", " False\n", - " False\n", - " False\n", + " True\n", " False\n", " False\n", " 1\n", - " 6812\n", - " 7\n", + " 5\n", " \n", " \n", - " 8110\n", - " WALK\n", - " WALK\n", + " 20468\n", + " SHARED3FREE\n", + " TNC_SINGLE\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 8.860000\n", - " 34.19784\n", - " 17.722496\n", + " 0.0\n", + " 9.440000\n", + " 13.24624\n", + " 14.917349\n", " ...\n", " False\n", " False\n", + " True\n", + " True\n", " False\n", - " False\n", - " False\n", + " True\n", " False\n", " False\n", " 0\n", - " 8110\n", - " 7\n", + " 20\n", " \n", " \n", - " 11013\n", - " DRIVEALONEFREE\n", - " DRIVEALONEFREE\n", + " 27055\n", + " SHARED3FREE\n", + " SHARED3FREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 20.689999\n", - " 18.17320\n", - " 15.097005\n", + " 13.160000\n", + " 8.53068\n", + " 25.088453\n", " ...\n", " False\n", + " False\n", " True\n", " True\n", " False\n", " True\n", " False\n", " False\n", - " 1\n", - " 11013\n", - " 1\n", + " 0\n", + " 5\n", " \n", " \n", - " 11016\n", - " DRIVEALONEFREE\n", - " DRIVEALONEFREE\n", + " 38877\n", + " SHARED2FREE\n", + " SHARED3FREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 14.710000\n", - " 12.85052\n", - " 10.971117\n", + " 6.730000\n", + " 11.04132\n", + " 11.040045\n", " ...\n", " False\n", + " False\n", " True\n", " True\n", " False\n", @@ -845,32 +1010,31 @@ " False\n", " False\n", " 0\n", - " 11016\n", - " 1\n", + " 5\n", " \n", " \n", - " 15403\n", - " DRIVEALONEFREE\n", - " DRIVEALONEFREE\n", + " 38904\n", + " SHARED2FREE\n", + " SHARED2FREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 15.160000\n", - " 10.64808\n", - " 26.092745\n", + " 22.410000\n", + " 17.29320\n", + " 46.920191\n", " ...\n", + " True\n", " False\n", " True\n", " True\n", " False\n", " True\n", - " False\n", + " True\n", " False\n", " 0\n", - " 15403\n", - " 1\n", + " 3\n", " \n", " \n", " ...\n", @@ -897,7 +1061,7 @@ " ...\n", " \n", " \n", - " 309760814\n", + " 310197956\n", " DRIVEALONEFREE\n", " DRIVEALONEFREE\n", " 0.0\n", @@ -905,95 +1069,95 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 24.510000\n", - " 11.41364\n", - " 27.217999\n", + " 29.900002\n", + " 4.21092\n", + " 69.245528\n", " ...\n", " False\n", - " True\n", - " True\n", " False\n", - " True\n", + " False\n", + " False\n", + " False\n", + " False\n", " False\n", " False\n", " 0\n", - " 309760814\n", " 1\n", " \n", " \n", - " 309760815\n", - " SHARED2FREE\n", - " SHARED2FREE\n", + " 310202376\n", + " DRIVEALONEFREE\n", + " BIKE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 19.349998\n", - " 16.96372\n", - " 14.591494\n", + " 5.480000\n", + " 3.82392\n", + " 19.277738\n", " ...\n", " False\n", - " True\n", - " True\n", " False\n", - " True\n", " False\n", " False\n", - " 1\n", - " 309760815\n", - " 3\n", + " False\n", + " False\n", + " False\n", + " False\n", + " 0\n", + " 8\n", " \n", " \n", - " 309790009\n", - " BIKE\n", - " BIKE\n", + " 310202384\n", + " DRIVEALONEFREE\n", + " DRIVEALONEFREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 21.410000\n", - " 19.44384\n", - " 84.280323\n", + " 5.480000\n", + " 3.82392\n", + " 19.277738\n", " ...\n", " False\n", - " True\n", - " True\n", " False\n", - " True\n", " False\n", " False\n", + " False\n", + " False\n", + " False\n", + " False\n", + " 0\n", " 1\n", - " 309790009\n", - " 8\n", " \n", " \n", - " 309796968\n", - " SHARED3FREE\n", - " SHARED3FREE\n", + " 310212634\n", + " DRIVEALONEFREE\n", + " DRIVEALONEFREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 17.340000\n", - " 13.66872\n", - " 30.549632\n", + " 12.840000\n", + " 5.32256\n", + " 5.457677\n", " ...\n", " False\n", + " False\n", + " False\n", " True\n", " True\n", " False\n", - " True\n", " False\n", " False\n", " 0\n", - " 309796968\n", - " 5\n", + " 1\n", " \n", " \n", - " 309796969\n", + " 310220296\n", " DRIVEALONEFREE\n", " DRIVEALONEFREE\n", " 0.0\n", @@ -1001,199 +1165,199 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 8.000000\n", - " 10.00692\n", - " 18.758547\n", + " 13.590000\n", + " 8.05768\n", + " 15.684208\n", " ...\n", " False\n", - " True\n", - " True\n", + " False\n", + " False\n", " False\n", " True\n", " False\n", " False\n", + " False\n", " 0\n", - " 309796969\n", " 1\n", " \n", " \n", "\n", - "

5314 rows × 535 columns

\n", + "

63935 rows × 536 columns

\n", "" ], "text/plain": [ " model_choice override_choice util_DRIVEALONEFREE_Unavailable \\\n", "tour_id \n", - "6812 WALK WALK 0.0 \n", - "8110 WALK WALK 0.0 \n", - "11013 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", - "11016 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", - "15403 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", + "1870 SHARED3FREE SHARED3FREE 0.0 \n", + "20468 SHARED3FREE TNC_SINGLE 0.0 \n", + "27055 SHARED3FREE SHARED3FREE 0.0 \n", + "38877 SHARED2FREE SHARED3FREE 0.0 \n", + "38904 SHARED2FREE SHARED2FREE 0.0 \n", "... ... ... ... \n", - "309760814 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", - "309760815 SHARED2FREE SHARED2FREE 0.0 \n", - "309790009 BIKE BIKE 0.0 \n", - "309796968 SHARED3FREE SHARED3FREE 0.0 \n", - "309796969 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", + "310197956 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", + "310202376 DRIVEALONEFREE BIKE 0.0 \n", + "310202384 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", + "310212634 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", + "310220296 DRIVEALONEFREE DRIVEALONEFREE 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_for_zero_auto_households \\\n", "tour_id \n", - "6812 1.0 \n", - "8110 1.0 \n", - "11013 0.0 \n", - "11016 0.0 \n", - "15403 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", + "38877 0.0 \n", + "38904 0.0 \n", "... ... \n", - "309760814 0.0 \n", - "309760815 0.0 \n", - "309790009 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", + "310197956 0.0 \n", + "310202376 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16 \\\n", "tour_id \n", - "6812 0.0 \n", - "8110 0.0 \n", - "11013 0.0 \n", - "11016 0.0 \n", - "15403 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", + "38877 0.0 \n", + "38904 0.0 \n", "... ... \n", - "309760814 0.0 \n", - "309760815 0.0 \n", - "309790009 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", + "310197956 0.0 \n", + "310202376 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_for_joint_tours \\\n", "tour_id \n", - "6812 0.0 \n", - "8110 0.0 \n", - "11013 0.0 \n", - "11016 0.0 \n", - "15403 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", + "38877 0.0 \n", + "38904 0.0 \n", "... ... \n", - "309760814 0.0 \n", - "309760815 0.0 \n", - "309790009 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", + "310197956 0.0 \n", + "310202376 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_if_didnt_drive_to_work \\\n", "tour_id \n", - "6812 0.0 \n", - "8110 0.0 \n", - "11013 0.0 \n", - "11016 0.0 \n", - "15403 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", + "38877 0.0 \n", + "38904 0.0 \n", "... ... \n", - "309760814 0.0 \n", - "309760815 0.0 \n", - "309790009 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", + "310197956 0.0 \n", + "310202376 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_In_vehicle_time \\\n", "tour_id \n", - "6812 5.480000 \n", - "8110 8.860000 \n", - "11013 20.689999 \n", - "11016 14.710000 \n", - "15403 15.160000 \n", + "1870 20.830002 \n", + "20468 9.440000 \n", + "27055 13.160000 \n", + "38877 6.730000 \n", + "38904 22.410000 \n", "... ... \n", - "309760814 24.510000 \n", - "309760815 19.349998 \n", - "309790009 21.410000 \n", - "309796968 17.340000 \n", - "309796969 8.000000 \n", + "310197956 29.900002 \n", + "310202376 5.480000 \n", + "310202384 5.480000 \n", + "310212634 12.840000 \n", + "310220296 13.590000 \n", "\n", " util_DRIVEALONEFREE_Terminal_time \\\n", "tour_id \n", - "6812 15.45016 \n", - "8110 34.19784 \n", - "11013 18.17320 \n", - "11016 12.85052 \n", - "15403 10.64808 \n", + "1870 21.26572 \n", + "20468 13.24624 \n", + "27055 8.53068 \n", + "38877 11.04132 \n", + "38904 17.29320 \n", "... ... \n", - "309760814 11.41364 \n", - "309760815 16.96372 \n", - "309790009 19.44384 \n", - "309796968 13.66872 \n", - "309796969 10.00692 \n", + "310197956 4.21092 \n", + "310202376 3.82392 \n", + "310202384 3.82392 \n", + "310212634 5.32256 \n", + "310220296 8.05768 \n", "\n", - " util_DRIVEALONEFREE_Operating_cost ... walk_ferry_available \\\n", - "tour_id ... \n", - "6812 2.156258 ... False \n", - "8110 17.722496 ... False \n", - "11013 15.097005 ... False \n", - "11016 10.971117 ... False \n", - "15403 26.092745 ... False \n", - "... ... ... ... \n", - "309760814 27.217999 ... False \n", - "309760815 14.591494 ... False \n", - "309790009 84.280323 ... False \n", - "309796968 30.549632 ... False \n", - "309796969 18.758547 ... False \n", + " util_DRIVEALONEFREE_Operating_cost ... walk_lrf_available \\\n", + "tour_id ... \n", + "1870 6.293324 ... False \n", + "20468 14.917349 ... False \n", + "27055 25.088453 ... False \n", + "38877 11.040045 ... False \n", + "38904 46.920191 ... True \n", + "... ... ... ... \n", + "310197956 69.245528 ... False \n", + "310202376 19.277738 ... False \n", + "310202384 19.277738 ... False \n", + "310212634 5.457677 ... False \n", + "310220296 15.684208 ... False \n", "\n", - " drive_local_available drive_commuter_available \\\n", - "tour_id \n", - "6812 False False \n", - "8110 False False \n", - "11013 True True \n", - "11016 True True \n", - "15403 True True \n", - "... ... ... \n", - "309760814 True True \n", - "309760815 True True \n", - "309790009 True True \n", - "309796968 True True \n", - "309796969 True True \n", + " walk_ferry_available drive_local_available \\\n", + "tour_id \n", + "1870 False True \n", + "20468 False True \n", + "27055 False True \n", + "38877 False True \n", + "38904 False True \n", + "... ... ... \n", + "310197956 False False \n", + "310202376 False False \n", + "310202384 False False \n", + "310212634 False False \n", + "310220296 False False \n", "\n", - " drive_express_available drive_heavyrail_available \\\n", - "tour_id \n", - "6812 False False \n", - "8110 False False \n", - "11013 False True \n", - "11016 False True \n", - "15403 False True \n", - "... ... ... \n", - "309760814 False True \n", - "309760815 False True \n", - "309790009 False True \n", - "309796968 False True \n", - "309796969 False True \n", + " drive_commuter_available drive_express_available \\\n", + "tour_id \n", + "1870 True False \n", + "20468 True False \n", + "27055 True False \n", + "38877 True False \n", + "38904 True False \n", + "... ... ... \n", + "310197956 False False \n", + "310202376 False False \n", + "310202384 False False \n", + "310212634 True True \n", + "310220296 False True \n", "\n", - " drive_lrf_available drive_ferry_available destination_in_cbd \\\n", - "tour_id \n", - "6812 False False 1 \n", - "8110 False False 0 \n", - "11013 False False 1 \n", - "11016 False False 0 \n", - "15403 False False 0 \n", - "... ... ... ... \n", - "309760814 False False 0 \n", - "309760815 False False 1 \n", - "309790009 False False 1 \n", - "309796968 False False 0 \n", - "309796969 False False 0 \n", + " drive_heavyrail_available drive_lrf_available \\\n", + "tour_id \n", + "1870 True False \n", + "20468 True False \n", + "27055 True False \n", + "38877 True False \n", + "38904 True True \n", + "... ... ... \n", + "310197956 False False \n", + "310202376 False False \n", + "310202384 False False \n", + "310212634 False False \n", + "310220296 False False \n", "\n", - " tour_id.1 override_choice_code \n", - "tour_id \n", - "6812 6812 7 \n", - "8110 8110 7 \n", - "11013 11013 1 \n", - "11016 11016 1 \n", - "15403 15403 1 \n", - "... ... ... \n", - "309760814 309760814 1 \n", - "309760815 309760815 3 \n", - "309790009 309790009 8 \n", - "309796968 309796968 5 \n", - "309796969 309796969 1 \n", + " drive_ferry_available destination_in_cbd override_choice_code \n", + "tour_id \n", + "1870 False 1 5 \n", + "20468 False 0 20 \n", + "27055 False 0 5 \n", + "38877 False 0 5 \n", + "38904 False 0 3 \n", + "... ... ... ... \n", + "310197956 False 0 1 \n", + "310202376 False 0 8 \n", + "310202384 False 0 1 \n", + "310212634 False 0 1 \n", + "310220296 False 0 1 \n", "\n", - "[5314 rows x 535 columns]" + "[63935 rows x 536 columns]" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1213,112 +1377,59 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:19:14.299439Z", + "iopub.status.busy": "2025-06-26T02:19:14.299343Z", + "iopub.status.idle": "2025-06-26T02:19:14.389799Z", + "shell.execute_reply": "2025-06-26T02:19:14.389326Z", + "shell.execute_reply.started": "2025-06-26T02:19:14.299429Z" + } + }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "problem: chosen-but-not-available (1 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (1 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: low-variance-data-co (1 issues)\n" - ] - }, { "data": { "text/plain": [ - "[(,\n", - " ┣ chosen_but_not_available: n example rows\n", - " ┃ 12 2 45, 312\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 58 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (,\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 87 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (,\n", - " ┣ chosen_but_not_available: n example rows\n", - " ┃ 10 4 66, 184, 222\n", - " ┃ 12 3 205, 394, 560\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 56 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (,\n", - " ┣ chosen_but_not_available: n example rows\n", - " ┃ 10 4 25, 73, 98\n", - " ┃ 12 2 129, 320\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 57 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (,\n", - " ┣ chosen_but_not_available: n example rows\n", - " ┃ 10 25 3, 108, 145\n", - " ┃ 12 5 10, 23, 135\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 88 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (,\n", - " ┣ chosen_but_not_available: n example rows\n", - " ┃ 10 5 118, 300, 303\n", - " ┃ 12 1 252\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 54 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (,\n", - " ┣ chosen_but_not_available: n example rows\n", - " ┃ 10 3 71, 163, 170\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 65 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (, dictx()),\n", - " (,\n", - " ┣ chosen_but_not_available: n example rows\n", - " ┃ 10 36 21, 23, 95\n", - " ┃ 12 15 46, 243, 449\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 108 util_BIKE_Unavailable_if_didnt_bike_to_work, u...),\n", - " (,\n", - " ┣ low_variance_data_co: n example cols\n", - " ┃ low_variance_co 106 util_Commuter_Rail, util_DRIVEALONEFREE_Unavai...)]" + "(,\n", + " [(, dictx()),\n", + " (, dictx()),\n", + " (, dictx()),\n", + " (, dictx()),\n", + " (, dictx()),\n", + " (, dictx()),\n", + " (, dictx()),\n", + " (, dictx()),\n", + " (, dictx()),\n", + " (, dictx())])" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.load_data()\n", "model.doctor(repair_ch_av=\"-\")" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:19:14.390341Z", + "iopub.status.busy": "2025-06-26T02:19:14.390244Z", + "iopub.status.idle": "2025-06-26T02:20:19.068878Z", + "shell.execute_reply": "2025-06-26T02:20:19.068547Z", + "shell.execute_reply.started": "2025-06-26T02:19:14.390332Z" + } + }, "outputs": [ { "data": { "text/html": [ - "

Iteration 168 [Optimization terminated successfully]

" + "

Iteration 189 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1330,7 +1441,7 @@ { "data": { "text/html": [ - "

Best LL = -6324.409943103907

" + "

Best LL = -76508.28923895479

" ], "text/plain": [ "" @@ -1361,13 +1472,22 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1375,56 +1495,51 @@ " -999\n", " -999.000000\n", " -999.000000\n", + " -999.000000\n", " -999.0\n", " -999.0\n", - " -999.0\n", + " 0.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " 1\n", " 1.000000\n", " 1.000000\n", + " 1.000000\n", " 1.0\n", " 1.0\n", - " 1.0\n", + " 0.0\n", " 1\n", - " \n", - " 1.000000\n", " \n", " \n", - " bike_ASC_auto_deficient_eatout\n", - " -95.051842\n", - " -1.569111\n", + " bike_ASC_auto_deficient_atwork\n", + " -1.054274\n", + " -1.054274\n", + " -0.807408\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -95.051842\n", " \n", " \n", - " bike_ASC_auto_sufficient_eatout\n", - " -1.464118\n", - " -1.200347\n", + " bike_ASC_auto_deficient_eatout\n", + " -1.458927\n", + " -1.458927\n", + " -1.569111\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.464118\n", " \n", " \n", - " bike_ASC_no_auto_eatout\n", - " 7.201849\n", - " 0.868071\n", + " bike_ASC_auto_deficient_escort\n", + " -3.980018\n", + " -3.980018\n", + " -4.527928\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 7.201849\n", " \n", " \n", " ...\n", @@ -1435,113 +1550,133 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " walk_ASC_no_auto_atwork\n", - " 12.824109\n", - " 6.669213\n", + " walk_transit_ASC_no_auto_work\n", + " 4.604150\n", + " 4.604150\n", + " 5.035417\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 12.824109\n", " \n", " \n", - " walk_transit_ASC_auto_deficient_atwork\n", - " 8.985177\n", - " -2.998829\n", + " walk_transit_CBD_ASC_atwork\n", + " 1.296785\n", + " 1.296785\n", + " 0.564000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 8.985177\n", " \n", " \n", - " walk_transit_ASC_auto_sufficient_atwork\n", - " 9.127996\n", - " -3.401027\n", + " walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social\n", + " 1.078110\n", + " 1.078110\n", + " 0.525000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 9.127996\n", " \n", " \n", - " walk_transit_ASC_no_auto_atwork\n", - " 21.234896\n", - " 2.704188\n", + " walk_transit_CBD_ASC_school_univ\n", + " 0.834010\n", + " 0.834010\n", + " 0.672000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 21.234896\n", " \n", " \n", - " walk_transit_CBD_ASC_atwork\n", - " 0.351949\n", - " 0.564000\n", + " walk_transit_CBD_ASC_work\n", + " 1.157993\n", + " 1.157993\n", + " 0.804000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.351949\n", " \n", " \n", "\n", - "

301 rows × 8 columns

\n", + "

303 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue nullvalue \\\n", - "-999 -999.000000 -999.000000 -999.0 \n", - "1 1.000000 1.000000 1.0 \n", - "bike_ASC_auto_deficient_eatout -95.051842 -1.569111 0.0 \n", - "bike_ASC_auto_sufficient_eatout -1.464118 -1.200347 0.0 \n", - "bike_ASC_no_auto_eatout 7.201849 0.868071 0.0 \n", - "... ... ... ... \n", - "walk_ASC_no_auto_atwork 12.824109 6.669213 0.0 \n", - "walk_transit_ASC_auto_deficient_atwork 8.985177 -2.998829 0.0 \n", - "walk_transit_ASC_auto_sufficient_atwork 9.127996 -3.401027 0.0 \n", - "walk_transit_ASC_no_auto_atwork 21.234896 2.704188 0.0 \n", - "walk_transit_CBD_ASC_atwork 0.351949 0.564000 0.0 \n", + " value best \\\n", + "param_name \n", + "-999 -999.000000 -999.000000 \n", + "1 1.000000 1.000000 \n", + "bike_ASC_auto_deficient_atwork -1.054274 -1.054274 \n", + "bike_ASC_auto_deficient_eatout -1.458927 -1.458927 \n", + "bike_ASC_auto_deficient_escort -3.980018 -3.980018 \n", + "... ... ... \n", + "walk_transit_ASC_no_auto_work 4.604150 4.604150 \n", + "walk_transit_CBD_ASC_atwork 1.296785 1.296785 \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth... 1.078110 1.078110 \n", + "walk_transit_CBD_ASC_school_univ 0.834010 0.834010 \n", + "walk_transit_CBD_ASC_work 1.157993 1.157993 \n", "\n", - " minimum maximum holdfast note \\\n", - "-999 -999.0 -999.0 1 \n", - "1 1.0 1.0 1 \n", - "bike_ASC_auto_deficient_eatout NaN NaN 0 \n", - "bike_ASC_auto_sufficient_eatout NaN NaN 0 \n", - "bike_ASC_no_auto_eatout NaN NaN 0 \n", - "... ... ... ... ... \n", - "walk_ASC_no_auto_atwork NaN NaN 0 \n", - "walk_transit_ASC_auto_deficient_atwork NaN NaN 0 \n", - "walk_transit_ASC_auto_sufficient_atwork NaN NaN 0 \n", - "walk_transit_ASC_no_auto_atwork NaN NaN 0 \n", - "walk_transit_CBD_ASC_atwork NaN NaN 0 \n", + " initvalue minimum \\\n", + "param_name \n", + "-999 -999.000000 -999.0 \n", + "1 1.000000 1.0 \n", + "bike_ASC_auto_deficient_atwork -0.807408 -inf \n", + "bike_ASC_auto_deficient_eatout -1.569111 -inf \n", + "bike_ASC_auto_deficient_escort -4.527928 -inf \n", + "... ... ... \n", + "walk_transit_ASC_no_auto_work 5.035417 -inf \n", + "walk_transit_CBD_ASC_atwork 0.564000 -inf \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth... 0.525000 -inf \n", + "walk_transit_CBD_ASC_school_univ 0.672000 -inf \n", + "walk_transit_CBD_ASC_work 0.804000 -inf \n", "\n", - " best \n", - "-999 -999.000000 \n", - "1 1.000000 \n", - "bike_ASC_auto_deficient_eatout -95.051842 \n", - "bike_ASC_auto_sufficient_eatout -1.464118 \n", - "bike_ASC_no_auto_eatout 7.201849 \n", - "... ... \n", - "walk_ASC_no_auto_atwork 12.824109 \n", - "walk_transit_ASC_auto_deficient_atwork 8.985177 \n", - "walk_transit_ASC_auto_sufficient_atwork 9.127996 \n", - "walk_transit_ASC_no_auto_atwork 21.234896 \n", - "walk_transit_CBD_ASC_atwork 0.351949 \n", + " maximum nullvalue \\\n", + "param_name \n", + "-999 -999.0 0.0 \n", + "1 1.0 0.0 \n", + "bike_ASC_auto_deficient_atwork inf 0.0 \n", + "bike_ASC_auto_deficient_eatout inf 0.0 \n", + "bike_ASC_auto_deficient_escort inf 0.0 \n", + "... ... ... \n", + "walk_transit_ASC_no_auto_work inf 0.0 \n", + "walk_transit_CBD_ASC_atwork inf 0.0 \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth... inf 0.0 \n", + "walk_transit_CBD_ASC_school_univ inf 0.0 \n", + "walk_transit_CBD_ASC_work inf 0.0 \n", "\n", - "[301 rows x 8 columns]" + " holdfast \n", + "param_name \n", + "-999 1 \n", + "1 1 \n", + "bike_ASC_auto_deficient_atwork 0 \n", + "bike_ASC_auto_deficient_eatout 0 \n", + "bike_ASC_auto_deficient_escort 0 \n", + "... ... \n", + "walk_transit_ASC_no_auto_work 0 \n", + "walk_transit_CBD_ASC_atwork 0 \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth... 0 \n", + "walk_transit_CBD_ASC_school_univ 0 \n", + "walk_transit_CBD_ASC_work 0 \n", + "\n", + "[303 rows x 7 columns]" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jpn/Git/aestival/repos/larch/src/larch/model/optimization.py:338: UserWarning: SLSQP may not play nicely with unbounded parameters\n", + "if you get poor results, consider setting global bounds with model.set_cap()\n", + " warnings.warn( # infinite bounds # )\n" + ] } ], "source": [ @@ -1550,25 +1685,135 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:20:19.069508Z", + "iopub.status.busy": "2025-06-26T02:20:19.069365Z", + "iopub.status.idle": "2025-06-26T02:22:22.469509Z", + "shell.execute_reply": "2025-06-26T02:22:22.469199Z", + "shell.execute_reply.started": "2025-06-26T02:20:19.069498Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_92897/3375301916.py:1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.calculate_parameter_covariance()\n", - "/Users/jeffnewman/LocalGit/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 1.1755327986150339e-46 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - "/var/folders/js/bk_dt9015j79_f6bxnc44dsr0000gp/T/ipykernel_92897/3375301916.py:1: PossibleOverspecification: WARNING: Model seems to have 58 parameter estimators with negative variance\n", - "- commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - "- drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - "- drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork\n", - "- drive_transit_ASC_no_auto_all\n", - "- and 54 more\n", + "/var/folders/gp/zzfchnm91870k7pvbkmbgwz80000gr/T/ipykernel_45605/3375301916.py:1: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", " model.calculate_parameter_covariance()\n" ] + }, + { + "data": { + 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 ValueStd Errt StatSignifLike RatioNull ValueValueStd Errt StatSignifNull Value
-999-999. 0.00 NA NA-999.00
1 1.00 0.00 NA NA 1.00
bike_ASC_auto_deficient_eatout-95.1 0.00 NA[**] 3.97 0.00
bike_ASC_auto_sufficient_eatout-1.46 0.459-3.19** NA 0.00
bike_ASC_no_auto_eatout 7.20 0.740 9.73*** NA 0.00
coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work 0.720 0.387 1.86 NA 0.00
coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work-0.0191 0.214-0.09 NA 0.00
coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social-1.14 0.276-4.13*** NA 0.00
coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork 0.0210 0.0998 0.21 NA 0.00
coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork-0.00393 0.0708-0.06 NA 0.00
coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social-0.0169 0.000785-21.58*** NA 0.00
coef_nest_AUTO 0.720 0.00 NA NA 1.00
coef_nest_AUTO_DRIVEALONE 0.350 0.00 NA NA 1.00
coef_nest_AUTO_SHAREDRIDE2 0.350 0.00 NA NA 1.00
coef_nest_AUTO_SHAREDRIDE3 0.350 0.00 NA NA 1.00
coef_nest_NONMOTORIZED 0.720 0.00 NA NA 1.00
coef_nest_RIDEHAIL 0.360 0.00 NA NA 1.00
coef_nest_TRANSIT 0.720 0.00 NA NA 1.00
coef_nest_TRANSIT_DRIVEACCESS 0.500 0.00 NA NA 1.00
coef_nest_TRANSIT_WALKACCESS 0.500 0.00 NA NA 1.00
commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-38.8 NA NA[***] BIG 0.00
drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.940 NA NA[] 0.00 0.00
drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-25.9 NA NA[***] 94.74 0.00
drive_transit_ASC_auto_deficient_eatout 14.7 1.77 8.30*** NA 0.00
drive_transit_ASC_auto_sufficient_eatout 12.1 1.88 6.45*** NA 0.00
drive_transit_ASC_no_auto_all 0.00 NA NA[] 0.00 0.00
drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 0.553 0.813 0.68 NA 0.00
express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-61.5 NA NA[***] 94.10 0.00
heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-11.3 1.64-6.91*** NA 0.00
joint_bike_ASC_auto_deficient_all-155. NA NA[***] 49.72 0.00
joint_bike_ASC_auto_sufficient_all-108. NA NA[***] 89.09 0.00
joint_bike_ASC_no_auto_all-47.7 NA NA[] 1.65 0.00
joint_drive_transit_ASC_auto_deficient_all-36.1 NA NA[] 0.00 0.00
joint_drive_transit_ASC_auto_sufficient_all-24.1 NA NA[] 0.00 0.00
joint_drive_transit_ASC_no_auto_all 0.00 0.00 NA NA 0.00
joint_sr2_ASC_auto_deficient_all 0.00 0.00 NA NA 0.00
joint_sr2_ASC_auto_sufficient_all 0.00 0.00 NA NA 0.00
joint_sr2_ASC_no_auto_all 0.00 0.00 NA NA 0.00
joint_sr3p_ASC_auto_deficient_all-5.34 1.92-2.79** NA 0.00
joint_sr3p_ASC_auto_sufficient_all-3.33 0.983-3.39*** NA 0.00
joint_sr3p_ASC_no_auto_all 0.977 0.798 1.22 NA 0.00
joint_taxi_ASC_auto_deficient_all-45.5 NA NA[***] 31.66 0.00
joint_taxi_ASC_auto_sufficient_all-11.7 0.00 NA NA 0.00
joint_taxi_ASC_no_auto_all-12.8 841.-0.02 NA 0.00
joint_tnc_shared_ASC_auto_deficient_all-37.8 NA NA[***] 42.60 0.00
joint_tnc_shared_ASC_auto_sufficient_all-13.2 0.00 NA NA 0.00
joint_tnc_shared_ASC_no_auto_all-24.5 NA NA[] 1.15 0.00
joint_tnc_single_ASC_auto_deficient_all-51.7 NA NA[***] 36.10 0.00
joint_tnc_single_ASC_auto_sufficient_all-14.0 0.00 NA NA 0.00
joint_tnc_single_ASC_no_auto_all-16.7 NA NA[] 0.72 0.00
joint_walk_ASC_auto_deficient_all-1.31 1.43-0.92 NA 0.00
joint_walk_ASC_auto_sufficient_all-3.63 1.11-3.27** NA 0.00
joint_walk_ASC_no_auto_all 0.968 1.21 0.80 NA 0.00
joint_walk_transit_ASC_auto_deficient_all 4.48 2.40 1.87 NA 0.00
joint_walk_transit_ASC_auto_sufficient_all-18.3 NA NA[] 0.00 0.00
joint_walk_transit_ASC_no_auto_all 13.6 1.87 7.24*** NA 0.00
local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-12.6 1.64-7.68*** NA 0.00
sr2_ASC_auto_deficient_eatout 0.235 0.415 0.57 NA 0.00
sr2_ASC_auto_sufficient_eatout 0.457 0.318 1.44 NA 0.00
sr2_ASC_no_auto_all 5.30 0.368 14.42*** NA 0.00
sr3p_ASC_auto_deficient_eatout-0.0844 0.448-0.19 NA 0.00
sr3p_ASC_auto_sufficient_eatout 0.502 0.313 1.60 NA 0.00
sr3p_ASC_no_auto_eatout 5.80 0.782 7.42*** NA 0.00
taxi_ASC_auto_deficient_eatout_othdiscr_social-34.3 NA NA[***] 10.47 0.00
taxi_ASC_auto_sufficient_eatout_othdiscr_social-2.38 0.505-4.71*** NA 0.00
taxi_ASC_no_auto_eatout_othdiscr_social-25.8 NA NA[] 0.00 0.00
tnc_shared_ASC_auto_deficient_eatout_othdiscr_social-2.92 1.01-2.89** NA 0.00
tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social-3.47 0.610-5.69*** NA 0.00
tnc_shared_ASC_no_auto_eatout_othdiscr_social 5.50 0.507 10.85*** NA 0.00
tnc_single_ASC_auto_deficient_eatout_othdiscr_social-30.0 NA NA[***] 15.06 0.00
tnc_single_ASC_auto_sufficient_eatout_othdiscr_social-2.09 0.375-5.57*** NA 0.00
tnc_single_ASC_no_auto_eatout_othdiscr_social 6.70 0.465 14.39*** NA 0.00
walk_ASC_auto_deficient_eatout 2.96 0.472 6.27*** NA 0.00
walk_ASC_auto_sufficient_eatout 0.804 0.390 2.06* NA 0.00
walk_ASC_no_auto_eatout 10.2 0.673 15.17*** NA 0.00
walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork 0.940 NA NA[] 0.00 0.00
walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-11.6 1.64-7.06*** NA 0.00
walk_transit_ASC_auto_deficient_eatout 12.3 1.69 7.30*** NA 0.00
walk_transit_ASC_auto_sufficient_eatout 10.8 1.67 6.43*** NA 0.00
walk_transit_ASC_no_auto_eatout 20.5 1.50 13.66*** NA 0.00
walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 0.691 0.157 4.40*** NA 0.00
bike_ASC_auto_deficient_escort-80.8 NA NA[***] 15.70 0.00
bike_ASC_auto_sufficient_escort-4.23 0.746-5.68*** NA 0.00
bike_ASC_no_auto_escort-82.6 NA NA[] 0.00 0.00
drive_transit_ASC_auto_deficient_escort 12.0 1.93 6.23*** NA 0.00
drive_transit_ASC_auto_sufficient_escort-38.4 NA NA[] 0.00 0.00
sr2_ASC_auto_deficient_escort-0.0289 0.415-0.07 NA 0.00
sr2_ASC_auto_sufficient_escort-0.446 0.436-1.02 NA 0.00
sr3p_ASC_auto_deficient_escort-0.242 0.420-0.58 NA 0.00
sr3p_ASC_auto_sufficient_escort-0.573 0.437-1.31 NA 0.00
sr3p_ASC_no_auto_escort-48.6 NA NA[] 0.00 0.00
taxi_ASC_auto_deficient_escort_othmaint_shopping 0.221 0.304 0.73 NA 0.00
taxi_ASC_auto_sufficient_escort_othmaint_shopping-1.87 0.295-6.35*** NA 0.00
taxi_ASC_no_auto_escort_othmaint_shopping 7.36 0.451 16.30*** NA 0.00
tnc_shared_ASC_auto_deficient_escort_othmaint_shopping-0.244 0.228-1.07 NA 0.00
tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping-2.71 0.286-9.46*** NA 0.00
tnc_shared_ASC_no_auto_escort_othmaint_shopping 6.66 0.440 15.13*** NA 0.00
tnc_single_ASC_auto_deficient_escort_othmaint_shopping 0.896 0.219 4.09*** NA 0.00
tnc_single_ASC_auto_sufficient_escort_othmaint_shopping-1.75 0.263-6.64*** NA 0.00
tnc_single_ASC_no_auto_escort_othmaint_shopping 7.44 0.437 17.05*** NA 0.00
walk_ASC_auto_deficient_escort-103. NA NA[*] 1.98 0.00
walk_ASC_auto_sufficient_escort-2.20 0.773-2.84** NA 0.00
walk_ASC_no_auto_escort 7.37 1.26 5.87*** NA 0.00
walk_transit_ASC_auto_deficient_escort 8.76 1.87 4.70*** NA 0.00
walk_transit_ASC_auto_sufficient_escort 7.57 1.77 4.29*** NA 0.00
walk_transit_ASC_no_auto_escort-44.3 NA NA[] 0.00 0.00
bike_ASC_auto_deficient_othdiscr 0.229 0.469 0.49 NA 0.00
bike_ASC_auto_sufficient_othdiscr-1.92 0.393-4.87*** NA 0.00
bike_ASC_no_auto_othdiscr 5.46 0.640 8.52*** NA 0.00
drive_transit_ASC_auto_deficient_othdiscr-61.4 NA NA[] 0.00 0.00
drive_transit_ASC_auto_sufficient_othdiscr 11.3 1.94 5.85*** NA 0.00
sr2_ASC_auto_deficient_othdiscr 0.739 0.378 1.96 NA 0.00
sr2_ASC_auto_sufficient_othdiscr 0.123 0.299 0.41 NA 0.00
sr3p_ASC_auto_deficient_othdiscr 0.935 0.364 2.57* NA 0.00
sr3p_ASC_auto_sufficient_othdiscr 0.246 0.295 0.83 NA 0.00
sr3p_ASC_no_auto_othdiscr 6.41 0.497 12.89*** NA 0.00
walk_ASC_auto_deficient_othdiscr 1.63 0.545 2.98** NA 0.00
walk_ASC_auto_sufficient_othdiscr 1.09 0.261 4.20*** NA 0.00
walk_ASC_no_auto_othdiscr 8.77 0.545 16.09*** NA 0.00
walk_transit_ASC_auto_deficient_othdiscr 13.3 1.67 7.97*** NA 0.00
walk_transit_ASC_auto_sufficient_othdiscr 11.3 1.65 6.84*** NA 0.00
walk_transit_ASC_no_auto_othdiscr 20.4 1.44 14.14*** NA 0.00
bike_ASC_auto_deficient_othmaint-1.55 0.792-1.96 NA 0.00
bike_ASC_auto_sufficient_othmaint-2.48 0.551-4.51*** NA 0.00
bike_ASC_no_auto_othmaint 7.09 0.520 13.63*** NA 0.00
drive_transit_ASC_auto_deficient_othmaint-65.9 NA NA[] 0.00 0.00
drive_transit_ASC_auto_sufficient_othmaint-32.2 NA NA[] 0.00 0.00
sr2_ASC_auto_deficient_othmaint-0.174 0.421-0.41 NA 0.00
sr2_ASC_auto_sufficient_othmaint 0.222 0.306 0.72 NA 0.00
sr3p_ASC_auto_deficient_othmaint-1.77 0.796-2.22* NA 0.00
sr3p_ASC_auto_sufficient_othmaint-0.251 0.322-0.78 NA 0.00
sr3p_ASC_no_auto_othmaint 4.59 0.844 5.44*** NA 0.00
walk_ASC_auto_deficient_othmaint 1.83 0.694 2.64** NA 0.00
walk_ASC_auto_sufficient_othmaint 0.890 0.354 2.51* NA 0.00
walk_ASC_no_auto_othmaint 6.29 0.883 7.13*** NA 0.00
walk_transit_ASC_auto_deficient_othmaint 9.41 1.92 4.90*** NA 0.00
walk_transit_ASC_auto_sufficient_othmaint 11.0 1.66 6.59*** NA 0.00
walk_transit_ASC_no_auto_othmaint 20.6 1.45 14.26*** NA 0.00
bike_ASC_auto_deficient_school-0.418 1.07-0.39 NA 0.00
bike_ASC_auto_sufficient_school-3.51 0.742-4.72*** NA 0.00
bike_ASC_no_auto_school-28.7 NA NA[] 0.00 0.00
coef_age010_trn_multiplier_school_univ-1.11 0.279-3.98*** NA 0.00
coef_age1619_da_multiplier_school_univ-1.60 0.496-3.22** NA 0.00
coef_age16p_sr_multiplier_school_univ_work_atwork-0.721 0.357-2.02* NA 0.00
coef_hhsize2_sr_multiplier_school_univ-0.534 0.528-1.01 NA 0.00
coef_ivt_school_univ-0.0212 0.00207-10.25*** NA 0.00
commuter_rail_ASC_school_univ-32.4 NA NA[***] 230.28 0.00
drive_ferry_ASC_school_univ 2.02 NA NA[] 0.00 0.00
drive_light_rail_ASC_school_univ-19.1 0.0154-BIG*** NA 0.00
drive_transit_ASC_auto_deficient_school-96.0 NA NA[***] BIG 0.00
drive_transit_ASC_auto_sufficient_school-46.2 153.-0.30 NA 0.00
drive_transit_CBD_ASC_school_univ 62.5 153. 0.41 NA 0.00
express_bus_ASC_school_univ 0.325 NA NA[] 0.00 0.00
heavy_rail_ASC_school_univ-12.4 305.-0.04 NA 0.00
local_bus_ASC_school_univ-13.9 305.-0.05 NA 0.00
sr2_ASC_auto_deficient_school 0.116 0.860 0.14 NA 0.00
sr2_ASC_auto_sufficient_school-1.65 0.362-4.55*** NA 0.00
sr3p_ASC_auto_deficient_school 0.721 0.848 0.85 NA 0.00
sr3p_ASC_auto_sufficient_school-1.19 0.353-3.36*** NA 0.00
sr3p_ASC_no_auto_school-6.02 NA NA[] 0.00 0.00
taxi_ASC_auto_deficient_school 1.59 0.932 1.71 NA 0.00
taxi_ASC_auto_sufficient_school-1.63 0.602-2.71** NA 0.00
taxi_ASC_no_auto_school_univ-7.00 0.00 NA NA 0.00
tnc_shared_ASC_auto_deficient_school 0.968 0.905 1.07 NA 0.00
tnc_shared_ASC_auto_sufficient_school-2.77 0.626-4.43*** NA 0.00
tnc_shared_ASC_no_auto_school-7.00 0.00 NA NA 0.00
tnc_single_ASC_auto_deficient_school-3.36 113.-0.03 NA 0.00
tnc_single_ASC_auto_sufficient_school-2.37 0.650-3.65*** NA 0.00
tnc_single_ASC_no_auto_school-7.00 0.00 NA NA 0.00
walk_ASC_auto_deficient_school 3.23 0.931 3.47*** NA 0.00
walk_ASC_auto_sufficient_school 0.692 0.421 1.64 NA 0.00
walk_ASC_no_auto_school 31.9 NA NA[***] 241.14 0.00
walk_ferry_ASC_school_univ 2.02 NA NA[] 0.00 0.00
walk_light_rail_ASC_school_univ-12.1 305.-0.04 NA 0.00
walk_transit_ASC_auto_deficient_school 17.1 305. 0.06 NA 0.00
walk_transit_ASC_auto_sufficient_school 14.2 305. 0.05 NA 0.00
walk_transit_ASC_no_auto_school 48.7 NA NA[***] BIG 0.00
walk_transit_CBD_ASC_school_univ 0.788 0.260 3.03** NA 0.00
bike_ASC_auto_deficient_shopping-0.982 0.499-1.97* NA 0.00
bike_ASC_auto_sufficient_shopping-4.77 0.991-4.81*** NA 0.00
bike_ASC_no_auto_shopping 6.35 0.481 13.21*** NA 0.00
drive_transit_ASC_auto_deficient_shopping-69.1 NA NA[] 0.00 0.00
drive_transit_ASC_auto_sufficient_shopping-74.4 NA NA[] 0.00 0.00
sr2_ASC_auto_deficient_shopping 0.341 0.336 1.01 NA 0.00
sr2_ASC_auto_sufficient_shopping 0.145 0.293 0.49 NA 0.00
sr3p_ASC_auto_deficient_shopping-0.262 0.376-0.70 NA 0.00
sr3p_ASC_auto_sufficient_shopping-0.296 0.301-0.98 NA 0.00
sr3p_ASC_no_auto_shopping 5.65 0.463 12.20*** NA 0.00
walk_ASC_auto_deficient_shopping 2.80 0.360 7.78*** NA 0.00
walk_ASC_auto_sufficient_shopping 0.473 0.249 1.90 NA 0.00
walk_ASC_no_auto_shopping 8.22 0.493 16.65*** NA 0.00
walk_transit_ASC_auto_deficient_shopping 11.3 1.67 6.73*** NA 0.00
walk_transit_ASC_auto_sufficient_shopping 9.75 1.66 5.86*** NA 0.00
walk_transit_ASC_no_auto_shopping 19.7 1.43 13.75*** NA 0.00
bike_ASC_auto_deficient_social-0.269 1.04-0.26 NA 0.00
bike_ASC_auto_sufficient_social-0.854 0.716-1.19 NA 0.00
bike_ASC_no_auto_social 5.51 0.583 9.46*** NA 0.00
drive_transit_ASC_auto_deficient_social-52.8 NA NA[] 0.00 0.00
drive_transit_ASC_auto_sufficient_social-48.0 NA NA[] 0.00 0.00
sr2_ASC_auto_deficient_social 1.43 0.463 3.08** NA 0.00
sr2_ASC_auto_sufficient_social 0.977 0.351 2.78** NA 0.00
sr3p_ASC_auto_deficient_social 0.676 0.546 1.24 NA 0.00
sr3p_ASC_auto_sufficient_social 0.757 0.362 2.09* NA 0.00
sr3p_ASC_no_auto_social 3.97 0.820 4.84*** NA 0.00
walk_ASC_auto_deficient_social 3.17 0.911 3.48*** NA 0.00
walk_ASC_auto_sufficient_social 2.66 0.491 5.42*** NA 0.00
walk_ASC_no_auto_social 6.44 0.683 9.43*** NA 0.00
walk_transit_ASC_auto_deficient_social 12.9 1.75 7.36*** NA 0.00
walk_transit_ASC_auto_sufficient_social 12.6 1.67 7.54*** NA 0.00
walk_transit_ASC_no_auto_social 19.0 1.46 13.02*** NA 0.00
bike_ASC_auto_deficient_univ-0.669 NA NA[] 0.00 0.00
bike_ASC_auto_sufficient_univ-1.94 NA NA[] 0.00 0.00
bike_ASC_no_auto_univ 4.29 NA NA[] 0.00 0.00
drive_transit_ASC_auto_deficient_univ 1.85 NA NA[] 0.00 0.00
drive_transit_ASC_auto_sufficient_univ 1.36 NA NA[] 0.00 0.00
sr2_ASC_auto_deficient_univ-1.69 NA NA[] 0.00 0.00
sr2_ASC_auto_sufficient_univ-1.86 NA NA[] 0.00 0.00
sr3p_ASC_auto_deficient_univ-1.73 NA NA[] 0.00 0.00
sr3p_ASC_auto_sufficient_univ-1.90 4.83e-14-BIG*** NA 0.00
sr3p_ASC_no_auto_univ-6.06 2.85e-18-BIG*** NA 0.00
taxi_ASC_auto_deficient_univ 4.25 0.00 NA[] 0.00 0.00
taxi_ASC_auto_sufficient_univ-0.313 0.00 NA[] 0.00 0.00
tnc_shared_ASC_auto_deficient_univ 3.25 0.00 NA[] 0.00 0.00
tnc_shared_ASC_auto_sufficient_univ-0.907 0.00 NA[] 0.00 0.00
tnc_shared_ASC_no_auto_univ-5.81 0.00 NA[] 0.00 0.00
tnc_single_ASC_auto_deficient_univ 1.02 0.00 NA[] 0.00 0.00
tnc_single_ASC_auto_sufficient_univ 0.209 0.00 NA[] 0.00 0.00
tnc_single_ASC_no_auto_univ-2.52 0.00 NA[] 0.00 0.00
walk_ASC_auto_deficient_univ 4.51 0.00 NA[] 0.00 0.00
walk_ASC_auto_sufficient_univ 1.06 0.00 NA[] 0.00 0.00
walk_ASC_no_auto_univ 6.41 0.00 NA[] 0.00 0.00
walk_transit_ASC_auto_deficient_univ 3.14 0.00 NA[] 0.00 0.00
walk_transit_ASC_auto_sufficient_univ 0.473 0.00 NA[] 0.00 0.00
walk_transit_ASC_no_auto_univ 8.79 0.00 NA[] 0.00 0.00
bike_ASC_auto_deficient_work 0.109 0.159 0.69 NA 0.00
bike_ASC_auto_sufficient_work-1.71 0.201-8.54*** NA 0.00
bike_ASC_no_auto_work 7.92 0.643 12.31*** NA 0.00
coef_hhsize1_sr_multiplier_work-0.679 0.174-3.90*** NA 0.00
coef_ivt_work-0.0131 0.000672-19.44*** NA 0.00
commuter_rail_ASC_work 12.1 0.561 21.60*** NA 0.00
drive_ferry_ASC_work 0.933 0.00 NA[] 0.00 0.00
drive_light_rail_ASC_work-36.0 NA NA[] 0.00 0.00
drive_transit_ASC_auto_deficient_work-10.8 0.493-21.96*** NA 0.00
drive_transit_ASC_auto_sufficient_work-11.9 0.496-24.03*** NA 0.00
drive_transit_CBD_ASC_work 1.43 0.593 2.41* NA 0.00
express_bus_ASC_work-54.6 NA NA[] 0.00 0.00
heavy_rail_ASC_work 11.0 0.185 59.50*** NA 0.00
local_bus_ASC_work 10.3 0.173 59.60*** NA 0.00
sr2_ASC_auto_deficient_work 0.463 0.367 1.26 NA 0.00
sr2_ASC_auto_sufficient_work-0.391 0.369-1.06 NA 0.00
sr3p_ASC_auto_deficient_work-0.0501 0.372-0.13 NA 0.00
sr3p_ASC_auto_sufficient_work-0.716 0.373-1.92 NA 0.00
sr3p_ASC_no_auto_work 5.29 0.646 8.18*** NA 0.00
taxi_ASC_auto_deficient_work-1.36 0.326-4.16*** NA 0.00
taxi_ASC_auto_sufficient_work-56.0 NA NA[***] 89.02 0.00
taxi_ASC_no_auto_work 9.43 0.651 14.50*** NA 0.00
tnc_shared_ASC_auto_deficient_work-2.43 0.420-5.80*** NA 0.00
tnc_shared_ASC_auto_sufficient_work-63.4 NA NA[***] 180.49 0.00
tnc_shared_ASC_no_auto_work-47.3 NA NA[] 0.00 0.00
tnc_single_ASC_auto_deficient_work-0.788 0.211-3.73*** NA 0.00
tnc_single_ASC_auto_sufficient_work-4.88 1.00-4.86*** NA 0.00
tnc_single_ASC_no_auto_work 10.3 0.630 16.35*** NA 0.00
walk_ASC_auto_deficient_work 2.13 0.200 10.62*** NA 0.00
walk_ASC_auto_sufficient_work 0.176 0.215 0.82 NA 0.00
walk_ASC_no_auto_work 10.4 0.654 15.83*** NA 0.00
walk_ferry_ASC_work 0.933 0.00 NA[] 0.00 0.00
walk_light_rail_ASC_work 11.2 0.178 62.93*** NA 0.00
walk_transit_ASC_auto_deficient_work-9.77 0.182-53.62*** NA 0.00
walk_transit_ASC_auto_sufficient_work-11.3 0.187-60.43*** NA 0.00
walk_transit_ASC_no_auto_work-0.818 0.576-1.42 NA 0.00
walk_transit_CBD_ASC_work 0.977 0.121 8.07*** NA 0.00
bike_ASC_auto_deficient_atwork-1.38 0.914-1.51 NA 0.00
bike_ASC_auto_sufficient_atwork 15.7 726. 0.02 NA 0.00
bike_ASC_no_auto_atwork-28.4 NA NA[] 0.00 0.00
coef_age010_trn_multiplier_atwork 0.000722 0.00 NA[] 0.00 0.00
coef_age1619_da_multiplier_atwork-0.807 1.03-0.79 NA 0.00
coef_ivt_atwork-0.0210 0.00255-8.26*** NA 0.00
drive_transit_ASC_auto_deficient_atwork-999. 0.00 NA[] 0.00 0.00
drive_transit_ASC_auto_sufficient_atwork-999. 0.00 NA[] 0.00 0.00
drive_transit_CBD_ASC_atwork 0.564 0.00 NA[] 0.00 0.00
sr2_ASC_auto_deficient_atwork-1.89 0.573-3.31*** NA 0.00
sr2_ASC_auto_sufficient_atwork-0.652 0.398-1.64 NA 0.00
sr3p_ASC_auto_deficient_atwork-2.38 0.580-4.11*** NA 0.00
sr3p_ASC_auto_sufficient_atwork-0.822 0.401-2.05* NA 0.00
sr3p_ASC_no_auto_atwork 6.24 0.790 7.90*** NA 0.00
taxi_ASC_auto_deficient_atwork-28.5 NA NA[***] 64.57 0.00
taxi_ASC_auto_sufficient_atwork-28.6 NA NA[***] 38.13 0.00
taxi_ASC_no_auto_atwork 10.2 0.970 10.52*** NA 0.00
tnc_shared_ASC_auto_deficient_atwork-3.91 0.639-6.12*** NA 0.00
tnc_shared_ASC_auto_sufficient_atwork-2.69 0.496-5.42*** NA 0.00
tnc_shared_ASC_no_auto_atwork 9.06 0.890 10.18*** NA 0.00
tnc_single_ASC_auto_deficient_atwork-3.11 0.611-5.09*** NA 0.00
tnc_single_ASC_auto_sufficient_atwork-2.63 0.529-4.97*** NA 0.00
tnc_single_ASC_no_auto_atwork 10.5 0.917 11.43*** NA 0.00
walk_ASC_auto_deficient_atwork 1.82 0.671 2.72** NA 0.00
walk_ASC_auto_sufficient_atwork 1.43 0.379 3.77*** NA 0.00
walk_ASC_no_auto_atwork 12.8 1.04 12.31*** NA 0.00
walk_transit_ASC_auto_deficient_atwork 8.99 1.76 5.10*** NA 0.00
walk_transit_ASC_auto_sufficient_atwork 9.13 1.81 5.04*** NA 0.00
walk_transit_ASC_no_auto_atwork 21.2 1.66 12.81*** NA 0.00
walk_transit_CBD_ASC_atwork 0.352 0.549 0.64 NA 0.00
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.parameter_summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "TojXWivZsx7M" - }, - "source": [ - "# Output Estimation Results" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from activitysim.estimation.larch import update_coefficients\n", - "result_dir = data.edb_directory/\"estimated\"\n", - "update_coefficients(\n", - " model, data, result_dir,\n", - " output_file=f\"{modelname}_coefficients_revised.csv\",\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write the model estimation report, including coefficient t-statistic and log likelihood" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeffnewman/LocalGit/larch/larch/util/excel.py:521: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.\n", - " xl = ExcelWriter(filename, engine='xlsxwriter_larch', model=model, **kwargs)\n" - ] - } - ], - "source": [ - "model.to_xlsx(\n", - " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", - " data_statistics=False,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Next Steps\n", - "\n", - "The final step is to either manually or automatically copy the `*_coefficients_revised.csv` file to the configs folder, rename it to `*_coefficients.csv`, and run ActivitySim in simulation mode." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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coefficient_namevalueconstrainParameter     
0coef_one1.000000T-999-999. 0.00 NA 0.00
1coef_nest_root1.000000T1 1.00 0.00 NA 0.00
2coef_nest_AUTO0.720000Tbike_ASC_auto_deficient_atwork-1.05 0.344-3.07** 0.00
3coef_nest_AUTO_DRIVEALONE0.350000Tbike_ASC_auto_deficient_eatout-1.46 0.396-3.69*** 0.00
4coef_nest_AUTO_SHAREDRIDE20.350000Tbike_ASC_auto_deficient_escort-3.98 0.406-9.79*** 0.00
............bike_ASC_auto_deficient_othdiscr-0.910 0.203-4.47*** 0.00
302walk_transit_CBD_ASC_atwork0.351950Fbike_ASC_auto_deficient_othmaint-2.06 0.323-6.37*** 0.00
303drive_transit_CBD_ASC_eatout_escort_othdiscr_o...0.553404Fbike_ASC_auto_deficient_school-1.20 0.964-1.24 0.00
304drive_transit_CBD_ASC_school_univ62.459413Fbike_ASC_auto_deficient_shopping-1.74 0.218-7.99*** 0.00
305drive_transit_CBD_ASC_work1.432631Fbike_ASC_auto_deficient_social 0.361 0.325 1.11 0.00
bike_ASC_auto_deficient_univ-0.669 NA NA 0.00
bike_ASC_auto_deficient_work-0.279 0.0926-3.01** 0.00
bike_ASC_auto_sufficient_atwork 15.7 470. 0.03 0.00
bike_ASC_auto_sufficient_eatout-1.85 0.159-11.60*** 0.00
bike_ASC_auto_sufficient_escort-5.65 0.337-16.76*** 0.00
bike_ASC_auto_sufficient_othdiscr-1.86 0.105-17.64*** 0.00
bike_ASC_auto_sufficient_othmaint-3.17 0.193-16.40*** 0.00
bike_ASC_auto_sufficient_school-2.21 0.919-2.40* 0.00
bike_ASC_auto_sufficient_shopping-3.14 0.138-22.70*** 0.00
bike_ASC_auto_sufficient_social-2.14 0.207-10.36*** 0.00
bike_ASC_auto_sufficient_univ-1.94 0.00657-295.46*** 0.00
bike_ASC_auto_sufficient_work-1.93 0.0814-23.72*** 0.00
bike_ASC_no_auto_atwork-0.910 76.5-0.01 0.00
bike_ASC_no_auto_eatout 0.428 75.9 0.01 0.00
bike_ASC_no_auto_escort-0.745 75.9-0.01 0.00
bike_ASC_no_auto_othdiscr-0.501 75.9-0.01 0.00
bike_ASC_no_auto_othmaint 1.53 75.9 0.02 0.00
bike_ASC_no_auto_school 13.5 109. 0.12 0.00
bike_ASC_no_auto_shopping 0.951 75.9 0.01 0.00
bike_ASC_no_auto_social 0.374 75.9 0.00 0.00
bike_ASC_no_auto_univ 4.29 0.00148 BIG*** 0.00
bike_ASC_no_auto_work 3.43 75.9 0.05 0.00
coef_age010_trn_multiplier_atwork 0.000722 0.00785 0.09 0.00
coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work 0.490 0.176 2.79** 0.00
coef_age010_trn_multiplier_school_univ-0.851 0.0900-9.46*** 0.00
coef_age1619_da_multiplier_atwork-0.175 0.205-0.85 0.00
coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work 0.0357 0.0425 0.84 0.00
coef_age1619_da_multiplier_school_univ-1.67 0.0748-22.31*** 0.00
coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social-1.58 0.0842-18.71*** 0.00
coef_age16p_sr_multiplier_school_univ_work_atwork-0.688 0.0682-10.08*** 0.00
coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork-0.0721 0.0312-2.31* 0.00
coef_hhsize1_sr_multiplier_work-0.822 0.0571-14.39*** 0.00
coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork-0.00404 0.0178-0.23 0.00
coef_hhsize2_sr_multiplier_school_univ-0.743 0.0912-8.15*** 0.00
coef_ivt_atwork-0.0112 0.000479-23.41*** 0.00
coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social-0.00691 0.000145-47.76*** 0.00
coef_ivt_school_univ-0.0103 0.000316-32.62*** 0.00
coef_ivt_work-0.00791 0.000147-53.62*** 0.00
coef_nest_AUTO 0.720 0.00 NA 1.00
coef_nest_AUTO_DRIVEALONE 0.350 0.00 NA 1.00
coef_nest_AUTO_SHAREDRIDE2 0.350 0.00 NA 1.00
coef_nest_AUTO_SHAREDRIDE3 0.350 0.00 NA 1.00
coef_nest_NONMOTORIZED 0.720 0.00 NA 1.00
coef_nest_RIDEHAIL 0.360 0.00 NA 1.00
coef_nest_TRANSIT 0.720 0.00 NA 1.00
coef_nest_TRANSIT_DRIVEACCESS 0.500 0.00 NA 1.00
coef_nest_TRANSIT_WALKACCESS 0.500 0.00 NA 1.00
coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork 0.0770 0.0494 1.56 0.00
coef_test_school_univ 0.518 0.913 0.57 0.00
commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.958 16.4-0.06 0.00
commuter_rail_ASC_school_univ-0.258 NA NA 0.00
commuter_rail_ASC_work 0.196 278. 0.00 0.00
drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.628 16.4-0.04 0.00
drive_ferry_ASC_school_univ 1.43 NA NA 0.00
drive_ferry_ASC_work 0.232 278. 0.00 0.00
drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.325 16.4-0.02 0.00
drive_light_rail_ASC_school_univ 0.691 NA NA 0.00
drive_light_rail_ASC_work 0.530 278. 0.00 0.00
drive_transit_ASC_auto_deficient_atwork-999. 0.000360-BIG*** 0.00
drive_transit_ASC_auto_deficient_eatout-0.173 16.4-0.01 0.00
drive_transit_ASC_auto_deficient_escort-2.19 16.4-0.13 0.00
drive_transit_ASC_auto_deficient_othdiscr-0.852 16.4-0.05 0.00
drive_transit_ASC_auto_deficient_othmaint-1.36 16.4-0.08 0.00
drive_transit_ASC_auto_deficient_school 1.59 NA NA 0.00
drive_transit_ASC_auto_deficient_shopping-1.58 16.4-0.10 0.00
drive_transit_ASC_auto_deficient_social 0.610 16.4 0.04 0.00
drive_transit_ASC_auto_deficient_univ 1.85 NA NA 0.00
drive_transit_ASC_auto_deficient_work-0.824 278.-0.00 0.00
drive_transit_ASC_auto_sufficient_atwork-999. 0.000176-BIG*** 0.00
drive_transit_ASC_auto_sufficient_eatout-1.65 16.4-0.10 0.00
drive_transit_ASC_auto_sufficient_escort-5.31 16.4-0.32 0.00
drive_transit_ASC_auto_sufficient_othdiscr-1.24 16.4-0.08 0.00
drive_transit_ASC_auto_sufficient_othmaint-2.80 16.4-0.17 0.00
drive_transit_ASC_auto_sufficient_school 0.105 NA NA 0.00
drive_transit_ASC_auto_sufficient_shopping-4.16 16.4-0.25 0.00
drive_transit_ASC_auto_sufficient_social-1.37 16.4-0.08 0.00
drive_transit_ASC_auto_sufficient_univ 1.36 NA NA 0.00
drive_transit_ASC_auto_sufficient_work-1.73 278.-0.01 0.00
drive_transit_ASC_no_auto_all 0.00 NA NA 0.00
drive_transit_CBD_ASC_atwork 0.564 NA NA 0.00
drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 1.37 0.142 9.63*** 0.00
drive_transit_CBD_ASC_school_univ 1.23 0.251 4.89*** 0.00
drive_transit_CBD_ASC_work 1.64 0.0698 23.45*** 0.00
express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.342 16.4-0.02 0.00
express_bus_ASC_school_univ-0.451 NA NA 0.00
express_bus_ASC_work-0.881 278.-0.00 0.00
heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.207 16.4-0.01 0.00
heavy_rail_ASC_school_univ 0.110 NA NA 0.00
heavy_rail_ASC_work 0.513 278. 0.00 0.00
joint_bike_ASC_auto_deficient_all-6.18 1.34-4.62*** 0.00
joint_bike_ASC_auto_sufficient_all-7.10 0.465-15.28*** 0.00
joint_bike_ASC_no_auto_all-2.67 1.08-2.48* 0.00
joint_drive_transit_ASC_auto_deficient_all-6.04 16.4-0.37 0.00
joint_drive_transit_ASC_auto_sufficient_all-7.79 16.4-0.48 0.00
joint_drive_transit_ASC_no_auto_all 0.00 0.00 NA 0.00
joint_sr2_ASC_auto_deficient_all 0.00 0.00 NA 0.00
joint_sr2_ASC_auto_sufficient_all 0.00 0.00 NA 0.00
joint_sr2_ASC_no_auto_all 0.00 0.00 NA 0.00
joint_sr3p_ASC_auto_deficient_all-1.49 0.258-5.77*** 0.00
joint_sr3p_ASC_auto_sufficient_all-2.29 0.133-17.17*** 0.00
joint_sr3p_ASC_no_auto_all 0.699 0.470 1.49 0.00
joint_taxi_ASC_auto_deficient_all-9.82 8.15-1.21 0.00
joint_taxi_ASC_auto_sufficient_all-11.7 0.00 NA 0.00
joint_taxi_ASC_no_auto_all-4.58 4.29-1.07 0.00
joint_tnc_shared_ASC_auto_deficient_all-11.2 17.0-0.66 0.00
joint_tnc_shared_ASC_auto_sufficient_all-13.2 0.00 NA 0.00
joint_tnc_shared_ASC_no_auto_all-4.72 2.18-2.17* 0.00
joint_tnc_single_ASC_auto_deficient_all-9.90 7.20-1.38 0.00
joint_tnc_single_ASC_auto_sufficient_all-14.0 0.00 NA 0.00
joint_tnc_single_ASC_no_auto_all-3.86 2.05-1.88 0.00
joint_walk_ASC_auto_deficient_all-2.22 0.376-5.89*** 0.00
joint_walk_ASC_auto_sufficient_all-4.34 0.245-17.76*** 0.00
joint_walk_ASC_no_auto_all-0.367 0.727-0.51 0.00
joint_walk_transit_ASC_auto_deficient_all-5.30 16.4-0.32 0.00
joint_walk_transit_ASC_auto_sufficient_all-18.3 655.-0.03 0.00
joint_walk_transit_ASC_no_auto_all 0.386 16.4 0.02 0.00
local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-1.18 16.4-0.07 0.00
local_bus_ASC_school_univ-0.819 NA NA 0.00
local_bus_ASC_work-0.179 278.-0.00 0.00
sr2_ASC_auto_deficient_atwork-1.49 0.132-11.34*** 0.00
sr2_ASC_auto_deficient_eatout 0.874 0.155 5.64*** 0.00
sr2_ASC_auto_deficient_escort 0.254 0.160 1.58 0.00
sr2_ASC_auto_deficient_othdiscr 1.06 0.131 8.13*** 0.00
sr2_ASC_auto_deficient_othmaint 0.584 0.145 4.02*** 0.00
sr2_ASC_auto_deficient_school 0.518 0.928 0.56 0.00
sr2_ASC_auto_deficient_shopping 0.522 0.127 4.09*** 0.00
sr2_ASC_auto_deficient_social 2.30 0.215 10.70*** 0.00
sr2_ASC_auto_deficient_univ-1.69 NA NA 0.00
sr2_ASC_auto_deficient_work 0.424 0.0892 4.76*** 0.00
sr2_ASC_auto_sufficient_atwork-0.772 0.0784-9.85*** 0.00
sr2_ASC_auto_sufficient_eatout 1.17 0.105 11.19*** 0.00
sr2_ASC_auto_sufficient_escort 0.427 0.129 3.31*** 0.00
sr2_ASC_auto_sufficient_othdiscr 0.830 0.101 8.23*** 0.00
sr2_ASC_auto_sufficient_othmaint 0.642 0.103 6.24*** 0.00
sr2_ASC_auto_sufficient_school-0.720 0.916-0.79 0.00
sr2_ASC_auto_sufficient_shopping 0.518 0.100 5.16*** 0.00
sr2_ASC_auto_sufficient_social 0.867 0.110 7.88*** 0.00
sr2_ASC_auto_sufficient_univ-1.86 NA NA 0.00
sr2_ASC_auto_sufficient_work-0.295 0.0860-3.43*** 0.00
sr2_ASC_no_auto_all 1.22 75.9 0.02 0.00
sr3p_ASC_auto_deficient_atwork-1.81 0.136-13.30*** 0.00
sr3p_ASC_auto_deficient_eatout 0.184 0.187 0.98 0.00
sr3p_ASC_auto_deficient_escort 0.0300 0.163 0.18 0.00
sr3p_ASC_auto_deficient_othdiscr 1.42 0.124 11.40*** 0.00
sr3p_ASC_auto_deficient_othmaint-0.607 0.223-2.71** 0.00
sr3p_ASC_auto_deficient_school 1.10 0.927 1.18 0.00
sr3p_ASC_auto_deficient_shopping 0.180 0.138 1.31 0.00
sr3p_ASC_auto_deficient_social 2.04 0.223 9.16*** 0.00
sr3p_ASC_auto_deficient_univ-1.73 1.83e-05-BIG*** 0.00
sr3p_ASC_auto_deficient_work-0.127 0.0922-1.38 0.00
sr3p_ASC_auto_sufficient_atwork-0.954 0.0797-11.97*** 0.00
sr3p_ASC_auto_sufficient_eatout 1.13 0.105 10.73*** 0.00
sr3p_ASC_auto_sufficient_escort 0.400 0.129 3.10** 0.00
sr3p_ASC_auto_sufficient_othdiscr 0.929 0.101 9.25*** 0.00
sr3p_ASC_auto_sufficient_othmaint 0.257 0.105 2.44* 0.00
sr3p_ASC_auto_sufficient_school-0.151 0.916-0.16 0.00
sr3p_ASC_auto_sufficient_shopping 0.275 0.101 2.72** 0.00
sr3p_ASC_auto_sufficient_social 0.870 0.110 7.91*** 0.00
sr3p_ASC_auto_sufficient_univ-1.90 2.57e-05-BIG*** 0.00
sr3p_ASC_auto_sufficient_work-0.662 0.0868-7.63*** 0.00
sr3p_ASC_no_auto_atwork 1.49 75.9 0.02 0.00
sr3p_ASC_no_auto_eatout 1.62 75.9 0.02 0.00
sr3p_ASC_no_auto_escort-1.87 75.9-0.02 0.00
sr3p_ASC_no_auto_othdiscr 1.43 75.9 0.02 0.00
sr3p_ASC_no_auto_othmaint 0.296 75.9 0.00 0.00
sr3p_ASC_no_auto_school-6.02 105.-0.06 0.00
sr3p_ASC_no_auto_shopping 0.881 75.9 0.01 0.00
sr3p_ASC_no_auto_social-1.17 75.9-0.02 0.00
sr3p_ASC_no_auto_univ-6.06 1.79e-05-BIG*** 0.00
sr3p_ASC_no_auto_work 0.614 75.9 0.01 0.00
taxi_ASC_auto_deficient_atwork-4.59 0.435-10.57*** 0.00
taxi_ASC_auto_deficient_eatout_othdiscr_social-3.26 0.483-6.76*** 0.00
taxi_ASC_auto_deficient_escort_othmaint_shopping-0.930 0.118-7.89*** 0.00
taxi_ASC_auto_deficient_school 0.840 0.935 0.90 0.00
taxi_ASC_auto_deficient_univ 4.25 1.62e-05 BIG*** 0.00
taxi_ASC_auto_deficient_work-2.04 0.205-9.95*** 0.00
taxi_ASC_auto_sufficient_atwork-3.34 0.150-22.22*** 0.00
taxi_ASC_auto_sufficient_eatout_othdiscr_social-3.93 0.246-15.96*** 0.00
taxi_ASC_auto_sufficient_escort_othmaint_shopping-2.78 0.0965-28.85*** 0.00
taxi_ASC_auto_sufficient_school-2.26 0.922-2.45* 0.00
taxi_ASC_auto_sufficient_univ-0.313 NA NA 0.00
taxi_ASC_auto_sufficient_work-4.85 0.303-16.01*** 0.00
taxi_ASC_no_auto_atwork 3.65 75.9 0.05 0.00
taxi_ASC_no_auto_eatout_othdiscr_social-0.571 75.9-0.01 0.00
taxi_ASC_no_auto_escort_othmaint_shopping 1.45 75.9 0.02 0.00
taxi_ASC_no_auto_school_univ-7.00 0.00 NA 0.00
taxi_ASC_no_auto_work 3.94 75.9 0.05 0.00
tnc_shared_ASC_auto_deficient_atwork-5.17 0.414-12.49*** 0.00
tnc_shared_ASC_auto_deficient_eatout_othdiscr_social-4.46 0.900-4.95*** 0.00
tnc_shared_ASC_auto_deficient_escort_othmaint_shopping-1.24 0.109-11.36*** 0.00
tnc_shared_ASC_auto_deficient_school 0.283 0.933 0.30 0.00
tnc_shared_ASC_auto_deficient_univ 3.25 1.25e-05 BIG*** 0.00
tnc_shared_ASC_auto_deficient_work-3.40 0.214-15.89*** 0.00
tnc_shared_ASC_auto_sufficient_atwork-4.08 0.166-24.64*** 0.00
tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social-4.32 0.199-21.67*** 0.00
tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping-3.29 0.0920-35.76*** 0.00
tnc_shared_ASC_auto_sufficient_school-3.62 0.927-3.91*** 0.00
tnc_shared_ASC_auto_sufficient_univ-0.907 NA NA 0.00
tnc_shared_ASC_auto_sufficient_work-6.48 0.411-15.77*** 0.00
tnc_shared_ASC_no_auto_atwork 3.06 75.9 0.04 0.00
tnc_shared_ASC_no_auto_eatout_othdiscr_social-0.0803 75.9-0.00 0.00
tnc_shared_ASC_no_auto_escort_othmaint_shopping 0.685 75.9 0.01 0.00
tnc_shared_ASC_no_auto_school-7.00 0.00 NA 0.00
tnc_shared_ASC_no_auto_univ-5.81 NA NA 0.00
tnc_shared_ASC_no_auto_work 2.23 75.9 0.03 0.00
tnc_single_ASC_auto_deficient_atwork-4.24 0.319-13.29*** 0.00
tnc_single_ASC_auto_deficient_eatout_othdiscr_social-3.27 0.436-7.50*** 0.00
tnc_single_ASC_auto_deficient_escort_othmaint_shopping-0.340 0.102-3.34*** 0.00
tnc_single_ASC_auto_deficient_school-0.0137 0.943-0.01 0.00
tnc_single_ASC_auto_deficient_univ 1.02 NA NA 0.00
tnc_single_ASC_auto_deficient_work-1.30 0.115-11.28*** 0.00
tnc_single_ASC_auto_sufficient_atwork-3.14 0.126-25.00*** 0.00
tnc_single_ASC_auto_sufficient_eatout_othdiscr_social-3.26 0.149-21.84*** 0.00
tnc_single_ASC_auto_sufficient_escort_othmaint_shopping-2.20 0.0833-26.43*** 0.00
tnc_single_ASC_auto_sufficient_school-2.07 0.920-2.25* 0.00
tnc_single_ASC_auto_sufficient_univ 0.209 5.09e-06 BIG*** 0.00
tnc_single_ASC_auto_sufficient_work-4.24 0.182-23.29*** 0.00
tnc_single_ASC_no_auto_atwork 4.26 75.9 0.06 0.00
tnc_single_ASC_no_auto_eatout_othdiscr_social 0.982 75.9 0.01 0.00
tnc_single_ASC_no_auto_escort_othmaint_shopping 1.60 75.9 0.02 0.00
tnc_single_ASC_no_auto_school-7.00 0.00 NA 0.00
tnc_single_ASC_no_auto_univ-2.52 NA NA 0.00
tnc_single_ASC_no_auto_work 5.31 75.9 0.07 0.00
walk_ASC_auto_deficient_atwork 0.227 0.123 1.84 0.00
walk_ASC_auto_deficient_eatout 2.17 0.198 10.96*** 0.00
walk_ASC_auto_deficient_escort-1.98 0.265-7.46*** 0.00
walk_ASC_auto_deficient_othdiscr 1.21 0.154 7.86*** 0.00
walk_ASC_auto_deficient_othmaint 0.145 0.208 0.70 0.00
walk_ASC_auto_deficient_school 2.61 0.931 2.81** 0.00
walk_ASC_auto_deficient_shopping 0.909 0.137 6.63*** 0.00
walk_ASC_auto_deficient_social 2.26 0.299 7.56*** 0.00
walk_ASC_auto_deficient_univ 4.51 1.92e-06 BIG*** 0.00
walk_ASC_auto_deficient_work 1.70 0.0955 17.84*** 0.00
walk_ASC_auto_sufficient_atwork 0.0776 0.0580 1.34 0.00
walk_ASC_auto_sufficient_eatout 0.613 0.106 5.78*** 0.00
walk_ASC_auto_sufficient_escort-1.74 0.117-14.88*** 0.00
walk_ASC_auto_sufficient_othdiscr 0.305 0.0829 3.68*** 0.00
walk_ASC_auto_sufficient_othmaint-0.168 0.0955-1.76 0.00
walk_ASC_auto_sufficient_school 0.374 0.917 0.41 0.00
walk_ASC_auto_sufficient_shopping-0.304 0.0811-3.75*** 0.00
walk_ASC_auto_sufficient_social 0.592 0.117 5.04*** 0.00
walk_ASC_auto_sufficient_univ 1.06 NA NA 0.00
walk_ASC_auto_sufficient_work-0.291 0.0787-3.70*** 0.00
walk_ASC_no_auto_atwork 6.63 75.9 0.09 0.00
walk_ASC_no_auto_eatout 4.41 75.9 0.06 0.00
walk_ASC_no_auto_escort 2.51 75.9 0.03 0.00
walk_ASC_no_auto_othdiscr 2.89 75.9 0.04 0.00
walk_ASC_no_auto_othmaint 0.930 75.9 0.01 0.00
walk_ASC_no_auto_school 17.8 109. 0.16 0.00
walk_ASC_no_auto_shopping 2.17 75.9 0.03 0.00
walk_ASC_no_auto_social 1.74 75.9 0.02 0.00
walk_ASC_no_auto_univ 6.41 NA NA 0.00
walk_ASC_no_auto_work 5.28 75.9 0.07 0.00
walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.239 16.4-0.01 0.00
walk_ferry_ASC_school_univ 0.858 NA NA 0.00
walk_ferry_ASC_work 0.189 278. 0.00 0.00
walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.136 16.4-0.01 0.00
walk_light_rail_ASC_school_univ 0.724 NA NA 0.00
walk_light_rail_ASC_work 0.786 278. 0.00 0.00
walk_transit_ASC_auto_deficient_atwork-2.63 16.4-0.16 0.00
walk_transit_ASC_auto_deficient_eatout-0.489 16.4-0.03 0.00
walk_transit_ASC_auto_deficient_escort-4.45 16.4-0.27 0.00
walk_transit_ASC_auto_deficient_othdiscr 0.873 16.4 0.05 0.00
walk_transit_ASC_auto_deficient_othmaint-2.97 16.4-0.18 0.00
walk_transit_ASC_auto_deficient_school 2.97 NA NA 0.00
walk_transit_ASC_auto_deficient_shopping-1.07 16.4-0.07 0.00
walk_transit_ASC_auto_deficient_social 1.32 16.4 0.08 0.00
walk_transit_ASC_auto_deficient_univ 3.14 0.00 NA 0.00
walk_transit_ASC_auto_deficient_work 0.159 278. 0.00 0.00
walk_transit_ASC_auto_sufficient_atwork-3.66 16.4-0.22 0.00
walk_transit_ASC_auto_sufficient_eatout-1.32 16.4-0.08 0.00
walk_transit_ASC_auto_sufficient_escort-4.82 16.4-0.29 0.00
walk_transit_ASC_auto_sufficient_othdiscr-0.914 16.4-0.06 0.00
walk_transit_ASC_auto_sufficient_othmaint-1.68 16.4-0.10 0.00
walk_transit_ASC_auto_sufficient_school 0.487 NA NA 0.00
walk_transit_ASC_auto_sufficient_shopping-2.35 16.4-0.14 0.00
walk_transit_ASC_auto_sufficient_social-0.647 16.4-0.04 0.00
walk_transit_ASC_auto_sufficient_univ 0.473 0.00 NA 0.00
walk_transit_ASC_auto_sufficient_work-1.37 278.-0.00 0.00
walk_transit_ASC_no_auto_atwork 3.13 66.9 0.05 0.00
walk_transit_ASC_no_auto_eatout 2.92 66.9 0.04 0.00
walk_transit_ASC_no_auto_escort-2.26 67.0-0.03 0.00
walk_transit_ASC_no_auto_othdiscr 2.80 66.9 0.04 0.00
walk_transit_ASC_no_auto_othmaint 2.59 66.9 0.04 0.00
walk_transit_ASC_no_auto_school 20.4 NA NA 0.00
walk_transit_ASC_no_auto_shopping 2.33 66.9 0.03 0.00
walk_transit_ASC_no_auto_social 2.06 66.9 0.03 0.00
walk_transit_ASC_no_auto_univ 8.79 0.00 NA 0.00
walk_transit_ASC_no_auto_work 4.60 285. 0.02 0.00
walk_transit_CBD_ASC_atwork 1.30 0.284 4.57*** 0.00
walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 1.08 0.0757 14.25*** 0.00
walk_transit_CBD_ASC_school_univ 0.834 0.114 7.30*** 0.00
walk_transit_CBD_ASC_work 1.16 0.0539 21.50*** 0.00
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.parameter_summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "TojXWivZsx7M" + }, + "source": [ + "# Output Estimation Results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:22:22.519480Z", + "iopub.status.busy": "2025-06-26T02:22:22.519378Z", + "iopub.status.idle": "2025-06-26T02:22:22.604985Z", + "shell.execute_reply": "2025-06-26T02:22:22.604582Z", + "shell.execute_reply.started": "2025-06-26T02:22:22.519470Z" + } + }, + "outputs": [], + "source": [ + "from activitysim.estimation.larch import update_coefficients\n", + "result_dir = data.edb_directory/\"estimated\"\n", + "update_coefficients(\n", + " model, data, result_dir,\n", + " output_file=f\"{modelname}_coefficients_revised.csv\",\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write the model estimation report, including coefficient t-statistic and log likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:22:22.605439Z", + "iopub.status.busy": "2025-06-26T02:22:22.605332Z", + "iopub.status.idle": "2025-06-26T02:22:33.125948Z", + "shell.execute_reply": "2025-06-26T02:22:33.125494Z", + "shell.execute_reply.started": "2025-06-26T02:22:22.605418Z" + } + }, + "outputs": [], + "source": [ + "model.to_xlsx(\n", + " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", + " data_statistics=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Steps\n", + "\n", + "The final step is to either manually or automatically copy the `*_coefficients_revised.csv` file to the configs folder, rename it to `*_coefficients.csv`, and run ActivitySim in simulation mode." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:22:33.126652Z", + "iopub.status.busy": "2025-06-26T02:22:33.126545Z", + "iopub.status.idle": "2025-06-26T02:22:33.132608Z", + "shell.execute_reply": "2025-06-26T02:22:33.132330Z", + "shell.execute_reply.started": "2025-06-26T02:22:33.126642Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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coefficient_namevalueconstrain
0coef_one1.000000T
1coef_nest_root1.000000T
2coef_nest_AUTO0.720000T
3coef_nest_AUTO_DRIVEALONE0.350000T
4coef_nest_AUTO_SHAREDRIDE20.350000T
............
304drive_transit_CBD_ASC_school_univ1.227148F
305drive_transit_CBD_ASC_work1.636347F
306drive_transit_CBD_ASC_atwork0.564000FF
307coef_test_eatout_escort_othdiscr_othmaint_shop...0.076990F
308coef_test_school_univ0.518279F
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309 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " coefficient_name value constrain\n", + "0 coef_one 1.000000 T\n", + "1 coef_nest_root 1.000000 T\n", + "2 coef_nest_AUTO 0.720000 T\n", + "3 coef_nest_AUTO_DRIVEALONE 0.350000 T\n", + "4 coef_nest_AUTO_SHAREDRIDE2 0.350000 T\n", + ".. ... ... ...\n", + "304 drive_transit_CBD_ASC_school_univ 1.227148 F\n", + "305 drive_transit_CBD_ASC_work 1.636347 F\n", + "306 drive_transit_CBD_ASC_atwork 0.564000 F\n", + "307 coef_test_eatout_escort_othdiscr_othmaint_shop... 0.076990 F\n", + "308 coef_test_school_univ 0.518279 F\n", + "\n", + "[309 rows x 3 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.read_csv(result_dir/f\"{modelname}_coefficients_revised.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modify Spec\n", + "\n", + "Here, we will demonstrate the process of re-estimating the model with a modified\n", + "SPEC file. This does *not* require re-running ActivitySim, it just requires\n", + "changing the SPEC file, and possibly the coefficients and/or coefficients template files, and re-running the Larch estimation only.\n", + "\n", + "The `backup` command we ran earlier made a backup copy of the\n", + "original spec file in the EDB directory.\n", + "This was not strictly necessary, but since we're about to modify it and\n", + "we may want undo our changes, it can be handy to keep a copy of the\n", + "original spec file around. Since we already have a backup copy, we'll make some \n", + "changes directly in the SPEC file. As an example here, we're going\n", + "to add a constant term to the model, with a coefficient that is shared in common across \n", + "several (but not all) purposes.\n", + "\n", + "For this demo we are editing \n", + "the model files using Python code to make the changes, but a user does not need\n", + "to change the file using Python; any CSV editor (e.g. Excel) can be used. We will\n", + "edit three files: the spec, the coefficients, and the coefficients template.\n", + "We're not going to edit any of the existing content in any of these files, instead \n", + "we will simply add a few lines to each to add an extra expression to the spec, and\n", + "the coefficients for that expression." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:22:33.133126Z", + "iopub.status.busy": "2025-06-26T02:22:33.133030Z", + "iopub.status.idle": "2025-06-26T02:22:33.135058Z", + "shell.execute_reply": "2025-06-26T02:22:33.134812Z", + "shell.execute_reply.started": "2025-06-26T02:22:33.133117Z" + } + }, + "outputs": [], + "source": [ + "with open(data.edb_directory / \"tour_mode_choice_SPEC.csv\", mode=\"a\") as f:\n", + " f.write(\"util_test,Additional DA constant,1,coef_test,,,,,,,,,,,,,,,,,,,,\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:22:33.135393Z", + "iopub.status.busy": "2025-06-26T02:22:33.135303Z", + "iopub.status.idle": "2025-06-26T02:22:33.137578Z", + "shell.execute_reply": "2025-06-26T02:22:33.137342Z", + "shell.execute_reply.started": "2025-06-26T02:22:33.135372Z" + } + }, + "outputs": [], + "source": [ + "with open(data.edb_directory / \"tour_mode_choice_coefficients_template.csv\", mode=\"a\") as f:\n", + " f.write(\"coef_test,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,\"\n", + " \"coef_test_school_univ,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,\"\n", + " \"coef_test_school_univ,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,\"\n", + " \"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:22:33.138092Z", + "iopub.status.busy": "2025-06-26T02:22:33.137967Z", + "iopub.status.idle": "2025-06-26T02:22:33.139786Z", + "shell.execute_reply": "2025-06-26T02:22:33.139542Z", + "shell.execute_reply.started": "2025-06-26T02:22:33.138083Z" + } + }, + "outputs": [], + "source": [ + "with open(data.edb_directory / \"tour_mode_choice_coefficients.csv\", mode=\"a\") as f:\n", + " f.write(\"coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F\\n\"\n", + " \"coef_test_school_univ,0,F\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to re-estimate the model, we just re-run the same steps as the original estimation above." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:23:19.234087Z", + "iopub.status.busy": "2025-06-26T02:23:19.233080Z", + "iopub.status.idle": "2025-06-26T02:23:34.736566Z", + "shell.execute_reply": "2025-06-26T02:23:34.736111Z", + "shell.execute_reply.started": "2025-06-26T02:23:19.234014Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_coefficients_template.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/tour_mode_choice/tour_mode_choice_values_combined.parquet\n" + ] + } + ], + "source": [ + "remodel, redata = component_model(modelname, edb_directory=str(data.edb_directory), return_data=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:23:37.086759Z", + "iopub.status.busy": "2025-06-26T02:23:37.086146Z", + "iopub.status.idle": "2025-06-26T02:23:39.392179Z", + "shell.execute_reply": "2025-06-26T02:23:39.391840Z", + "shell.execute_reply.started": "2025-06-26T02:23:37.086721Z" + } + }, + "outputs": [], + "source": [ + "remodel.extend(model2)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:23:40.195214Z", + "iopub.status.busy": "2025-06-26T02:23:40.194549Z", + "iopub.status.idle": "2025-06-26T02:27:37.904716Z", + "shell.execute_reply": "2025-06-26T02:27:37.904362Z", + "shell.execute_reply.started": "2025-06-26T02:23:40.195175Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "

Iteration 200 [Iteration limit reached]

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Best LL = -76502.09343717809

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0
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11.000000
bike_ASC_auto_deficient_atwork-1.059385
bike_ASC_auto_deficient_eatout-1.201250
bike_ASC_auto_deficient_escort-3.880330
bike_ASC_auto_deficient_othdiscr-0.528102
bike_ASC_auto_deficient_othmaint-1.629975
bike_ASC_auto_deficient_school-1.182724
bike_ASC_auto_deficient_shopping-1.304686
bike_ASC_auto_deficient_social0.592094
bike_ASC_auto_deficient_univ-0.669235
bike_ASC_auto_deficient_work-0.360143
bike_ASC_auto_sufficient_atwork15.720172
bike_ASC_auto_sufficient_eatout-1.297285
bike_ASC_auto_sufficient_escort-5.264113
bike_ASC_auto_sufficient_othdiscr-1.491319
bike_ASC_auto_sufficient_othmaint-2.511651
bike_ASC_auto_sufficient_school-2.201436
bike_ASC_auto_sufficient_shopping-2.777945
bike_ASC_auto_sufficient_social-1.828315
bike_ASC_auto_sufficient_univ-1.939783
bike_ASC_auto_sufficient_work-2.058472
bike_ASC_no_auto_atwork-0.911319
bike_ASC_no_auto_eatout0.509502
bike_ASC_no_auto_escort-0.730310
bike_ASC_no_auto_othdiscr-0.404361
bike_ASC_no_auto_othmaint1.614051
bike_ASC_no_auto_school13.403780
bike_ASC_no_auto_shopping1.048175
bike_ASC_no_auto_social0.485746
bike_ASC_no_auto_univ4.294516
bike_ASC_no_auto_work3.427768
coef_age010_trn_multiplier_atwork0.000722
coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work0.363546
coef_age010_trn_multiplier_school_univ-0.874785
coef_age1619_da_multiplier_atwork-0.192883
coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work0.000513
coef_age1619_da_multiplier_school_univ-1.677960
coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social-1.451060
coef_age16p_sr_multiplier_school_univ_work_atwork-0.648072
coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork-0.071030
coef_hhsize1_sr_multiplier_work-0.836007
coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork0.002091
coef_hhsize2_sr_multiplier_school_univ-0.747693
coef_ivt_atwork-0.011188
coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social-0.006884
coef_ivt_school_univ-0.010386
coef_ivt_work-0.007950
coef_nest_AUTO0.720000
coef_nest_AUTO_DRIVEALONE0.350000
coef_nest_AUTO_SHAREDRIDE20.350000
coef_nest_AUTO_SHAREDRIDE30.350000
coef_nest_NONMOTORIZED0.720000
coef_nest_RIDEHAIL0.360000
coef_nest_TRANSIT0.720000
coef_nest_TRANSIT_DRIVEACCESS0.500000
coef_nest_TRANSIT_WALKACCESS0.500000
coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork0.076990
coef_test_school_univ0.518279
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: eatout, dtype: object0.339513
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: escort, dtype: object0.000000
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: othdiscr, dtype: object0.230600
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: othmaint, dtype: object0.332934
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: shopping, dtype: object0.244712
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: social, dtype: object0.196574
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: work, dtype: object-0.030571
coefficient_name\\ncoef_test coef_test_school_univ\\ncoef_test coef_test_school_univ\\nName: school, dtype: object0.278054
coefficient_name\\ncoef_test coef_test_school_univ\\ncoef_test coef_test_school_univ\\nName: univ, dtype: object0.000000
commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.744041
commuter_rail_ASC_school_univ-0.247252
commuter_rail_ASC_work0.157688
drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.393253
drive_ferry_ASC_school_univ1.448664
drive_ferry_ASC_work0.193999
drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.091901
drive_light_rail_ASC_school_univ0.706784
drive_light_rail_ASC_work0.506525
drive_transit_ASC_auto_deficient_atwork-998.819580
drive_transit_ASC_auto_deficient_eatout0.104696
drive_transit_ASC_auto_deficient_escort-2.135178
drive_transit_ASC_auto_deficient_othdiscr-0.725510
drive_transit_ASC_auto_deficient_othmaint-1.174854
drive_transit_ASC_auto_deficient_school1.572565
drive_transit_ASC_auto_deficient_shopping-1.430278
drive_transit_ASC_auto_deficient_social0.594346
drive_transit_ASC_auto_deficient_univ1.850118
drive_transit_ASC_auto_deficient_work-0.911001
drive_transit_ASC_auto_sufficient_atwork-999.214661
drive_transit_ASC_auto_sufficient_eatout-1.297555
drive_transit_ASC_auto_sufficient_escort-5.253544
drive_transit_ASC_auto_sufficient_othdiscr-1.114939
drive_transit_ASC_auto_sufficient_othmaint-2.442900
drive_transit_ASC_auto_sufficient_school0.154528
drive_transit_ASC_auto_sufficient_shopping-3.967262
drive_transit_ASC_auto_sufficient_social-1.343284
drive_transit_ASC_auto_sufficient_univ1.358775
drive_transit_ASC_auto_sufficient_work-1.831235
drive_transit_ASC_no_auto_all0.000000
drive_transit_CBD_ASC_atwork0.564000
drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social1.413499
drive_transit_CBD_ASC_school_univ1.203794
drive_transit_CBD_ASC_work1.616319
express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.088784
express_bus_ASC_school_univ-0.449853
express_bus_ASC_work-0.920137
heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork0.040071
heavy_rail_ASC_school_univ0.126501
heavy_rail_ASC_work0.496146
joint_bike_ASC_auto_deficient_all-6.159645
joint_bike_ASC_auto_sufficient_all-6.948677
joint_bike_ASC_no_auto_all-2.667145
joint_drive_transit_ASC_auto_deficient_all-6.040459
joint_drive_transit_ASC_auto_sufficient_all-7.802616
joint_drive_transit_ASC_no_auto_all0.000000
joint_sr2_ASC_auto_deficient_all0.000000
joint_sr2_ASC_auto_sufficient_all0.000000
joint_sr2_ASC_no_auto_all0.000000
joint_sr3p_ASC_auto_deficient_all-1.508271
joint_sr3p_ASC_auto_sufficient_all-2.268077
joint_sr3p_ASC_no_auto_all0.680128
joint_taxi_ASC_auto_deficient_all-9.817099
joint_taxi_ASC_auto_sufficient_all-11.709900
joint_taxi_ASC_no_auto_all-4.583211
joint_tnc_shared_ASC_auto_deficient_all-11.157463
joint_tnc_shared_ASC_auto_sufficient_all-13.205000
joint_tnc_shared_ASC_no_auto_all-4.699899
joint_tnc_single_ASC_auto_deficient_all-9.898289
joint_tnc_single_ASC_auto_sufficient_all-14.015900
joint_tnc_single_ASC_no_auto_all-3.890298
joint_walk_ASC_auto_deficient_all-2.049580
joint_walk_ASC_auto_sufficient_all-4.151795
joint_walk_ASC_no_auto_all-0.294038
joint_walk_transit_ASC_auto_deficient_all-5.295731
joint_walk_transit_ASC_auto_sufficient_all-18.264534
joint_walk_transit_ASC_no_auto_all0.350958
local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.943275
local_bus_ASC_school_univ-0.811253
local_bus_ASC_work-0.210576
sr2_ASC_auto_deficient_atwork-1.571425
sr2_ASC_auto_deficient_eatout1.323994
sr2_ASC_auto_deficient_escort0.560298
sr2_ASC_auto_deficient_othdiscr1.316437
sr2_ASC_auto_deficient_othmaint1.027389
sr2_ASC_auto_deficient_school0.521971
sr2_ASC_auto_deficient_shopping0.800981
sr2_ASC_auto_deficient_social2.418469
sr2_ASC_auto_deficient_univ-1.692235
sr2_ASC_auto_deficient_work0.248251
sr2_ASC_auto_sufficient_atwork-0.810446
sr2_ASC_auto_sufficient_eatout1.632701
sr2_ASC_auto_sufficient_escort0.754348
sr2_ASC_auto_sufficient_othdiscr1.091357
sr2_ASC_auto_sufficient_othmaint1.094521
sr2_ASC_auto_sufficient_school-0.720077
sr2_ASC_auto_sufficient_shopping0.805726
sr2_ASC_auto_sufficient_social1.065900
sr2_ASC_auto_sufficient_univ-1.859427
sr2_ASC_auto_sufficient_work-0.477609
sr2_ASC_no_auto_all1.173523
sr3p_ASC_auto_deficient_atwork-1.881594
sr3p_ASC_auto_deficient_eatout0.638190
sr3p_ASC_auto_deficient_escort0.335767
sr3p_ASC_auto_deficient_othdiscr1.672007
sr3p_ASC_auto_deficient_othmaint-0.202298
sr3p_ASC_auto_deficient_school1.094613
sr3p_ASC_auto_deficient_shopping0.457352
sr3p_ASC_auto_deficient_social2.155012
sr3p_ASC_auto_deficient_univ-1.727742
sr3p_ASC_auto_deficient_work-0.289309
sr3p_ASC_auto_sufficient_atwork-1.003854
sr3p_ASC_auto_sufficient_eatout1.576426
sr3p_ASC_auto_sufficient_escort0.729492
sr3p_ASC_auto_sufficient_othdiscr1.182849
sr3p_ASC_auto_sufficient_othmaint0.726042
sr3p_ASC_auto_sufficient_school-0.151086
sr3p_ASC_auto_sufficient_shopping0.560767
sr3p_ASC_auto_sufficient_social1.073137
sr3p_ASC_auto_sufficient_univ-1.904710
sr3p_ASC_auto_sufficient_work-0.848535
sr3p_ASC_no_auto_atwork1.665939
sr3p_ASC_no_auto_eatout1.528129
sr3p_ASC_no_auto_escort-1.866823
sr3p_ASC_no_auto_othdiscr1.403607
sr3p_ASC_no_auto_othmaint0.194971
sr3p_ASC_no_auto_school-6.024155
sr3p_ASC_no_auto_shopping0.846912
sr3p_ASC_no_auto_social-1.209366
sr3p_ASC_no_auto_univ-6.056001
sr3p_ASC_no_auto_work0.522935
taxi_ASC_auto_deficient_atwork-4.750529
taxi_ASC_auto_deficient_eatout_othdiscr_social-3.164652
taxi_ASC_auto_deficient_escort_othmaint_shopping-0.502553
taxi_ASC_auto_deficient_school0.899118
taxi_ASC_auto_deficient_univ4.249200
taxi_ASC_auto_deficient_work-2.299959
taxi_ASC_auto_sufficient_atwork-3.311568
taxi_ASC_auto_sufficient_eatout_othdiscr_social-3.474973
taxi_ASC_auto_sufficient_escort_othmaint_shopping-2.325136
taxi_ASC_auto_sufficient_school-2.230947
taxi_ASC_auto_sufficient_univ-0.313100
taxi_ASC_auto_sufficient_work-5.138446
taxi_ASC_no_auto_atwork3.627714
taxi_ASC_no_auto_eatout_othdiscr_social-0.460021
taxi_ASC_no_auto_escort_othmaint_shopping1.536364
taxi_ASC_no_auto_school_univ-7.000000
taxi_ASC_no_auto_work3.889335
tnc_shared_ASC_auto_deficient_atwork-5.208859
tnc_shared_ASC_auto_deficient_eatout_othdiscr_social-4.431790
tnc_shared_ASC_auto_deficient_escort_othmaint_shopping-0.796074
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tnc_shared_ASC_auto_deficient_univ3.250000
tnc_shared_ASC_auto_deficient_work-3.733595
tnc_shared_ASC_auto_sufficient_atwork-4.094668
tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social-3.879623
tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping-2.832616
tnc_shared_ASC_auto_sufficient_school-3.620468
tnc_shared_ASC_auto_sufficient_univ-0.906800
tnc_shared_ASC_auto_sufficient_work-6.741197
tnc_shared_ASC_no_auto_atwork3.035906
tnc_shared_ASC_no_auto_eatout_othdiscr_social-0.000529
tnc_shared_ASC_no_auto_escort_othmaint_shopping0.776143
tnc_shared_ASC_no_auto_school-7.000000
tnc_shared_ASC_no_auto_univ-5.811600
tnc_shared_ASC_no_auto_work2.211617
tnc_single_ASC_auto_deficient_atwork-4.359982
tnc_single_ASC_auto_deficient_eatout_othdiscr_social-3.121747
tnc_single_ASC_auto_deficient_escort_othmaint_shopping0.092823
tnc_single_ASC_auto_deficient_school0.059727
tnc_single_ASC_auto_deficient_univ1.022100
tnc_single_ASC_auto_deficient_work-1.472138
tnc_single_ASC_auto_sufficient_atwork-3.135939
tnc_single_ASC_auto_sufficient_eatout_othdiscr_social-2.826943
tnc_single_ASC_auto_sufficient_escort_othmaint_shopping-1.756842
tnc_single_ASC_auto_sufficient_school-2.018347
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tnc_single_ASC_no_auto_eatout_othdiscr_social1.063350
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tnc_single_ASC_no_auto_univ-2.519000
tnc_single_ASC_no_auto_work5.312088
walk_ASC_auto_deficient_atwork0.174536
walk_ASC_auto_deficient_eatout2.703990
walk_ASC_auto_deficient_escort-1.602460
walk_ASC_auto_deficient_othdiscr1.568287
walk_ASC_auto_deficient_othmaint0.642511
walk_ASC_auto_deficient_school2.647520
walk_ASC_auto_deficient_shopping1.312088
walk_ASC_auto_deficient_social2.482888
walk_ASC_auto_deficient_univ4.505910
walk_ASC_auto_deficient_work1.589921
walk_ASC_auto_sufficient_atwork0.078581
walk_ASC_auto_sufficient_eatout1.185624
walk_ASC_auto_sufficient_escort-1.315039
walk_ASC_auto_sufficient_othdiscr0.660807
walk_ASC_auto_sufficient_othmaint0.439213
walk_ASC_auto_sufficient_school0.382641
walk_ASC_auto_sufficient_shopping0.081775
walk_ASC_auto_sufficient_social0.911126
walk_ASC_auto_sufficient_univ1.060766
walk_ASC_auto_sufficient_work-0.417450
walk_ASC_no_auto_atwork6.611279
walk_ASC_no_auto_eatout4.520665
walk_ASC_no_auto_escort2.558862
walk_ASC_no_auto_othdiscr2.961389
walk_ASC_no_auto_othmaint1.056949
walk_ASC_no_auto_school17.800582
walk_ASC_no_auto_shopping2.263652
walk_ASC_no_auto_social1.844138
walk_ASC_no_auto_univ6.408967
walk_ASC_no_auto_work5.277625
walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.034691
walk_ferry_ASC_school_univ0.903288
walk_ferry_ASC_work0.075113
walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork0.115212
walk_light_rail_ASC_school_univ0.720550
walk_light_rail_ASC_work0.730077
walk_transit_ASC_auto_deficient_atwork-2.844239
walk_transit_ASC_auto_deficient_eatout-0.205432
walk_transit_ASC_auto_deficient_escort-4.464762
walk_transit_ASC_auto_deficient_othdiscr0.997111
walk_transit_ASC_auto_deficient_othmaint-2.906453
walk_transit_ASC_auto_deficient_school3.003413
walk_transit_ASC_auto_deficient_shopping-0.898698
walk_transit_ASC_auto_deficient_social1.324930
walk_transit_ASC_auto_deficient_univ3.136256
walk_transit_ASC_auto_deficient_work0.066276
walk_transit_ASC_auto_sufficient_atwork-3.764087
walk_transit_ASC_auto_sufficient_eatout-0.985317
walk_transit_ASC_auto_sufficient_escort-4.683494
walk_transit_ASC_auto_sufficient_othdiscr-0.771461
walk_transit_ASC_auto_sufficient_othmaint-1.309230
walk_transit_ASC_auto_sufficient_school0.522457
walk_transit_ASC_auto_sufficient_shopping-2.202302
walk_transit_ASC_auto_sufficient_social-0.595214
walk_transit_ASC_auto_sufficient_univ0.473116
walk_transit_ASC_auto_sufficient_work-1.474310
walk_transit_ASC_no_auto_atwork2.978048
walk_transit_ASC_no_auto_eatout2.764558
walk_transit_ASC_no_auto_escort-2.263431
walk_transit_ASC_no_auto_othdiscr2.638916
walk_transit_ASC_no_auto_othmaint2.431884
walk_transit_ASC_no_auto_school20.462674
walk_transit_ASC_no_auto_shopping2.182496
walk_transit_ASC_no_auto_social1.900415
walk_transit_ASC_no_auto_univ8.786037
walk_transit_ASC_no_auto_work4.631023
walk_transit_CBD_ASC_atwork1.184225
walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social1.088968
walk_transit_CBD_ASC_school_univ0.826484
walk_transit_CBD_ASC_work1.146513
logloss1.0933400042471608
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0
-9990.000000e+00
10.000000e+00
bike_ASC_auto_deficient_atwork1.774086e-05
bike_ASC_auto_deficient_eatout5.086225e-06
bike_ASC_auto_deficient_escort1.041342e-05
bike_ASC_auto_deficient_othdiscr-5.778635e-07
bike_ASC_auto_deficient_othmaint-6.765009e-06
bike_ASC_auto_deficient_school2.953352e-05
bike_ASC_auto_deficient_shopping-9.789016e-07
bike_ASC_auto_deficient_social-1.500595e-05
bike_ASC_auto_deficient_univ0.000000e+00
bike_ASC_auto_deficient_work-2.177390e-05
bike_ASC_auto_sufficient_atwork7.360022e-11
bike_ASC_auto_sufficient_eatout5.699513e-06
bike_ASC_auto_sufficient_escort-1.189039e-05
bike_ASC_auto_sufficient_othdiscr-3.053639e-06
bike_ASC_auto_sufficient_othmaint6.207501e-06
bike_ASC_auto_sufficient_school-3.955640e-05
bike_ASC_auto_sufficient_shopping2.371630e-06
bike_ASC_auto_sufficient_social8.079152e-06
bike_ASC_auto_sufficient_univ0.000000e+00
bike_ASC_auto_sufficient_work-7.377310e-05
bike_ASC_no_auto_atwork-1.079009e-07
bike_ASC_no_auto_eatout-1.351470e-05
bike_ASC_no_auto_escort-6.632985e-07
bike_ASC_no_auto_othdiscr-7.848992e-06
bike_ASC_no_auto_othmaint-1.700614e-05
bike_ASC_no_auto_school5.416195e-05
bike_ASC_no_auto_shopping-1.106327e-05
bike_ASC_no_auto_social-1.724981e-05
bike_ASC_no_auto_univ0.000000e+00
bike_ASC_no_auto_work-4.669114e-05
coef_age010_trn_multiplier_atwork0.000000e+00
coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work-4.351067e-05
coef_age010_trn_multiplier_school_univ-6.148052e-06
coef_age1619_da_multiplier_atwork2.370563e-06
coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work7.865522e-05
coef_age1619_da_multiplier_school_univ5.794259e-05
coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social6.559047e-06
coef_age16p_sr_multiplier_school_univ_work_atwork2.000044e-04
coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork7.346641e-05
coef_hhsize1_sr_multiplier_work-3.556634e-05
coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork-1.320963e-04
coef_hhsize2_sr_multiplier_school_univ-2.237656e-06
coef_ivt_atwork5.354940e-05
coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social1.361858e-03
coef_ivt_school_univ5.392887e-04
coef_ivt_work1.007546e-03
coef_nest_AUTO1.718013e-03
coef_nest_AUTO_DRIVEALONE3.960085e-04
coef_nest_AUTO_SHAREDRIDE2-4.321150e-05
coef_nest_AUTO_SHAREDRIDE3-4.203121e-17
coef_nest_NONMOTORIZED2.445183e-04
coef_nest_RIDEHAIL3.970494e-02
coef_nest_TRANSIT-6.735168e-03
coef_nest_TRANSIT_DRIVEACCESS-5.412309e-03
coef_nest_TRANSIT_WALKACCESS-4.990662e-03
coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork0.000000e+00
coef_test_school_univ0.000000e+00
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: eatout, dtype: object2.972870e-05
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: escort, dtype: object0.000000e+00
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: othdiscr, dtype: object-3.077223e-05
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: othmaint, dtype: object2.351673e-05
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: shopping, dtype: object-9.832621e-06
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: social, dtype: object-2.774498e-05
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: work, dtype: object5.099878e-05
coefficient_name\\ncoef_test coef_test_school_univ\\ncoef_test coef_test_school_univ\\nName: school, dtype: object2.043797e-04
coefficient_name\\ncoef_test coef_test_school_univ\\ncoef_test coef_test_school_univ\\nName: univ, dtype: object0.000000e+00
commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-1.076357e-05
commuter_rail_ASC_school_univ4.538199e-05
commuter_rail_ASC_work2.680006e-05
drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-1.539882e-05
drive_ferry_ASC_school_univ-1.492963e-05
drive_ferry_ASC_work5.248967e-05
drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-3.564786e-05
drive_light_rail_ASC_school_univ-5.604245e-06
drive_light_rail_ASC_work3.779577e-05
drive_transit_ASC_auto_deficient_atwork0.000000e+00
drive_transit_ASC_auto_deficient_eatout4.925097e-06
drive_transit_ASC_auto_deficient_escort-1.075357e-05
drive_transit_ASC_auto_deficient_othdiscr-9.490300e-06
drive_transit_ASC_auto_deficient_othmaint1.225259e-06
drive_transit_ASC_auto_deficient_school6.513910e-05
drive_transit_ASC_auto_deficient_shopping-1.342895e-05
drive_transit_ASC_auto_deficient_social-7.970583e-07
drive_transit_ASC_auto_deficient_univ0.000000e+00
drive_transit_ASC_auto_deficient_work-2.589444e-05
drive_transit_ASC_auto_sufficient_atwork0.000000e+00
drive_transit_ASC_auto_sufficient_eatout-1.080784e-05
drive_transit_ASC_auto_sufficient_escort-2.626942e-05
drive_transit_ASC_auto_sufficient_othdiscr-7.774693e-06
drive_transit_ASC_auto_sufficient_othmaint-1.165773e-05
drive_transit_ASC_auto_sufficient_school2.119160e-05
drive_transit_ASC_auto_sufficient_shopping-1.340120e-07
drive_transit_ASC_auto_sufficient_social-8.214597e-06
drive_transit_ASC_auto_sufficient_univ0.000000e+00
drive_transit_ASC_auto_sufficient_work-4.622149e-05
drive_transit_ASC_no_auto_all0.000000e+00
drive_transit_CBD_ASC_atwork0.000000e+00
drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social-4.684988e-06
drive_transit_CBD_ASC_school_univ-3.805396e-05
drive_transit_CBD_ASC_work-6.488983e-05
express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-1.885124e-05
express_bus_ASC_school_univ5.641507e-06
express_bus_ASC_work-9.987402e-07
heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-1.544009e-05
heavy_rail_ASC_school_univ-4.234502e-05
heavy_rail_ASC_work-2.393953e-04
joint_bike_ASC_auto_deficient_all-6.478420e-06
joint_bike_ASC_auto_sufficient_all-4.580153e-05
joint_bike_ASC_no_auto_all7.966080e-06
joint_drive_transit_ASC_auto_deficient_all-6.199588e-06
joint_drive_transit_ASC_auto_sufficient_all4.372034e-06
joint_drive_transit_ASC_no_auto_all0.000000e+00
joint_sr2_ASC_auto_deficient_all0.000000e+00
joint_sr2_ASC_auto_sufficient_all0.000000e+00
joint_sr2_ASC_no_auto_all0.000000e+00
joint_sr3p_ASC_auto_deficient_all-1.008236e-05
joint_sr3p_ASC_auto_sufficient_all2.534002e-05
joint_sr3p_ASC_no_auto_all5.235127e-06
joint_taxi_ASC_auto_deficient_all-1.164850e-07
joint_taxi_ASC_auto_sufficient_all0.000000e+00
joint_taxi_ASC_no_auto_all-3.151328e-07
joint_tnc_shared_ASC_auto_deficient_all-2.107226e-08
joint_tnc_shared_ASC_auto_sufficient_all0.000000e+00
joint_tnc_shared_ASC_no_auto_all-1.881990e-05
joint_tnc_single_ASC_auto_deficient_all-1.783185e-07
joint_tnc_single_ASC_auto_sufficient_all0.000000e+00
joint_tnc_single_ASC_no_auto_all3.023204e-05
joint_walk_ASC_auto_deficient_all-3.050589e-05
joint_walk_ASC_auto_sufficient_all-1.286366e-05
joint_walk_ASC_no_auto_all-1.617279e-05
joint_walk_transit_ASC_auto_deficient_all-6.415354e-06
joint_walk_transit_ASC_auto_sufficient_all-2.728968e-11
joint_walk_transit_ASC_no_auto_all-5.541879e-06
local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork1.259957e-04
local_bus_ASC_school_univ-5.596945e-05
local_bus_ASC_work1.016212e-04
sr2_ASC_auto_deficient_atwork2.242653e-05
sr2_ASC_auto_deficient_eatout9.816584e-07
sr2_ASC_auto_deficient_escort5.813310e-06
sr2_ASC_auto_deficient_othdiscr-1.246664e-05
sr2_ASC_auto_deficient_othmaint-1.373838e-05
sr2_ASC_auto_deficient_school2.466906e-05
sr2_ASC_auto_deficient_shopping1.962366e-05
sr2_ASC_auto_deficient_social-1.133811e-05
sr2_ASC_auto_deficient_univ0.000000e+00
sr2_ASC_auto_deficient_work3.366868e-05
sr2_ASC_auto_sufficient_atwork-3.856744e-05
sr2_ASC_auto_sufficient_eatout-6.401070e-06
sr2_ASC_auto_sufficient_escort3.483448e-05
sr2_ASC_auto_sufficient_othdiscr-1.727323e-04
sr2_ASC_auto_sufficient_othmaint8.908591e-05
sr2_ASC_auto_sufficient_school-7.725139e-05
sr2_ASC_auto_sufficient_shopping-5.510640e-05
sr2_ASC_auto_sufficient_social3.858880e-05
sr2_ASC_auto_sufficient_univ0.000000e+00
sr2_ASC_auto_sufficient_work1.019533e-04
sr2_ASC_no_auto_all1.418570e-05
sr3p_ASC_auto_deficient_atwork-4.858277e-05
sr3p_ASC_auto_deficient_eatout-6.183223e-06
sr3p_ASC_auto_deficient_escort-2.721905e-05
sr3p_ASC_auto_deficient_othdiscr6.869172e-06
sr3p_ASC_auto_deficient_othmaint-1.584337e-05
sr3p_ASC_auto_deficient_school-6.263611e-05
sr3p_ASC_auto_deficient_shopping1.021365e-05
sr3p_ASC_auto_deficient_social-2.315194e-05
sr3p_ASC_auto_deficient_univ0.000000e+00
sr3p_ASC_auto_deficient_work-3.019121e-05
sr3p_ASC_auto_sufficient_atwork1.375365e-04
sr3p_ASC_auto_sufficient_eatout4.993787e-05
sr3p_ASC_auto_sufficient_escort-6.984512e-05
sr3p_ASC_auto_sufficient_othdiscr1.181772e-04
sr3p_ASC_auto_sufficient_othmaint-4.887767e-06
sr3p_ASC_auto_sufficient_school-6.574727e-05
sr3p_ASC_auto_sufficient_shopping2.914554e-05
sr3p_ASC_auto_sufficient_social-1.779069e-05
sr3p_ASC_auto_sufficient_univ0.000000e+00
sr3p_ASC_auto_sufficient_work1.751686e-04
sr3p_ASC_no_auto_atwork1.470191e-05
sr3p_ASC_no_auto_eatout-6.560895e-06
sr3p_ASC_no_auto_escort-1.740187e-06
sr3p_ASC_no_auto_othdiscr2.448092e-05
sr3p_ASC_no_auto_othmaint3.640557e-05
sr3p_ASC_no_auto_school-2.101477e-09
sr3p_ASC_no_auto_shopping-2.347155e-05
sr3p_ASC_no_auto_social2.268209e-05
sr3p_ASC_no_auto_univ0.000000e+00
sr3p_ASC_no_auto_work-3.225057e-06
taxi_ASC_auto_deficient_atwork-3.207089e-06
taxi_ASC_auto_deficient_eatout_othdiscr_social-1.003595e-05
taxi_ASC_auto_deficient_escort_othmaint_shopping-4.396800e-05
taxi_ASC_auto_deficient_school-6.716802e-05
taxi_ASC_auto_deficient_univ0.000000e+00
taxi_ASC_auto_deficient_work6.370923e-05
taxi_ASC_auto_sufficient_atwork-2.581602e-05
taxi_ASC_auto_sufficient_eatout_othdiscr_social-1.609204e-05
taxi_ASC_auto_sufficient_escort_othmaint_shopping-4.463775e-05
taxi_ASC_auto_sufficient_school7.337408e-05
taxi_ASC_auto_sufficient_univ0.000000e+00
taxi_ASC_auto_sufficient_work-1.088371e-05
taxi_ASC_no_auto_atwork-5.568698e-06
taxi_ASC_no_auto_eatout_othdiscr_social-1.507503e-05
taxi_ASC_no_auto_escort_othmaint_shopping-1.285605e-05
taxi_ASC_no_auto_school_univ0.000000e+00
taxi_ASC_no_auto_work-2.252879e-05
tnc_shared_ASC_auto_deficient_atwork-2.735320e-06
tnc_shared_ASC_auto_deficient_eatout_othdiscr_social-5.208046e-06
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nit200
nfev211
njev200
status9
message'Iteration limit reached'
successFalse
elapsed_time0:01:46.414845
method'slsqp'
n_cases69971
iteration_number200
loglike-76502.09343717809
" + ], + "text/plain": [ + "┣ x: -999 -999.000000\n", + "┃ 1 1.000000\n", + "┃ bike_ASC_auto_deficient_atwork -1.059385\n", + "┃ bike_ASC_auto_deficient_eatout -1.201250\n", + "┃ bike_ASC_auto_deficient_escort -3.880330\n", + "┃ ... \n", + "┃ walk_transit_ASC_no_auto_work 4.631023\n", + "┃ walk_transit_CBD_ASC_atwork 1.184225\n", + "┃ walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 1.088968\n", + "┃ walk_transit_CBD_ASC_school_univ 0.826484\n", + "┃ walk_transit_CBD_ASC_work 1.146513\n", + "┃ Length: 312, dtype: float64\n", + "┣ logloss: 1.0933400042471608\n", + "┣ d_logloss: -999 0.000000\n", + "┃ 1 0.000000\n", + "┃ bike_ASC_auto_deficient_atwork 0.000018\n", + "┃ bike_ASC_auto_deficient_eatout 0.000005\n", + "┃ bike_ASC_auto_deficient_escort 0.000010\n", + "┃ ... \n", + "┃ walk_transit_ASC_no_auto_work 0.000049\n", + "┃ walk_transit_CBD_ASC_atwork -0.000097\n", + "┃ walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social 0.000019\n", + "┃ walk_transit_CBD_ASC_school_univ 0.000020\n", + "┃ walk_transit_CBD_ASC_work 0.000038\n", + "┃ Length: 312, dtype: float64\n", + "┣ nit: 200\n", + "┣ nfev: 211\n", + "┣ njev: 200\n", + "┣ status: 9\n", + "┣ message: 'Iteration limit reached'\n", + "┣ success: False\n", + "┣ elapsed_time: datetime.timedelta(seconds=106, microseconds=414845)\n", + "┣ method: 'slsqp'\n", + "┣ n_cases: 69971\n", + "┣ iteration_number: 200\n", + "┣ loglike: -76502.09343717809" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "remodel.estimate(maxiter=200)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can easily review the parameter estimates from the original and\n", + "revised models side by side to see what changed." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:27:37.905478Z", + "iopub.status.busy": "2025-06-26T02:27:37.905380Z", + "iopub.status.idle": "2025-06-26T02:27:37.943038Z", + "shell.execute_reply": "2025-06-26T02:27:37.942668Z", + "shell.execute_reply.started": "2025-06-26T02:27:37.905469Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelremodel
ValueStd Errt StatSignifNull ValueValueStd Errt StatSignifNull Value
Parameter
-999-999.0.00NA0.0-999.0.00NA0.0
11.000.00NA0.01.000.00NA0.0
bike_ASC_auto_deficient_atwork-1.050.344-3.07**0.0-1.060.340-3.11**0.0
bike_ASC_auto_deficient_eatout-1.460.396-3.69***0.0-1.200.594-2.02*0.0
bike_ASC_auto_deficient_escort-3.980.406-9.79***0.0-3.880.559-6.94***0.0
bike_ASC_auto_deficient_othdiscr-0.9100.203-4.47***0.0-0.5280.418-1.260.0
bike_ASC_auto_deficient_othmaint-2.060.323-6.37***0.0-1.630.448-3.64***0.0
bike_ASC_auto_deficient_school-1.200.964-1.240.0-1.180.997-1.190.0
bike_ASC_auto_deficient_shopping-1.740.218-7.99***0.0-1.300.363-3.59***0.0
bike_ASC_auto_deficient_social0.3610.3251.110.00.5920.5321.110.0
bike_ASC_auto_deficient_univ-0.669NANA0.0-0.6690.00146-458.48***0.0
bike_ASC_auto_deficient_work-0.2790.0926-3.01**0.0-0.3600.0922-3.90***0.0
bike_ASC_auto_sufficient_atwork15.7470.0.030.015.7463.0.030.0
bike_ASC_auto_sufficient_eatout-1.850.159-11.60***0.0-1.300.409-3.17**0.0
bike_ASC_auto_sufficient_escort-5.650.337-16.76***0.0-5.260.453-11.62***0.0
bike_ASC_auto_sufficient_othdiscr-1.860.105-17.64***0.0-1.490.382-3.91***0.0
bike_ASC_auto_sufficient_othmaint-3.170.193-16.40***0.0-2.510.349-7.20***0.0
bike_ASC_auto_sufficient_school-2.210.919-2.40*0.0-2.200.953-2.31*0.0
bike_ASC_auto_sufficient_shopping-3.140.138-22.70***0.0-2.780.326-8.52***0.0
bike_ASC_auto_sufficient_social-2.140.207-10.36***0.0-1.830.471-3.88***0.0
bike_ASC_auto_sufficient_univ-1.940.00657-295.46***0.0-1.940.00650-298.24***0.0
bike_ASC_auto_sufficient_work-1.930.0814-23.72***0.0-2.060.0822-25.06***0.0
bike_ASC_no_auto_atwork-0.91076.5-0.010.0-0.91187.8-0.010.0
bike_ASC_no_auto_eatout0.42875.90.010.00.51087.20.010.0
bike_ASC_no_auto_escort-0.74575.9-0.010.0-0.73087.2-0.010.0
bike_ASC_no_auto_othdiscr-0.50175.9-0.010.0-0.40487.2-0.000.0
bike_ASC_no_auto_othmaint1.5375.90.020.01.6187.20.020.0
bike_ASC_no_auto_school13.5109.0.120.013.4116.0.120.0
bike_ASC_no_auto_shopping0.95175.90.010.01.0587.20.010.0
bike_ASC_no_auto_social0.37475.90.000.00.48687.20.010.0
bike_ASC_no_auto_univ4.290.00148BIG***0.04.290.00688624.05***0.0
bike_ASC_no_auto_work3.4375.90.050.03.4387.20.040.0
coef_age010_trn_multiplier_atwork0.0007220.007850.090.00.000722NANA0.0
coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work0.4900.1762.79**0.00.3640.1752.08*0.0
coef_age010_trn_multiplier_school_univ-0.8510.0900-9.46***0.0-0.8750.0898-9.74***0.0
coef_age1619_da_multiplier_atwork-0.1750.205-0.850.0-0.1930.205-0.940.0
coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work0.03570.04250.840.00.0005130.04240.010.0
coef_age1619_da_multiplier_school_univ-1.670.0748-22.31***0.0-1.680.0748-22.42***0.0
coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social-1.580.0842-18.71***0.0-1.450.0814-17.82***0.0
coef_age16p_sr_multiplier_school_univ_work_atwork-0.6880.0682-10.08***0.0-0.6480.0683-9.49***0.0
coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork-0.07210.0312-2.31*0.0-0.07100.0312-2.27*0.0
coef_hhsize1_sr_multiplier_work-0.8220.0571-14.39***0.0-0.8360.0577-14.48***0.0
coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork-0.004040.0178-0.230.00.002090.01780.120.0
coef_hhsize2_sr_multiplier_school_univ-0.7430.0912-8.15***0.0-0.7480.0912-8.20***0.0
coef_ivt_atwork-0.01120.000479-23.41***0.0-0.01120.000478-23.41***0.0
coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social-0.006910.000145-47.76***0.0-0.006880.000144-47.65***0.0
coef_ivt_school_univ-0.01030.000316-32.62***0.0-0.01040.000318-32.71***0.0
coef_ivt_work-0.007910.000147-53.62***0.0-0.007950.000148-53.70***0.0
coef_nest_AUTO0.7200.00NA1.00.7200.00NA1.0
coef_nest_AUTO_DRIVEALONE0.3500.00NA1.00.3500.00NA1.0
coef_nest_AUTO_SHAREDRIDE20.3500.00NA1.00.3500.00NA1.0
coef_nest_AUTO_SHAREDRIDE30.3500.00NA1.00.3500.00NA1.0
coef_nest_NONMOTORIZED0.7200.00NA1.00.7200.00NA1.0
coef_nest_RIDEHAIL0.3600.00NA1.00.3600.00NA1.0
coef_nest_TRANSIT0.7200.00NA1.00.7200.00NA1.0
coef_nest_TRANSIT_DRIVEACCESS0.5000.00NA1.00.5000.00NA1.0
coef_nest_TRANSIT_WALKACCESS0.5000.00NA1.00.5000.00NA1.0
coef_test_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork0.07700.04941.560.00.0770NANA0.0
coef_test_school_univ0.5180.9130.570.00.518NANA0.0
commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.95816.4-0.060.0-0.74420.6-0.040.0
commuter_rail_ASC_school_univ-0.258NANA0.0-0.247NANA0.0
commuter_rail_ASC_work0.196278.0.000.00.158NANA0.0
drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.62816.4-0.040.0-0.39320.6-0.020.0
drive_ferry_ASC_school_univ1.43NANA0.01.45NANA0.0
drive_ferry_ASC_work0.232278.0.000.00.194NANA0.0
drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.32516.4-0.020.0-0.091920.6-0.000.0
drive_light_rail_ASC_school_univ0.691NANA0.00.707NANA0.0
drive_light_rail_ASC_work0.530278.0.000.00.507NANA0.0
drive_transit_ASC_auto_deficient_atwork-999.0.000360-BIG***0.0-999.0.000231-BIG***0.0
drive_transit_ASC_auto_deficient_eatout-0.17316.4-0.010.00.10520.60.010.0
drive_transit_ASC_auto_deficient_escort-2.1916.4-0.130.0-2.1420.6-0.100.0
drive_transit_ASC_auto_deficient_othdiscr-0.85216.4-0.050.0-0.72620.6-0.040.0
drive_transit_ASC_auto_deficient_othmaint-1.3616.4-0.080.0-1.1720.6-0.060.0
drive_transit_ASC_auto_deficient_school1.59NANA0.01.57NANA0.0
drive_transit_ASC_auto_deficient_shopping-1.5816.4-0.100.0-1.4320.6-0.070.0
drive_transit_ASC_auto_deficient_social0.61016.40.040.00.59420.60.030.0
drive_transit_ASC_auto_deficient_univ1.85NANA0.01.85NANA0.0
drive_transit_ASC_auto_deficient_work-0.824278.-0.000.0-0.911NANA0.0
drive_transit_ASC_auto_sufficient_atwork-999.0.000176-BIG***0.0-999.NANA0.0
drive_transit_ASC_auto_sufficient_eatout-1.6516.4-0.100.0-1.3020.6-0.060.0
drive_transit_ASC_auto_sufficient_escort-5.3116.4-0.320.0-5.2520.6-0.260.0
drive_transit_ASC_auto_sufficient_othdiscr-1.2416.4-0.080.0-1.1120.6-0.050.0
drive_transit_ASC_auto_sufficient_othmaint-2.8016.4-0.170.0-2.4420.6-0.120.0
drive_transit_ASC_auto_sufficient_school0.105NANA0.00.155NANA0.0
drive_transit_ASC_auto_sufficient_shopping-4.1616.4-0.250.0-3.9720.6-0.190.0
drive_transit_ASC_auto_sufficient_social-1.3716.4-0.080.0-1.3420.6-0.070.0
drive_transit_ASC_auto_sufficient_univ1.36NANA0.01.360.000291BIG***0.0
drive_transit_ASC_auto_sufficient_work-1.73278.-0.010.0-1.83NANA0.0
drive_transit_ASC_no_auto_all0.00NANA0.00.000.0002930.000.0
drive_transit_CBD_ASC_atwork0.564NANA0.00.564NANA0.0
drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social1.370.1429.63***0.01.410.1429.95***0.0
drive_transit_CBD_ASC_school_univ1.230.2514.89***0.01.200.2534.76***0.0
drive_transit_CBD_ASC_work1.640.069823.45***0.01.620.069723.19***0.0
express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.34216.4-0.020.0-0.088820.6-0.000.0
express_bus_ASC_school_univ-0.451NANA0.0-0.450NANA0.0
express_bus_ASC_work-0.881278.-0.000.0-0.920NANA0.0
heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.20716.4-0.010.00.040120.60.000.0
heavy_rail_ASC_school_univ0.110NANA0.00.127NANA0.0
heavy_rail_ASC_work0.513278.0.000.00.496NANA0.0
joint_bike_ASC_auto_deficient_all-6.181.34-4.62***0.0-6.161.42-4.35***0.0
joint_bike_ASC_auto_sufficient_all-7.100.465-15.28***0.0-6.950.465-14.96***0.0
joint_bike_ASC_no_auto_all-2.671.08-2.48*0.0-2.671.14-2.35*0.0
joint_drive_transit_ASC_auto_deficient_all-6.0416.4-0.370.0-6.0420.6-0.290.0
joint_drive_transit_ASC_auto_sufficient_all-7.7916.4-0.480.0-7.8020.6-0.380.0
joint_drive_transit_ASC_no_auto_all0.000.00NA0.00.000.00NA0.0
joint_sr2_ASC_auto_deficient_all0.000.00NA0.00.000.00NA0.0
joint_sr2_ASC_auto_sufficient_all0.000.00NA0.00.000.00NA0.0
joint_sr2_ASC_no_auto_all0.000.00NA0.00.000.00NA0.0
joint_sr3p_ASC_auto_deficient_all-1.490.258-5.77***0.0-1.510.260-5.80***0.0
joint_sr3p_ASC_auto_sufficient_all-2.290.133-17.17***0.0-2.270.131-17.25***0.0
joint_sr3p_ASC_no_auto_all0.6990.4701.490.00.6800.4711.440.0
joint_taxi_ASC_auto_deficient_all-9.828.15-1.210.0-9.828.64-1.140.0
joint_taxi_ASC_auto_sufficient_all-11.70.00NA0.0-11.70.00NA0.0
joint_taxi_ASC_no_auto_all-4.584.29-1.070.0-4.584.45-1.030.0
joint_tnc_shared_ASC_auto_deficient_all-11.217.0-0.660.0-11.218.0-0.620.0
joint_tnc_shared_ASC_auto_sufficient_all-13.20.00NA0.0-13.20.00NA0.0
joint_tnc_shared_ASC_no_auto_all-4.722.18-2.17*0.0-4.702.31-2.03*0.0
joint_tnc_single_ASC_auto_deficient_all-9.907.20-1.380.0-9.907.63-1.300.0
joint_tnc_single_ASC_auto_sufficient_all-14.00.00NA0.0-14.00.00NA0.0
joint_tnc_single_ASC_no_auto_all-3.862.05-1.880.0-3.892.20-1.770.0
joint_walk_ASC_auto_deficient_all-2.220.376-5.89***0.0-2.050.372-5.51***0.0
joint_walk_ASC_auto_sufficient_all-4.340.245-17.76***0.0-4.150.240-17.32***0.0
joint_walk_ASC_no_auto_all-0.3670.727-0.510.0-0.2940.735-0.400.0
joint_walk_transit_ASC_auto_deficient_all-5.3016.4-0.320.0-5.3020.6-0.260.0
joint_walk_transit_ASC_auto_sufficient_all-18.3655.-0.030.0-18.3620.-0.030.0
joint_walk_transit_ASC_no_auto_all0.38616.40.020.00.35120.60.020.0
local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-1.1816.4-0.070.0-0.94320.6-0.050.0
local_bus_ASC_school_univ-0.819NANA0.0-0.811NANA0.0
local_bus_ASC_work-0.179278.-0.000.0-0.211NANA0.0
sr2_ASC_auto_deficient_atwork-1.490.132-11.34***0.0-1.570.133-11.82***0.0
sr2_ASC_auto_deficient_eatout0.8740.1555.64***0.01.320.4073.25**0.0
sr2_ASC_auto_deficient_escort0.2540.1601.580.00.5600.3311.690.0
sr2_ASC_auto_deficient_othdiscr1.060.1318.13***0.01.320.3893.39***0.0
sr2_ASC_auto_deficient_othmaint0.5840.1454.02***0.01.030.3273.14**0.0
sr2_ASC_auto_deficient_school0.5180.9280.560.00.5220.9610.540.0
sr2_ASC_auto_deficient_shopping0.5220.1274.09***0.00.8010.3192.51*0.0
sr2_ASC_auto_deficient_social2.300.21510.70***0.02.420.4735.12***0.0
sr2_ASC_auto_deficient_univ-1.69NANA0.0-1.690.000112-BIG***0.0
sr2_ASC_auto_deficient_work0.4240.08924.76***0.00.2480.09002.76**0.0
sr2_ASC_auto_sufficient_atwork-0.7720.0784-9.85***0.0-0.8100.0784-10.34***0.0
sr2_ASC_auto_sufficient_eatout1.170.10511.19***0.01.630.3904.18***0.0
sr2_ASC_auto_sufficient_escort0.4270.1293.31***0.00.7540.3172.38*0.0
sr2_ASC_auto_sufficient_othdiscr0.8300.1018.23***0.01.090.3802.87**0.0
sr2_ASC_auto_sufficient_othmaint0.6420.1036.24***0.01.090.3123.51***0.0
sr2_ASC_auto_sufficient_school-0.7200.916-0.790.0-0.7200.950-0.760.0
sr2_ASC_auto_sufficient_shopping0.5180.1005.16***0.00.8060.3102.60**0.0
sr2_ASC_auto_sufficient_social0.8670.1107.88***0.01.070.4382.43*0.0
sr2_ASC_auto_sufficient_univ-1.86NANA0.0-1.866.12e-05-BIG***0.0
sr2_ASC_auto_sufficient_work-0.2950.0860-3.43***0.0-0.4780.0868-5.50***0.0
sr2_ASC_no_auto_all1.2275.90.020.01.1787.20.010.0
sr3p_ASC_auto_deficient_atwork-1.810.136-13.30***0.0-1.880.137-13.71***0.0
sr3p_ASC_auto_deficient_eatout0.1840.1870.980.00.6380.4201.520.0
sr3p_ASC_auto_deficient_escort0.03000.1630.180.00.3360.3321.010.0
sr3p_ASC_auto_deficient_othdiscr1.420.12411.40***0.01.670.3874.32***0.0
sr3p_ASC_auto_deficient_othmaint-0.6070.223-2.71**0.0-0.2020.371-0.550.0
sr3p_ASC_auto_deficient_school1.100.9271.180.01.090.9611.140.0
sr3p_ASC_auto_deficient_shopping0.1800.1381.310.00.4570.3241.410.0
sr3p_ASC_auto_deficient_social2.040.2239.16***0.02.160.4764.52***0.0
sr3p_ASC_auto_deficient_univ-1.731.83e-05-BIG***0.0-1.73NANA0.0
sr3p_ASC_auto_deficient_work-0.1270.0922-1.380.0-0.2890.0929-3.11**0.0
sr3p_ASC_auto_sufficient_atwork-0.9540.0797-11.97***0.0-1.000.0798-12.59***0.0
sr3p_ASC_auto_sufficient_eatout1.130.10510.73***0.01.580.3904.04***0.0
sr3p_ASC_auto_sufficient_escort0.4000.1293.10**0.00.7290.3172.30*0.0
sr3p_ASC_auto_sufficient_othdiscr0.9290.1019.25***0.01.180.3803.12**0.0
sr3p_ASC_auto_sufficient_othmaint0.2570.1052.44*0.00.7260.3132.32*0.0
sr3p_ASC_auto_sufficient_school-0.1510.916-0.160.0-0.1510.950-0.160.0
sr3p_ASC_auto_sufficient_shopping0.2750.1012.72**0.00.5610.3101.810.0
sr3p_ASC_auto_sufficient_social0.8700.1107.91***0.01.070.4382.45*0.0
sr3p_ASC_auto_sufficient_univ-1.902.57e-05-BIG***0.0-1.901.95e-05-BIG***0.0
sr3p_ASC_auto_sufficient_work-0.6620.0868-7.63***0.0-0.8490.0876-9.68***0.0
sr3p_ASC_no_auto_atwork1.4975.90.020.01.6787.20.020.0
sr3p_ASC_no_auto_eatout1.6275.90.020.01.5387.20.020.0
sr3p_ASC_no_auto_escort-1.8775.9-0.020.0-1.8787.3-0.020.0
sr3p_ASC_no_auto_othdiscr1.4375.90.020.01.4087.20.020.0
sr3p_ASC_no_auto_othmaint0.29675.90.000.00.19587.20.000.0
sr3p_ASC_no_auto_school-6.02105.-0.060.0-6.02112.-0.050.0
sr3p_ASC_no_auto_shopping0.88175.90.010.00.84787.20.010.0
sr3p_ASC_no_auto_social-1.1775.9-0.020.0-1.2187.2-0.010.0
sr3p_ASC_no_auto_univ-6.061.79e-05-BIG***0.0-6.065.30e-05-BIG***0.0
sr3p_ASC_no_auto_work0.61475.90.010.00.52387.20.010.0
taxi_ASC_auto_deficient_atwork-4.590.435-10.57***0.0-4.750.459-10.34***0.0
taxi_ASC_auto_deficient_eatout_othdiscr_social-3.260.483-6.76***0.0-3.160.647-4.89***0.0
taxi_ASC_auto_deficient_escort_othmaint_shopping-0.9300.118-7.89***0.0-0.5030.313-1.600.0
taxi_ASC_auto_deficient_school0.8400.9350.900.00.8990.9680.930.0
taxi_ASC_auto_deficient_univ4.251.62e-05BIG***0.04.254.12e-06BIG***0.0
taxi_ASC_auto_deficient_work-2.040.205-9.95***0.0-2.300.226-10.19***0.0
taxi_ASC_auto_sufficient_atwork-3.340.150-22.22***0.0-3.310.148-22.34***0.0
taxi_ASC_auto_sufficient_eatout_othdiscr_social-3.930.246-15.96***0.0-3.470.416-8.35***0.0
taxi_ASC_auto_sufficient_escort_othmaint_shopping-2.780.0965-28.85***0.0-2.330.306-7.60***0.0
taxi_ASC_auto_sufficient_school-2.260.922-2.45*0.0-2.230.956-2.33*0.0
taxi_ASC_auto_sufficient_univ-0.313NANA0.0-0.313NANA0.0
taxi_ASC_auto_sufficient_work-4.850.303-16.01***0.0-5.140.361-14.22***0.0
taxi_ASC_no_auto_atwork3.6575.90.050.03.6387.20.040.0
taxi_ASC_no_auto_eatout_othdiscr_social-0.57175.9-0.010.0-0.46087.2-0.010.0
taxi_ASC_no_auto_escort_othmaint_shopping1.4575.90.020.01.5487.20.020.0
taxi_ASC_no_auto_school_univ-7.000.00NA0.0-7.000.00NA0.0
taxi_ASC_no_auto_work3.9475.90.050.03.8987.20.040.0
tnc_shared_ASC_auto_deficient_atwork-5.170.414-12.49***0.0-5.210.410-12.71***0.0
tnc_shared_ASC_auto_deficient_eatout_othdiscr_social-4.460.900-4.95***0.0-4.431.17-3.78***0.0
tnc_shared_ASC_auto_deficient_escort_othmaint_shopping-1.240.109-11.36***0.0-0.7960.310-2.57*0.0
tnc_shared_ASC_auto_deficient_school0.2830.9330.300.00.3120.9660.320.0
tnc_shared_ASC_auto_deficient_univ3.251.25e-05BIG***0.03.25NANA0.0
tnc_shared_ASC_auto_deficient_work-3.400.214-15.89***0.0-3.730.231-16.16***0.0
tnc_shared_ASC_auto_sufficient_atwork-4.080.166-24.64***0.0-4.090.167-24.51***0.0
tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social-4.320.199-21.67***0.0-3.880.393-9.86***0.0
tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping-3.290.0920-35.76***0.0-2.830.305-9.30***0.0
tnc_shared_ASC_auto_sufficient_school-3.620.927-3.91***0.0-3.620.961-3.77***0.0
tnc_shared_ASC_auto_sufficient_univ-0.907NANA0.0-0.907NANA0.0
tnc_shared_ASC_auto_sufficient_work-6.480.411-15.77***0.0-6.740.419-16.11***0.0
tnc_shared_ASC_no_auto_atwork3.0675.90.040.03.0487.20.030.0
tnc_shared_ASC_no_auto_eatout_othdiscr_social-0.080375.9-0.000.0-0.00052987.2-0.000.0
tnc_shared_ASC_no_auto_escort_othmaint_shopping0.68575.90.010.00.77687.20.010.0
tnc_shared_ASC_no_auto_school-7.000.00NA0.0-7.000.00NA0.0
tnc_shared_ASC_no_auto_univ-5.81NANA0.0-5.81NANA0.0
tnc_shared_ASC_no_auto_work2.2375.90.030.02.2187.20.030.0
tnc_single_ASC_auto_deficient_atwork-4.240.319-13.29***0.0-4.360.331-13.19***0.0
tnc_single_ASC_auto_deficient_eatout_othdiscr_social-3.270.436-7.50***0.0-3.120.599-5.21***0.0
tnc_single_ASC_auto_deficient_escort_othmaint_shopping-0.3400.102-3.34***0.00.09280.3080.300.0
tnc_single_ASC_auto_deficient_school-0.01370.943-0.010.00.05970.9750.060.0
tnc_single_ASC_auto_deficient_univ1.02NANA0.01.022.45e-06BIG***0.0
tnc_single_ASC_auto_deficient_work-1.300.115-11.28***0.0-1.470.118-12.49***0.0
tnc_single_ASC_auto_sufficient_atwork-3.140.126-25.00***0.0-3.140.125-25.04***0.0
tnc_single_ASC_auto_sufficient_eatout_othdiscr_social-3.260.149-21.84***0.0-2.830.371-7.62***0.0
tnc_single_ASC_auto_sufficient_escort_othmaint_shopping-2.200.0833-26.43***0.0-1.760.302-5.81***0.0
tnc_single_ASC_auto_sufficient_school-2.070.920-2.25*0.0-2.020.953-2.12*0.0
tnc_single_ASC_auto_sufficient_univ0.2095.09e-06BIG***0.00.2092.79e-06BIG***0.0
tnc_single_ASC_auto_sufficient_work-4.240.182-23.29***0.0-4.330.179-24.13***0.0
tnc_single_ASC_no_auto_atwork4.2675.90.060.04.2487.20.050.0
tnc_single_ASC_no_auto_eatout_othdiscr_social0.98275.90.010.01.0687.20.010.0
tnc_single_ASC_no_auto_escort_othmaint_shopping1.6075.90.020.01.7087.20.020.0
tnc_single_ASC_no_auto_school-7.000.00NA0.0-7.000.00NA0.0
tnc_single_ASC_no_auto_univ-2.52NANA0.0-2.525.36e-06-BIG***0.0
tnc_single_ASC_no_auto_work5.3175.90.070.05.3187.20.060.0
walk_ASC_auto_deficient_atwork0.2270.1231.840.00.1750.1241.410.0
walk_ASC_auto_deficient_eatout2.170.19810.96***0.02.700.4256.37***0.0
walk_ASC_auto_deficient_escort-1.980.265-7.46***0.0-1.600.396-4.04***0.0
walk_ASC_auto_deficient_othdiscr1.210.1547.86***0.01.570.3983.94***0.0
walk_ASC_auto_deficient_othmaint0.1450.2080.700.00.6430.3601.790.0
walk_ASC_auto_deficient_school2.610.9312.81**0.02.650.9642.75**0.0
walk_ASC_auto_deficient_shopping0.9090.1376.63***0.01.310.3234.06***0.0
walk_ASC_auto_deficient_social2.260.2997.56***0.02.480.5164.82***0.0
walk_ASC_auto_deficient_univ4.511.92e-06BIG***0.04.51NANA0.0
walk_ASC_auto_deficient_work1.700.095517.84***0.01.590.096216.52***0.0
walk_ASC_auto_sufficient_atwork0.07760.05801.340.00.07860.05791.360.0
walk_ASC_auto_sufficient_eatout0.6130.1065.78***0.01.190.3913.04**0.0
walk_ASC_auto_sufficient_escort-1.740.117-14.88***0.0-1.320.313-4.20***0.0
walk_ASC_auto_sufficient_othdiscr0.3050.08293.68***0.00.6610.3761.760.0
walk_ASC_auto_sufficient_othmaint-0.1680.0955-1.760.00.4390.3101.420.0
walk_ASC_auto_sufficient_school0.3740.9170.410.00.3830.9510.400.0
walk_ASC_auto_sufficient_shopping-0.3040.0811-3.75***0.00.08180.3050.270.0
walk_ASC_auto_sufficient_social0.5920.1175.04***0.00.9110.4392.08*0.0
walk_ASC_auto_sufficient_univ1.06NANA0.01.061.94e-07BIG***0.0
walk_ASC_auto_sufficient_work-0.2910.0787-3.70***0.0-0.4170.0795-5.25***0.0
walk_ASC_no_auto_atwork6.6375.90.090.06.6187.20.080.0
walk_ASC_no_auto_eatout4.4175.90.060.04.5287.20.050.0
walk_ASC_no_auto_escort2.5175.90.030.02.5687.20.030.0
walk_ASC_no_auto_othdiscr2.8975.90.040.02.9687.20.030.0
walk_ASC_no_auto_othmaint0.93075.90.010.01.0687.20.010.0
walk_ASC_no_auto_school17.8109.0.160.017.8116.0.150.0
walk_ASC_no_auto_shopping2.1775.90.030.02.2687.20.030.0
walk_ASC_no_auto_social1.7475.90.020.01.8487.20.020.0
walk_ASC_no_auto_univ6.41NANA0.06.413.50e-09BIG***0.0
walk_ASC_no_auto_work5.2875.90.070.05.2887.20.060.0
walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.23916.4-0.010.0-0.034720.6-0.000.0
walk_ferry_ASC_school_univ0.858NANA0.00.903NANA0.0
walk_ferry_ASC_work0.189278.0.000.00.0751NANA0.0
walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork-0.13616.4-0.010.00.11520.60.010.0
walk_light_rail_ASC_school_univ0.724NANA0.00.721NANA0.0
walk_light_rail_ASC_work0.786278.0.000.00.730NANA0.0
walk_transit_ASC_auto_deficient_atwork-2.6316.4-0.160.0-2.8420.6-0.140.0
walk_transit_ASC_auto_deficient_eatout-0.48916.4-0.030.0-0.20520.6-0.010.0
walk_transit_ASC_auto_deficient_escort-4.4516.4-0.270.0-4.4620.6-0.220.0
walk_transit_ASC_auto_deficient_othdiscr0.87316.40.050.00.99720.60.050.0
walk_transit_ASC_auto_deficient_othmaint-2.9716.4-0.180.0-2.9120.6-0.140.0
walk_transit_ASC_auto_deficient_school2.97NANA0.03.00NANA0.0
walk_transit_ASC_auto_deficient_shopping-1.0716.4-0.070.0-0.89920.6-0.040.0
walk_transit_ASC_auto_deficient_social1.3216.40.080.01.3220.60.060.0
walk_transit_ASC_auto_deficient_univ3.140.00NA0.03.140.00NA0.0
walk_transit_ASC_auto_deficient_work0.159278.0.000.00.0663NANA0.0
walk_transit_ASC_auto_sufficient_atwork-3.6616.4-0.220.0-3.7620.6-0.180.0
walk_transit_ASC_auto_sufficient_eatout-1.3216.4-0.080.0-0.98520.6-0.050.0
walk_transit_ASC_auto_sufficient_escort-4.8216.4-0.290.0-4.6820.6-0.230.0
walk_transit_ASC_auto_sufficient_othdiscr-0.91416.4-0.060.0-0.77120.6-0.040.0
walk_transit_ASC_auto_sufficient_othmaint-1.6816.4-0.100.0-1.3120.6-0.060.0
walk_transit_ASC_auto_sufficient_school0.487NANA0.00.522NANA0.0
walk_transit_ASC_auto_sufficient_shopping-2.3516.4-0.140.0-2.2020.6-0.110.0
walk_transit_ASC_auto_sufficient_social-0.64716.4-0.040.0-0.59520.6-0.030.0
walk_transit_ASC_auto_sufficient_univ0.4730.00NA0.00.4730.00NA0.0
walk_transit_ASC_auto_sufficient_work-1.37278.-0.000.0-1.47NANA0.0
walk_transit_ASC_no_auto_atwork3.1366.90.050.02.9873.60.040.0
walk_transit_ASC_no_auto_eatout2.9266.90.040.02.7673.60.040.0
walk_transit_ASC_no_auto_escort-2.2667.0-0.030.0-2.2673.7-0.030.0
walk_transit_ASC_no_auto_othdiscr2.8066.90.040.02.6473.60.040.0
walk_transit_ASC_no_auto_othmaint2.5966.90.040.02.4373.60.030.0
walk_transit_ASC_no_auto_school20.4NANA0.020.5NANA0.0
walk_transit_ASC_no_auto_shopping2.3366.90.030.02.1873.60.030.0
walk_transit_ASC_no_auto_social2.0666.90.030.01.9073.60.030.0
walk_transit_ASC_no_auto_univ8.790.00NA0.08.790.00NA0.0
walk_transit_ASC_no_auto_work4.60285.0.020.04.63NANA0.0
walk_transit_CBD_ASC_atwork1.300.2844.57***0.01.180.2774.28***0.0
walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social1.080.075714.25***0.01.090.075614.41***0.0
walk_transit_CBD_ASC_school_univ0.8340.1147.30***0.00.8260.1147.24***0.0
walk_transit_CBD_ASC_work1.160.053921.50***0.01.150.053921.27***0.0
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: eatout, dtype: object0.3400.1901.780.0
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: escort, dtype: object0.00NANA0.0
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: othdiscr, dtype: object0.2310.1851.240.0
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: othmaint, dtype: object0.3330.1502.22*0.0
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: shopping, dtype: object0.2450.1491.640.0
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: social, dtype: object0.1970.2140.920.0
coefficient_name\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\ncoef_test coef_test_eatout_escort_othdiscr_othmaint_shop...\\nName: work, dtype: object-0.03060.0255-1.200.0
coefficient_name\\ncoef_test coef_test_school_univ\\ncoef_test coef_test_school_univ\\nName: school, dtype: object0.2780.4740.590.0
coefficient_name\\ncoef_test coef_test_school_univ\\ncoef_test coef_test_school_univ\\nName: univ, dtype: object0.00NANA0.0
\n", - "

307 rows × 3 columns

\n", "
" ], "text/plain": [ - " coefficient_name value constrain\n", - "0 coef_one 1.000000 T\n", - "1 coef_nest_root 1.000000 T\n", - "2 coef_nest_AUTO 0.720000 T\n", - "3 coef_nest_AUTO_DRIVEALONE 0.350000 T\n", - "4 coef_nest_AUTO_SHAREDRIDE2 0.350000 T\n", - ".. ... ... ...\n", - "302 walk_transit_CBD_ASC_atwork 0.351950 F\n", - "303 drive_transit_CBD_ASC_eatout_escort_othdiscr_o... 0.553404 F\n", - "304 drive_transit_CBD_ASC_school_univ 62.459413 F\n", - "305 drive_transit_CBD_ASC_work 1.432631 F\n", - "306 drive_transit_CBD_ASC_atwork 0.564000 F\n", + " model \\\n", + " Value Std Err \n", + "Parameter \n", + "-999 -999.  0.00 \n", + "1  1.00  0.00 \n", + "bike_ASC_auto_deficient_atwork -1.05  0.344 \n", + "bike_ASC_auto_deficient_eatout -1.46  0.396 \n", + "bike_ASC_auto_deficient_escort -3.98  0.406 \n", + "bike_ASC_auto_deficient_othdiscr -0.910  0.203 \n", + "bike_ASC_auto_deficient_othmaint -2.06  0.323 \n", + "bike_ASC_auto_deficient_school -1.20  0.964 \n", + "bike_ASC_auto_deficient_shopping -1.74  0.218 \n", + "bike_ASC_auto_deficient_social  0.361  0.325 \n", + "bike_ASC_auto_deficient_univ -0.669  NA \n", + "bike_ASC_auto_deficient_work -0.279  0.0926 \n", + "bike_ASC_auto_sufficient_atwork  15.7  470. \n", + "bike_ASC_auto_sufficient_eatout -1.85  0.159 \n", + "bike_ASC_auto_sufficient_escort -5.65  0.337 \n", + "bike_ASC_auto_sufficient_othdiscr -1.86  0.105 \n", + "bike_ASC_auto_sufficient_othmaint -3.17  0.193 \n", + "bike_ASC_auto_sufficient_school -2.21  0.919 \n", + "bike_ASC_auto_sufficient_shopping -3.14  0.138 \n", + "bike_ASC_auto_sufficient_social -2.14  0.207 \n", + "bike_ASC_auto_sufficient_univ -1.94  0.00657 \n", + "bike_ASC_auto_sufficient_work -1.93  0.0814 \n", + "bike_ASC_no_auto_atwork -0.910  76.5 \n", + "bike_ASC_no_auto_eatout  0.428  75.9 \n", + "bike_ASC_no_auto_escort -0.745  75.9 \n", + "bike_ASC_no_auto_othdiscr -0.501  75.9 \n", + "bike_ASC_no_auto_othmaint  1.53  75.9 \n", + "bike_ASC_no_auto_school  13.5  109. \n", + "bike_ASC_no_auto_shopping  0.951  75.9 \n", + "bike_ASC_no_auto_social  0.374  75.9 \n", + "bike_ASC_no_auto_univ  4.29  0.00148 \n", + "bike_ASC_no_auto_work  3.43  75.9 \n", + "coef_age010_trn_multiplier_atwork  0.000722  0.00785 \n", + "coef_age010_trn_multiplier_eatout_escort_othdis...  0.490  0.176 \n", + "coef_age010_trn_multiplier_school_univ -0.851  0.0900 \n", + "coef_age1619_da_multiplier_atwork -0.175  0.205 \n", + "coef_age1619_da_multiplier_eatout_escort_othdis...  0.0357  0.0425 \n", + "coef_age1619_da_multiplier_school_univ -1.67  0.0748 \n", + "coef_age16p_sr_multiplier_eatout_escort_othdisc... -1.58  0.0842 \n", + "coef_age16p_sr_multiplier_school_univ_work_atwork -0.688  0.0682 \n", + "coef_hhsize1_sr_multiplier_eatout_escort_othdis... -0.0721  0.0312 \n", + "coef_hhsize1_sr_multiplier_work -0.822  0.0571 \n", + "coef_hhsize2_sr_multiplier_eatout_escort_othdis... -0.00404  0.0178 \n", + "coef_hhsize2_sr_multiplier_school_univ -0.743  0.0912 \n", + "coef_ivt_atwork -0.0112  0.000479 \n", + "coef_ivt_eatout_escort_othdiscr_othmaint_shoppi... -0.00691  0.000145 \n", + "coef_ivt_school_univ -0.0103  0.000316 \n", + "coef_ivt_work -0.00791  0.000147 \n", + "coef_nest_AUTO  0.720  0.00 \n", + "coef_nest_AUTO_DRIVEALONE  0.350  0.00 \n", + "coef_nest_AUTO_SHAREDRIDE2  0.350  0.00 \n", + "coef_nest_AUTO_SHAREDRIDE3  0.350  0.00 \n", + "coef_nest_NONMOTORIZED  0.720  0.00 \n", + "coef_nest_RIDEHAIL  0.360  0.00 \n", + "coef_nest_TRANSIT  0.720  0.00 \n", + "coef_nest_TRANSIT_DRIVEACCESS  0.500  0.00 \n", + "coef_nest_TRANSIT_WALKACCESS  0.500  0.00 \n", + "coef_test_eatout_escort_othdiscr_othmaint_shopp...  0.0770  0.0494 \n", + "coef_test_school_univ  0.518  0.913 \n", + "commuter_rail_ASC_eatout_escort_othdiscr_othmai... -0.958  16.4 \n", + "commuter_rail_ASC_school_univ -0.258  NA \n", + "commuter_rail_ASC_work  0.196  278. \n", + "drive_ferry_ASC_eatout_escort_othdiscr_othmaint... -0.628  16.4 \n", + "drive_ferry_ASC_school_univ  1.43  NA \n", + "drive_ferry_ASC_work  0.232  278. \n", + "drive_light_rail_ASC_eatout_escort_othdiscr_oth... -0.325  16.4 \n", + "drive_light_rail_ASC_school_univ  0.691  NA \n", + "drive_light_rail_ASC_work  0.530  278. \n", + "drive_transit_ASC_auto_deficient_atwork -999.  0.000360 \n", + "drive_transit_ASC_auto_deficient_eatout -0.173  16.4 \n", + "drive_transit_ASC_auto_deficient_escort -2.19  16.4 \n", + "drive_transit_ASC_auto_deficient_othdiscr -0.852  16.4 \n", + "drive_transit_ASC_auto_deficient_othmaint -1.36  16.4 \n", + "drive_transit_ASC_auto_deficient_school  1.59  NA \n", + "drive_transit_ASC_auto_deficient_shopping -1.58  16.4 \n", + "drive_transit_ASC_auto_deficient_social  0.610  16.4 \n", + "drive_transit_ASC_auto_deficient_univ  1.85  NA \n", + "drive_transit_ASC_auto_deficient_work -0.824  278. \n", + "drive_transit_ASC_auto_sufficient_atwork -999.  0.000176 \n", + "drive_transit_ASC_auto_sufficient_eatout -1.65  16.4 \n", + "drive_transit_ASC_auto_sufficient_escort -5.31  16.4 \n", + "drive_transit_ASC_auto_sufficient_othdiscr -1.24  16.4 \n", + "drive_transit_ASC_auto_sufficient_othmaint -2.80  16.4 \n", + "drive_transit_ASC_auto_sufficient_school  0.105  NA \n", + "drive_transit_ASC_auto_sufficient_shopping -4.16  16.4 \n", + "drive_transit_ASC_auto_sufficient_social -1.37  16.4 \n", + "drive_transit_ASC_auto_sufficient_univ  1.36  NA \n", + "drive_transit_ASC_auto_sufficient_work -1.73  278. \n", + "drive_transit_ASC_no_auto_all  0.00  NA \n", + "drive_transit_CBD_ASC_atwork  0.564  NA \n", + "drive_transit_CBD_ASC_eatout_escort_othdiscr_ot...  1.37  0.142 \n", + "drive_transit_CBD_ASC_school_univ  1.23  0.251 \n", + "drive_transit_CBD_ASC_work  1.64  0.0698 \n", + "express_bus_ASC_eatout_escort_othdiscr_othmaint... -0.342  16.4 \n", + "express_bus_ASC_school_univ -0.451  NA \n", + "express_bus_ASC_work -0.881  278. \n", + "heavy_rail_ASC_eatout_escort_othdiscr_othmaint_... -0.207  16.4 \n", + "heavy_rail_ASC_school_univ  0.110  NA \n", + "heavy_rail_ASC_work  0.513  278. \n", + "joint_bike_ASC_auto_deficient_all -6.18  1.34 \n", + "joint_bike_ASC_auto_sufficient_all -7.10  0.465 \n", + "joint_bike_ASC_no_auto_all -2.67  1.08 \n", + "joint_drive_transit_ASC_auto_deficient_all -6.04  16.4 \n", + "joint_drive_transit_ASC_auto_sufficient_all -7.79  16.4 \n", + "joint_drive_transit_ASC_no_auto_all  0.00  0.00 \n", + "joint_sr2_ASC_auto_deficient_all  0.00  0.00 \n", + "joint_sr2_ASC_auto_sufficient_all  0.00  0.00 \n", + "joint_sr2_ASC_no_auto_all  0.00  0.00 \n", + "joint_sr3p_ASC_auto_deficient_all -1.49  0.258 \n", + "joint_sr3p_ASC_auto_sufficient_all -2.29  0.133 \n", + "joint_sr3p_ASC_no_auto_all  0.699  0.470 \n", + "joint_taxi_ASC_auto_deficient_all -9.82  8.15 \n", + "joint_taxi_ASC_auto_sufficient_all -11.7  0.00 \n", + "joint_taxi_ASC_no_auto_all -4.58  4.29 \n", + "joint_tnc_shared_ASC_auto_deficient_all -11.2  17.0 \n", + "joint_tnc_shared_ASC_auto_sufficient_all -13.2  0.00 \n", + "joint_tnc_shared_ASC_no_auto_all -4.72  2.18 \n", + "joint_tnc_single_ASC_auto_deficient_all -9.90  7.20 \n", + "joint_tnc_single_ASC_auto_sufficient_all -14.0  0.00 \n", + "joint_tnc_single_ASC_no_auto_all -3.86  2.05 \n", + "joint_walk_ASC_auto_deficient_all -2.22  0.376 \n", + "joint_walk_ASC_auto_sufficient_all -4.34  0.245 \n", + "joint_walk_ASC_no_auto_all -0.367  0.727 \n", + "joint_walk_transit_ASC_auto_deficient_all -5.30  16.4 \n", + "joint_walk_transit_ASC_auto_sufficient_all -18.3  655. \n", + "joint_walk_transit_ASC_no_auto_all  0.386  16.4 \n", + "local_bus_ASC_eatout_escort_othdiscr_othmaint_s... -1.18  16.4 \n", + "local_bus_ASC_school_univ -0.819  NA \n", + "local_bus_ASC_work -0.179  278. \n", + "sr2_ASC_auto_deficient_atwork -1.49  0.132 \n", + "sr2_ASC_auto_deficient_eatout  0.874  0.155 \n", + "sr2_ASC_auto_deficient_escort  0.254  0.160 \n", + "sr2_ASC_auto_deficient_othdiscr  1.06  0.131 \n", + "sr2_ASC_auto_deficient_othmaint  0.584  0.145 \n", + "sr2_ASC_auto_deficient_school  0.518  0.928 \n", + "sr2_ASC_auto_deficient_shopping  0.522  0.127 \n", + "sr2_ASC_auto_deficient_social  2.30  0.215 \n", + "sr2_ASC_auto_deficient_univ -1.69  NA \n", + "sr2_ASC_auto_deficient_work  0.424  0.0892 \n", + "sr2_ASC_auto_sufficient_atwork -0.772  0.0784 \n", + "sr2_ASC_auto_sufficient_eatout  1.17  0.105 \n", + "sr2_ASC_auto_sufficient_escort  0.427  0.129 \n", + "sr2_ASC_auto_sufficient_othdiscr  0.830  0.101 \n", + "sr2_ASC_auto_sufficient_othmaint  0.642  0.103 \n", + "sr2_ASC_auto_sufficient_school -0.720  0.916 \n", + "sr2_ASC_auto_sufficient_shopping  0.518  0.100 \n", + "sr2_ASC_auto_sufficient_social  0.867  0.110 \n", + "sr2_ASC_auto_sufficient_univ -1.86  NA \n", + "sr2_ASC_auto_sufficient_work -0.295  0.0860 \n", + "sr2_ASC_no_auto_all  1.22  75.9 \n", + "sr3p_ASC_auto_deficient_atwork -1.81  0.136 \n", + "sr3p_ASC_auto_deficient_eatout  0.184  0.187 \n", + "sr3p_ASC_auto_deficient_escort  0.0300  0.163 \n", + "sr3p_ASC_auto_deficient_othdiscr  1.42  0.124 \n", + "sr3p_ASC_auto_deficient_othmaint -0.607  0.223 \n", + "sr3p_ASC_auto_deficient_school  1.10  0.927 \n", + "sr3p_ASC_auto_deficient_shopping  0.180  0.138 \n", + "sr3p_ASC_auto_deficient_social  2.04  0.223 \n", + "sr3p_ASC_auto_deficient_univ -1.73  1.83e-05 \n", + "sr3p_ASC_auto_deficient_work -0.127  0.0922 \n", + "sr3p_ASC_auto_sufficient_atwork -0.954  0.0797 \n", + "sr3p_ASC_auto_sufficient_eatout  1.13  0.105 \n", + "sr3p_ASC_auto_sufficient_escort  0.400  0.129 \n", + "sr3p_ASC_auto_sufficient_othdiscr  0.929  0.101 \n", + "sr3p_ASC_auto_sufficient_othmaint  0.257  0.105 \n", + "sr3p_ASC_auto_sufficient_school -0.151  0.916 \n", + "sr3p_ASC_auto_sufficient_shopping  0.275  0.101 \n", + "sr3p_ASC_auto_sufficient_social  0.870  0.110 \n", + "sr3p_ASC_auto_sufficient_univ -1.90  2.57e-05 \n", + "sr3p_ASC_auto_sufficient_work -0.662  0.0868 \n", + "sr3p_ASC_no_auto_atwork  1.49  75.9 \n", + "sr3p_ASC_no_auto_eatout  1.62  75.9 \n", + "sr3p_ASC_no_auto_escort -1.87  75.9 \n", + "sr3p_ASC_no_auto_othdiscr  1.43  75.9 \n", + "sr3p_ASC_no_auto_othmaint  0.296  75.9 \n", + "sr3p_ASC_no_auto_school -6.02  105. \n", + "sr3p_ASC_no_auto_shopping  0.881  75.9 \n", + "sr3p_ASC_no_auto_social -1.17  75.9 \n", + "sr3p_ASC_no_auto_univ -6.06  1.79e-05 \n", + "sr3p_ASC_no_auto_work  0.614  75.9 \n", + "taxi_ASC_auto_deficient_atwork -4.59  0.435 \n", + "taxi_ASC_auto_deficient_eatout_othdiscr_social -3.26  0.483 \n", + "taxi_ASC_auto_deficient_escort_othmaint_shopping -0.930  0.118 \n", + "taxi_ASC_auto_deficient_school  0.840  0.935 \n", + "taxi_ASC_auto_deficient_univ  4.25  1.62e-05 \n", + "taxi_ASC_auto_deficient_work -2.04  0.205 \n", + "taxi_ASC_auto_sufficient_atwork -3.34  0.150 \n", + "taxi_ASC_auto_sufficient_eatout_othdiscr_social -3.93  0.246 \n", + "taxi_ASC_auto_sufficient_escort_othmaint_shopping -2.78  0.0965 \n", + "taxi_ASC_auto_sufficient_school -2.26  0.922 \n", + "taxi_ASC_auto_sufficient_univ -0.313  NA \n", + "taxi_ASC_auto_sufficient_work -4.85  0.303 \n", + "taxi_ASC_no_auto_atwork  3.65  75.9 \n", + "taxi_ASC_no_auto_eatout_othdiscr_social -0.571  75.9 \n", + "taxi_ASC_no_auto_escort_othmaint_shopping  1.45  75.9 \n", + "taxi_ASC_no_auto_school_univ -7.00  0.00 \n", + "taxi_ASC_no_auto_work  3.94  75.9 \n", + "tnc_shared_ASC_auto_deficient_atwork -5.17  0.414 \n", + "tnc_shared_ASC_auto_deficient_eatout_othdiscr_s... -4.46  0.900 \n", + "tnc_shared_ASC_auto_deficient_escort_othmaint_s... -1.24  0.109 \n", + "tnc_shared_ASC_auto_deficient_school  0.283  0.933 \n", + "tnc_shared_ASC_auto_deficient_univ  3.25  1.25e-05 \n", + "tnc_shared_ASC_auto_deficient_work -3.40  0.214 \n", + "tnc_shared_ASC_auto_sufficient_atwork -4.08  0.166 \n", + "tnc_shared_ASC_auto_sufficient_eatout_othdiscr_... -4.32  0.199 \n", + "tnc_shared_ASC_auto_sufficient_escort_othmaint_... -3.29  0.0920 \n", + "tnc_shared_ASC_auto_sufficient_school -3.62  0.927 \n", + "tnc_shared_ASC_auto_sufficient_univ -0.907  NA \n", + "tnc_shared_ASC_auto_sufficient_work -6.48  0.411 \n", + "tnc_shared_ASC_no_auto_atwork  3.06  75.9 \n", + "tnc_shared_ASC_no_auto_eatout_othdiscr_social -0.0803  75.9 \n", + "tnc_shared_ASC_no_auto_escort_othmaint_shopping  0.685  75.9 \n", + "tnc_shared_ASC_no_auto_school -7.00  0.00 \n", + "tnc_shared_ASC_no_auto_univ -5.81  NA \n", + "tnc_shared_ASC_no_auto_work  2.23  75.9 \n", + "tnc_single_ASC_auto_deficient_atwork -4.24  0.319 \n", + "tnc_single_ASC_auto_deficient_eatout_othdiscr_s... -3.27  0.436 \n", + "tnc_single_ASC_auto_deficient_escort_othmaint_s... -0.340  0.102 \n", + "tnc_single_ASC_auto_deficient_school -0.0137  0.943 \n", + "tnc_single_ASC_auto_deficient_univ  1.02  NA \n", + "tnc_single_ASC_auto_deficient_work -1.30  0.115 \n", + "tnc_single_ASC_auto_sufficient_atwork -3.14  0.126 \n", + "tnc_single_ASC_auto_sufficient_eatout_othdiscr_... -3.26  0.149 \n", + "tnc_single_ASC_auto_sufficient_escort_othmaint_... -2.20  0.0833 \n", + "tnc_single_ASC_auto_sufficient_school -2.07  0.920 \n", + "tnc_single_ASC_auto_sufficient_univ  0.209  5.09e-06 \n", + "tnc_single_ASC_auto_sufficient_work -4.24  0.182 \n", + "tnc_single_ASC_no_auto_atwork  4.26  75.9 \n", + "tnc_single_ASC_no_auto_eatout_othdiscr_social  0.982  75.9 \n", + "tnc_single_ASC_no_auto_escort_othmaint_shopping  1.60  75.9 \n", + "tnc_single_ASC_no_auto_school -7.00  0.00 \n", + "tnc_single_ASC_no_auto_univ -2.52  NA \n", + "tnc_single_ASC_no_auto_work  5.31  75.9 \n", + "walk_ASC_auto_deficient_atwork  0.227  0.123 \n", + "walk_ASC_auto_deficient_eatout  2.17  0.198 \n", + "walk_ASC_auto_deficient_escort -1.98  0.265 \n", + "walk_ASC_auto_deficient_othdiscr  1.21  0.154 \n", + "walk_ASC_auto_deficient_othmaint  0.145  0.208 \n", + "walk_ASC_auto_deficient_school  2.61  0.931 \n", + "walk_ASC_auto_deficient_shopping  0.909  0.137 \n", + "walk_ASC_auto_deficient_social  2.26  0.299 \n", + "walk_ASC_auto_deficient_univ  4.51  1.92e-06 \n", + "walk_ASC_auto_deficient_work  1.70  0.0955 \n", + "walk_ASC_auto_sufficient_atwork  0.0776  0.0580 \n", + "walk_ASC_auto_sufficient_eatout  0.613  0.106 \n", + "walk_ASC_auto_sufficient_escort -1.74  0.117 \n", + "walk_ASC_auto_sufficient_othdiscr  0.305  0.0829 \n", + "walk_ASC_auto_sufficient_othmaint -0.168  0.0955 \n", + "walk_ASC_auto_sufficient_school  0.374  0.917 \n", + "walk_ASC_auto_sufficient_shopping -0.304  0.0811 \n", + "walk_ASC_auto_sufficient_social  0.592  0.117 \n", + "walk_ASC_auto_sufficient_univ  1.06  NA \n", + "walk_ASC_auto_sufficient_work -0.291  0.0787 \n", + "walk_ASC_no_auto_atwork  6.63  75.9 \n", + "walk_ASC_no_auto_eatout  4.41  75.9 \n", + "walk_ASC_no_auto_escort  2.51  75.9 \n", + "walk_ASC_no_auto_othdiscr  2.89  75.9 \n", + "walk_ASC_no_auto_othmaint  0.930  75.9 \n", + "walk_ASC_no_auto_school  17.8  109. \n", + "walk_ASC_no_auto_shopping  2.17  75.9 \n", + "walk_ASC_no_auto_social  1.74  75.9 \n", + "walk_ASC_no_auto_univ  6.41  NA \n", + "walk_ASC_no_auto_work  5.28  75.9 \n", + "walk_ferry_ASC_eatout_escort_othdiscr_othmaint_... -0.239  16.4 \n", + "walk_ferry_ASC_school_univ  0.858  NA \n", + "walk_ferry_ASC_work  0.189  278. \n", + "walk_light_rail_ASC_eatout_escort_othdiscr_othm... -0.136  16.4 \n", + "walk_light_rail_ASC_school_univ  0.724  NA \n", + "walk_light_rail_ASC_work  0.786  278. \n", + "walk_transit_ASC_auto_deficient_atwork -2.63  16.4 \n", + "walk_transit_ASC_auto_deficient_eatout -0.489  16.4 \n", + "walk_transit_ASC_auto_deficient_escort -4.45  16.4 \n", + "walk_transit_ASC_auto_deficient_othdiscr  0.873  16.4 \n", + "walk_transit_ASC_auto_deficient_othmaint -2.97  16.4 \n", + "walk_transit_ASC_auto_deficient_school  2.97  NA \n", + "walk_transit_ASC_auto_deficient_shopping -1.07  16.4 \n", + "walk_transit_ASC_auto_deficient_social  1.32  16.4 \n", + "walk_transit_ASC_auto_deficient_univ  3.14  0.00 \n", + "walk_transit_ASC_auto_deficient_work  0.159  278. \n", + "walk_transit_ASC_auto_sufficient_atwork -3.66  16.4 \n", + "walk_transit_ASC_auto_sufficient_eatout -1.32  16.4 \n", + "walk_transit_ASC_auto_sufficient_escort -4.82  16.4 \n", + "walk_transit_ASC_auto_sufficient_othdiscr -0.914  16.4 \n", + "walk_transit_ASC_auto_sufficient_othmaint -1.68  16.4 \n", + "walk_transit_ASC_auto_sufficient_school  0.487  NA \n", + "walk_transit_ASC_auto_sufficient_shopping -2.35  16.4 \n", + "walk_transit_ASC_auto_sufficient_social -0.647  16.4 \n", + "walk_transit_ASC_auto_sufficient_univ  0.473  0.00 \n", + "walk_transit_ASC_auto_sufficient_work -1.37  278. \n", + "walk_transit_ASC_no_auto_atwork  3.13  66.9 \n", + "walk_transit_ASC_no_auto_eatout  2.92  66.9 \n", + "walk_transit_ASC_no_auto_escort -2.26  67.0 \n", + "walk_transit_ASC_no_auto_othdiscr  2.80  66.9 \n", + "walk_transit_ASC_no_auto_othmaint  2.59  66.9 \n", + "walk_transit_ASC_no_auto_school  20.4  NA \n", + "walk_transit_ASC_no_auto_shopping  2.33  66.9 \n", + "walk_transit_ASC_no_auto_social  2.06  66.9 \n", + "walk_transit_ASC_no_auto_univ  8.79  0.00 \n", + "walk_transit_ASC_no_auto_work  4.60  285. \n", + "walk_transit_CBD_ASC_atwork  1.30  0.284 \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth...  1.08  0.0757 \n", + "walk_transit_CBD_ASC_school_univ  0.834  0.114 \n", + "walk_transit_CBD_ASC_work  1.16  0.0539 \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_school... \n", + "coefficient_name\\ncoef_test coef_test_school... \n", + "\n", + " \\\n", + " t Stat Signif Null Value \n", + "Parameter \n", + "-999  NA 0.0 \n", + "1  NA 0.0 \n", + "bike_ASC_auto_deficient_atwork -3.07 ** 0.0 \n", + "bike_ASC_auto_deficient_eatout -3.69 *** 0.0 \n", + "bike_ASC_auto_deficient_escort -9.79 *** 0.0 \n", + "bike_ASC_auto_deficient_othdiscr -4.47 *** 0.0 \n", + "bike_ASC_auto_deficient_othmaint -6.37 *** 0.0 \n", + "bike_ASC_auto_deficient_school -1.24 0.0 \n", + "bike_ASC_auto_deficient_shopping -7.99 *** 0.0 \n", + "bike_ASC_auto_deficient_social  1.11 0.0 \n", + "bike_ASC_auto_deficient_univ  NA 0.0 \n", + "bike_ASC_auto_deficient_work -3.01 ** 0.0 \n", + "bike_ASC_auto_sufficient_atwork  0.03 0.0 \n", + "bike_ASC_auto_sufficient_eatout -11.60 *** 0.0 \n", + "bike_ASC_auto_sufficient_escort -16.76 *** 0.0 \n", + "bike_ASC_auto_sufficient_othdiscr -17.64 *** 0.0 \n", + "bike_ASC_auto_sufficient_othmaint -16.40 *** 0.0 \n", + "bike_ASC_auto_sufficient_school -2.40 * 0.0 \n", + "bike_ASC_auto_sufficient_shopping -22.70 *** 0.0 \n", + "bike_ASC_auto_sufficient_social -10.36 *** 0.0 \n", + "bike_ASC_auto_sufficient_univ -295.46 *** 0.0 \n", + "bike_ASC_auto_sufficient_work -23.72 *** 0.0 \n", + "bike_ASC_no_auto_atwork -0.01 0.0 \n", + "bike_ASC_no_auto_eatout  0.01 0.0 \n", + "bike_ASC_no_auto_escort -0.01 0.0 \n", + "bike_ASC_no_auto_othdiscr -0.01 0.0 \n", + "bike_ASC_no_auto_othmaint  0.02 0.0 \n", + "bike_ASC_no_auto_school  0.12 0.0 \n", + "bike_ASC_no_auto_shopping  0.01 0.0 \n", + "bike_ASC_no_auto_social  0.00 0.0 \n", + "bike_ASC_no_auto_univ  BIG *** 0.0 \n", + "bike_ASC_no_auto_work  0.05 0.0 \n", + "coef_age010_trn_multiplier_atwork  0.09 0.0 \n", + "coef_age010_trn_multiplier_eatout_escort_othdis...  2.79 ** 0.0 \n", + "coef_age010_trn_multiplier_school_univ -9.46 *** 0.0 \n", + "coef_age1619_da_multiplier_atwork -0.85 0.0 \n", + "coef_age1619_da_multiplier_eatout_escort_othdis...  0.84 0.0 \n", + "coef_age1619_da_multiplier_school_univ -22.31 *** 0.0 \n", + "coef_age16p_sr_multiplier_eatout_escort_othdisc... -18.71 *** 0.0 \n", + "coef_age16p_sr_multiplier_school_univ_work_atwork -10.08 *** 0.0 \n", + "coef_hhsize1_sr_multiplier_eatout_escort_othdis... -2.31 * 0.0 \n", + "coef_hhsize1_sr_multiplier_work -14.39 *** 0.0 \n", + "coef_hhsize2_sr_multiplier_eatout_escort_othdis... -0.23 0.0 \n", + "coef_hhsize2_sr_multiplier_school_univ -8.15 *** 0.0 \n", + "coef_ivt_atwork -23.41 *** 0.0 \n", + "coef_ivt_eatout_escort_othdiscr_othmaint_shoppi... -47.76 *** 0.0 \n", + "coef_ivt_school_univ -32.62 *** 0.0 \n", + "coef_ivt_work -53.62 *** 0.0 \n", + "coef_nest_AUTO  NA 1.0 \n", + "coef_nest_AUTO_DRIVEALONE  NA 1.0 \n", + "coef_nest_AUTO_SHAREDRIDE2  NA 1.0 \n", + "coef_nest_AUTO_SHAREDRIDE3  NA 1.0 \n", + "coef_nest_NONMOTORIZED  NA 1.0 \n", + "coef_nest_RIDEHAIL  NA 1.0 \n", + "coef_nest_TRANSIT  NA 1.0 \n", + "coef_nest_TRANSIT_DRIVEACCESS  NA 1.0 \n", + "coef_nest_TRANSIT_WALKACCESS  NA 1.0 \n", + "coef_test_eatout_escort_othdiscr_othmaint_shopp...  1.56 0.0 \n", + "coef_test_school_univ  0.57 0.0 \n", + "commuter_rail_ASC_eatout_escort_othdiscr_othmai... -0.06 0.0 \n", + "commuter_rail_ASC_school_univ  NA 0.0 \n", + "commuter_rail_ASC_work  0.00 0.0 \n", + "drive_ferry_ASC_eatout_escort_othdiscr_othmaint... -0.04 0.0 \n", + "drive_ferry_ASC_school_univ  NA 0.0 \n", + "drive_ferry_ASC_work  0.00 0.0 \n", + "drive_light_rail_ASC_eatout_escort_othdiscr_oth... -0.02 0.0 \n", + "drive_light_rail_ASC_school_univ  NA 0.0 \n", + "drive_light_rail_ASC_work  0.00 0.0 \n", + "drive_transit_ASC_auto_deficient_atwork -BIG *** 0.0 \n", + "drive_transit_ASC_auto_deficient_eatout -0.01 0.0 \n", + "drive_transit_ASC_auto_deficient_escort -0.13 0.0 \n", + "drive_transit_ASC_auto_deficient_othdiscr -0.05 0.0 \n", + "drive_transit_ASC_auto_deficient_othmaint -0.08 0.0 \n", + "drive_transit_ASC_auto_deficient_school  NA 0.0 \n", + "drive_transit_ASC_auto_deficient_shopping -0.10 0.0 \n", + "drive_transit_ASC_auto_deficient_social  0.04 0.0 \n", + "drive_transit_ASC_auto_deficient_univ  NA 0.0 \n", + "drive_transit_ASC_auto_deficient_work -0.00 0.0 \n", + "drive_transit_ASC_auto_sufficient_atwork -BIG *** 0.0 \n", + "drive_transit_ASC_auto_sufficient_eatout -0.10 0.0 \n", + "drive_transit_ASC_auto_sufficient_escort -0.32 0.0 \n", + "drive_transit_ASC_auto_sufficient_othdiscr -0.08 0.0 \n", + "drive_transit_ASC_auto_sufficient_othmaint -0.17 0.0 \n", + "drive_transit_ASC_auto_sufficient_school  NA 0.0 \n", + "drive_transit_ASC_auto_sufficient_shopping -0.25 0.0 \n", + "drive_transit_ASC_auto_sufficient_social -0.08 0.0 \n", + "drive_transit_ASC_auto_sufficient_univ  NA 0.0 \n", + "drive_transit_ASC_auto_sufficient_work -0.01 0.0 \n", + "drive_transit_ASC_no_auto_all  NA 0.0 \n", + "drive_transit_CBD_ASC_atwork  NA 0.0 \n", + "drive_transit_CBD_ASC_eatout_escort_othdiscr_ot...  9.63 *** 0.0 \n", + "drive_transit_CBD_ASC_school_univ  4.89 *** 0.0 \n", + "drive_transit_CBD_ASC_work  23.45 *** 0.0 \n", + "express_bus_ASC_eatout_escort_othdiscr_othmaint... -0.02 0.0 \n", + "express_bus_ASC_school_univ  NA 0.0 \n", + "express_bus_ASC_work -0.00 0.0 \n", + "heavy_rail_ASC_eatout_escort_othdiscr_othmaint_... -0.01 0.0 \n", + "heavy_rail_ASC_school_univ  NA 0.0 \n", + "heavy_rail_ASC_work  0.00 0.0 \n", + "joint_bike_ASC_auto_deficient_all -4.62 *** 0.0 \n", + "joint_bike_ASC_auto_sufficient_all -15.28 *** 0.0 \n", + "joint_bike_ASC_no_auto_all -2.48 * 0.0 \n", + "joint_drive_transit_ASC_auto_deficient_all -0.37 0.0 \n", + "joint_drive_transit_ASC_auto_sufficient_all -0.48 0.0 \n", + "joint_drive_transit_ASC_no_auto_all  NA 0.0 \n", + "joint_sr2_ASC_auto_deficient_all  NA 0.0 \n", + "joint_sr2_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_sr2_ASC_no_auto_all  NA 0.0 \n", + "joint_sr3p_ASC_auto_deficient_all -5.77 *** 0.0 \n", + "joint_sr3p_ASC_auto_sufficient_all -17.17 *** 0.0 \n", + "joint_sr3p_ASC_no_auto_all  1.49 0.0 \n", + "joint_taxi_ASC_auto_deficient_all -1.21 0.0 \n", + "joint_taxi_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_taxi_ASC_no_auto_all -1.07 0.0 \n", + "joint_tnc_shared_ASC_auto_deficient_all -0.66 0.0 \n", + "joint_tnc_shared_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_tnc_shared_ASC_no_auto_all -2.17 * 0.0 \n", + "joint_tnc_single_ASC_auto_deficient_all -1.38 0.0 \n", + "joint_tnc_single_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_tnc_single_ASC_no_auto_all -1.88 0.0 \n", + "joint_walk_ASC_auto_deficient_all -5.89 *** 0.0 \n", + "joint_walk_ASC_auto_sufficient_all -17.76 *** 0.0 \n", + "joint_walk_ASC_no_auto_all -0.51 0.0 \n", + "joint_walk_transit_ASC_auto_deficient_all -0.32 0.0 \n", + "joint_walk_transit_ASC_auto_sufficient_all -0.03 0.0 \n", + "joint_walk_transit_ASC_no_auto_all  0.02 0.0 \n", + "local_bus_ASC_eatout_escort_othdiscr_othmaint_s... -0.07 0.0 \n", + "local_bus_ASC_school_univ  NA 0.0 \n", + "local_bus_ASC_work -0.00 0.0 \n", + "sr2_ASC_auto_deficient_atwork -11.34 *** 0.0 \n", + "sr2_ASC_auto_deficient_eatout  5.64 *** 0.0 \n", + "sr2_ASC_auto_deficient_escort  1.58 0.0 \n", + "sr2_ASC_auto_deficient_othdiscr  8.13 *** 0.0 \n", + "sr2_ASC_auto_deficient_othmaint  4.02 *** 0.0 \n", + "sr2_ASC_auto_deficient_school  0.56 0.0 \n", + "sr2_ASC_auto_deficient_shopping  4.09 *** 0.0 \n", + "sr2_ASC_auto_deficient_social  10.70 *** 0.0 \n", + "sr2_ASC_auto_deficient_univ  NA 0.0 \n", + "sr2_ASC_auto_deficient_work  4.76 *** 0.0 \n", + "sr2_ASC_auto_sufficient_atwork -9.85 *** 0.0 \n", + "sr2_ASC_auto_sufficient_eatout  11.19 *** 0.0 \n", + "sr2_ASC_auto_sufficient_escort  3.31 *** 0.0 \n", + "sr2_ASC_auto_sufficient_othdiscr  8.23 *** 0.0 \n", + "sr2_ASC_auto_sufficient_othmaint  6.24 *** 0.0 \n", + "sr2_ASC_auto_sufficient_school -0.79 0.0 \n", + "sr2_ASC_auto_sufficient_shopping  5.16 *** 0.0 \n", + "sr2_ASC_auto_sufficient_social  7.88 *** 0.0 \n", + "sr2_ASC_auto_sufficient_univ  NA 0.0 \n", + "sr2_ASC_auto_sufficient_work -3.43 *** 0.0 \n", + "sr2_ASC_no_auto_all  0.02 0.0 \n", + "sr3p_ASC_auto_deficient_atwork -13.30 *** 0.0 \n", + "sr3p_ASC_auto_deficient_eatout  0.98 0.0 \n", + "sr3p_ASC_auto_deficient_escort  0.18 0.0 \n", + "sr3p_ASC_auto_deficient_othdiscr  11.40 *** 0.0 \n", + "sr3p_ASC_auto_deficient_othmaint -2.71 ** 0.0 \n", + "sr3p_ASC_auto_deficient_school  1.18 0.0 \n", + "sr3p_ASC_auto_deficient_shopping  1.31 0.0 \n", + "sr3p_ASC_auto_deficient_social  9.16 *** 0.0 \n", + "sr3p_ASC_auto_deficient_univ -BIG *** 0.0 \n", + "sr3p_ASC_auto_deficient_work -1.38 0.0 \n", + "sr3p_ASC_auto_sufficient_atwork -11.97 *** 0.0 \n", + "sr3p_ASC_auto_sufficient_eatout  10.73 *** 0.0 \n", + "sr3p_ASC_auto_sufficient_escort  3.10 ** 0.0 \n", + "sr3p_ASC_auto_sufficient_othdiscr  9.25 *** 0.0 \n", + "sr3p_ASC_auto_sufficient_othmaint  2.44 * 0.0 \n", + "sr3p_ASC_auto_sufficient_school -0.16 0.0 \n", + "sr3p_ASC_auto_sufficient_shopping  2.72 ** 0.0 \n", + "sr3p_ASC_auto_sufficient_social  7.91 *** 0.0 \n", + "sr3p_ASC_auto_sufficient_univ -BIG *** 0.0 \n", + "sr3p_ASC_auto_sufficient_work -7.63 *** 0.0 \n", + "sr3p_ASC_no_auto_atwork  0.02 0.0 \n", + "sr3p_ASC_no_auto_eatout  0.02 0.0 \n", + "sr3p_ASC_no_auto_escort -0.02 0.0 \n", + "sr3p_ASC_no_auto_othdiscr  0.02 0.0 \n", + "sr3p_ASC_no_auto_othmaint  0.00 0.0 \n", + "sr3p_ASC_no_auto_school -0.06 0.0 \n", + "sr3p_ASC_no_auto_shopping  0.01 0.0 \n", + "sr3p_ASC_no_auto_social -0.02 0.0 \n", + "sr3p_ASC_no_auto_univ -BIG *** 0.0 \n", + "sr3p_ASC_no_auto_work  0.01 0.0 \n", + "taxi_ASC_auto_deficient_atwork -10.57 *** 0.0 \n", + "taxi_ASC_auto_deficient_eatout_othdiscr_social -6.76 *** 0.0 \n", + "taxi_ASC_auto_deficient_escort_othmaint_shopping -7.89 *** 0.0 \n", + "taxi_ASC_auto_deficient_school  0.90 0.0 \n", + "taxi_ASC_auto_deficient_univ  BIG *** 0.0 \n", + "taxi_ASC_auto_deficient_work -9.95 *** 0.0 \n", + "taxi_ASC_auto_sufficient_atwork -22.22 *** 0.0 \n", + "taxi_ASC_auto_sufficient_eatout_othdiscr_social -15.96 *** 0.0 \n", + "taxi_ASC_auto_sufficient_escort_othmaint_shopping -28.85 *** 0.0 \n", + "taxi_ASC_auto_sufficient_school -2.45 * 0.0 \n", + "taxi_ASC_auto_sufficient_univ  NA 0.0 \n", + "taxi_ASC_auto_sufficient_work -16.01 *** 0.0 \n", + "taxi_ASC_no_auto_atwork  0.05 0.0 \n", + "taxi_ASC_no_auto_eatout_othdiscr_social -0.01 0.0 \n", + "taxi_ASC_no_auto_escort_othmaint_shopping  0.02 0.0 \n", + "taxi_ASC_no_auto_school_univ  NA 0.0 \n", + "taxi_ASC_no_auto_work  0.05 0.0 \n", + "tnc_shared_ASC_auto_deficient_atwork -12.49 *** 0.0 \n", + "tnc_shared_ASC_auto_deficient_eatout_othdiscr_s... -4.95 *** 0.0 \n", + "tnc_shared_ASC_auto_deficient_escort_othmaint_s... -11.36 *** 0.0 \n", + "tnc_shared_ASC_auto_deficient_school  0.30 0.0 \n", + "tnc_shared_ASC_auto_deficient_univ  BIG *** 0.0 \n", + "tnc_shared_ASC_auto_deficient_work -15.89 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_atwork -24.64 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_eatout_othdiscr_... -21.67 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_escort_othmaint_... -35.76 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_school -3.91 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_univ  NA 0.0 \n", + "tnc_shared_ASC_auto_sufficient_work -15.77 *** 0.0 \n", + "tnc_shared_ASC_no_auto_atwork  0.04 0.0 \n", + "tnc_shared_ASC_no_auto_eatout_othdiscr_social -0.00 0.0 \n", + "tnc_shared_ASC_no_auto_escort_othmaint_shopping  0.01 0.0 \n", + "tnc_shared_ASC_no_auto_school  NA 0.0 \n", + "tnc_shared_ASC_no_auto_univ  NA 0.0 \n", + "tnc_shared_ASC_no_auto_work  0.03 0.0 \n", + "tnc_single_ASC_auto_deficient_atwork -13.29 *** 0.0 \n", + "tnc_single_ASC_auto_deficient_eatout_othdiscr_s... -7.50 *** 0.0 \n", + "tnc_single_ASC_auto_deficient_escort_othmaint_s... -3.34 *** 0.0 \n", + "tnc_single_ASC_auto_deficient_school -0.01 0.0 \n", + "tnc_single_ASC_auto_deficient_univ  NA 0.0 \n", + "tnc_single_ASC_auto_deficient_work -11.28 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_atwork -25.00 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_eatout_othdiscr_... -21.84 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_escort_othmaint_... -26.43 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_school -2.25 * 0.0 \n", + "tnc_single_ASC_auto_sufficient_univ  BIG *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_work -23.29 *** 0.0 \n", + "tnc_single_ASC_no_auto_atwork  0.06 0.0 \n", + "tnc_single_ASC_no_auto_eatout_othdiscr_social  0.01 0.0 \n", + "tnc_single_ASC_no_auto_escort_othmaint_shopping  0.02 0.0 \n", + "tnc_single_ASC_no_auto_school  NA 0.0 \n", + "tnc_single_ASC_no_auto_univ  NA 0.0 \n", + "tnc_single_ASC_no_auto_work  0.07 0.0 \n", + "walk_ASC_auto_deficient_atwork  1.84 0.0 \n", + "walk_ASC_auto_deficient_eatout  10.96 *** 0.0 \n", + "walk_ASC_auto_deficient_escort -7.46 *** 0.0 \n", + "walk_ASC_auto_deficient_othdiscr  7.86 *** 0.0 \n", + "walk_ASC_auto_deficient_othmaint  0.70 0.0 \n", + "walk_ASC_auto_deficient_school  2.81 ** 0.0 \n", + "walk_ASC_auto_deficient_shopping  6.63 *** 0.0 \n", + "walk_ASC_auto_deficient_social  7.56 *** 0.0 \n", + "walk_ASC_auto_deficient_univ  BIG *** 0.0 \n", + "walk_ASC_auto_deficient_work  17.84 *** 0.0 \n", + "walk_ASC_auto_sufficient_atwork  1.34 0.0 \n", + "walk_ASC_auto_sufficient_eatout  5.78 *** 0.0 \n", + "walk_ASC_auto_sufficient_escort -14.88 *** 0.0 \n", + "walk_ASC_auto_sufficient_othdiscr  3.68 *** 0.0 \n", + "walk_ASC_auto_sufficient_othmaint -1.76 0.0 \n", + "walk_ASC_auto_sufficient_school  0.41 0.0 \n", + "walk_ASC_auto_sufficient_shopping -3.75 *** 0.0 \n", + "walk_ASC_auto_sufficient_social  5.04 *** 0.0 \n", + "walk_ASC_auto_sufficient_univ  NA 0.0 \n", + "walk_ASC_auto_sufficient_work -3.70 *** 0.0 \n", + "walk_ASC_no_auto_atwork  0.09 0.0 \n", + "walk_ASC_no_auto_eatout  0.06 0.0 \n", + "walk_ASC_no_auto_escort  0.03 0.0 \n", + "walk_ASC_no_auto_othdiscr  0.04 0.0 \n", + "walk_ASC_no_auto_othmaint  0.01 0.0 \n", + "walk_ASC_no_auto_school  0.16 0.0 \n", + "walk_ASC_no_auto_shopping  0.03 0.0 \n", + "walk_ASC_no_auto_social  0.02 0.0 \n", + "walk_ASC_no_auto_univ  NA 0.0 \n", + "walk_ASC_no_auto_work  0.07 0.0 \n", + "walk_ferry_ASC_eatout_escort_othdiscr_othmaint_... -0.01 0.0 \n", + "walk_ferry_ASC_school_univ  NA 0.0 \n", + "walk_ferry_ASC_work  0.00 0.0 \n", + "walk_light_rail_ASC_eatout_escort_othdiscr_othm... -0.01 0.0 \n", + "walk_light_rail_ASC_school_univ  NA 0.0 \n", + "walk_light_rail_ASC_work  0.00 0.0 \n", + "walk_transit_ASC_auto_deficient_atwork -0.16 0.0 \n", + "walk_transit_ASC_auto_deficient_eatout -0.03 0.0 \n", + "walk_transit_ASC_auto_deficient_escort -0.27 0.0 \n", + "walk_transit_ASC_auto_deficient_othdiscr  0.05 0.0 \n", + "walk_transit_ASC_auto_deficient_othmaint -0.18 0.0 \n", + "walk_transit_ASC_auto_deficient_school  NA 0.0 \n", + "walk_transit_ASC_auto_deficient_shopping -0.07 0.0 \n", + "walk_transit_ASC_auto_deficient_social  0.08 0.0 \n", + "walk_transit_ASC_auto_deficient_univ  NA 0.0 \n", + "walk_transit_ASC_auto_deficient_work  0.00 0.0 \n", + "walk_transit_ASC_auto_sufficient_atwork -0.22 0.0 \n", + "walk_transit_ASC_auto_sufficient_eatout -0.08 0.0 \n", + "walk_transit_ASC_auto_sufficient_escort -0.29 0.0 \n", + "walk_transit_ASC_auto_sufficient_othdiscr -0.06 0.0 \n", + "walk_transit_ASC_auto_sufficient_othmaint -0.10 0.0 \n", + "walk_transit_ASC_auto_sufficient_school  NA 0.0 \n", + "walk_transit_ASC_auto_sufficient_shopping -0.14 0.0 \n", + "walk_transit_ASC_auto_sufficient_social -0.04 0.0 \n", + "walk_transit_ASC_auto_sufficient_univ  NA 0.0 \n", + "walk_transit_ASC_auto_sufficient_work -0.00 0.0 \n", + "walk_transit_ASC_no_auto_atwork  0.05 0.0 \n", + "walk_transit_ASC_no_auto_eatout  0.04 0.0 \n", + "walk_transit_ASC_no_auto_escort -0.03 0.0 \n", + "walk_transit_ASC_no_auto_othdiscr  0.04 0.0 \n", + "walk_transit_ASC_no_auto_othmaint  0.04 0.0 \n", + "walk_transit_ASC_no_auto_school  NA 0.0 \n", + "walk_transit_ASC_no_auto_shopping  0.03 0.0 \n", + "walk_transit_ASC_no_auto_social  0.03 0.0 \n", + "walk_transit_ASC_no_auto_univ  NA 0.0 \n", + "walk_transit_ASC_no_auto_work  0.02 0.0 \n", + "walk_transit_CBD_ASC_atwork  4.57 *** 0.0 \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth...  14.25 *** 0.0 \n", + "walk_transit_CBD_ASC_school_univ  7.30 *** 0.0 \n", + "walk_transit_CBD_ASC_work  21.50 *** 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_eatout... \n", + "coefficient_name\\ncoef_test coef_test_school... \n", + "coefficient_name\\ncoef_test coef_test_school... \n", + "\n", + " remodel \\\n", + " Value Std Err \n", + "Parameter \n", + "-999 -999.  0.00 \n", + "1  1.00  0.00 \n", + "bike_ASC_auto_deficient_atwork -1.06  0.340 \n", + "bike_ASC_auto_deficient_eatout -1.20  0.594 \n", + "bike_ASC_auto_deficient_escort -3.88  0.559 \n", + "bike_ASC_auto_deficient_othdiscr -0.528  0.418 \n", + "bike_ASC_auto_deficient_othmaint -1.63  0.448 \n", + "bike_ASC_auto_deficient_school -1.18  0.997 \n", + "bike_ASC_auto_deficient_shopping -1.30  0.363 \n", + "bike_ASC_auto_deficient_social  0.592  0.532 \n", + "bike_ASC_auto_deficient_univ -0.669  0.00146 \n", + "bike_ASC_auto_deficient_work -0.360  0.0922 \n", + "bike_ASC_auto_sufficient_atwork  15.7  463. \n", + "bike_ASC_auto_sufficient_eatout -1.30  0.409 \n", + "bike_ASC_auto_sufficient_escort -5.26  0.453 \n", + "bike_ASC_auto_sufficient_othdiscr -1.49  0.382 \n", + "bike_ASC_auto_sufficient_othmaint -2.51  0.349 \n", + "bike_ASC_auto_sufficient_school -2.20  0.953 \n", + "bike_ASC_auto_sufficient_shopping -2.78  0.326 \n", + "bike_ASC_auto_sufficient_social -1.83  0.471 \n", + "bike_ASC_auto_sufficient_univ -1.94  0.00650 \n", + "bike_ASC_auto_sufficient_work -2.06  0.0822 \n", + "bike_ASC_no_auto_atwork -0.911  87.8 \n", + "bike_ASC_no_auto_eatout  0.510  87.2 \n", + "bike_ASC_no_auto_escort -0.730  87.2 \n", + "bike_ASC_no_auto_othdiscr -0.404  87.2 \n", + "bike_ASC_no_auto_othmaint  1.61  87.2 \n", + "bike_ASC_no_auto_school  13.4  116. \n", + "bike_ASC_no_auto_shopping  1.05  87.2 \n", + "bike_ASC_no_auto_social  0.486  87.2 \n", + "bike_ASC_no_auto_univ  4.29  0.00688 \n", + "bike_ASC_no_auto_work  3.43  87.2 \n", + "coef_age010_trn_multiplier_atwork  0.000722  NA \n", + "coef_age010_trn_multiplier_eatout_escort_othdis...  0.364  0.175 \n", + "coef_age010_trn_multiplier_school_univ -0.875  0.0898 \n", + "coef_age1619_da_multiplier_atwork -0.193  0.205 \n", + "coef_age1619_da_multiplier_eatout_escort_othdis...  0.000513  0.0424 \n", + "coef_age1619_da_multiplier_school_univ -1.68  0.0748 \n", + "coef_age16p_sr_multiplier_eatout_escort_othdisc... -1.45  0.0814 \n", + "coef_age16p_sr_multiplier_school_univ_work_atwork -0.648  0.0683 \n", + "coef_hhsize1_sr_multiplier_eatout_escort_othdis... -0.0710  0.0312 \n", + "coef_hhsize1_sr_multiplier_work -0.836  0.0577 \n", + "coef_hhsize2_sr_multiplier_eatout_escort_othdis...  0.00209  0.0178 \n", + "coef_hhsize2_sr_multiplier_school_univ -0.748  0.0912 \n", + "coef_ivt_atwork -0.0112  0.000478 \n", + "coef_ivt_eatout_escort_othdiscr_othmaint_shoppi... -0.00688  0.000144 \n", + "coef_ivt_school_univ -0.0104  0.000318 \n", + "coef_ivt_work -0.00795  0.000148 \n", + "coef_nest_AUTO  0.720  0.00 \n", + "coef_nest_AUTO_DRIVEALONE  0.350  0.00 \n", + "coef_nest_AUTO_SHAREDRIDE2  0.350  0.00 \n", + "coef_nest_AUTO_SHAREDRIDE3  0.350  0.00 \n", + "coef_nest_NONMOTORIZED  0.720  0.00 \n", + "coef_nest_RIDEHAIL  0.360  0.00 \n", + "coef_nest_TRANSIT  0.720  0.00 \n", + "coef_nest_TRANSIT_DRIVEACCESS  0.500  0.00 \n", + "coef_nest_TRANSIT_WALKACCESS  0.500  0.00 \n", + "coef_test_eatout_escort_othdiscr_othmaint_shopp...  0.0770  NA \n", + "coef_test_school_univ  0.518  NA \n", + "commuter_rail_ASC_eatout_escort_othdiscr_othmai... -0.744  20.6 \n", + "commuter_rail_ASC_school_univ -0.247  NA \n", + "commuter_rail_ASC_work  0.158  NA \n", + "drive_ferry_ASC_eatout_escort_othdiscr_othmaint... -0.393  20.6 \n", + "drive_ferry_ASC_school_univ  1.45  NA \n", + "drive_ferry_ASC_work  0.194  NA \n", + "drive_light_rail_ASC_eatout_escort_othdiscr_oth... -0.0919  20.6 \n", + "drive_light_rail_ASC_school_univ  0.707  NA \n", + "drive_light_rail_ASC_work  0.507  NA \n", + "drive_transit_ASC_auto_deficient_atwork -999.  0.000231 \n", + "drive_transit_ASC_auto_deficient_eatout  0.105  20.6 \n", + "drive_transit_ASC_auto_deficient_escort -2.14  20.6 \n", + "drive_transit_ASC_auto_deficient_othdiscr -0.726  20.6 \n", + "drive_transit_ASC_auto_deficient_othmaint -1.17  20.6 \n", + "drive_transit_ASC_auto_deficient_school  1.57  NA \n", + "drive_transit_ASC_auto_deficient_shopping -1.43  20.6 \n", + "drive_transit_ASC_auto_deficient_social  0.594  20.6 \n", + "drive_transit_ASC_auto_deficient_univ  1.85  NA \n", + "drive_transit_ASC_auto_deficient_work -0.911  NA \n", + "drive_transit_ASC_auto_sufficient_atwork -999.  NA \n", + "drive_transit_ASC_auto_sufficient_eatout -1.30  20.6 \n", + "drive_transit_ASC_auto_sufficient_escort -5.25  20.6 \n", + "drive_transit_ASC_auto_sufficient_othdiscr -1.11  20.6 \n", + "drive_transit_ASC_auto_sufficient_othmaint -2.44  20.6 \n", + "drive_transit_ASC_auto_sufficient_school  0.155  NA \n", + "drive_transit_ASC_auto_sufficient_shopping -3.97  20.6 \n", + "drive_transit_ASC_auto_sufficient_social -1.34  20.6 \n", + "drive_transit_ASC_auto_sufficient_univ  1.36  0.000291 \n", + "drive_transit_ASC_auto_sufficient_work -1.83  NA \n", + "drive_transit_ASC_no_auto_all  0.00  0.000293 \n", + "drive_transit_CBD_ASC_atwork  0.564  NA \n", + "drive_transit_CBD_ASC_eatout_escort_othdiscr_ot...  1.41  0.142 \n", + "drive_transit_CBD_ASC_school_univ  1.20  0.253 \n", + "drive_transit_CBD_ASC_work  1.62  0.0697 \n", + "express_bus_ASC_eatout_escort_othdiscr_othmaint... -0.0888  20.6 \n", + "express_bus_ASC_school_univ -0.450  NA \n", + "express_bus_ASC_work -0.920  NA \n", + "heavy_rail_ASC_eatout_escort_othdiscr_othmaint_...  0.0401  20.6 \n", + "heavy_rail_ASC_school_univ  0.127  NA \n", + "heavy_rail_ASC_work  0.496  NA \n", + "joint_bike_ASC_auto_deficient_all -6.16  1.42 \n", + "joint_bike_ASC_auto_sufficient_all -6.95  0.465 \n", + "joint_bike_ASC_no_auto_all -2.67  1.14 \n", + "joint_drive_transit_ASC_auto_deficient_all -6.04  20.6 \n", + "joint_drive_transit_ASC_auto_sufficient_all -7.80  20.6 \n", + "joint_drive_transit_ASC_no_auto_all  0.00  0.00 \n", + "joint_sr2_ASC_auto_deficient_all  0.00  0.00 \n", + "joint_sr2_ASC_auto_sufficient_all  0.00  0.00 \n", + "joint_sr2_ASC_no_auto_all  0.00  0.00 \n", + "joint_sr3p_ASC_auto_deficient_all -1.51  0.260 \n", + "joint_sr3p_ASC_auto_sufficient_all -2.27  0.131 \n", + "joint_sr3p_ASC_no_auto_all  0.680  0.471 \n", + "joint_taxi_ASC_auto_deficient_all -9.82  8.64 \n", + "joint_taxi_ASC_auto_sufficient_all -11.7  0.00 \n", + "joint_taxi_ASC_no_auto_all -4.58  4.45 \n", + "joint_tnc_shared_ASC_auto_deficient_all -11.2  18.0 \n", + "joint_tnc_shared_ASC_auto_sufficient_all -13.2  0.00 \n", + "joint_tnc_shared_ASC_no_auto_all -4.70  2.31 \n", + "joint_tnc_single_ASC_auto_deficient_all -9.90  7.63 \n", + "joint_tnc_single_ASC_auto_sufficient_all -14.0  0.00 \n", + "joint_tnc_single_ASC_no_auto_all -3.89  2.20 \n", + "joint_walk_ASC_auto_deficient_all -2.05  0.372 \n", + "joint_walk_ASC_auto_sufficient_all -4.15  0.240 \n", + "joint_walk_ASC_no_auto_all -0.294  0.735 \n", + "joint_walk_transit_ASC_auto_deficient_all -5.30  20.6 \n", + "joint_walk_transit_ASC_auto_sufficient_all -18.3  620. \n", + "joint_walk_transit_ASC_no_auto_all  0.351  20.6 \n", + "local_bus_ASC_eatout_escort_othdiscr_othmaint_s... -0.943  20.6 \n", + "local_bus_ASC_school_univ -0.811  NA \n", + "local_bus_ASC_work -0.211  NA \n", + "sr2_ASC_auto_deficient_atwork -1.57  0.133 \n", + "sr2_ASC_auto_deficient_eatout  1.32  0.407 \n", + "sr2_ASC_auto_deficient_escort  0.560  0.331 \n", + "sr2_ASC_auto_deficient_othdiscr  1.32  0.389 \n", + "sr2_ASC_auto_deficient_othmaint  1.03  0.327 \n", + "sr2_ASC_auto_deficient_school  0.522  0.961 \n", + "sr2_ASC_auto_deficient_shopping  0.801  0.319 \n", + "sr2_ASC_auto_deficient_social  2.42  0.473 \n", + "sr2_ASC_auto_deficient_univ -1.69  0.000112 \n", + "sr2_ASC_auto_deficient_work  0.248  0.0900 \n", + "sr2_ASC_auto_sufficient_atwork -0.810  0.0784 \n", + "sr2_ASC_auto_sufficient_eatout  1.63  0.390 \n", + "sr2_ASC_auto_sufficient_escort  0.754  0.317 \n", + "sr2_ASC_auto_sufficient_othdiscr  1.09  0.380 \n", + "sr2_ASC_auto_sufficient_othmaint  1.09  0.312 \n", + "sr2_ASC_auto_sufficient_school -0.720  0.950 \n", + "sr2_ASC_auto_sufficient_shopping  0.806  0.310 \n", + "sr2_ASC_auto_sufficient_social  1.07  0.438 \n", + "sr2_ASC_auto_sufficient_univ -1.86  6.12e-05 \n", + "sr2_ASC_auto_sufficient_work -0.478  0.0868 \n", + "sr2_ASC_no_auto_all  1.17  87.2 \n", + "sr3p_ASC_auto_deficient_atwork -1.88  0.137 \n", + "sr3p_ASC_auto_deficient_eatout  0.638  0.420 \n", + "sr3p_ASC_auto_deficient_escort  0.336  0.332 \n", + "sr3p_ASC_auto_deficient_othdiscr  1.67  0.387 \n", + "sr3p_ASC_auto_deficient_othmaint -0.202  0.371 \n", + "sr3p_ASC_auto_deficient_school  1.09  0.961 \n", + "sr3p_ASC_auto_deficient_shopping  0.457  0.324 \n", + "sr3p_ASC_auto_deficient_social  2.16  0.476 \n", + "sr3p_ASC_auto_deficient_univ -1.73  NA \n", + "sr3p_ASC_auto_deficient_work -0.289  0.0929 \n", + "sr3p_ASC_auto_sufficient_atwork -1.00  0.0798 \n", + "sr3p_ASC_auto_sufficient_eatout  1.58  0.390 \n", + "sr3p_ASC_auto_sufficient_escort  0.729  0.317 \n", + "sr3p_ASC_auto_sufficient_othdiscr  1.18  0.380 \n", + "sr3p_ASC_auto_sufficient_othmaint  0.726  0.313 \n", + "sr3p_ASC_auto_sufficient_school -0.151  0.950 \n", + "sr3p_ASC_auto_sufficient_shopping  0.561  0.310 \n", + "sr3p_ASC_auto_sufficient_social  1.07  0.438 \n", + "sr3p_ASC_auto_sufficient_univ -1.90  1.95e-05 \n", + "sr3p_ASC_auto_sufficient_work -0.849  0.0876 \n", + "sr3p_ASC_no_auto_atwork  1.67  87.2 \n", + "sr3p_ASC_no_auto_eatout  1.53  87.2 \n", + "sr3p_ASC_no_auto_escort -1.87  87.3 \n", + "sr3p_ASC_no_auto_othdiscr  1.40  87.2 \n", + "sr3p_ASC_no_auto_othmaint  0.195  87.2 \n", + "sr3p_ASC_no_auto_school -6.02  112. \n", + "sr3p_ASC_no_auto_shopping  0.847  87.2 \n", + "sr3p_ASC_no_auto_social -1.21  87.2 \n", + "sr3p_ASC_no_auto_univ -6.06  5.30e-05 \n", + "sr3p_ASC_no_auto_work  0.523  87.2 \n", + "taxi_ASC_auto_deficient_atwork -4.75  0.459 \n", + "taxi_ASC_auto_deficient_eatout_othdiscr_social -3.16  0.647 \n", + "taxi_ASC_auto_deficient_escort_othmaint_shopping -0.503  0.313 \n", + "taxi_ASC_auto_deficient_school  0.899  0.968 \n", + "taxi_ASC_auto_deficient_univ  4.25  4.12e-06 \n", + "taxi_ASC_auto_deficient_work -2.30  0.226 \n", + "taxi_ASC_auto_sufficient_atwork -3.31  0.148 \n", + "taxi_ASC_auto_sufficient_eatout_othdiscr_social -3.47  0.416 \n", + "taxi_ASC_auto_sufficient_escort_othmaint_shopping -2.33  0.306 \n", + "taxi_ASC_auto_sufficient_school -2.23  0.956 \n", + "taxi_ASC_auto_sufficient_univ -0.313  NA \n", + "taxi_ASC_auto_sufficient_work -5.14  0.361 \n", + "taxi_ASC_no_auto_atwork  3.63  87.2 \n", + "taxi_ASC_no_auto_eatout_othdiscr_social -0.460  87.2 \n", + "taxi_ASC_no_auto_escort_othmaint_shopping  1.54  87.2 \n", + "taxi_ASC_no_auto_school_univ -7.00  0.00 \n", + "taxi_ASC_no_auto_work  3.89  87.2 \n", + "tnc_shared_ASC_auto_deficient_atwork -5.21  0.410 \n", + "tnc_shared_ASC_auto_deficient_eatout_othdiscr_s... -4.43  1.17 \n", + "tnc_shared_ASC_auto_deficient_escort_othmaint_s... -0.796  0.310 \n", + "tnc_shared_ASC_auto_deficient_school  0.312  0.966 \n", + "tnc_shared_ASC_auto_deficient_univ  3.25  NA \n", + "tnc_shared_ASC_auto_deficient_work -3.73  0.231 \n", + "tnc_shared_ASC_auto_sufficient_atwork -4.09  0.167 \n", + "tnc_shared_ASC_auto_sufficient_eatout_othdiscr_... -3.88  0.393 \n", + "tnc_shared_ASC_auto_sufficient_escort_othmaint_... -2.83  0.305 \n", + "tnc_shared_ASC_auto_sufficient_school -3.62  0.961 \n", + "tnc_shared_ASC_auto_sufficient_univ -0.907  NA \n", + "tnc_shared_ASC_auto_sufficient_work -6.74  0.419 \n", + "tnc_shared_ASC_no_auto_atwork  3.04  87.2 \n", + "tnc_shared_ASC_no_auto_eatout_othdiscr_social -0.000529  87.2 \n", + "tnc_shared_ASC_no_auto_escort_othmaint_shopping  0.776  87.2 \n", + "tnc_shared_ASC_no_auto_school -7.00  0.00 \n", + "tnc_shared_ASC_no_auto_univ -5.81  NA \n", + "tnc_shared_ASC_no_auto_work  2.21  87.2 \n", + "tnc_single_ASC_auto_deficient_atwork -4.36  0.331 \n", + "tnc_single_ASC_auto_deficient_eatout_othdiscr_s... -3.12  0.599 \n", + "tnc_single_ASC_auto_deficient_escort_othmaint_s...  0.0928  0.308 \n", + "tnc_single_ASC_auto_deficient_school  0.0597  0.975 \n", + "tnc_single_ASC_auto_deficient_univ  1.02  2.45e-06 \n", + "tnc_single_ASC_auto_deficient_work -1.47  0.118 \n", + "tnc_single_ASC_auto_sufficient_atwork -3.14  0.125 \n", + "tnc_single_ASC_auto_sufficient_eatout_othdiscr_... -2.83  0.371 \n", + "tnc_single_ASC_auto_sufficient_escort_othmaint_... -1.76  0.302 \n", + "tnc_single_ASC_auto_sufficient_school -2.02  0.953 \n", + "tnc_single_ASC_auto_sufficient_univ  0.209  2.79e-06 \n", + "tnc_single_ASC_auto_sufficient_work -4.33  0.179 \n", + "tnc_single_ASC_no_auto_atwork  4.24  87.2 \n", + "tnc_single_ASC_no_auto_eatout_othdiscr_social  1.06  87.2 \n", + "tnc_single_ASC_no_auto_escort_othmaint_shopping  1.70  87.2 \n", + "tnc_single_ASC_no_auto_school -7.00  0.00 \n", + "tnc_single_ASC_no_auto_univ -2.52  5.36e-06 \n", + "tnc_single_ASC_no_auto_work  5.31  87.2 \n", + "walk_ASC_auto_deficient_atwork  0.175  0.124 \n", + "walk_ASC_auto_deficient_eatout  2.70  0.425 \n", + "walk_ASC_auto_deficient_escort -1.60  0.396 \n", + "walk_ASC_auto_deficient_othdiscr  1.57  0.398 \n", + "walk_ASC_auto_deficient_othmaint  0.643  0.360 \n", + "walk_ASC_auto_deficient_school  2.65  0.964 \n", + "walk_ASC_auto_deficient_shopping  1.31  0.323 \n", + "walk_ASC_auto_deficient_social  2.48  0.516 \n", + "walk_ASC_auto_deficient_univ  4.51  NA \n", + "walk_ASC_auto_deficient_work  1.59  0.0962 \n", + "walk_ASC_auto_sufficient_atwork  0.0786  0.0579 \n", + "walk_ASC_auto_sufficient_eatout  1.19  0.391 \n", + "walk_ASC_auto_sufficient_escort -1.32  0.313 \n", + "walk_ASC_auto_sufficient_othdiscr  0.661  0.376 \n", + "walk_ASC_auto_sufficient_othmaint  0.439  0.310 \n", + "walk_ASC_auto_sufficient_school  0.383  0.951 \n", + "walk_ASC_auto_sufficient_shopping  0.0818  0.305 \n", + "walk_ASC_auto_sufficient_social  0.911  0.439 \n", + "walk_ASC_auto_sufficient_univ  1.06  1.94e-07 \n", + "walk_ASC_auto_sufficient_work -0.417  0.0795 \n", + "walk_ASC_no_auto_atwork  6.61  87.2 \n", + "walk_ASC_no_auto_eatout  4.52  87.2 \n", + "walk_ASC_no_auto_escort  2.56  87.2 \n", + "walk_ASC_no_auto_othdiscr  2.96  87.2 \n", + "walk_ASC_no_auto_othmaint  1.06  87.2 \n", + "walk_ASC_no_auto_school  17.8  116. \n", + "walk_ASC_no_auto_shopping  2.26  87.2 \n", + "walk_ASC_no_auto_social  1.84  87.2 \n", + "walk_ASC_no_auto_univ  6.41  3.50e-09 \n", + "walk_ASC_no_auto_work  5.28  87.2 \n", + "walk_ferry_ASC_eatout_escort_othdiscr_othmaint_... -0.0347  20.6 \n", + "walk_ferry_ASC_school_univ  0.903  NA \n", + "walk_ferry_ASC_work  0.0751  NA \n", + "walk_light_rail_ASC_eatout_escort_othdiscr_othm...  0.115  20.6 \n", + "walk_light_rail_ASC_school_univ  0.721  NA \n", + "walk_light_rail_ASC_work  0.730  NA \n", + "walk_transit_ASC_auto_deficient_atwork -2.84  20.6 \n", + "walk_transit_ASC_auto_deficient_eatout -0.205  20.6 \n", + "walk_transit_ASC_auto_deficient_escort -4.46  20.6 \n", + "walk_transit_ASC_auto_deficient_othdiscr  0.997  20.6 \n", + "walk_transit_ASC_auto_deficient_othmaint -2.91  20.6 \n", + "walk_transit_ASC_auto_deficient_school  3.00  NA \n", + "walk_transit_ASC_auto_deficient_shopping -0.899  20.6 \n", + "walk_transit_ASC_auto_deficient_social  1.32  20.6 \n", + "walk_transit_ASC_auto_deficient_univ  3.14  0.00 \n", + "walk_transit_ASC_auto_deficient_work  0.0663  NA \n", + "walk_transit_ASC_auto_sufficient_atwork -3.76  20.6 \n", + "walk_transit_ASC_auto_sufficient_eatout -0.985  20.6 \n", + "walk_transit_ASC_auto_sufficient_escort -4.68  20.6 \n", + "walk_transit_ASC_auto_sufficient_othdiscr -0.771  20.6 \n", + "walk_transit_ASC_auto_sufficient_othmaint -1.31  20.6 \n", + "walk_transit_ASC_auto_sufficient_school  0.522  NA \n", + "walk_transit_ASC_auto_sufficient_shopping -2.20  20.6 \n", + "walk_transit_ASC_auto_sufficient_social -0.595  20.6 \n", + "walk_transit_ASC_auto_sufficient_univ  0.473  0.00 \n", + "walk_transit_ASC_auto_sufficient_work -1.47  NA \n", + "walk_transit_ASC_no_auto_atwork  2.98  73.6 \n", + "walk_transit_ASC_no_auto_eatout  2.76  73.6 \n", + "walk_transit_ASC_no_auto_escort -2.26  73.7 \n", + "walk_transit_ASC_no_auto_othdiscr  2.64  73.6 \n", + "walk_transit_ASC_no_auto_othmaint  2.43  73.6 \n", + "walk_transit_ASC_no_auto_school  20.5  NA \n", + "walk_transit_ASC_no_auto_shopping  2.18  73.6 \n", + "walk_transit_ASC_no_auto_social  1.90  73.6 \n", + "walk_transit_ASC_no_auto_univ  8.79  0.00 \n", + "walk_transit_ASC_no_auto_work  4.63  NA \n", + "walk_transit_CBD_ASC_atwork  1.18  0.277 \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth...  1.09  0.0756 \n", + "walk_transit_CBD_ASC_school_univ  0.826  0.114 \n", + "walk_transit_CBD_ASC_work  1.15  0.0539 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  0.340  0.190 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  0.00  NA \n", + "coefficient_name\\ncoef_test coef_test_eatout...  0.231  0.185 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  0.333  0.150 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  0.245  0.149 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  0.197  0.214 \n", + "coefficient_name\\ncoef_test coef_test_eatout... -0.0306  0.0255 \n", + "coefficient_name\\ncoef_test coef_test_school...  0.278  0.474 \n", + "coefficient_name\\ncoef_test coef_test_school...  0.00  NA \n", "\n", - "[307 rows x 3 columns]" + " \n", + " t Stat Signif Null Value \n", + "Parameter \n", + "-999  NA 0.0 \n", + "1  NA 0.0 \n", + "bike_ASC_auto_deficient_atwork -3.11 ** 0.0 \n", + "bike_ASC_auto_deficient_eatout -2.02 * 0.0 \n", + "bike_ASC_auto_deficient_escort -6.94 *** 0.0 \n", + "bike_ASC_auto_deficient_othdiscr -1.26 0.0 \n", + "bike_ASC_auto_deficient_othmaint -3.64 *** 0.0 \n", + "bike_ASC_auto_deficient_school -1.19 0.0 \n", + "bike_ASC_auto_deficient_shopping -3.59 *** 0.0 \n", + "bike_ASC_auto_deficient_social  1.11 0.0 \n", + "bike_ASC_auto_deficient_univ -458.48 *** 0.0 \n", + "bike_ASC_auto_deficient_work -3.90 *** 0.0 \n", + "bike_ASC_auto_sufficient_atwork  0.03 0.0 \n", + "bike_ASC_auto_sufficient_eatout -3.17 ** 0.0 \n", + "bike_ASC_auto_sufficient_escort -11.62 *** 0.0 \n", + "bike_ASC_auto_sufficient_othdiscr -3.91 *** 0.0 \n", + "bike_ASC_auto_sufficient_othmaint -7.20 *** 0.0 \n", + "bike_ASC_auto_sufficient_school -2.31 * 0.0 \n", + "bike_ASC_auto_sufficient_shopping -8.52 *** 0.0 \n", + "bike_ASC_auto_sufficient_social -3.88 *** 0.0 \n", + "bike_ASC_auto_sufficient_univ -298.24 *** 0.0 \n", + "bike_ASC_auto_sufficient_work -25.06 *** 0.0 \n", + "bike_ASC_no_auto_atwork -0.01 0.0 \n", + "bike_ASC_no_auto_eatout  0.01 0.0 \n", + "bike_ASC_no_auto_escort -0.01 0.0 \n", + "bike_ASC_no_auto_othdiscr -0.00 0.0 \n", + "bike_ASC_no_auto_othmaint  0.02 0.0 \n", + "bike_ASC_no_auto_school  0.12 0.0 \n", + "bike_ASC_no_auto_shopping  0.01 0.0 \n", + "bike_ASC_no_auto_social  0.01 0.0 \n", + "bike_ASC_no_auto_univ  624.05 *** 0.0 \n", + "bike_ASC_no_auto_work  0.04 0.0 \n", + "coef_age010_trn_multiplier_atwork  NA 0.0 \n", + "coef_age010_trn_multiplier_eatout_escort_othdis...  2.08 * 0.0 \n", + "coef_age010_trn_multiplier_school_univ -9.74 *** 0.0 \n", + "coef_age1619_da_multiplier_atwork -0.94 0.0 \n", + "coef_age1619_da_multiplier_eatout_escort_othdis...  0.01 0.0 \n", + "coef_age1619_da_multiplier_school_univ -22.42 *** 0.0 \n", + "coef_age16p_sr_multiplier_eatout_escort_othdisc... -17.82 *** 0.0 \n", + "coef_age16p_sr_multiplier_school_univ_work_atwork -9.49 *** 0.0 \n", + "coef_hhsize1_sr_multiplier_eatout_escort_othdis... -2.27 * 0.0 \n", + "coef_hhsize1_sr_multiplier_work -14.48 *** 0.0 \n", + "coef_hhsize2_sr_multiplier_eatout_escort_othdis...  0.12 0.0 \n", + "coef_hhsize2_sr_multiplier_school_univ -8.20 *** 0.0 \n", + "coef_ivt_atwork -23.41 *** 0.0 \n", + "coef_ivt_eatout_escort_othdiscr_othmaint_shoppi... -47.65 *** 0.0 \n", + "coef_ivt_school_univ -32.71 *** 0.0 \n", + "coef_ivt_work -53.70 *** 0.0 \n", + "coef_nest_AUTO  NA 1.0 \n", + "coef_nest_AUTO_DRIVEALONE  NA 1.0 \n", + "coef_nest_AUTO_SHAREDRIDE2  NA 1.0 \n", + "coef_nest_AUTO_SHAREDRIDE3  NA 1.0 \n", + "coef_nest_NONMOTORIZED  NA 1.0 \n", + "coef_nest_RIDEHAIL  NA 1.0 \n", + "coef_nest_TRANSIT  NA 1.0 \n", + "coef_nest_TRANSIT_DRIVEACCESS  NA 1.0 \n", + "coef_nest_TRANSIT_WALKACCESS  NA 1.0 \n", + "coef_test_eatout_escort_othdiscr_othmaint_shopp...  NA 0.0 \n", + "coef_test_school_univ  NA 0.0 \n", + "commuter_rail_ASC_eatout_escort_othdiscr_othmai... -0.04 0.0 \n", + "commuter_rail_ASC_school_univ  NA 0.0 \n", + "commuter_rail_ASC_work  NA 0.0 \n", + "drive_ferry_ASC_eatout_escort_othdiscr_othmaint... -0.02 0.0 \n", + "drive_ferry_ASC_school_univ  NA 0.0 \n", + "drive_ferry_ASC_work  NA 0.0 \n", + "drive_light_rail_ASC_eatout_escort_othdiscr_oth... -0.00 0.0 \n", + "drive_light_rail_ASC_school_univ  NA 0.0 \n", + "drive_light_rail_ASC_work  NA 0.0 \n", + "drive_transit_ASC_auto_deficient_atwork -BIG *** 0.0 \n", + "drive_transit_ASC_auto_deficient_eatout  0.01 0.0 \n", + "drive_transit_ASC_auto_deficient_escort -0.10 0.0 \n", + "drive_transit_ASC_auto_deficient_othdiscr -0.04 0.0 \n", + "drive_transit_ASC_auto_deficient_othmaint -0.06 0.0 \n", + "drive_transit_ASC_auto_deficient_school  NA 0.0 \n", + "drive_transit_ASC_auto_deficient_shopping -0.07 0.0 \n", + "drive_transit_ASC_auto_deficient_social  0.03 0.0 \n", + "drive_transit_ASC_auto_deficient_univ  NA 0.0 \n", + "drive_transit_ASC_auto_deficient_work  NA 0.0 \n", + "drive_transit_ASC_auto_sufficient_atwork  NA 0.0 \n", + "drive_transit_ASC_auto_sufficient_eatout -0.06 0.0 \n", + "drive_transit_ASC_auto_sufficient_escort -0.26 0.0 \n", + "drive_transit_ASC_auto_sufficient_othdiscr -0.05 0.0 \n", + "drive_transit_ASC_auto_sufficient_othmaint -0.12 0.0 \n", + "drive_transit_ASC_auto_sufficient_school  NA 0.0 \n", + "drive_transit_ASC_auto_sufficient_shopping -0.19 0.0 \n", + "drive_transit_ASC_auto_sufficient_social -0.07 0.0 \n", + "drive_transit_ASC_auto_sufficient_univ  BIG *** 0.0 \n", + "drive_transit_ASC_auto_sufficient_work  NA 0.0 \n", + "drive_transit_ASC_no_auto_all  0.00 0.0 \n", + "drive_transit_CBD_ASC_atwork  NA 0.0 \n", + "drive_transit_CBD_ASC_eatout_escort_othdiscr_ot...  9.95 *** 0.0 \n", + "drive_transit_CBD_ASC_school_univ  4.76 *** 0.0 \n", + "drive_transit_CBD_ASC_work  23.19 *** 0.0 \n", + "express_bus_ASC_eatout_escort_othdiscr_othmaint... -0.00 0.0 \n", + "express_bus_ASC_school_univ  NA 0.0 \n", + "express_bus_ASC_work  NA 0.0 \n", + "heavy_rail_ASC_eatout_escort_othdiscr_othmaint_...  0.00 0.0 \n", + "heavy_rail_ASC_school_univ  NA 0.0 \n", + "heavy_rail_ASC_work  NA 0.0 \n", + "joint_bike_ASC_auto_deficient_all -4.35 *** 0.0 \n", + "joint_bike_ASC_auto_sufficient_all -14.96 *** 0.0 \n", + "joint_bike_ASC_no_auto_all -2.35 * 0.0 \n", + "joint_drive_transit_ASC_auto_deficient_all -0.29 0.0 \n", + "joint_drive_transit_ASC_auto_sufficient_all -0.38 0.0 \n", + "joint_drive_transit_ASC_no_auto_all  NA 0.0 \n", + "joint_sr2_ASC_auto_deficient_all  NA 0.0 \n", + "joint_sr2_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_sr2_ASC_no_auto_all  NA 0.0 \n", + "joint_sr3p_ASC_auto_deficient_all -5.80 *** 0.0 \n", + "joint_sr3p_ASC_auto_sufficient_all -17.25 *** 0.0 \n", + "joint_sr3p_ASC_no_auto_all  1.44 0.0 \n", + "joint_taxi_ASC_auto_deficient_all -1.14 0.0 \n", + "joint_taxi_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_taxi_ASC_no_auto_all -1.03 0.0 \n", + "joint_tnc_shared_ASC_auto_deficient_all -0.62 0.0 \n", + "joint_tnc_shared_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_tnc_shared_ASC_no_auto_all -2.03 * 0.0 \n", + "joint_tnc_single_ASC_auto_deficient_all -1.30 0.0 \n", + "joint_tnc_single_ASC_auto_sufficient_all  NA 0.0 \n", + "joint_tnc_single_ASC_no_auto_all -1.77 0.0 \n", + "joint_walk_ASC_auto_deficient_all -5.51 *** 0.0 \n", + "joint_walk_ASC_auto_sufficient_all -17.32 *** 0.0 \n", + "joint_walk_ASC_no_auto_all -0.40 0.0 \n", + "joint_walk_transit_ASC_auto_deficient_all -0.26 0.0 \n", + "joint_walk_transit_ASC_auto_sufficient_all -0.03 0.0 \n", + "joint_walk_transit_ASC_no_auto_all  0.02 0.0 \n", + "local_bus_ASC_eatout_escort_othdiscr_othmaint_s... -0.05 0.0 \n", + "local_bus_ASC_school_univ  NA 0.0 \n", + "local_bus_ASC_work  NA 0.0 \n", + "sr2_ASC_auto_deficient_atwork -11.82 *** 0.0 \n", + "sr2_ASC_auto_deficient_eatout  3.25 ** 0.0 \n", + "sr2_ASC_auto_deficient_escort  1.69 0.0 \n", + "sr2_ASC_auto_deficient_othdiscr  3.39 *** 0.0 \n", + "sr2_ASC_auto_deficient_othmaint  3.14 ** 0.0 \n", + "sr2_ASC_auto_deficient_school  0.54 0.0 \n", + "sr2_ASC_auto_deficient_shopping  2.51 * 0.0 \n", + "sr2_ASC_auto_deficient_social  5.12 *** 0.0 \n", + "sr2_ASC_auto_deficient_univ -BIG *** 0.0 \n", + "sr2_ASC_auto_deficient_work  2.76 ** 0.0 \n", + "sr2_ASC_auto_sufficient_atwork -10.34 *** 0.0 \n", + "sr2_ASC_auto_sufficient_eatout  4.18 *** 0.0 \n", + "sr2_ASC_auto_sufficient_escort  2.38 * 0.0 \n", + "sr2_ASC_auto_sufficient_othdiscr  2.87 ** 0.0 \n", + "sr2_ASC_auto_sufficient_othmaint  3.51 *** 0.0 \n", + "sr2_ASC_auto_sufficient_school -0.76 0.0 \n", + "sr2_ASC_auto_sufficient_shopping  2.60 ** 0.0 \n", + "sr2_ASC_auto_sufficient_social  2.43 * 0.0 \n", + "sr2_ASC_auto_sufficient_univ -BIG *** 0.0 \n", + "sr2_ASC_auto_sufficient_work -5.50 *** 0.0 \n", + "sr2_ASC_no_auto_all  0.01 0.0 \n", + "sr3p_ASC_auto_deficient_atwork -13.71 *** 0.0 \n", + "sr3p_ASC_auto_deficient_eatout  1.52 0.0 \n", + "sr3p_ASC_auto_deficient_escort  1.01 0.0 \n", + "sr3p_ASC_auto_deficient_othdiscr  4.32 *** 0.0 \n", + "sr3p_ASC_auto_deficient_othmaint -0.55 0.0 \n", + "sr3p_ASC_auto_deficient_school  1.14 0.0 \n", + "sr3p_ASC_auto_deficient_shopping  1.41 0.0 \n", + "sr3p_ASC_auto_deficient_social  4.52 *** 0.0 \n", + "sr3p_ASC_auto_deficient_univ  NA 0.0 \n", + "sr3p_ASC_auto_deficient_work -3.11 ** 0.0 \n", + "sr3p_ASC_auto_sufficient_atwork -12.59 *** 0.0 \n", + "sr3p_ASC_auto_sufficient_eatout  4.04 *** 0.0 \n", + "sr3p_ASC_auto_sufficient_escort  2.30 * 0.0 \n", + "sr3p_ASC_auto_sufficient_othdiscr  3.12 ** 0.0 \n", + "sr3p_ASC_auto_sufficient_othmaint  2.32 * 0.0 \n", + "sr3p_ASC_auto_sufficient_school -0.16 0.0 \n", + "sr3p_ASC_auto_sufficient_shopping  1.81 0.0 \n", + "sr3p_ASC_auto_sufficient_social  2.45 * 0.0 \n", + "sr3p_ASC_auto_sufficient_univ -BIG *** 0.0 \n", + "sr3p_ASC_auto_sufficient_work -9.68 *** 0.0 \n", + "sr3p_ASC_no_auto_atwork  0.02 0.0 \n", + "sr3p_ASC_no_auto_eatout  0.02 0.0 \n", + "sr3p_ASC_no_auto_escort -0.02 0.0 \n", + "sr3p_ASC_no_auto_othdiscr  0.02 0.0 \n", + "sr3p_ASC_no_auto_othmaint  0.00 0.0 \n", + "sr3p_ASC_no_auto_school -0.05 0.0 \n", + "sr3p_ASC_no_auto_shopping  0.01 0.0 \n", + "sr3p_ASC_no_auto_social -0.01 0.0 \n", + "sr3p_ASC_no_auto_univ -BIG *** 0.0 \n", + "sr3p_ASC_no_auto_work  0.01 0.0 \n", + "taxi_ASC_auto_deficient_atwork -10.34 *** 0.0 \n", + "taxi_ASC_auto_deficient_eatout_othdiscr_social -4.89 *** 0.0 \n", + "taxi_ASC_auto_deficient_escort_othmaint_shopping -1.60 0.0 \n", + "taxi_ASC_auto_deficient_school  0.93 0.0 \n", + "taxi_ASC_auto_deficient_univ  BIG *** 0.0 \n", + "taxi_ASC_auto_deficient_work -10.19 *** 0.0 \n", + "taxi_ASC_auto_sufficient_atwork -22.34 *** 0.0 \n", + "taxi_ASC_auto_sufficient_eatout_othdiscr_social -8.35 *** 0.0 \n", + "taxi_ASC_auto_sufficient_escort_othmaint_shopping -7.60 *** 0.0 \n", + "taxi_ASC_auto_sufficient_school -2.33 * 0.0 \n", + "taxi_ASC_auto_sufficient_univ  NA 0.0 \n", + "taxi_ASC_auto_sufficient_work -14.22 *** 0.0 \n", + "taxi_ASC_no_auto_atwork  0.04 0.0 \n", + "taxi_ASC_no_auto_eatout_othdiscr_social -0.01 0.0 \n", + "taxi_ASC_no_auto_escort_othmaint_shopping  0.02 0.0 \n", + "taxi_ASC_no_auto_school_univ  NA 0.0 \n", + "taxi_ASC_no_auto_work  0.04 0.0 \n", + "tnc_shared_ASC_auto_deficient_atwork -12.71 *** 0.0 \n", + "tnc_shared_ASC_auto_deficient_eatout_othdiscr_s... -3.78 *** 0.0 \n", + "tnc_shared_ASC_auto_deficient_escort_othmaint_s... -2.57 * 0.0 \n", + "tnc_shared_ASC_auto_deficient_school  0.32 0.0 \n", + "tnc_shared_ASC_auto_deficient_univ  NA 0.0 \n", + "tnc_shared_ASC_auto_deficient_work -16.16 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_atwork -24.51 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_eatout_othdiscr_... -9.86 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_escort_othmaint_... -9.30 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_school -3.77 *** 0.0 \n", + "tnc_shared_ASC_auto_sufficient_univ  NA 0.0 \n", + "tnc_shared_ASC_auto_sufficient_work -16.11 *** 0.0 \n", + "tnc_shared_ASC_no_auto_atwork  0.03 0.0 \n", + "tnc_shared_ASC_no_auto_eatout_othdiscr_social -0.00 0.0 \n", + "tnc_shared_ASC_no_auto_escort_othmaint_shopping  0.01 0.0 \n", + "tnc_shared_ASC_no_auto_school  NA 0.0 \n", + "tnc_shared_ASC_no_auto_univ  NA 0.0 \n", + "tnc_shared_ASC_no_auto_work  0.03 0.0 \n", + "tnc_single_ASC_auto_deficient_atwork -13.19 *** 0.0 \n", + "tnc_single_ASC_auto_deficient_eatout_othdiscr_s... -5.21 *** 0.0 \n", + "tnc_single_ASC_auto_deficient_escort_othmaint_s...  0.30 0.0 \n", + "tnc_single_ASC_auto_deficient_school  0.06 0.0 \n", + "tnc_single_ASC_auto_deficient_univ  BIG *** 0.0 \n", + "tnc_single_ASC_auto_deficient_work -12.49 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_atwork -25.04 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_eatout_othdiscr_... -7.62 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_escort_othmaint_... -5.81 *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_school -2.12 * 0.0 \n", + "tnc_single_ASC_auto_sufficient_univ  BIG *** 0.0 \n", + "tnc_single_ASC_auto_sufficient_work -24.13 *** 0.0 \n", + "tnc_single_ASC_no_auto_atwork  0.05 0.0 \n", + "tnc_single_ASC_no_auto_eatout_othdiscr_social  0.01 0.0 \n", + "tnc_single_ASC_no_auto_escort_othmaint_shopping  0.02 0.0 \n", + "tnc_single_ASC_no_auto_school  NA 0.0 \n", + "tnc_single_ASC_no_auto_univ -BIG *** 0.0 \n", + "tnc_single_ASC_no_auto_work  0.06 0.0 \n", + "walk_ASC_auto_deficient_atwork  1.41 0.0 \n", + "walk_ASC_auto_deficient_eatout  6.37 *** 0.0 \n", + "walk_ASC_auto_deficient_escort -4.04 *** 0.0 \n", + "walk_ASC_auto_deficient_othdiscr  3.94 *** 0.0 \n", + "walk_ASC_auto_deficient_othmaint  1.79 0.0 \n", + "walk_ASC_auto_deficient_school  2.75 ** 0.0 \n", + "walk_ASC_auto_deficient_shopping  4.06 *** 0.0 \n", + "walk_ASC_auto_deficient_social  4.82 *** 0.0 \n", + "walk_ASC_auto_deficient_univ  NA 0.0 \n", + "walk_ASC_auto_deficient_work  16.52 *** 0.0 \n", + "walk_ASC_auto_sufficient_atwork  1.36 0.0 \n", + "walk_ASC_auto_sufficient_eatout  3.04 ** 0.0 \n", + "walk_ASC_auto_sufficient_escort -4.20 *** 0.0 \n", + "walk_ASC_auto_sufficient_othdiscr  1.76 0.0 \n", + "walk_ASC_auto_sufficient_othmaint  1.42 0.0 \n", + "walk_ASC_auto_sufficient_school  0.40 0.0 \n", + "walk_ASC_auto_sufficient_shopping  0.27 0.0 \n", + "walk_ASC_auto_sufficient_social  2.08 * 0.0 \n", + "walk_ASC_auto_sufficient_univ  BIG *** 0.0 \n", + "walk_ASC_auto_sufficient_work -5.25 *** 0.0 \n", + "walk_ASC_no_auto_atwork  0.08 0.0 \n", + "walk_ASC_no_auto_eatout  0.05 0.0 \n", + "walk_ASC_no_auto_escort  0.03 0.0 \n", + "walk_ASC_no_auto_othdiscr  0.03 0.0 \n", + "walk_ASC_no_auto_othmaint  0.01 0.0 \n", + "walk_ASC_no_auto_school  0.15 0.0 \n", + "walk_ASC_no_auto_shopping  0.03 0.0 \n", + "walk_ASC_no_auto_social  0.02 0.0 \n", + "walk_ASC_no_auto_univ  BIG *** 0.0 \n", + "walk_ASC_no_auto_work  0.06 0.0 \n", + "walk_ferry_ASC_eatout_escort_othdiscr_othmaint_... -0.00 0.0 \n", + "walk_ferry_ASC_school_univ  NA 0.0 \n", + "walk_ferry_ASC_work  NA 0.0 \n", + "walk_light_rail_ASC_eatout_escort_othdiscr_othm...  0.01 0.0 \n", + "walk_light_rail_ASC_school_univ  NA 0.0 \n", + "walk_light_rail_ASC_work  NA 0.0 \n", + "walk_transit_ASC_auto_deficient_atwork -0.14 0.0 \n", + "walk_transit_ASC_auto_deficient_eatout -0.01 0.0 \n", + "walk_transit_ASC_auto_deficient_escort -0.22 0.0 \n", + "walk_transit_ASC_auto_deficient_othdiscr  0.05 0.0 \n", + "walk_transit_ASC_auto_deficient_othmaint -0.14 0.0 \n", + "walk_transit_ASC_auto_deficient_school  NA 0.0 \n", + "walk_transit_ASC_auto_deficient_shopping -0.04 0.0 \n", + "walk_transit_ASC_auto_deficient_social  0.06 0.0 \n", + "walk_transit_ASC_auto_deficient_univ  NA 0.0 \n", + "walk_transit_ASC_auto_deficient_work  NA 0.0 \n", + "walk_transit_ASC_auto_sufficient_atwork -0.18 0.0 \n", + "walk_transit_ASC_auto_sufficient_eatout -0.05 0.0 \n", + "walk_transit_ASC_auto_sufficient_escort -0.23 0.0 \n", + "walk_transit_ASC_auto_sufficient_othdiscr -0.04 0.0 \n", + "walk_transit_ASC_auto_sufficient_othmaint -0.06 0.0 \n", + "walk_transit_ASC_auto_sufficient_school  NA 0.0 \n", + "walk_transit_ASC_auto_sufficient_shopping -0.11 0.0 \n", + "walk_transit_ASC_auto_sufficient_social -0.03 0.0 \n", + "walk_transit_ASC_auto_sufficient_univ  NA 0.0 \n", + "walk_transit_ASC_auto_sufficient_work  NA 0.0 \n", + "walk_transit_ASC_no_auto_atwork  0.04 0.0 \n", + "walk_transit_ASC_no_auto_eatout  0.04 0.0 \n", + "walk_transit_ASC_no_auto_escort -0.03 0.0 \n", + "walk_transit_ASC_no_auto_othdiscr  0.04 0.0 \n", + "walk_transit_ASC_no_auto_othmaint  0.03 0.0 \n", + "walk_transit_ASC_no_auto_school  NA 0.0 \n", + "walk_transit_ASC_no_auto_shopping  0.03 0.0 \n", + "walk_transit_ASC_no_auto_social  0.03 0.0 \n", + "walk_transit_ASC_no_auto_univ  NA 0.0 \n", + "walk_transit_ASC_no_auto_work  NA 0.0 \n", + "walk_transit_CBD_ASC_atwork  4.28 *** 0.0 \n", + "walk_transit_CBD_ASC_eatout_escort_othdiscr_oth...  14.41 *** 0.0 \n", + "walk_transit_CBD_ASC_school_univ  7.24 *** 0.0 \n", + "walk_transit_CBD_ASC_work  21.27 *** 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  1.78 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  NA 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  1.24 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  2.22 * 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  1.64 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout...  0.92 0.0 \n", + "coefficient_name\\ncoef_test coef_test_eatout... -1.20 0.0 \n", + "coefficient_name\\ncoef_test coef_test_school...  0.59 0.0 \n", + "coefficient_name\\ncoef_test coef_test_school...  NA 0.0 " ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "pd.read_csv(result_dir/f\"{modelname}_coefficients_revised.csv\")" + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.parameter_summary().data,\n", + " \"remodel\": remodel.parameter_summary().data,\n", + " }, axis=1).fillna(\"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2025-06-26T02:27:37.943631Z", + "iopub.status.busy": "2025-06-26T02:27:37.943503Z", + "iopub.status.idle": "2025-06-26T02:27:38.022217Z", + "shell.execute_reply": "2025-06-26T02:27:38.021934Z", + "shell.execute_reply.started": "2025-06-26T02:27:37.943621Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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modelremodel
Number of CasesAggregate69971.00000069971.000000
Log Likelihood at ConvergenceAggregate-76508.289239-76502.093437
Per Case-1.093429-1.093340
Log Likelihood at Null ParametersAggregate-142469.530288-142469.530288
Per Case-2.036123-2.036123
Rho Squared w.r.t. Null ParametersAggregate0.4629850.463028
\n", + "
" + ], + "text/plain": [ + " model remodel\n", + "Number of Cases Aggregate 69971.000000 69971.000000\n", + "Log Likelihood at Convergence Aggregate -76508.289239 -76502.093437\n", + " Per Case -1.093429 -1.093340\n", + "Log Likelihood at Null Parameters Aggregate -142469.530288 -142469.530288\n", + " Per Case -2.036123 -2.036123\n", + "Rho Squared w.r.t. Null Parameters Aggregate 0.462985 0.463028" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with pd.option_context('display.max_rows', 999):\n", + " display(pd.concat({\n", + " \"model\": model.estimation_statistics_raw(),\n", + " \"remodel\": remodel.estimation_statistics_raw(),\n", + " }, axis=1).fillna(\"\"))" ] } ], @@ -4539,7 +13023,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -4553,7 +13037,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/18_atwork_subtour_freq.ipynb b/activitysim/examples/example_estimation/notebooks/18_atwork_subtour_freq.ipynb index 4bd5cd1f3e..9d92886e00 100644 --- a/activitysim/examples/example_estimation/notebooks/18_atwork_subtour_freq.ipynb +++ b/activitysim/examples/example_estimation/notebooks/18_atwork_subtour_freq.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -68,12 +115,27 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_frequency/atwork_subtour_frequency_coefficients.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/atwork_subtour_frequency/atwork_subtour_frequency_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_frequency/atwork_subtour_frequency_values_combined.parquet\n" + ] + } + ], "source": [ "modelname = \"atwork_subtour_frequency\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -752,11 +814,11 @@ " util_individual_discretionary_tours_made_by_full_time_worker\n", " util_individual_discretionary_tours_made_by_part_time_worker\n", " ...\n", - " TERMINAL\n", - " household_density\n", - " employment_density\n", - " density_index\n", - " is_cbd\n", + " trPkTotal\n", + " trOpRetail\n", + " trOpTotal\n", + " nmRetail\n", + " nmTotal\n", " num_maint_shop_escort\n", " num_joint_discr\n", " num_joint_maint_shop_eat\n", @@ -790,31 +852,31 @@ " \n", " \n", " \n", - " 2998943\n", - " maint\n", - " maint\n", + " 2966594\n", + " no_subtours\n", + " eat\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 2.48345\n", - " 26.073171\n", - " 8.048780\n", - " 6.150212\n", - " False\n", + " 9.119016\n", + " 6.446184\n", + " 9.035333\n", + " 5.256966\n", + " 6.831275\n", " 0\n", " 0\n", " 0\n", " False\n", - " 3\n", + " 1\n", " \n", " \n", - " 3060361\n", + " 3046667\n", " eat\n", " eat\n", " 1.0\n", @@ -826,45 +888,45 @@ " 0.0\n", " 0.0\n", " ...\n", - " 2.09035\n", - " 20.666667\n", - " 4.107527\n", - " 3.426505\n", - " False\n", + " 7.771767\n", + " 5.298242\n", + " 7.602426\n", + " 6.443514\n", + " 7.965548\n", " 0\n", " 0\n", " 0\n", - " True\n", + " False\n", " 1\n", " \n", " \n", - " 4422914\n", - " eat\n", + " 3048143\n", + " no_subtours\n", " eat\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 5.35435\n", - " 139.333333\n", - " 418.518519\n", - " 104.532377\n", - " False\n", + " 7.771767\n", + " 5.298242\n", + " 7.602426\n", + " 6.443514\n", + " 7.965548\n", " 0\n", " 0\n", " 0\n", - " False\n", + " True\n", " 1\n", " \n", " \n", - " 4440298\n", - " maint\n", - " maint\n", + " 3177498\n", + " no_subtours\n", + " eat\n", " 1.0\n", " 0.0\n", " 0.0\n", @@ -874,39 +936,39 @@ " 0.0\n", " 0.0\n", " ...\n", - " 5.22542\n", - " 97.634722\n", - " 550.205552\n", - " 82.920387\n", - " False\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0\n", " 0\n", " 0\n", - " False\n", - " 3\n", + " True\n", + " 1\n", " \n", " \n", - " 4496796\n", - " maint\n", + " 3191848\n", + " eat\n", " maint\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 4.73802\n", - " 117.769796\n", - " 246.205869\n", - " 79.663609\n", - " False\n", + " 3.278544\n", + " 1.239060\n", + " 3.280429\n", + " 4.284159\n", + " 6.249753\n", " 0\n", " 0\n", " 0\n", - " False\n", + " True\n", " 3\n", " \n", " \n", @@ -934,258 +996,258 @@ " ...\n", " \n", " \n", - " 302923742\n", - " maint\n", - " maint\n", + " 308156039\n", + " no_subtours\n", + " eat\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 2.37546\n", - " 19.153846\n", - " 5.907692\n", - " 4.515087\n", - " False\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 3.086628\n", + " 5.647842\n", " 0\n", " 0\n", " 0\n", - " True\n", - " 3\n", + " False\n", + " 1\n", " \n", " \n", - " 302942602\n", - " eat\n", - " eat\n", + " 308227666\n", + " no_subtours\n", + " maint\n", + " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 2.81406\n", - " 16.068376\n", - " 21.136752\n", - " 9.128669\n", - " False\n", + " 7.890189\n", + " 4.493279\n", + " 7.643024\n", + " 5.529241\n", + " 8.876000\n", " 0\n", " 0\n", " 0\n", " True\n", - " 1\n", + " 3\n", " \n", " \n", - " 302942643\n", - " maint\n", - " maint\n", + " 308260138\n", + " no_subtours\n", + " eat\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 2.81406\n", - " 16.068376\n", - " 21.136752\n", - " 9.128669\n", - " False\n", + " 7.614481\n", + " 3.838266\n", + " 7.011926\n", + " 5.169891\n", + " 8.595128\n", " 0\n", " 0\n", " 0\n", " False\n", - " 3\n", + " 1\n", " \n", " \n", - " 305120481\n", - " maint\n", - " maint\n", + " 309080753\n", + " no_subtours\n", + " eat\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 8.54946\n", - " 55.606634\n", - " 142.984438\n", - " 40.036459\n", - " False\n", + " 9.892019\n", + " 6.979741\n", + " 9.773601\n", + " 6.557982\n", + " 8.518901\n", " 0\n", " 0\n", " 0\n", " False\n", - " 3\n", + " 1\n", " \n", " \n", - " 308000690\n", - " maint\n", + " 309112036\n", " maint\n", - " 0.0\n", + " eat\n", " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.0\n", + " 0.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", - " 4.64648\n", - " 196.395950\n", - " 178.779465\n", - " 93.587057\n", - " False\n", + " 0.222696\n", + " 0.016077\n", + " 0.153189\n", + " 0.000000\n", + " 0.000000\n", " 0\n", " 0\n", " 0\n", - " False\n", - " 3\n", + " True\n", + " 1\n", " \n", " \n", "\n", - "

460 rows × 182 columns

\n", + "

5947 rows × 182 columns

\n", "
" ], "text/plain": [ " model_choice override_choice util_dummy_for_full_time_worker \\\n", "tour_id \n", - "2998943 maint maint 1.0 \n", - "3060361 eat eat 1.0 \n", - "4422914 eat eat 1.0 \n", - "4440298 maint maint 1.0 \n", - "4496796 maint maint 1.0 \n", + "2966594 no_subtours eat 1.0 \n", + "3046667 eat eat 1.0 \n", + "3048143 no_subtours eat 1.0 \n", + "3177498 no_subtours eat 1.0 \n", + "3191848 eat maint 1.0 \n", "... ... ... ... \n", - "302923742 maint maint 1.0 \n", - "302942602 eat eat 1.0 \n", - "302942643 maint maint 1.0 \n", - "305120481 maint maint 1.0 \n", - "308000690 maint maint 0.0 \n", + "308156039 no_subtours eat 1.0 \n", + "308227666 no_subtours maint 0.0 \n", + "308260138 no_subtours eat 1.0 \n", + "309080753 no_subtours eat 1.0 \n", + "309112036 maint eat 1.0 \n", "\n", " util_dummy_for_non_full_time_worker util_dummy_for_non_workers \\\n", "tour_id \n", - "2998943 0.0 0.0 \n", - "3060361 0.0 0.0 \n", - "4422914 0.0 0.0 \n", - "4440298 0.0 0.0 \n", - "4496796 0.0 0.0 \n", + "2966594 0.0 0.0 \n", + "3046667 0.0 0.0 \n", + "3048143 0.0 0.0 \n", + "3177498 0.0 0.0 \n", + "3191848 0.0 0.0 \n", "... ... ... \n", - "302923742 0.0 0.0 \n", - "302942602 0.0 0.0 \n", - "302942643 0.0 0.0 \n", - "305120481 0.0 0.0 \n", - "308000690 1.0 0.0 \n", + "308156039 0.0 0.0 \n", + "308227666 1.0 0.0 \n", + "308260138 0.0 0.0 \n", + "309080753 0.0 0.0 \n", + "309112036 0.0 0.0 \n", "\n", " util_medium_hh_income_dummy util_high_hh_income_dummy \\\n", "tour_id \n", - "2998943 0.0 0.0 \n", - "3060361 0.0 0.0 \n", - "4422914 0.0 0.0 \n", - "4440298 0.0 0.0 \n", - "4496796 0.0 0.0 \n", + "2966594 0.0 0.0 \n", + "3046667 0.0 0.0 \n", + "3048143 0.0 0.0 \n", + "3177498 0.0 0.0 \n", + "3191848 0.0 0.0 \n", "... ... ... \n", - "302923742 0.0 1.0 \n", - "302942602 0.0 1.0 \n", - "302942643 0.0 1.0 \n", - "305120481 0.0 1.0 \n", - "308000690 0.0 0.0 \n", + "308156039 0.0 0.0 \n", + "308227666 0.0 0.0 \n", + "308260138 0.0 0.0 \n", + "309080753 0.0 0.0 \n", + "309112036 0.0 0.0 \n", "\n", " util_zero_cars_owned_by_hh_dummy \\\n", "tour_id \n", - "2998943 1.0 \n", - "3060361 0.0 \n", - "4422914 1.0 \n", - "4440298 0.0 \n", - "4496796 1.0 \n", + "2966594 0.0 \n", + "3046667 0.0 \n", + "3048143 0.0 \n", + "3177498 0.0 \n", + "3191848 0.0 \n", "... ... \n", - "302923742 0.0 \n", - "302942602 0.0 \n", - "302942643 0.0 \n", - "305120481 0.0 \n", - "308000690 1.0 \n", + "308156039 0.0 \n", + "308227666 0.0 \n", + "308260138 0.0 \n", + "309080753 1.0 \n", + "309112036 0.0 \n", "\n", " util_individual_discretionary_tours_made_by_full_time_worker \\\n", "tour_id \n", - "2998943 0.0 \n", - "3060361 0.0 \n", - "4422914 0.0 \n", - "4440298 0.0 \n", - "4496796 0.0 \n", + "2966594 0.0 \n", + "3046667 0.0 \n", + "3048143 0.0 \n", + "3177498 0.0 \n", + "3191848 0.0 \n", "... ... \n", - "302923742 0.0 \n", - "302942602 0.0 \n", - "302942643 0.0 \n", - "305120481 0.0 \n", - "308000690 0.0 \n", + "308156039 0.0 \n", + "308227666 0.0 \n", + "308260138 0.0 \n", + "309080753 0.0 \n", + "309112036 0.0 \n", "\n", " util_individual_discretionary_tours_made_by_part_time_worker ... \\\n", "tour_id ... \n", - "2998943 0.0 ... \n", - "3060361 0.0 ... \n", - "4422914 0.0 ... \n", - "4440298 0.0 ... \n", - "4496796 0.0 ... \n", + "2966594 0.0 ... \n", + "3046667 0.0 ... \n", + "3048143 0.0 ... \n", + "3177498 0.0 ... \n", + "3191848 0.0 ... \n", "... ... ... \n", - "302923742 0.0 ... \n", - "302942602 0.0 ... \n", - "302942643 0.0 ... \n", - "305120481 0.0 ... \n", - "308000690 0.0 ... \n", + "308156039 0.0 ... \n", + "308227666 0.0 ... \n", + "308260138 0.0 ... \n", + "309080753 0.0 ... \n", + "309112036 0.0 ... \n", "\n", - " TERMINAL household_density employment_density density_index \\\n", - "tour_id \n", - "2998943 2.48345 26.073171 8.048780 6.150212 \n", - "3060361 2.09035 20.666667 4.107527 3.426505 \n", - "4422914 5.35435 139.333333 418.518519 104.532377 \n", - "4440298 5.22542 97.634722 550.205552 82.920387 \n", - "4496796 4.73802 117.769796 246.205869 79.663609 \n", - "... ... ... ... ... \n", - "302923742 2.37546 19.153846 5.907692 4.515087 \n", - "302942602 2.81406 16.068376 21.136752 9.128669 \n", - "302942643 2.81406 16.068376 21.136752 9.128669 \n", - "305120481 8.54946 55.606634 142.984438 40.036459 \n", - "308000690 4.64648 196.395950 178.779465 93.587057 \n", + " trPkTotal trOpRetail trOpTotal nmRetail nmTotal \\\n", + "tour_id \n", + "2966594 9.119016 6.446184 9.035333 5.256966 6.831275 \n", + "3046667 7.771767 5.298242 7.602426 6.443514 7.965548 \n", + "3048143 7.771767 5.298242 7.602426 6.443514 7.965548 \n", + "3177498 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "3191848 3.278544 1.239060 3.280429 4.284159 6.249753 \n", + "... ... ... ... ... ... \n", + "308156039 0.000000 0.000000 0.000000 3.086628 5.647842 \n", + "308227666 7.890189 4.493279 7.643024 5.529241 8.876000 \n", + "308260138 7.614481 3.838266 7.011926 5.169891 8.595128 \n", + "309080753 9.892019 6.979741 9.773601 6.557982 8.518901 \n", + "309112036 0.222696 0.016077 0.153189 0.000000 0.000000 \n", "\n", - " is_cbd num_maint_shop_escort num_joint_discr \\\n", - "tour_id \n", - "2998943 False 0 0 \n", - "3060361 False 0 0 \n", - "4422914 False 0 0 \n", - "4440298 False 0 0 \n", - "4496796 False 0 0 \n", - "... ... ... ... \n", - "302923742 False 0 0 \n", - "302942602 False 0 0 \n", - "302942643 False 0 0 \n", - "305120481 False 0 0 \n", - "308000690 False 0 0 \n", + " num_maint_shop_escort num_joint_discr num_joint_maint_shop_eat \\\n", + "tour_id \n", + "2966594 0 0 0 \n", + "3046667 0 0 0 \n", + "3048143 0 0 0 \n", + "3177498 0 0 0 \n", + "3191848 0 0 0 \n", + "... ... ... ... \n", + "308156039 0 0 0 \n", + "308227666 0 0 0 \n", + "308260138 0 0 0 \n", + "309080753 0 0 0 \n", + "309112036 0 0 0 \n", "\n", - " num_joint_maint_shop_eat work_tour_is_SOV override_choice_code \n", - "tour_id \n", - "2998943 0 False 3 \n", - "3060361 0 True 1 \n", - "4422914 0 False 1 \n", - "4440298 0 False 3 \n", - "4496796 0 False 3 \n", - "... ... ... ... \n", - "302923742 0 True 3 \n", - "302942602 0 True 1 \n", - "302942643 0 False 3 \n", - "305120481 0 False 3 \n", - "308000690 0 False 3 \n", + " work_tour_is_SOV override_choice_code \n", + "tour_id \n", + "2966594 False 1 \n", + "3046667 False 1 \n", + "3048143 True 1 \n", + "3177498 True 1 \n", + "3191848 True 3 \n", + "... ... ... \n", + "308156039 False 1 \n", + "308227666 True 3 \n", + "308260138 False 1 \n", + "309080753 False 1 \n", + "309112036 True 1 \n", "\n", - "[460 rows x 182 columns]" + "[5947 rows x 182 columns]" ] }, "execution_count": 6, @@ -1211,17 +1273,10 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 075 [Optimization terminated successfully]

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Iteration 100 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1233,7 +1288,7 @@ { "data": { "text/html": [ - "

Best LL = -311.0816324383027

" + "

Best LL = -4832.223778692503

" ], "text/plain": [ "" @@ -1264,70 +1319,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coefficient_at_work_sub_tour_asc_business1\n", - " 1.421407\n", + " -0.350395\n", + " -0.350395\n", " -0.5372\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 1.421407\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_business2\n", - " -0.130778\n", + " -2.211281\n", + " -2.211281\n", " -2.1337\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.130778\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_eat\n", - " 5.809056\n", + " 0.440311\n", + " 0.440311\n", " 0.8576\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 5.809056\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_eat_business\n", - " -23.415363\n", + " -0.860366\n", + " -0.860366\n", " -0.9721\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -23.415363\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_maint\n", - " 12.910479\n", + " -0.423469\n", + " -0.423469\n", " -0.6198\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 12.910479\n", " \n", " \n", " ...\n", @@ -1338,122 +1397,120 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_business1\n", - " -0.526876\n", + " -0.633755\n", + " -0.633755\n", " -0.3391\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.526876\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_business2\n", " 0.000000\n", + " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_eat\n", " 0.000000\n", + " 0.000000\n", " 0.0000\n", " 0.0\n", - " NaN\n", - " NaN\n", + " 0.0\n", + " 0.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_eat_business\n", - " -19.116288\n", + " -0.847707\n", + " -0.847707\n", " -0.3391\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -19.116288\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_maint\n", - " 0.559350\n", + " -0.020836\n", + " -0.020836\n", " 0.1762\n", + " -50.0\n", + " 50.0\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.559350\n", " \n", " \n", "\n", - "

110 rows × 8 columns

\n", + "

110 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coefficient_at_work_sub_tour_asc_business1 1.421407 -0.5372 \n", - "coefficient_at_work_sub_tour_asc_business2 -0.130778 -2.1337 \n", - "coefficient_at_work_sub_tour_asc_eat 5.809056 0.8576 \n", - "coefficient_at_work_sub_tour_asc_eat_business -23.415363 -0.9721 \n", - "coefficient_at_work_sub_tour_asc_maint 12.910479 -0.6198 \n", - "... ... ... \n", - "coefficient_zero_cars_owned_by_hh_dummy_business1 -0.526876 -0.3391 \n", - "coefficient_zero_cars_owned_by_hh_dummy_business2 0.000000 0.0000 \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat 0.000000 0.0000 \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... -19.116288 -0.3391 \n", - "coefficient_zero_cars_owned_by_hh_dummy_maint 0.559350 0.1762 \n", + " value best \\\n", + "param_name \n", + "coefficient_at_work_sub_tour_asc_business1 -0.350395 -0.350395 \n", + "coefficient_at_work_sub_tour_asc_business2 -2.211281 -2.211281 \n", + "coefficient_at_work_sub_tour_asc_eat 0.440311 0.440311 \n", + "coefficient_at_work_sub_tour_asc_eat_business -0.860366 -0.860366 \n", + "coefficient_at_work_sub_tour_asc_maint -0.423469 -0.423469 \n", + "... ... ... \n", + "coefficient_zero_cars_owned_by_hh_dummy_business1 -0.633755 -0.633755 \n", + "coefficient_zero_cars_owned_by_hh_dummy_business2 0.000000 0.000000 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat 0.000000 0.000000 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... -0.847707 -0.847707 \n", + "coefficient_zero_cars_owned_by_hh_dummy_maint -0.020836 -0.020836 \n", "\n", - " nullvalue minimum \\\n", - "coefficient_at_work_sub_tour_asc_business1 0.0 NaN \n", - "coefficient_at_work_sub_tour_asc_business2 0.0 NaN \n", - "coefficient_at_work_sub_tour_asc_eat 0.0 NaN \n", - "coefficient_at_work_sub_tour_asc_eat_business 0.0 NaN \n", - "coefficient_at_work_sub_tour_asc_maint 0.0 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coefficient_at_work_sub_tour_asc_business1 -0.5372 -50.0 \n", + "coefficient_at_work_sub_tour_asc_business2 -2.1337 -50.0 \n", + "coefficient_at_work_sub_tour_asc_eat 0.8576 -50.0 \n", + "coefficient_at_work_sub_tour_asc_eat_business -0.9721 -50.0 \n", + "coefficient_at_work_sub_tour_asc_maint -0.6198 -50.0 \n", "... ... ... \n", - "coefficient_zero_cars_owned_by_hh_dummy_business1 0.0 NaN \n", - "coefficient_zero_cars_owned_by_hh_dummy_business2 0.0 NaN \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat 0.0 NaN \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... 0.0 NaN \n", - "coefficient_zero_cars_owned_by_hh_dummy_maint 0.0 NaN \n", + "coefficient_zero_cars_owned_by_hh_dummy_business1 -0.3391 -50.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_business2 0.0000 0.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat 0.0000 0.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... -0.3391 -50.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_maint 0.1762 -50.0 \n", "\n", - " maximum holdfast note \\\n", - "coefficient_at_work_sub_tour_asc_business1 NaN 0 \n", - "coefficient_at_work_sub_tour_asc_business2 NaN 0 \n", - "coefficient_at_work_sub_tour_asc_eat NaN 0 \n", - "coefficient_at_work_sub_tour_asc_eat_business NaN 0 \n", - "coefficient_at_work_sub_tour_asc_maint NaN 0 \n", - "... ... ... ... \n", - "coefficient_zero_cars_owned_by_hh_dummy_business1 NaN 0 \n", - "coefficient_zero_cars_owned_by_hh_dummy_business2 NaN 1 \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat NaN 1 \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... NaN 0 \n", - "coefficient_zero_cars_owned_by_hh_dummy_maint NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coefficient_at_work_sub_tour_asc_business1 50.0 0.0 \n", + "coefficient_at_work_sub_tour_asc_business2 50.0 0.0 \n", + "coefficient_at_work_sub_tour_asc_eat 50.0 0.0 \n", + "coefficient_at_work_sub_tour_asc_eat_business 50.0 0.0 \n", + "coefficient_at_work_sub_tour_asc_maint 50.0 0.0 \n", + "... ... ... \n", + "coefficient_zero_cars_owned_by_hh_dummy_business1 50.0 0.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_business2 0.0 0.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat 0.0 0.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... 50.0 0.0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_maint 50.0 0.0 \n", "\n", - " best \n", - "coefficient_at_work_sub_tour_asc_business1 1.421407 \n", - "coefficient_at_work_sub_tour_asc_business2 -0.130778 \n", - "coefficient_at_work_sub_tour_asc_eat 5.809056 \n", - "coefficient_at_work_sub_tour_asc_eat_business -23.415363 \n", - "coefficient_at_work_sub_tour_asc_maint 12.910479 \n", - "... ... \n", - "coefficient_zero_cars_owned_by_hh_dummy_business1 -0.526876 \n", - "coefficient_zero_cars_owned_by_hh_dummy_business2 0.000000 \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat 0.000000 \n", - "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... -19.116288 \n", - "coefficient_zero_cars_owned_by_hh_dummy_maint 0.559350 \n", + " holdfast \n", + "param_name \n", + "coefficient_at_work_sub_tour_asc_business1 0 \n", + "coefficient_at_work_sub_tour_asc_business2 0 \n", + "coefficient_at_work_sub_tour_asc_eat 0 \n", + "coefficient_at_work_sub_tour_asc_eat_business 0 \n", + "coefficient_at_work_sub_tour_asc_maint 0 \n", + "... ... \n", + "coefficient_zero_cars_owned_by_hh_dummy_business1 0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_business2 1 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat 1 \n", + "coefficient_zero_cars_owned_by_hh_dummy_eat_bus... 0 \n", + "coefficient_zero_cars_owned_by_hh_dummy_maint 0 \n", "\n", - "[110 rows x 8 columns]" + "[110 rows x 7 columns]" ] }, "metadata": {}, @@ -1463,12 +1520,8 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate(method='SLSQP')\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 5.5647113087279e-33 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: RuntimeWarning: invalid value encountered in sqrt\n", - " model.estimate(method='SLSQP')\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -1484,446 +1537,446 @@ " \n", " \n", " coefficient_at_work_sub_tour_asc_business1\n", - " 1.421407e+00\n", + " -0.350395\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_business2\n", - " -1.307783e-01\n", + " -2.211281\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_eat\n", - " 5.809056e+00\n", + " 0.440311\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_eat_business\n", - " -2.341536e+01\n", + " -0.860366\n", " \n", " \n", " coefficient_at_work_sub_tour_asc_maint\n", - " 1.291048e+01\n", + " -0.423469\n", " \n", " \n", " coefficient_auto_accessibility_to_retail_for_work_taz_business1\n", - " -2.350832e-01\n", + " 0.078156\n", " \n", " \n", " coefficient_auto_accessibility_to_retail_for_work_taz_business2\n", - " 2.951134e+00\n", + " 0.295708\n", " \n", " \n", " coefficient_auto_accessibility_to_retail_for_work_taz_eat\n", - " -7.610481e-01\n", + " 0.137215\n", " \n", " \n", " coefficient_auto_accessibility_to_retail_for_work_taz_eat_business\n", - " 1.515548e+00\n", + " -0.262951\n", " \n", " \n", " coefficient_auto_accessibility_to_retail_for_work_taz_maint\n", - " -3.200651e+00\n", + " 0.021772\n", " \n", " \n", " coefficient_dummy_for_drive_alone_mode_for_work_tour_business1\n", - " 3.943802e+00\n", + " 0.949692\n", " \n", " \n", " coefficient_dummy_for_drive_alone_mode_for_work_tour_business2\n", - " -7.285121e+00\n", + " 1.745346\n", " \n", " \n", " coefficient_dummy_for_drive_alone_mode_for_work_tour_eat\n", - " 3.216240e+00\n", + " 0.488173\n", " \n", " \n", " coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business\n", - " 2.333483e+00\n", + " 1.784643\n", " \n", " \n", " coefficient_dummy_for_drive_alone_mode_for_work_tour_maint\n", - " 3.865797e+00\n", + " 1.106347\n", " \n", " \n", " coefficient_dummy_for_full_time_worker_business1\n", - " -1.136529e+01\n", + " -7.402415\n", " \n", " \n", " coefficient_dummy_for_full_time_worker_business2\n", - " -1.383392e+01\n", + " -13.824988\n", " \n", " \n", " coefficient_dummy_for_full_time_worker_eat\n", - " -9.545916e+00\n", + " -7.551797\n", " \n", " \n", " coefficient_dummy_for_full_time_worker_eat_business\n", - " -1.119217e+01\n", + " -14.974823\n", " \n", " \n", " coefficient_dummy_for_full_time_worker_maint\n", - " -5.880704e+00\n", + " -8.063977\n", " \n", " \n", " coefficient_dummy_for_non_full_time_worker_business1\n", - " -2.370101e+00\n", + " -8.104781\n", " \n", " \n", " coefficient_dummy_for_non_full_time_worker_business2\n", - " -1.272316e+01\n", + " -14.812593\n", " \n", " \n", " coefficient_dummy_for_non_full_time_worker_eat\n", - " -1.386629e+00\n", + " -8.749492\n", " \n", " \n", " coefficient_dummy_for_non_full_time_worker_eat_business\n", - " -4.083109e+01\n", + " -14.493442\n", " \n", " \n", " coefficient_dummy_for_non_full_time_worker_maint\n", - " 3.103983e+00\n", + " -8.046692\n", " \n", " \n", " coefficient_dummy_for_non_workers_business1\n", - " -5.000000e+00\n", + " -5.000000\n", " \n", " \n", " coefficient_dummy_for_non_workers_business2\n", - " -5.000000e+00\n", + " -5.000000\n", " \n", " \n", " coefficient_dummy_for_non_workers_eat\n", - " -3.678488e-14\n", + " 0.000000\n", " \n", " \n", " coefficient_dummy_for_non_workers_eat_business\n", - " -5.000000e+00\n", + " -5.000000\n", " \n", " \n", " coefficient_dummy_for_non_workers_maint\n", - " -5.000000e+00\n", + " -5.000000\n", " \n", " \n", " coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1\n", - " 5.542765e-14\n", + " 0.000000\n", " \n", " \n", " coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2\n", - " -6.800386e-14\n", + " 0.000000\n", " \n", " \n", " coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat\n", - " -7.803277e-15\n", + " 0.000000\n", " \n", " \n", " coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business\n", - " 9.964378e-15\n", + " 0.000000\n", " \n", " \n", " coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint\n", - " -4.465435e-01\n", + " -0.857652\n", " \n", " \n", " coefficient_high_hh_income_dummy_business1\n", - " -5.699484e+00\n", + " 1.051824\n", " \n", " \n", " coefficient_high_hh_income_dummy_business2\n", - " 1.817758e+01\n", + " 1.775205\n", " \n", " \n", " coefficient_high_hh_income_dummy_eat\n", - " -5.360138e+00\n", + " 0.987000\n", " \n", " \n", " coefficient_high_hh_income_dummy_eat_business\n", - " 5.949348e+00\n", + " 2.165299\n", " \n", " \n", " coefficient_high_hh_income_dummy_maint\n", - " -6.899602e+00\n", + " 0.188372\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_full_time_worker_business1\n", - " -1.423867e+00\n", + " 0.746021\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_full_time_worker_business2\n", - " 1.949725e+01\n", + " 0.764494\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_full_time_worker_eat\n", - " -1.457980e+00\n", + " 0.315100\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business\n", - " -1.111294e+01\n", + " 1.303968\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_full_time_worker_maint\n", - " -1.711559e+00\n", + " 0.661317\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_part_time_worker_business1\n", - " 1.701675e+00\n", + " 0.057399\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_part_time_worker_business2\n", - " 1.531338e+00\n", + " 2.149968\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_part_time_worker_eat\n", - " 9.815649e-01\n", + " 0.034146\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business\n", - " 2.152744e-01\n", + " 1.416263\n", " \n", " \n", " coefficient_individual_discretionary_tours_made_by_part_time_worker_maint\n", - " 2.494477e-01\n", + " 1.021525\n", " \n", " \n", " coefficient_individual_eating_out_tours_made_by_person_business1\n", - " 4.249081e-01\n", + " 0.263548\n", " \n", " \n", " coefficient_individual_eating_out_tours_made_by_person_business2\n", - " 1.688422e+01\n", + " 1.149846\n", " \n", " \n", " coefficient_individual_eating_out_tours_made_by_person_eat\n", - " -2.110385e-01\n", + " 0.231645\n", " \n", " \n", " coefficient_individual_eating_out_tours_made_by_person_eat_business\n", - " -1.375749e+01\n", + " 1.418076\n", " \n", " \n", " coefficient_individual_eating_out_tours_made_by_person_maint\n", - " 8.477993e-01\n", + " 1.125285\n", " \n", " \n", " coefficient_log_of_the_work_tour_duration_business1\n", - " 1.079743e+01\n", + " 1.007122\n", " \n", " \n", " coefficient_log_of_the_work_tour_duration_business2\n", - " -4.104113e+01\n", + " 1.771287\n", " \n", " \n", " coefficient_log_of_the_work_tour_duration_eat\n", - " 9.912072e+00\n", + " 1.458489\n", " \n", " \n", " coefficient_log_of_the_work_tour_duration_eat_business\n", - " 1.921602e+01\n", + " 3.447598\n", " \n", " \n", " coefficient_log_of_the_work_tour_duration_maint\n", - " 1.044261e+01\n", + " 1.642504\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1\n", - " 5.245200e+00\n", + " -0.181697\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2\n", - " -5.957409e+00\n", + " -0.328201\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat\n", - " 5.734339e+00\n", + " -0.013286\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business\n", - " -1.136555e+01\n", + " -0.788124\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint\n", - " 5.726820e+00\n", + " 0.694707\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1\n", - " -1.437920e+01\n", + " -0.005852\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2\n", - " 1.824373e-01\n", + " -0.444998\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat\n", - " 6.645087e+00\n", + " -0.120943\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business\n", - " -4.922608e-01\n", + " -1.170010\n", " \n", " \n", " coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint\n", - " 6.390638e+00\n", + " 0.088504\n", " \n", " \n", " coefficient_medium_hh_income_dummy_business1\n", - " -3.286293e+00\n", + " 0.374955\n", " \n", " \n", " coefficient_medium_hh_income_dummy_business2\n", - " 2.006070e+00\n", + " 1.384595\n", " \n", " \n", " coefficient_medium_hh_income_dummy_eat\n", - " -2.530097e+00\n", + " 0.467235\n", " \n", " \n", " coefficient_medium_hh_income_dummy_eat_business\n", - " 1.050098e+01\n", + " 1.373736\n", " \n", " \n", " coefficient_medium_hh_income_dummy_maint\n", - " -3.095965e+00\n", + " -0.005820\n", " \n", " \n", " coefficient_participation_in_joint_discretionary_tours_business1\n", - " 8.878328e-01\n", + " 0.075698\n", " \n", " \n", " coefficient_participation_in_joint_discretionary_tours_business2\n", - " -4.199835e-01\n", + " -0.191769\n", " \n", " \n", " coefficient_participation_in_joint_discretionary_tours_eat\n", - " -2.448175e-01\n", + " 0.454516\n", " \n", " \n", " coefficient_participation_in_joint_discretionary_tours_eat_business\n", - " -2.723876e+00\n", + " -0.681551\n", " \n", " \n", " coefficient_participation_in_joint_discretionary_tours_maint\n", - " 2.745844e+00\n", + " 0.588105\n", " \n", " \n", " coefficient_participation_in_joint_shop_main_eat_tours_business1\n", - " 3.561155e+00\n", + " 0.151268\n", " \n", " \n", " coefficient_participation_in_joint_shop_main_eat_tours_business2\n", - " -2.800502e-01\n", + " 0.232018\n", " \n", " \n", " coefficient_participation_in_joint_shop_main_eat_tours_eat\n", - " 1.988637e+00\n", + " -0.054088\n", " \n", " \n", " coefficient_participation_in_joint_shop_main_eat_tours_eat_business\n", - " -6.545173e+00\n", + " 0.928998\n", " \n", " \n", " coefficient_participation_in_joint_shop_main_eat_tours_maint\n", - " 2.179332e+00\n", + " -0.354297\n", " \n", " \n", " coefficient_two_work_tours_by_person_business1\n", - " 3.694023e+00\n", + " 0.343052\n", " \n", " \n", " coefficient_two_work_tours_by_person_business2\n", - " -4.104683e+00\n", + " 0.672564\n", " \n", " \n", " coefficient_two_work_tours_by_person_eat\n", - " 1.927236e+00\n", + " -0.903976\n", " \n", " \n", " coefficient_two_work_tours_by_person_eat_business\n", - " -5.424974e+00\n", + " -1.029881\n", " \n", " \n", " coefficient_two_work_tours_by_person_maint\n", - " 3.205997e+00\n", + " 0.215840\n", " \n", " \n", " coefficient_walk_accessibility_to_retail_for_work_taz_business1\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coefficient_walk_accessibility_to_retail_for_work_taz_business2\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coefficient_walk_accessibility_to_retail_for_work_taz_eat\n", - " 5.329379e-02\n", + " 0.054262\n", " \n", " \n", " coefficient_walk_accessibility_to_retail_for_work_taz_eat_business\n", - " -4.690165e-01\n", + " 0.248444\n", " \n", " \n", " coefficient_walk_accessibility_to_retail_for_work_taz_maint\n", - " 3.930491e-01\n", + " 0.051297\n", " \n", " \n", " coefficient_workplace_suburban_area_dummy_business1\n", - " -1.102000e-01\n", + " -0.077046\n", " \n", " \n", " coefficient_workplace_suburban_area_dummy_business2\n", - " -2.204000e-01\n", + " -0.214908\n", " \n", " \n", " coefficient_workplace_suburban_area_dummy_eat\n", - " -2.916000e-01\n", + " -0.497064\n", " \n", " \n", " coefficient_workplace_suburban_area_dummy_eat_business\n", - " -4.018000e-01\n", + " -0.496303\n", " \n", " \n", " coefficient_workplace_suburban_area_dummy_maint\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coefficient_workplace_urban_area_dummy_business1\n", - " 1.735107e+00\n", + " -0.069849\n", " \n", " \n", " coefficient_workplace_urban_area_dummy_business2\n", - " 1.555922e+00\n", + " -0.530073\n", " \n", " \n", " coefficient_workplace_urban_area_dummy_eat\n", - " 4.533256e+00\n", + " -0.630025\n", " \n", " \n", " coefficient_workplace_urban_area_dummy_eat_business\n", - " -2.308496e+01\n", + " -0.435462\n", " \n", " \n", " coefficient_workplace_urban_area_dummy_maint\n", - " 1.338238e+01\n", + " -0.212890\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_business1\n", - " -5.268759e-01\n", + " -0.633755\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_business2\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_eat\n", - " 0.000000e+00\n", + " 0.000000\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_eat_business\n", - " -1.911629e+01\n", + " -0.847707\n", " \n", " \n", " coefficient_zero_cars_owned_by_hh_dummy_maint\n", - " 5.593497e-01\n", + " -0.020836\n", " \n", " \n", - "loglike-311.0816324383027d_loglike\n", + "
logloss0.8125481383373976d_logloss\n", " \n", " \n", " \n", @@ -1933,103 +1986,103 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2069,227 +2122,227 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2301,31 +2354,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2333,27 +2386,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2365,52 +2418,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coefficient_at_work_sub_tour_asc_business1-1.481164e-04-2.181632e-05
coefficient_at_work_sub_tour_asc_business2-2.230083e-05-7.974611e-06
coefficient_at_work_sub_tour_asc_eat1.390294e-046.885959e-05
coefficient_at_work_sub_tour_asc_eat_business1.539374e-04-5.704460e-06
coefficient_at_work_sub_tour_asc_maint-1.225496e-04-3.336420e-05
coefficient_auto_accessibility_to_retail_for_work_taz_business1-1.603725e-031.678059e-05
coefficient_auto_accessibility_to_retail_for_work_taz_business2-2.219798e-04-7.241747e-05
coefficient_auto_accessibility_to_retail_for_work_taz_eat1.752293e-03-6.082835e-05
coefficient_auto_accessibility_to_retail_for_work_taz_eat_business1.320801e-038.064174e-05
coefficient_auto_accessibility_to_retail_for_work_taz_maint-1.247389e-033.582349e-05
coefficient_dummy_for_drive_alone_mode_for_work_tour_business1-9.593191e-05-4.107213e-05
coefficient_dummy_for_drive_alone_mode_for_work_tour_business2-1.354692e-055.986918e-05
coefficient_dummy_for_drive_alone_mode_for_work_tour_eat-2.279001e-047.033209e-05
coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business4.011519e-04-9.447444e-06
coefficient_dummy_for_drive_alone_mode_for_work_tour_maint-6.377298e-05-7.968169e-05
coefficient_dummy_for_full_time_worker_business1-1.605243e-042.817589e-05
coefficient_dummy_for_full_time_worker_business2-2.230083e-053.226183e-05
coefficient_dummy_for_full_time_worker_eat7.303158e-051.003712e-04
coefficient_dummy_for_full_time_worker_eat_business1.539374e-041.549190e-05
coefficient_dummy_for_full_time_worker_maint-4.414386e-05-1.763008e-04
coefficient_dummy_for_non_full_time_worker_business11.240794e-05-4.999221e-05
coefficient_dummy_for_non_full_time_worker_business2-7.261864e-17-4.023644e-05
coefficient_dummy_for_non_full_time_worker_eat6.599780e-05-3.151158e-05
coefficient_dummy_for_non_full_time_worker_eat_business-1.086362e-18-2.119636e-05
coefficient_dummy_for_non_full_time_worker_maint-7.840574e-051.429366e-04
coefficient_dummy_for_non_workers_business1
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint-9.311590e-05-1.714819e-04
coefficient_high_hh_income_dummy_business1-1.000616e-04-1.191730e-05
coefficient_high_hh_income_dummy_business2-8.753913e-06-7.059313e-05
coefficient_high_hh_income_dummy_eat-2.221195e-06-5.126677e-05
coefficient_high_hh_income_dummy_eat_business2.213370e-048.255506e-05
coefficient_high_hh_income_dummy_maint-1.103003e-045.122214e-05
coefficient_individual_discretionary_tours_made_by_full_time_worker_business1-7.053640e-05-1.226983e-05
coefficient_individual_discretionary_tours_made_by_full_time_worker_business2-2.791062e-057.360525e-05
coefficient_individual_discretionary_tours_made_by_full_time_worker_eat1.818022e-04-3.188124e-05
coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business-2.707632e-066.305075e-07
coefficient_individual_discretionary_tours_made_by_full_time_worker_maint-8.064758e-05-3.008468e-05
coefficient_individual_discretionary_tours_made_by_part_time_worker_business1-9.603037e-06-2.903966e-05
coefficient_individual_discretionary_tours_made_by_part_time_worker_business2-1.629515e-19-1.376988e-05
coefficient_individual_discretionary_tours_made_by_part_time_worker_eat-3.348668e-072.058728e-05
coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business-1.124861e-22-3.015319e-05
coefficient_individual_discretionary_tours_made_by_part_time_worker_maint9.937904e-065.237545e-05
coefficient_individual_eating_out_tours_made_by_person_business1-7.862028e-05-1.667468e-04
coefficient_individual_eating_out_tours_made_by_person_business2-8.753608e-06-3.264570e-05
coefficient_individual_eating_out_tours_made_by_person_eat3.564089e-052.212368e-05
coefficient_individual_eating_out_tours_made_by_person_eat_business-1.309491e-079.515601e-06
coefficient_individual_eating_out_tours_made_by_person_maint5.186395e-051.677532e-04
coefficient_log_of_the_work_tour_duration_business1-3.971369e-041.043468e-04
coefficient_log_of_the_work_tour_duration_business2-2.991023e-058.913429e-06
coefficient_log_of_the_work_tour_duration_eat3.149096e-041.295254e-05
coefficient_log_of_the_work_tour_duration_eat_business3.910540e-04-1.066273e-04
coefficient_log_of_the_work_tour_duration_maint-2.789165e-04-1.958540e-05
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business18.932382e-064.643980e-05
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2-1.299699e-05-2.188999e-05
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat-8.667554e-051.650779e-04
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business-1.119625e-084.743178e-05
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint9.075134e-05-2.370595e-04
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1-4.914227e-102.357735e-05
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2-2.173636e-16-1.188907e-05
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat-2.658044e-067.570065e-05
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business-7.964540e-24-3.983432e-05
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint2.658536e-06-4.755461e-05
coefficient_medium_hh_income_dummy_business1-5.984683e-053.482667e-05
coefficient_medium_hh_income_dummy_business2-8.619829e-275.769942e-05
coefficient_medium_hh_income_dummy_eat1.446985e-04-5.137537e-06
coefficient_medium_hh_income_dummy_eat_business-6.509501e-05-5.711674e-05
coefficient_medium_hh_income_dummy_maint-1.975668e-05-3.027181e-05
coefficient_participation_in_joint_discretionary_tours_business11.633148e-05-5.693164e-05
coefficient_participation_in_joint_discretionary_tours_business2-3.971146e-267.289406e-05
coefficient_participation_in_joint_discretionary_tours_eat8.138286e-051.061065e-04
coefficient_participation_in_joint_discretionary_tours_eat_business-5.416119e-05-9.568225e-05
coefficient_participation_in_joint_discretionary_tours_maint-4.355316e-05-2.638670e-05
coefficient_participation_in_joint_shop_main_eat_tours_business1-1.005486e-044.632880e-05
coefficient_participation_in_joint_shop_main_eat_tours_business2-1.490847e-157.216085e-07
coefficient_participation_in_joint_shop_main_eat_tours_eat1.024581e-04-6.996621e-05
coefficient_participation_in_joint_shop_main_eat_tours_eat_business-8.904994e-071.708229e-06
coefficient_participation_in_joint_shop_main_eat_tours_maint-1.018973e-062.120756e-05
coefficient_two_work_tours_by_person_business17.076435e-059.533131e-05
coefficient_two_work_tours_by_person_business2-3.049929e-10-7.711784e-06
coefficient_two_work_tours_by_person_eat1.336457e-05-5.696558e-06
coefficient_two_work_tours_by_person_eat_business-1.430779e-06-5.510631e-05
coefficient_two_work_tours_by_person_maint-8.269783e-05-2.681666e-05
coefficient_walk_accessibility_to_retail_for_work_taz_business1
coefficient_walk_accessibility_to_retail_for_work_taz_eat2.395850e-038.877549e-05
coefficient_walk_accessibility_to_retail_for_work_taz_eat_business-5.362286e-045.191476e-05
coefficient_walk_accessibility_to_retail_for_work_taz_maint-6.315696e-04-3.267917e-05
coefficient_workplace_suburban_area_dummy_business10.000000e+00-2.911572e-05
coefficient_workplace_suburban_area_dummy_business20.000000e+00-3.969147e-06
coefficient_workplace_suburban_area_dummy_eat0.000000e+002.978185e-05
coefficient_workplace_suburban_area_dummy_eat_business0.000000e+00-8.309287e-06
coefficient_workplace_suburban_area_dummy_maint
coefficient_workplace_urban_area_dummy_business1-1.481164e-047.299400e-06
coefficient_workplace_urban_area_dummy_business2-2.230083e-05-4.005464e-06
coefficient_workplace_urban_area_dummy_eat1.390294e-043.907774e-05
coefficient_workplace_urban_area_dummy_eat_business1.539374e-042.604827e-06
coefficient_workplace_urban_area_dummy_maint-1.225496e-04-4.497650e-05
coefficient_zero_cars_owned_by_hh_dummy_business1-2.241883e-04-1.324894e-05
coefficient_zero_cars_owned_by_hh_dummy_business2
coefficient_zero_cars_owned_by_hh_dummy_eat_business-4.538836e-09-8.057484e-05
coefficient_zero_cars_owned_by_hh_dummy_maint-3.467043e-05-2.405112e-05
nit75nfev115njev75status0message'Optimization terminated successfully'successTrueelapsed_time0:00:01.501390method'SLSQP'n_cases460iteration_number75logloss0.6762644183441363" + "nit100nfev104njev100status0message'Optimization terminated successfully'successTrueelapsed_time0:00:00.874759method'SLSQP'n_cases5947iteration_number100loglike-4832.223778692503" ], "text/plain": [ - "┣ x: coefficient_at_work_sub_tour_asc_business1 1.421407\n", - "┃ coefficient_at_work_sub_tour_asc_business2 -0.130778\n", - "┃ coefficient_at_work_sub_tour_asc_eat 5.809056\n", - "┃ coefficient_at_work_sub_tour_asc_eat_business -23.415363\n", - "┃ coefficient_at_work_sub_tour_asc_maint 12.910479\n", - "┃ ... \n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_business1 -0.526876\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_business2 0.000000\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_eat 0.000000\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_eat_business -19.116288\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_maint 0.559350\n", + "┣ x: coefficient_at_work_sub_tour_asc_business1 -0.350395\n", + "┃ coefficient_at_work_sub_tour_asc_business2 -2.211281\n", + "┃ coefficient_at_work_sub_tour_asc_eat 0.440311\n", + "┃ coefficient_at_work_sub_tour_asc_eat_business -0.860366\n", + "┃ coefficient_at_work_sub_tour_asc_maint -0.423469\n", + "┃ ... \n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_business1 -0.633755\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_business2 0.000000\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_eat 0.000000\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_eat_business -0.847707\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_maint -0.020836\n", "┃ Length: 110, dtype: float64\n", - "┣ loglike: -311.0816324383027\n", - "┣ d_loglike: coefficient_at_work_sub_tour_asc_business1 -1.481164e-04\n", - "┃ coefficient_at_work_sub_tour_asc_business2 -2.230083e-05\n", - "┃ coefficient_at_work_sub_tour_asc_eat 1.390294e-04\n", - "┃ coefficient_at_work_sub_tour_asc_eat_business 1.539374e-04\n", - "┃ coefficient_at_work_sub_tour_asc_maint -1.225496e-04\n", - "┃ ... \n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_business1 -2.241883e-04\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_business2 0.000000e+00\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_eat 0.000000e+00\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_eat_business -4.538836e-09\n", - "┃ coefficient_zero_cars_owned_by_hh_dummy_maint -3.467043e-05\n", + "┣ logloss: 0.8125481383373976\n", + "┣ d_logloss: coefficient_at_work_sub_tour_asc_business1 -0.000022\n", + "┃ coefficient_at_work_sub_tour_asc_business2 -0.000008\n", + "┃ coefficient_at_work_sub_tour_asc_eat 0.000069\n", + "┃ coefficient_at_work_sub_tour_asc_eat_business -0.000006\n", + "┃ coefficient_at_work_sub_tour_asc_maint -0.000033\n", + "┃ ... \n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_business1 -0.000013\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_business2 0.000000\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_eat 0.000000\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_eat_business -0.000081\n", + "┃ coefficient_zero_cars_owned_by_hh_dummy_maint -0.000024\n", "┃ Length: 110, dtype: float64\n", - "┣ nit: 75\n", - "┣ nfev: 115\n", - "┣ njev: 75\n", + "┣ nit: 100\n", + "┣ nfev: 104\n", + "┣ njev: 100\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=1, microseconds=501390)\n", + "┣ elapsed_time: datetime.timedelta(microseconds=874759)\n", "┣ method: 'SLSQP'\n", - "┣ n_cases: 460\n", - "┣ iteration_number: 75\n", - "┣ logloss: 0.6762644183441363" + "┣ n_cases: 5947\n", + "┣ iteration_number: 100\n", + "┣ loglike: -4832.223778692503" ] }, "execution_count": 7, @@ -2419,7 +2472,7 @@ } ], "source": [ - "model.estimate(method='SLSQP')" + "model.estimate(method='SLSQP', maxiter=900)" ] }, { @@ -2437,1120 +2490,1036 @@ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coefficient_at_work_sub_tour_asc_business1 1.42 NA NA[***] 29.59 0.00
coefficient_at_work_sub_tour_asc_business2-0.131 1.51e+04-0.00 NA 0.00
coefficient_at_work_sub_tour_asc_eat 5.81 484. 0.01 NA 0.00
coefficient_at_work_sub_tour_asc_eat_business-23.4 NA NA[***] BIG 0.00
coefficient_at_work_sub_tour_asc_maint 12.9 523. 0.02 NA 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_business1-0.235 NA NA[***] 181.30 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_business2 2.95 2.55e+03 0.00 NA 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_eat-0.761 NA NA[***] 714.04 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_eat_business 1.52 NA NA[***] 41.92 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_maint-3.20 NA NA[***] BIG 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_business1 3.94 NA NA[***] 77.39 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_business2-7.29 NA NA[] 0.02 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_eat 3.22 NA NA[***] 201.21 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business 2.33 NA NA[] 1.40 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_maint 3.87 NA NA[***] 79.47 0.00
coefficient_dummy_for_full_time_worker_business1-11.4 NA NA[***] BIG 0.00
coefficient_dummy_for_full_time_worker_business2-13.8 NA NA[**] 5.21 0.00
coefficient_dummy_for_full_time_worker_eat-9.55 NA NA[***] 827.25 0.00
coefficient_dummy_for_full_time_worker_eat_business-11.2 NA NA[***] BIG 0.00
coefficient_dummy_for_full_time_worker_maint-5.88 260.-0.02 NA 0.00
coefficient_dummy_for_non_full_time_worker_business1-2.37 1.09e+03-0.00 NA 0.00
coefficient_dummy_for_non_full_time_worker_business2-12.7 NA NA[] 0.00 0.00
coefficient_dummy_for_non_full_time_worker_eat-1.39 1.96e+03-0.00 NA 0.00
coefficient_dummy_for_non_full_time_worker_eat_business-40.8 NA NA[] 1.24 0.00
coefficient_dummy_for_non_full_time_worker_maint 3.10 2.40e+03 0.00 NA 0.00
coefficient_dummy_for_non_workers_business1-5.00 NA NA NA 0.00fixed value
coefficient_dummy_for_non_workers_business2-5.00 NA NA NA 0.00fixed value
coefficient_dummy_for_non_workers_eat 0.00 NA NA NA 0.00fixed value
coefficient_dummy_for_non_workers_eat_business-5.00 NA NA NA 0.00fixed value
coefficient_dummy_for_non_workers_maint-5.00 NA NA NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1 0.00 NA NA NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2 0.00 NA NA NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat 0.00 NA NA NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business 0.00 NA NA NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint-0.447 0.821-0.54 NA 0.00
coefficient_high_hh_income_dummy_business1-5.70 NA NA[***] 900.87 0.00
coefficient_high_hh_income_dummy_business2 18.2 NA NA[***] 5.91 0.00
coefficient_high_hh_income_dummy_eat-5.36 NA NA[***] 296.55 0.00
coefficient_high_hh_income_dummy_eat_business 5.95 430. 0.01 NA 0.00
coefficient_high_hh_income_dummy_maint-6.90 NA NA[***] BIG 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_business1-1.42 540.-0.00 NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_business2 19.5 4.34e+03 0.00 NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_eat-1.46 540.-0.00 NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business-11.1 806.-0.01 NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_maint-1.71 540.-0.00 NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_business1 1.70 NA NA[] 0.74 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_business2 1.53 0.000562 BIG*** NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_eat 0.982 NA NA[] 0.40 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business 0.215 0.000123 BIG*** NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_maint 0.249 NA NA[] 0.02 0.00
coefficient_individual_eating_out_tours_made_by_person_business1 0.425 NA NA[] 0.26 0.00
coefficient_individual_eating_out_tours_made_by_person_business2 16.9 NA NA[**] 4.62 0.00
coefficient_individual_eating_out_tours_made_by_person_eat-0.211 NA NA[] 0.12 0.00
coefficient_individual_eating_out_tours_made_by_person_eat_business-13.8 2.41e+03-0.01 NA 0.00
coefficient_individual_eating_out_tours_made_by_person_maint 0.848 NA NA[] 1.11 0.00
coefficient_log_of_the_work_tour_duration_business1 10.8 NA NA[***] BIG 0.00
coefficient_log_of_the_work_tour_duration_business2-41.0 4.06e+03-0.01 NA 0.00
coefficient_log_of_the_work_tour_duration_eat 9.91 NA NA[***] BIG 0.00
coefficient_log_of_the_work_tour_duration_eat_business 19.2 NA NA[***] 151.19 0.00
coefficient_log_of_the_work_tour_duration_maint 10.4 NA NA[***] BIG 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1 5.25 NA NA[***] 17.02 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2-5.96 1.41e+04-0.00 NA 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat 5.73 NA NA[***] 170.02 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business-11.4 7.01e+03-0.00 NA 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint 5.73 NA NA[***] 19.07 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1-14.4 NA NA[] 0.00 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2 0.182 NA NA[] 0.00 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat 6.65 5.15e+03 0.00 NA 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business-0.492 NA NA[] 0.00 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint 6.39 5.15e+03 0.00 NA 0.00
coefficient_medium_hh_income_dummy_business1-3.29 1.13e+03-0.00 NA 0.00
coefficient_medium_hh_income_dummy_business2 2.01 NA NA[] 0.00 0.00
coefficient_medium_hh_income_dummy_eat-2.53 1.13e+03-0.00 NA 0.00
coefficient_medium_hh_income_dummy_eat_business 10.5 1.27e+03 0.01 NA 0.00
coefficient_medium_hh_income_dummy_maint-3.10 1.13e+03-0.00 NA 0.00
coefficient_participation_in_joint_discretionary_tours_business1 0.888 NA NA[] 0.23 0.00
coefficient_participation_in_joint_discretionary_tours_business2-0.420 NA NA[] 0.00 0.00
coefficient_participation_in_joint_discretionary_tours_eat-0.245 NA NA[] 0.02 0.00
coefficient_participation_in_joint_discretionary_tours_eat_business-2.72 NA NA[] 0.00 0.00
coefficient_participation_in_joint_discretionary_tours_maint 2.75 NA NA[*] 2.92 0.00
coefficient_participation_in_joint_shop_main_eat_tours_business1 3.56 1.13e+03 0.00 NA 0.00
coefficient_participation_in_joint_shop_main_eat_tours_business2-0.280 NA NA[] 0.00 0.00
coefficient_participation_in_joint_shop_main_eat_tours_eat 1.99 1.13e+03 0.00 NA 0.00
coefficient_participation_in_joint_shop_main_eat_tours_eat_business-6.55 1.38e+03-0.00 NA 0.00
coefficient_participation_in_joint_shop_main_eat_tours_maint 2.18 1.13e+03 0.00 NA 0.00
coefficient_two_work_tours_by_person_business1 3.69 NA NA[**] 5.16 0.00
coefficient_two_work_tours_by_person_business2-4.10 2.72e+03-0.00 NA 0.00
coefficient_two_work_tours_by_person_eat 1.93 NA NA[**] 4.09 0.00
coefficient_two_work_tours_by_person_eat_business-5.42 NA NA[] 0.00 0.00
coefficient_two_work_tours_by_person_maint 3.21 NA NA[**] 4.07 0.00
coefficient_walk_accessibility_to_retail_for_work_taz_business1 0.00 NA NA NA 0.00fixed value
coefficient_walk_accessibility_to_retail_for_work_taz_business2 0.00 NA NA NA 0.00fixed value
coefficient_walk_accessibility_to_retail_for_work_taz_eat 0.0533 0.234 0.23 NA 0.00
coefficient_walk_accessibility_to_retail_for_work_taz_eat_business-0.469 0.669-0.70 NA 0.00
coefficient_walk_accessibility_to_retail_for_work_taz_maint 0.393 0.308 1.28 NA 0.00
coefficient_workplace_suburban_area_dummy_business1-0.110 0.00 NA[] 0.00 0.00
coefficient_workplace_suburban_area_dummy_business2-0.220 0.00 NA[] 0.00 0.00
coefficient_workplace_suburban_area_dummy_eat-0.292 0.00 NA[] 0.00 0.00
coefficient_workplace_suburban_area_dummy_eat_business-0.402 0.00 NA[] 0.00 0.00
coefficient_workplace_suburban_area_dummy_maint 0.00 NA NA NA 0.00fixed value
coefficient_workplace_urban_area_dummy_business1 1.74 NA NA[***] 41.10 0.00
coefficient_workplace_urban_area_dummy_business2 1.56 NA NA[]-0.00 0.00
coefficient_workplace_urban_area_dummy_eat 4.53 NA NA[***] 890.88 0.00
coefficient_workplace_urban_area_dummy_eat_business-23.1 NA NA[***] BIG 0.00
coefficient_workplace_urban_area_dummy_maint 13.4 579. 0.02 NA 0.00
coefficient_zero_cars_owned_by_hh_dummy_business1-0.527 0.582-0.90 NA 0.00
coefficient_zero_cars_owned_by_hh_dummy_business2 0.00 NA NA NA 0.00fixed value
coefficient_zero_cars_owned_by_hh_dummy_eat 0.00 NA NA NA 0.00fixed value
coefficient_zero_cars_owned_by_hh_dummy_eat_business-19.1 1.36e+04-0.00 NA 0.00
coefficient_zero_cars_owned_by_hh_dummy_maint 0.559 0.426 1.31 NA 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coefficient_at_work_sub_tour_asc_business1-0.350 1.10e+03-0.00 0.00
coefficient_at_work_sub_tour_asc_business2-2.21 2.21e+03-0.00 0.00
coefficient_at_work_sub_tour_asc_eat 0.440 NA NA 0.00
coefficient_at_work_sub_tour_asc_eat_business-0.860 495.-0.00 0.00
coefficient_at_work_sub_tour_asc_maint-0.423 NA NA 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_business1 0.0782 NA NA 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_business2 0.296 NA NA 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_eat 0.137 NA NA 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_eat_business-0.263 NA NA 0.00
coefficient_auto_accessibility_to_retail_for_work_taz_maint 0.0218 NA NA 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_business1 0.950 NA NA 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_business2 1.75 NA NA 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_eat 0.488 NA NA 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business 1.78 NA NA 0.00
coefficient_dummy_for_drive_alone_mode_for_work_tour_maint 1.11 NA NA 0.00
coefficient_dummy_for_full_time_worker_business1-7.40 552.-0.01 0.00
coefficient_dummy_for_full_time_worker_business2-13.8 1.67e+03-0.01 0.00
coefficient_dummy_for_full_time_worker_eat-7.55 1.25e+03-0.01 0.00
coefficient_dummy_for_full_time_worker_eat_business-15.0 2.70e+03-0.01 0.00
coefficient_dummy_for_full_time_worker_maint-8.06 1.60e+03-0.01 0.00
coefficient_dummy_for_non_full_time_worker_business1-8.10 687.-0.01 0.00
coefficient_dummy_for_non_full_time_worker_business2-14.8 1.52e+03-0.01 0.00
coefficient_dummy_for_non_full_time_worker_eat-8.75 1.58e+03-0.01 0.00
coefficient_dummy_for_non_full_time_worker_eat_business-14.5 2.34e+03-0.01 0.00
coefficient_dummy_for_non_full_time_worker_maint-8.05 2.29e+03-0.00 0.00
coefficient_dummy_for_non_workers_business1-5.00 0.00 NA 0.00fixed value
coefficient_dummy_for_non_workers_business2-5.00 0.00 NA 0.00fixed value
coefficient_dummy_for_non_workers_eat 0.00 0.00 NA 0.00fixed value
coefficient_dummy_for_non_workers_eat_business-5.00 0.00 NA 0.00fixed value
coefficient_dummy_for_non_workers_maint-5.00 0.00 NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1 0.00 0.00 NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2 0.00 0.00 NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat 0.00 0.00 NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business 0.00 0.00 NA 0.00fixed value
coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint-0.858 0.324-2.64** 0.00
coefficient_high_hh_income_dummy_business1 1.05 733. 0.00 0.00
coefficient_high_hh_income_dummy_business2 1.78 733. 0.00 0.00
coefficient_high_hh_income_dummy_eat 0.987 733. 0.00 0.00
coefficient_high_hh_income_dummy_eat_business 2.17 733. 0.00 0.00
coefficient_high_hh_income_dummy_maint 0.188 733. 0.00 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_business1 0.746 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_business2 0.764 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_eat 0.315 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business 1.30 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_full_time_worker_maint 0.661 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_business1 0.0574 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_business2 2.15 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_eat 0.0341 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business 1.42 NA NA 0.00
coefficient_individual_discretionary_tours_made_by_part_time_worker_maint 1.02 NA NA 0.00
coefficient_individual_eating_out_tours_made_by_person_business1 0.264 NA NA 0.00
coefficient_individual_eating_out_tours_made_by_person_business2 1.15 NA NA 0.00
coefficient_individual_eating_out_tours_made_by_person_eat 0.232 NA NA 0.00
coefficient_individual_eating_out_tours_made_by_person_eat_business 1.42 NA NA 0.00
coefficient_individual_eating_out_tours_made_by_person_maint 1.13 NA NA 0.00
coefficient_log_of_the_work_tour_duration_business1 1.01 288. 0.00 0.00
coefficient_log_of_the_work_tour_duration_business2 1.77 288. 0.01 0.00
coefficient_log_of_the_work_tour_duration_eat 1.46 288. 0.01 0.00
coefficient_log_of_the_work_tour_duration_eat_business 3.45 288. 0.01 0.00
coefficient_log_of_the_work_tour_duration_maint 1.64 288. 0.01 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1-0.182 NA NA 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2-0.328 NA NA 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat-0.0133 NA NA 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business-0.788 NA NA 0.00
coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint 0.695 NA NA 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1-0.00585 1.19e+03-0.00 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2-0.445 1.19e+03-0.00 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat-0.121 1.19e+03-0.00 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business-1.17 1.19e+03-0.00 0.00
coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint 0.0885 1.19e+03 0.00 0.00
coefficient_medium_hh_income_dummy_business1 0.375 772. 0.00 0.00
coefficient_medium_hh_income_dummy_business2 1.38 772. 0.00 0.00
coefficient_medium_hh_income_dummy_eat 0.467 772. 0.00 0.00
coefficient_medium_hh_income_dummy_eat_business 1.37 772. 0.00 0.00
coefficient_medium_hh_income_dummy_maint-0.00582 772.-0.00 0.00
coefficient_participation_in_joint_discretionary_tours_business1 0.0757 3.02e+03 0.00 0.00
coefficient_participation_in_joint_discretionary_tours_business2-0.192 3.02e+03-0.00 0.00
coefficient_participation_in_joint_discretionary_tours_eat 0.455 3.02e+03 0.00 0.00
coefficient_participation_in_joint_discretionary_tours_eat_business-0.682 3.02e+03-0.00 0.00
coefficient_participation_in_joint_discretionary_tours_maint 0.588 3.02e+03 0.00 0.00
coefficient_participation_in_joint_shop_main_eat_tours_business1 0.151 1.04e+03 0.00 0.00
coefficient_participation_in_joint_shop_main_eat_tours_business2 0.232 1.04e+03 0.00 0.00
coefficient_participation_in_joint_shop_main_eat_tours_eat-0.0541 1.04e+03-0.00 0.00
coefficient_participation_in_joint_shop_main_eat_tours_eat_business 0.929 1.04e+03 0.00 0.00
coefficient_participation_in_joint_shop_main_eat_tours_maint-0.354 1.04e+03-0.00 0.00
coefficient_two_work_tours_by_person_business1 0.343 NA NA 0.00
coefficient_two_work_tours_by_person_business2 0.673 NA NA 0.00
coefficient_two_work_tours_by_person_eat-0.904 NA NA 0.00
coefficient_two_work_tours_by_person_eat_business-1.03 NA NA 0.00
coefficient_two_work_tours_by_person_maint 0.216 NA NA 0.00
coefficient_walk_accessibility_to_retail_for_work_taz_business1 0.00 0.00 NA 0.00fixed value
coefficient_walk_accessibility_to_retail_for_work_taz_business2 0.00 0.00 NA 0.00fixed value
coefficient_walk_accessibility_to_retail_for_work_taz_eat 0.0543 0.0307 1.77 0.00
coefficient_walk_accessibility_to_retail_for_work_taz_eat_business 0.248 0.125 1.98* 0.00
coefficient_walk_accessibility_to_retail_for_work_taz_maint 0.0513 0.0415 1.24 0.00
coefficient_workplace_suburban_area_dummy_business1-0.0770 695.-0.00 0.00
coefficient_workplace_suburban_area_dummy_business2-0.215 1.23e+03-0.00 0.00
coefficient_workplace_suburban_area_dummy_eat-0.497 235.-0.00 0.00
coefficient_workplace_suburban_area_dummy_eat_business-0.496 5.05e+03-0.00 0.00
coefficient_workplace_suburban_area_dummy_maint 0.00 0.00 NA 0.00fixed value
coefficient_workplace_urban_area_dummy_business1-0.0698 NA NA 0.00
coefficient_workplace_urban_area_dummy_business2-0.530 1.18e+03-0.00 0.00
coefficient_workplace_urban_area_dummy_eat-0.630 NA NA 0.00
coefficient_workplace_urban_area_dummy_eat_business-0.435 5.37e+03-0.00 0.00
coefficient_workplace_urban_area_dummy_maint-0.213 305.-0.00 0.00
coefficient_zero_cars_owned_by_hh_dummy_business1-0.634 0.312-2.03* 0.00
coefficient_zero_cars_owned_by_hh_dummy_business2 0.00 0.00 NA 0.00fixed value
coefficient_zero_cars_owned_by_hh_dummy_eat 0.00 0.00 NA 0.00fixed value
coefficient_zero_cars_owned_by_hh_dummy_eat_business-0.848 1.51-0.56 0.00
coefficient_zero_cars_owned_by_hh_dummy_maint-0.0208 0.253-0.08 0.00
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -3597,18 +3566,7 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -3660,31 +3618,31 @@ " \n", " 0\n", " coefficient_dummy_for_full_time_worker_business1\n", - " -11.365293\n", + " -7.402415\n", " F\n", " \n", " \n", " 1\n", " coefficient_dummy_for_full_time_worker_business2\n", - " -13.833915\n", + " -13.824988\n", " F\n", " \n", " \n", " 2\n", " coefficient_dummy_for_full_time_worker_eat\n", - " -9.545916\n", + " -7.551797\n", " F\n", " \n", " \n", " 3\n", " coefficient_dummy_for_full_time_worker_eat_bus...\n", - " -11.192172\n", + " -14.974823\n", " F\n", " \n", " \n", " 4\n", " coefficient_dummy_for_full_time_worker_maint\n", - " -5.880704\n", + " -8.063977\n", " F\n", " \n", " \n", @@ -3696,25 +3654,25 @@ " \n", " 127\n", " coefficient_at_work_sub_tour_asc_business2\n", - " -0.130778\n", + " -2.211281\n", " F\n", " \n", " \n", " 128\n", " coefficient_at_work_sub_tour_asc_eat\n", - " 5.809056\n", + " 0.440311\n", " F\n", " \n", " \n", " 129\n", " coefficient_at_work_sub_tour_asc_eat_business\n", - " -23.415363\n", + " -0.860366\n", " F\n", " \n", " \n", " 130\n", " coefficient_at_work_sub_tour_asc_maint\n", - " 12.910479\n", + " -0.423469\n", " F\n", " \n", " \n", @@ -3730,16 +3688,16 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coefficient_dummy_for_full_time_worker_business1 -11.365293 F\n", - "1 coefficient_dummy_for_full_time_worker_business2 -13.833915 F\n", - "2 coefficient_dummy_for_full_time_worker_eat -9.545916 F\n", - "3 coefficient_dummy_for_full_time_worker_eat_bus... -11.192172 F\n", - "4 coefficient_dummy_for_full_time_worker_maint -5.880704 F\n", + "0 coefficient_dummy_for_full_time_worker_business1 -7.402415 F\n", + "1 coefficient_dummy_for_full_time_worker_business2 -13.824988 F\n", + "2 coefficient_dummy_for_full_time_worker_eat -7.551797 F\n", + "3 coefficient_dummy_for_full_time_worker_eat_bus... -14.974823 F\n", + "4 coefficient_dummy_for_full_time_worker_maint -8.063977 F\n", ".. ... ... ...\n", - "127 coefficient_at_work_sub_tour_asc_business2 -0.130778 F\n", - "128 coefficient_at_work_sub_tour_asc_eat 5.809056 F\n", - "129 coefficient_at_work_sub_tour_asc_eat_business -23.415363 F\n", - "130 coefficient_at_work_sub_tour_asc_maint 12.910479 F\n", + "127 coefficient_at_work_sub_tour_asc_business2 -2.211281 F\n", + "128 coefficient_at_work_sub_tour_asc_eat 0.440311 F\n", + "129 coefficient_at_work_sub_tour_asc_eat_business -0.860366 F\n", + "130 coefficient_at_work_sub_tour_asc_maint -0.423469 F\n", "131 coefficient_at_work_sub_tour_asc_no_subtours 0.000000 T\n", "\n", "[132 rows x 3 columns]" @@ -3762,7 +3720,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -3776,7 +3734,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/19_atwork_subtour_dest.ipynb b/activitysim/examples/example_estimation/notebooks/19_atwork_subtour_dest.ipynb index a340efca73..5219560f6c 100644 --- a/activitysim/examples/example_estimation/notebooks/19_atwork_subtour_dest.ipynb +++ b/activitysim/examples/example_estimation/notebooks/19_atwork_subtour_dest.ipynb @@ -26,30 +26,74 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import larch # !conda install larch #for estimation\n", + "import larch as lx\n", "import pandas as pd\n", - "import numpy as np\n", - "import yaml \n", - "import larch.util.excel\n", - "import os" + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -72,10 +116,27 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_destination/atwork_subtour_destination_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_destination/atwork_subtour_destination_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_destination/atwork_subtour_destination_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_destination/atwork_subtour_destination_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_destination/atwork_subtour_destination_landuse.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_destination/atwork_subtour_destination_size_terms.csv\n" + ] + } + ], "source": [ "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -236,148 +297,88 @@ " \n", " \n", " tour_id\n", - " variable\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " ...\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", - " 190\n", + " alt_dest\n", + " util_distance_piecewise_linear_from_0_to_1_miles\n", + " util_distance_piecewise_linear_from_1_to_2_miles\n", + " util_distance_piecewise_linear_from_2_to_5_miles\n", + " util_distance_piecewise_linear_from_5_to_15_miles\n", + " util_distance_piecewise_linear_for_15_plus_miles\n", + " util_size_variable_atwork\n", + " util_no_attractions_atwork_size_variable_is_0\n", + " util_mode_choice_logsum\n", + " util_sample_of_alternatives_correction_factor\n", " \n", " \n", " \n", " \n", " 0\n", - " 2998927\n", - " util_distance_piecewise_linear_for_15_plus_miles\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 2966559\n", + " 4\n", + " 0.99\n", + " 0.00\n", + " 0.00\n", " 0.0\n", " 0.0\n", + " 7.026095\n", + " False\n", + " 14.641064\n", + " 3.669233\n", " \n", " \n", " 1\n", - " 2998927\n", - " util_distance_piecewise_linear_from_0_to_1_miles\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " ...\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", + " 2966559\n", + " 9\n", + " 1.00\n", + " 0.98\n", + " 0.00\n", + " 0.0\n", + " 0.0\n", + " 7.841204\n", + " False\n", + " 13.896585\n", + " 4.331944\n", " \n", " \n", " 2\n", - " 2998927\n", - " util_distance_piecewise_linear_from_1_to_2_miles\n", - " 1.0\n", - " 0.9299999475479126\n", - " 0.7599999904632568\n", - " 0.7699999809265137\n", - " 0.4700000286102295\n", - " 0.2799999713897705\n", - " 0.12000000476837158\n", - " 0.25999999046325684\n", - " ...\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", - " 1.0\n", + " 2966559\n", + " 11\n", + " 1.00\n", + " 0.35\n", + " 0.00\n", + " 0.0\n", + " 0.0\n", + " 7.778631\n", + " False\n", + " 14.171065\n", + " 3.202032\n", " \n", " \n", " 3\n", - " 2998927\n", - " util_distance_piecewise_linear_from_2_to_5_miles\n", - " 0.029999971389770508\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 2966559\n", + " 12\n", + " 0.89\n", + " 0.00\n", + " 0.00\n", " 0.0\n", " 0.0\n", - " ...\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 2.9600000381469727\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", - " 3.0\n", + " 7.385584\n", + " False\n", + " 14.520744\n", + " 4.616777\n", " \n", " \n", " 4\n", - " 2998927\n", - " util_distance_piecewise_linear_from_5_to_15_miles\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 2966559\n", + " 14\n", + " 0.87\n", + " 0.00\n", + " 0.00\n", " 0.0\n", - " ...\n", - " 0.8899998664855957\n", - " 1.8000001907348633\n", - " 1.3299999237060547\n", - " 0.48999977111816406\n", " 0.0\n", - " 1.0799999237060547\n", - " 0.9899997711181641\n", - " 1.070000171661377\n", - " 1.929999828338623\n", - " 2.130000114440918\n", + " 6.755056\n", + " False\n", + " 14.588424\n", + " 4.538306\n", " \n", " \n", " ...\n", @@ -392,235 +393,188 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", " \n", " \n", - " 4171\n", - " 308000674\n", - " util_distance_piecewise_linear_from_5_to_15_miles\n", - " 1.4200000762939453\n", - " 1.3600001335144043\n", - " 1.2899999618530273\n", - " 1.1700000762939453\n", - " 0.9699997901916504\n", - " 0.9699997901916504\n", - 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"

4176 rows × 192 columns

\n", + "

125551 rows × 11 columns

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464 rows × 6 columns

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6036 rows × 6 columns

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" income_segment \n", - "0 1 \n", - "1 1 \n", - "2 1 \n", - "3 1 \n", - "4 1 \n", - ".. ... \n", - "459 4 \n", - "460 4 \n", - "461 4 \n", - "462 4 \n", - "463 1 \n", + " income_segment \n", + "0 1 \n", + "1 1 \n", + "2 1 \n", + "3 1 \n", + "4 1 \n", + "... ... \n", + "6031 1 \n", + "6032 1 \n", + "6033 1 \n", + "6034 1 \n", + "6035 1 \n", "\n", - "[464 rows x 6 columns]" + "[6036 rows x 6 columns]" ] }, "execution_count": 7, @@ -912,9 +866,9 @@ " 7\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 3\n", " 5.89564\n", " 2.875000\n", @@ -936,9 +890,9 @@ " 19\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.84871\n", " 5.195214\n", @@ -960,9 +914,9 @@ " 38\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.53231\n", " 80.470405\n", @@ -984,9 +938,9 @@ " 20\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 2\n", " 5.64330\n", " 7.947368\n", @@ -1008,9 +962,9 @@ " 86\n", " ...\n", " 0\n", - " 0.00000\n", + " 0.0\n", " 72.14684\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.52555\n", " 38.187500\n", @@ -1043,188 +997,188 @@ " ...\n", " \n", " \n", - " 186\n", - " 4\n", - " 4\n", - " 1\n", - " 2779\n", - " 8062\n", - " 376.0\n", - " 172.0\n", - " 15.00000\n", - " 1760\n", - " 1178\n", + " 1450\n", + " 34\n", + " 34\n", + " 9\n", + " 2724\n", + " 6493\n", + " 1320.0\n", + " 630.0\n", + " 69.00000\n", + " 1046\n", + " 1013\n", " ...\n", - " 3\n", - " 0.00000\n", - " 0.00000\n", + " 4\n", + " 0.0\n", " 0.00000\n", + " 0.0\n", " 1\n", - " 2.04173\n", - " 14.860963\n", - " 9.411765\n", - " 5.762347\n", + " 1.12116\n", + " 3.896996\n", + " 1.496423\n", + " 1.081235\n", " False\n", " \n", " \n", - " 187\n", - " 4\n", - " 4\n", - " 1\n", - " 1492\n", - " 4139\n", - " 214.0\n", - " 116.0\n", - " 10.00000\n", - " 808\n", - " 603\n", + " 1451\n", + " 34\n", + " 34\n", + " 9\n", + " 2016\n", + " 4835\n", + " 664.0\n", + " 379.0\n", + " 43.00000\n", + " 757\n", + " 757\n", " ...\n", - " 3\n", - 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190 rows × 28 columns

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LabelDescriptionExpressionatwork
0util_distance_piecewise_linear_from_0_to_1_milesDistance, piecewise linear from 0 to 1 miles@skims['DIST'].clip(0,1)coef_distance_piecewise_linear_from_0_to_1_miles
1util_distance_piecewise_linear_from_1_to_2_milesDistance, piecewise linear from 1 to 2 miles@(skims['DIST']-1).clip(0,1)coef_distance_piecewise_linear_from_1_to_2_miles
2util_distance_piecewise_linear_from_2_to_5_milesDistance, piecewise linear from 2 to 5 miles@(skims['DIST']-2).clip(0,3)coef_distance_piecewise_linear_from_2_to_5_miles
3util_distance_piecewise_linear_from_5_to_15_milesDistance, piecewise linear from 5 to 15 miles@(skims['DIST']-5).clip(0,10)coef_distance_piecewise_linear_from_5_to_15_miles
4util_distance_piecewise_linear_for_15_plus_milesDistance, piecewise linear for 15+ miles@(skims['DIST']-15.0).clip(0)coef_distance_piecewise_linear_for_15_plus_miles
5util_no_attractions_atwork_size_variable_is_0No attractions, atwork size_term variable is 0size_term==0coef_no_attractions_atwork_size_variable_is_0
6util_mode_choice_logsumMode choice logsummode_choice_logsumcoef_mode_choice_logsum
7util_sample_of_alternatives_correction_factorSample of alternatives correction factor@np.minimum(np.log(df.pick_count/df.prob), 60)coef_sample_of_alternatives_correction_factor
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Iteration 006 [Optimization terminated successfully]

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x\n", " \n", " \n", " \n", @@ -1738,7 +1865,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1746,27 +1873,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1777,38 +1904,37 @@ " \n", " \n", " \n", - "
atwork_HEREMPN-1.707324-1.371795
atwork_RETEMPN
coef_distance_piecewise_linear_for_15_plus_miles-0.204500-0.326384
coef_distance_piecewise_linear_from_0_to_1_miles-1.364154-0.840401
coef_distance_piecewise_linear_from_1_to_2_miles-0.739532-0.905173
coef_distance_piecewise_linear_from_2_to_5_miles-0.504526-0.577554
coef_distance_piecewise_linear_from_5_to_15_miles-0.073403-0.193532
coef_mode_choice_logsum0.5032710.403737
coef_no_attractions_atwork_size_variable_is_01.000000
tolerance8.076677745948512e-06
stepsarray([1., 1., 1., 1., 1., 1., 1., 1.])
message'Optimization terminated successfully.'
elapsed_time0:00:00.121814
method'BHHH'
n_cases464
iteration_number8
logloss5.021271650749392
" + "
tolerance8.315604351559683e-06stepsarray([1., 1., 1., 1., 1., 1.])message'Optimization terminated successfully'elapsed_time0:00:00.285360method'bhhh'n_cases6036iteration_number6
" ], "text/plain": [ - "┣ loglike: -2329.8700459477177\n", - "┣ x: atwork_HEREMPN -1.707324\n", + "┣ loglike: -12720.40641937967\n", + "┣ x: atwork_HEREMPN -1.371795\n", "┃ atwork_RETEMPN -0.298406\n", - "┃ coef_distance_piecewise_linear_for_15_plus_miles -0.204500\n", - "┃ coef_distance_piecewise_linear_from_0_to_1_miles -1.364154\n", - "┃ coef_distance_piecewise_linear_from_1_to_2_miles -0.739532\n", - "┃ coef_distance_piecewise_linear_from_2_to_5_miles -0.504526\n", - "┃ coef_distance_piecewise_linear_from_5_to_15_miles -0.073403\n", - "┃ coef_mode_choice_logsum 0.503271\n", + "┃ coef_distance_piecewise_linear_for_15_plus_miles -0.326384\n", + "┃ coef_distance_piecewise_linear_from_0_to_1_miles -0.840401\n", + "┃ coef_distance_piecewise_linear_from_1_to_2_miles -0.905173\n", + "┃ coef_distance_piecewise_linear_from_2_to_5_miles -0.577554\n", + "┃ coef_distance_piecewise_linear_from_5_to_15_miles -0.193532\n", + "┃ coef_mode_choice_logsum 0.403737\n", "┃ coef_no_attractions_atwork_size_variable_is_0 -999.000000\n", "┃ coef_sample_of_alternatives_correction_factor 1.000000\n", "┃ dtype: float64\n", - "┣ tolerance: 8.076677745948512e-06\n", - "┣ steps: array([1., 1., 1., 1., 1., 1., 1., 1.])\n", - "┣ message: 'Optimization terminated successfully.'\n", - "┣ elapsed_time: datetime.timedelta(microseconds=121814)\n", - "┣ method: 'BHHH'\n", - "┣ n_cases: 464\n", - "┣ iteration_number: 8\n", - "┣ logloss: 5.021271650749392" + "┣ tolerance: 8.315604351559683e-06\n", + "┣ steps: array([1., 1., 1., 1., 1., 1.])\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ elapsed_time: datetime.timedelta(microseconds=285360)\n", + "┣ method: 'bhhh'\n", + "┣ n_cases: 6036\n", + "┣ iteration_number: 6" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.estimate(method='BHHH', options={'maxiter':1000})" + "model.estimate(method='bhhh', options={'maxiter':1000})" ] }, { @@ -1820,119 +1946,145 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Value Std Err t Stat Signif Null Value Constrained
atwork_HEREMPN-1.71 0.203-8.39*** 0.00
atwork_RETEMPN-0.298 NA NA 0.00fixed value
coef_distance_piecewise_linear_for_15_plus_miles-0.205 1.79e-14-BIG*** 0.00
coef_distance_piecewise_linear_from_0_to_1_miles-1.36 0.402-3.39*** 0.00
coef_distance_piecewise_linear_from_1_to_2_miles-0.740 0.222-3.33*** 0.00
coef_distance_piecewise_linear_from_2_to_5_miles-0.505 0.0702-7.19*** 0.00
coef_distance_piecewise_linear_from_5_to_15_miles-0.0734 0.0897-0.82 0.00
coef_mode_choice_logsum 0.503 0.0702 7.17*** 0.00
coef_no_attractions_atwork_size_variable_is_0-999. NA NA 0.00fixed value
coef_sample_of_alternatives_correction_factor 1.00 NA NA 0.00fixed value
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
atwork_HEREMPN-1.37 0.0710-19.32*** 0.00
atwork_RETEMPN-0.298 0.00 NA 0.00fixed value
coef_distance_piecewise_linear_for_15_plus_miles-0.326 0.0607-5.38*** 0.00
coef_distance_piecewise_linear_from_0_to_1_miles-0.840 0.0954-8.81*** 0.00
coef_distance_piecewise_linear_from_1_to_2_miles-0.905 0.0554-16.34*** 0.00
coef_distance_piecewise_linear_from_2_to_5_miles-0.578 0.0278-20.77*** 0.00
coef_distance_piecewise_linear_from_5_to_15_miles-0.194 0.0173-11.20*** 0.00
coef_mode_choice_logsum 0.404 0.0244 16.54*** 0.00
coef_no_attractions_atwork_size_variable_is_0-999. 0.00 NA 0.00fixed value
coef_sample_of_alternatives_correction_factor 1.00 0.00 NA 0.00fixed value
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1953,7 +2105,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -1970,7 +2122,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1989,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2242,9 +2394,9 @@ " atwork\n", " atwork\n", " 0.000000\n", - " 0.803595\n", + " 0.745241\n", " 0.000000\n", - " 0.196405\n", + " 0.254759\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -2400,7 +2552,7 @@ "10 othmaint non_mandatory 0.000000 0.482000 0.000000 0.518000 \n", "11 social non_mandatory 0.000000 0.522000 0.000000 0.478000 \n", "12 othdiscr non_mandatory 0.252252 0.212212 0.000000 0.272272 \n", - "13 atwork atwork 0.000000 0.803595 0.000000 0.196405 \n", + "13 atwork atwork 0.000000 0.745241 0.000000 0.254759 \n", "14 work trip 0.000000 0.166667 0.166667 0.166667 \n", "15 escort trip 0.001000 0.225000 0.000000 0.144000 \n", "16 shopping trip 0.001000 0.999000 0.000000 0.000000 \n", @@ -2435,7 +2587,7 @@ "21 0.000000 0.000000 0.000000 0.000 0.000000 0.591409 0.407592 " ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2456,7 +2608,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -2480,7 +2632,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2513,31 +2665,31 @@ " \n", " 0\n", " coef_distance_piecewise_linear_from_0_to_1_miles\n", - " -1.364154\n", + " -0.840401\n", " F\n", " \n", " \n", " 1\n", " coef_distance_piecewise_linear_from_1_to_2_miles\n", - " -0.739532\n", + " -0.905173\n", " F\n", " \n", " \n", " 2\n", " coef_distance_piecewise_linear_from_2_to_5_miles\n", - " -0.504526\n", + " -0.577554\n", " F\n", " \n", " \n", " 3\n", " coef_distance_piecewise_linear_from_5_to_15_miles\n", - " -0.073403\n", + " -0.193532\n", " F\n", " \n", " \n", " 4\n", " coef_distance_piecewise_linear_for_15_plus_miles\n", - " -0.204500\n", + " -0.326384\n", " F\n", " \n", " \n", @@ -2555,7 +2707,7 @@ " \n", " 7\n", " coef_mode_choice_logsum\n", - " 0.503271\n", + " 0.403737\n", " F\n", " \n", " \n", @@ -2570,18 +2722,18 @@ ], "text/plain": [ " coefficient_name value constrain\n", - "0 coef_distance_piecewise_linear_from_0_to_1_miles -1.364154 F\n", - "1 coef_distance_piecewise_linear_from_1_to_2_miles -0.739532 F\n", - "2 coef_distance_piecewise_linear_from_2_to_5_miles -0.504526 F\n", - "3 coef_distance_piecewise_linear_from_5_to_15_miles -0.073403 F\n", - "4 coef_distance_piecewise_linear_for_15_plus_miles -0.204500 F\n", + "0 coef_distance_piecewise_linear_from_0_to_1_miles -0.840401 F\n", + "1 coef_distance_piecewise_linear_from_1_to_2_miles -0.905173 F\n", + "2 coef_distance_piecewise_linear_from_2_to_5_miles -0.577554 F\n", + "3 coef_distance_piecewise_linear_from_5_to_15_miles -0.193532 F\n", + "4 coef_distance_piecewise_linear_for_15_plus_miles -0.326384 F\n", "5 coef_size_variable_atwork 1.000000 T\n", "6 coef_no_attractions_atwork_size_variable_is_0 -999.000000 T\n", - "7 coef_mode_choice_logsum 0.503271 F\n", + "7 coef_mode_choice_logsum 0.403737 F\n", "8 coef_sample_of_alternatives_correction_factor 1.000000 T" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2592,7 +2744,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2860,9 +3012,9 @@ " atwork\n", " atwork\n", " 0.000000\n", - " 0.803595\n", + " 0.745241\n", " 0.000000\n", - " 0.196405\n", + " 0.254759\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3026,7 +3178,7 @@ "10 10 othmaint non_mandatory 0.000000 0.482000 0.000000 \n", "11 11 social non_mandatory 0.000000 0.522000 0.000000 \n", "12 12 othdiscr non_mandatory 0.252252 0.212212 0.000000 \n", - "13 13 atwork atwork 0.000000 0.803595 0.000000 \n", + "13 13 atwork atwork 0.000000 0.745241 0.000000 \n", "14 14 work trip 0.000000 0.166667 0.166667 \n", "15 15 escort trip 0.001000 0.225000 0.000000 \n", "16 16 shopping trip 0.001000 0.999000 0.000000 \n", @@ -3050,7 +3202,7 @@ "10 0.518000 0.000000 0.000000 0.000000 0.000 0.000000 0.000000 \n", "11 0.478000 0.000000 0.000000 0.000000 0.000 0.000000 0.000000 \n", "12 0.272272 0.165165 0.000000 0.000000 0.000 0.098098 0.000000 \n", - "13 0.196405 0.000000 0.000000 0.000000 0.000 0.000000 0.000000 \n", + "13 0.254759 0.000000 0.000000 0.000000 0.000 0.000000 0.000000 \n", "14 0.166667 0.166667 0.166667 0.166667 0.000 0.000000 0.000000 \n", "15 0.144000 0.000000 0.000000 0.000000 0.464 0.166000 0.000000 \n", "16 0.000000 0.000000 0.000000 0.000000 0.000 0.000000 0.000000 \n", @@ -3085,7 +3237,7 @@ "21 0.407592 " ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -3102,7 +3254,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -3116,7 +3268,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/20_atwork_subtour_scheduling.ipynb b/activitysim/examples/example_estimation/notebooks/20_atwork_subtour_scheduling.ipynb index ae20d36942..1de74014a3 100644 --- a/activitysim/examples/example_estimation/notebooks/20_atwork_subtour_scheduling.ipynb +++ b/activitysim/examples/example_estimation/notebooks/20_atwork_subtour_scheduling.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -70,11 +117,13 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/activitysim/activitysim/estimation/larch/__init__.py:18: DtypeWarning: Columns (15,16,17,18,19,20,33,34,35,36,37,38,50,51,52,53,54,55,66,67,68,69,70,71,81,82,83,84,85,86,95,96,97,98,99,100,108,109,110,111,112,113,120,121,122,123,124,125,131,132,133,134,135,136,141,142,143,144,145,146,150,151,152,153,154,155,158,159,160,161,162,163,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " return m(*args, **kwargs)\n" + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_scheduling/tour_scheduling_atwork_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_scheduling/atwork_subtour_scheduling_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_scheduling/atwork_subtour_scheduling_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/atwork_subtour_scheduling/atwork_subtour_scheduling_choosers_combined.parquet\n" ] } ], @@ -82,7 +131,12 @@ "modelname = \"atwork_subtour_scheduling\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -1095,14 +1149,14 @@ " tour_count\n", " tour_category\n", " ...\n", - " COLLFTE\n", - " COLLPTE\n", - " TOPOLOGY\n", - " TERMINAL\n", - " household_density\n", - " employment_density\n", - " density_index\n", - " is_cbd\n", + " auOpRetail\n", + " auOpTotal\n", + " trPkRetail\n", + " trPkTotal\n", + " trOpRetail\n", + " trOpTotal\n", + " nmRetail\n", + " nmTotal\n", " start_previous\n", " end_previous\n", " \n", @@ -1110,34 +1164,34 @@ " \n", " \n", " 0\n", - " 2998927\n", - " 114\n", - " 114\n", - " 73144\n", - " maint\n", + " 2966559\n", + " 86\n", + " 113\n", + " 72355\n", + " eat\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.48345\n", - " 26.073171\n", - " 8.048780\n", - " 6.150212\n", - " False\n", + " 9.915345\n", + " 12.430580\n", + " 6.550726\n", + " 9.119016\n", + " 6.446184\n", + " 9.035333\n", + " 5.256966\n", + " 6.831275\n", " 5\n", " 5\n", " \n", " \n", " 1\n", - " 3060326\n", - " 85\n", - " 85\n", - " 74642\n", + " 3046632\n", + " 145\n", + " 90\n", + " 74308\n", " eat\n", " 1\n", " 1\n", @@ -1145,23 +1199,23 @@ " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.09035\n", - " 20.666667\n", - " 4.107527\n", - " 3.426505\n", - " False\n", + " 10.259606\n", + " 12.727344\n", + " 5.452325\n", + " 7.771767\n", + " 5.298242\n", + " 7.602426\n", + " 6.443514\n", + " 7.965548\n", " 5\n", " 5\n", " \n", " \n", " 2\n", - " 4422879\n", - " 124\n", + " 3048108\n", + " 85\n", " 124\n", - " 107875\n", + " 74344\n", " eat\n", " 1\n", " 1\n", @@ -1169,47 +1223,47 @@ " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 5.35435\n", - " 139.333333\n", - " 418.518519\n", - " 104.532377\n", - " False\n", + " 10.259606\n", + " 12.727344\n", + " 5.452325\n", + " 7.771767\n", + " 5.298242\n", + " 7.602426\n", + " 6.443514\n", + " 7.965548\n", " 5\n", " 5\n", " \n", " \n", " 3\n", - " 4440282\n", - " 154\n", - " 154\n", - " 108299\n", - " maint\n", + " 3177463\n", + " 99\n", + " 55\n", + " 77499\n", + " eat\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " ...\n", - " 2035.58118\n", - " 20.60887\n", - " 2\n", - " 5.22542\n", - " 97.634722\n", - " 550.205552\n", - " 82.920387\n", - " False\n", + " 7.714071\n", + " 10.313852\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 5\n", " 5\n", " \n", " \n", " 4\n", - " 4496780\n", - " 89\n", - " 89\n", - " 109677\n", + " 3191832\n", + " 99\n", + " 145\n", + " 77849\n", " maint\n", " 1\n", " 1\n", @@ -1217,14 +1271,14 @@ " 1\n", " atwork\n", " ...\n", - " 690.54974\n", - " 0.00000\n", - " 3\n", - " 4.73802\n", - " 117.769796\n", - " 246.205869\n", - " 79.663609\n", - " False\n", + " 9.763398\n", + " 12.307147\n", + " 1.228620\n", + " 3.278544\n", + " 1.239060\n", + " 3.280429\n", + " 4.284159\n", + " 6.249753\n", " 5\n", " 5\n", " \n", @@ -1253,184 +1307,184 @@ " ...\n", " \n", " \n", - " 459\n", - " 302923726\n", - " 145\n", - " 145\n", - " 7388383\n", - " maint\n", + " 6031\n", + " 308156004\n", + " 99\n", + " 124\n", + " 7516000\n", + " eat\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.37546\n", - " 19.153846\n", - " 5.907692\n", - " 4.515087\n", - " False\n", + " 9.566058\n", + " 12.139587\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 3.086628\n", + " 5.647842\n", " 5\n", " 5\n", " \n", " \n", - " 460\n", - " 302942567\n", - " 85\n", - " 85\n", - " 7388843\n", - " eat\n", + " 6032\n", + " 308227650\n", + " 155\n", + " 88\n", + " 7517747\n", + " maint\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.81406\n", - " 16.068376\n", - " 21.136752\n", - " 9.128669\n", - " False\n", + " 10.260330\n", + " 12.748307\n", + " 4.719610\n", + " 7.890189\n", + " 4.493279\n", + " 7.643024\n", + " 5.529241\n", + " 8.876000\n", " 5\n", " 5\n", " \n", " \n", - " 461\n", - " 302942627\n", - " 135\n", - " 135\n", - " 7388844\n", - " maint\n", + " 6033\n", + " 308260103\n", + " 102\n", + " 113\n", + " 7518539\n", + " eat\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 2.81406\n", - " 16.068376\n", - " 21.136752\n", - " 9.128669\n", - " False\n", + " 10.294354\n", + " 12.880095\n", + " 4.408064\n", + " 7.614481\n", + " 3.838266\n", + " 7.011926\n", + " 5.169891\n", + " 8.595128\n", " 5\n", " 5\n", " \n", " \n", - " 462\n", - " 305120465\n", - " 112\n", - " 112\n", - " 7441962\n", - " maint\n", + " 6034\n", + " 309080718\n", + " 55\n", + " 113\n", + " 7538554\n", + " eat\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 1\n", - " 8.54946\n", - " 55.606634\n", - " 142.984438\n", - " 40.036459\n", - " False\n", + " 10.345456\n", + " 12.953264\n", + " 7.103743\n", + " 9.892019\n", + " 6.979741\n", + " 9.773601\n", + " 6.557982\n", + " 8.518901\n", " 5\n", " 5\n", " \n", " \n", - " 463\n", - " 308000674\n", - " 124\n", - " 124\n", - " 7512211\n", - " maint\n", + " 6035\n", + " 309112001\n", + " 125\n", + " 113\n", + " 7539317\n", + " eat\n", " 1\n", " 1\n", " 1\n", " 1\n", " atwork\n", " ...\n", - " 0.00000\n", - " 0.00000\n", - " 2\n", - " 4.64648\n", - " 196.395950\n", - " 178.779465\n", - " 93.587057\n", - " False\n", + " 6.494711\n", + " 8.713819\n", + " 0.019735\n", + " 0.222696\n", + " 0.016077\n", + " 0.153189\n", + " 0.000000\n", + " 0.000000\n", " 5\n", " 5\n", " \n", " \n", "\n", - "

464 rows × 162 columns

\n", + "

6036 rows × 162 columns

\n", "" ], "text/plain": [ - " tour_id model_choice override_choice person_id tour_type \\\n", - "0 2998927 114 114 73144 maint \n", - "1 3060326 85 85 74642 eat \n", - "2 4422879 124 124 107875 eat \n", - "3 4440282 154 154 108299 maint \n", - "4 4496780 89 89 109677 maint \n", - ".. ... ... ... ... ... \n", - "459 302923726 145 145 7388383 maint \n", - "460 302942567 85 85 7388843 eat \n", - "461 302942627 135 135 7388844 maint \n", - "462 305120465 112 112 7441962 maint \n", - "463 308000674 124 124 7512211 maint \n", + " tour_id model_choice override_choice person_id tour_type \\\n", + "0 2966559 86 113 72355 eat \n", + "1 3046632 145 90 74308 eat \n", + "2 3048108 85 124 74344 eat \n", + "3 3177463 99 55 77499 eat \n", + "4 3191832 99 145 77849 maint \n", + "... ... ... ... ... ... \n", + "6031 308156004 99 124 7516000 eat \n", + "6032 308227650 155 88 7517747 maint \n", + "6033 308260103 102 113 7518539 eat \n", + "6034 309080718 55 113 7538554 eat \n", + "6035 309112001 125 113 7539317 eat \n", "\n", - " tour_type_count tour_type_num tour_num tour_count tour_category ... \\\n", - "0 1 1 1 1 atwork ... \n", - "1 1 1 1 1 atwork ... \n", - "2 1 1 1 1 atwork ... \n", - "3 1 1 1 1 atwork ... \n", - "4 1 1 1 1 atwork ... \n", - ".. ... ... ... ... ... ... \n", - "459 1 1 1 1 atwork ... \n", - "460 1 1 1 1 atwork ... \n", - "461 1 1 1 1 atwork ... \n", - "462 1 1 1 1 atwork ... \n", - "463 1 1 1 1 atwork ... \n", + " tour_type_count tour_type_num tour_num tour_count tour_category ... \\\n", + "0 1 1 1 1 atwork ... \n", + "1 1 1 1 1 atwork ... \n", + "2 1 1 1 1 atwork ... \n", + "3 1 1 1 1 atwork ... \n", + "4 1 1 1 1 atwork ... \n", + "... ... ... ... ... ... ... \n", + "6031 1 1 1 1 atwork ... \n", + "6032 1 1 1 1 atwork ... \n", + "6033 1 1 1 1 atwork ... \n", + "6034 1 1 1 1 atwork ... \n", + "6035 1 1 1 1 atwork ... \n", "\n", - " COLLFTE COLLPTE TOPOLOGY TERMINAL household_density \\\n", - "0 0.00000 0.00000 1 2.48345 26.073171 \n", - "1 0.00000 0.00000 1 2.09035 20.666667 \n", - "2 0.00000 0.00000 1 5.35435 139.333333 \n", - "3 2035.58118 20.60887 2 5.22542 97.634722 \n", - "4 690.54974 0.00000 3 4.73802 117.769796 \n", - ".. ... ... ... ... ... \n", - "459 0.00000 0.00000 1 2.37546 19.153846 \n", - "460 0.00000 0.00000 1 2.81406 16.068376 \n", - "461 0.00000 0.00000 1 2.81406 16.068376 \n", - "462 0.00000 0.00000 1 8.54946 55.606634 \n", - "463 0.00000 0.00000 2 4.64648 196.395950 \n", + " auOpRetail auOpTotal trPkRetail trPkTotal trOpRetail trOpTotal \\\n", + "0 9.915345 12.430580 6.550726 9.119016 6.446184 9.035333 \n", + "1 10.259606 12.727344 5.452325 7.771767 5.298242 7.602426 \n", + "2 10.259606 12.727344 5.452325 7.771767 5.298242 7.602426 \n", + "3 7.714071 10.313852 0.000000 0.000000 0.000000 0.000000 \n", + "4 9.763398 12.307147 1.228620 3.278544 1.239060 3.280429 \n", + "... ... ... ... ... ... ... \n", + "6031 9.566058 12.139587 0.000000 0.000000 0.000000 0.000000 \n", + "6032 10.260330 12.748307 4.719610 7.890189 4.493279 7.643024 \n", + "6033 10.294354 12.880095 4.408064 7.614481 3.838266 7.011926 \n", + "6034 10.345456 12.953264 7.103743 9.892019 6.979741 9.773601 \n", + "6035 6.494711 8.713819 0.019735 0.222696 0.016077 0.153189 \n", "\n", - " employment_density density_index is_cbd start_previous end_previous \n", - "0 8.048780 6.150212 False 5 5 \n", - "1 4.107527 3.426505 False 5 5 \n", - "2 418.518519 104.532377 False 5 5 \n", - "3 550.205552 82.920387 False 5 5 \n", - "4 246.205869 79.663609 False 5 5 \n", - ".. ... ... ... ... ... \n", - "459 5.907692 4.515087 False 5 5 \n", - "460 21.136752 9.128669 False 5 5 \n", - "461 21.136752 9.128669 False 5 5 \n", - "462 142.984438 40.036459 False 5 5 \n", - "463 178.779465 93.587057 False 5 5 \n", + " nmRetail nmTotal start_previous end_previous \n", + "0 5.256966 6.831275 5 5 \n", + "1 6.443514 7.965548 5 5 \n", + "2 6.443514 7.965548 5 5 \n", + "3 0.000000 0.000000 5 5 \n", + "4 4.284159 6.249753 5 5 \n", + "... ... ... ... ... \n", + "6031 3.086628 5.647842 5 5 \n", + "6032 5.529241 8.876000 5 5 \n", + "6033 5.169891 8.595128 5 5 \n", + "6034 6.557982 8.518901 5 5 \n", + "6035 0.000000 0.000000 5 5 \n", "\n", - "[464 rows x 162 columns]" + "[6036 rows x 162 columns]" ] }, "execution_count": 6, @@ -1476,148 +1530,148 @@ " \n", " \n", " tour_id\n", - " variable\n", - " 0\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", + " start\n", + " end\n", + " duration\n", + " tdd\n", + " mode_choice_logsum\n", + " util_early_start_at_5\n", + " util_am_peak_start_at_6\n", + " util_am_peak_start_at_7\n", + " util_am_peak_start_at_8\n", " ...\n", - " 180\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", + " util_duration_shift_for_number_of_mandatory_tours\n", + " util_start_shift_for_number_of_joint_tours\n", + " util_duration_shift_for_number_of_joint_tours\n", + " util_start_shift_for_number_of_individual_nonmandatory_tours\n", + " util_duration_shift_for_number_of_individual_nonmandatory_tours\n", + " util_dummy_for_business_related_purpose_and_duration_from_0_to_1\n", + " util_dummy_for_eating_out_purpose_and_duration_of_1_hour\n", + " util_dummy_for_eating_out_purpose_and_departure_at_11\n", + " util_dummy_for_eating_out_purpose_and_departure_at_12\n", + " util_dummy_for_eating_out_purpose_and_departure_at_13\n", " \n", " \n", " \n", " \n", " 0\n", - " 2998927\n", - " duration\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 2966559\n", + " 10\n", + " 10\n", " 0\n", + " 85\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", " ...\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 1\n", - " 2998927\n", - " end\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 2966559\n", + " 10\n", + " 11\n", + " 1\n", + " 86\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", " ...\n", + " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 2\n", - " 2998927\n", - " mode_choice_logsum\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 2966559\n", + " 10\n", + " 12\n", + " 2\n", + " 87\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", " ...\n", + " 2\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 3\n", - " 2998927\n", - " start\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 2966559\n", + " 10\n", + " 13\n", + " 3\n", + " 88\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", " ...\n", + " 3\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " 4\n", - " 2998927\n", - " util_am_peak_end\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 2966559\n", + " 10\n", + " 14\n", + " 4\n", + " 89\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", " ...\n", + " 4\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", " ...\n", @@ -1644,197 +1698,301 @@ " ...\n", " \n", " \n", - " 24587\n", - " 308000674\n", - " util_start_shift_for_number_of_individual_nonm...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 457514\n", + " 309112001\n", + " 21\n", + " 22\n", + " 1\n", + " 185\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", " ...\n", + " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 24588\n", - " 308000674\n", - " util_start_shift_for_number_of_joint_tours\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 457515\n", + " 309112001\n", + " 21\n", + " 23\n", + " 2\n", + " 186\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " ...\n", + " 2\n", " 0\n", " 0\n", " 0\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 24589\n", - " 308000674\n", - " util_start_shift_for_number_of_mandatory_tours\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " ...\n", - " 0\n", - " 0\n", - " 0\n", + " 457516\n", + " 309112001\n", + " 22\n", + " 22\n", " 0\n", + " 187\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " ...\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 24590\n", - " 308000674\n", - " util_start_shift_for_outbound_auto_travel_time...\n", - " 60.10000228881836\n", - " 60.10000228881836\n", - " 60.10000228881836\n", - " 60.10000228881836\n", - " 60.10000228881836\n", - " 60.10000228881836\n", - " 60.10000228881836\n", - " 60.10000228881836\n", - " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 457517\n", + " 309112001\n", + " 22\n", + " 23\n", + " 1\n", + " 188\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " ...\n", + " 1\n", " 0\n", " 0\n", " 0\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", - " 24591\n", - " 308000674\n", - " util_start_shift_for_subsequent_sub_tour_of_sa...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " 457518\n", + " 309112001\n", + " 23\n", + " 23\n", " 0\n", + " 189\n", " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", " ...\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", + " False\n", + " False\n", + " False\n", + " False\n", + " False\n", " \n", " \n", "\n", - "

24592 rows × 192 columns

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457519 rows × 55 columns

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"[24592 rows x 192 columns]" + " util_dummy_for_eating_out_purpose_and_departure_at_11 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "457514 False \n", + "457515 False \n", + "457516 False \n", + "457517 False \n", + "457518 False \n", + "\n", + " util_dummy_for_eating_out_purpose_and_departure_at_12 \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "457514 False \n", + "457515 False \n", + "457516 False \n", + "457517 False \n", + "457518 False \n", + "\n", + " util_dummy_for_eating_out_purpose_and_departure_at_13 \n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "457514 False \n", + "457515 False \n", + "457516 False \n", + "457517 False \n", + "457518 False \n", + "\n", + "[457519 rows x 55 columns]" ] }, "execution_count": 7, @@ -1864,9 +2022,44 @@ "name": "stderr", "output_type": "stream", "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" + "problem: nan_utility has (190 issues)\n", + "problem: chosen_but_not_available has (12 issues)\n" ] }, + { + "data": { + "text/plain": [ + "(,\n", + " ┣ chosen_but_not_available: altid n example rows\n", + " ┃ 0 88 1 5445\n", + " ┃ 1 100 2 1344, 5240\n", + " ┃ 2 102 1 4696\n", + " ┃ 3 103 1 3099\n", + " ┃ 4 107 1 2364\n", + " ┃ 5 113 2 3454, 4647\n", + " ┃ 6 114 4 1537, 2506, 2800\n", + " ┃ 7 115 1 1791\n", + " ┃ 8 116 1 4089\n", + " ┃ 9 120 1 2595\n", + " ┃ 10 125 3 797, 1766, 2716\n", + " ┃ 11 138 1 2003)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.doctor(repair_nan_utility=True)\n", + "model.doctor(repair_ch_av=\"-\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -1882,7 +2075,7 @@ { "data": { "text/html": [ - "

Best LL = -1401.9305368056416

" + "

Best LL = -17373.947698365682

" ], "text/plain": [ "" @@ -1913,778 +2106,732 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " coef_am_peak_end\n", - " -1.373169\n", + " -2.543876\n", + " -2.543876\n", " -2.928312\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.373169\n", " \n", " \n", " coef_am_peak_start_at_6\n", - " -20.592814\n", + " -5.790935\n", + " -5.790935\n", " -6.156718\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -20.592814\n", " \n", " \n", " coef_am_peak_start_at_7\n", - " -2.826984\n", + " -4.238642\n", + " -4.238642\n", " -4.061708\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.826984\n", " \n", " \n", " coef_am_peak_start_at_8\n", - " -1.196528\n", + " -2.371798\n", + " -2.371798\n", " -2.330535\n", - 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" -2.204068\n", " \n", " \n", " coef_evening_end_at_19_20_21\n", - " -4.521037\n", + " -2.460690\n", + " -2.460690\n", " -2.319982\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -4.521037\n", " \n", " \n", " coef_evening_start_at_19_20_21\n", - " -0.325741\n", + " -0.786571\n", + " -0.786571\n", " -1.015090\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.325741\n", " \n", " \n", " coef_late_end_at_22_23\n", - " -11.516565\n", + " -2.391664\n", + " -2.391664\n", " -2.319982\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -11.516565\n", " \n", " \n", " coef_late_start_at_22_23\n", - " -5.142925\n", + " -0.890587\n", + " -0.890587\n", " -0.737570\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -5.142925\n", " \n", " \n", " coef_midday_end_at_10_11_12\n", - " -1.305196\n", + " -2.145575\n", + " -2.145575\n", " -2.297264\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.305196\n", " \n", " \n", " coef_midday_end_at_13_14\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_midday_start_at_10_11_12\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.0\n", + " 0.0\n", " 0.0\n", - " -25.0\n", - " 25.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", " coef_midday_start_at_13_14_15\n", - " -1.051562\n", + " -0.665045\n", + " -0.665045\n", " -0.775022\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.051562\n", " \n", " \n", " coef_pm_peak_end_at_15\n", - " -1.432981\n", + " -0.586475\n", + " -0.586475\n", " -0.578344\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -1.432981\n", " \n", " \n", " coef_pm_peak_end_at_16\n", - " -2.055109\n", + " -1.337297\n", + " -1.337297\n", " -1.094087\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.055109\n", " \n", " \n", " coef_pm_peak_end_at_17\n", - " -2.404252\n", + " -1.432487\n", + " -1.432487\n", " -1.165847\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -2.404252\n", " \n", " \n", " coef_pm_peak_end_at_18\n", - " -3.500372\n", + " -1.702240\n", + " -1.702240\n", " -1.496131\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -3.500372\n", " \n", " \n", " coef_pm_peak_start_at_16_17_18\n", - " -0.798588\n", + " -0.000431\n", + " -0.000431\n", " -0.227528\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.798588\n", " \n", " \n", " coef_start_shift_for_business_related_\n", - " -0.083357\n", + " -0.071618\n", + " -0.071618\n", " -0.111300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.083357\n", " \n", " \n", " coef_start_shift_for_first_sub_tour_of_same_work_tour\n", - " -0.168324\n", + " -0.458549\n", + " -0.458549\n", " -0.543300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.168324\n", " \n", " \n", " coef_start_shift_for_inbound_auto_travel_time_off_peak\n", - " -0.099019\n", + " -0.011025\n", + " -0.011025\n", " 0.000650\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.099019\n", " \n", " \n", " coef_start_shift_for_number_of_individual_nonmandatory_tours\n", - " 0.026777\n", + " -0.019137\n", + " -0.019137\n", " -0.012800\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.026777\n", " \n", " \n", " coef_start_shift_for_number_of_joint_tours\n", - " -0.019722\n", + " 0.025502\n", + " 0.025502\n", " -0.020600\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.019722\n", " \n", " \n", " coef_start_shift_for_number_of_mandatory_tours\n", - " 0.059018\n", + " -0.067479\n", + " -0.067479\n", " -0.019300\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.059018\n", " \n", " \n", " coef_start_shift_for_outbound_auto_travel_time_off_peak\n", - " 0.100349\n", + " 0.010616\n", + " 0.010616\n", " 0.000650\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " 0.100349\n", " \n", " \n", " coef_start_shift_for_subsequent_sub_tour_of_same_work_tour\n", - " -0.360206\n", + " -0.117673\n", + " -0.117673\n", " -0.184400\n", - " 0.0\n", " -25.0\n", " 25.0\n", + " 0.0\n", " 0\n", - " \n", - " -0.360206\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue \\\n", - "coef_am_peak_end -1.373169 -2.928312 \n", - "coef_am_peak_start_at_6 -20.592814 -6.156718 \n", - "coef_am_peak_start_at_7 -2.826984 -4.061708 \n", - "coef_am_peak_start_at_8 -1.196528 -2.330535 \n", - "coef_am_peak_start_at_9 -1.619897 -1.881593 \n", - "coef_dummy_for_business_related_purpose_and_dur... -0.663129 -1.543000 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 1.151827 1.511000 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 2.625436 2.721000 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 2.360981 2.122000 \n", - "coef_dummy_for_eating_out_purpose_and_duration_... 0.310405 0.399900 \n", - "coef_duration_of_0_hours 9.959985 -0.906682 \n", - "coef_duration_of_11_to_13_hours -0.374055 0.300000 \n", - "coef_duration_of_14_to_18_hours 0.000000 0.000000 \n", - "coef_duration_of_1_hour 0.000000 0.000000 \n", - "coef_duration_of_2_to_3_hours 9.181283 -1.362176 \n", - "coef_duration_of_4_to_5_hours -0.483141 -0.819618 \n", - "coef_duration_of_6_to_7_hours 10.535613 1.088111 \n", - "coef_duration_of_8_to_10_hours 10.402183 1.734039 \n", - "coef_duration_shift_for_business_related_ 0.600044 0.264600 \n", - "coef_duration_shift_for_first_sub_tour_of_same_... 0.319885 -0.399200 \n", - "coef_duration_shift_for_inbound_auto_travel_tim... -0.118575 0.009810 \n", - "coef_duration_shift_for_number_of_individual_no... 0.009894 -0.042200 \n", - "coef_duration_shift_for_number_of_joint_tours -0.577681 -0.249700 \n", - "coef_duration_shift_for_number_of_mandatory_tours -1.058788 -0.770200 \n", - "coef_duration_shift_for_outbound_auto_travel_ti... 0.145324 0.009810 \n", - "coef_duration_shift_for_subsequent_sub_tour_of_... -10.333603 -0.184400 \n", - "coef_early_end_at_5_6 -25.000000 -2.928312 \n", - "coef_early_start_at_5 -2.204068 -7.765548 \n", - "coef_evening_end_at_19_20_21 -4.521037 -2.319982 \n", - "coef_evening_start_at_19_20_21 -0.325741 -1.015090 \n", - "coef_late_end_at_22_23 -11.516565 -2.319982 \n", - "coef_late_start_at_22_23 -5.142925 -0.737570 \n", - "coef_midday_end_at_10_11_12 -1.305196 -2.297264 \n", - "coef_midday_end_at_13_14 0.000000 0.000000 \n", - "coef_midday_start_at_10_11_12 0.000000 0.000000 \n", - "coef_midday_start_at_13_14_15 -1.051562 -0.775022 \n", - "coef_pm_peak_end_at_15 -1.432981 -0.578344 \n", - "coef_pm_peak_end_at_16 -2.055109 -1.094087 \n", - "coef_pm_peak_end_at_17 -2.404252 -1.165847 \n", - "coef_pm_peak_end_at_18 -3.500372 -1.496131 \n", - "coef_pm_peak_start_at_16_17_18 -0.798588 -0.227528 \n", - "coef_start_shift_for_business_related_ -0.083357 -0.111300 \n", - "coef_start_shift_for_first_sub_tour_of_same_wor... -0.168324 -0.543300 \n", - "coef_start_shift_for_inbound_auto_travel_time_o... -0.099019 0.000650 \n", - "coef_start_shift_for_number_of_individual_nonma... 0.026777 -0.012800 \n", - "coef_start_shift_for_number_of_joint_tours -0.019722 -0.020600 \n", - "coef_start_shift_for_number_of_mandatory_tours 0.059018 -0.019300 \n", - "coef_start_shift_for_outbound_auto_travel_time_... 0.100349 0.000650 \n", - "coef_start_shift_for_subsequent_sub_tour_of_sam... -0.360206 -0.184400 \n", + " value best \\\n", + "param_name \n", + "coef_am_peak_end -2.543876 -2.543876 \n", + "coef_am_peak_start_at_6 -5.790935 -5.790935 \n", + "coef_am_peak_start_at_7 -4.238642 -4.238642 \n", + "coef_am_peak_start_at_8 -2.371798 -2.371798 \n", + "coef_am_peak_start_at_9 -2.106247 -2.106247 \n", + "coef_dummy_for_business_related_purpose_and_dur... -1.656086 -1.656086 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 1.371453 1.371453 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 2.454861 2.454861 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 1.832150 1.832150 \n", + "coef_dummy_for_eating_out_purpose_and_duration_... 0.534786 0.534786 \n", + "coef_duration_of_0_hours -1.056506 -1.056506 \n", + "coef_duration_of_11_to_13_hours 0.292100 0.292100 \n", + "coef_duration_of_14_to_18_hours 0.000000 0.000000 \n", + "coef_duration_of_1_hour 0.000000 0.000000 \n", + "coef_duration_of_2_to_3_hours -1.478463 -1.478463 \n", + "coef_duration_of_4_to_5_hours -1.032651 -1.032651 \n", + "coef_duration_of_6_to_7_hours 1.074781 1.074781 \n", + "coef_duration_of_8_to_10_hours 1.476278 1.476278 \n", + "coef_duration_shift_for_business_related_ 0.237322 0.237322 \n", + "coef_duration_shift_for_first_sub_tour_of_same_... -0.199899 -0.199899 \n", + "coef_duration_shift_for_inbound_auto_travel_tim... 0.022979 0.022979 \n", + "coef_duration_shift_for_number_of_individual_no... -0.075913 -0.075913 \n", + "coef_duration_shift_for_number_of_joint_tours -0.234618 -0.234618 \n", + "coef_duration_shift_for_number_of_mandatory_tours -0.909117 -0.909117 \n", + "coef_duration_shift_for_outbound_auto_travel_ti... -0.002863 -0.002863 \n", + "coef_duration_shift_for_subsequent_sub_tour_of_... -0.371148 -0.371148 \n", + "coef_early_end_at_5_6 -2.693000 -2.693000 \n", + "coef_early_start_at_5 -7.645209 -7.645209 \n", + "coef_evening_end_at_19_20_21 -2.460690 -2.460690 \n", + "coef_evening_start_at_19_20_21 -0.786571 -0.786571 \n", + "coef_late_end_at_22_23 -2.391664 -2.391664 \n", + "coef_late_start_at_22_23 -0.890587 -0.890587 \n", + "coef_midday_end_at_10_11_12 -2.145575 -2.145575 \n", + "coef_midday_end_at_13_14 0.000000 0.000000 \n", + "coef_midday_start_at_10_11_12 0.000000 0.000000 \n", + "coef_midday_start_at_13_14_15 -0.665045 -0.665045 \n", + "coef_pm_peak_end_at_15 -0.586475 -0.586475 \n", + "coef_pm_peak_end_at_16 -1.337297 -1.337297 \n", + "coef_pm_peak_end_at_17 -1.432487 -1.432487 \n", + "coef_pm_peak_end_at_18 -1.702240 -1.702240 \n", + "coef_pm_peak_start_at_16_17_18 -0.000431 -0.000431 \n", + "coef_start_shift_for_business_related_ -0.071618 -0.071618 \n", + "coef_start_shift_for_first_sub_tour_of_same_wor... -0.458549 -0.458549 \n", + "coef_start_shift_for_inbound_auto_travel_time_o... -0.011025 -0.011025 \n", + "coef_start_shift_for_number_of_individual_nonma... -0.019137 -0.019137 \n", + "coef_start_shift_for_number_of_joint_tours 0.025502 0.025502 \n", + "coef_start_shift_for_number_of_mandatory_tours -0.067479 -0.067479 \n", + "coef_start_shift_for_outbound_auto_travel_time_... 0.010616 0.010616 \n", + "coef_start_shift_for_subsequent_sub_tour_of_sam... -0.117673 -0.117673 \n", "\n", - " nullvalue minimum \\\n", - "coef_am_peak_end 0.0 -25.0 \n", - "coef_am_peak_start_at_6 0.0 -25.0 \n", - "coef_am_peak_start_at_7 0.0 -25.0 \n", - "coef_am_peak_start_at_8 0.0 -25.0 \n", - "coef_am_peak_start_at_9 0.0 -25.0 \n", - "coef_dummy_for_business_related_purpose_and_dur... 0.0 -25.0 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 0.0 -25.0 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 0.0 -25.0 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 0.0 -25.0 \n", - "coef_dummy_for_eating_out_purpose_and_duration_... 0.0 -25.0 \n", - "coef_duration_of_0_hours 0.0 -25.0 \n", - "coef_duration_of_11_to_13_hours 0.0 -25.0 \n", - "coef_duration_of_14_to_18_hours 0.0 -25.0 \n", - "coef_duration_of_1_hour 0.0 -25.0 \n", - "coef_duration_of_2_to_3_hours 0.0 -25.0 \n", - "coef_duration_of_4_to_5_hours 0.0 -25.0 \n", - "coef_duration_of_6_to_7_hours 0.0 -25.0 \n", - "coef_duration_of_8_to_10_hours 0.0 -25.0 \n", - "coef_duration_shift_for_business_related_ 0.0 -25.0 \n", - "coef_duration_shift_for_first_sub_tour_of_same_... 0.0 -25.0 \n", - "coef_duration_shift_for_inbound_auto_travel_tim... 0.0 -25.0 \n", - "coef_duration_shift_for_number_of_individual_no... 0.0 -25.0 \n", - "coef_duration_shift_for_number_of_joint_tours 0.0 -25.0 \n", - "coef_duration_shift_for_number_of_mandatory_tours 0.0 -25.0 \n", - "coef_duration_shift_for_outbound_auto_travel_ti... 0.0 -25.0 \n", - "coef_duration_shift_for_subsequent_sub_tour_of_... 0.0 -25.0 \n", - "coef_early_end_at_5_6 0.0 -25.0 \n", - "coef_early_start_at_5 0.0 -25.0 \n", - "coef_evening_end_at_19_20_21 0.0 -25.0 \n", - "coef_evening_start_at_19_20_21 0.0 -25.0 \n", - "coef_late_end_at_22_23 0.0 -25.0 \n", - "coef_late_start_at_22_23 0.0 -25.0 \n", - "coef_midday_end_at_10_11_12 0.0 -25.0 \n", - "coef_midday_end_at_13_14 0.0 -25.0 \n", - "coef_midday_start_at_10_11_12 0.0 -25.0 \n", - "coef_midday_start_at_13_14_15 0.0 -25.0 \n", - "coef_pm_peak_end_at_15 0.0 -25.0 \n", - "coef_pm_peak_end_at_16 0.0 -25.0 \n", - 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" maximum holdfast note \\\n", - "coef_am_peak_end 25.0 0 \n", - "coef_am_peak_start_at_6 25.0 0 \n", - "coef_am_peak_start_at_7 25.0 0 \n", - "coef_am_peak_start_at_8 25.0 0 \n", - "coef_am_peak_start_at_9 25.0 0 \n", - "coef_dummy_for_business_related_purpose_and_dur... 25.0 0 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 25.0 0 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 25.0 0 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 25.0 0 \n", - "coef_dummy_for_eating_out_purpose_and_duration_... 25.0 0 \n", - "coef_duration_of_0_hours 25.0 0 \n", - "coef_duration_of_11_to_13_hours 25.0 0 \n", - "coef_duration_of_14_to_18_hours 25.0 1 \n", - "coef_duration_of_1_hour 25.0 1 \n", - "coef_duration_of_2_to_3_hours 25.0 0 \n", - "coef_duration_of_4_to_5_hours 25.0 0 \n", - "coef_duration_of_6_to_7_hours 25.0 0 \n", - "coef_duration_of_8_to_10_hours 25.0 0 \n", - "coef_duration_shift_for_business_related_ 25.0 0 \n", - "coef_duration_shift_for_first_sub_tour_of_same_... 25.0 0 \n", - "coef_duration_shift_for_inbound_auto_travel_tim... 25.0 0 \n", - "coef_duration_shift_for_number_of_individual_no... 25.0 0 \n", - "coef_duration_shift_for_number_of_joint_tours 25.0 0 \n", - "coef_duration_shift_for_number_of_mandatory_tours 25.0 0 \n", - "coef_duration_shift_for_outbound_auto_travel_ti... 25.0 0 \n", - "coef_duration_shift_for_subsequent_sub_tour_of_... 25.0 0 \n", - "coef_early_end_at_5_6 25.0 0 \n", - "coef_early_start_at_5 25.0 0 \n", - "coef_evening_end_at_19_20_21 25.0 0 \n", - "coef_evening_start_at_19_20_21 25.0 0 \n", - "coef_late_end_at_22_23 25.0 0 \n", - "coef_late_start_at_22_23 25.0 0 \n", - "coef_midday_end_at_10_11_12 25.0 0 \n", - "coef_midday_end_at_13_14 25.0 1 \n", - "coef_midday_start_at_10_11_12 25.0 1 \n", - "coef_midday_start_at_13_14_15 25.0 0 \n", - "coef_pm_peak_end_at_15 25.0 0 \n", - "coef_pm_peak_end_at_16 25.0 0 \n", - "coef_pm_peak_end_at_17 25.0 0 \n", - "coef_pm_peak_end_at_18 25.0 0 \n", - "coef_pm_peak_start_at_16_17_18 25.0 0 \n", - "coef_start_shift_for_business_related_ 25.0 0 \n", - "coef_start_shift_for_first_sub_tour_of_same_wor... 25.0 0 \n", - "coef_start_shift_for_inbound_auto_travel_time_o... 25.0 0 \n", - "coef_start_shift_for_number_of_individual_nonma... 25.0 0 \n", - "coef_start_shift_for_number_of_joint_tours 25.0 0 \n", - "coef_start_shift_for_number_of_mandatory_tours 25.0 0 \n", - "coef_start_shift_for_outbound_auto_travel_time_... 25.0 0 \n", - "coef_start_shift_for_subsequent_sub_tour_of_sam... 25.0 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_am_peak_end 25.0 0.0 \n", + "coef_am_peak_start_at_6 25.0 0.0 \n", + "coef_am_peak_start_at_7 25.0 0.0 \n", + "coef_am_peak_start_at_8 25.0 0.0 \n", + "coef_am_peak_start_at_9 25.0 0.0 \n", + "coef_dummy_for_business_related_purpose_and_dur... 25.0 0.0 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 25.0 0.0 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 25.0 0.0 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 25.0 0.0 \n", + "coef_dummy_for_eating_out_purpose_and_duration_... 25.0 0.0 \n", + "coef_duration_of_0_hours 25.0 0.0 \n", + "coef_duration_of_11_to_13_hours 25.0 0.0 \n", + "coef_duration_of_14_to_18_hours 0.0 0.0 \n", + "coef_duration_of_1_hour 0.0 0.0 \n", + "coef_duration_of_2_to_3_hours 25.0 0.0 \n", + "coef_duration_of_4_to_5_hours 25.0 0.0 \n", + "coef_duration_of_6_to_7_hours 25.0 0.0 \n", + "coef_duration_of_8_to_10_hours 25.0 0.0 \n", + "coef_duration_shift_for_business_related_ 25.0 0.0 \n", + "coef_duration_shift_for_first_sub_tour_of_same_... 25.0 0.0 \n", + "coef_duration_shift_for_inbound_auto_travel_tim... 25.0 0.0 \n", + "coef_duration_shift_for_number_of_individual_no... 25.0 0.0 \n", + "coef_duration_shift_for_number_of_joint_tours 25.0 0.0 \n", + "coef_duration_shift_for_number_of_mandatory_tours 25.0 0.0 \n", + "coef_duration_shift_for_outbound_auto_travel_ti... 25.0 0.0 \n", + "coef_duration_shift_for_subsequent_sub_tour_of_... 25.0 0.0 \n", + "coef_early_end_at_5_6 25.0 0.0 \n", + "coef_early_start_at_5 25.0 0.0 \n", + "coef_evening_end_at_19_20_21 25.0 0.0 \n", + "coef_evening_start_at_19_20_21 25.0 0.0 \n", + "coef_late_end_at_22_23 25.0 0.0 \n", + "coef_late_start_at_22_23 25.0 0.0 \n", + "coef_midday_end_at_10_11_12 25.0 0.0 \n", + "coef_midday_end_at_13_14 0.0 0.0 \n", + "coef_midday_start_at_10_11_12 0.0 0.0 \n", + "coef_midday_start_at_13_14_15 25.0 0.0 \n", + "coef_pm_peak_end_at_15 25.0 0.0 \n", + "coef_pm_peak_end_at_16 25.0 0.0 \n", + "coef_pm_peak_end_at_17 25.0 0.0 \n", + "coef_pm_peak_end_at_18 25.0 0.0 \n", + "coef_pm_peak_start_at_16_17_18 25.0 0.0 \n", + "coef_start_shift_for_business_related_ 25.0 0.0 \n", + "coef_start_shift_for_first_sub_tour_of_same_wor... 25.0 0.0 \n", + "coef_start_shift_for_inbound_auto_travel_time_o... 25.0 0.0 \n", + "coef_start_shift_for_number_of_individual_nonma... 25.0 0.0 \n", + "coef_start_shift_for_number_of_joint_tours 25.0 0.0 \n", + "coef_start_shift_for_number_of_mandatory_tours 25.0 0.0 \n", + "coef_start_shift_for_outbound_auto_travel_time_... 25.0 0.0 \n", + "coef_start_shift_for_subsequent_sub_tour_of_sam... 25.0 0.0 \n", "\n", - " best \n", - "coef_am_peak_end -1.373169 \n", - "coef_am_peak_start_at_6 -20.592814 \n", - "coef_am_peak_start_at_7 -2.826984 \n", - "coef_am_peak_start_at_8 -1.196528 \n", - "coef_am_peak_start_at_9 -1.619897 \n", - "coef_dummy_for_business_related_purpose_and_dur... -0.663129 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 1.151827 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 2.625436 \n", - "coef_dummy_for_eating_out_purpose_and_departure... 2.360981 \n", - "coef_dummy_for_eating_out_purpose_and_duration_... 0.310405 \n", - "coef_duration_of_0_hours 9.959985 \n", - "coef_duration_of_11_to_13_hours -0.374055 \n", - "coef_duration_of_14_to_18_hours 0.000000 \n", - "coef_duration_of_1_hour 0.000000 \n", - "coef_duration_of_2_to_3_hours 9.181283 \n", - "coef_duration_of_4_to_5_hours -0.483141 \n", - "coef_duration_of_6_to_7_hours 10.535613 \n", - "coef_duration_of_8_to_10_hours 10.402183 \n", - "coef_duration_shift_for_business_related_ 0.600044 \n", - "coef_duration_shift_for_first_sub_tour_of_same_... 0.319885 \n", - "coef_duration_shift_for_inbound_auto_travel_tim... -0.118575 \n", - "coef_duration_shift_for_number_of_individual_no... 0.009894 \n", - "coef_duration_shift_for_number_of_joint_tours -0.577681 \n", - "coef_duration_shift_for_number_of_mandatory_tours -1.058788 \n", - "coef_duration_shift_for_outbound_auto_travel_ti... 0.145324 \n", - "coef_duration_shift_for_subsequent_sub_tour_of_... -10.333603 \n", - "coef_early_end_at_5_6 -25.000000 \n", - "coef_early_start_at_5 -2.204068 \n", - "coef_evening_end_at_19_20_21 -4.521037 \n", - "coef_evening_start_at_19_20_21 -0.325741 \n", - "coef_late_end_at_22_23 -11.516565 \n", - "coef_late_start_at_22_23 -5.142925 \n", - "coef_midday_end_at_10_11_12 -1.305196 \n", - "coef_midday_end_at_13_14 0.000000 \n", - "coef_midday_start_at_10_11_12 0.000000 \n", - "coef_midday_start_at_13_14_15 -1.051562 \n", - "coef_pm_peak_end_at_15 -1.432981 \n", - "coef_pm_peak_end_at_16 -2.055109 \n", - "coef_pm_peak_end_at_17 -2.404252 \n", - "coef_pm_peak_end_at_18 -3.500372 \n", - "coef_pm_peak_start_at_16_17_18 -0.798588 \n", - "coef_start_shift_for_business_related_ -0.083357 \n", - "coef_start_shift_for_first_sub_tour_of_same_wor... -0.168324 \n", - "coef_start_shift_for_inbound_auto_travel_time_o... -0.099019 \n", - "coef_start_shift_for_number_of_individual_nonma... 0.026777 \n", - "coef_start_shift_for_number_of_joint_tours -0.019722 \n", - "coef_start_shift_for_number_of_mandatory_tours 0.059018 \n", - "coef_start_shift_for_outbound_auto_travel_time_... 0.100349 \n", - "coef_start_shift_for_subsequent_sub_tour_of_sam... -0.360206 " + " holdfast \n", + "param_name \n", + "coef_am_peak_end 0 \n", + "coef_am_peak_start_at_6 0 \n", + "coef_am_peak_start_at_7 0 \n", + "coef_am_peak_start_at_8 0 \n", + "coef_am_peak_start_at_9 0 \n", + "coef_dummy_for_business_related_purpose_and_dur... 0 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 0 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 0 \n", + "coef_dummy_for_eating_out_purpose_and_departure... 0 \n", + "coef_dummy_for_eating_out_purpose_and_duration_... 0 \n", + "coef_duration_of_0_hours 0 \n", + "coef_duration_of_11_to_13_hours 0 \n", + "coef_duration_of_14_to_18_hours 1 \n", + "coef_duration_of_1_hour 1 \n", + "coef_duration_of_2_to_3_hours 0 \n", + "coef_duration_of_4_to_5_hours 0 \n", + "coef_duration_of_6_to_7_hours 0 \n", + "coef_duration_of_8_to_10_hours 0 \n", + "coef_duration_shift_for_business_related_ 0 \n", + "coef_duration_shift_for_first_sub_tour_of_same_... 0 \n", + "coef_duration_shift_for_inbound_auto_travel_tim... 0 \n", + "coef_duration_shift_for_number_of_individual_no... 0 \n", + "coef_duration_shift_for_number_of_joint_tours 0 \n", + "coef_duration_shift_for_number_of_mandatory_tours 0 \n", + "coef_duration_shift_for_outbound_auto_travel_ti... 0 \n", + "coef_duration_shift_for_subsequent_sub_tour_of_... 0 \n", + "coef_early_end_at_5_6 0 \n", + "coef_early_start_at_5 0 \n", + "coef_evening_end_at_19_20_21 0 \n", + "coef_evening_start_at_19_20_21 0 \n", + "coef_late_end_at_22_23 0 \n", + "coef_late_start_at_22_23 0 \n", + "coef_midday_end_at_10_11_12 0 \n", + "coef_midday_end_at_13_14 1 \n", + "coef_midday_start_at_10_11_12 1 \n", + "coef_midday_start_at_13_14_15 0 \n", + "coef_pm_peak_end_at_15 0 \n", + "coef_pm_peak_end_at_16 0 \n", + "coef_pm_peak_end_at_17 0 \n", + "coef_pm_peak_end_at_18 0 \n", + "coef_pm_peak_start_at_16_17_18 0 \n", + "coef_start_shift_for_business_related_ 0 \n", + "coef_start_shift_for_first_sub_tour_of_same_wor... 0 \n", + "coef_start_shift_for_inbound_auto_travel_time_o... 0 \n", + "coef_start_shift_for_number_of_individual_nonma... 0 \n", + "coef_start_shift_for_number_of_joint_tours 0 \n", + "coef_start_shift_for_number_of_mandatory_tours 0 \n", + "coef_start_shift_for_outbound_auto_travel_time_... 0 \n", + "coef_start_shift_for_subsequent_sub_tour_of_sam... 0 " ] }, "metadata": {}, "output_type": "display_data" }, - 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" 1.151827\n", + " 1.371453\n", " \n", " \n", " coef_dummy_for_eating_out_purpose_and_departure_at_12\n", - " 2.625436\n", + " 2.454861\n", " \n", " \n", " coef_dummy_for_eating_out_purpose_and_departure_at_13\n", - " 2.360981\n", + " 1.832150\n", " \n", " \n", " coef_dummy_for_eating_out_purpose_and_duration_of_1_hour\n", - " 0.310405\n", + " 0.534786\n", " \n", " \n", " coef_duration_of_0_hours\n", - " 9.959985\n", + " -1.056506\n", " \n", " \n", " coef_duration_of_11_to_13_hours\n", - " -0.374055\n", + " 0.292100\n", " \n", " \n", " coef_duration_of_14_to_18_hours\n", @@ -2754,79 +2901,79 @@ " \n", " \n", " coef_duration_of_2_to_3_hours\n", - " 9.181283\n", + " -1.478463\n", " \n", " \n", " coef_duration_of_4_to_5_hours\n", - " -0.483141\n", + " -1.032651\n", " \n", " \n", " coef_duration_of_6_to_7_hours\n", - " 10.535613\n", + " 1.074781\n", " \n", " \n", " coef_duration_of_8_to_10_hours\n", - " 10.402183\n", + " 1.476278\n", " \n", " \n", " coef_duration_shift_for_business_related_\n", - " 0.600044\n", + " 0.237322\n", " \n", " \n", " coef_duration_shift_for_first_sub_tour_of_same_work_tour\n", - " 0.319885\n", + " -0.199899\n", " \n", " \n", " coef_duration_shift_for_inbound_auto_travel_time_off_peak\n", - " -0.118575\n", + " 0.022979\n", " \n", " \n", " coef_duration_shift_for_number_of_individual_nonmandatory_tours\n", - " 0.009894\n", + " -0.075913\n", " \n", " \n", " coef_duration_shift_for_number_of_joint_tours\n", - " -0.577681\n", + " -0.234618\n", " \n", " \n", " coef_duration_shift_for_number_of_mandatory_tours\n", - " -1.058788\n", + " -0.909117\n", " \n", " \n", " coef_duration_shift_for_outbound_auto_travel_time_off_peak\n", - " 0.145324\n", + " -0.002863\n", " \n", " \n", " coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour\n", - " -10.333603\n", + " -0.371148\n", " \n", " \n", " coef_early_end_at_5_6\n", - " -25.000000\n", + " -2.693000\n", " \n", " \n", " coef_early_start_at_5\n", - " -2.204068\n", + " -7.645209\n", " \n", " \n", " coef_evening_end_at_19_20_21\n", - " -4.521037\n", + " -2.460690\n", " \n", " \n", " coef_evening_start_at_19_20_21\n", - " -0.325741\n", + " -0.786571\n", " \n", " \n", " coef_late_end_at_22_23\n", - " -11.516565\n", + " -2.391664\n", " \n", " \n", " coef_late_start_at_22_23\n", - " -5.142925\n", + " -0.890587\n", " \n", " \n", " coef_midday_end_at_10_11_12\n", - " -1.305196\n", + " -2.145575\n", " \n", " \n", " coef_midday_end_at_13_14\n", @@ -2838,62 +2985,62 @@ " \n", " \n", " coef_midday_start_at_13_14_15\n", - " -1.051562\n", + " -0.665045\n", " \n", " \n", " coef_pm_peak_end_at_15\n", - " -1.432981\n", + " -0.586475\n", " \n", " \n", " coef_pm_peak_end_at_16\n", - " -2.055109\n", + " -1.337297\n", " \n", " \n", " coef_pm_peak_end_at_17\n", - " -2.404252\n", + " -1.432487\n", " \n", " \n", " coef_pm_peak_end_at_18\n", - " -3.500372\n", + " -1.702240\n", " \n", " \n", " coef_pm_peak_start_at_16_17_18\n", - " -0.798588\n", + " -0.000431\n", " \n", " \n", " coef_start_shift_for_business_related_\n", - " -0.083357\n", + " -0.071618\n", " \n", " \n", " coef_start_shift_for_first_sub_tour_of_same_work_tour\n", - " -0.168324\n", + " -0.458549\n", " \n", " \n", " coef_start_shift_for_inbound_auto_travel_time_off_peak\n", - " -0.099019\n", + " -0.011025\n", " \n", " \n", " coef_start_shift_for_number_of_individual_nonmandatory_tours\n", - " 0.026777\n", + " -0.019137\n", " \n", " \n", " coef_start_shift_for_number_of_joint_tours\n", - " -0.019722\n", + " 0.025502\n", " \n", " \n", " coef_start_shift_for_number_of_mandatory_tours\n", - " 0.059018\n", + " -0.067479\n", " \n", " \n", " coef_start_shift_for_outbound_auto_travel_time_off_peak\n", - " 0.100349\n", + " 0.010616\n", " \n", " \n", " coef_start_shift_for_subsequent_sub_tour_of_same_work_tour\n", - " -0.360206\n", + " -0.117673\n", " \n", " \n", - "loglike-1401.9305368056416d_loglike\n", + "
logloss2.887476765558531d_logloss\n", " \n", " \n", " \n", @@ -2903,51 +3050,51 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2959,79 +3106,79 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3043,185 +3190,185 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - "
coef_am_peak_end1.755671e-04-4.854218e-05
coef_am_peak_start_at_6-3.041241e-081.910552e-05
coef_am_peak_start_at_7-8.670115e-059.916948e-07
coef_am_peak_start_at_85.028242e-054.037261e-05
coef_am_peak_start_at_95.798423e-041.440668e-04
coef_dummy_for_business_related_purpose_and_duration_from_0_to_11.601202e-04-1.280892e-04
coef_dummy_for_eating_out_purpose_and_departure_at_11-3.475790e-051.803296e-04
coef_dummy_for_eating_out_purpose_and_departure_at_12-5.671365e-05-1.483346e-04
coef_dummy_for_eating_out_purpose_and_departure_at_13-2.578419e-051.713493e-04
coef_dummy_for_eating_out_purpose_and_duration_of_1_hour1.872976e-04-2.135625e-04
coef_duration_of_0_hours-1.158083e-038.415289e-05
coef_duration_of_11_to_13_hours-1.673798e-06-6.167370e-06
coef_duration_of_14_to_18_hours
coef_duration_of_2_to_3_hours9.972871e-04-1.347427e-04
coef_duration_of_4_to_5_hours1.179130e-041.170007e-05
coef_duration_of_6_to_7_hours-3.119839e-04-7.452797e-05
coef_duration_of_8_to_10_hours1.749841e-06-4.591330e-06
coef_duration_shift_for_business_related_-1.422810e-031.631608e-04
coef_duration_shift_for_first_sub_tour_of_same_work_tour-6.454765e-046.894456e-05
coef_duration_shift_for_inbound_auto_travel_time_off_peak8.510358e-03-1.645782e-04
coef_duration_shift_for_number_of_individual_nonmandatory_tours-1.352429e-03-1.965446e-04
coef_duration_shift_for_number_of_joint_tours1.961158e-042.409234e-04
coef_duration_shift_for_number_of_mandatory_tours-6.872100e-04-9.571494e-05
coef_duration_shift_for_outbound_auto_travel_time_off_peak7.485785e-035.935593e-05
coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour-5.553217e-06-5.141939e-05
coef_early_end_at_5_6-4.444583e-111.627725e-05
coef_early_start_at_55.404961e-057.249674e-05
coef_evening_end_at_19_20_21-3.938375e-04-1.112621e-04
coef_evening_start_at_19_20_21-1.613864e-041.220311e-04
coef_late_end_at_22_23-3.810622e-04-5.597541e-05
coef_late_start_at_22_23-1.853500e-06-9.478339e-05
coef_midday_end_at_10_11_126.960928e-04-2.023533e-05
coef_midday_end_at_13_14
coef_midday_start_at_13_14_158.833428e-058.980809e-05
coef_pm_peak_end_at_15-2.278624e-04-2.232845e-04
coef_pm_peak_end_at_16-3.377131e-05-4.780466e-05
coef_pm_peak_end_at_17-4.096157e-043.154057e-04
coef_pm_peak_end_at_18-4.205497e-04-1.485193e-04
coef_pm_peak_start_at_16_17_18-8.339350e-04-3.850429e-05
coef_start_shift_for_business_related_1.194878e-04-2.801495e-06
coef_start_shift_for_first_sub_tour_of_same_work_tour-1.481936e-021.192141e-04
coef_start_shift_for_inbound_auto_travel_time_off_peak-1.201511e-017.248793e-05
coef_start_shift_for_number_of_individual_nonmandatory_tours-1.882400e-03-4.273167e-05
coef_start_shift_for_number_of_joint_tours-1.517039e-034.702349e-05
coef_start_shift_for_number_of_mandatory_tours-1.475028e-02-4.354135e-05
coef_start_shift_for_outbound_auto_travel_time_off_peak-1.163942e-01-4.170920e-05
coef_start_shift_for_subsequent_sub_tour_of_same_work_tour2.856907e-051.286129e-04
nit68nfev132njev68status0message'Optimization terminated successfully'successTrueelapsed_time0:00:01.774115method'slsqp'n_cases464iteration_number68logloss3.021402018977676" + "nit68nfev75njev68status0message'Optimization terminated successfully'successTrueelapsed_time0:00:05.198235method'slsqp'n_cases6036iteration_number68loglike-17373.947698365682" ], "text/plain": [ - "┣ x: coef_am_peak_end -1.373169\n", - "┃ coef_am_peak_start_at_6 -20.592814\n", - "┃ coef_am_peak_start_at_7 -2.826984\n", - "┃ coef_am_peak_start_at_8 -1.196528\n", - "┃ coef_am_peak_start_at_9 -1.619897\n", - "┃ coef_dummy_for_business_related_purpose_and_duration_from_0_to_1 -0.663129\n", - "┃ coef_dummy_for_eating_out_purpose_and_departure_at_11 1.151827\n", - "┃ coef_dummy_for_eating_out_purpose_and_departure_at_12 2.625436\n", - "┃ coef_dummy_for_eating_out_purpose_and_departure_at_13 2.360981\n", - "┃ coef_dummy_for_eating_out_purpose_and_duration_of_1_hour 0.310405\n", - "┃ coef_duration_of_0_hours 9.959985\n", - 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"┃ coef_evening_end_at_19_20_21 -4.521037\n", - "┃ coef_evening_start_at_19_20_21 -0.325741\n", - "┃ coef_late_end_at_22_23 -11.516565\n", - "┃ coef_late_start_at_22_23 -5.142925\n", - "┃ coef_midday_end_at_10_11_12 -1.305196\n", - "┃ coef_midday_end_at_13_14 0.000000\n", - "┃ coef_midday_start_at_10_11_12 0.000000\n", - "┃ coef_midday_start_at_13_14_15 -1.051562\n", - "┃ coef_pm_peak_end_at_15 -1.432981\n", - "┃ coef_pm_peak_end_at_16 -2.055109\n", - "┃ coef_pm_peak_end_at_17 -2.404252\n", - "┃ coef_pm_peak_end_at_18 -3.500372\n", - "┃ coef_pm_peak_start_at_16_17_18 -0.798588\n", - "┃ coef_start_shift_for_business_related_ -0.083357\n", - "┃ coef_start_shift_for_first_sub_tour_of_same_work_tour -0.168324\n", - "┃ coef_start_shift_for_inbound_auto_travel_time_off_peak -0.099019\n", - "┃ coef_start_shift_for_number_of_individual_nonmandatory_tours 0.026777\n", - "┃ coef_start_shift_for_number_of_joint_tours -0.019722\n", - "┃ coef_start_shift_for_number_of_mandatory_tours 0.059018\n", - 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"┃ coef_midday_start_at_13_14_15 8.833428e-05\n", - "┃ coef_pm_peak_end_at_15 -2.278624e-04\n", - "┃ coef_pm_peak_end_at_16 -3.377131e-05\n", - "┃ coef_pm_peak_end_at_17 -4.096157e-04\n", - "┃ coef_pm_peak_end_at_18 -4.205497e-04\n", - "┃ coef_pm_peak_start_at_16_17_18 -8.339350e-04\n", - "┃ coef_start_shift_for_business_related_ 1.194878e-04\n", - "┃ coef_start_shift_for_first_sub_tour_of_same_work_tour -1.481936e-02\n", - "┃ coef_start_shift_for_inbound_auto_travel_time_off_peak -1.201511e-01\n", - "┃ coef_start_shift_for_number_of_individual_nonmandatory_tours -1.882400e-03\n", - "┃ coef_start_shift_for_number_of_joint_tours -1.517039e-03\n", - "┃ coef_start_shift_for_number_of_mandatory_tours -1.475028e-02\n", - "┃ coef_start_shift_for_outbound_auto_travel_time_off_peak -1.163942e-01\n", - "┃ coef_start_shift_for_subsequent_sub_tour_of_same_work_tour 2.856907e-05\n", + "┃ coef_midday_start_at_13_14_15 8.980809e-05\n", + "┃ coef_pm_peak_end_at_15 -2.232845e-04\n", + "┃ coef_pm_peak_end_at_16 -4.780466e-05\n", + "┃ coef_pm_peak_end_at_17 3.154057e-04\n", + "┃ coef_pm_peak_end_at_18 -1.485193e-04\n", + "┃ coef_pm_peak_start_at_16_17_18 -3.850429e-05\n", + "┃ coef_start_shift_for_business_related_ -2.801495e-06\n", + "┃ coef_start_shift_for_first_sub_tour_of_same_work_tour 1.192141e-04\n", + "┃ coef_start_shift_for_inbound_auto_travel_time_off_peak 7.248793e-05\n", + "┃ coef_start_shift_for_number_of_individual_nonmandatory_tours -4.273167e-05\n", + "┃ coef_start_shift_for_number_of_joint_tours 4.702349e-05\n", + "┃ coef_start_shift_for_number_of_mandatory_tours -4.354135e-05\n", + "┃ coef_start_shift_for_outbound_auto_travel_time_off_peak -4.170920e-05\n", + "┃ coef_start_shift_for_subsequent_sub_tour_of_same_work_tour 1.286129e-04\n", "┃ dtype: float64\n", "┣ nit: 68\n", - "┣ nfev: 132\n", + "┣ nfev: 75\n", "┣ njev: 68\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=1, microseconds=774115)\n", + "┣ elapsed_time: datetime.timedelta(seconds=5, microseconds=198235)\n", "┣ method: 'slsqp'\n", - "┣ n_cases: 464\n", + "┣ n_cases: 6036\n", "┣ iteration_number: 68\n", - "┣ logloss: 3.021402018977676" + "┣ loglike: -17373.947698365682" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.estimate()" + "model.estimate(maxiter=900)" ] }, { @@ -3233,519 +3380,496 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_am_peak_end-1.37 0.551-2.49* NA 0.00
coef_am_peak_start_at_6-20.6 7.33-2.81** NA 0.00
coef_am_peak_start_at_7-2.83 0.615-4.59*** NA 0.00
coef_am_peak_start_at_8-1.20 0.405-2.96** NA 0.00
coef_am_peak_start_at_9-1.62 0.372-4.35*** NA 0.00
coef_dummy_for_business_related_purpose_and_duration_from_0_to_1-0.663 0.597-1.11 NA 0.00
coef_dummy_for_eating_out_purpose_and_departure_at_11 1.15 0.559 2.06* NA 0.00
coef_dummy_for_eating_out_purpose_and_departure_at_12 2.63 0.598 4.39*** NA 0.00
coef_dummy_for_eating_out_purpose_and_departure_at_13 2.36 0.785 3.01** NA 0.00
coef_dummy_for_eating_out_purpose_and_duration_of_1_hour 0.310 0.516 0.60 NA 0.00
coef_duration_of_0_hours 9.96 1.24 8.06*** NA 0.00
coef_duration_of_11_to_13_hours-0.374 773.-0.00 NA 0.00
coef_duration_of_14_to_18_hours 0.00 NA NA NA 0.00fixed value
coef_duration_of_1_hour 0.00 NA NA NA 0.00fixed value
coef_duration_of_2_to_3_hours 9.18 1.26 7.27*** NA 0.00
coef_duration_of_4_to_5_hours-0.483 0.399-1.21 NA 0.00
coef_duration_of_6_to_7_hours 10.5 1.48 7.11*** NA 0.00
coef_duration_of_8_to_10_hours 10.4 1.83 5.70*** NA 0.00
coef_duration_shift_for_business_related_ 0.600 0.143 4.20*** NA 0.00
coef_duration_shift_for_first_sub_tour_of_same_work_tour 0.320 0.665 0.48 NA 0.00
coef_duration_shift_for_inbound_auto_travel_time_off_peak-0.119 0.0948-1.25 NA 0.00
coef_duration_shift_for_number_of_individual_nonmandatory_tours 0.00989 0.0574 0.17 NA 0.00
coef_duration_shift_for_number_of_joint_tours-0.578 0.294-1.96* NA 0.00
coef_duration_shift_for_number_of_mandatory_tours-1.06 0.652-1.62 NA 0.00
coef_duration_shift_for_outbound_auto_travel_time_off_peak 0.145 0.0956 1.52 NA 0.00
coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour-10.3 424.-0.02 NA 0.00
coef_early_end_at_5_6-25.0 NA NA[*] 3.06 0.00coef_early_end_at_5_6 ≥ -25.0
coef_early_start_at_5-2.20 1.30-1.70 NA 0.00
coef_evening_end_at_19_20_21-4.52 1.26-3.59*** NA 0.00
coef_evening_start_at_19_20_21-0.326 1.53-0.21 NA 0.00
coef_late_end_at_22_23-11.5 51.4-0.22 NA 0.00
coef_late_start_at_22_23-5.14 736.-0.01 NA 0.00
coef_midday_end_at_10_11_12-1.31 0.238-5.49*** NA 0.00
coef_midday_end_at_13_14 0.00 NA NA NA 0.00fixed value
coef_midday_start_at_10_11_12 0.00 NA NA NA 0.00fixed value
coef_midday_start_at_13_14_15-1.05 0.239-4.41*** NA 0.00
coef_pm_peak_end_at_15-1.43 0.260-5.50*** NA 0.00
coef_pm_peak_end_at_16-2.06 0.451-4.56*** NA 0.00
coef_pm_peak_end_at_17-2.40 0.556-4.33*** NA 0.00
coef_pm_peak_end_at_18-3.50 0.738-4.74*** NA 0.00
coef_pm_peak_start_at_16_17_18-0.799 0.642-1.24 NA 0.00
coef_start_shift_for_business_related_-0.0834 0.144-0.58 NA 0.00
coef_start_shift_for_first_sub_tour_of_same_work_tour-0.168 0.135-1.25 NA 0.00
coef_start_shift_for_inbound_auto_travel_time_off_peak-0.0990 0.0641-1.54 NA 0.00
coef_start_shift_for_number_of_individual_nonmandatory_tours 0.0268 0.0292 0.92 NA 0.00
coef_start_shift_for_number_of_joint_tours-0.0197 0.122-0.16 NA 0.00
coef_start_shift_for_number_of_mandatory_tours 0.0590 0.0887 0.67 NA 0.00
coef_start_shift_for_outbound_auto_travel_time_off_peak 0.100 0.0650 1.54 NA 0.00
coef_start_shift_for_subsequent_sub_tour_of_same_work_tour-0.360 0.423-0.85 NA 0.00
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_am_peak_end-2.54 0.162-15.68*** 0.00
coef_am_peak_start_at_6-5.79 0.649-8.93*** 0.00
coef_am_peak_start_at_7-4.24 0.200-21.18*** 0.00
coef_am_peak_start_at_8-2.37 0.127-18.63*** 0.00
coef_am_peak_start_at_9-2.11 0.111-19.05*** 0.00
coef_dummy_for_business_related_purpose_and_duration_from_0_to_1-1.66 0.153-10.81*** 0.00
coef_dummy_for_eating_out_purpose_and_departure_at_11 1.37 0.130 10.56*** 0.00
coef_dummy_for_eating_out_purpose_and_departure_at_12 2.45 0.153 16.00*** 0.00
coef_dummy_for_eating_out_purpose_and_departure_at_13 1.83 0.204 8.98*** 0.00
coef_dummy_for_eating_out_purpose_and_duration_of_1_hour 0.535 0.128 4.17*** 0.00
coef_duration_of_0_hours-1.06 0.214-4.94*** 0.00
coef_duration_of_11_to_13_hours 0.292 5.22 0.06 0.00
coef_duration_of_14_to_18_hours 0.00 0.00 NA 0.00fixed value
coef_duration_of_1_hour 0.00 0.00 NA 0.00fixed value
coef_duration_of_2_to_3_hours-1.48 0.199-7.43*** 0.00
coef_duration_of_4_to_5_hours-1.03 0.160-6.46*** 0.00
coef_duration_of_6_to_7_hours 1.07 0.241 4.46*** 0.00
coef_duration_of_8_to_10_hours 1.48 0.409 3.61*** 0.00
coef_duration_shift_for_business_related_ 0.237 0.0416 5.71*** 0.00
coef_duration_shift_for_first_sub_tour_of_same_work_tour-0.200 0.159-1.26 0.00
coef_duration_shift_for_inbound_auto_travel_time_off_peak 0.0230 0.0131 1.76 0.00
coef_duration_shift_for_number_of_individual_nonmandatory_tours-0.0759 0.0200-3.79*** 0.00
coef_duration_shift_for_number_of_joint_tours-0.235 0.0632-3.71*** 0.00
coef_duration_shift_for_number_of_mandatory_tours-0.909 0.154-5.90*** 0.00
coef_duration_shift_for_outbound_auto_travel_time_off_peak-0.00286 0.0130-0.22 0.00
coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour-0.371 0.303-1.22 0.00
coef_early_end_at_5_6-2.69 0.834-3.23** 0.00
coef_early_start_at_5-7.65 1.48-5.16*** 0.00
coef_evening_end_at_19_20_21-2.46 0.364-6.75*** 0.00
coef_evening_start_at_19_20_21-0.787 0.597-1.32 0.00
coef_late_end_at_22_23-2.39 1.21-1.98* 0.00
coef_late_start_at_22_23-0.891 1.85-0.48 0.00
coef_midday_end_at_10_11_12-2.15 0.0685-31.31*** 0.00
coef_midday_end_at_13_14 0.00 0.00 NA 0.00fixed value
coef_midday_start_at_10_11_12 0.00 0.00 NA 0.00fixed value
coef_midday_start_at_13_14_15-0.665 0.0663-10.04*** 0.00
coef_pm_peak_end_at_15-0.586 0.0676-8.68*** 0.00
coef_pm_peak_end_at_16-1.34 0.134-10.01*** 0.00
coef_pm_peak_end_at_17-1.43 0.165-8.68*** 0.00
coef_pm_peak_end_at_18-1.70 0.211-8.07*** 0.00
coef_pm_peak_start_at_16_17_18-0.000431 0.181-0.00 0.00
coef_start_shift_for_business_related_-0.0716 0.0540-1.33 0.00
coef_start_shift_for_first_sub_tour_of_same_work_tour-0.459 0.0714-6.42*** 0.00
coef_start_shift_for_inbound_auto_travel_time_off_peak-0.0110 0.0130-0.85 0.00
coef_start_shift_for_number_of_individual_nonmandatory_tours-0.0191 0.0140-1.37 0.00
coef_start_shift_for_number_of_joint_tours 0.0255 0.0364 0.70 0.00
coef_start_shift_for_number_of_mandatory_tours-0.0675 0.0648-1.04 0.00
coef_start_shift_for_outbound_auto_travel_time_off_peak 0.0106 0.0127 0.83 0.00
coef_start_shift_for_subsequent_sub_tour_of_same_work_tour-0.118 0.167-0.71 0.00
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -3766,7 +3890,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -3787,20 +3911,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model.to_xlsx(\n", " result_dir/f\"{modelname}_model_estimation.xlsx\", \n", @@ -3819,7 +3932,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -3852,31 +3965,31 @@ " \n", " 0\n", " coef_early_start_at_5\n", - " -2.204068\n", + " -7.645209\n", " F\n", " \n", " \n", " 1\n", " coef_am_peak_start_at_6\n", - " -20.592814\n", + " -5.790935\n", " F\n", " \n", " \n", " 2\n", " coef_am_peak_start_at_7\n", - " -2.826984\n", + " -4.238642\n", " F\n", " \n", " \n", " 3\n", " coef_am_peak_start_at_8\n", - " -1.196528\n", + " -2.371798\n", " F\n", " \n", " \n", " 4\n", " coef_am_peak_start_at_9\n", - " -1.619897\n", + " -2.106247\n", " F\n", " \n", " \n", @@ -3888,43 +4001,43 @@ " \n", " 6\n", " coef_midday_start_at_13_14_15\n", - " -1.051562\n", + " -0.665045\n", " F\n", " \n", " \n", " 7\n", " coef_pm_peak_start_at_16_17_18\n", - " -0.798588\n", + " -0.000431\n", " F\n", " \n", " \n", " 8\n", " coef_evening_start_at_19_20_21\n", - " -0.325741\n", + " -0.786571\n", " F\n", " \n", " \n", " 9\n", " coef_late_start_at_22_23\n", - " -5.142925\n", + " -0.890587\n", " F\n", " \n", " \n", " 10\n", " coef_early_end_at_5_6\n", - " -25.000000\n", + " -2.693000\n", " F\n", " \n", " \n", " 11\n", " coef_am_peak_end\n", - " -1.373169\n", + " -2.543876\n", " F\n", " \n", " \n", " 12\n", " coef_midday_end_at_10_11_12\n", - " -1.305196\n", + " -2.145575\n", " F\n", " \n", " \n", @@ -3936,43 +4049,43 @@ " \n", " 14\n", " coef_pm_peak_end_at_15\n", - " -1.432981\n", + " -0.586475\n", " F\n", " \n", " \n", " 15\n", " coef_pm_peak_end_at_16\n", - " -2.055109\n", + " -1.337297\n", " F\n", " \n", " \n", " 16\n", " coef_pm_peak_end_at_17\n", - " -2.404252\n", + " -1.432487\n", " F\n", " \n", " \n", " 17\n", " coef_pm_peak_end_at_18\n", - " -3.500372\n", + " -1.702240\n", " F\n", " \n", " \n", " 18\n", " coef_evening_end_at_19_20_21\n", - " -4.521037\n", + " -2.460690\n", " F\n", " \n", " \n", " 19\n", " coef_late_end_at_22_23\n", - " -11.516565\n", + " -2.391664\n", " F\n", " \n", " \n", " 20\n", " coef_duration_of_0_hours\n", - " 9.959985\n", + " -1.056506\n", " F\n", " \n", " \n", @@ -3984,31 +4097,31 @@ " \n", " 22\n", " coef_duration_of_2_to_3_hours\n", - " 9.181283\n", + " -1.478463\n", " F\n", " \n", " \n", " 23\n", " coef_duration_of_4_to_5_hours\n", - " -0.483141\n", + " -1.032651\n", " F\n", " \n", " \n", " 24\n", " coef_duration_of_6_to_7_hours\n", - " 10.535613\n", + " 1.074781\n", " F\n", " \n", " \n", " 25\n", " coef_duration_of_8_to_10_hours\n", - " 10.402183\n", + " 1.476278\n", " F\n", " \n", " \n", " 26\n", " coef_duration_of_11_to_13_hours\n", - " -0.374055\n", + " 0.292100\n", " F\n", " \n", " \n", @@ -4020,127 +4133,127 @@ " \n", " 28\n", " coef_start_shift_for_outbound_auto_travel_time...\n", - " 0.100349\n", + " 0.010616\n", " F\n", " \n", " \n", " 29\n", " coef_start_shift_for_inbound_auto_travel_time_...\n", - " -0.099019\n", + " -0.011025\n", " F\n", " \n", " \n", " 30\n", " coef_duration_shift_for_outbound_auto_travel_t...\n", - " 0.145324\n", + " -0.002863\n", " F\n", " \n", " \n", " 31\n", " coef_duration_shift_for_inbound_auto_travel_ti...\n", - " -0.118575\n", + " 0.022979\n", " F\n", " \n", " \n", " 32\n", " coef_start_shift_for_business_related_\n", - " -0.083357\n", + " -0.071618\n", " F\n", " \n", " \n", " 33\n", " coef_duration_shift_for_business_related_\n", - " 0.600044\n", + " 0.237322\n", " F\n", " \n", " \n", " 34\n", " coef_start_shift_for_first_sub_tour_of_same_wo...\n", - " -0.168324\n", + " -0.458549\n", " F\n", " \n", " \n", " 35\n", " coef_duration_shift_for_first_sub_tour_of_same...\n", - " 0.319885\n", + " -0.199899\n", " F\n", " \n", " \n", " 36\n", " coef_start_shift_for_subsequent_sub_tour_of_sa...\n", - " -0.360206\n", + " -0.117673\n", " F\n", " \n", " \n", " 37\n", " coef_duration_shift_for_subsequent_sub_tour_of...\n", - " -10.333603\n", + " -0.371148\n", " F\n", " \n", " \n", " 38\n", " coef_start_shift_for_number_of_mandatory_tours\n", - " 0.059018\n", + " -0.067479\n", " F\n", " \n", " \n", " 39\n", " coef_duration_shift_for_number_of_mandatory_tours\n", - " -1.058788\n", + " -0.909117\n", " F\n", " \n", " \n", " 40\n", " coef_start_shift_for_number_of_joint_tours\n", - " -0.019722\n", + " 0.025502\n", " F\n", " \n", " \n", " 41\n", " coef_duration_shift_for_number_of_joint_tours\n", - " -0.577681\n", + " -0.234618\n", " F\n", " \n", " \n", " 42\n", " coef_start_shift_for_number_of_individual_nonm...\n", - " 0.026777\n", + " -0.019137\n", " F\n", " \n", " \n", " 43\n", " coef_duration_shift_for_number_of_individual_n...\n", - " 0.009894\n", + " -0.075913\n", " F\n", " \n", " \n", " 44\n", " coef_dummy_for_business_related_purpose_and_du...\n", - " -0.663129\n", + " -1.656086\n", " F\n", " \n", " \n", " 45\n", " coef_dummy_for_eating_out_purpose_and_duration...\n", - " 0.310405\n", + " 0.534786\n", " F\n", " \n", " \n", " 46\n", " coef_dummy_for_eating_out_purpose_and_departur...\n", - " 1.151827\n", + " 1.371453\n", " F\n", " \n", " \n", " 47\n", " coef_dummy_for_eating_out_purpose_and_departur...\n", - " 2.625436\n", + " 2.454861\n", " F\n", " \n", " \n", " 48\n", " coef_dummy_for_eating_out_purpose_and_departur...\n", - " 2.360981\n", + " 1.832150\n", " F\n", " \n", " \n", @@ -4148,59 +4261,59 @@ "" ], "text/plain": [ - " coefficient_name value constrain\n", - "0 coef_early_start_at_5 -2.204068 F\n", - "1 coef_am_peak_start_at_6 -20.592814 F\n", - "2 coef_am_peak_start_at_7 -2.826984 F\n", - "3 coef_am_peak_start_at_8 -1.196528 F\n", - "4 coef_am_peak_start_at_9 -1.619897 F\n", - "5 coef_midday_start_at_10_11_12 0.000000 T\n", - "6 coef_midday_start_at_13_14_15 -1.051562 F\n", - "7 coef_pm_peak_start_at_16_17_18 -0.798588 F\n", - "8 coef_evening_start_at_19_20_21 -0.325741 F\n", - "9 coef_late_start_at_22_23 -5.142925 F\n", - "10 coef_early_end_at_5_6 -25.000000 F\n", - "11 coef_am_peak_end -1.373169 F\n", - "12 coef_midday_end_at_10_11_12 -1.305196 F\n", - "13 coef_midday_end_at_13_14 0.000000 T\n", - "14 coef_pm_peak_end_at_15 -1.432981 F\n", - "15 coef_pm_peak_end_at_16 -2.055109 F\n", - "16 coef_pm_peak_end_at_17 -2.404252 F\n", - "17 coef_pm_peak_end_at_18 -3.500372 F\n", - "18 coef_evening_end_at_19_20_21 -4.521037 F\n", - "19 coef_late_end_at_22_23 -11.516565 F\n", - "20 coef_duration_of_0_hours 9.959985 F\n", - "21 coef_duration_of_1_hour 0.000000 T\n", - "22 coef_duration_of_2_to_3_hours 9.181283 F\n", - "23 coef_duration_of_4_to_5_hours -0.483141 F\n", - "24 coef_duration_of_6_to_7_hours 10.535613 F\n", - "25 coef_duration_of_8_to_10_hours 10.402183 F\n", - "26 coef_duration_of_11_to_13_hours -0.374055 F\n", - "27 coef_duration_of_14_to_18_hours 0.000000 T\n", - "28 coef_start_shift_for_outbound_auto_travel_time... 0.100349 F\n", - "29 coef_start_shift_for_inbound_auto_travel_time_... -0.099019 F\n", - "30 coef_duration_shift_for_outbound_auto_travel_t... 0.145324 F\n", - "31 coef_duration_shift_for_inbound_auto_travel_ti... -0.118575 F\n", - "32 coef_start_shift_for_business_related_ -0.083357 F\n", - "33 coef_duration_shift_for_business_related_ 0.600044 F\n", - "34 coef_start_shift_for_first_sub_tour_of_same_wo... -0.168324 F\n", - "35 coef_duration_shift_for_first_sub_tour_of_same... 0.319885 F\n", - "36 coef_start_shift_for_subsequent_sub_tour_of_sa... -0.360206 F\n", - "37 coef_duration_shift_for_subsequent_sub_tour_of... -10.333603 F\n", - "38 coef_start_shift_for_number_of_mandatory_tours 0.059018 F\n", - "39 coef_duration_shift_for_number_of_mandatory_tours -1.058788 F\n", - "40 coef_start_shift_for_number_of_joint_tours -0.019722 F\n", - "41 coef_duration_shift_for_number_of_joint_tours -0.577681 F\n", - "42 coef_start_shift_for_number_of_individual_nonm... 0.026777 F\n", - "43 coef_duration_shift_for_number_of_individual_n... 0.009894 F\n", - "44 coef_dummy_for_business_related_purpose_and_du... -0.663129 F\n", - "45 coef_dummy_for_eating_out_purpose_and_duration... 0.310405 F\n", - "46 coef_dummy_for_eating_out_purpose_and_departur... 1.151827 F\n", - "47 coef_dummy_for_eating_out_purpose_and_departur... 2.625436 F\n", - "48 coef_dummy_for_eating_out_purpose_and_departur... 2.360981 F" + " coefficient_name value constrain\n", + "0 coef_early_start_at_5 -7.645209 F\n", + "1 coef_am_peak_start_at_6 -5.790935 F\n", + "2 coef_am_peak_start_at_7 -4.238642 F\n", + "3 coef_am_peak_start_at_8 -2.371798 F\n", + "4 coef_am_peak_start_at_9 -2.106247 F\n", + "5 coef_midday_start_at_10_11_12 0.000000 T\n", + "6 coef_midday_start_at_13_14_15 -0.665045 F\n", + "7 coef_pm_peak_start_at_16_17_18 -0.000431 F\n", + "8 coef_evening_start_at_19_20_21 -0.786571 F\n", + "9 coef_late_start_at_22_23 -0.890587 F\n", + "10 coef_early_end_at_5_6 -2.693000 F\n", + "11 coef_am_peak_end -2.543876 F\n", + "12 coef_midday_end_at_10_11_12 -2.145575 F\n", + "13 coef_midday_end_at_13_14 0.000000 T\n", + "14 coef_pm_peak_end_at_15 -0.586475 F\n", + "15 coef_pm_peak_end_at_16 -1.337297 F\n", + "16 coef_pm_peak_end_at_17 -1.432487 F\n", + "17 coef_pm_peak_end_at_18 -1.702240 F\n", + "18 coef_evening_end_at_19_20_21 -2.460690 F\n", + "19 coef_late_end_at_22_23 -2.391664 F\n", + "20 coef_duration_of_0_hours -1.056506 F\n", + "21 coef_duration_of_1_hour 0.000000 T\n", + "22 coef_duration_of_2_to_3_hours -1.478463 F\n", + "23 coef_duration_of_4_to_5_hours -1.032651 F\n", + "24 coef_duration_of_6_to_7_hours 1.074781 F\n", + "25 coef_duration_of_8_to_10_hours 1.476278 F\n", + "26 coef_duration_of_11_to_13_hours 0.292100 F\n", + "27 coef_duration_of_14_to_18_hours 0.000000 T\n", + "28 coef_start_shift_for_outbound_auto_travel_time... 0.010616 F\n", + "29 coef_start_shift_for_inbound_auto_travel_time_... -0.011025 F\n", + "30 coef_duration_shift_for_outbound_auto_travel_t... -0.002863 F\n", + "31 coef_duration_shift_for_inbound_auto_travel_ti... 0.022979 F\n", + "32 coef_start_shift_for_business_related_ -0.071618 F\n", + "33 coef_duration_shift_for_business_related_ 0.237322 F\n", + "34 coef_start_shift_for_first_sub_tour_of_same_wo... -0.458549 F\n", + "35 coef_duration_shift_for_first_sub_tour_of_same... -0.199899 F\n", + "36 coef_start_shift_for_subsequent_sub_tour_of_sa... -0.117673 F\n", + "37 coef_duration_shift_for_subsequent_sub_tour_of... -0.371148 F\n", + "38 coef_start_shift_for_number_of_mandatory_tours -0.067479 F\n", + "39 coef_duration_shift_for_number_of_mandatory_tours -0.909117 F\n", + "40 coef_start_shift_for_number_of_joint_tours 0.025502 F\n", + "41 coef_duration_shift_for_number_of_joint_tours -0.234618 F\n", + "42 coef_start_shift_for_number_of_individual_nonm... -0.019137 F\n", + "43 coef_duration_shift_for_number_of_individual_n... -0.075913 F\n", + "44 coef_dummy_for_business_related_purpose_and_du... -1.656086 F\n", + "45 coef_dummy_for_eating_out_purpose_and_duration... 0.534786 F\n", + "46 coef_dummy_for_eating_out_purpose_and_departur... 1.371453 F\n", + "47 coef_dummy_for_eating_out_purpose_and_departur... 2.454861 F\n", + "48 coef_dummy_for_eating_out_purpose_and_departur... 1.832150 F" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4217,7 +4330,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -4231,7 +4344,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/21_stop_frequency.ipynb b/activitysim/examples/example_estimation/notebooks/21_stop_frequency.ipynb index 3de8e64272..ee1914bfd6 100644 --- a/activitysim/examples/example_estimation/notebooks/21_stop_frequency.ipynb +++ b/activitysim/examples/example_estimation/notebooks/21_stop_frequency.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -73,8 +120,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/activitysim/activitysim/estimation/larch/general.py:321: UserWarning: coefficient dataframe missing 'constrain' column, setting all to 'F'\n", - " warnings.warn(\"coefficient dataframe missing 'constrain' column, setting all to 'F'\")\n" + "/Users/jpn/Git/est-mode/activitysim/activitysim/estimation/larch/general.py:359: UserWarning: coefficient dataframe missing 'constrain' column, setting all to 'F'\n", + " warnings.warn(\n" ] } ], @@ -82,7 +129,12 @@ "modelname = \"stop_frequency\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "\n", + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -1195,10 +1247,10 @@ " coef_alternative_specific_constant_for_outboun...\n", " coef_alternative_specific_constant_for_outboun...\n", " coef_alternative_specific_constant_for_outboun...\n", - " -3.934\n", - " -3.934\n", - " -3.934\n", - " -3.934\n", + " coef_alternative_specific_constant_for_outboun...\n", + " coef_alternative_specific_constant_for_outboun...\n", + " coef_alternative_specific_constant_for_outboun...\n", + " coef_alternative_specific_constant_for_outboun...\n", " \n", " \n", " 40\n", @@ -1208,19 +1260,19 @@ " NaN\n", " coef_alternative_specific_constant_for_return_...\n", " coef_alternative_specific_constant_for_return_...\n", - " -2.139\n", + " coef_alternative_specific_constant_for_return_...\n", " NaN\n", " coef_alternative_specific_constant_for_return_...\n", " coef_alternative_specific_constant_for_return_...\n", - " -2.139\n", + " coef_alternative_specific_constant_for_return_...\n", " NaN\n", " coef_alternative_specific_constant_for_return_...\n", " coef_alternative_specific_constant_for_return_...\n", - " -2.139\n", + " coef_alternative_specific_constant_for_return_...\n", " NaN\n", " coef_alternative_specific_constant_for_return_...\n", " coef_alternative_specific_constant_for_return_...\n", - " -2.139\n", + " coef_alternative_specific_constant_for_return_...\n", " \n", " \n", " 41\n", @@ -1537,7 +1589,7 @@ "37 coef_dummy_for_distance_in_miles \n", "38 coef_no_stops_if_tour_mode_is_drivetransit \n", "39 NaN \n", - "40 -2.139 \n", + "40 coef_alternative_specific_constant_for_return_... \n", "41 coef_alternative_specific_constant_for_the_tot... \n", "42 coef_number_of_subtours_in_the_tour \n", "\n", @@ -1717,7 +1769,7 @@ "37 coef_dummy_for_distance_in_miles \n", "38 coef_no_stops_if_tour_mode_is_drivetransit \n", "39 coef_alternative_specific_constant_for_outboun... \n", - "40 -2.139 \n", + "40 coef_alternative_specific_constant_for_return_... \n", "41 coef_alternative_specific_constant_for_the_tot... \n", "42 coef_number_of_subtours_in_the_tour \n", "\n", @@ -1897,7 +1949,7 @@ "37 coef_dummy_for_distance_in_miles \n", "38 coef_no_stops_if_tour_mode_is_drivetransit \n", "39 coef_alternative_specific_constant_for_outboun... \n", - "40 -2.139 \n", + "40 coef_alternative_specific_constant_for_return_... \n", "41 coef_alternative_specific_constant_for_the_tot... \n", "42 coef_number_of_subtours_in_the_tour \n", "\n", @@ -1941,7 +1993,7 @@ "36 coef_dummy_for_distance_less_than_20_miles \n", "37 coef_dummy_for_distance_in_miles \n", "38 coef_no_stops_if_tour_mode_is_drivetransit \n", - "39 -3.934 \n", + "39 coef_alternative_specific_constant_for_outboun... \n", "40 NaN \n", "41 coef_alternative_specific_constant_for_the_tot... \n", "42 coef_number_of_subtours_in_the_tour \n", @@ -1986,7 +2038,7 @@ "36 coef_dummy_for_distance_less_than_20_miles \n", "37 coef_dummy_for_distance_in_miles \n", "38 coef_no_stops_if_tour_mode_is_drivetransit \n", - "39 -3.934 \n", + "39 coef_alternative_specific_constant_for_outboun... \n", "40 coef_alternative_specific_constant_for_return_... \n", "41 coef_alternative_specific_constant_for_the_tot... \n", "42 coef_number_of_subtours_in_the_tour \n", @@ -2031,7 +2083,7 @@ "36 coef_dummy_for_distance_less_than_20_miles \n", "37 coef_dummy_for_distance_in_miles \n", "38 coef_no_stops_if_tour_mode_is_drivetransit \n", - "39 -3.934 \n", + "39 coef_alternative_specific_constant_for_outboun... \n", "40 coef_alternative_specific_constant_for_return_... \n", "41 coef_alternative_specific_constant_for_the_tot... \n", "42 coef_number_of_subtours_in_the_tour \n", @@ -2076,8 +2128,8 @@ "36 coef_dummy_for_distance_less_than_20_miles \n", "37 coef_dummy_for_distance_in_miles \n", "38 coef_no_stops_if_tour_mode_is_drivetransit \n", - "39 -3.934 \n", - "40 -2.139 \n", + "39 coef_alternative_specific_constant_for_outboun... \n", + "40 coef_alternative_specific_constant_for_return_... \n", "41 coef_alternative_specific_constant_for_the_tot... \n", "42 coef_number_of_subtours_in_the_tour " ] @@ -2175,9 +2227,9 @@ " \n", " \n", " \n", - " 2961920\n", - " 0out_0in\n", - " 0out_0in\n", + " 2966594\n", + " 0out_1in\n", + " 0out_1in\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2191,17 +2243,17 @@ " False\n", " 0\n", " 0\n", + " 1\n", " 0\n", " 0\n", - " 0\n", - " 6.859067\n", - " 7.751024\n", - " 0\n", + " 6.446184\n", + " 6.869385\n", + " 1\n", " \n", " \n", - " 2970120\n", - " 2out_1in\n", - " 2out_1in\n", + " 2967783\n", + " 0out_0in\n", + " 0out_0in\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2218,12 +2270,12 @@ " 0\n", " 0\n", " 0\n", - " 0.000000\n", - " 0.000000\n", - " 1\n", + " 6.384615\n", + " 7.341247\n", + " 0\n", " \n", " \n", - " 2998943\n", + " 2968726\n", " 0out_0in\n", " 0out_0in\n", " 0.0\n", @@ -2236,19 +2288,19 @@ " 0.0\n", " ...\n", " False\n", - " False\n", + " True\n", " 0\n", " 0\n", - " 1\n", " 0\n", " 0\n", - " 6.024261\n", - " 7.519040\n", - " 2\n", + " 0\n", + " 5.447277\n", + " 6.565208\n", + " 1\n", " \n", " \n", - " 3013252\n", - " 0out_0in\n", + " 2970858\n", + " 1out_1in\n", " 0out_0in\n", " 0.0\n", " 0.0\n", @@ -2266,12 +2318,12 @@ " 0\n", " 0\n", " 0\n", - " 5.672122\n", - " 6.595380\n", + " 6.787018\n", + " 7.692237\n", " 1\n", " \n", " \n", - " 3015794\n", + " 2973728\n", " 0out_0in\n", " 0out_0in\n", " 0.0\n", @@ -2290,8 +2342,8 @@ " 0\n", " 0\n", " 0\n", - " 6.252582\n", - " 5.865708\n", + " 6.611336\n", + " 5.693881\n", " 3\n", " \n", " \n", @@ -2319,38 +2371,38 @@ " ...\n", " \n", " \n", - " 308070309\n", - " 0out_0in\n", + " 309081532\n", + " 2out_0in\n", " 0out_0in\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", + " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " ...\n", " False\n", - " True\n", - " 0\n", + " False\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 8.181251\n", - " 8.326834\n", " 0\n", + " 4.619027\n", + " 6.475182\n", + " 3\n", " \n", " \n", - " 308073875\n", - " 1out_0in\n", - " 1out_0in\n", + " 309090634\n", + " 0out_1in\n", + " 0out_0in\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 1.0\n", + " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2362,14 +2414,14 @@ " 0\n", " 0\n", " 0\n", - " 0.000000\n", - " 0.000000\n", - " 2\n", + " 4.800090\n", + " 6.198316\n", + " 1\n", " \n", " \n", - " 308090603\n", - " 0out_0in\n", + " 309101950\n", " 0out_0in\n", + " 1out_1in\n", " 0.0\n", " 0.0\n", " 0.0\n", @@ -2386,22 +2438,22 @@ " 0\n", " 0\n", " 0\n", - " 7.629996\n", - " 7.282405\n", + " 0.000000\n", + " 0.000000\n", " 1\n", " \n", " \n", - " 308105896\n", + " 309107362\n", " 0out_0in\n", " 0out_0in\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " ...\n", " False\n", " False\n", @@ -2410,181 +2462,181 @@ " 0\n", " 0\n", " 0\n", - " 7.149388\n", - " 6.679189\n", - " 2\n", + " 0.000000\n", + " 0.000000\n", + " 4\n", " \n", " \n", - " 308122624\n", + " 309112036\n", " 0out_0in\n", " 0out_0in\n", " 0.0\n", " 0.0\n", " 0.0\n", " 1.0\n", - " 0.0\n", " 1.0\n", " 0.0\n", " 0.0\n", + " 0.0\n", " ...\n", " False\n", " False\n", " 0\n", " 0\n", + " 1\n", " 0\n", " 0\n", - " 0\n", - " 5.117139\n", - " 7.355598\n", - " 0\n", + " 0.000000\n", + " 0.000000\n", + " 4\n", " \n", " \n", "\n", - "

2124 rows × 220 columns

\n", + "

22637 rows × 220 columns

\n", "" ], "text/plain": [ " model_choice override_choice util_middle_to_low_income_hh \\\n", "tour_id \n", - "2961920 0out_0in 0out_0in 0.0 \n", - "2970120 2out_1in 2out_1in 0.0 \n", - "2998943 0out_0in 0out_0in 0.0 \n", - "3013252 0out_0in 0out_0in 0.0 \n", - "3015794 0out_0in 0out_0in 0.0 \n", + "2966594 0out_1in 0out_1in 0.0 \n", + "2967783 0out_0in 0out_0in 0.0 \n", + "2968726 0out_0in 0out_0in 0.0 \n", + "2970858 1out_1in 0out_0in 0.0 \n", + "2973728 0out_0in 0out_0in 0.0 \n", "... ... ... ... \n", - "308070309 0out_0in 0out_0in 0.0 \n", - "308073875 1out_0in 1out_0in 0.0 \n", - "308090603 0out_0in 0out_0in 0.0 \n", - "308105896 0out_0in 0out_0in 0.0 \n", - "308122624 0out_0in 0out_0in 0.0 \n", + "309081532 2out_0in 0out_0in 0.0 \n", + "309090634 0out_1in 0out_0in 0.0 \n", + "309101950 0out_0in 1out_1in 0.0 \n", + "309107362 0out_0in 0out_0in 0.0 \n", + "309112036 0out_0in 0out_0in 0.0 \n", "\n", " util_mid_to_high_income_hh util_high_income_hh \\\n", "tour_id \n", - "2961920 0.0 0.0 \n", - "2970120 0.0 0.0 \n", - "2998943 0.0 0.0 \n", - "3013252 0.0 0.0 \n", - "3015794 0.0 0.0 \n", + "2966594 0.0 0.0 \n", + "2967783 0.0 0.0 \n", + "2968726 0.0 0.0 \n", + "2970858 0.0 0.0 \n", + "2973728 0.0 0.0 \n", "... ... ... \n", - "308070309 0.0 0.0 \n", - "308073875 0.0 0.0 \n", - "308090603 0.0 0.0 \n", - "308105896 0.0 0.0 \n", - "308122624 0.0 0.0 \n", + "309081532 0.0 0.0 \n", + "309090634 0.0 0.0 \n", + "309101950 0.0 0.0 \n", + "309107362 0.0 0.0 \n", + "309112036 0.0 0.0 \n", "\n", " util_number_of_hh_persons util_number_of_full_time_workers_in_hh \\\n", "tour_id \n", - "2961920 1.0 1.0 \n", - "2970120 1.0 1.0 \n", - "2998943 1.0 1.0 \n", - "3013252 1.0 1.0 \n", - "3015794 1.0 0.0 \n", + "2966594 1.0 1.0 \n", + "2967783 1.0 1.0 \n", + "2968726 1.0 1.0 \n", + "2970858 1.0 1.0 \n", + "2973728 1.0 0.0 \n", "... ... ... \n", - "308070309 1.0 0.0 \n", - "308073875 1.0 1.0 \n", - "308090603 1.0 1.0 \n", - "308105896 1.0 0.0 \n", - "308122624 1.0 0.0 \n", + "309081532 1.0 1.0 \n", + "309090634 1.0 0.0 \n", + "309101950 1.0 1.0 \n", + "309107362 1.0 1.0 \n", + "309112036 1.0 1.0 \n", "\n", " util_number_of_students_in_hh \\\n", "tour_id \n", - "2961920 0.0 \n", - "2970120 0.0 \n", - "2998943 0.0 \n", - "3013252 0.0 \n", - "3015794 0.0 \n", + "2966594 0.0 \n", + "2967783 0.0 \n", + "2968726 0.0 \n", + "2970858 0.0 \n", + "2973728 0.0 \n", "... ... \n", - "308070309 0.0 \n", - "308073875 0.0 \n", - "308090603 0.0 \n", - "308105896 1.0 \n", - "308122624 1.0 \n", + "309081532 0.0 \n", + "309090634 0.0 \n", + "309101950 0.0 \n", + "309107362 0.0 \n", + "309112036 0.0 \n", "\n", " util_num_kids_between_0_and_4_including_years_old \\\n", "tour_id \n", - "2961920 0.0 \n", - "2970120 0.0 \n", - "2998943 0.0 \n", - "3013252 0.0 \n", - "3015794 0.0 \n", + "2966594 0.0 \n", + "2967783 0.0 \n", + "2968726 0.0 \n", + "2970858 0.0 \n", + "2973728 0.0 \n", "... ... \n", - "308070309 0.0 \n", - "308073875 0.0 \n", - "308090603 0.0 \n", - "308105896 0.0 \n", - "308122624 0.0 \n", + "309081532 0.0 \n", + "309090634 0.0 \n", + "309101950 0.0 \n", + "309107362 0.0 \n", + "309112036 0.0 \n", "\n", " util_presence_of_kids_between_0_and_4_including_years_old ... \\\n", "tour_id ... \n", - "2961920 0.0 ... \n", - "2970120 0.0 ... \n", - "2998943 0.0 ... \n", - "3013252 0.0 ... \n", - "3015794 0.0 ... \n", + "2966594 0.0 ... \n", + "2967783 0.0 ... \n", + "2968726 0.0 ... \n", + "2970858 0.0 ... \n", + "2973728 0.0 ... \n", "... ... ... \n", - "308070309 0.0 ... \n", - "308073875 0.0 ... \n", - "308090603 0.0 ... \n", - "308105896 0.0 ... \n", - "308122624 0.0 ... \n", + "309081532 0.0 ... \n", + "309090634 0.0 ... \n", + "309101950 0.0 ... \n", + "309107362 0.0 ... \n", + "309112036 0.0 ... \n", "\n", " tour_mode_is_drive_transit tour_mode_is_non_motorized \\\n", "tour_id \n", - "2961920 False False \n", - "2970120 False False \n", - "2998943 False False \n", - "3013252 False False \n", - "3015794 False False \n", + "2966594 False False \n", + "2967783 False False \n", + "2968726 False True \n", + "2970858 False False \n", + "2973728 False False \n", "... ... ... \n", - "308070309 False True \n", - "308073875 False False \n", - "308090603 False False \n", - "308105896 False False \n", - "308122624 False False \n", + "309081532 False False \n", + "309090634 False False \n", + "309101950 False False \n", + "309107362 False False \n", + "309112036 False False \n", "\n", " num_school_tours num_univ_tours num_atwork_subtours \\\n", "tour_id \n", - "2961920 0 0 0 \n", - "2970120 0 0 0 \n", - "2998943 0 0 1 \n", - "3013252 0 0 0 \n", - "3015794 0 0 0 \n", + "2966594 0 0 1 \n", + "2967783 0 0 0 \n", + "2968726 0 0 0 \n", + "2970858 0 0 0 \n", + "2973728 0 0 0 \n", "... ... ... ... \n", - "308070309 0 0 0 \n", - "308073875 0 0 0 \n", - "308090603 0 0 0 \n", - "308105896 0 0 0 \n", - "308122624 0 0 0 \n", + "309081532 0 0 0 \n", + "309090634 0 0 0 \n", + "309101950 0 0 0 \n", + "309107362 0 0 0 \n", + "309112036 0 0 1 \n", "\n", " num_hh_shop_tours num_hh_maint_tours hhacc pracc \\\n", "tour_id \n", - "2961920 0 0 6.859067 7.751024 \n", - "2970120 0 0 0.000000 0.000000 \n", - "2998943 0 0 6.024261 7.519040 \n", - "3013252 0 0 5.672122 6.595380 \n", - "3015794 0 0 6.252582 5.865708 \n", + "2966594 0 0 6.446184 6.869385 \n", + "2967783 0 0 6.384615 7.341247 \n", + "2968726 0 0 5.447277 6.565208 \n", + "2970858 0 0 6.787018 7.692237 \n", + "2973728 0 0 6.611336 5.693881 \n", "... ... ... ... ... \n", - "308070309 0 0 8.181251 8.326834 \n", - "308073875 0 0 0.000000 0.000000 \n", - "308090603 0 0 7.629996 7.282405 \n", - "308105896 0 0 7.149388 6.679189 \n", - "308122624 0 0 5.117139 7.355598 \n", + "309081532 0 0 4.619027 6.475182 \n", + "309090634 0 0 4.800090 6.198316 \n", + "309101950 0 0 0.000000 0.000000 \n", + "309107362 0 0 0.000000 0.000000 \n", + "309112036 0 0 0.000000 0.000000 \n", "\n", " destination_area_type \n", "tour_id \n", - "2961920 0 \n", - "2970120 1 \n", - "2998943 2 \n", - "3013252 1 \n", - "3015794 3 \n", + "2966594 1 \n", + "2967783 0 \n", + "2968726 1 \n", + "2970858 1 \n", + "2973728 3 \n", "... ... \n", - "308070309 0 \n", - "308073875 2 \n", - "308090603 1 \n", - "308105896 2 \n", - "308122624 0 \n", + "309081532 3 \n", + "309090634 1 \n", + "309101950 1 \n", + "309107362 4 \n", + "309112036 4 \n", "\n", - "[2124 rows x 220 columns]" + "[22637 rows x 220 columns]" ] }, "execution_count": 7, @@ -2610,26 +2662,10 @@ "execution_count": 8, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ - "

Iteration 130 [Optimization terminated successfully]

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Iteration 081 [Optimization terminated successfully]

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Best LL = -6094.555918549999

" + "

Best LL = -78188.35046478933

" ], "text/plain": [ "" @@ -2672,70 +2708,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " -2.139\n", - " -2.139000\n", - " -2.139\n", - " -2.139\n", - " -2.139\n", - " -2.139\n", - " 1\n", - " \n", - " -2.139000\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in\n", + " -0.817659\n", + " -0.817659\n", + " -0.833\n", + " -inf\n", + " inf\n", + " 0.0\n", + " 0\n", " \n", " \n", - " -3.934\n", - " -3.934000\n", - " -3.934\n", - " -3.934\n", - " -3.934\n", - " -3.934\n", - " 1\n", - " \n", - " -3.934000\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork\n", + " -3.857785\n", + " -3.857785\n", + " -3.896\n", + " -inf\n", + " inf\n", + " 0.0\n", + " 0\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in\n", - " -0.741225\n", - " -0.833\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout\n", + " -2.243908\n", + " -2.243908\n", + " -2.190\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " -0.741225\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in\n", - " -2.594489\n", - " -2.613\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort\n", + " -2.254734\n", + " -2.254734\n", + " -2.173\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " -2.594489\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in\n", - " -0.447476\n", - " -0.445\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr\n", + " -1.509245\n", + " -1.509245\n", + " -1.581\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " -0.447476\n", " \n", " \n", " ...\n", @@ -2746,122 +2786,120 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", " coef_primary_destination_accessibility_log_of_it_\n", - " 0.170704\n", + " 0.191205\n", + " 0.191205\n", " 0.180\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " 0.170704\n", " \n", " \n", " coef_subtour_departure_less_than_or_equal_to_11am\n", - " 0.266339\n", + " 0.352247\n", + " 0.352247\n", " 0.310\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " 0.266339\n", " \n", " \n", " coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_\n", - " -0.152384\n", + " 0.023141\n", + " 0.023141\n", " 0.020\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " -0.152384\n", " \n", " \n", " coef_subtour_duration_in_hours_integer_\n", - " 0.681811\n", + " 0.555413\n", + " 0.555413\n", " 0.560\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " 0.681811\n", " \n", " \n", " coef_subtour_return_time_greater_or_equal_to_2pm\n", - " 1.549569\n", + " 0.206446\n", + " 0.206446\n", " 0.340\n", - " 0.000\n", - " NaN\n", - " NaN\n", + " -inf\n", + " inf\n", + " 0.0\n", " 0\n", - " \n", - " 1.549569\n", " \n", " \n", "\n", - "

188 rows × 8 columns

\n", + "

188 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue \\\n", - "-2.139 -2.139000 -2.139 \n", - "-3.934 -3.934000 -3.934 \n", - "coef_alternative_specific_constant_for_outbound... -0.741225 -0.833 \n", - "coef_alternative_specific_constant_for_outbound... -2.594489 -2.613 \n", - "coef_alternative_specific_constant_for_return_s... -0.447476 -0.445 \n", - "... ... ... \n", - "coef_primary_destination_accessibility_log_of_it_ 0.170704 0.180 \n", - "coef_subtour_departure_less_than_or_equal_to_11am 0.266339 0.310 \n", - "coef_subtour_distance_in_miles_from_tour_destin... -0.152384 0.020 \n", - "coef_subtour_duration_in_hours_integer_ 0.681811 0.560 \n", - "coef_subtour_return_time_greater_or_equal_to_2pm 1.549569 0.340 \n", + " value best \\\n", + "param_name \n", + "coef_alternative_specific_constant_for_outbound... -0.817659 -0.817659 \n", + "coef_alternative_specific_constant_for_outbound... -3.857785 -3.857785 \n", + "coef_alternative_specific_constant_for_outbound... -2.243908 -2.243908 \n", + "coef_alternative_specific_constant_for_outbound... -2.254734 -2.254734 \n", + "coef_alternative_specific_constant_for_outbound... -1.509245 -1.509245 \n", + "... ... ... \n", + "coef_primary_destination_accessibility_log_of_it_ 0.191205 0.191205 \n", + "coef_subtour_departure_less_than_or_equal_to_11am 0.352247 0.352247 \n", + "coef_subtour_distance_in_miles_from_tour_destin... 0.023141 0.023141 \n", + "coef_subtour_duration_in_hours_integer_ 0.555413 0.555413 \n", + "coef_subtour_return_time_greater_or_equal_to_2pm 0.206446 0.206446 \n", "\n", - " nullvalue minimum \\\n", - "-2.139 -2.139 -2.139 \n", - "-3.934 -3.934 -3.934 \n", - "coef_alternative_specific_constant_for_outbound... 0.000 NaN \n", - "coef_alternative_specific_constant_for_outbound... 0.000 NaN \n", - "coef_alternative_specific_constant_for_return_s... 0.000 NaN \n", + " initvalue minimum \\\n", + "param_name \n", + "coef_alternative_specific_constant_for_outbound... -0.833 -inf \n", + "coef_alternative_specific_constant_for_outbound... -3.896 -inf \n", + "coef_alternative_specific_constant_for_outbound... -2.190 -inf \n", + "coef_alternative_specific_constant_for_outbound... -2.173 -inf \n", + "coef_alternative_specific_constant_for_outbound... -1.581 -inf \n", "... ... ... \n", - "coef_primary_destination_accessibility_log_of_it_ 0.000 NaN \n", - "coef_subtour_departure_less_than_or_equal_to_11am 0.000 NaN \n", - "coef_subtour_distance_in_miles_from_tour_destin... 0.000 NaN \n", - "coef_subtour_duration_in_hours_integer_ 0.000 NaN \n", - "coef_subtour_return_time_greater_or_equal_to_2pm 0.000 NaN \n", + "coef_primary_destination_accessibility_log_of_it_ 0.180 -inf \n", + "coef_subtour_departure_less_than_or_equal_to_11am 0.310 -inf \n", + "coef_subtour_distance_in_miles_from_tour_destin... 0.020 -inf \n", + "coef_subtour_duration_in_hours_integer_ 0.560 -inf \n", + "coef_subtour_return_time_greater_or_equal_to_2pm 0.340 -inf \n", "\n", - " maximum holdfast note \\\n", - "-2.139 -2.139 1 \n", - "-3.934 -3.934 1 \n", - "coef_alternative_specific_constant_for_outbound... NaN 0 \n", - "coef_alternative_specific_constant_for_outbound... NaN 0 \n", - "coef_alternative_specific_constant_for_return_s... NaN 0 \n", - "... ... ... ... \n", - "coef_primary_destination_accessibility_log_of_it_ NaN 0 \n", - "coef_subtour_departure_less_than_or_equal_to_11am NaN 0 \n", - "coef_subtour_distance_in_miles_from_tour_destin... NaN 0 \n", - "coef_subtour_duration_in_hours_integer_ NaN 0 \n", - "coef_subtour_return_time_greater_or_equal_to_2pm NaN 0 \n", + " maximum nullvalue \\\n", + "param_name \n", + "coef_alternative_specific_constant_for_outbound... inf 0.0 \n", + "coef_alternative_specific_constant_for_outbound... inf 0.0 \n", + "coef_alternative_specific_constant_for_outbound... inf 0.0 \n", + "coef_alternative_specific_constant_for_outbound... inf 0.0 \n", + "coef_alternative_specific_constant_for_outbound... inf 0.0 \n", + "... ... ... \n", + "coef_primary_destination_accessibility_log_of_it_ inf 0.0 \n", + "coef_subtour_departure_less_than_or_equal_to_11am inf 0.0 \n", + "coef_subtour_distance_in_miles_from_tour_destin... inf 0.0 \n", + "coef_subtour_duration_in_hours_integer_ inf 0.0 \n", + "coef_subtour_return_time_greater_or_equal_to_2pm inf 0.0 \n", "\n", - " best \n", - "-2.139 -2.139000 \n", - "-3.934 -3.934000 \n", - "coef_alternative_specific_constant_for_outbound... -0.741225 \n", - "coef_alternative_specific_constant_for_outbound... -2.594489 \n", - "coef_alternative_specific_constant_for_return_s... -0.447476 \n", + " holdfast \n", + "param_name \n", + "coef_alternative_specific_constant_for_outbound... 0 \n", + "coef_alternative_specific_constant_for_outbound... 0 \n", + "coef_alternative_specific_constant_for_outbound... 0 \n", + "coef_alternative_specific_constant_for_outbound... 0 \n", + "coef_alternative_specific_constant_for_outbound... 0 \n", "... ... \n", - "coef_primary_destination_accessibility_log_of_it_ 0.170704 \n", - "coef_subtour_departure_less_than_or_equal_to_11am 0.266339 \n", - "coef_subtour_distance_in_miles_from_tour_destin... -0.152384 \n", - "coef_subtour_duration_in_hours_integer_ 0.681811 \n", - "coef_subtour_return_time_greater_or_equal_to_2pm 1.549569 \n", + "coef_primary_destination_accessibility_log_of_it_ 0 \n", + "coef_subtour_departure_less_than_or_equal_to_11am 0 \n", + "coef_subtour_distance_in_miles_from_tour_destin... 0 \n", + "coef_subtour_duration_in_hours_integer_ 0 \n", + "coef_subtour_return_time_greater_or_equal_to_2pm 0 \n", "\n", - "[188 rows x 8 columns]" + "[188 rows x 7 columns]" ] }, "metadata": {}, @@ -2871,10 +2909,9 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate(method='SLSQP', options={\"maxiter\": 1000})\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 3.0254868525048906e-31 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/optimization.py:338: UserWarning: SLSQP may not play nicely with unbounded parameters\n", + "if you get poor results, consider setting global bounds with model.set_cap()\n", + " warnings.warn( # infinite bounds # )\n" ] }, { @@ -2889,3290 +2926,3126 @@ " \n", " \n", " \n", - " -2.139\n", - " -2.139000\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in\n", + " -0.817659\n", " \n", " \n", - " -3.934\n", - " -3.934000\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork\n", + " -3.857785\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in\n", - " -0.741225\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout\n", + " -2.243908\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in\n", - " -2.594489\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort\n", + " -2.254734\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in\n", - " -0.447476\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr\n", + " -1.509245\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in\n", - " -1.657817\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint\n", + " -1.850181\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in\n", - " 0.039441\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_school\n", + " -2.103501\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in\n", - " 0.754748\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping\n", + " -1.374719\n", " \n", " \n", - " coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours\n", - " -1.998832\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_social\n", + " -1.100007\n", " \n", " \n", - " coef_dummy_for_all_stops_made_by_transit\n", - " -0.672704\n", + " coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ\n", + " -2.583745\n", " \n", " \n", - " coef_dummy_for_distance_in_miles\n", - " 0.043974\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in\n", + " -2.553493\n", " \n", " \n", - " coef_dummy_for_distance_less_than_20_miles\n", - " -0.588175\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_atwork\n", + " -5.670203\n", " \n", " \n", - " coef_dummy_for_female\n", - " 0.282632\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout\n", + " -4.501523\n", " \n", " \n", - " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours\n", - " 0.713048\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort\n", + " -4.225203\n", " \n", " \n", - " coef_dummy_for_walking_to_all_stops\n", - " -1.491072\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr\n", + " -3.357879\n", " \n", " \n", - " coef_evening_arrival_19_00_interacted_with_return_tours\n", - " 0.606441\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint\n", + " -3.532505\n", " \n", " \n", - " coef_high_income_hh\n", - " 0.240000\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_school\n", + " -3.932072\n", " \n", " \n", - " coef_mid_to_high_income_hh\n", - " 0.230000\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping\n", + " -3.001964\n", " \n", " \n", - " coef_middle_to_low_income_hh\n", - " 0.170000\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_social\n", + " -2.787392\n", " \n", " \n", - " coef_no_stops_if_tour_mode_is_drivetransit\n", - " -999.000000\n", + " coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ\n", + " -3.625877\n", " \n", " \n", - " coef_num_kids_between_5_and_15_including_years_old\n", - " 0.089718\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in\n", + " -3.914672\n", " \n", " \n", - " coef_number_of_adults_16_years_old_\n", - " 0.074203\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_atwork\n", + " -7.451244\n", " \n", " \n", - " coef_number_of_cars_number_of_workers\n", - " -0.065713\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout\n", + " -5.213504\n", " \n", " \n", - " coef_number_of_escort_tours_tours_undertaken_by_the_person\n", - " 0.097792\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort\n", + " -4.713943\n", " \n", " \n", - " coef_number_of_hh_persons\n", - " -0.328159\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr\n", + " -4.262005\n", " \n", " \n", - " coef_number_of_school_tours_tours_undertaken_by_the_person\n", - " 0.130473\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint\n", + " -5.370287\n", " \n", " \n", - " coef_number_of_shop_tours_undertaken_by_the_houshold\n", - " -0.328294\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_school\n", + " -5.865306\n", " \n", " \n", - " coef_number_of_students_in_hh\n", - " 0.315297\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping\n", + " -4.443339\n", " \n", " \n", - " coef_number_of_subtours_in_the_tour\n", - " 0.337091\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_social\n", + " -4.476231\n", " \n", " \n", - " coef_number_of_university_tours_tours_undertaken_by_the_person\n", - " -0.480000\n", + " coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ\n", + " -5.123628\n", " \n", " \n", - " coef_number_of_work_tours_undertaken_by_the_person\n", - " -0.174762\n", + " coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in\n", + " -1.861054\n", " \n", " \n", - " coef_presence_of_kids_between_0_and_4_including_years_old\n", - " 0.557476\n", + " coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in\n", + " -4.027317\n", " \n", " \n", - " coef_presence_of_kids_between_5_and_15_including_years_old\n", - " 0.117544\n", + " coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in\n", + " -5.079709\n", " \n", " \n", - " -2.672\n", - " -2.672000\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in\n", + " -0.422070\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in_school\n", - " -1.768772\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_atwork\n", + " -3.667812\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in_school\n", - " -4.305875\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_eatout\n", + " -1.667177\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_school\n", - " -6.209404\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_escort\n", + " -0.883632\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in_school\n", - " -1.229114\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_othdiscr\n", + " -0.944455\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_3in_school\n", - " -3.185728\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint\n", + " -0.606891\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in\n", - " 1.573277\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_school\n", + " -1.203184\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school\n", - " 2.178796\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_shopping\n", + " -1.155398\n", " \n", " \n", - " coef_arrival_later_than_17_00_\n", - " 1.373657\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_social\n", + " -1.262568\n", " \n", " \n", - " coef_dummy_for_distance_in_miles_school\n", - " 0.092023\n", + " coef_alternative_specific_constant_for_return_stops_0out_1in_univ\n", + " -1.892703\n", " \n", " \n", - " coef_dummy_for_female_school\n", - " 0.687687\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in\n", + " -1.737740\n", " \n", " \n", - " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_\n", - " 1.904120\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_atwork\n", + " -5.352597\n", " \n", " \n", - " coef_dummy_for_walking_to_all_stops_school\n", - " -1.525112\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_eatout\n", + " -3.384664\n", " \n", " \n", - " coef_number_of_cars_number_of_workers_school\n", - " 0.282408\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_escort\n", + " -2.356788\n", " \n", " \n", - " coef_number_of_escort_tours_tours_undertaken_by_the_person_school\n", - " 2.320226\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr\n", + " -2.247941\n", " \n", " \n", - " coef_number_of_hh_persons_school\n", - " -0.602730\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint\n", + " -1.549591\n", " \n", " \n", - " coef_presence_of_kids_between_5_and_15_including_years_old_school\n", - " 0.021083\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_school\n", + " -2.618009\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ\n", - " -2.211722\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_shopping\n", + " -2.252362\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ\n", - " -4.042853\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_social\n", + " -2.751879\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ\n", - " -4.641257\n", + " coef_alternative_specific_constant_for_return_stops_0out_2in_univ\n", + " -3.564173\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in_univ\n", - " -1.574239\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in\n", + " -2.056255\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in_univ\n", - " -3.434788\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_atwork\n", + " -6.484627\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_3in_univ\n", - " -3.860338\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_eatout\n", + " -4.733147\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ\n", - " 2.019764\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr\n", + " -2.820768\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ\n", - " 2.117237\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_othmaint\n", + " -2.348697\n", " \n", " \n", - " coef_arrival_later_than_17_00__univ\n", - " 0.397767\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_school\n", + " -3.360598\n", " \n", " \n", - " coef_dummy_for_female_univ\n", - " 0.527348\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_shopping\n", + " -3.086157\n", " \n", " \n", - " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ\n", - " 1.086324\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_social\n", + " -3.617917\n", " \n", " \n", - " coef_hh_accesibility_for_inbound_tours_interaction\n", - " 0.243672\n", + " coef_alternative_specific_constant_for_return_stops_0out_3in_univ\n", + " -3.549561\n", " \n", " \n", - " coef_number_of_escort_tours_tours_undertaken_by_the_person_univ\n", - " 1.858956\n", + " coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in\n", + " -1.349229\n", " \n", " \n", - " coef_number_of_hh_persons_univ\n", - " -0.326432\n", + " coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in\n", + " -3.006175\n", " \n", " \n", - " coef_number_of_vehicles\n", - " 0.265859\n", + " coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in\n", + " -3.092591\n", " \n", " \n", - " coef_presence_of_kids_between_5_and_15_including_years_old_univ\n", - " -0.061482\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in\n", + " -0.033904\n", " \n", " \n", - " -1.783\n", - " -1.783000\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in\n", + " 0.619994\n", " \n", " \n", - " -2.874\n", - " -2.874000\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_atwork\n", + " 2.196478\n", " \n", " \n", - " -3.379\n", - " -3.379000\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout\n", + " 0.956558\n", " \n", " \n", - " 1.497\n", - " 1.497000\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr\n", + " 0.779563\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in_social\n", - " -1.292414\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint\n", + " 0.525190\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_social\n", - " -42.649173\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping\n", + " 0.518875\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in\n", - " -4.158467\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social\n", + " 0.620383\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in\n", - " -28.920114\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ\n", + " 1.218010\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in_social\n", - " 0.474303\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in\n", + " 0.555526\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in_social\n", - " -2.400345\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout\n", + " 1.979106\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_3in_social\n", - " -3.106704\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort\n", + " -1.796251\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in\n", - " -1.489538\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othdiscr\n", + " 1.148950\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in\n", - " -3.529456\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint\n", + " 0.489201\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social\n", - " 2.605627\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school\n", + " 1.203689\n", + " \n", + " \n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping\n", + " 0.411626\n", " \n", " \n", " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_social\n", - " -9.808088\n", + " 0.866347\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in\n", - " -0.168353\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ\n", + " 1.958267\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in\n", - " -26.078121\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in\n", + " -0.057283\n", " \n", " \n", - " coef_arrival_later_than_17_00__social\n", - " -0.470057\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in\n", + " 0.608179\n", " \n", " \n", - " coef_at_least_one_kid_and_one_adult_participate_in_the_tour\n", - " 2.116069\n", + " coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in\n", + " 1.429603\n", " \n", " \n", - " coef_dummy_for_a_return_visiting_tour\n", - " -1.746712\n", + " coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours\n", + " -1.913742\n", " \n", " \n", - " coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg\n", - " 0.344025\n", + " coef_arrival_later_than_17_00_\n", + " 1.839504\n", " \n", " \n", - " coef_dummy_for_an_outbound_visiting_tour\n", - " -0.742278\n", + " coef_arrival_later_than_17_00__othdiscr\n", + " -0.637546\n", " \n", " \n", - " coef_dummy_for_distance_in_miles_social\n", - " -0.078778\n", + " coef_arrival_later_than_17_00__social\n", + " -0.708359\n", " \n", " \n", - " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_\n", - " 1.141918\n", + " coef_arrival_later_than_17_00__univ\n", + " 0.352924\n", " \n", " \n", - " coef_dummy_for_walking_to_all_stops_social\n", - " -2.087075\n", + " coef_at_least_one_kid_and_one_adult_participate_in_the_tour\n", + " 0.598380\n", " \n", " \n", - " coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction\n", - " -0.027490\n", + " coef_dummy_for_a_return_visiting_tour\n", + " -0.542418\n", " \n", " \n", - " coef_number_of_shop_tours_undertaken_by_the_person\n", - " -0.177893\n", + " coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg\n", + " 0.637918\n", " \n", " \n", - " coef_number_of_vehicles_social\n", - " -0.154123\n", + " coef_dummy_for_all_stops_made_by_transit\n", + " -0.698140\n", " \n", " \n", - " coef_number_of_work_tours_undertaken_by_the_person_social\n", - " -0.010226\n", + " coef_dummy_for_an_outbound_visiting_tour\n", + " -0.547383\n", " \n", " \n", - " -3.024\n", - " -3.024000\n", + " coef_dummy_for_distance_in_miles\n", + " 0.008199\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping\n", - " -1.491871\n", + " coef_dummy_for_distance_in_miles_othdiscr\n", + " -0.016398\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping\n", - " -3.340709\n", + " coef_dummy_for_distance_in_miles_othmaint\n", + " 0.024622\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping\n", - " -4.537979\n", + " coef_dummy_for_distance_in_miles_school\n", + " 0.048727\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in_shopping\n", - " -1.234250\n", + " coef_dummy_for_distance_in_miles_shopping\n", + " 0.042050\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in_shopping\n", - " -2.580404\n", + " coef_dummy_for_distance_in_miles_social\n", + " -0.011161\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping\n", - " 0.585255\n", + " coef_dummy_for_distance_less_than_10_miles_\n", + " 0.311571\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping\n", - " -0.015580\n", + " coef_dummy_for_distance_less_than_20_miles\n", + " -0.209813\n", " \n", " \n", - " coef_dummy_for_distance_in_miles_shopping\n", - " 0.021405\n", + " coef_dummy_for_distance_less_than_20_miles_\n", + " -0.403250\n", " \n", " \n", " coef_dummy_for_distance_less_than_5_miles\n", - " 0.152609\n", + " 0.391002\n", " \n", " \n", - " coef_dummy_for_female_shopping\n", - " 0.339336\n", + " coef_dummy_for_distance_less_than_5_miles_escort\n", + " 0.287884\n", " \n", " \n", - " coef_dummy_for_only_adults_participate_in_the_tour\n", - " 1.542207\n", + " coef_dummy_for_female\n", + " 0.231116\n", " \n", " \n", - " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping\n", - " 3.102092\n", + " coef_dummy_for_female_othmaint\n", + " 0.298798\n", " \n", " \n", - " coef_dummy_for_walking_to_all_stops_shopping\n", - " -1.883904\n", + " coef_dummy_for_female_school\n", + " 0.518603\n", " \n", " \n", - " coef_num_kids_between_5_and_15_including_years_old_shopping\n", - " -0.047846\n", + " coef_dummy_for_female_shopping\n", + " 0.199726\n", " \n", " \n", - " coef_number_of_hh_persons_shopping\n", - " -0.086986\n", + " coef_dummy_for_female_univ\n", + " 0.539334\n", " \n", " \n", - " coef_number_of_maintenace_tours_tours_undertaken_by_the_person\n", - " -0.325799\n", + " coef_dummy_for_only_adults_participate_in_the_tour\n", + " 0.241206\n", " \n", " \n", - " coef_number_of_shop_tours_undertaken_by_the_houshold_shopping\n", - " 0.147901\n", + " coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_\n", + " 0.270000\n", " \n", " \n", - " coef_number_of_university_tours_tours_undertaken_by_the_person_shopping\n", - " -0.670900\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours\n", + " 0.629566\n", " \n", " \n", - " coef_number_of_work_tours_undertaken_by_the_person_shopping\n", - " -0.437195\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_\n", + " 1.296845\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout\n", - " -1.879526\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_\n", + " 0.935859\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout\n", - " -32.613372\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__escort\n", + " 0.468894\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout\n", - " -3.738703\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othdiscr\n", + " 0.843539\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in_eatout\n", - " -1.687121\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint\n", + " 0.103716\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in_eatout\n", - " -3.033355\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping\n", + " 1.035541\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_3in_eatout\n", - " -4.837278\n", + " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ\n", + " 0.771998\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout\n", - " -31.986519\n", + " coef_dummy_for_walking_to_all_stops\n", + " -1.590379\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout\n", - " -30.916248\n", + " coef_dummy_for_walking_to_all_stops_escort\n", + " -2.041910\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort\n", - " -2.225477\n", + " coef_dummy_for_walking_to_all_stops_othdiscr\n", + " -2.338073\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort\n", - " -4.857476\n", + " coef_dummy_for_walking_to_all_stops_othmaint\n", + " -1.321421\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort\n", - " -4.792453\n", + " coef_dummy_for_walking_to_all_stops_school\n", + " -2.074837\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in_escort\n", - " -1.279087\n", + " coef_dummy_for_walking_to_all_stops_shopping\n", + " -1.462311\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in_escort\n", - " -2.459341\n", + " coef_dummy_for_walking_to_all_stops_social\n", + " -1.649814\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort\n", - " -12.346112\n", + " coef_evening_arrival_19_00_interacted_with_return_tours\n", + " 0.297829\n", " \n", " \n", - " coef_dummy_for_distance_less_than_5_miles_escort\n", - " 0.269808\n", + " coef_hh_accesibility_for_inbound_tours_interaction\n", + " 0.258426\n", " \n", " \n", 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coef_num_kids_between_5_and_15_including_years_old\n", + " 0.083356\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint\n", - " -2.122249\n", + " coef_num_kids_between_5_and_15_including_years_old_shopping\n", + " 0.033141\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint\n", - " -3.470020\n", + " coef_number_of_adults_16_years_old_\n", + " 0.006177\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint\n", - " -4.230867\n", + " coef_number_of_cars_number_of_workers\n", + " 0.195317\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint\n", - " -0.833758\n", + " coef_number_of_cars_number_of_workers_school\n", + " 0.630311\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint\n", - " -1.186628\n", + " coef_number_of_eating_tours_tours_undertaken_by_the_person\n", + " -0.424859\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint\n", - " 0.315206\n", + " coef_number_of_escort_tours_tours_undertaken_by_the_person\n", + " 0.285502\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint\n", - " -31.005508\n", + " coef_number_of_escort_tours_tours_undertaken_by_the_person_escort\n", + " -0.077934\n", " \n", " \n", - " coef_dummy_for_distance_in_miles_othmaint\n", - " 0.102023\n", + " coef_number_of_escort_tours_tours_undertaken_by_the_person_school\n", + " 1.290196\n", " \n", " \n", - " coef_dummy_for_distance_less_than_20_miles_\n", - " -0.621770\n", + " coef_number_of_escort_tours_tours_undertaken_by_the_person_univ\n", + " 0.984566\n", " \n", " \n", - " coef_dummy_for_female_othmaint\n", - " 0.281419\n", + " coef_number_of_hh_persons\n", + " -0.291501\n", " \n", " \n", - " coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint\n", - " 0.168631\n", + " coef_number_of_hh_persons_escort\n", + " -0.289093\n", " \n", " \n", - " coef_dummy_for_walking_to_all_stops_othmaint\n", - " -2.298979\n", + " coef_number_of_hh_persons_school\n", + " -0.521641\n", " \n", " \n", - " coef_middle_to_low_income_hh_\n", - " 0.170000\n", + " coef_number_of_hh_persons_shopping\n", + " -0.164111\n", " \n", " \n", - " coef_number_of_maintenace_tours_undertaken_by_the_houshold\n", - " -0.264644\n", + " coef_number_of_hh_persons_univ\n", + " -0.296528\n", " \n", " \n", - " coef_number_of_persons_participating_in_the_tour_return_stops_interaction\n", - " 0.626322\n", + " coef_number_of_maintenace_tours_tours_undertaken_by_the_person\n", + " -0.281780\n", " \n", " \n", - " coef_number_of_shool_tours_tours_undertaken_by_the_person\n", - " -29.423035\n", + " coef_number_of_maintenace_tours_tours_undertaken_by_the_person_othdiscr\n", + " -0.273666\n", " \n", " \n", - " coef_number_of_shop_tours_undertaken_by_the_person_othmaint\n", - " 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coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr\n", - " -2.956642\n", + " coef_number_of_shop_tours_undertaken_by_the_houshold\n", + " 0.194165\n", " \n", " \n", - " coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr\n", - " -4.394706\n", + " coef_number_of_shop_tours_undertaken_by_the_houshold_shopping\n", + " -0.142075\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr\n", - " -2.518386\n", + " coef_number_of_shop_tours_undertaken_by_the_person\n", + " -0.303706\n", " \n", " \n", - " coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr\n", - " -3.024368\n", + " coef_number_of_shop_tours_undertaken_by_the_person_othdiscr\n", + " -0.641366\n", " \n", " \n", - " coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr\n", - " 1.088281\n", + " coef_number_of_shop_tours_undertaken_by_the_person_othmaint\n", + " -0.175208\n", " \n", " \n", - " 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0
coef_alternative_specific_constant_for_outbound_stops_1out_0in1.318164e-07
coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork-3.450424e-05
coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout-2.357602e-05
coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort7.327103e-06
coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr5.830160e-05
coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint-1.687364e-05
coef_alternative_specific_constant_for_outbound_stops_1out_0in_school-1.669586e-05
coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping-1.179775e-08
coef_alternative_specific_constant_for_outbound_stops_1out_0in_social1.831328e-05
coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ1.659316e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in3.044727e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_atwork1.175946e-08
coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout1.022786e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort1.105196e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr3.555996e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint-1.893055e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_school3.445422e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping3.263881e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_social-2.496547e-05
coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ1.438293e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in-1.071751e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_atwork-1.888854e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout1.167128e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort1.356432e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr-3.000084e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint1.163413e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_school-2.151305e-06
coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping-1.610268e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_social1.204101e-05
coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ-2.399461e-05
coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in1.866585e-05
coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in9.984701e-06
coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in-2.645255e-05
coef_alternative_specific_constant_for_return_stops_0out_1in-4.708365e-05
coef_alternative_specific_constant_for_return_stops_0out_1in_atwork5.651507e-06
coef_alternative_specific_constant_for_return_stops_0out_1in_eatout1.418406e-06
coef_alternative_specific_constant_for_return_stops_0out_1in_escort-1.108521e-04
coef_alternative_specific_constant_for_return_stops_0out_1in_othdiscr3.754350e-05
coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint-5.058401e-05
coef_alternative_specific_constant_for_return_stops_0out_1in_school-1.317771e-05
coef_alternative_specific_constant_for_return_stops_0out_1in_shopping-1.353818e-05
coef_alternative_specific_constant_for_return_stops_0out_1in_social9.274282e-06
coef_alternative_specific_constant_for_return_stops_0out_1in_univ1.265812e-05
coef_alternative_specific_constant_for_return_stops_0out_2in-1.013444e-04
coef_alternative_specific_constant_for_return_stops_0out_2in_atwork-5.726811e-06
coef_alternative_specific_constant_for_return_stops_0out_2in_eatout2.119117e-06
coef_alternative_specific_constant_for_return_stops_0out_2in_escort7.074619e-05
coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr6.714927e-06
coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint6.821290e-06
coef_alternative_specific_constant_for_return_stops_0out_2in_school2.925330e-05
coef_alternative_specific_constant_for_return_stops_0out_2in_shopping-4.080487e-05
coef_alternative_specific_constant_for_return_stops_0out_2in_social-4.285482e-05
coef_alternative_specific_constant_for_return_stops_0out_2in_univ3.661192e-05
coef_alternative_specific_constant_for_return_stops_0out_3in1.133758e-04
coef_alternative_specific_constant_for_return_stops_0out_3in_atwork9.086276e-06
coef_alternative_specific_constant_for_return_stops_0out_3in_eatout4.586623e-06
coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr2.426809e-05
coef_alternative_specific_constant_for_return_stops_0out_3in_othmaint-2.486703e-05
coef_alternative_specific_constant_for_return_stops_0out_3in_school-2.227797e-05
coef_alternative_specific_constant_for_return_stops_0out_3in_shopping2.495105e-05
coef_alternative_specific_constant_for_return_stops_0out_3in_social-6.029497e-05
coef_alternative_specific_constant_for_return_stops_0out_3in_univ1.155529e-05
coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in-3.870571e-06
coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in7.661858e-06
coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in-4.833521e-06
coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in6.276561e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in-3.395874e-06
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_atwork3.600197e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout4.455986e-07
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr-2.367744e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint-1.442248e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping-8.406942e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social1.283479e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ-1.771563e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in1.798862e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout-2.174096e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort4.005556e-06
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othdiscr-8.102586e-06
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint-7.835219e-06
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school1.969169e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping-1.891110e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_social-1.234210e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ1.908165e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in-1.399555e-07
coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in1.065266e-05
coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in-2.973298e-05
coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours4.011337e-05
coef_arrival_later_than_17_00_9.509009e-06
coef_arrival_later_than_17_00__othdiscr5.323204e-05
coef_arrival_later_than_17_00__social-3.236359e-05
coef_arrival_later_than_17_00__univ6.463924e-06
coef_at_least_one_kid_and_one_adult_participate_in_the_tour5.107909e-05
coef_dummy_for_a_return_visiting_tour3.250338e-05
coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg-2.201652e-05
coef_dummy_for_all_stops_made_by_transit2.881609e-05
coef_dummy_for_an_outbound_visiting_tour6.196096e-06
coef_dummy_for_distance_in_miles9.385043e-06
coef_dummy_for_distance_in_miles_othdiscr-7.834462e-06
coef_dummy_for_distance_in_miles_othmaint4.302748e-05
coef_dummy_for_distance_in_miles_school2.634834e-05
coef_dummy_for_distance_in_miles_shopping-3.883054e-06
coef_dummy_for_distance_in_miles_social-2.876791e-06
coef_dummy_for_distance_less_than_10_miles_8.995802e-05
coef_dummy_for_distance_less_than_20_miles-2.204602e-05
coef_dummy_for_distance_less_than_20_miles_-1.281965e-04
coef_dummy_for_distance_less_than_5_miles-1.119062e-05
coef_dummy_for_distance_less_than_5_miles_escort-1.207377e-05
coef_dummy_for_female-8.480269e-06
coef_dummy_for_female_othmaint-4.926674e-05
coef_dummy_for_female_school1.424463e-05
coef_dummy_for_female_shopping-4.273230e-05
coef_dummy_for_female_univ3.630606e-05
coef_dummy_for_only_adults_participate_in_the_tour1.089846e-05
coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_0.000000e+00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours-4.743285e-05
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_-9.652111e-05
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_1.664266e-05
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__escort-5.502657e-06
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othdiscr-3.421032e-06
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint-2.300422e-05
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping3.276266e-06
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ-1.959322e-05
coef_dummy_for_walking_to_all_stops6.006629e-06
coef_dummy_for_walking_to_all_stops_escort-3.338229e-05
coef_dummy_for_walking_to_all_stops_othdiscr3.003054e-06
coef_dummy_for_walking_to_all_stops_othmaint5.544972e-06
coef_dummy_for_walking_to_all_stops_school5.590612e-05
coef_dummy_for_walking_to_all_stops_shopping-6.986420e-06
coef_dummy_for_walking_to_all_stops_social-1.812323e-05
coef_evening_arrival_19_00_interacted_with_return_tours-1.075717e-04
coef_hh_accesibility_for_inbound_tours_interaction1.545214e-04
coef_high_income_hh0.000000e+00
coef_mid_to_high_income_hh0.000000e+00
coef_middle_to_low_income_hh0.000000e+00
coef_middle_to_low_income_hh_0.000000e+00
coef_middle_to_low_income_hh__atwork1.591693e-05
coef_no_stops_if_tour_mode_is_drivetransit0.000000e+00
coef_num_kids_between_5_and_15_including_years_old1.811798e-04
coef_num_kids_between_5_and_15_including_years_old_shopping-2.177398e-05
coef_number_of_adults_16_years_old_1.607333e-04
coef_number_of_cars_number_of_workers-5.548331e-05
coef_number_of_cars_number_of_workers_school-4.595882e-05
coef_number_of_eating_tours_tours_undertaken_by_the_person-1.221656e-05
coef_number_of_escort_tours_tours_undertaken_by_the_person3.915582e-05
coef_number_of_escort_tours_tours_undertaken_by_the_person_escort-4.632281e-05
coef_number_of_escort_tours_tours_undertaken_by_the_person_school-1.496317e-06
coef_number_of_escort_tours_tours_undertaken_by_the_person_univ5.820883e-06
coef_number_of_hh_persons-9.384152e-05
coef_number_of_hh_persons_escort4.408343e-05
coef_number_of_hh_persons_school-4.397796e-05
coef_number_of_hh_persons_shopping3.401635e-05
coef_number_of_hh_persons_univ-6.991701e-05
coef_number_of_maintenace_tours_tours_undertaken_by_the_person-1.812134e-05
coef_number_of_maintenace_tours_tours_undertaken_by_the_person_othdiscr-2.534184e-05
coef_number_of_maintenace_tours_undertaken_by_the_houshold2.050003e-06
coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction6.755108e-06
coef_number_of_persons_participating_in_the_tour_return_stops_interaction-5.553747e-05
coef_number_of_school_tours_tours_undertaken_by_the_person3.616606e-06
coef_number_of_shool_tours_tours_undertaken_by_the_person-4.217392e-05
coef_number_of_shool_tours_tours_undertaken_by_the_person_othdiscr7.289526e-06
coef_number_of_shop_tours_undertaken_by_the_houshold5.577565e-05
coef_number_of_shop_tours_undertaken_by_the_houshold_shopping1.055865e-05
coef_number_of_shop_tours_undertaken_by_the_person2.191567e-05
coef_number_of_shop_tours_undertaken_by_the_person_othdiscr1.735752e-05
coef_number_of_shop_tours_undertaken_by_the_person_othmaint-1.989246e-05
coef_number_of_students_in_hh-3.900317e-05
coef_number_of_students_in_hh_escort-8.924947e-05
coef_number_of_subtours_in_the_tour-1.634519e-05
coef_number_of_university_tours_tours_undertaken_by_the_person0.000000e+00
coef_number_of_university_tours_tours_undertaken_by_the_person_othmaint0.000000e+00
coef_number_of_university_tours_tours_undertaken_by_the_person_shopping0.000000e+00
coef_number_of_vehicles-6.107892e-05
coef_number_of_vehicles_social4.943979e-05
coef_number_of_work_tours_undertaken_by_the_person-4.194501e-05
coef_number_of_work_tours_undertaken_by_the_person_escort-9.605595e-07
coef_number_of_work_tours_undertaken_by_the_person_othdiscr3.079891e-05
coef_number_of_work_tours_undertaken_by_the_person_othmaint-2.034031e-05
coef_number_of_work_tours_undertaken_by_the_person_shopping-2.318647e-05
coef_number_of_work_tours_undertaken_by_the_person_social-4.019493e-05
coef_presence_of_kids_between_0_and_4_including_years_old-9.532148e-05
coef_presence_of_kids_between_5_and_15_including_years_old1.004783e-04
coef_presence_of_kids_between_5_and_15_including_years_old_school-1.763120e-05
coef_presence_of_kids_between_5_and_15_including_years_old_univ-4.857618e-06
coef_primary_destination_accessibility_log_of_it_0.170704-4.708946e-06
coef_subtour_departure_less_than_or_equal_to_11am0.266339-3.435878e-05
coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_-0.1523849.929553e-05
coef_subtour_duration_in_hours_integer_-1.478819e-04
coef_subtour_return_time_greater_or_equal_to_2pm-2.289188e-05
nit81nfev82njev81status0message'Optimization terminated successfully'successTrueelapsed_time0:00:17.187577method'SLSQP'n_cases69971iteration_number81loglike-78188.35046478933" + ], + "text/plain": [ + "┣ x: coef_alternative_specific_constant_for_outbound_stops_1out_0in -0.817659\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork -3.857785\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout -2.243908\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort -2.254734\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr -1.509245\n", + "┃ ... \n", + "┃ coef_primary_destination_accessibility_log_of_it_ 0.191205\n", + "┃ coef_subtour_departure_less_than_or_equal_to_11am 0.352247\n", + "┃ coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_ 0.023141\n", + "┃ coef_subtour_duration_in_hours_integer_ 0.555413\n", + "┃ coef_subtour_return_time_greater_or_equal_to_2pm 0.206446\n", + "┃ Length: 188, dtype: float64\n", + "┣ logloss: 1.1174393743806623\n", + "┣ d_logloss: coef_alternative_specific_constant_for_outbound_stops_1out_0in 1.318164e-07\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork -3.450424e-05\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout -2.357602e-05\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort 7.327103e-06\n", + "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr 5.830160e-05\n", + "┃ ... \n", + "┃ coef_primary_destination_accessibility_log_of_it_ -4.708946e-06\n", + "┃ coef_subtour_departure_less_than_or_equal_to_11am -3.435878e-05\n", + "┃ coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_ 9.929553e-05\n", + "┃ coef_subtour_duration_in_hours_integer_ -1.478819e-04\n", + "┃ coef_subtour_return_time_greater_or_equal_to_2pm -2.289188e-05\n", + "┃ Length: 188, dtype: float64\n", + "┣ nit: 81\n", + "┣ nfev: 82\n", + "┣ njev: 81\n", + "┣ status: 0\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ success: True\n", + "┣ elapsed_time: datetime.timedelta(seconds=17, microseconds=187577)\n", + "┣ method: 'SLSQP'\n", + "┣ n_cases: 69971\n", + "┣ iteration_number: 81\n", + "┣ loglike: -78188.35046478933" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.estimate(method='SLSQP', options={\"maxiter\": 1000})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimated coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - "
coef_subtour_duration_in_hours_integer_0.681811 ValueStd Errt StatSignifNull Value
coef_subtour_return_time_greater_or_equal_to_2pm1.549569
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0Parameter     
-2.1390.000000e+00coef_alternative_specific_constant_for_outbound_stops_1out_0in-0.818 0.0344-23.78*** 0.00
-3.9340.000000e+00coef_alternative_specific_constant_for_outbound_stops_1out_0in_atwork-3.86 0.249-15.49*** 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in-7.969702e-05coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout-2.24 0.102-22.05*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in-7.162521e-04coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort-2.25 0.0741-30.45*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in-6.335846e-05coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr-1.51 0.0658-22.93*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in9.540429e-05coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint-1.85 0.0875-21.14*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in-5.140413e-04coef_alternative_specific_constant_for_outbound_stops_1out_0in_school-2.10 0.0714-29.46*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in-8.954661e-05coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping-1.37 0.0511-26.89*** 0.00
coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours-2.097418e-04coef_alternative_specific_constant_for_outbound_stops_1out_0in_social-1.10 0.565-1.95 0.00
coef_dummy_for_all_stops_made_by_transit5.963845e-05coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ-2.58 0.148-17.51*** 0.00
coef_dummy_for_distance_in_miles-2.339326e-03coef_alternative_specific_constant_for_outbound_stops_2out_0in-2.55 0.0622-41.08*** 0.00
coef_dummy_for_distance_less_than_20_miles-4.727787e-04coef_alternative_specific_constant_for_outbound_stops_2out_0in_atwork-5.67 0.275-20.66*** 0.00
coef_dummy_for_female2.329415e-05coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout-4.50 0.236-19.10*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours-2.446071e-04coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort-4.23 0.158-26.72*** 0.00
coef_dummy_for_walking_to_all_stops-2.009061e-04coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr-3.36 0.116-28.93*** 0.00
coef_evening_arrival_19_00_interacted_with_return_tours-2.608074e-04coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint-3.53 0.157-22.55*** 0.00
coef_high_income_hh0.000000e+00coef_alternative_specific_constant_for_outbound_stops_2out_0in_school-3.93 0.122-32.36*** 0.00
coef_mid_to_high_income_hh0.000000e+00coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping-3.00 0.0869-34.53*** 0.00
coef_middle_to_low_income_hh0.000000e+00coef_alternative_specific_constant_for_outbound_stops_2out_0in_social-2.79 0.582-4.79*** 0.00
coef_no_stops_if_tour_mode_is_drivetransit0.000000e+00coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ-3.63 0.193-18.79*** 0.00
coef_num_kids_between_5_and_15_including_years_old1.941456e-04coef_alternative_specific_constant_for_outbound_stops_3out_0in-3.91 0.0934-41.93*** 0.00
coef_number_of_adults_16_years_old_2.205314e-04coef_alternative_specific_constant_for_outbound_stops_3out_0in_atwork-7.45 0.358-20.80*** 0.00
coef_number_of_cars_number_of_workers5.994133e-04coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout-5.21 0.325-16.02*** 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person-5.224365e-05coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort-4.71 0.202-23.35*** 0.00
coef_number_of_hh_persons1.586957e-03coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr-4.26 0.163-26.11*** 0.00
coef_number_of_school_tours_tours_undertaken_by_the_person-3.034153e-05coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint-5.37 0.326-16.47*** 0.00
coef_number_of_shop_tours_undertaken_by_the_houshold1.116601e-04coef_alternative_specific_constant_for_outbound_stops_3out_0in_school-5.87 0.244-24.09*** 0.00
coef_number_of_students_in_hh1.126486e-04coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping-4.44 0.143-30.98*** 0.00
coef_number_of_subtours_in_the_tour-4.322246e-04coef_alternative_specific_constant_for_outbound_stops_3out_0in_social-4.48 0.653-6.86*** 0.00
coef_number_of_university_tours_tours_undertaken_by_the_person0.000000e+00coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ-5.12 0.304-16.88*** 0.00
coef_number_of_work_tours_undertaken_by_the_person-3.832504e-04coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_1out_0in-1.86 0.132-14.07*** 0.00
coef_presence_of_kids_between_0_and_4_including_years_old-2.811261e-04coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in-4.03 0.366-11.00*** 0.00
coef_presence_of_kids_between_5_and_15_including_years_old1.838518e-04coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in-5.08 0.509-9.97*** 0.00
-2.6720.000000e+00coef_alternative_specific_constant_for_return_stops_0out_1in-0.422 0.0376-11.23*** 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_school3.622369e-04coef_alternative_specific_constant_for_return_stops_0out_1in_atwork-3.67 0.250-14.69*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_school-1.073515e-04coef_alternative_specific_constant_for_return_stops_0out_1in_eatout-1.67 0.0960-17.36*** 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_school5.591613e-07coef_alternative_specific_constant_for_return_stops_0out_1in_escort-0.884 0.0729-12.12*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_school-4.218724e-04coef_alternative_specific_constant_for_return_stops_0out_1in_othdiscr-0.944 0.0691-13.68*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_school-1.172540e-04coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint-0.607 0.0995-6.10*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in6.638463e-05coef_alternative_specific_constant_for_return_stops_0out_1in_school-1.20 0.0752-16.01*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school-4.148872e-05coef_alternative_specific_constant_for_return_stops_0out_1in_shopping-1.16 0.0523-22.10*** 0.00
coef_arrival_later_than_17_00_3.242621e-05coef_alternative_specific_constant_for_return_stops_0out_1in_social-1.26 0.260-4.86*** 0.00
coef_dummy_for_distance_in_miles_school1.257368e-04coef_alternative_specific_constant_for_return_stops_0out_1in_univ-1.89 0.143-13.28*** 0.00
coef_dummy_for_female_school1.151809e-04coef_alternative_specific_constant_for_return_stops_0out_2in-1.74 0.0605-28.73*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_1.609739e-04coef_alternative_specific_constant_for_return_stops_0out_2in_atwork-5.35 0.270-19.83*** 0.00
coef_dummy_for_walking_to_all_stops_school-2.948826e-05coef_alternative_specific_constant_for_return_stops_0out_2in_eatout-3.38 0.161-21.08*** 0.00
coef_number_of_cars_number_of_workers_school-1.267875e-04coef_alternative_specific_constant_for_return_stops_0out_2in_escort-2.36 0.104-22.69*** 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person_school-1.086302e-04coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr-2.25 0.0973-23.10*** 0.00
coef_number_of_hh_persons_school-8.934922e-05coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint-1.55 0.121-12.84*** 0.00
coef_presence_of_kids_between_5_and_15_including_years_old_school2.925349e-04coef_alternative_specific_constant_for_return_stops_0out_2in_school-2.62 0.101-25.83*** 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ2.297287e-04coef_alternative_specific_constant_for_return_stops_0out_2in_shopping-2.25 0.0774-29.09*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ-7.201840e-04coef_alternative_specific_constant_for_return_stops_0out_2in_social-2.75 0.290-9.48*** 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ3.180905e-04coef_alternative_specific_constant_for_return_stops_0out_2in_univ-3.56 0.199-17.95*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_univ-6.094370e-04coef_alternative_specific_constant_for_return_stops_0out_3in-2.06 0.0653-31.50*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_univ2.159121e-04coef_alternative_specific_constant_for_return_stops_0out_3in_atwork-6.48 0.304-21.31*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_univ-2.067752e-04coef_alternative_specific_constant_for_return_stops_0out_3in_eatout-4.73 0.275-17.24*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ-1.224656e-04coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr-2.82 0.121-23.37*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ1.115042e-04coef_alternative_specific_constant_for_return_stops_0out_3in_othmaint-2.35 0.149-15.81*** 0.00
coef_arrival_later_than_17_00__univ3.883451e-05coef_alternative_specific_constant_for_return_stops_0out_3in_school-3.36 0.124-27.19*** 0.00
coef_dummy_for_female_univ-2.006300e-04coef_alternative_specific_constant_for_return_stops_0out_3in_shopping-3.09 0.0889-34.71*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ1.801912e-05coef_alternative_specific_constant_for_return_stops_0out_3in_social-3.62 0.345-10.49*** 0.00
coef_hh_accesibility_for_inbound_tours_interaction1.451782e-04coef_alternative_specific_constant_for_return_stops_0out_3in_univ-3.55 0.207-17.16*** 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person_univ4.571784e-05coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in-1.35 0.109-12.40*** 0.00
coef_number_of_hh_persons_univ-9.257971e-05coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in-3.01 0.298-10.08*** 0.00
coef_number_of_vehicles-3.492440e-05coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_3in-3.09 0.319-9.71*** 0.00
coef_presence_of_kids_between_5_and_15_including_years_old_univ-1.005346e-04coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in-0.0339 0.0356-0.95 0.00
-1.7830.000000e+00coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in 0.620 0.180 3.44*** 0.00
-2.8740.000000e+00coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_atwork 2.20 0.379 5.79*** 0.00
-3.3790.000000e+00coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout 0.957 0.397 2.41* 0.00
1.4970.000000e+00coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr 0.780 0.146 5.35*** 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_social-1.680088e-04coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint 0.525 0.198 2.65** 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_social-1.534541e-16coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping 0.519 0.123 4.22*** 0.00
coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in-1.142652e-04coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social 0.620 0.318 1.95 0.00
coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in-2.817966e-11coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ 1.22 0.294 4.14*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_social2.375526e-04coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in 0.556 0.128 4.35*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_social-2.745402e-08coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout 1.98 0.616 3.21** 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_social-8.463137e-05coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort-1.80 1.19-1.51 0.00
coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in6.231017e-04coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othdiscr 1.15 0.241 4.76*** 0.00
coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in8.245417e-05coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint 0.489 0.418 1.17 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social-1.206021e-04coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school 1.20 0.317 3.80*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_social-1.513475e-06coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping 0.412 0.245 1.68 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in-2.522694e-04coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_social 0.866 0.592 1.46 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in-3.539804e-12coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ 1.96 0.383 5.12*** 0.00
coef_arrival_later_than_17_00__social4.029635e-04coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in-0.0573 0.238-0.24 0.00
coef_at_least_one_kid_and_one_adult_participate_in_the_tour3.720282e-04coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in 0.608 0.463 1.31 0.00
coef_dummy_for_a_return_visiting_tour3.077125e-04coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_2out_3in 1.43 0.759 1.88 0.00
coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg1.591081e-04coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours-1.91 0.0370-51.75*** 0.00
coef_dummy_for_an_outbound_visiting_tour9.181048e-05coef_arrival_later_than_17_00_ 1.84 0.0758 24.27*** 0.00
coef_dummy_for_distance_in_miles_social6.278670e-04coef_arrival_later_than_17_00__othdiscr-0.638 0.0589-10.83*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_5.519594e-04coef_arrival_later_than_17_00__social-0.708 0.0742-9.55*** 0.00
coef_dummy_for_walking_to_all_stops_social4.496402e-04coef_arrival_later_than_17_00__univ 0.353 0.112 3.14** 0.00
coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction5.942079e-07coef_at_least_one_kid_and_one_adult_participate_in_the_tour 0.598 0.257 2.33* 0.00
coef_number_of_shop_tours_undertaken_by_the_person1.577034e-04coef_dummy_for_a_return_visiting_tour-0.542 0.250-2.17* 0.00
coef_number_of_vehicles_social1.013531e-03coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg 0.638 0.173 3.69*** 0.00
coef_number_of_work_tours_undertaken_by_the_person_social7.685343e-04coef_dummy_for_all_stops_made_by_transit-0.698 0.0392-17.80*** 0.00
-3.0240.000000e+00coef_dummy_for_an_outbound_visiting_tour-0.547 0.560-0.98 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping2.666344e-05coef_dummy_for_distance_in_miles 0.00820 0.00214 3.83*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping6.780320e-05coef_dummy_for_distance_in_miles_othdiscr-0.0164 0.00521-3.15** 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping-7.271615e-04coef_dummy_for_distance_in_miles_othmaint 0.0246 0.00536 4.59*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_shopping-9.635243e-06coef_dummy_for_distance_in_miles_school 0.0487 0.00546 8.92*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_shopping5.507170e-05coef_dummy_for_distance_in_miles_shopping 0.0421 0.00629 6.68*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping-5.236271e-05coef_dummy_for_distance_in_miles_social-0.0112 0.00621-1.80 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping-7.154384e-05coef_dummy_for_distance_less_than_10_miles_ 0.312 0.0676 4.61*** 0.00
coef_dummy_for_distance_in_miles_shopping2.523396e-04coef_dummy_for_distance_less_than_20_miles-0.210 0.0571-3.67*** 0.00
coef_dummy_for_distance_less_than_5_miles-2.476980e-05coef_dummy_for_distance_less_than_20_miles_-0.403 0.109-3.71*** 0.00
coef_dummy_for_female_shopping4.740709e-04coef_dummy_for_distance_less_than_5_miles 0.391 0.0569 6.87*** 0.00
coef_dummy_for_only_adults_participate_in_the_tour4.017317e-04coef_dummy_for_distance_less_than_5_miles_escort 0.288 0.0736 3.91*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping-4.228203e-05coef_dummy_for_female 0.231 0.0317 7.29*** 0.00
coef_dummy_for_walking_to_all_stops_shopping-3.690879e-04coef_dummy_for_female_othmaint 0.299 0.0693 4.31*** 0.00
coef_num_kids_between_5_and_15_including_years_old_shopping-1.102340e-04coef_dummy_for_female_school 0.519 0.0577 9.00*** 0.00
coef_number_of_hh_persons_shopping2.463465e-04coef_dummy_for_female_shopping 0.200 0.0467 4.28*** 0.00
coef_number_of_maintenace_tours_tours_undertaken_by_the_person7.302562e-05coef_dummy_for_female_univ 0.539 0.111 4.84*** 0.00
coef_number_of_shop_tours_undertaken_by_the_houshold_shopping-2.407855e-05coef_dummy_for_only_adults_participate_in_the_tour 0.241 0.194 1.25 0.00
coef_number_of_university_tours_tours_undertaken_by_the_person_shopping0.000000e+00coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_ 0.270 NA NA 0.00
coef_number_of_work_tours_undertaken_by_the_person_shopping8.243323e-05coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours 0.630 0.0404 15.57*** 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout-3.755981e-04coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_ 1.30 0.0744 17.43*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout-1.868019e-12coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ 0.936 0.0765 12.24*** 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout4.022851e-04coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__escort 0.469 0.163 2.88** 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_eatout-1.344298e-04coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othdiscr 0.844 0.118 7.13*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_eatout-1.392861e-04coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint 0.104 0.161 0.65 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_eatout1.106246e-04coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping 1.04 0.156 6.66*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout-1.796611e-14coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ 0.772 0.320 2.41* 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout-1.259540e-14coef_dummy_for_walking_to_all_stops-1.59 0.0898-17.72*** 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort-4.940134e-04coef_dummy_for_walking_to_all_stops_escort-2.04 0.265-7.71*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort1.495955e-04coef_dummy_for_walking_to_all_stops_othdiscr-2.34 0.153-15.29*** 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort-3.241983e-04coef_dummy_for_walking_to_all_stops_othmaint-1.32 0.170-7.79*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_escort9.510081e-04coef_dummy_for_walking_to_all_stops_school-2.07 0.112-18.53*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_escort-2.900724e-04coef_dummy_for_walking_to_all_stops_shopping-1.46 0.109-13.36*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort-1.512127e-06coef_dummy_for_walking_to_all_stops_social-1.65 0.150-10.97*** 0.00
coef_dummy_for_distance_less_than_5_miles_escort2.673121e-05coef_evening_arrival_19_00_interacted_with_return_tours 0.298 0.0385 7.73*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__escort-3.600725e-05coef_hh_accesibility_for_inbound_tours_interaction 0.258 0.0256 10.09*** 0.00
coef_dummy_for_walking_to_all_stops_escort1.191655e-04coef_high_income_hh 0.240 NA NA 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person_escort-7.410121e-05coef_mid_to_high_income_hh 0.230 NA NA 0.00
coef_number_of_hh_persons_escort-3.140191e-04coef_middle_to_low_income_hh 0.170 2.03e-14 BIG*** 0.00
coef_number_of_students_in_hh_escort1.269734e-04coef_middle_to_low_income_hh_ 0.170 1.42e-14 BIG*** 0.00
coef_number_of_work_tours_undertaken_by_the_person_escort-2.773780e-05coef_middle_to_low_income_hh__atwork 0.369 0.262 1.41 0.00
-2.4620.000000e+00coef_no_stops_if_tour_mode_is_drivetransit-999. NA NA 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint1.221742e-04coef_num_kids_between_5_and_15_including_years_old 0.0834 0.0434 1.92 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint3.550288e-05coef_num_kids_between_5_and_15_including_years_old_shopping 0.0331 0.0347 0.95 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint-3.087708e-04coef_number_of_adults_16_years_old_ 0.00618 0.0229 0.27 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint4.516106e-04coef_number_of_cars_number_of_workers 0.195 0.0414 4.72*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint3.975618e-04coef_number_of_cars_number_of_workers_school 0.630 0.0881 7.16*** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint-6.769433e-05coef_number_of_eating_tours_tours_undertaken_by_the_person-0.425 0.178-2.39* 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint-4.805955e-14coef_number_of_escort_tours_tours_undertaken_by_the_person 0.286 0.0527 5.42*** 0.00
coef_dummy_for_distance_in_miles_othmaint-1.679261e-04coef_number_of_escort_tours_tours_undertaken_by_the_person_escort-0.0779 0.0376-2.07* 0.00
coef_dummy_for_distance_less_than_20_miles_3.465085e-05coef_number_of_escort_tours_tours_undertaken_by_the_person_school 1.29 0.133 9.72*** 0.00
coef_dummy_for_female_othmaint1.685909e-04coef_number_of_escort_tours_tours_undertaken_by_the_person_univ 0.985 0.191 5.15*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint-1.142635e-04coef_number_of_hh_persons-0.292 0.0225-12.98*** 0.00
coef_dummy_for_walking_to_all_stops_othmaint-3.369012e-04coef_number_of_hh_persons_escort-0.289 0.0247-11.72*** 0.00
coef_middle_to_low_income_hh_0.000000e+00coef_number_of_hh_persons_school-0.522 0.0197-26.42*** 0.00
coef_number_of_maintenace_tours_undertaken_by_the_houshold1.869470e-04coef_number_of_hh_persons_shopping-0.164 0.0143-11.47*** 0.00
coef_number_of_persons_participating_in_the_tour_return_stops_interaction1.532520e-05coef_number_of_hh_persons_univ-0.297 0.0417-7.12*** 0.00
coef_number_of_shool_tours_tours_undertaken_by_the_person-9.850134e-13coef_number_of_maintenace_tours_tours_undertaken_by_the_person-0.282 0.0682-4.13*** 0.00
coef_number_of_shop_tours_undertaken_by_the_person_othmaint1.727916e-04coef_number_of_maintenace_tours_tours_undertaken_by_the_person_othdiscr-0.274 0.0896-3.05** 0.00
coef_number_of_university_tours_tours_undertaken_by_the_person_othmaint0.000000e+00coef_number_of_maintenace_tours_undertaken_by_the_houshold 0.280 0.157 1.78 0.00
coef_number_of_work_tours_undertaken_by_the_person_othmaint-1.965224e-04coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction-0.531 0.165-3.22** 0.00
-0.9210.000000e+00coef_number_of_persons_participating_in_the_tour_return_stops_interaction 0.488 0.0633 7.72*** 0.00
0.9390.000000e+00coef_number_of_school_tours_tours_undertaken_by_the_person-1.54 0.467-3.30*** 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr-3.506169e-04coef_number_of_shool_tours_tours_undertaken_by_the_person-1.56 0.258-6.05*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr1.907176e-04coef_number_of_shool_tours_tours_undertaken_by_the_person_othdiscr-0.844 0.100-8.43*** 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr3.189979e-04coef_number_of_shop_tours_undertaken_by_the_houshold 0.194 0.245 0.79 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr2.028679e-04coef_number_of_shop_tours_undertaken_by_the_houshold_shopping-0.142 0.117-1.21 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr1.562541e-05coef_number_of_shop_tours_undertaken_by_the_person-0.304 0.0956-3.18** 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr2.583440e-04coef_number_of_shop_tours_undertaken_by_the_person_othdiscr-0.641 0.0747-8.59*** 0.00
coef_arrival_later_than_17_00__othdiscr1.080590e-04coef_number_of_shop_tours_undertaken_by_the_person_othmaint-0.175 0.0767-2.28* 0.00
coef_dummy_for_distance_in_miles_othdiscr9.492548e-04coef_number_of_students_in_hh 0.206 0.0209 9.85*** 0.00
coef_dummy_for_distance_less_than_10_miles_1.588828e-04coef_number_of_students_in_hh_escort 0.217 0.0357 6.07*** 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othdiscr1.826016e-04coef_number_of_subtours_in_the_tour 0.185 0.0354 5.21*** 0.00
coef_dummy_for_walking_to_all_stops_othdiscr1.223109e-04coef_number_of_university_tours_tours_undertaken_by_the_person-0.480 NA NA 0.00
coef_number_of_maintenace_tours_tours_undertaken_by_the_person_othdiscr-7.663028e-04coef_number_of_university_tours_tours_undertaken_by_the_person_othmaint-0.625 4.04e-15-BIG*** 0.00
coef_number_of_shool_tours_tours_undertaken_by_the_person_othdiscr4.152763e-04coef_number_of_university_tours_tours_undertaken_by_the_person_shopping-0.671 7.34e-15-BIG*** 0.00
coef_number_of_shop_tours_undertaken_by_the_person_othdiscr3.120237e-04coef_number_of_vehicles 0.190 0.0605 3.14** 0.00
coef_number_of_work_tours_undertaken_by_the_person_othdiscr3.092441e-04coef_number_of_vehicles_social-0.158 0.0333-4.76*** 0.00
-3.6710.000000e+00coef_number_of_work_tours_undertaken_by_the_person-0.182 0.0552-3.30*** 0.00
-3.8960.000000e+00coef_number_of_work_tours_undertaken_by_the_person_escort-0.222 0.0707-3.15** 0.00
-7.36100000000000150.000000e+00coef_number_of_work_tours_undertaken_by_the_person_othdiscr-0.547 0.0742-7.36*** 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_atwork-3.052719e-04coef_number_of_work_tours_undertaken_by_the_person_othmaint-0.426 0.0859-4.96*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_atwork-1.668958e-04coef_number_of_work_tours_undertaken_by_the_person_shopping-0.599 0.0588-10.19*** 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_atwork2.048973e-04coef_number_of_work_tours_undertaken_by_the_person_social-0.198 0.0971-2.04* 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_atwork1.185991e-04coef_presence_of_kids_between_0_and_4_including_years_old 0.738 0.0493 14.98*** 0.00
coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_0.000000e+00coef_presence_of_kids_between_5_and_15_including_years_old 0.174 0.0692 2.52* 0.00
coef_middle_to_low_income_hh__atwork-1.187817e-03coef_presence_of_kids_between_5_and_15_including_years_old_school 0.258 0.0737 3.50*** 0.00
coef_number_of_eating_tours_tours_undertaken_by_the_person4.166759e-04coef_presence_of_kids_between_5_and_15_including_years_old_univ 0.755 0.165 4.58*** 0.00
coef_primary_destination_accessibility_log_of_it_-3.977574e-03coef_primary_destination_accessibility_log_of_it_ 0.191 0.0120 15.92*** 0.00
coef_subtour_departure_less_than_or_equal_to_11am-7.903092e-04coef_subtour_departure_less_than_or_equal_to_11am 0.352 0.0707 4.98*** 0.00
coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_-4.153043e-03coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_ 0.0231 0.0171 1.35 0.00
coef_subtour_duration_in_hours_integer_-2.149064e-03coef_subtour_duration_in_hours_integer_ 0.555 0.0253 21.94*** 0.00
coef_subtour_return_time_greater_or_equal_to_2pm1.538038e-04coef_subtour_return_time_greater_or_equal_to_2pm 0.206 0.209 0.99 0.00
nit130nfev327njev130status0message'Optimization terminated successfully'successTrueelapsed_time0:01:25.611724method'SLSQP'n_cases5778iteration_number130logloss1.0547864171945307" - ], - "text/plain": [ - "┣ x: -2.139 -2.139000\n", - "┃ -3.934 -3.934000\n", - "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in -0.741225\n", - "┃ coef_alternative_specific_constant_for_outbound_stops_2out_0in -2.594489\n", - "┃ coef_alternative_specific_constant_for_return_stops_0out_1in -0.447476\n", - "┃ ... \n", - "┃ coef_primary_destination_accessibility_log_of_it_ 0.170704\n", - "┃ coef_subtour_departure_less_than_or_equal_to_11am 0.266339\n", - "┃ coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_ -0.152384\n", - "┃ coef_subtour_duration_in_hours_integer_ 0.681811\n", - "┃ coef_subtour_return_time_greater_or_equal_to_2pm 1.549569\n", - "┃ Length: 188, dtype: float64\n", - "┣ loglike: -6094.555918549999\n", - "┣ d_loglike: -2.139 0.000000\n", - "┃ -3.934 0.000000\n", - "┃ coef_alternative_specific_constant_for_outbound_stops_1out_0in -0.000080\n", - "┃ coef_alternative_specific_constant_for_outbound_stops_2out_0in -0.000716\n", - "┃ coef_alternative_specific_constant_for_return_stops_0out_1in -0.000063\n", - "┃ ... \n", - "┃ coef_primary_destination_accessibility_log_of_it_ -0.003978\n", - "┃ coef_subtour_departure_less_than_or_equal_to_11am -0.000790\n", - "┃ coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_ -0.004153\n", - "┃ coef_subtour_duration_in_hours_integer_ -0.002149\n", - "┃ coef_subtour_return_time_greater_or_equal_to_2pm 0.000154\n", - "┃ Length: 188, dtype: float64\n", - "┣ nit: 130\n", - "┣ nfev: 327\n", - "┣ njev: 130\n", - "┣ status: 0\n", - "┣ message: 'Optimization terminated successfully'\n", - "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=85, microseconds=611724)\n", - "┣ method: 'SLSQP'\n", - "┣ n_cases: 5778\n", - "┣ iteration_number: 130\n", - "┣ logloss: 1.0547864171945307" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.estimate(method='SLSQP', options={\"maxiter\": 1000})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Estimated coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value
-2.139-2.14 0.00 NA NA-2.14
-3.934-3.93 0.00 NA NA-3.93
coef_alternative_specific_constant_for_outbound_stops_1out_0in-0.741 0.0909-8.16*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in-2.59 0.167-15.51*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in-0.447 0.0942-4.75*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in-1.66 0.136-12.17*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_0out_2in 0.0394 0.0697 0.57 NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in 0.755 0.335 2.25* NA 0.00
coef_am_peak_departure_between_6am_and_7_am_including_interacted_with_outbound_tours-2.00 0.132-15.17*** NA 0.00
coef_dummy_for_all_stops_made_by_transit-0.673 0.0870-7.73*** NA 0.00
coef_dummy_for_distance_in_miles 0.0440 0.0256 1.72 NA 0.00
coef_dummy_for_distance_less_than_20_miles-0.588 0.310-1.90 NA 0.00
coef_dummy_for_female 0.283 0.108 2.63** NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_11_hours 0.713 0.139 5.13*** NA 0.00
coef_dummy_for_walking_to_all_stops-1.49 0.218-6.84*** NA 0.00
coef_evening_arrival_19_00_interacted_with_return_tours 0.606 0.127 4.78*** NA 0.00
coef_high_income_hh 0.240 1.15e-07 BIG*** NA 0.00
coef_mid_to_high_income_hh 0.230 3.09e-07 BIG*** NA 0.00
coef_middle_to_low_income_hh 0.170 1.05e-06 BIG*** NA 0.00
coef_no_stops_if_tour_mode_is_drivetransit-999. 9.87e-07-BIG*** NA 0.00
coef_num_kids_between_5_and_15_including_years_old 0.0897 0.206 0.44 NA 0.00
coef_number_of_adults_16_years_old_ 0.0742 0.0890 0.83 NA 0.00
coef_number_of_cars_number_of_workers-0.0657 0.116-0.57 NA 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person 0.0978 0.216 0.45 NA 0.00
coef_number_of_hh_persons-0.328 0.0857-3.83*** NA 0.00
coef_number_of_school_tours_tours_undertaken_by_the_person 0.130 1.43 0.09 NA 0.00
coef_number_of_shop_tours_undertaken_by_the_houshold-0.328 0.660-0.50 NA 0.00
coef_number_of_students_in_hh 0.315 0.0885 3.56*** NA 0.00
coef_number_of_subtours_in_the_tour 0.337 0.128 2.63** NA 0.00
coef_number_of_university_tours_tours_undertaken_by_the_person-0.480 8.22e-07-BIG*** NA 0.00
coef_number_of_work_tours_undertaken_by_the_person-0.175 0.201-0.87 NA 0.00
coef_presence_of_kids_between_0_and_4_including_years_old 0.557 0.200 2.78** NA 0.00
coef_presence_of_kids_between_5_and_15_including_years_old 0.118 0.306 0.38 NA 0.00
-2.672-2.67 0.00 NA NA-2.67
coef_alternative_specific_constant_for_outbound_stops_1out_0in_school-1.77 0.250-7.07*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_school-4.31 0.594-7.26*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_school-6.21 1.12-5.53*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_school-1.23 0.243-5.05*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_school-3.19 0.436-7.30*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in 1.57 0.577 2.72** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_school 2.18 1.22 1.78 NA 0.00
coef_arrival_later_than_17_00_ 1.37 0.387 3.55*** NA 0.00
coef_dummy_for_distance_in_miles_school 0.0920 0.0697 1.32 NA 0.00
coef_dummy_for_female_school 0.688 0.282 2.44* NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours_ 1.90 0.388 4.91*** NA 0.00
coef_dummy_for_walking_to_all_stops_school-1.53 0.539-2.83** NA 0.00
coef_number_of_cars_number_of_workers_school 0.282 0.300 0.94 NA 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person_school 2.32 0.591 3.93*** NA 0.00
coef_number_of_hh_persons_school-0.603 0.0890-6.78*** NA 0.00
coef_presence_of_kids_between_5_and_15_including_years_old_school 0.0211 0.346 0.06 NA 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_univ-2.21 0.395-5.59*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_univ-4.04 0.629-6.42*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_univ-4.64 0.752-6.17*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_univ-1.57 0.395-3.98*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_univ-3.43 0.560-6.13*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_univ-3.86 0.649-5.95*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_univ 2.02 0.718 2.81** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_univ 2.12 1.26 1.68 NA 0.00
coef_arrival_later_than_17_00__univ 0.398 0.350 1.14 NA 0.00
coef_dummy_for_female_univ 0.527 0.361 1.46 NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__univ 1.09 0.968 1.12 NA 0.00
coef_hh_accesibility_for_inbound_tours_interaction 0.244 0.0583 4.18*** NA 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person_univ 1.86 1.35 1.37 NA 0.00
coef_number_of_hh_persons_univ-0.326 0.139-2.35* NA 0.00
coef_number_of_vehicles 0.266 0.201 1.32 NA 0.00
coef_presence_of_kids_between_5_and_15_including_years_old_univ-0.0615 0.651-0.09 NA 0.00
-1.783-1.78 0.00 NA NA-1.78
-2.874-2.87 0.00 NA NA-2.87
-3.379-3.38 0.00 NA NA-3.38
1.497 1.50 0.00 NA NA 1.50
coef_alternative_specific_constant_for_outbound_stops_1out_0in_social-1.29 0.598-2.16* NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_social-42.6 8.43e-07-BIG*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_2out_0in-4.16 1.09-3.82*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_on_joint_tours_3out_0in-28.9 4.10e-07-BIG*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_social 0.474 1.29 0.37 NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_social-2.40 1.50-1.61 NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_social-3.11 1.65-1.88 NA 0.00
coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_1in-1.49 0.366-4.07*** NA 0.00
coef_alternative_specific_constant_for_return_stops_on_joint_tours_0out_2in-3.53 0.880-4.01*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_social 2.61 1.22 2.14* NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_social-9.81 921.-0.01 NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_0out_2in-0.168 0.519-0.32 NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_on_joint_tours_1out_3in-26.1 1.12e-07-BIG*** NA 0.00
coef_arrival_later_than_17_00__social-0.470 0.240-1.96 NA 0.00
coef_at_least_one_kid_and_one_adult_participate_in_the_tour 2.12 1.01 2.09* NA 0.00
coef_dummy_for_a_return_visiting_tour-1.75 1.28-1.36 NA 0.00
coef_dummy_for_a_visiting_tour_with_both_outbound_and_return_leg 0.344 0.644 0.53 NA 0.00
coef_dummy_for_an_outbound_visiting_tour-0.742 0.617-1.20 NA 0.00
coef_dummy_for_distance_in_miles_social-0.0788 0.0646-1.22 NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_3_hours_ 1.14 0.248 4.60*** NA 0.00
coef_dummy_for_walking_to_all_stops_social-2.09 0.423-4.94*** NA 0.00
coef_number_of_persons_participating_in_the_tour_outgoing_stops_interaction-0.0275 0.310-0.09 NA 0.00
coef_number_of_shop_tours_undertaken_by_the_person-0.178 0.302-0.59 NA 0.00
coef_number_of_vehicles_social-0.154 0.132-1.17 NA 0.00
coef_number_of_work_tours_undertaken_by_the_person_social-0.0102 0.288-0.04 NA 0.00
-3.024-3.02 0.00 NA NA-3.02
coef_alternative_specific_constant_for_outbound_stops_1out_0in_shopping-1.49 0.156-9.57*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_shopping-3.34 0.273-12.24*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_shopping-4.54 0.447-10.16*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_shopping-1.23 0.155-7.95*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_shopping-2.58 0.221-11.70*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_shopping 0.585 0.339 1.72 NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_shopping-0.0156 1.03-0.02 NA 0.00
coef_dummy_for_distance_in_miles_shopping 0.0214 0.0384 0.56 NA 0.00
coef_dummy_for_distance_less_than_5_miles 0.153 0.172 0.89 NA 0.00
coef_dummy_for_female_shopping 0.339 0.162 2.09* NA 0.00
coef_dummy_for_only_adults_participate_in_the_tour 1.54 0.764 2.02* NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__shopping 3.10 0.813 3.82*** NA 0.00
coef_dummy_for_walking_to_all_stops_shopping-1.88 0.331-5.69*** NA 0.00
coef_num_kids_between_5_and_15_including_years_old_shopping-0.0478 0.155-0.31 NA 0.00
coef_number_of_hh_persons_shopping-0.0870 0.0495-1.76 NA 0.00
coef_number_of_maintenace_tours_tours_undertaken_by_the_person-0.326 0.218-1.49 NA 0.00
coef_number_of_shop_tours_undertaken_by_the_houshold_shopping 0.148 0.276 0.54 NA 0.00
coef_number_of_university_tours_tours_undertaken_by_the_person_shopping-0.671 3.90e-07-BIG*** NA 0.00
coef_number_of_work_tours_undertaken_by_the_person_shopping-0.437 0.186-2.35* NA 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_eatout-1.88 0.316-5.94*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_eatout-32.6 1.42e-07-BIG*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_eatout-3.74 0.648-5.77*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_eatout-1.69 0.321-5.26*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_eatout-3.03 0.462-6.56*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_eatout-4.84 1.04-4.64*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_eatout-32.0 2.36e-07-BIG*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_eatout-30.9 2.73e-07-BIG*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_1out_0in_escort-2.23 0.253-8.78*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_escort-4.86 0.729-6.67*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_escort-4.79 0.731-6.56*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_escort-1.28 0.241-5.31*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_escort-2.46 0.315-7.81*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_escort-12.3 923.-0.01 NA 0.00
coef_dummy_for_distance_less_than_5_miles_escort 0.270 0.240 1.12 NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__escort 1.40 0.752 1.86 NA 0.00
coef_dummy_for_walking_to_all_stops_escort-0.341 0.889-0.38 NA 0.00
coef_number_of_escort_tours_tours_undertaken_by_the_person_escort-0.260 0.147-1.77 NA 0.00
coef_number_of_hh_persons_escort-0.179 0.0901-1.98* NA 0.00
coef_number_of_students_in_hh_escort 0.161 0.159 1.01 NA 0.00
coef_number_of_work_tours_undertaken_by_the_person_escort-0.158 0.289-0.55 NA 0.00
-2.462-2.46 0.00 NA NA-2.46
coef_alternative_specific_constant_for_outbound_stops_1out_0in_othmaint-2.12 0.292-7.26*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_othmaint-3.47 0.501-6.92*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_othmaint-4.23 0.775-5.46*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_1in_othmaint-0.834 0.287-2.91** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_othmaint-1.19 0.307-3.87*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othmaint 0.315 0.650 0.48 NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_2out_3in_othmaint-31.0 8.73e-08-BIG*** NA 0.00
coef_dummy_for_distance_in_miles_othmaint 0.102 0.0656 1.55 NA 0.00
coef_dummy_for_distance_less_than_20_miles_-0.622 0.451-1.38 NA 0.00
coef_dummy_for_female_othmaint 0.281 0.254 1.11 NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othmaint 0.169 0.746 0.23 NA 0.00
coef_dummy_for_walking_to_all_stops_othmaint-2.30 0.751-3.06** NA 0.00
coef_middle_to_low_income_hh_ 0.170 1.58e-08 BIG*** NA 0.00
coef_number_of_maintenace_tours_undertaken_by_the_houshold-0.265 0.426-0.62 NA 0.00
coef_number_of_persons_participating_in_the_tour_return_stops_interaction 0.626 0.233 2.69** NA 0.00
coef_number_of_shool_tours_tours_undertaken_by_the_person-29.4 3.81e-08-BIG*** NA 0.00
coef_number_of_shop_tours_undertaken_by_the_person_othmaint-0.200 0.253-0.79 NA 0.00
coef_number_of_university_tours_tours_undertaken_by_the_person_othmaint-0.625 3.47e-08-BIG*** NA 0.00
coef_number_of_work_tours_undertaken_by_the_person_othmaint-0.358 0.317-1.13 NA 0.00
-0.921-0.921 0.00 NA NA-0.92
0.939 0.939 0.00 NA NA 0.94
coef_alternative_specific_constant_for_outbound_stops_1out_0in_othdiscr-1.39 0.171-8.11*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_2out_0in_othdiscr-2.96 0.290-10.20*** NA 0.00
coef_alternative_specific_constant_for_outbound_stops_3out_0in_othdiscr-4.39 0.503-8.73*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_othdiscr-2.52 0.258-9.77*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_othdiscr-3.02 0.316-9.58*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_othdiscr 1.09 0.410 2.65** NA 0.00
coef_arrival_later_than_17_00__othdiscr-0.367 0.208-1.76 NA 0.00
coef_dummy_for_distance_in_miles_othdiscr 0.0493 0.0477 1.03 NA 0.00
coef_dummy_for_distance_less_than_10_miles_-0.135 0.250-0.54 NA 0.00
coef_dummy_for_the_duration_of_the_tour_being_equal_or_greater_than_or_equal_to_9_hours__othdiscr 0.254 0.398 0.64 NA 0.00
coef_dummy_for_walking_to_all_stops_othdiscr-2.06 0.429-4.81*** NA 0.00
coef_number_of_maintenace_tours_tours_undertaken_by_the_person_othdiscr-0.621 0.322-1.92 NA 0.00
coef_number_of_shool_tours_tours_undertaken_by_the_person_othdiscr-0.595 0.410-1.45 NA 0.00
coef_number_of_shop_tours_undertaken_by_the_person_othdiscr-0.607 0.236-2.57* NA 0.00
coef_number_of_work_tours_undertaken_by_the_person_othdiscr-0.475 0.243-1.96 NA 0.00
-3.671-3.67 0.00 NA NA-3.67
-3.896-3.90 0.00 NA NA-3.90
-7.3610000000000015-7.36 0.00 NA NA-7.36
coef_alternative_specific_constant_for_outbound_stops_2out_0in_atwork-5.61 0.354-15.85*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_2in_atwork-6.03 0.427-14.13*** NA 0.00
coef_alternative_specific_constant_for_return_stops_0out_3in_atwork-8.02 1.03-7.75*** NA 0.00
coef_alternative_specific_constant_for_the_total_number_of_stops_1out_3in_atwork 2.56 1.04 2.45* NA 0.00
coef_dummy_for_subtour_origin_tour_destination_at_exurban_or_rual_areatypes_6_or_7_ 0.270 0.00 NA[] 0.00 0.00
coef_middle_to_low_income_hh__atwork 0.939 0.357 2.63** NA 0.00
coef_number_of_eating_tours_tours_undertaken_by_the_person-0.250 0.553-0.45 NA 0.00
coef_primary_destination_accessibility_log_of_it_ 0.171 0.0435 3.93*** NA 0.00
coef_subtour_departure_less_than_or_equal_to_11am 0.266 0.259 1.03 NA 0.00
coef_subtour_distance_in_miles_from_tour_destination_to_subtour_primary_destination_one_way_-0.152 0.0816-1.87 NA 0.00
coef_subtour_duration_in_hours_integer_ 0.682 0.0974 7.00*** NA 0.00
coef_subtour_return_time_greater_or_equal_to_2pm 1.55 0.707 2.19* NA 0.00
" + "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -6295,67 +6168,67 @@ " 3\n", " coef_number_of_hh_persons\n", " Number of HH Persons\n", - " -0.328159\n", + " -0.291501\n", " \n", " \n", " 4\n", " coef_number_of_students_in_hh\n", " Number of Students in HH\n", - " 0.315297\n", + " 0.206028\n", " \n", " \n", " 5\n", " coef_presence_of_kids_between_0_and_4_includin...\n", " Presence of Kids between 0 and 4 (including) y...\n", - " 0.557476\n", + " 0.738257\n", " \n", " \n", " 6\n", " coef_num_kids_between_5_and_15_including_years...\n", " Num kids between 5 and 15 (including) years old\n", - " 0.089718\n", + " 0.083356\n", " \n", " \n", " 7\n", " coef_presence_of_kids_between_5_and_15_includi...\n", " Presence of kids between 5 and 15 (including) ...\n", - " 0.117544\n", + " 0.174006\n", " \n", " \n", " 8\n", " coef_number_of_adults_16_years_old_\n", " Number of Adults (>= 16 years old)\n", - " 0.074203\n", + " 0.006177\n", " \n", " \n", " 9\n", " coef_number_of_cars_number_of_workers\n", " Number of Cars > Number of Workers\n", - " -0.065713\n", + " 0.195317\n", " \n", " \n", " 10\n", " coef_dummy_for_female\n", " Dummy for female\n", - " 0.282632\n", + " 0.231116\n", " \n", " \n", " 11\n", " coef_dummy_for_all_stops_made_by_transit\n", " Dummy for all stops made by transit\n", - " -0.672704\n", + " -0.698140\n", " \n", " \n", " 12\n", " coef_dummy_for_walking_to_all_stops\n", " Dummy for walking to all stops\n", - " -1.491072\n", + " -1.590379\n", " \n", " \n", " 13\n", " coef_number_of_work_tours_undertaken_by_the_pe...\n", " Number of work tours undertaken by the person\n", - " -0.174762\n", + " -0.182288\n", " \n", " \n", " 14\n", @@ -6367,49 +6240,49 @@ " 15\n", " coef_number_of_school_tours_tours_undertaken_b...\n", " Number of school tours tours undertaken by the...\n", - " 0.130473\n", + " -1.539830\n", " \n", " \n", " 16\n", " coef_number_of_escort_tours_tours_undertaken_b...\n", " Number of escort tours tours undertaken by the...\n", - " 0.097792\n", + " 0.285502\n", " \n", " \n", " 17\n", " coef_number_of_shop_tours_undertaken_by_the_ho...\n", " Number of shop tours undertaken by the houshold\n", - " -0.328294\n", + " 0.194165\n", " \n", " \n", " 18\n", " coef_am_peak_departure_between_6am_and_7_am_in...\n", " AM Peak departure between 6AM and 7 AM (includ...\n", - " -1.998832\n", + " -1.913742\n", " \n", " \n", " 19\n", " coef_evening_arrival_19_00_interacted_with_ret...\n", " Evening Arrival (>=19:00) Interacted with retu...\n", - " 0.606441\n", + " 0.297829\n", " \n", " \n", " 20\n", " coef_dummy_for_the_duration_of_the_tour_being_...\n", " Dummy for the duration of the tour being equal...\n", - " 0.713048\n", + " 0.629566\n", " \n", " \n", " 21\n", " coef_dummy_for_distance_less_than_20_miles\n", " dummy for distance less than 20 Miles\n", - " -0.588175\n", + " -0.209813\n", " \n", " \n", " 22\n", " coef_dummy_for_distance_in_miles\n", " dummy for distance in miles\n", - " 0.043974\n", + " 0.008199\n", " \n", " \n", " 23\n", @@ -6421,55 +6294,55 @@ " 24\n", " coef_alternative_specific_constant_for_return_...\n", " Alternative specific constant for return stops\n", - " -0.447476\n", + " -0.422070\n", " \n", " \n", " 25\n", " coef_number_of_subtours_in_the_tour\n", " Number of subtours in the tour\n", - " 0.337091\n", + " 0.184545\n", " \n", " \n", " 26\n", " coef_alternative_specific_constant_for_return_...\n", " Alternative specific constant for return stops\n", - " -1.657817\n", + " -1.737740\n", " \n", " \n", " 27\n", " coef_alternative_specific_constant_for_the_tot...\n", " Alternative specific constant for the total nu...\n", - " 0.039441\n", + " -0.033904\n", " \n", " \n", " 28\n", " coef_alternative_specific_constant_for_return_...\n", " Alternative specific constant for return stops\n", - " -2.139000\n", + " -2.056255\n", " \n", " \n", " 29\n", " coef_alternative_specific_constant_for_outboun...\n", " Alternative specific constant for outbound stops\n", - " -0.741225\n", + " -0.817659\n", " \n", " \n", " 30\n", " coef_alternative_specific_constant_for_outboun...\n", " Alternative specific constant for outbound stops\n", - " -2.594489\n", + " -2.553493\n", " \n", " \n", " 31\n", " coef_alternative_specific_constant_for_the_tot...\n", " Alternative specific constant for the total nu...\n", - " 0.754748\n", + " 0.555526\n", " \n", " \n", " 32\n", " coef_alternative_specific_constant_for_outboun...\n", " Alternative specific constant for outbound stops\n", - " -3.934000\n", + " -3.914672\n", " \n", " \n", "\n", @@ -6515,36 +6388,36 @@ "0 Middle to Low Income HH 0.170000 \n", "1 Mid to High Income HH 0.230000 \n", "2 High Income HH 0.240000 \n", - "3 Number of HH Persons -0.328159 \n", - "4 Number of Students in HH 0.315297 \n", - "5 Presence of Kids between 0 and 4 (including) y... 0.557476 \n", - "6 Num kids between 5 and 15 (including) years old 0.089718 \n", - "7 Presence of kids between 5 and 15 (including) ... 0.117544 \n", - "8 Number of Adults (>= 16 years old) 0.074203 \n", - "9 Number of Cars > Number of Workers -0.065713 \n", - "10 Dummy for female 0.282632 \n", - "11 Dummy for all stops made by transit -0.672704 \n", - "12 Dummy for walking to all stops -1.491072 \n", - "13 Number of work tours undertaken by the person -0.174762 \n", + "3 Number of HH Persons -0.291501 \n", + "4 Number of Students in HH 0.206028 \n", + "5 Presence of Kids between 0 and 4 (including) y... 0.738257 \n", + "6 Num kids between 5 and 15 (including) years old 0.083356 \n", + "7 Presence of kids between 5 and 15 (including) ... 0.174006 \n", + "8 Number of Adults (>= 16 years old) 0.006177 \n", + "9 Number of Cars > Number of Workers 0.195317 \n", + "10 Dummy for female 0.231116 \n", + "11 Dummy for all stops made by transit -0.698140 \n", + "12 Dummy for walking to all stops -1.590379 \n", + "13 Number of work tours undertaken by the person -0.182288 \n", "14 Number of university tours tours undertaken by... -0.480000 \n", - "15 Number of school tours tours undertaken by the... 0.130473 \n", - "16 Number of escort tours tours undertaken by the... 0.097792 \n", - "17 Number of shop tours undertaken by the houshold -0.328294 \n", - "18 AM Peak departure between 6AM and 7 AM (includ... -1.998832 \n", - "19 Evening Arrival (>=19:00) Interacted with retu... 0.606441 \n", - "20 Dummy for the duration of the tour being equal... 0.713048 \n", - "21 dummy for distance less than 20 Miles -0.588175 \n", - "22 dummy for distance in miles 0.043974 \n", + "15 Number of school tours tours undertaken by the... -1.539830 \n", + "16 Number of escort tours tours undertaken by the... 0.285502 \n", + "17 Number of shop tours undertaken by the houshold 0.194165 \n", + "18 AM Peak departure between 6AM and 7 AM (includ... -1.913742 \n", + "19 Evening Arrival (>=19:00) Interacted with retu... 0.297829 \n", + "20 Dummy for the duration of the tour being equal... 0.629566 \n", + "21 dummy for distance less than 20 Miles -0.209813 \n", + "22 dummy for distance in miles 0.008199 \n", "23 No stops if tour mode is driveTransit -999.000000 \n", - "24 Alternative specific constant for return stops -0.447476 \n", - "25 Number of subtours in the tour 0.337091 \n", - "26 Alternative specific constant for return stops -1.657817 \n", - "27 Alternative specific constant for the total nu... 0.039441 \n", - "28 Alternative specific constant for return stops -2.139000 \n", - "29 Alternative specific constant for outbound stops -0.741225 \n", - "30 Alternative specific constant for outbound stops -2.594489 \n", - "31 Alternative specific constant for the total nu... 0.754748 \n", - "32 Alternative specific constant for outbound stops -3.934000 " + "24 Alternative specific constant for return stops -0.422070 \n", + "25 Number of subtours in the tour 0.184545 \n", + "26 Alternative specific constant for return stops -1.737740 \n", + "27 Alternative specific constant for the total nu... -0.033904 \n", + "28 Alternative specific constant for return stops -2.056255 \n", + "29 Alternative specific constant for outbound stops -0.817659 \n", + "30 Alternative specific constant for outbound stops -2.553493 \n", + "31 Alternative specific constant for the total nu... 0.555526 \n", + "32 Alternative specific constant for outbound stops -3.914672 " ] }, "execution_count": 12, @@ -6564,7 +6437,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -6578,7 +6451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/22_trip_dest.ipynb b/activitysim/examples/example_estimation/notebooks/22_trip_dest.ipynb index 576e87bfe5..2965cb74f0 100644 --- a/activitysim/examples/example_estimation/notebooks/22_trip_dest.ipynb +++ b/activitysim/examples/example_estimation/notebooks/22_trip_dest.ipynb @@ -26,30 +26,74 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import larch # !conda install larch #for estimation\n", + "import larch as lx\n", "import pandas as pd\n", - "import numpy as np\n", - "import yaml \n", - "import larch.util.excel\n", - "import os" + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -72,10 +116,35 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading from output-est-mode/estimation_data_bundle/trip_destination/trip_destination_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/trip_destination/trip_destination_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/trip_destination/trip_destination_alternatives_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/trip_destination/trip_destination_choosers_combined.parquet\n", + "loading from output-est-mode/estimation_data_bundle/trip_destination/trip_destination_landuse.csv\n", + "loading from output-est-mode/estimation_data_bundle/trip_destination/trip_destination_size_terms.csv\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jpn/Git/est-mode/activitysim/activitysim/estimation/larch/location_choice.py:548: UserWarning: Removed 5419 choosers with invalid (zero-sized) observed choice\n", + " return location_choice_model(\n" + ] + } + ], "source": [ "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -290,148 +359,118 @@ " \n", " \n", " trip_id\n", - " variable\n", - " 1\n", - " 2\n", - " 3\n", - " 4\n", - " 5\n", - " 6\n", - " 7\n", - " 8\n", - " ...\n", - " 181\n", - " 182\n", - " 183\n", - " 184\n", - " 185\n", - " 186\n", - " 187\n", - " 188\n", - " 189\n", - " 190\n", + " dest_taz\n", + " util_size_term\n", + " util_no_attractions\n", + " util_distance_inbound\n", + " util_distance_outbound\n", + " util_distance_joint\n", + " util_prox_home_outbound\n", + " util_prox_home_inbound\n", + " util_prox_dest_outbound\n", + " util_prox_dest_inbound\n", + " util_sample_of_alternatives_correction_factor\n", + " util_mode_choice_logsum_os\n", + " util_stop_not_accessible_by_this_tour_mode\n", + " util_mode_choice_logsum_sd\n", + " util_dest_not_accessible_by_this_tour_mode\n", " \n", " \n", " \n", " \n", " 0\n", - " 123229\n", - " util_dest_not_accessible_by_this_tour_mode\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " ...\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", + " 439241\n", + " 2\n", + " 6.117394\n", " False\n", + " 0.000000\n", + " 14.110000\n", + " 0.0\n", + " 6.89\n", + " 0.000000\n", + " 7.22\n", + " 0.00\n", + " 6.128602\n", + " -0.847005\n", " False\n", + " -0.633253\n", " False\n", " \n", " \n", " 1\n", - " 123229\n", - " util_distance_inbound\n", - " 6.390000343322754\n", - " 6.170000076293945\n", - " 5.920000076293945\n", - " 5.810000419616699\n", - " 5.299999713897705\n", - " 5.170000076293945\n", - " 4.840000152587891\n", - " 4.630000114440918\n", - " ...\n", - " 9.819999694824219\n", - " 11.25\n", - " 10.449999809265137\n", - " 8.970000267028809\n", - " 7.920000076293945\n", - " 9.600000381469727\n", - " 9.889999389648438\n", - " 10.079999923706055\n", - " 11.899999618530273\n", - " 12.170000076293945\n", + " 439241\n", + " 11\n", + " 7.726934\n", + " False\n", + " 0.000000\n", + " 12.280001\n", + " 0.0\n", + " 5.90\n", + " 0.000000\n", + " 6.38\n", + " 0.00\n", + " 3.637608\n", + " -0.558405\n", + " False\n", + " -0.497479\n", + " False\n", " \n", " \n", " 2\n", - " 123229\n", - " util_distance_joint\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 439241\n", + " 16\n", + " 7.935721\n", + " False\n", + " 0.000000\n", + " 14.280000\n", " 0.0\n", + " 6.81\n", + " 0.000000\n", + " 7.47\n", + " 0.00\n", + " 3.634621\n", + " -0.681632\n", + " False\n", + " -0.639591\n", + " False\n", " \n", " \n", " 3\n", - " 123229\n", - " util_distance_outbound\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", + " 439241\n", + " 21\n", + " 5.953480\n", + " False\n", + " 0.000000\n", + " 12.920000\n", " 0.0\n", + " 6.13\n", + " 0.000000\n", + " 6.79\n", + " 0.00\n", + " 5.476918\n", + " -0.536647\n", + " False\n", + " -0.524008\n", + " False\n", " \n", " \n", " 4\n", - " 123229\n", - " util_mode_choice_logsum_os\n", - " -1.1826465123669618\n", - " -1.1342039462845261\n", - " -1.0045888362609303\n", - " -0.9695035841251944\n", - " -0.9086842324484546\n", - " -0.7911335856893744\n", - " -1.0394341719374236\n", - " -0.735128183005223\n", - " ...\n", - " -0.5685844758479193\n", - " -0.7106540688185471\n", - " -0.6324772374186627\n", - " -0.5127860821542347\n", - " -0.427736842041275\n", - " -0.5989989879176718\n", - " -0.6352947097849363\n", - " -0.6732994275559964\n", - " -0.8319610659002946\n", - " -0.8302145411677482\n", + " 439241\n", + " 82\n", + " 5.890218\n", + " False\n", + " 0.000000\n", + " 9.790000\n", + " 0.0\n", + " 4.83\n", + " 0.000000\n", + " 4.96\n", + " 0.00\n", + " 5.218103\n", + " -0.383122\n", + " False\n", + " -0.265333\n", + " False\n", " \n", " \n", " ...\n", @@ -451,230 +490,213 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 39139\n", - " 2478375757\n", - " util_prox_home_inbound\n", - " 5.659999847412109\n", - " 5.570000171661377\n", - " 5.5\n", - " 5.329999923706055\n", - " 5.119999885559082\n", - " 5.190000057220459\n", - " 5.019999980926514\n", - " 4.809999942779541\n", - " ...\n", - " 6.260000228881836\n", - " 5.440000057220459\n", - " 5.110000133514404\n", - " 4.889999866485596\n", - " 4.329999923706055\n", - " 4.5\n", - " 3.990000009536743\n", - " 3.4200000762939453\n", - " 4.159999847412109\n", - " 4.650000095367432\n", - " \n", - " \n", - " 39140\n", - " 2478375757\n", - " util_prox_home_outbound\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " ...\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", - " 0.0\n", " \n", " \n", - " 39141\n", - " 2478375757\n", - " util_sample_of_alternatives_correction_factor\n", - " 5.969689780743902\n", - " 5.229744858144034\n", - " 6.651885938411212\n", - " 5.457984599784746\n", - " 4.557500241901908\n", - " 5.683456777589848\n", - " 4.880351408439651\n", - " 5.056172176409885\n", - " ...\n", - " 6.167159960103807\n", - " 5.635222918217218\n", - " 4.9055149705471015\n", - " 5.197733187456251\n", - " 4.545857557157909\n", - " 5.146011961990624\n", - " 5.677734711636542\n", - " 4.0921031600858315\n", - " 5.525577058677637\n", - " 6.765177014979627\n", - " \n", - " \n", - " 39142\n", - " 2478375757\n", - " util_size_term\n", - " 5.8928092787280315\n", - " 6.61382841501164\n", - " 5.174023179275996\n", - " 6.331334576117736\n", - " 7.181350090272405\n", - " 6.062963941307298\n", - " 6.824432363972737\n", - " 6.596881001099327\n", - " ...\n", - " 5.988058753973149\n", - " 6.313073295919817\n", - " 6.959507591274848\n", - " 6.611773525937498\n", - " 7.122336273469753\n", - " 6.565080445969589\n", - " 5.957654370567811\n", - " 7.481461558469711\n", - " 6.2347224418357365\n", - " 5.096060413644514\n", - " \n", - " \n", - " 39143\n", - " 2478375757\n", - " util_stop_not_accessible_by_this_tour_mode\n", - " False\n", - " False\n", - " False\n", + " 854498\n", + " 2481262477\n", + " 1314\n", + " 6.565007\n", " False\n", + " 21.299999\n", + " 0.000000\n", + " 0.0\n", + " 0.00\n", + " 10.650000\n", + " 0.00\n", + " 10.65\n", + " 3.869640\n", + " -3.466365\n", " False\n", + " -3.466365\n", " False\n", + " \n", + " \n", + " 854499\n", + " 2481262477\n", + " 1315\n", + " 5.491167\n", " False\n", + " 14.500000\n", + " 0.000000\n", + " 0.0\n", + " 0.00\n", + " 7.250000\n", + " 0.00\n", + " 7.25\n", + " 2.986897\n", + " -2.475631\n", " False\n", - " ...\n", + " -2.476182\n", " False\n", + " \n", + " \n", + " 854500\n", + " 2481262477\n", + " 1318\n", + " 4.109496\n", " False\n", + " 35.799999\n", + " 0.000000\n", + " 0.0\n", + " 0.00\n", + " 17.900000\n", + " 0.00\n", + " 17.90\n", + " 7.056036\n", + " -6.066198\n", " False\n", + " -6.054759\n", " False\n", + " \n", + " \n", + " 854501\n", + " 2481262477\n", + " 1324\n", + " 5.586113\n", " False\n", + " 40.349998\n", + " 0.000000\n", + " 0.0\n", + " 0.00\n", + " 19.950001\n", + " 0.00\n", + " 20.40\n", + " 6.153502\n", + " -6.458255\n", " False\n", + " -6.443607\n", " False\n", + " \n", + " \n", + " 854502\n", + " 2481262477\n", + " 1361\n", + " 7.160701\n", " False\n", + " 60.360001\n", + " 0.000000\n", + " 0.0\n", + " 0.00\n", + " 30.180000\n", + " 0.00\n", + " 30.18\n", + " 7.103621\n", + " -9.888594\n", " False\n", + " -9.902823\n", " False\n", " \n", " \n", "\n", - "

39144 rows × 192 columns

\n", + "

854503 rows × 16 columns

\n", "" ], "text/plain": [ - " trip_id variable \\\n", - "0 123229 util_dest_not_accessible_by_this_tour_mode \n", - "1 123229 util_distance_inbound \n", - "2 123229 util_distance_joint \n", - "3 123229 util_distance_outbound \n", - "4 123229 util_mode_choice_logsum_os \n", - "... ... ... \n", - "39139 2478375757 util_prox_home_inbound \n", - "39140 2478375757 util_prox_home_outbound \n", - "39141 2478375757 util_sample_of_alternatives_correction_factor \n", - "39142 2478375757 util_size_term \n", - "39143 2478375757 util_stop_not_accessible_by_this_tour_mode \n", + " trip_id dest_taz util_size_term util_no_attractions \\\n", + "0 439241 2 6.117394 False \n", + "1 439241 11 7.726934 False \n", + "2 439241 16 7.935721 False \n", + "3 439241 21 5.953480 False \n", + "4 439241 82 5.890218 False \n", + "... ... ... ... ... \n", + "854498 2481262477 1314 6.565007 False \n", + "854499 2481262477 1315 5.491167 False \n", + "854500 2481262477 1318 4.109496 False \n", + "854501 2481262477 1324 5.586113 False \n", + "854502 2481262477 1361 7.160701 False \n", + "\n", + " util_distance_inbound util_distance_outbound util_distance_joint \\\n", + "0 0.000000 14.110000 0.0 \n", + "1 0.000000 12.280001 0.0 \n", + "2 0.000000 14.280000 0.0 \n", + "3 0.000000 12.920000 0.0 \n", + "4 0.000000 9.790000 0.0 \n", + "... ... ... ... \n", + "854498 21.299999 0.000000 0.0 \n", + "854499 14.500000 0.000000 0.0 \n", + "854500 35.799999 0.000000 0.0 \n", + "854501 40.349998 0.000000 0.0 \n", + "854502 60.360001 0.000000 0.0 \n", "\n", - " 1 2 3 \\\n", - "0 False False False \n", - "1 6.390000343322754 6.170000076293945 5.920000076293945 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 -1.1826465123669618 -1.1342039462845261 -1.0045888362609303 \n", - "... ... ... ... \n", - "39139 5.659999847412109 5.570000171661377 5.5 \n", - "39140 0.0 0.0 0.0 \n", - "39141 5.969689780743902 5.229744858144034 6.651885938411212 \n", - "39142 5.8928092787280315 6.61382841501164 5.174023179275996 \n", - "39143 False False False \n", + " util_prox_home_outbound util_prox_home_inbound \\\n", + "0 6.89 0.000000 \n", + "1 5.90 0.000000 \n", + "2 6.81 0.000000 \n", + "3 6.13 0.000000 \n", + "4 4.83 0.000000 \n", + "... ... ... \n", + "854498 0.00 10.650000 \n", + "854499 0.00 7.250000 \n", + "854500 0.00 17.900000 \n", + "854501 0.00 19.950001 \n", + "854502 0.00 30.180000 \n", "\n", - " 4 5 6 \\\n", - "0 False False False \n", - "1 5.810000419616699 5.299999713897705 5.170000076293945 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 -0.9695035841251944 -0.9086842324484546 -0.7911335856893744 \n", - "... ... ... ... \n", - "39139 5.329999923706055 5.119999885559082 5.190000057220459 \n", - "39140 0.0 0.0 0.0 \n", - "39141 5.457984599784746 4.557500241901908 5.683456777589848 \n", - "39142 6.331334576117736 7.181350090272405 6.062963941307298 \n", - "39143 False False False \n", + " util_prox_dest_outbound util_prox_dest_inbound \\\n", + "0 7.22 0.00 \n", + "1 6.38 0.00 \n", + "2 7.47 0.00 \n", + "3 6.79 0.00 \n", + "4 4.96 0.00 \n", + "... ... ... \n", + "854498 0.00 10.65 \n", + "854499 0.00 7.25 \n", + "854500 0.00 17.90 \n", + "854501 0.00 20.40 \n", + "854502 0.00 30.18 \n", "\n", - " 7 8 ... 181 \\\n", - "0 False False ... False \n", - "1 4.840000152587891 4.630000114440918 ... 9.819999694824219 \n", - "2 0.0 0.0 ... 0.0 \n", - "3 0.0 0.0 ... 0.0 \n", - "4 -1.0394341719374236 -0.735128183005223 ... -0.5685844758479193 \n", - "... ... ... ... ... \n", - "39139 5.019999980926514 4.809999942779541 ... 6.260000228881836 \n", - "39140 0.0 0.0 ... 0.0 \n", - "39141 4.880351408439651 5.056172176409885 ... 6.167159960103807 \n", - "39142 6.824432363972737 6.596881001099327 ... 5.988058753973149 \n", - "39143 False False ... False \n", + " util_sample_of_alternatives_correction_factor \\\n", + "0 6.128602 \n", + "1 3.637608 \n", + "2 3.634621 \n", + "3 5.476918 \n", + "4 5.218103 \n", + "... ... \n", + "854498 3.869640 \n", + "854499 2.986897 \n", + "854500 7.056036 \n", + "854501 6.153502 \n", + "854502 7.103621 \n", "\n", - " 182 183 184 \\\n", - "0 False False False \n", - "1 11.25 10.449999809265137 8.970000267028809 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 -0.7106540688185471 -0.6324772374186627 -0.5127860821542347 \n", - "... ... ... ... \n", - "39139 5.440000057220459 5.110000133514404 4.889999866485596 \n", - "39140 0.0 0.0 0.0 \n", - "39141 5.635222918217218 4.9055149705471015 5.197733187456251 \n", - "39142 6.313073295919817 6.959507591274848 6.611773525937498 \n", - "39143 False False False \n", + " util_mode_choice_logsum_os \\\n", + "0 -0.847005 \n", + "1 -0.558405 \n", + "2 -0.681632 \n", + "3 -0.536647 \n", + "4 -0.383122 \n", + "... ... \n", + "854498 -3.466365 \n", + "854499 -2.475631 \n", + "854500 -6.066198 \n", + "854501 -6.458255 \n", + "854502 -9.888594 \n", "\n", - " 185 186 187 \\\n", - "0 False False False \n", - "1 7.920000076293945 9.600000381469727 9.889999389648438 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 -0.427736842041275 -0.5989989879176718 -0.6352947097849363 \n", - "... ... ... ... \n", - "39139 4.329999923706055 4.5 3.990000009536743 \n", - "39140 0.0 0.0 0.0 \n", - "39141 4.545857557157909 5.146011961990624 5.677734711636542 \n", - "39142 7.122336273469753 6.565080445969589 5.957654370567811 \n", - "39143 False False False \n", + " util_stop_not_accessible_by_this_tour_mode \\\n", + "0 False \n", + "1 False \n", + "2 False \n", + "3 False \n", + "4 False \n", + "... ... \n", + "854498 False \n", + "854499 False \n", + "854500 False \n", + "854501 False \n", + "854502 False \n", "\n", - " 188 189 190 \n", - "0 False False False \n", - "1 10.079999923706055 11.899999618530273 12.170000076293945 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 -0.6732994275559964 -0.8319610659002946 -0.8302145411677482 \n", - "... ... ... ... \n", - "39139 3.4200000762939453 4.159999847412109 4.650000095367432 \n", - "39140 0.0 0.0 0.0 \n", - "39141 4.0921031600858315 5.525577058677637 6.765177014979627 \n", - "39142 7.481461558469711 6.2347224418357365 5.096060413644514 \n", - "39143 False False False \n", + " util_mode_choice_logsum_sd util_dest_not_accessible_by_this_tour_mode \n", + "0 -0.633253 False \n", + "1 -0.497479 False \n", + "2 -0.639591 False \n", + "3 -0.524008 False \n", + "4 -0.265333 False \n", + "... ... ... \n", + "854498 -3.466365 False \n", + "854499 -2.476182 False \n", + "854500 -6.054759 False \n", + "854501 -6.443607 False \n", + "854502 -9.902823 False \n", "\n", - "[39144 rows x 192 columns]" + "[854503 rows x 16 columns]" ] }, "execution_count": 6, @@ -724,130 +746,142 @@ " override_choice\n", " person_id\n", " household_id\n", - " tour_id\n", " primary_purpose\n", " trip_num\n", " outbound\n", " trip_count\n", + " destination\n", + " ...\n", " purpose\n", " next_trip_id\n", - " destination\n", - " origin\n", - " failed\n", " tour_mode\n", " trip_period\n", " is_joint\n", + " tour_leg_dest\n", + " purpose_index_num\n", + " tour_mode_is_walk\n", + " tour_mode_is_bike\n", " destination_logsum\n", " \n", " \n", " \n", " \n", " 0\n", - " 123229\n", - " 7\n", - " 69\n", - " 375\n", - " 375\n", - " 15403\n", - " othmaint\n", + " 439241\n", + " 93\n", + " 178\n", + " 1339\n", + " 1339\n", + " eatout\n", " 1\n", - " False\n", - " 3\n", + " True\n", + " 2\n", + " 187\n", + " ...\n", " shopping\n", - " 123230\n", - " 105\n", - " 67\n", - " False\n", + " 439242\n", " DRIVEALONEFREE\n", - " MD\n", + " PM\n", + " False\n", + " 187.0\n", + " 2\n", + " False\n", " False\n", " NaN\n", " \n", " \n", " 1\n", - " 123230\n", - " 9\n", - " 106\n", - " 375\n", - " 375\n", - " 15403\n", - " othmaint\n", + " 497189\n", + " 169\n", + " 99\n", + " 1515\n", + " 1515\n", + " shopping\n", + " 1\n", + " False\n", " 2\n", + " 170\n", + " ...\n", + " othdiscr\n", + " 497190\n", + " DRIVEALONEFREE\n", + " EV\n", " False\n", - " 3\n", - " eatout\n", - " 123231\n", - " 105\n", - " 69\n", + " 170.0\n", + " 6\n", " False\n", - " DRIVEALONEFREE\n", - " MD\n", " False\n", " NaN\n", " \n", " \n", " 2\n", - " 206561\n", - " 7\n", - " 9\n", - " 629\n", - " 629\n", - " 25820\n", - " univ\n", + " 536181\n", + " 184\n", + " 177\n", + " 1634\n", + " 1634\n", + " othmaint\n", " 1\n", - " True\n", + " False\n", " 2\n", - " escort\n", - " 206562\n", - " 12\n", - " 131\n", + " 178\n", + " ...\n", + " othmaint\n", + " 536182\n", + " DRIVEALONEFREE\n", + " PM\n", + " False\n", + " 178.0\n", + " 4\n", " False\n", - " WALK_HVY\n", - " MD\n", " False\n", " NaN\n", " \n", " \n", " 3\n", - " 265617\n", - " 141\n", - " 141\n", - " 809\n", - " 809\n", - " 33202\n", - " shopping\n", + " 593909\n", + " 183\n", + " 136\n", + " 1810\n", + " 1810\n", + " othmaint\n", + " 1\n", + " False\n", + " 3\n", + " 183\n", + " ...\n", + " escort\n", + " 593910\n", + " SHARED2FREE\n", + " AM\n", + " False\n", + " 183.0\n", " 1\n", - " True\n", - " 2\n", - " shopping\n", - " 265618\n", - " 117\n", - " 138\n", " False\n", - " DRIVEALONEFREE\n", - " MD\n", " False\n", " NaN\n", " \n", " \n", " 4\n", - " 265621\n", - " 127\n", - " 127\n", - " 809\n", - " 809\n", - " 33202\n", - " shopping\n", - " 1\n", - " False\n", + " 593910\n", + " 89\n", + " 188\n", + " 1810\n", + " 1810\n", + " othmaint\n", " 2\n", + " False\n", + " 3\n", + " 183\n", + " ...\n", " othmaint\n", - " 265622\n", - " 138\n", - " 117\n", + " 593911\n", + " SHARED2FREE\n", + " AM\n", + " False\n", + " 183.0\n", + " 4\n", " False\n", - " DRIVEALONEFREE\n", - " MD\n", " False\n", " NaN\n", " \n", @@ -872,176 +906,201 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 2791\n", - " 2473349145\n", - " 7\n", - " 121\n", - " 7540698\n", - " 2849363\n", - " 309168643\n", + " 36442\n", + " 2480879045\n", + " 1304\n", + " 1306\n", + " 7563655\n", + " 2872320\n", " othdiscr\n", " 1\n", - " True\n", + " False\n", " 2\n", - " social\n", - " 2473349146\n", - " 56\n", - " 92\n", + " 1293\n", + " ...\n", + " othdiscr\n", + " 2480879046\n", + " DRIVEALONEFREE\n", + " MD\n", + " False\n", + " 1293.0\n", + " 6\n", " False\n", - " SHARED3FREE\n", - " PM\n", " False\n", " NaN\n", " \n", " \n", - " 2792\n", - " 2473471869\n", - " 25\n", - " 134\n", - " 7541072\n", - " 2849737\n", - " 309183983\n", - " univ\n", + " 36443\n", + " 2480879069\n", + " 1298\n", + " 1299\n", + " 7563655\n", + " 2872320\n", + " othmaint\n", " 1\n", " False\n", + " 4\n", + " 1293\n", + " ...\n", + " shopping\n", + " 2480879070\n", + " DRIVEALONEFREE\n", + " PM\n", + " False\n", + " 1293.0\n", " 2\n", - " escort\n", - " 2473471870\n", - " 117\n", - " 107\n", " False\n", - " TAXI\n", - " AM\n", " False\n", " NaN\n", " \n", " \n", - " 2793\n", - " 2477980533\n", - " 7\n", - " 117\n", - " 7554818\n", - " 2863483\n", - " 309747566\n", + " 36444\n", + " 2480879070\n", + " 1291\n", + " 1293\n", + " 7563655\n", + " 2872320\n", " othmaint\n", - " 1\n", - " False\n", " 2\n", - " shopping\n", - " 2477980534\n", - " 20\n", - " 177\n", " False\n", + " 4\n", + " 1293\n", + " ...\n", + " eatout\n", + " 2480879071\n", " DRIVEALONEFREE\n", " PM\n", " False\n", + " 1293.0\n", + " 3\n", + " False\n", + " False\n", " NaN\n", " \n", " \n", - " 2794\n", - " 2478086525\n", - " 7\n", - " 48\n", - " 7555141\n", - " 2863806\n", - " 309760815\n", - " shopping\n", - " 1\n", + " 36445\n", + " 2480879071\n", + " 1293\n", + " 1293\n", + " 7563655\n", + " 2872320\n", + " othmaint\n", + " 3\n", + " False\n", + " 4\n", + " 1293\n", + " ...\n", + " othdiscr\n", + " 2480879072\n", + " DRIVEALONEFREE\n", + " PM\n", " False\n", - " 2\n", - " shopping\n", - " 2478086526\n", - " 85\n", - " 41\n", + " 1293.0\n", + " 6\n", " False\n", - " SHARED2FREE\n", - " PM\n", " False\n", " NaN\n", " \n", " \n", - " 2795\n", - " 2478375757\n", - " 126\n", - " 127\n", - " 7556023\n", - " 2864688\n", - " 309796969\n", + " 36446\n", + " 2481262477\n", + " 1309\n", + " 1302\n", + " 7564824\n", + " 2873489\n", " othdiscr\n", " 1\n", " False\n", " 2\n", - " escort\n", - " 2478375758\n", - " 136\n", - " 128\n", - " False\n", + " 1310\n", + " ...\n", + " shopping\n", + " 2481262478\n", " DRIVEALONEFREE\n", - " EV\n", + " PM\n", + " False\n", + " 1310.0\n", + " 2\n", + " False\n", " False\n", " NaN\n", " \n", " \n", "\n", - "

2796 rows × 19 columns

\n", + "

36447 rows × 23 columns

\n", "" ], "text/plain": [ - " trip_id model_choice override_choice person_id household_id \\\n", - "0 123229 7 69 375 375 \n", - "1 123230 9 106 375 375 \n", - "2 206561 7 9 629 629 \n", - "3 265617 141 141 809 809 \n", - "4 265621 127 127 809 809 \n", - "... ... ... ... ... ... \n", - "2791 2473349145 7 121 7540698 2849363 \n", - "2792 2473471869 25 134 7541072 2849737 \n", - "2793 2477980533 7 117 7554818 2863483 \n", - "2794 2478086525 7 48 7555141 2863806 \n", - "2795 2478375757 126 127 7556023 2864688 \n", + " trip_id model_choice override_choice person_id household_id \\\n", + "0 439241 93 178 1339 1339 \n", + "1 497189 169 99 1515 1515 \n", + "2 536181 184 177 1634 1634 \n", + "3 593909 183 136 1810 1810 \n", + "4 593910 89 188 1810 1810 \n", + "... ... ... ... ... ... \n", + "36442 2480879045 1304 1306 7563655 2872320 \n", + "36443 2480879069 1298 1299 7563655 2872320 \n", + "36444 2480879070 1291 1293 7563655 2872320 \n", + "36445 2480879071 1293 1293 7563655 2872320 \n", + "36446 2481262477 1309 1302 7564824 2873489 \n", "\n", - " tour_id primary_purpose trip_num outbound trip_count purpose \\\n", - "0 15403 othmaint 1 False 3 shopping \n", - "1 15403 othmaint 2 False 3 eatout \n", - "2 25820 univ 1 True 2 escort \n", - "3 33202 shopping 1 True 2 shopping \n", - "4 33202 shopping 1 False 2 othmaint \n", - "... ... ... ... ... ... ... \n", - "2791 309168643 othdiscr 1 True 2 social \n", - "2792 309183983 univ 1 False 2 escort \n", - "2793 309747566 othmaint 1 False 2 shopping \n", - "2794 309760815 shopping 1 False 2 shopping \n", - "2795 309796969 othdiscr 1 False 2 escort \n", + " primary_purpose trip_num outbound trip_count destination ... \\\n", + "0 eatout 1 True 2 187 ... \n", + "1 shopping 1 False 2 170 ... \n", + "2 othmaint 1 False 2 178 ... \n", + "3 othmaint 1 False 3 183 ... \n", + "4 othmaint 2 False 3 183 ... \n", + "... ... ... ... ... ... ... \n", + "36442 othdiscr 1 False 2 1293 ... \n", + "36443 othmaint 1 False 4 1293 ... \n", + "36444 othmaint 2 False 4 1293 ... \n", + "36445 othmaint 3 False 4 1293 ... \n", + "36446 othdiscr 1 False 2 1310 ... \n", "\n", - " next_trip_id destination origin failed tour_mode trip_period \\\n", - "0 123230 105 67 False DRIVEALONEFREE MD \n", - "1 123231 105 69 False DRIVEALONEFREE MD \n", - "2 206562 12 131 False WALK_HVY MD \n", - "3 265618 117 138 False DRIVEALONEFREE MD \n", - "4 265622 138 117 False DRIVEALONEFREE MD \n", - "... ... ... ... ... ... ... \n", - "2791 2473349146 56 92 False SHARED3FREE PM \n", - "2792 2473471870 117 107 False TAXI AM \n", - "2793 2477980534 20 177 False DRIVEALONEFREE PM \n", - "2794 2478086526 85 41 False SHARED2FREE PM \n", - "2795 2478375758 136 128 False DRIVEALONEFREE EV \n", + " purpose next_trip_id tour_mode trip_period is_joint \\\n", + "0 shopping 439242 DRIVEALONEFREE PM False \n", + "1 othdiscr 497190 DRIVEALONEFREE EV False \n", + "2 othmaint 536182 DRIVEALONEFREE PM False \n", + "3 escort 593910 SHARED2FREE AM False \n", + "4 othmaint 593911 SHARED2FREE AM False \n", + "... ... ... ... ... ... \n", + "36442 othdiscr 2480879046 DRIVEALONEFREE MD False \n", + "36443 shopping 2480879070 DRIVEALONEFREE PM False \n", + "36444 eatout 2480879071 DRIVEALONEFREE PM False \n", + "36445 othdiscr 2480879072 DRIVEALONEFREE PM False \n", + "36446 shopping 2481262478 DRIVEALONEFREE PM False \n", "\n", - " is_joint destination_logsum \n", - "0 False NaN \n", - "1 False NaN \n", - "2 False NaN \n", - "3 False NaN \n", - "4 False NaN \n", - "... ... ... \n", - "2791 False NaN \n", - "2792 False NaN \n", - "2793 False NaN \n", - "2794 False NaN \n", - "2795 False NaN \n", + " tour_leg_dest purpose_index_num tour_mode_is_walk tour_mode_is_bike \\\n", + "0 187.0 2 False False \n", + "1 170.0 6 False False \n", + "2 178.0 4 False False \n", + "3 183.0 1 False False \n", + "4 183.0 4 False False \n", + "... ... ... ... ... \n", + "36442 1293.0 6 False False \n", + "36443 1293.0 2 False False \n", + "36444 1293.0 3 False False \n", + "36445 1293.0 6 False False \n", + "36446 1310.0 2 False False \n", "\n", - "[2796 rows x 19 columns]" + " destination_logsum \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "... ... \n", + "36442 NaN \n", + "36443 NaN \n", + "36444 NaN \n", + "36445 NaN \n", + "36446 NaN \n", + "\n", + "[36447 rows x 23 columns]" ] }, "execution_count": 7, @@ -1148,9 +1207,9 @@ " 7\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 3\n", " 5.89564\n", " 2.875000\n", @@ -1172,9 +1231,9 @@ " 19\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.84871\n", " 5.195214\n", @@ -1196,9 +1255,9 @@ " 38\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.53231\n", " 80.470405\n", @@ -1220,9 +1279,9 @@ " 20\n", " ...\n", " 0\n", + " 0.0\n", " 0.00000\n", - " 0.00000\n", - " 0.00000\n", + " 0.0\n", " 2\n", " 5.64330\n", " 7.947368\n", @@ -1244,9 +1303,9 @@ " 86\n", " ...\n", " 0\n", - " 0.00000\n", + " 0.0\n", " 72.14684\n", - " 0.00000\n", + " 0.0\n", " 1\n", " 5.52555\n", " 38.187500\n", @@ -1279,188 +1338,188 @@ " ...\n", " \n", " \n", - " 186\n", - " 4\n", - " 4\n", - " 1\n", - " 2779\n", - " 8062\n", - " 376.0\n", - " 172.0\n", - " 15.00000\n", - " 1760\n", - " 1178\n", + " 1450\n", + " 34\n", + " 34\n", + " 9\n", + " 2724\n", + " 6493\n", + " 1320.0\n", + " 630.0\n", + " 69.00000\n", + " 1046\n", + " 1013\n", " ...\n", - 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190 rows × 28 columns

\n", + "

1454 rows × 28 columns

\n", "" ], "text/plain": [ - " DISTRICT SD county_id TOTHH TOTPOP TOTACRE RESACRE CIACRE \\\n", - "zone_id \n", - "1 1 1 1 46 82 20.3 1.0 15.00000 \n", - "2 1 1 1 134 240 31.1 1.0 24.79297 \n", - "3 1 1 1 267 476 14.7 1.0 2.31799 \n", - "4 1 1 1 151 253 19.3 1.0 18.00000 \n", - "5 1 1 1 611 1069 52.7 1.0 15.00000 \n", - "... ... .. ... ... ... ... ... ... \n", - "186 4 4 1 2779 8062 376.0 172.0 15.00000 \n", - "187 4 4 1 1492 4139 214.0 116.0 10.00000 \n", - "188 4 4 1 753 4072 232.0 11.0 178.00000 \n", - "189 4 4 1 3546 8476 201.0 72.0 6.00000 \n", - "190 4 4 1 968 1647 1381.0 14.0 28.00000 \n", + " DISTRICT SD county_id TOTHH TOTPOP TOTACRE RESACRE CIACRE \\\n", + "zone_id \n", + "1 1 1 1 46 82 20.3 1.0 15.00000 \n", + "2 1 1 1 134 240 31.1 1.0 24.79297 \n", + "3 1 1 1 267 476 14.7 1.0 2.31799 \n", + "4 1 1 1 151 253 19.3 1.0 18.00000 \n", + "5 1 1 1 611 1069 52.7 1.0 15.00000 \n", + "... ... .. ... ... ... ... ... ... \n", + "1450 34 34 9 2724 6493 1320.0 630.0 69.00000 \n", + "1451 34 34 9 2016 4835 664.0 379.0 43.00000 \n", + "1452 34 34 9 2178 5055 1068.0 602.0 35.00000 \n", + "1453 34 34 9 298 779 14195.0 429.0 4.00000 \n", + "1454 34 34 9 1068 2337 10469.0 1114.0 27.00000 \n", "\n", - " TOTEMP AGE0519 ... area_type HSENROLL COLLFTE \\\n", - "zone_id ... \n", - "1 27318 7 ... 0 0.00000 0.00000 \n", - "2 42078 19 ... 0 0.00000 0.00000 \n", - "3 2445 38 ... 0 0.00000 0.00000 \n", - "4 22434 20 ... 0 0.00000 0.00000 \n", - "5 15662 86 ... 0 0.00000 72.14684 \n", - "... ... ... ... ... ... ... \n", - "186 1760 1178 ... 3 0.00000 0.00000 \n", - "187 808 603 ... 3 0.00000 0.00000 \n", - "188 4502 1117 ... 2 3961.04761 17397.79102 \n", - "189 226 1057 ... 2 0.00000 0.00000 \n", - "190 1010 114 ... 3 0.00000 0.00000 \n", + " TOTEMP AGE0519 ... area_type HSENROLL COLLFTE COLLPTE \\\n", + "zone_id ... \n", + "1 27318 7 ... 0 0.0 0.00000 0.0 \n", + "2 42078 19 ... 0 0.0 0.00000 0.0 \n", + "3 2445 38 ... 0 0.0 0.00000 0.0 \n", + "4 22434 20 ... 0 0.0 0.00000 0.0 \n", + "5 15662 86 ... 0 0.0 72.14684 0.0 \n", + "... ... ... ... ... ... ... ... \n", + "1450 1046 1013 ... 4 0.0 0.00000 0.0 \n", + "1451 757 757 ... 4 0.0 0.00000 0.0 \n", + "1452 2110 789 ... 4 0.0 0.00000 0.0 \n", + "1453 922 88 ... 5 0.0 0.00000 0.0 \n", + "1454 607 418 ... 5 0.0 0.00000 0.0 \n", "\n", - " COLLPTE TOPOLOGY TERMINAL household_density \\\n", - "zone_id \n", - "1 0.00000 3 5.89564 2.875000 \n", - "2 0.00000 1 5.84871 5.195214 \n", - "3 0.00000 1 5.53231 80.470405 \n", - "4 0.00000 2 5.64330 7.947368 \n", - "5 0.00000 1 5.52555 38.187500 \n", - "... ... ... ... ... \n", - "186 0.00000 1 2.04173 14.860963 \n", - "187 0.00000 2 1.73676 11.841270 \n", - "188 11152.93652 1 2.28992 3.984127 \n", - "189 0.00000 1 2.88773 45.461538 \n", - "190 0.00000 1 2.60309 23.047619 \n", + " TOPOLOGY TERMINAL household_density employment_density \\\n", + "zone_id \n", + "1 3 5.89564 2.875000 1707.375000 \n", + "2 1 5.84871 5.195214 1631.374751 \n", + "3 1 5.53231 80.470405 736.891913 \n", + "4 2 5.64330 7.947368 1180.736842 \n", + "5 1 5.52555 38.187500 978.875000 \n", + "... ... ... ... ... \n", + "1450 1 1.12116 3.896996 1.496423 \n", + "1451 1 1.17116 4.777251 1.793839 \n", + "1452 1 1.17587 3.419152 3.312402 \n", + "1453 1 1.01972 0.688222 2.129330 \n", + "1454 1 0.95542 0.936021 0.531989 \n", "\n", - " employment_density density_index is_cbd \n", - "zone_id \n", - "1 1707.375000 2.870167 False \n", - "2 1631.374751 5.178722 False \n", - "3 736.891913 72.547987 False \n", - "4 1180.736842 7.894233 False \n", - "5 978.875000 36.753679 False \n", - "... ... ... ... \n", - "186 9.411765 5.762347 False \n", - "187 6.412698 4.159890 False \n", - "188 23.820106 3.413233 False \n", - "189 2.897436 2.723836 False \n", - "190 24.047619 11.768501 False \n", + " density_index is_cbd \n", + "zone_id \n", + "1 2.870167 False \n", + "2 5.178722 False \n", + "3 72.547987 False \n", + "4 7.894233 False \n", + "5 36.753679 False \n", + "... ... ... \n", + "1450 1.081235 False \n", + "1451 1.304140 False \n", + "1452 1.682465 False \n", + "1453 0.520115 False \n", + "1454 0.339203 False \n", "\n", - "[190 rows x 28 columns]" + "[1454 rows x 28 columns]" ] }, "execution_count": 8, @@ -1541,7 +1600,7 @@ " 1\n", " util_no_attractions\n", " no attractions\n", - " @size_terms.get(df.dest_taz, df.purpose) == 0\n", + " @size_terms.get(df.dest_taz, df.purpose) == 0 ...\n", " coef_UNAVAILABLE\n", " coef_UNAVAILABLE\n", " coef_UNAVAILABLE\n", @@ -1784,7 +1843,7 @@ "\n", " Expression \\\n", "0 @np.log1p(size_terms.get(df.dest_taz, df.purpo... \n", - "1 @size_terms.get(df.dest_taz, df.purpose) == 0 \n", + "1 @size_terms.get(df.dest_taz, df.purpose) == 0 ... \n", "2 @(~df.is_joint & ~df.outbound) * (_od_DIST + _... \n", "3 @(~df.is_joint & df.outbound) * (_od_DIST + _d... \n", "4 @df.is_joint * (_od_DIST + _dp_DIST) \n", @@ -2112,13 +2171,6 @@ "execution_count": 11, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n" - ] - }, { "data": { "text/html": [ @@ -2134,7 +2186,7 @@ { "data": { "text/html": [ - "

Best LL = -12569.486099970745

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Best LL = -78919.86749443343

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" -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -2.099140\n", " \n", " \n", " escort_TOTHH\n", " -6.907755\n", " -6.907755\n", + " -6.907755\n", + " -6.907755\n", + " -6.907755\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -6.907755\n", " \n", " \n", " othdiscr_HEREMPN\n", - " -6.000000\n", + " -4.394168\n", + " -4.394168\n", " -1.301953\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " othdiscr_HSENROLL\n", - " -6.000000\n", + " -3.310820\n", + " -3.310820\n", " -2.322788\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " othdiscr_OTHEMPN\n", - " -4.741924\n", + " -2.825965\n", + " -2.825965\n", " -1.801810\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -4.741924\n", " \n", " \n", " othdiscr_RETEMPN\n", - " -0.420810\n", + " -2.884521\n", + " -2.884521\n", " -1.551169\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -0.420810\n", " \n", " \n", " othdiscr_TOTHH\n", " -1.378326\n", " -1.378326\n", + " -1.378326\n", + " -1.378326\n", + " -1.378326\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -1.378326\n", " \n", " \n", " othmaint_HEREMPN\n", - " -6.000000\n", + " -1.693356\n", + " -1.693356\n", " -0.657780\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " othmaint_RETEMPN\n", - " -4.468097\n", + " -0.351270\n", + " -0.351270\n", " -0.731888\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -4.468097\n", " \n", " \n", " othmaint_TOTHH\n", " -6.907755\n", " -6.907755\n", + " -6.907755\n", + " -6.907755\n", + " -6.907755\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -6.907755\n", " \n", " \n", " shopping_RETEMPN\n", " -6.000000\n", + " -6.000000\n", " -0.001001\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " shopping_TOTHH\n", " -6.907755\n", " -6.907755\n", + " -6.907755\n", + " -6.907755\n", + " -6.907755\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -6.907755\n", " \n", " \n", " social_HEREMPN\n", - " -6.000000\n", + " -1.516222\n", + " -1.516222\n", " -0.738145\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " social_RETEMPN\n", - " -3.463457\n", + " -0.098839\n", + " -0.098839\n", " -0.652005\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -3.463457\n", " \n", " \n", " social_TOTHH\n", " -6.907755\n", " -6.907755\n", + " -6.907755\n", + " -6.907755\n", + " -6.907755\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -6.907755\n", " \n", " \n", " univ_COLLFTE\n", " -6.000000\n", + " -6.000000\n", " -0.524249\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " univ_COLLPTE\n", " -6.000000\n", + " -6.000000\n", " -0.896488\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " univ_TOTHH\n", " -6.907755\n", " -6.907755\n", + " -6.907755\n", + " -6.907755\n", + " -6.907755\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " -6.907755\n", " \n", " \n", " work_AGREMPN\n", - " 1.145159\n", + " 0.122431\n", + " 0.122431\n", " 0.000000\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " 1.145159\n", " \n", " \n", " work_FPSEMPN\n", " -6.000000\n", + " -6.000000\n", " 0.000000\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " work_HEREMPN\n", - " -4.551520\n", + " -1.959508\n", + " -1.959508\n", " 0.000000\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -4.551520\n", " \n", " \n", " work_MWTEMPN\n", - " -6.000000\n", + " -5.322959\n", + " -5.322959\n", " 0.000000\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -6.000000\n", " \n", " \n", " work_OTHEMPN\n", - " -5.205310\n", + " -2.512682\n", + " -2.512682\n", " 0.000000\n", + " -6.000000\n", + " 6.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 0\n", - " \n", - " -5.205310\n", " \n", " \n", " work_RETEMPN\n", " 0.000000\n", " 0.000000\n", + " 0.000000\n", + " 0.000000\n", + " 0.000000\n", " 0.0\n", - " -6.0\n", - " 6.0\n", " 1\n", - " \n", - " 0.000000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " value initvalue nullvalue minimum \\\n", - "coef_UNAVAILABLE -999.000000 -999.000000 0.0 NaN \n", - "coef_distance_joint -0.376392 -0.123800 0.0 NaN \n", - "coef_mode_choice_logsum 0.163028 1.821000 0.0 NaN \n", - "coef_one 1.000000 1.000000 0.0 NaN \n", - "coef_prox_dest_outbound_work -0.359504 -0.260000 0.0 NaN \n", - "coef_prox_home_inbound_work -0.174104 -0.150000 0.0 NaN \n", - "coef_prox_home_outbound_work -0.522711 -0.380000 0.0 NaN \n", - "coef_util_distance_atwork -0.122335 -0.122335 0.0 NaN \n", - "coef_util_distance_eatout -0.366193 -0.102900 0.0 NaN \n", - "coef_util_distance_escort -0.337785 -0.149100 0.0 NaN \n", - "coef_util_distance_othdiscr -0.408787 -0.126172 0.0 NaN \n", - "coef_util_distance_othmaint -0.257971 -0.096200 0.0 NaN \n", - "coef_util_distance_school -0.105600 -0.105600 0.0 NaN \n", - "coef_util_distance_shopping -0.390483 -0.119200 0.0 NaN \n", - "coef_util_distance_social -0.338764 -0.132900 0.0 NaN \n", - "coef_util_distance_univ -0.186380 -0.061300 0.0 NaN \n", - "coef_util_distance_work_inbound -0.094761 0.147813 0.0 NaN \n", - "coef_util_distance_work_outbound -0.300511 -0.049726 0.0 NaN \n", - "eatout_HEREMPN -4.274182 -1.354796 0.0 -6.0 \n", - "eatout_RETEMPN -0.298406 -0.298406 0.0 -6.0 \n", - "escort_AGE0519 -1.513508 -0.767871 0.0 -6.0 \n", - "escort_HEREMPN -6.000000 -1.937942 0.0 -6.0 \n", - "escort_HSENROLL -3.172185 -1.795767 0.0 -6.0 \n", - "escort_RETEMPN -2.099140 -1.491655 0.0 -6.0 \n", - "escort_TOTHH -6.907755 -6.907755 0.0 -6.0 \n", - "othdiscr_HEREMPN -6.000000 -1.301953 0.0 -6.0 \n", - "othdiscr_HSENROLL -6.000000 -2.322788 0.0 -6.0 \n", - "othdiscr_OTHEMPN -4.741924 -1.801810 0.0 -6.0 \n", - "othdiscr_RETEMPN -0.420810 -1.551169 0.0 -6.0 \n", - "othdiscr_TOTHH -1.378326 -1.378326 0.0 -6.0 \n", - "othmaint_HEREMPN -6.000000 -0.657780 0.0 -6.0 \n", - "othmaint_RETEMPN -4.468097 -0.731888 0.0 -6.0 \n", - "othmaint_TOTHH -6.907755 -6.907755 0.0 -6.0 \n", - "shopping_RETEMPN -6.000000 -0.001001 0.0 -6.0 \n", - "shopping_TOTHH -6.907755 -6.907755 0.0 -6.0 \n", - "social_HEREMPN -6.000000 -0.738145 0.0 -6.0 \n", - "social_RETEMPN -3.463457 -0.652005 0.0 -6.0 \n", - "social_TOTHH -6.907755 -6.907755 0.0 -6.0 \n", - "univ_COLLFTE -6.000000 -0.524249 0.0 -6.0 \n", - "univ_COLLPTE -6.000000 -0.896488 0.0 -6.0 \n", - "univ_TOTHH -6.907755 -6.907755 0.0 -6.0 \n", - "work_AGREMPN 1.145159 0.000000 0.0 -6.0 \n", - "work_FPSEMPN -6.000000 0.000000 0.0 -6.0 \n", - "work_HEREMPN -4.551520 0.000000 0.0 -6.0 \n", - "work_MWTEMPN -6.000000 0.000000 0.0 -6.0 \n", - "work_OTHEMPN -5.205310 0.000000 0.0 -6.0 \n", - "work_RETEMPN 0.000000 0.000000 0.0 -6.0 \n", + " value best initvalue \\\n", + "param_name \n", + "coef_UNAVAILABLE -999.000000 -999.000000 -999.000000 \n", + "coef_distance_joint -0.222590 -0.222590 -0.123800 \n", + "coef_mode_choice_logsum 0.908189 0.908189 1.821000 \n", + "coef_one 1.000000 1.000000 1.000000 \n", + "coef_prox_dest_outbound_work -0.278949 -0.278949 -0.260000 \n", + "coef_prox_home_inbound_work -0.121975 -0.121975 -0.150000 \n", + "coef_prox_home_outbound_work -0.419012 -0.419012 -0.380000 \n", + "coef_util_distance_atwork -0.122335 -0.122335 -0.122335 \n", + "coef_util_distance_eatout -0.219723 -0.219723 -0.102900 \n", + "coef_util_distance_escort -0.235858 -0.235858 -0.149100 \n", + "coef_util_distance_othdiscr -0.241573 -0.241573 -0.126172 \n", + "coef_util_distance_othmaint -0.177610 -0.177610 -0.096200 \n", + "coef_util_distance_school -0.105600 -0.105600 -0.105600 \n", + "coef_util_distance_shopping -0.227090 -0.227090 -0.119200 \n", + "coef_util_distance_social -0.227558 -0.227558 -0.132900 \n", + "coef_util_distance_univ -0.139388 -0.139388 -0.061300 \n", + "coef_util_distance_work_inbound 0.089849 0.089849 0.147813 \n", + "coef_util_distance_work_outbound -0.138174 -0.138174 -0.049726 \n", + "eatout_HEREMPN -2.550718 -2.550718 -1.354796 \n", + "eatout_RETEMPN -0.298406 -0.298406 -0.298406 \n", + "escort_AGE0519 -0.039579 -0.039579 -0.767871 \n", + "escort_HEREMPN -2.848681 -2.848681 -1.937942 \n", + "escort_HSENROLL -2.260284 -2.260284 -1.795768 \n", + "escort_RETEMPN -0.886457 -0.886457 -1.491655 \n", + "escort_TOTHH -6.907755 -6.907755 -6.907755 \n", + "othdiscr_HEREMPN -4.394168 -4.394168 -1.301953 \n", + "othdiscr_HSENROLL -3.310820 -3.310820 -2.322788 \n", + "othdiscr_OTHEMPN -2.825965 -2.825965 -1.801810 \n", + "othdiscr_RETEMPN -2.884521 -2.884521 -1.551169 \n", + "othdiscr_TOTHH -1.378326 -1.378326 -1.378326 \n", + "othmaint_HEREMPN -1.693356 -1.693356 -0.657780 \n", + "othmaint_RETEMPN -0.351270 -0.351270 -0.731888 \n", + "othmaint_TOTHH -6.907755 -6.907755 -6.907755 \n", + "shopping_RETEMPN -6.000000 -6.000000 -0.001001 \n", + "shopping_TOTHH -6.907755 -6.907755 -6.907755 \n", + "social_HEREMPN -1.516222 -1.516222 -0.738145 \n", + "social_RETEMPN -0.098839 -0.098839 -0.652005 \n", + "social_TOTHH -6.907755 -6.907755 -6.907755 \n", + "univ_COLLFTE -6.000000 -6.000000 -0.524249 \n", + "univ_COLLPTE -6.000000 -6.000000 -0.896488 \n", + "univ_TOTHH -6.907755 -6.907755 -6.907755 \n", + "work_AGREMPN 0.122431 0.122431 0.000000 \n", + "work_FPSEMPN -6.000000 -6.000000 0.000000 \n", + "work_HEREMPN -1.959508 -1.959508 0.000000 \n", + "work_MWTEMPN -5.322959 -5.322959 0.000000 \n", + "work_OTHEMPN -2.512682 -2.512682 0.000000 \n", + "work_RETEMPN 0.000000 0.000000 0.000000 \n", "\n", - " maximum holdfast note best \n", - "coef_UNAVAILABLE NaN 1 -999.000000 \n", - "coef_distance_joint NaN 0 -0.376392 \n", - "coef_mode_choice_logsum NaN 0 0.163028 \n", - "coef_one NaN 1 1.000000 \n", - "coef_prox_dest_outbound_work NaN 0 -0.359504 \n", - "coef_prox_home_inbound_work NaN 0 -0.174104 \n", - "coef_prox_home_outbound_work NaN 0 -0.522711 \n", - "coef_util_distance_atwork NaN 0 -0.122335 \n", - "coef_util_distance_eatout NaN 0 -0.366193 \n", - "coef_util_distance_escort NaN 0 -0.337785 \n", - "coef_util_distance_othdiscr NaN 0 -0.408787 \n", - "coef_util_distance_othmaint NaN 0 -0.257971 \n", - "coef_util_distance_school NaN 0 -0.105600 \n", - "coef_util_distance_shopping NaN 0 -0.390483 \n", - "coef_util_distance_social NaN 0 -0.338764 \n", - "coef_util_distance_univ NaN 0 -0.186380 \n", - "coef_util_distance_work_inbound NaN 0 -0.094761 \n", - "coef_util_distance_work_outbound NaN 0 -0.300511 \n", - "eatout_HEREMPN 6.0 0 -4.274182 \n", - "eatout_RETEMPN 6.0 1 -0.298406 \n", - "escort_AGE0519 6.0 0 -1.513508 \n", - "escort_HEREMPN 6.0 0 -6.000000 \n", - "escort_HSENROLL 6.0 0 -3.172185 \n", - "escort_RETEMPN 6.0 0 -2.099140 \n", - "escort_TOTHH 6.0 1 -6.907755 \n", - "othdiscr_HEREMPN 6.0 0 -6.000000 \n", - "othdiscr_HSENROLL 6.0 0 -6.000000 \n", - "othdiscr_OTHEMPN 6.0 0 -4.741924 \n", - "othdiscr_RETEMPN 6.0 0 -0.420810 \n", - "othdiscr_TOTHH 6.0 1 -1.378326 \n", - "othmaint_HEREMPN 6.0 0 -6.000000 \n", - "othmaint_RETEMPN 6.0 0 -4.468097 \n", - "othmaint_TOTHH 6.0 1 -6.907755 \n", - "shopping_RETEMPN 6.0 0 -6.000000 \n", - "shopping_TOTHH 6.0 1 -6.907755 \n", - "social_HEREMPN 6.0 0 -6.000000 \n", - "social_RETEMPN 6.0 0 -3.463457 \n", - "social_TOTHH 6.0 1 -6.907755 \n", - "univ_COLLFTE 6.0 0 -6.000000 \n", - "univ_COLLPTE 6.0 0 -6.000000 \n", - "univ_TOTHH 6.0 1 -6.907755 \n", - "work_AGREMPN 6.0 0 1.145159 \n", - "work_FPSEMPN 6.0 0 -6.000000 \n", - "work_HEREMPN 6.0 0 -4.551520 \n", - "work_MWTEMPN 6.0 0 -6.000000 \n", - "work_OTHEMPN 6.0 0 -5.205310 \n", - "work_RETEMPN 6.0 1 0.000000 " + " minimum maximum nullvalue holdfast \n", + "param_name \n", + "coef_UNAVAILABLE -999.000000 -999.000000 0.0 1 \n", + "coef_distance_joint -25.000000 25.000000 0.0 0 \n", + "coef_mode_choice_logsum -25.000000 25.000000 0.0 0 \n", + "coef_one 1.000000 1.000000 0.0 1 \n", + "coef_prox_dest_outbound_work -25.000000 25.000000 0.0 0 \n", + "coef_prox_home_inbound_work -25.000000 25.000000 0.0 0 \n", + "coef_prox_home_outbound_work -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_atwork -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_eatout -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_escort -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_othdiscr -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_othmaint -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_school -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_shopping -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_social -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_univ -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_work_inbound -25.000000 25.000000 0.0 0 \n", + "coef_util_distance_work_outbound -25.000000 25.000000 0.0 0 \n", + "eatout_HEREMPN -6.000000 6.000000 0.0 0 \n", + "eatout_RETEMPN -0.298406 -0.298406 0.0 1 \n", + "escort_AGE0519 -6.000000 6.000000 0.0 0 \n", + "escort_HEREMPN -6.000000 6.000000 0.0 0 \n", + "escort_HSENROLL -6.000000 6.000000 0.0 0 \n", + "escort_RETEMPN -6.000000 6.000000 0.0 0 \n", + "escort_TOTHH -6.907755 -6.907755 0.0 1 \n", + "othdiscr_HEREMPN -6.000000 6.000000 0.0 0 \n", + "othdiscr_HSENROLL -6.000000 6.000000 0.0 0 \n", + "othdiscr_OTHEMPN -6.000000 6.000000 0.0 0 \n", + "othdiscr_RETEMPN -6.000000 6.000000 0.0 0 \n", + "othdiscr_TOTHH -1.378326 -1.378326 0.0 1 \n", + "othmaint_HEREMPN -6.000000 6.000000 0.0 0 \n", + "othmaint_RETEMPN -6.000000 6.000000 0.0 0 \n", + "othmaint_TOTHH -6.907755 -6.907755 0.0 1 \n", + "shopping_RETEMPN -6.000000 6.000000 0.0 0 \n", + "shopping_TOTHH -6.907755 -6.907755 0.0 1 \n", + "social_HEREMPN -6.000000 6.000000 0.0 0 \n", + "social_RETEMPN -6.000000 6.000000 0.0 0 \n", + "social_TOTHH -6.907755 -6.907755 0.0 1 \n", + "univ_COLLFTE -6.000000 6.000000 0.0 0 \n", + "univ_COLLPTE -6.000000 6.000000 0.0 0 \n", + "univ_TOTHH -6.907755 -6.907755 0.0 1 \n", + "work_AGREMPN -6.000000 6.000000 0.0 0 \n", + "work_FPSEMPN -6.000000 6.000000 0.0 0 \n", + "work_HEREMPN -6.000000 6.000000 0.0 0 \n", + "work_MWTEMPN -6.000000 6.000000 0.0 0 \n", + "work_OTHEMPN -6.000000 6.000000 0.0 0 \n", + "work_RETEMPN 0.000000 0.000000 0.0 1 " ] }, "metadata": {}, @@ -2803,12 +2819,8 @@ "name": "stderr", "output_type": "stream", "text": [ - ":1: PossibleOverspecification: WARNING: Model is possibly over-specified (hessian is nearly singular).\n", - " model.estimate(method='SLSQP', options={'maxiter':1000})\n", - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/larch/larch/linalg/__init__.py:18: UserWarning: minimum eig 8.29804005517378e-15 in general_inverse\n", - " warnings.warn(f\"minimum eig {min_eig} in general_inverse\")\n", - ":1: RuntimeWarning: invalid value encountered in sqrt\n", - " model.estimate(method='SLSQP', options={'maxiter':1000})\n" + "/Users/jpn/Git/est-mode/larch/src/larch/model/jaxmodel.py:1156: PossibleOverspecification: Model is possibly over-specified (hessian is nearly singular).\n", + " self.calculate_parameter_covariance()\n" ] }, { @@ -2828,11 +2840,11 @@ " \n", " \n", " coef_distance_joint\n", - " -0.376392\n", + " -0.222590\n", " \n", " \n", " coef_mode_choice_logsum\n", - " 0.163028\n", + " 0.908189\n", " \n", " \n", " coef_one\n", @@ -2840,15 +2852,15 @@ " \n", " \n", " coef_prox_dest_outbound_work\n", - " -0.359504\n", + " -0.278949\n", " \n", " \n", " coef_prox_home_inbound_work\n", - " -0.174104\n", + " -0.121975\n", " \n", " \n", " coef_prox_home_outbound_work\n", - " -0.522711\n", + " -0.419012\n", " \n", " \n", " coef_util_distance_atwork\n", @@ -2856,19 +2868,19 @@ " \n", " \n", " coef_util_distance_eatout\n", - " -0.366193\n", + " -0.219723\n", " \n", " \n", " coef_util_distance_escort\n", - " -0.337785\n", + " -0.235858\n", " \n", " \n", " coef_util_distance_othdiscr\n", - " -0.408787\n", + " -0.241573\n", " \n", " \n", " coef_util_distance_othmaint\n", - " -0.257971\n", + " -0.177610\n", " \n", " \n", " coef_util_distance_school\n", @@ -2876,27 +2888,27 @@ " \n", " \n", " coef_util_distance_shopping\n", - " -0.390483\n", + " -0.227090\n", " \n", " \n", " coef_util_distance_social\n", - " -0.338764\n", + " -0.227558\n", " \n", " \n", " coef_util_distance_univ\n", - " -0.186380\n", + " -0.139388\n", " \n", " \n", " coef_util_distance_work_inbound\n", - " -0.094761\n", + " 0.089849\n", " \n", " \n", " coef_util_distance_work_outbound\n", - " -0.300511\n", + " -0.138174\n", " \n", " \n", " eatout_HEREMPN\n", - " -4.274182\n", + " -2.550718\n", " \n", " \n", " eatout_RETEMPN\n", @@ -2904,39 +2916,39 @@ " \n", " \n", " escort_AGE0519\n", - " -1.513508\n", + " -0.039579\n", " \n", " \n", " escort_HEREMPN\n", - " -6.000000\n", + " -2.848681\n", " \n", " \n", " escort_HSENROLL\n", - " -3.172185\n", + " -2.260284\n", " \n", " \n", " escort_RETEMPN\n", - " -2.099140\n", + " -0.886457\n", " \n", " \n", " escort_TOTHH\n", - " -6.000000\n", + " -6.907755\n", " \n", " \n", " othdiscr_HEREMPN\n", - " -6.000000\n", + " -4.394168\n", " \n", " \n", " othdiscr_HSENROLL\n", - " -6.000000\n", + " -3.310820\n", " \n", " \n", " othdiscr_OTHEMPN\n", - " -4.741924\n", + " -2.825965\n", " \n", " \n", " othdiscr_RETEMPN\n", - " -0.420810\n", + " -2.884521\n", " \n", " \n", " othdiscr_TOTHH\n", @@ -2944,15 +2956,15 @@ " \n", " \n", " othmaint_HEREMPN\n", - " -6.000000\n", + " -1.693356\n", " \n", " \n", " othmaint_RETEMPN\n", - " -4.468097\n", + " -0.351270\n", " \n", " \n", " othmaint_TOTHH\n", - " -6.000000\n", + " -6.907755\n", " \n", " \n", " shopping_RETEMPN\n", @@ -2960,19 +2972,19 @@ " \n", " \n", " shopping_TOTHH\n", - " -6.000000\n", + " -6.907755\n", " \n", " \n", " social_HEREMPN\n", - " -6.000000\n", + " -1.516222\n", " \n", " \n", " social_RETEMPN\n", - " -3.463457\n", + " -0.098839\n", " \n", " \n", " social_TOTHH\n", - " -6.000000\n", + " -6.907755\n", " \n", " \n", " univ_COLLFTE\n", @@ -2984,11 +2996,11 @@ " \n", " \n", " univ_TOTHH\n", - " -6.000000\n", + " -6.907755\n", " \n", " \n", " work_AGREMPN\n", - " 1.145159\n", + " 0.122431\n", " \n", " \n", " work_FPSEMPN\n", @@ -2996,22 +3008,22 @@ " \n", " \n", " work_HEREMPN\n", - " -4.551520\n", + " -1.959508\n", " \n", " \n", " work_MWTEMPN\n", - " -6.000000\n", + " -5.322959\n", " \n", " \n", " work_OTHEMPN\n", - " -5.205310\n", + " -2.512682\n", " \n", " \n", " work_RETEMPN\n", " 0.000000\n", " \n", " \n", - "loglike-12569.486099970745d_loglike\n", + "
logloss2.543504818049292d_logloss\n", " \n", " \n", " \n", @@ -3025,11 +3037,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3037,15 +3049,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3053,19 +3065,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3073,27 +3085,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3101,19 +3113,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3121,19 +3133,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3141,11 +3153,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3153,7 +3165,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3161,11 +3173,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3173,11 +3185,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3185,140 +3197,140 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - "
coef_distance_joint-0.0015450.000054
coef_mode_choice_logsum-0.0371690.000028
coef_one
coef_prox_dest_outbound_work-0.004856-0.000016
coef_prox_home_inbound_work-0.0006900.000044
coef_prox_home_outbound_work0.006258-0.000010
coef_util_distance_atwork
coef_util_distance_eatout0.0009010.000017
coef_util_distance_escort0.0016000.000004
coef_util_distance_othdiscr-0.002904-0.000030
coef_util_distance_othmaint0.002059-0.000009
coef_util_distance_school
coef_util_distance_shopping-0.0016370.000006
coef_util_distance_social-0.000562-0.000076
coef_util_distance_univ-0.0014910.000026
coef_util_distance_work_inbound0.001402-0.000026
coef_util_distance_work_outbound0.0099840.000069
eatout_HEREMPN0.000060-0.000049
eatout_RETEMPN
escort_AGE0519-0.000103-0.000144
escort_HEREMPN-0.2823510.000048
escort_HSENROLL-0.0001000.000044
escort_RETEMPN0.0002190.000042
escort_TOTHH
othdiscr_HEREMPN-0.0837150.000056
othdiscr_HSENROLL-0.013300-0.000088
othdiscr_OTHEMPN-0.0001250.000216
othdiscr_RETEMPN-0.000008-0.000059
othdiscr_TOTHH
othmaint_HEREMPN-14.917882-0.000220
othmaint_RETEMPN0.000013-0.000006
othmaint_TOTHH
shopping_RETEMPN-6.303186-0.005801
shopping_TOTHH
social_HEREMPN-2.2981350.000007
social_RETEMPN-0.000206-0.000072
social_TOTHH
univ_COLLFTE-12.165359-0.003131
univ_COLLPTE-12.781555-0.002441
univ_TOTHH
work_AGREMPN0.000453-0.000051
work_FPSEMPN-0.905637-0.000190
work_HEREMPN0.0002220.000081
work_MWTEMPN-0.021745-0.000196
work_OTHEMPN-0.000419-0.000045
work_RETEMPN0.000000
nit87nfev147njev87status0message'Optimization terminated successfully'successTrueelapsed_time0:00:21.469689method'SLSQP'n_cases2490iteration_number87logloss5.04798638553042" + "nit87nfev92njev87status0message'Optimization terminated successfully'successTrueelapsed_time0:00:19.360953method'SLSQP'n_cases31028iteration_number87loglike-78919.86749443343" ], "text/plain": [ "┣ x: coef_UNAVAILABLE -999.000000\n", - "┃ coef_distance_joint -0.376392\n", - "┃ coef_mode_choice_logsum 0.163028\n", + "┃ coef_distance_joint -0.222590\n", + "┃ coef_mode_choice_logsum 0.908189\n", "┃ coef_one 1.000000\n", - "┃ coef_prox_dest_outbound_work -0.359504\n", - "┃ coef_prox_home_inbound_work -0.174104\n", - "┃ coef_prox_home_outbound_work -0.522711\n", + "┃ coef_prox_dest_outbound_work -0.278949\n", + "┃ coef_prox_home_inbound_work -0.121975\n", + "┃ coef_prox_home_outbound_work -0.419012\n", "┃ coef_util_distance_atwork -0.122335\n", - "┃ coef_util_distance_eatout -0.366193\n", - "┃ coef_util_distance_escort -0.337785\n", - "┃ coef_util_distance_othdiscr -0.408787\n", - "┃ coef_util_distance_othmaint -0.257971\n", + "┃ coef_util_distance_eatout -0.219723\n", + "┃ coef_util_distance_escort -0.235858\n", + "┃ coef_util_distance_othdiscr -0.241573\n", + "┃ coef_util_distance_othmaint -0.177610\n", "┃ coef_util_distance_school -0.105600\n", - "┃ coef_util_distance_shopping -0.390483\n", - "┃ coef_util_distance_social -0.338764\n", - "┃ coef_util_distance_univ -0.186380\n", - "┃ coef_util_distance_work_inbound -0.094761\n", - "┃ coef_util_distance_work_outbound -0.300511\n", - "┃ eatout_HEREMPN -4.274182\n", + "┃ coef_util_distance_shopping -0.227090\n", + "┃ coef_util_distance_social -0.227558\n", + "┃ coef_util_distance_univ -0.139388\n", + "┃ coef_util_distance_work_inbound 0.089849\n", + "┃ coef_util_distance_work_outbound -0.138174\n", + "┃ eatout_HEREMPN -2.550718\n", "┃ eatout_RETEMPN -0.298406\n", - "┃ escort_AGE0519 -1.513508\n", - "┃ escort_HEREMPN -6.000000\n", - "┃ escort_HSENROLL -3.172185\n", - "┃ escort_RETEMPN -2.099140\n", - "┃ escort_TOTHH -6.000000\n", - "┃ othdiscr_HEREMPN -6.000000\n", - "┃ othdiscr_HSENROLL -6.000000\n", - "┃ othdiscr_OTHEMPN -4.741924\n", - "┃ othdiscr_RETEMPN -0.420810\n", + "┃ escort_AGE0519 -0.039579\n", + "┃ escort_HEREMPN -2.848681\n", + "┃ escort_HSENROLL -2.260284\n", + "┃ escort_RETEMPN -0.886457\n", + "┃ escort_TOTHH -6.907755\n", + "┃ othdiscr_HEREMPN -4.394168\n", + "┃ othdiscr_HSENROLL -3.310820\n", + "┃ othdiscr_OTHEMPN -2.825965\n", + "┃ othdiscr_RETEMPN -2.884521\n", "┃ othdiscr_TOTHH -1.378326\n", - "┃ othmaint_HEREMPN -6.000000\n", - "┃ othmaint_RETEMPN -4.468097\n", - "┃ othmaint_TOTHH -6.000000\n", + "┃ othmaint_HEREMPN -1.693356\n", + "┃ othmaint_RETEMPN -0.351270\n", + "┃ othmaint_TOTHH -6.907755\n", "┃ shopping_RETEMPN -6.000000\n", - "┃ shopping_TOTHH -6.000000\n", - "┃ social_HEREMPN -6.000000\n", - "┃ social_RETEMPN -3.463457\n", - "┃ social_TOTHH -6.000000\n", + "┃ shopping_TOTHH -6.907755\n", + "┃ social_HEREMPN -1.516222\n", + "┃ social_RETEMPN -0.098839\n", + "┃ social_TOTHH -6.907755\n", "┃ univ_COLLFTE -6.000000\n", "┃ univ_COLLPTE -6.000000\n", - "┃ univ_TOTHH -6.000000\n", - "┃ work_AGREMPN 1.145159\n", + "┃ univ_TOTHH -6.907755\n", + "┃ work_AGREMPN 0.122431\n", "┃ work_FPSEMPN -6.000000\n", - "┃ work_HEREMPN -4.551520\n", - "┃ work_MWTEMPN -6.000000\n", - "┃ work_OTHEMPN -5.205310\n", + "┃ work_HEREMPN -1.959508\n", + "┃ work_MWTEMPN -5.322959\n", + "┃ work_OTHEMPN -2.512682\n", "┃ work_RETEMPN 0.000000\n", "┃ dtype: float64\n", - "┣ loglike: -12569.486099970745\n", - "┣ d_loglike: coef_UNAVAILABLE 0.000000\n", - "┃ coef_distance_joint -0.001545\n", - "┃ coef_mode_choice_logsum -0.037169\n", - "┃ coef_one 0.000000\n", - "┃ coef_prox_dest_outbound_work -0.004856\n", - "┃ coef_prox_home_inbound_work -0.000690\n", - "┃ coef_prox_home_outbound_work 0.006258\n", - "┃ coef_util_distance_atwork 0.000000\n", - "┃ coef_util_distance_eatout 0.000901\n", - "┃ coef_util_distance_escort 0.001600\n", - "┃ coef_util_distance_othdiscr -0.002904\n", - "┃ coef_util_distance_othmaint 0.002059\n", - "┃ coef_util_distance_school 0.000000\n", - "┃ coef_util_distance_shopping -0.001637\n", - "┃ coef_util_distance_social -0.000562\n", - "┃ coef_util_distance_univ -0.001491\n", - "┃ coef_util_distance_work_inbound 0.001402\n", - "┃ coef_util_distance_work_outbound 0.009984\n", - "┃ eatout_HEREMPN 0.000060\n", - "┃ eatout_RETEMPN 0.000000\n", - "┃ escort_AGE0519 -0.000103\n", - "┃ escort_HEREMPN -0.282351\n", - "┃ escort_HSENROLL -0.000100\n", - "┃ escort_RETEMPN 0.000219\n", - "┃ escort_TOTHH 0.000000\n", - "┃ othdiscr_HEREMPN -0.083715\n", - "┃ othdiscr_HSENROLL -0.013300\n", - "┃ othdiscr_OTHEMPN -0.000125\n", - "┃ othdiscr_RETEMPN -0.000008\n", - "┃ othdiscr_TOTHH 0.000000\n", - "┃ othmaint_HEREMPN -14.917882\n", - "┃ othmaint_RETEMPN 0.000013\n", - "┃ othmaint_TOTHH 0.000000\n", - "┃ shopping_RETEMPN -6.303186\n", - "┃ shopping_TOTHH 0.000000\n", - "┃ social_HEREMPN -2.298135\n", - "┃ social_RETEMPN -0.000206\n", - "┃ social_TOTHH 0.000000\n", - "┃ univ_COLLFTE -12.165359\n", - "┃ univ_COLLPTE -12.781555\n", - "┃ univ_TOTHH 0.000000\n", - "┃ work_AGREMPN 0.000453\n", - "┃ work_FPSEMPN -0.905637\n", - "┃ work_HEREMPN 0.000222\n", - "┃ work_MWTEMPN -0.021745\n", - "┃ work_OTHEMPN -0.000419\n", - "┃ work_RETEMPN 0.000000\n", + "┣ logloss: 2.543504818049292\n", + "┣ d_logloss: coef_UNAVAILABLE 0.000000\n", + "┃ coef_distance_joint 0.000054\n", + "┃ coef_mode_choice_logsum 0.000028\n", + "┃ coef_one 0.000000\n", + "┃ coef_prox_dest_outbound_work -0.000016\n", + "┃ coef_prox_home_inbound_work 0.000044\n", + "┃ coef_prox_home_outbound_work -0.000010\n", + "┃ coef_util_distance_atwork 0.000000\n", + "┃ coef_util_distance_eatout 0.000017\n", + "┃ coef_util_distance_escort 0.000004\n", + "┃ coef_util_distance_othdiscr -0.000030\n", + "┃ coef_util_distance_othmaint -0.000009\n", + "┃ coef_util_distance_school 0.000000\n", + "┃ coef_util_distance_shopping 0.000006\n", + "┃ coef_util_distance_social -0.000076\n", + "┃ coef_util_distance_univ 0.000026\n", + "┃ coef_util_distance_work_inbound -0.000026\n", + "┃ coef_util_distance_work_outbound 0.000069\n", + "┃ eatout_HEREMPN -0.000049\n", + "┃ eatout_RETEMPN 0.000000\n", + "┃ escort_AGE0519 -0.000144\n", + "┃ escort_HEREMPN 0.000048\n", + "┃ escort_HSENROLL 0.000044\n", + "┃ escort_RETEMPN 0.000042\n", + "┃ escort_TOTHH 0.000000\n", + "┃ othdiscr_HEREMPN 0.000056\n", + "┃ othdiscr_HSENROLL -0.000088\n", + "┃ othdiscr_OTHEMPN 0.000216\n", + "┃ othdiscr_RETEMPN -0.000059\n", + "┃ othdiscr_TOTHH 0.000000\n", + "┃ othmaint_HEREMPN -0.000220\n", + "┃ othmaint_RETEMPN -0.000006\n", + "┃ othmaint_TOTHH 0.000000\n", + "┃ shopping_RETEMPN -0.005801\n", + "┃ shopping_TOTHH 0.000000\n", + "┃ social_HEREMPN 0.000007\n", + "┃ social_RETEMPN -0.000072\n", + "┃ social_TOTHH 0.000000\n", + "┃ univ_COLLFTE -0.003131\n", + "┃ univ_COLLPTE -0.002441\n", + "┃ univ_TOTHH 0.000000\n", + "┃ work_AGREMPN -0.000051\n", + "┃ work_FPSEMPN -0.000190\n", + "┃ work_HEREMPN 0.000081\n", + "┃ work_MWTEMPN -0.000196\n", + "┃ work_OTHEMPN -0.000045\n", + "┃ work_RETEMPN 0.000000\n", "┃ dtype: float64\n", "┣ nit: 87\n", - "┣ nfev: 147\n", + "┣ nfev: 92\n", "┣ njev: 87\n", "┣ status: 0\n", "┣ message: 'Optimization terminated successfully'\n", "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=21, microseconds=469689)\n", + "┣ elapsed_time: datetime.timedelta(seconds=19, microseconds=360953)\n", "┣ method: 'SLSQP'\n", - "┣ n_cases: 2490\n", + "┣ n_cases: 31028\n", "┣ iteration_number: 87\n", - "┣ logloss: 5.04798638553042" + "┣ loglike: -78919.86749443343" ] }, "execution_count": 11, @@ -3345,490 +3357,469 @@ { "data": { "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Std Err t Stat Signif Like Ratio Null Value Constrained
coef_UNAVAILABLE-999. NA NA NA 0.00fixed value
coef_distance_joint-0.376 0.0762-4.94*** NA 0.00
coef_mode_choice_logsum 0.163 0.00797 20.46*** NA 0.00
coef_one 1.00 NA NA NA 0.00fixed value
coef_prox_dest_outbound_work-0.360 441.-0.00 NA 0.00
coef_prox_home_inbound_work-0.174 0.0368-4.73*** NA 0.00
coef_prox_home_outbound_work-0.523 441.-0.00 NA 0.00
coef_util_distance_atwork-0.122 2.43e-06-BIG*** NA 0.00
coef_util_distance_eatout-0.366 0.0630-5.81*** NA 0.00
coef_util_distance_escort-0.338 0.0329-10.28*** NA 0.00
coef_util_distance_othdiscr-0.409 0.0322-12.71*** NA 0.00
coef_util_distance_othmaint-0.258 0.0368-7.02*** NA 0.00
coef_util_distance_school-0.106 4.08e-06-BIG*** NA 0.00
coef_util_distance_shopping-0.390 0.0251-15.56*** NA 0.00
coef_util_distance_social-0.339 0.0631-5.36*** NA 0.00
coef_util_distance_univ-0.186 0.0428-4.35*** NA 0.00
coef_util_distance_work_inbound-0.0948 441.-0.00 NA 0.00
coef_util_distance_work_outbound-0.301 0.0272-11.05*** NA 0.00
eatout_HEREMPN-4.27 1.02-4.19*** NA 0.00
eatout_RETEMPN-0.298 NA NA NA 0.00fixed value
escort_AGE0519-1.51 2.88-0.53 NA 0.00
escort_HEREMPN-6.00 NA NA[***] 93.05 0.00escort_HEREMPN ≥ -6.0
escort_HSENROLL-3.17 2.87-1.10 NA 0.00
escort_RETEMPN-2.10 3.12-0.67 NA 0.00
escort_TOTHH-6.91 NA NA NA 0.00fixed value
othdiscr_HEREMPN-6.00 NA NA[***] 63.28 0.00othdiscr_HEREMPN ≥ -6.0
othdiscr_HSENROLL-6.00 NA NA[***] 23.13 0.00othdiscr_HSENROLL ≥ -6.0
othdiscr_OTHEMPN-4.74 2.07-2.29* NA 0.00
othdiscr_RETEMPN-0.421 0.383-1.10 NA 0.00
othdiscr_TOTHH-1.38 NA NA NA 0.00fixed value
othmaint_HEREMPN-6.00 NA NA[***] 93.87 0.00othmaint_HEREMPN ≥ -6.0
othmaint_RETEMPN-4.47 0.487-9.18*** NA 0.00
othmaint_TOTHH-6.91 NA NA NA 0.00fixed value
shopping_RETEMPN-6.00 NA NA[***] 107.89 0.00shopping_RETEMPN ≥ -6.0
shopping_TOTHH-6.91 NA NA NA 0.00fixed value
social_HEREMPN-6.00 NA NA[***] 33.82 0.00social_HEREMPN ≥ -6.0
social_RETEMPN-3.46 0.666-5.20*** NA 0.00
social_TOTHH-6.91 NA NA NA 0.00fixed value
univ_COLLFTE-6.00 NA NA[***] 379.06 0.00univ_COLLFTE ≥ -6.0
univ_COLLPTE-6.00 NA NA[***] 390.32 0.00univ_COLLPTE ≥ -6.0
univ_TOTHH-6.91 NA NA NA 0.00fixed value
work_AGREMPN 1.15 0.433 2.65** NA 0.00
work_FPSEMPN-6.00 NA NA[***] 647.25 0.00work_FPSEMPN ≥ -6.0
work_HEREMPN-4.55 0.535-8.51*** NA 0.00
work_MWTEMPN-6.00 NA NA[***] 106.49 0.00work_MWTEMPN ≥ -6.0
work_OTHEMPN-5.21 1.18-4.42*** NA 0.00
work_RETEMPN 0.00 NA NA NA 0.00fixed value
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 ValueStd Errt StatSignifNull ValueConstrained
Parameter      
coef_UNAVAILABLE-999. 0.00 NA 0.00fixed value
coef_distance_joint-0.223 0.0134-16.56*** 0.00
coef_mode_choice_logsum 0.908 0.0111 82.17*** 0.00
coef_one 1.00 0.00 NA 0.00fixed value
coef_prox_dest_outbound_work-0.279 158.-0.00 0.00
coef_prox_home_inbound_work-0.122 0.00393-31.03*** 0.00
coef_prox_home_outbound_work-0.419 158.-0.00 0.00
coef_util_distance_atwork-0.122 NA NA 0.00
coef_util_distance_eatout-0.220 0.0130-16.94*** 0.00
coef_util_distance_escort-0.236 0.00671-35.15*** 0.00
coef_util_distance_othdiscr-0.242 0.00636-37.96*** 0.00
coef_util_distance_othmaint-0.178 0.00584-30.41*** 0.00
coef_util_distance_school-0.106 NA NA 0.00
coef_util_distance_shopping-0.227 0.00490-46.30*** 0.00
coef_util_distance_social-0.228 0.0116-19.56*** 0.00
coef_util_distance_univ-0.139 0.00887-15.71*** 0.00
coef_util_distance_work_inbound 0.0898 158. 0.00 0.00
coef_util_distance_work_outbound-0.138 0.00378-36.55*** 0.00
eatout_HEREMPN-2.55 0.279-9.16*** 0.00
eatout_RETEMPN-0.298 0.00 NA 0.00fixed value
escort_AGE0519-0.0396 NA NA 0.00
escort_HEREMPN-2.85 NA NA 0.00
escort_HSENROLL-2.26 NA NA 0.00
escort_RETEMPN-0.886 NA NA 0.00
escort_TOTHH-6.91 0.00 NA 0.00fixed value
othdiscr_HEREMPN-4.39 0.449-9.78*** 0.00
othdiscr_HSENROLL-3.31 0.341-9.70*** 0.00
othdiscr_OTHEMPN-2.83 0.181-15.63*** 0.00
othdiscr_RETEMPN-2.88 0.379-7.60*** 0.00
othdiscr_TOTHH-1.38 0.00 NA 0.00fixed value
othmaint_HEREMPN-1.69 NA NA 0.00
othmaint_RETEMPN-0.351 NA NA 0.00
othmaint_TOTHH-6.91 0.00 NA 0.00fixed value
shopping_RETEMPN-6.00 0.00 NA 0.00shopping_RETEMPN ≥ -6.0
shopping_TOTHH-6.91 0.00 NA 0.00fixed value
social_HEREMPN-1.52 NA NA 0.00
social_RETEMPN-0.0988 NA NA 0.00
social_TOTHH-6.91 0.00 NA 0.00fixed value
univ_COLLFTE-6.00 0.00 NA 0.00univ_COLLFTE ≥ -6.0
univ_COLLPTE-6.00 0.00 NA 0.00univ_COLLPTE ≥ -6.0
univ_TOTHH-6.91 0.00 NA 0.00fixed value
work_AGREMPN 0.122 0.169 0.73 0.00
work_FPSEMPN-6.00 5.27e-09-BIG*** 0.00work_FPSEMPN ≥ -6.0
work_HEREMPN-1.96 0.0705-27.78*** 0.00
work_MWTEMPN-5.32 0.384-13.86*** 0.00
work_OTHEMPN-2.51 0.0972-25.86*** 0.00
work_RETEMPN 0.00 0.00 NA 0.00fixed value
\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -4157,12 +4148,12 @@ " work\n", " trip\n", " 0.000000\n", - " 0.240157\n", - " 0.000595\n", - " 0.002534\n", - " 0.001318\n", - " 0.754800\n", - " 0.000595\n", + " 0.423805\n", + " 0.001051\n", + " 0.059726\n", + " 0.034350\n", + " 0.479002\n", + " 0.002067\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4172,15 +4163,15 @@ " 15\n", " escort\n", " trip\n", - " 0.002577\n", - " 0.315809\n", + " 0.000651\n", + " 0.268207\n", " 0.000000\n", - " 0.006387\n", + " 0.037695\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.567232\n", - " 0.107996\n", + " 0.625554\n", + " 0.067893\n", " 0.000000\n", " 0.000000\n", " \n", @@ -4205,9 +4196,9 @@ " eatout\n", " trip\n", " 0.000000\n", - " 0.981581\n", + " 0.904850\n", " 0.000000\n", - " 0.018419\n", + " 0.095150\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4220,10 +4211,10 @@ " 18\n", " othmaint\n", " trip\n", - " 0.066899\n", - " 0.767274\n", + " 0.001125\n", + " 0.791941\n", " 0.000000\n", - " 0.165826\n", + " 0.206934\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4236,10 +4227,10 @@ " 19\n", " social\n", " trip\n", - " 0.028736\n", - " 0.900036\n", + " 0.000888\n", + " 0.804213\n", " 0.000000\n", - " 0.071228\n", + " 0.194899\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4252,15 +4243,15 @@ " 20\n", " othdiscr\n", " trip\n", - " 0.273261\n", - " 0.711905\n", + " 0.605815\n", + " 0.134341\n", " 0.000000\n", - " 0.002688\n", - " 0.009458\n", + " 0.029688\n", + " 0.142442\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.002688\n", + " 0.087714\n", " 0.000000\n", " 0.000000\n", " \n", @@ -4300,13 +4291,13 @@ "11 social non_mandatory 0.000000 0.522000 0.000000 0.478000 \n", "12 othdiscr non_mandatory 0.252252 0.212212 0.000000 0.272272 \n", "13 atwork atwork 0.000000 0.742000 0.000000 0.258000 \n", - "14 work trip 0.000000 0.240157 0.000595 0.002534 \n", - "15 escort trip 0.002577 0.315809 0.000000 0.006387 \n", + "14 work trip 0.000000 0.423805 0.001051 0.059726 \n", + "15 escort trip 0.000651 0.268207 0.000000 0.037695 \n", "16 shopping trip 0.287459 0.712541 0.000000 0.000000 \n", - "17 eatout trip 0.000000 0.981581 0.000000 0.018419 \n", - "18 othmaint trip 0.066899 0.767274 0.000000 0.165826 \n", - "19 social trip 0.028736 0.900036 0.000000 0.071228 \n", - "20 othdiscr trip 0.273261 0.711905 0.000000 0.002688 \n", + "17 eatout trip 0.000000 0.904850 0.000000 0.095150 \n", + "18 othmaint trip 0.001125 0.791941 0.000000 0.206934 \n", + "19 social trip 0.000888 0.804213 0.000000 0.194899 \n", + "20 othdiscr trip 0.605815 0.134341 0.000000 0.029688 \n", "21 univ trip 0.167856 0.000000 0.000000 0.000000 \n", "\n", " OTHEMPN AGREMPN MWTEMPN AGE0519 HSENROLL COLLFTE COLLPTE \n", @@ -4324,13 +4315,13 @@ "11 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12 0.165165 0.000000 0.000000 0.000000 0.098098 0.000000 0.000000 \n", "13 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "14 0.001318 0.754800 0.000595 0.000000 0.000000 0.000000 0.000000 \n", - "15 0.000000 0.000000 0.000000 0.567232 0.107996 0.000000 0.000000 \n", + "14 0.034350 0.479002 0.002067 0.000000 0.000000 0.000000 0.000000 \n", + "15 0.000000 0.000000 0.000000 0.625554 0.067893 0.000000 0.000000 \n", "16 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "17 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "18 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "19 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "20 0.009458 0.000000 0.000000 0.000000 0.002688 0.000000 0.000000 \n", + "20 0.142442 0.000000 0.000000 0.000000 0.087714 0.000000 0.000000 \n", "21 0.000000 0.000000 0.000000 0.000000 0.000000 0.416072 0.416072 " ] }, @@ -4424,31 +4415,31 @@ " \n", " 2\n", " coef_mode_choice_logsum\n", - " 0.163028\n", + " 0.908189\n", " F\n", " \n", " \n", " 3\n", " coef_distance_joint\n", - " -0.376392\n", + " -0.222590\n", " F\n", " \n", " \n", " 4\n", " coef_util_distance_work_outbound\n", - " -0.300511\n", + " -0.138174\n", " F\n", " \n", " \n", " 5\n", " coef_util_distance_work_inbound\n", - " -0.094761\n", + " 0.089849\n", " F\n", " \n", " \n", " 6\n", " coef_util_distance_univ\n", - " -0.186380\n", + " -0.139388\n", " F\n", " \n", " \n", @@ -4460,37 +4451,37 @@ " \n", " 8\n", " coef_util_distance_escort\n", - " -0.337785\n", + " -0.235858\n", " F\n", " \n", " \n", " 9\n", " coef_util_distance_shopping\n", - " -0.390483\n", + " -0.227090\n", " F\n", " \n", " \n", " 10\n", " coef_util_distance_eatout\n", - " -0.366193\n", + " -0.219723\n", " F\n", " \n", " \n", " 11\n", " coef_util_distance_othmaint\n", - " -0.257971\n", + " -0.177610\n", " F\n", " \n", " \n", " 12\n", " coef_util_distance_social\n", - " -0.338764\n", + " -0.227558\n", " F\n", " \n", " \n", " 13\n", " coef_util_distance_othdiscr\n", - " -0.408787\n", + " -0.241573\n", " F\n", " \n", " \n", @@ -4502,19 +4493,19 @@ " \n", " 15\n", " coef_prox_home_outbound_work\n", - " -0.522711\n", + " -0.419012\n", " F\n", " \n", " \n", " 16\n", " coef_prox_home_inbound_work\n", - " -0.174104\n", + " -0.121975\n", " F\n", " \n", " \n", " 17\n", " coef_prox_dest_outbound_work\n", - " -0.359504\n", + " -0.278949\n", " F\n", " \n", " \n", @@ -4525,22 +4516,22 @@ " coefficient_name value constrain\n", "0 coef_UNAVAILABLE -999.000000 T\n", "1 coef_one 1.000000 T\n", - "2 coef_mode_choice_logsum 0.163028 F\n", - "3 coef_distance_joint -0.376392 F\n", - "4 coef_util_distance_work_outbound -0.300511 F\n", - "5 coef_util_distance_work_inbound -0.094761 F\n", - "6 coef_util_distance_univ -0.186380 F\n", + "2 coef_mode_choice_logsum 0.908189 F\n", + "3 coef_distance_joint -0.222590 F\n", + "4 coef_util_distance_work_outbound -0.138174 F\n", + "5 coef_util_distance_work_inbound 0.089849 F\n", + "6 coef_util_distance_univ -0.139388 F\n", "7 coef_util_distance_school -0.105600 F\n", - "8 coef_util_distance_escort -0.337785 F\n", - "9 coef_util_distance_shopping -0.390483 F\n", - "10 coef_util_distance_eatout -0.366193 F\n", - "11 coef_util_distance_othmaint -0.257971 F\n", - "12 coef_util_distance_social -0.338764 F\n", - "13 coef_util_distance_othdiscr -0.408787 F\n", + "8 coef_util_distance_escort -0.235858 F\n", + "9 coef_util_distance_shopping -0.227090 F\n", + "10 coef_util_distance_eatout -0.219723 F\n", + "11 coef_util_distance_othmaint -0.177610 F\n", + "12 coef_util_distance_social -0.227558 F\n", + "13 coef_util_distance_othdiscr -0.241573 F\n", "14 coef_util_distance_atwork -0.122335 F\n", - "15 coef_prox_home_outbound_work -0.522711 F\n", - "16 coef_prox_home_inbound_work -0.174104 F\n", - "17 coef_prox_dest_outbound_work -0.359504 F" + "15 coef_prox_home_outbound_work -0.419012 F\n", + "16 coef_prox_home_inbound_work -0.121975 F\n", + "17 coef_prox_dest_outbound_work -0.278949 F" ] }, "execution_count": 17, @@ -4839,12 +4830,12 @@ " work\n", " trip\n", " 0.000000\n", - " 0.240157\n", - " 0.000595\n", - " 0.002534\n", - " 0.001318\n", - " 0.754800\n", - " 0.000595\n", + " 0.423805\n", + " 0.001051\n", + " 0.059726\n", + " 0.034350\n", + " 0.479002\n", + " 0.002067\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4855,15 +4846,15 @@ " 15\n", " escort\n", " trip\n", - " 0.002577\n", - " 0.315809\n", + " 0.000651\n", + " 0.268207\n", " 0.000000\n", - " 0.006387\n", + " 0.037695\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.567232\n", - " 0.107996\n", + " 0.625554\n", + " 0.067893\n", " 0.000000\n", " 0.000000\n", " \n", @@ -4890,9 +4881,9 @@ " eatout\n", " trip\n", " 0.000000\n", - " 0.981581\n", + " 0.904850\n", " 0.000000\n", - " 0.018419\n", + " 0.095150\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4906,10 +4897,10 @@ " 18\n", " othmaint\n", " trip\n", - " 0.066899\n", - " 0.767274\n", + " 0.001125\n", + " 0.791941\n", " 0.000000\n", - " 0.165826\n", + " 0.206934\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4923,10 +4914,10 @@ " 19\n", " social\n", " trip\n", - " 0.028736\n", - " 0.900036\n", + " 0.000888\n", + " 0.804213\n", " 0.000000\n", - " 0.071228\n", + " 0.194899\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -4940,15 +4931,15 @@ " 20\n", " othdiscr\n", " trip\n", - " 0.273261\n", - " 0.711905\n", + " 0.605815\n", + " 0.134341\n", " 0.000000\n", - " 0.002688\n", - " 0.009458\n", + " 0.029688\n", + " 0.142442\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.002688\n", + " 0.087714\n", " 0.000000\n", " 0.000000\n", " \n", @@ -4989,13 +4980,13 @@ "11 11 social non_mandatory 0.000000 0.522000 0.000000 \n", "12 12 othdiscr non_mandatory 0.252252 0.212212 0.000000 \n", "13 13 atwork atwork 0.000000 0.742000 0.000000 \n", - "14 14 work trip 0.000000 0.240157 0.000595 \n", - "15 15 escort trip 0.002577 0.315809 0.000000 \n", + "14 14 work trip 0.000000 0.423805 0.001051 \n", + "15 15 escort trip 0.000651 0.268207 0.000000 \n", "16 16 shopping trip 0.287459 0.712541 0.000000 \n", - "17 17 eatout trip 0.000000 0.981581 0.000000 \n", - "18 18 othmaint trip 0.066899 0.767274 0.000000 \n", - "19 19 social trip 0.028736 0.900036 0.000000 \n", - "20 20 othdiscr trip 0.273261 0.711905 0.000000 \n", + "17 17 eatout trip 0.000000 0.904850 0.000000 \n", + "18 18 othmaint trip 0.001125 0.791941 0.000000 \n", + "19 19 social trip 0.000888 0.804213 0.000000 \n", + "20 20 othdiscr trip 0.605815 0.134341 0.000000 \n", "21 21 univ trip 0.167856 0.000000 0.000000 \n", "\n", " HEREMPN OTHEMPN AGREMPN MWTEMPN AGE0519 HSENROLL COLLFTE \\\n", @@ -5013,13 +5004,13 @@ "11 0.478000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "12 0.272272 0.165165 0.000000 0.000000 0.000000 0.098098 0.000000 \n", "13 0.258000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "14 0.002534 0.001318 0.754800 0.000595 0.000000 0.000000 0.000000 \n", - "15 0.006387 0.000000 0.000000 0.000000 0.567232 0.107996 0.000000 \n", + "14 0.059726 0.034350 0.479002 0.002067 0.000000 0.000000 0.000000 \n", + "15 0.037695 0.000000 0.000000 0.000000 0.625554 0.067893 0.000000 \n", "16 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "17 0.018419 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "18 0.165826 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "19 0.071228 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "20 0.002688 0.009458 0.000000 0.000000 0.000000 0.002688 0.000000 \n", + "17 0.095150 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "18 0.206934 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "19 0.194899 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "20 0.029688 0.142442 0.000000 0.000000 0.000000 0.087714 0.000000 \n", "21 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.416072 \n", "\n", " COLLPTE \n", @@ -5064,7 +5055,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -5078,7 +5069,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/23_trip_mode_choice.ipynb b/activitysim/examples/example_estimation/notebooks/23_trip_mode_choice.ipynb index acf84e2740..a38eb92392 100644 --- a/activitysim/examples/example_estimation/notebooks/23_trip_mode_choice.ipynb +++ b/activitysim/examples/example_estimation/notebooks/23_trip_mode_choice.ipynb @@ -34,27 +34,74 @@ "id": "s53VwlPwtNnr", "outputId": "d1208b7a-c1f2-4b0b-c439-bf312fe12be0" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "JAX not found. Some functionality will be unavailable.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'larch': '6.0.32',\n", + " 'sharrow': '2.13.0',\n", + " 'numpy': '1.26.4',\n", + " 'pandas': '1.5.3',\n", + " 'xarray': '2024.3.0',\n", + " 'numba': '0.60.0'}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import os\n", - "import larch # !conda install larch -c conda-forge # for estimation\n", - "import pandas as pd" + "import larch as lx\n", + "import pandas as pd\n", + "\n", + "lx.versions()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We'll work in our `test` directory, where ActivitySim has saved the estimation data bundles." + "For this demo, we will assume that you have already run ActivitySim in estimation\n", + "mode, and saved the required estimation data bundles (EDB's) to disk. See\n", + "the [first notebook](./01_estimation_mode.ipynb) for details. The following module\n", + "will run a script to set everything up if the example data is not already available." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EDB directory already populated.\n" + ] + }, + { + "data": { + "text/plain": [ + "PosixPath('test-estimation-data/activitysim-prototype-mtc-extended')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "os.chdir('test')" + "from est_mode_setup import prepare\n", + "\n", + "prepare()" ] }, { @@ -70,11 +117,13 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/Users/jeffnewman/OneDrive - Cambridge Systematics/Git/activitysim/activitysim/estimation/larch/mode_choice.py:126: DtypeWarning: Columns (411) have mixed types.Specify dtype option on import or set low_memory=False.\n", - " return mode_choice_model(\n" + "loading from output-est-mode/estimation_data_bundle/trip_mode_choice/trip_mode_choice_coefficients.csv\n", + "loading from output-est-mode/estimation_data_bundle/trip_mode_choice/trip_mode_choice_coefficients_template.csv\n", + "loading spec from output-est-mode/estimation_data_bundle/trip_mode_choice/trip_mode_choice_SPEC.csv\n", + "loading from output-est-mode/estimation_data_bundle/trip_mode_choice/trip_mode_choice_values_combined.parquet\n" ] } ], @@ -82,7 +131,11 @@ "modelname = \"trip_mode_choice\"\n", "\n", "from activitysim.estimation.larch import component_model\n", - "model, data = component_model(modelname, return_data=True)" + "model, data = component_model(\n", + " modelname,\n", + " edb_directory=f\"output-est-mode/estimation_data_bundle/{modelname}/\",\n", + " return_data=True,\n", + ")" ] }, { @@ -685,15 +738,15 @@ " util_DRIVEALONEFREE_In_vehicle_time\n", " util_DRIVEALONEFREE_Terminal_time\n", " ...\n", - " drive_lrf_available_inbound\n", - " drive_express_available_outbound\n", - " drive_express_available_inbound\n", " drive_heavyrail_available_outbound\n", " drive_heavyrail_available_inbound\n", " drive_commuter_available_outbound\n", " drive_commuter_available_inbound\n", " walk_ferry_available\n", " drive_ferry_available\n", + " distance\n", + " distance_walk_od\n", + " distance_bike_od\n", " override_choice_code\n", " \n", " \n", @@ -723,17 +776,17 @@ " \n", " \n", " \n", - " 6812\n", - " 54497\n", + " 1870\n", + " 14961\n", " WALK\n", " WALK\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 2.72\n", - " 7.72508\n", + " 0.0\n", + " 10.52\n", + " 10.63286\n", " ...\n", " False\n", " False\n", @@ -741,23 +794,23 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", + " 3.78\n", + " 3.78\n", + " 3.78\n", " 7\n", " \n", " \n", - " 6812\n", - " 54501\n", + " 1870\n", + " 14965\n", " WALK\n", " WALK\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 2.76\n", - " 7.72508\n", + " 0.0\n", + " 10.31\n", + " 10.63286\n", " ...\n", " False\n", " False\n", @@ -765,71 +818,71 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", + " 3.79\n", + " 3.79\n", + " 3.79\n", " 7\n", " \n", " \n", - " 8110\n", - " 64881\n", - " WALK\n", - " WALK\n", + " 20468\n", + " 163745\n", + " WALK_LOC\n", + " WALK_LOC\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 4.43\n", - " 17.09892\n", + " 0.0\n", + " 4.72\n", + " 6.62312\n", " ...\n", + " True\n", " False\n", + " True\n", " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " 7\n", + " 2.07\n", + " 2.07\n", + " 2.07\n", + " 9\n", " \n", " \n", - " 8110\n", - " 64885\n", - " WALK\n", - " WALK\n", + " 20468\n", + " 163749\n", + " WALK_LOC\n", + " TNC_SINGLE\n", " 0.0\n", - " 1.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 4.43\n", - " 17.09892\n", + " 0.0\n", + " 4.72\n", + " 6.62312\n", " ...\n", " False\n", + " True\n", " False\n", + " True\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " False\n", - " 7\n", + " 2.07\n", + " 2.07\n", + " 2.07\n", + " 20\n", " \n", " \n", - " 11013\n", - " 88105\n", - " DRIVEALONEFREE\n", + " 27055\n", + " 216441\n", + " SHARED3FREE\n", " DRIVEALONEFREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 10.53\n", - " 9.08660\n", + " 6.77\n", + " 4.26534\n", " ...\n", " False\n", " False\n", @@ -837,9 +890,9 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", + " 2.49\n", + " 2.49\n", + " 2.49\n", " 1\n", " \n", " \n", @@ -867,17 +920,17 @@ " ...\n", " \n", " \n", - " 309796968\n", - " 2478375745\n", - " SHARED3FREE\n", - " SHARED3FREE\n", + " 310202384\n", + " 2481619077\n", + " DRIVEALONEFREE\n", + " DRIVEALONEFREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 8.84\n", - " 6.83436\n", + " 2.74\n", + " 1.91196\n", " ...\n", " False\n", " False\n", @@ -885,23 +938,23 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", - " 5\n", + " 1.66\n", + " 2.55\n", + " 2.55\n", + " 1\n", " \n", " \n", - " 309796968\n", - " 2478375749\n", - " SHARED3FREE\n", - " SHARED3FREE\n", + " 310212634\n", + " 2481701073\n", + " DRIVEALONEFREE\n", + " DRIVEALONEFREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 8.50\n", - " 6.83436\n", + " 6.42\n", + " 2.66128\n", " ...\n", " False\n", " False\n", @@ -909,14 +962,14 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", - " 5\n", + " 2.57\n", + " 2.57\n", + " 2.57\n", + " 1\n", " \n", " \n", - " 309796969\n", - " 2478375753\n", + " 310212634\n", + " 2481701077\n", " DRIVEALONEFREE\n", " DRIVEALONEFREE\n", " 0.0\n", @@ -924,8 +977,8 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 4.00\n", - " 5.00346\n", + " 6.42\n", + " 2.66128\n", " ...\n", " False\n", " False\n", @@ -933,23 +986,23 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", + " 2.57\n", + " 2.57\n", + " 2.57\n", " 1\n", " \n", " \n", - " 309796969\n", - " 2478375757\n", - " TNC_SHARED\n", - " TNC_SHARED\n", + " 310220296\n", + " 2481762369\n", + " DRIVEALONEFREE\n", + " DRIVEALONEFREE\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", " 0.0\n", - " 1.89\n", - " 9.29502\n", + " 6.72\n", + " 4.02884\n", " ...\n", " False\n", " False\n", @@ -957,14 +1010,14 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", - " 21\n", + " 2.90\n", + " 2.90\n", + " 2.90\n", + " 1\n", " \n", " \n", - " 309796969\n", - " 2478375758\n", + " 310220296\n", + " 2481762373\n", " DRIVEALONEFREE\n", " DRIVEALONEFREE\n", " 0.0\n", @@ -972,8 +1025,8 @@ " 0.0\n", " 0.0\n", " 0.0\n", - " 3.73\n", - " 4.29156\n", + " 6.87\n", + " 4.02884\n", " ...\n", " False\n", " False\n", @@ -981,228 +1034,214 @@ " False\n", " False\n", " False\n", - " False\n", - " False\n", - " False\n", + " 2.90\n", + " 2.90\n", + " 2.90\n", " 1\n", " \n", " \n", "\n", - "

14352 rows × 475 columns

\n", + "

176389 rows × 480 columns

\n", "" ], "text/plain": [ " trip_id model_choice override_choice \\\n", "tour_id \n", - "6812 54497 WALK WALK \n", - "6812 54501 WALK WALK \n", - "8110 64881 WALK WALK \n", - "8110 64885 WALK WALK \n", - "11013 88105 DRIVEALONEFREE DRIVEALONEFREE \n", + "1870 14961 WALK WALK \n", + "1870 14965 WALK WALK \n", + "20468 163745 WALK_LOC WALK_LOC \n", + "20468 163749 WALK_LOC TNC_SINGLE \n", + "27055 216441 SHARED3FREE DRIVEALONEFREE \n", "... ... ... ... \n", - "309796968 2478375745 SHARED3FREE SHARED3FREE \n", - "309796968 2478375749 SHARED3FREE SHARED3FREE \n", - "309796969 2478375753 DRIVEALONEFREE DRIVEALONEFREE \n", - "309796969 2478375757 TNC_SHARED TNC_SHARED \n", - "309796969 2478375758 DRIVEALONEFREE DRIVEALONEFREE \n", + "310202384 2481619077 DRIVEALONEFREE DRIVEALONEFREE \n", + "310212634 2481701073 DRIVEALONEFREE DRIVEALONEFREE \n", + "310212634 2481701077 DRIVEALONEFREE DRIVEALONEFREE \n", + "310220296 2481762369 DRIVEALONEFREE DRIVEALONEFREE \n", + "310220296 2481762373 DRIVEALONEFREE DRIVEALONEFREE \n", "\n", " util_DRIVEALONEFREE_Unavailable \\\n", "tour_id \n", - "6812 0.0 \n", - "6812 0.0 \n", - "8110 0.0 \n", - "8110 0.0 \n", - "11013 0.0 \n", + "1870 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", "... ... \n", - "309796968 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_for_zero_auto_households \\\n", "tour_id \n", - "6812 1.0 \n", - "6812 1.0 \n", - "8110 1.0 \n", - "8110 1.0 \n", - "11013 0.0 \n", + "1870 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", "... ... \n", - "309796968 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16 \\\n", "tour_id \n", - "6812 0.0 \n", - "6812 0.0 \n", - "8110 0.0 \n", - "8110 0.0 \n", - "11013 0.0 \n", + "1870 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", "... ... \n", - "309796968 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_for_joint_tours \\\n", "tour_id \n", - "6812 0.0 \n", - "6812 0.0 \n", - "8110 0.0 \n", - "8110 0.0 \n", - "11013 0.0 \n", + "1870 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", "... ... \n", - "309796968 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_Unavailable_if_didnt_drive_to_work \\\n", "tour_id \n", - "6812 0.0 \n", - "6812 0.0 \n", - "8110 0.0 \n", - "8110 0.0 \n", - "11013 0.0 \n", + "1870 0.0 \n", + "1870 0.0 \n", + "20468 0.0 \n", + "20468 0.0 \n", + "27055 0.0 \n", "... ... \n", - "309796968 0.0 \n", - "309796968 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", - "309796969 0.0 \n", + "310202384 0.0 \n", + "310212634 0.0 \n", + "310212634 0.0 \n", + "310220296 0.0 \n", + "310220296 0.0 \n", "\n", " util_DRIVEALONEFREE_In_vehicle_time \\\n", "tour_id \n", - "6812 2.72 \n", - "6812 2.76 \n", - "8110 4.43 \n", - "8110 4.43 \n", - "11013 10.53 \n", + "1870 10.52 \n", + "1870 10.31 \n", + "20468 4.72 \n", + "20468 4.72 \n", + "27055 6.77 \n", "... ... \n", - "309796968 8.84 \n", - "309796968 8.50 \n", - "309796969 4.00 \n", - "309796969 1.89 \n", - "309796969 3.73 \n", + "310202384 2.74 \n", + "310212634 6.42 \n", + "310212634 6.42 \n", + "310220296 6.72 \n", + "310220296 6.87 \n", "\n", " util_DRIVEALONEFREE_Terminal_time ... \\\n", "tour_id ... \n", - "6812 7.72508 ... \n", - "6812 7.72508 ... \n", - "8110 17.09892 ... \n", - "8110 17.09892 ... \n", - "11013 9.08660 ... \n", + "1870 10.63286 ... \n", + "1870 10.63286 ... \n", + "20468 6.62312 ... \n", + "20468 6.62312 ... \n", + "27055 4.26534 ... \n", "... ... ... \n", - "309796968 6.83436 ... \n", - "309796968 6.83436 ... \n", - "309796969 5.00346 ... \n", - "309796969 9.29502 ... \n", - "309796969 4.29156 ... \n", - "\n", - " drive_lrf_available_inbound drive_express_available_outbound \\\n", - "tour_id \n", - "6812 False False \n", - "6812 False False \n", - "8110 False False \n", - "8110 False False \n", - "11013 False False \n", - "... ... ... \n", - "309796968 False False \n", - "309796968 False False \n", - "309796969 False False \n", - "309796969 False False \n", - "309796969 False False \n", - "\n", - " drive_express_available_inbound \\\n", - "tour_id \n", - "6812 False \n", - "6812 False \n", - "8110 False \n", - "8110 False \n", - "11013 False \n", - "... ... \n", - "309796968 False \n", - "309796968 False \n", - "309796969 False \n", - "309796969 False \n", - "309796969 False \n", + "310202384 1.91196 ... \n", + "310212634 2.66128 ... \n", + "310212634 2.66128 ... \n", + "310220296 4.02884 ... \n", + "310220296 4.02884 ... \n", "\n", " drive_heavyrail_available_outbound \\\n", "tour_id \n", - "6812 False \n", - "6812 False \n", - "8110 False \n", - "8110 False \n", - "11013 False \n", + "1870 False \n", + "1870 False \n", + "20468 True \n", + "20468 False \n", + "27055 False \n", "... ... \n", - "309796968 False \n", - "309796968 False \n", - "309796969 False \n", - "309796969 False \n", - "309796969 False \n", + "310202384 False \n", + "310212634 False \n", + "310212634 False \n", + "310220296 False \n", + "310220296 False \n", "\n", " drive_heavyrail_available_inbound \\\n", "tour_id \n", - "6812 False \n", - "6812 False \n", - "8110 False \n", - "8110 False \n", - "11013 False \n", + "1870 False \n", + "1870 False \n", + "20468 False \n", + "20468 True \n", + "27055 False \n", "... ... \n", - "309796968 False \n", - "309796968 False \n", - "309796969 False \n", - "309796969 False \n", - "309796969 False \n", + "310202384 False \n", + "310212634 False \n", + "310212634 False \n", + "310220296 False \n", + "310220296 False \n", "\n", " drive_commuter_available_outbound \\\n", "tour_id \n", - "6812 False \n", - "6812 False \n", - "8110 False \n", - "8110 False \n", - "11013 False \n", + "1870 False \n", + "1870 False \n", + "20468 True \n", + "20468 False \n", + "27055 False \n", "... ... \n", - "309796968 False \n", - "309796968 False \n", - "309796969 False \n", - "309796969 False \n", - "309796969 False \n", + "310202384 False \n", + "310212634 False \n", + "310212634 False \n", + "310220296 False \n", + "310220296 False \n", "\n", " drive_commuter_available_inbound walk_ferry_available \\\n", "tour_id \n", - "6812 False False \n", - "6812 False False \n", - "8110 False False \n", - "8110 False False \n", - "11013 False False \n", + "1870 False False \n", + "1870 False False \n", + "20468 False False \n", + "20468 True False \n", + "27055 False False \n", "... ... ... \n", - "309796968 False False \n", - "309796968 False False \n", - "309796969 False False \n", - "309796969 False False \n", - "309796969 False False \n", + "310202384 False False \n", + "310212634 False False \n", + "310212634 False False \n", + "310220296 False False \n", + "310220296 False False \n", "\n", - " drive_ferry_available override_choice_code \n", - "tour_id \n", - "6812 False 7 \n", - "6812 False 7 \n", - "8110 False 7 \n", - "8110 False 7 \n", - "11013 False 1 \n", - "... ... ... \n", - "309796968 False 5 \n", - "309796968 False 5 \n", - "309796969 False 1 \n", - "309796969 False 21 \n", - "309796969 False 1 \n", + " drive_ferry_available distance distance_walk_od \\\n", + "tour_id \n", + "1870 False 3.78 3.78 \n", + "1870 False 3.79 3.79 \n", + "20468 False 2.07 2.07 \n", + "20468 False 2.07 2.07 \n", + "27055 False 2.49 2.49 \n", + "... ... ... ... \n", + "310202384 False 1.66 2.55 \n", + "310212634 False 2.57 2.57 \n", + "310212634 False 2.57 2.57 \n", + "310220296 False 2.90 2.90 \n", + "310220296 False 2.90 2.90 \n", + "\n", + " distance_bike_od override_choice_code \n", + "tour_id \n", + "1870 3.78 7 \n", + "1870 3.79 7 \n", + "20468 2.07 9 \n", + "20468 2.07 20 \n", + "27055 2.49 1 \n", + "... ... ... \n", + "310202384 2.55 1 \n", + "310212634 2.57 1 \n", + "310212634 2.57 1 \n", + "310220296 2.90 1 \n", + "310220296 2.90 1 \n", "\n", - "[14352 rows x 475 columns]" + "[176389 rows x 480 columns]" ] }, "execution_count": 6, @@ -1228,41 +1267,10 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "req_data does not request avail_ca or avail_co but it is set and being provided\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (1 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (1 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (1 issues)\n", - "problem: low-variance-data-co (1 issues)\n", - "problem: chosen-but-not-available (2 issues)\n", - "problem: low-variance-data-co (1 issues)\n" - ] - }, { "data": { "text/plain": [ - "-10094.898223413013" + "-94787.94601063678" ] }, "execution_count": 7, @@ -1271,7 +1279,6 @@ } ], "source": [ - "model.load_data()\n", "model.doctor(repair_ch_av='-')\n", "model.loglike()" ] @@ -1284,7 +1291,7 @@ { "data": { "text/html": [ - "

Iteration 110 [Optimization terminated successfully]

" + "

Iteration 304 [Optimization terminated successfully]

" ], "text/plain": [ "" @@ -1296,7 +1303,7 @@ { "data": { "text/html": [ - "

Best LL = -7541.342641931288

" + "

Best LL = -83882.48668326798

" ], "text/plain": [ "" @@ -1327,70 +1334,74 @@ " \n", " \n", " value\n", + " best\n", " initvalue\n", - " nullvalue\n", " minimum\n", " maximum\n", + " nullvalue\n", " holdfast\n", - " note\n", - " best\n", + " \n", + " \n", + " param_name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " -999\n", " -999.000000\n", + " -999.000000\n", " -999.0000\n", " -999.0\n", " -999.0\n", - " -999.0\n", + " 0.0\n", " 1\n", - " \n", - " -999.000000\n", " \n", " \n", " 1\n", " 1.000000\n", + " 1.000000\n", " 1.0000\n", " 1.0\n", " 1.0\n", - " 1.0\n", + " 0.0\n", " 1\n", - " \n", - " 1.000000\n", " \n", " \n", " coef_age010_trn\n", - " 0.360551\n", + " 0.131028\n", + " 0.131028\n", " 0.0000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.360551\n", " \n", " \n", " coef_age1619_da\n", - " 0.344530\n", + " 0.432972\n", + " 0.432972\n", " 0.0000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.344530\n", " \n", " \n", " coef_age16p_sr\n", - " -0.151886\n", + " -0.403317\n", + " -0.403317\n", " 0.0000\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.151886\n", " \n", " \n", " ...\n", @@ -1401,114 +1412,106 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " coef_sr3p_ASC_sr3p_othdiscr\n", - " 0.844944\n", - " 0.7398\n", + " coef_walk_transit_ASC_walk_school\n", + " -1.566510\n", + " -1.566510\n", + " -1.1828\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " 0.844944\n", " \n", " \n", - " coef_sr3p_ASC_walk_othdiscr\n", - " -5.841028\n", - " -0.5272\n", + " coef_walk_transit_ASC_walk_shopping\n", + " -1.250219\n", + " -1.250219\n", + " -0.1943\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -5.841028\n", " \n", " \n", - " coef_walk_transit_ASC_sr2_othdiscr\n", - " -1.232224\n", - " -2.7590\n", + " coef_walk_transit_ASC_walk_social\n", + " -0.846190\n", + " -0.846190\n", + " -0.7651\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.232224\n", " \n", " \n", - " coef_walk_transit_ASC_sr3p_othdiscr\n", - " -1.199264\n", - " -2.5126\n", + " coef_walk_transit_ASC_walk_univ\n", + " -1.031100\n", + " -1.031100\n", + " -1.0311\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -1.199264\n", " \n", " \n", - " coef_walk_transit_ASC_walk_othdiscr\n", - " -0.980497\n", - " -0.4997\n", + " coef_walk_transit_ASC_walk_work\n", + " 0.296352\n", + " 0.296352\n", + " 0.4432\n", + " -inf\n", + " inf\n", " 0.0\n", - " NaN\n", - " NaN\n", " 0\n", - " \n", - " -0.980497\n", " \n", " \n", "\n", - "

272 rows × 8 columns

\n", + "

272 rows × 7 columns

\n", "" ], "text/plain": [ - " value initvalue nullvalue \\\n", - "-999 -999.000000 -999.0000 -999.0 \n", - "1 1.000000 1.0000 1.0 \n", - "coef_age010_trn 0.360551 0.0000 0.0 \n", - "coef_age1619_da 0.344530 0.0000 0.0 \n", - "coef_age16p_sr -0.151886 0.0000 0.0 \n", - "... ... ... ... \n", - "coef_sr3p_ASC_sr3p_othdiscr 0.844944 0.7398 0.0 \n", - "coef_sr3p_ASC_walk_othdiscr -5.841028 -0.5272 0.0 \n", - "coef_walk_transit_ASC_sr2_othdiscr -1.232224 -2.7590 0.0 \n", - "coef_walk_transit_ASC_sr3p_othdiscr -1.199264 -2.5126 0.0 \n", - "coef_walk_transit_ASC_walk_othdiscr -0.980497 -0.4997 0.0 \n", + " value best initvalue \\\n", + "param_name \n", + "-999 -999.000000 -999.000000 -999.0000 \n", + "1 1.000000 1.000000 1.0000 \n", + "coef_age010_trn 0.131028 0.131028 0.0000 \n", + "coef_age1619_da 0.432972 0.432972 0.0000 \n", + "coef_age16p_sr -0.403317 -0.403317 0.0000 \n", + "... ... ... ... \n", + "coef_walk_transit_ASC_walk_school -1.566510 -1.566510 -1.1828 \n", + "coef_walk_transit_ASC_walk_shopping -1.250219 -1.250219 -0.1943 \n", + "coef_walk_transit_ASC_walk_social -0.846190 -0.846190 -0.7651 \n", + "coef_walk_transit_ASC_walk_univ -1.031100 -1.031100 -1.0311 \n", + "coef_walk_transit_ASC_walk_work 0.296352 0.296352 0.4432 \n", "\n", - " minimum maximum holdfast note \\\n", - "-999 -999.0 -999.0 1 \n", - "1 1.0 1.0 1 \n", - "coef_age010_trn NaN NaN 0 \n", - "coef_age1619_da NaN NaN 0 \n", - "coef_age16p_sr NaN NaN 0 \n", - "... ... ... ... ... \n", - "coef_sr3p_ASC_sr3p_othdiscr NaN NaN 0 \n", - "coef_sr3p_ASC_walk_othdiscr NaN NaN 0 \n", - "coef_walk_transit_ASC_sr2_othdiscr NaN NaN 0 \n", - "coef_walk_transit_ASC_sr3p_othdiscr NaN NaN 0 \n", - "coef_walk_transit_ASC_walk_othdiscr NaN NaN 0 \n", + " minimum maximum nullvalue holdfast \n", + "param_name \n", + "-999 -999.0 -999.0 0.0 1 \n", + "1 1.0 1.0 0.0 1 \n", + "coef_age010_trn -inf inf 0.0 0 \n", + "coef_age1619_da -inf inf 0.0 0 \n", + "coef_age16p_sr -inf inf 0.0 0 \n", + "... ... ... ... ... \n", + "coef_walk_transit_ASC_walk_school -inf inf 0.0 0 \n", + "coef_walk_transit_ASC_walk_shopping -inf inf 0.0 0 \n", + "coef_walk_transit_ASC_walk_social -inf inf 0.0 0 \n", + "coef_walk_transit_ASC_walk_univ -inf inf 0.0 0 \n", + "coef_walk_transit_ASC_walk_work -inf inf 0.0 0 \n", "\n", - " best \n", - "-999 -999.000000 \n", - "1 1.000000 \n", - "coef_age010_trn 0.360551 \n", - "coef_age1619_da 0.344530 \n", - "coef_age16p_sr -0.151886 \n", - "... ... \n", - "coef_sr3p_ASC_sr3p_othdiscr 0.844944 \n", - "coef_sr3p_ASC_walk_othdiscr -5.841028 \n", - "coef_walk_transit_ASC_sr2_othdiscr -1.232224 \n", - "coef_walk_transit_ASC_sr3p_othdiscr -1.199264 \n", - "coef_walk_transit_ASC_walk_othdiscr -0.980497 \n", - "\n", - "[272 rows x 8 columns]" + "[272 rows x 7 columns]" ] }, "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jpn/Git/est-mode/larch/src/larch/model/optimization.py:338: UserWarning: SLSQP may not play nicely with unbounded parameters\n", + "if you get poor results, consider setting global bounds with model.set_cap()\n", + " warnings.warn( # infinite bounds # )\n" + ] + }, { "data": { "text/html": [ @@ -1530,173 +1533,461 @@ " \n", " \n", " coef_age010_trn\n", - " 0.360551\n", + " 0.131028\n", " \n", " \n", " coef_age1619_da\n", - " 0.344530\n", + " 0.432972\n", " \n", " \n", " coef_age16p_sr\n", - " -0.151886\n", + " -0.403317\n", " \n", " \n", " coef_bike_ASC_rh\n", " -7.000000\n", " \n", " \n", - " coef_bike_ASC_walk_work\n", - " -2.659172\n", + " coef_bike_ASC_walk_eatout\n", + " -1.688820\n", " \n", " \n", - " coef_drive_transit_ASC_commuter_work\n", - " 0.426505\n", + " coef_bike_ASC_walk_escort\n", + " -13.520300\n", " \n", " \n", - " coef_drive_transit_ASC_express_work\n", - " -0.355400\n", + " coef_bike_ASC_walk_othdiscr\n", + " -1.324400\n", " \n", " \n", - " coef_drive_transit_ASC_ferry_work\n", - " 0.527700\n", + " coef_bike_ASC_walk_othmaint\n", + " -0.712317\n", " \n", " \n", - " coef_drive_transit_ASC_heavyrail_work\n", - " -11.994128\n", + " coef_bike_ASC_walk_school\n", + " -2.411542\n", " \n", " \n", - " coef_drive_transit_ASC_lightrail_work\n", - " -10.300241\n", + " coef_bike_ASC_walk_shopping\n", + " -1.577746\n", " \n", " \n", - " coef_drive_transit_ASC_rh\n", - " -162.171583\n", + " coef_bike_ASC_walk_social\n", + " -13.238401\n", " \n", " \n", - " coef_hhsize1_sr\n", - " -0.890929\n", + " coef_bike_ASC_walk_univ\n", + " -0.339900\n", " \n", " \n", - " coef_hhsize2_sr\n", - " 0.039653\n", + " coef_bike_ASC_walk_work\n", + " -2.059726\n", " \n", " \n", - " coef_ivt_work\n", - " -0.010155\n", + " coef_drive_transit_ASC_commuter_escort\n", + " 0.544200\n", " \n", " \n", - " coef_joint_auto_ASC_rh_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork\n", + " 6.898234\n", " \n", " \n", - " coef_joint_auto_ASC_sr2_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_commuter_univ_school\n", + " 3.472482\n", " \n", " \n", - " coef_joint_auto_ASC_sr3p_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_commuter_work\n", + " 0.928536\n", " \n", " \n", - " coef_joint_auto_ASC_walk_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_express_escort\n", + " 0.754700\n", " \n", " \n", - " coef_joint_bike_ASC_rh_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork\n", + " 0.673466\n", " \n", " \n", - " coef_joint_bike_ASC_walk_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_express_univ_school\n", + " -0.091011\n", " \n", " \n", - " coef_joint_drive_transit_ASC_commuter_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_express_work\n", + " 0.063342\n", " \n", " \n", - " coef_joint_drive_transit_ASC_express_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_ferry_escort\n", + " 0.600500\n", " \n", " \n", - " coef_joint_drive_transit_ASC_ferry_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork\n", + " -1.435555\n", " \n", " \n", - " coef_joint_drive_transit_ASC_heavyrail_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_ferry_univ_school\n", + " 0.935424\n", " \n", " \n", - " coef_joint_drive_transit_ASC_lightrail_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_ferry_work\n", + " 0.556372\n", " \n", " \n", - " coef_joint_drive_transit_ASC_rh_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_heavyrail_escort\n", + " -3.973160\n", " \n", " \n", - " coef_joint_ride_hail_ASC_sr2_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork\n", + " 0.985065\n", " \n", " \n", - " coef_joint_ride_hail_ASC_sr3p_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_heavyrail_univ_school\n", + " 0.729763\n", " \n", " \n", - " coef_joint_ride_hail_ASC_taxi_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_heavyrail_work\n", + " 0.821984\n", " \n", " \n", - " coef_joint_ride_hail_ASC_tnc_shared\n", - " 0.000000\n", + " coef_drive_transit_ASC_lightrail_escort\n", + " 2.308376\n", " \n", " \n", - " coef_joint_ride_hail_ASC_tnc_single_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork\n", + " 0.501609\n", " \n", " \n", - " coef_joint_ride_hail_ASC_walk_transit\n", - " 0.000000\n", + " coef_drive_transit_ASC_lightrail_univ_school\n", + " 3.943847\n", " \n", " \n", - " coef_joint_ride_hail_ASC_walk_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_lightrail_work\n", + " 0.635303\n", " \n", " \n", - " coef_joint_walk_ASC_rh_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_drive_transit_ASC_rh\n", + " -3.908149\n", " \n", " \n", - " coef_joint_walk_transit_ASC_commuter_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_hhsize1_sr\n", + " -0.789606\n", " \n", " \n", - " coef_joint_walk_transit_ASC_express_work_univ_school_escort_atwork\n", - " 0.000000\n", + " coef_hhsize2_sr\n", + " -0.058339\n", + " \n", + " \n", + " coef_ivt_escort_shopping_eatout_othdiscr_atwork\n", + " -0.011979\n", + " \n", + " \n", + " coef_ivt_othmaint_social\n", + " -0.008160\n", + " \n", + " \n", + " coef_ivt_univ_school\n", + " -0.016233\n", + " \n", + " \n", + " coef_ivt_work\n", + " -0.014958\n", + " \n", + " \n", + " coef_joint_auto_ASC_rh_shopping_eatout_othmaint_social_othdiscr\n", + " -7.345956\n", + " \n", + " \n", + " coef_joint_auto_ASC_rh_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_auto_ASC_sr2_eatout_othmaint_social_othdiscr\n", + " 1.280849\n", + " \n", + " \n", + " coef_joint_auto_ASC_sr2_shopping\n", + " 2.351884\n", + " \n", + " \n", + " coef_joint_auto_ASC_sr2_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_auto_ASC_sr3p_eatout_othmaint_social_othdiscr\n", + " 1.246167\n", + " \n", + " \n", + " coef_joint_auto_ASC_sr3p_shopping\n", + " -19.045412\n", + " \n", + " \n", + " coef_joint_auto_ASC_sr3p_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_auto_ASC_walk_eatout_othmaint_social_othdiscr\n", + " -0.098984\n", + " \n", + " \n", + " coef_joint_auto_ASC_walk_shopping\n", + " -23.468800\n", + " \n", + " \n", + " coef_joint_auto_ASC_walk_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_bike_ASC_rh_shopping_eatout_othmaint_social_othdiscr\n", + " -12.305749\n", + " \n", + " \n", + " coef_joint_bike_ASC_rh_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_bike_ASC_walk_eatout\n", + " -15.558800\n", + " \n", + " \n", + " coef_joint_bike_ASC_walk_othdiscr\n", + " -14.444000\n", + " \n", + " \n", + " coef_joint_bike_ASC_walk_othmaint\n", + " -13.519200\n", + " \n", + " \n", + " coef_joint_bike_ASC_walk_shopping\n", + " -7.047600\n", + " \n", + " \n", + " coef_joint_bike_ASC_walk_social\n", + " -26.171400\n", + " \n", + " \n", + " coef_joint_bike_ASC_walk_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr\n", + " 0.512900\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_commuter_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr\n", + " -0.805943\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_express_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr\n", + " 2.029768\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_ferry_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr\n", + " 3.190747\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_heavyrail_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr\n", + " 0.539200\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_lightrail_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr\n", + " 2.050928\n", + " \n", + " \n", + " coef_joint_drive_transit_ASC_rh_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_sr2_shopping_eatout_othmaint_social_othdiscr\n", + " -6.819150\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_sr2_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_sr3p_shopping_eatout_othmaint_social_othdiscr\n", + " -6.822328\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_sr3p_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_taxi_shopping_eatout_othmaint_social_othdiscr\n", + " -7.000000\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_taxi_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_tnc_shared\n", + " -0.345956\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_tnc_single_shopping_eatout_othmaint_social_othdiscr\n", + " -4.733900\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_tnc_single_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_walk_shopping_eatout_othmaint_social_othdiscr\n", + " -7.012565\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_walk_transit\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_ride_hail_ASC_walk_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_walk_ASC_rh_shopping_eatout_othmaint_social_othdiscr\n", + " -3.048765\n", + " \n", + " \n", + " coef_joint_walk_ASC_rh_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr\n", + " 0.512900\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_commuter_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr\n", + " 0.664800\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_express_work_univ_school_escort_atwork\n", + " 0.000000\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr\n", + " 0.646600\n", " \n", " \n", " coef_joint_walk_transit_ASC_ferry_work_univ_school_escort_atwork\n", " 0.000000\n", " \n", " \n", + " coef_joint_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr\n", + " 2.769790\n", + " \n", + " \n", " coef_joint_walk_transit_ASC_heavyrail_work_univ_school_escort_atwork\n", " 0.000000\n", " \n", " \n", + " coef_joint_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr\n", + " 0.727835\n", + " \n", + " \n", " coef_joint_walk_transit_ASC_lightrail_work_univ_school_escort_atwork\n", " 0.000000\n", " \n", " \n", + " coef_joint_walk_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr\n", + " 0.073906\n", + " \n", + " \n", " coef_joint_walk_transit_ASC_rh_work_univ_school_escort_atwork\n", " 0.000000\n", " \n", " \n", + " coef_joint_walk_transit_ASC_sr2_eatout\n", + " -14.913400\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr2_othdiscr\n", + " -14.980100\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr2_othmaint\n", + " -5.324150\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr2_shopping\n", + " -21.576800\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr2_social\n", + " -20.502200\n", + " \n", + " \n", " coef_joint_walk_transit_ASC_sr2_work_univ_school_escort_atwork\n", " 0.000000\n", " \n", " \n", + " coef_joint_walk_transit_ASC_sr3p_eatout\n", + " -16.758700\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr3p_othdiscr\n", + " -16.735400\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr3p_othmaint\n", + " -3.973990\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr3p_shopping\n", + " -25.595300\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_sr3p_social\n", + " -34.737800\n", + " \n", + " \n", " coef_joint_walk_transit_ASC_sr3p_work_univ_school_escort_atwork\n", " 0.000000\n", " \n", " \n", + " coef_joint_walk_transit_ASC_walk_eatout\n", + " 3.421757\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_walk_othdiscr\n", + " -1.023876\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_walk_othmaint\n", + " 0.282606\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_walk_shopping\n", + " 0.717138\n", + " \n", + " \n", + " coef_joint_walk_transit_ASC_walk_social\n", + " -2.272422\n", + " \n", + " \n", " coef_joint_walk_transit_ASC_walk_work_univ_school_escort_atwork\n", " 0.000000\n", " \n", @@ -1737,3414 +2028,3144 @@ " 0.500000\n", " \n", " \n", - " coef_ride_hail_ASC_sr2_work\n", - " -24.964153\n", + " coef_ride_hail_ASC_sr2_eatout_social_othdiscr\n", + " -6.474676\n", + " \n", + " \n", + " coef_ride_hail_ASC_sr2_escort_shopping_othmaint\n", + " -3.837659\n", + " \n", + " \n", + " coef_ride_hail_ASC_sr2_school\n", + " -1.923960\n", + " \n", + " \n", + " coef_ride_hail_ASC_sr2_univ\n", + " -4.337200\n", + " \n", + " \n", + " coef_ride_hail_ASC_sr2_work\n", + " -12.531625\n", + " \n", + " \n", + " coef_ride_hail_ASC_sr3p_eatout_social_othdiscr\n", + " -6.907989\n", + " \n", + " \n", + " coef_ride_hail_ASC_sr3p_school\n", + " -8.857391\n", + " \n", + " \n", + " coef_ride_hail_ASC_sr3p_univ\n", + " -4.922000\n", " \n", " \n", " coef_ride_hail_ASC_sr3p_work_escort_shopping_othmaint_atwork\n", - " -29.661854\n", + " -10.063483\n", + " \n", + " \n", + " coef_ride_hail_ASC_taxi_eatout_social_othdiscr\n", + " -2.560139\n", + " \n", + " \n", + " coef_ride_hail_ASC_taxi_escort_shopping_othmaint\n", + " -3.667690\n", + " \n", + " \n", + " coef_ride_hail_ASC_taxi_school\n", + " 0.194629\n", + " \n", + " \n", + " coef_ride_hail_ASC_taxi_univ\n", + " -1.599400\n", " \n", " \n", " coef_ride_hail_ASC_taxi_work\n", - " -2.519566\n", + " -2.416191\n", " \n", " \n", " coef_ride_hail_ASC_tnc_shared\n", - " 23.268241\n", + " 0.940193\n", + " \n", + " \n", + " coef_ride_hail_ASC_tnc_single_eatout_social_othdiscr\n", + " 3.020074\n", + " \n", + " \n", + " coef_ride_hail_ASC_tnc_single_escort_shopping_othmaint\n", + " 2.436588\n", + " \n", + " \n", + " coef_ride_hail_ASC_tnc_single_school\n", + " 2.043069\n", + " \n", + " \n", + " coef_ride_hail_ASC_tnc_single_univ\n", + " 0.108100\n", " \n", " \n", " coef_ride_hail_ASC_tnc_single_work\n", - " 23.984210\n", + " 2.658594\n", + " \n", + " \n", + " coef_ride_hail_ASC_walk_eatout_social_othdiscr\n", + " -1.924015\n", + " \n", + " \n", + " coef_ride_hail_ASC_walk_school\n", + " -0.961041\n", " \n", " \n", " coef_ride_hail_ASC_walk_transit\n", - " 22.114587\n", + " 1.263022\n", + " \n", + " \n", + " coef_ride_hail_ASC_walk_univ_escort_shopping_othmaint\n", + " -3.676198\n", " \n", " \n", " coef_ride_hail_ASC_walk_work\n", - " 21.771188\n", + " 1.329192\n", + " \n", + " \n", + " coef_sov_ASC_rh_escort_shopping_eatout_othmaint_social_othdiscr\n", + " 0.000000\n", + " \n", + " \n", + " coef_sov_ASC_rh_school\n", + " -9.212494\n", + " \n", + " \n", + " coef_sov_ASC_rh_univ\n", + " -6.649000\n", " 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coef_sr2_ASC_sr2_social0.768269coef_walk_transit_ASC_sr2_shopping-9.388409e-06
coef_sr2_ASC_walk_social-2.850657coef_walk_transit_ASC_sr2_social9.230328e-07
coef_sr3p_ASC_sr2_social-0.122347coef_walk_transit_ASC_sr2_univ0.000000e+00
coef_sr3p_ASC_sr3p_social1.210687coef_walk_transit_ASC_sr2_work-1.261236e-04
coef_sr3p_ASC_walk_social-3.080113coef_walk_transit_ASC_sr3p_eatout6.744761e-06
coef_walk_transit_ASC_sr2_social-2.064534coef_walk_transit_ASC_sr3p_escort-2.229624e-07
coef_walk_transit_ASC_sr3p_social-21.630900coef_walk_transit_ASC_sr3p_othdiscr-1.310142e-05
coef_walk_transit_ASC_walk_social-2.314604coef_walk_transit_ASC_sr3p_othmaint-1.028683e-16
coef_bike_ASC_walk_othdiscr-1.397187coef_walk_transit_ASC_sr3p_school1.154167e-04
coef_joint_bike_ASC_walk_othdiscr-14.444000coef_walk_transit_ASC_sr3p_shopping-5.588721e-05
coef_joint_walk_transit_ASC_sr2_othdiscr-14.980100coef_walk_transit_ASC_sr3p_social-1.622526e-16
coef_joint_walk_transit_ASC_sr3p_othdiscr-16.735568coef_walk_transit_ASC_sr3p_univ0.000000e+00
coef_joint_walk_transit_ASC_walk_othdiscr-25.948045coef_walk_transit_ASC_sr3p_work-1.697982e-05
coef_sov_ASC_walk_othdiscr-4.939783coef_walk_transit_ASC_walk_eatout-1.290994e-04
coef_sr2_ASC_sr2_othdiscr0.855704coef_walk_transit_ASC_walk_escort1.459589e-06
coef_sr2_ASC_walk_othdiscr-2.470470coef_walk_transit_ASC_walk_othdiscr-6.642421e-05
coef_sr3p_ASC_sr2_othdiscr-0.417630coef_walk_transit_ASC_walk_othmaint-5.294680e-05
coef_sr3p_ASC_sr3p_othdiscr0.844944coef_walk_transit_ASC_walk_school9.694344e-05
coef_sr3p_ASC_walk_othdiscr-5.841028coef_walk_transit_ASC_walk_shopping4.504021e-05
coef_walk_transit_ASC_sr2_othdiscr-1.232224coef_walk_transit_ASC_walk_social-7.533156e-05
coef_walk_transit_ASC_sr3p_othdiscr-1.199264coef_walk_transit_ASC_walk_univ0.000000e+00
coef_walk_transit_ASC_walk_othdiscr-0.980497coef_walk_transit_ASC_walk_work-1.629846e-04
loglike-7541.342641931288d_loglike\n", + "
nit304nfev317njev304status0message'Optimization terminated successfully'successTrueelapsed_time0:03:32.168906method'SLSQP'n_cases162587iteration_number304loglike-83882.486683268" + ], + "text/plain": [ + "┣ x: -999 -999.000000\n", + "┃ 1 1.000000\n", + "┃ coef_age010_trn 0.131028\n", + "┃ coef_age1619_da 0.432972\n", + "┃ coef_age16p_sr -0.403317\n", + "┃ ... \n", + "┃ coef_walk_transit_ASC_walk_school -1.566510\n", + "┃ coef_walk_transit_ASC_walk_shopping -1.250219\n", + "┃ coef_walk_transit_ASC_walk_social -0.846190\n", + "┃ coef_walk_transit_ASC_walk_univ -1.031100\n", + "┃ coef_walk_transit_ASC_walk_work 0.296352\n", + "┃ Length: 272, dtype: float64\n", + "┣ logloss: 0.5159237004389526\n", + "┣ d_logloss: -999 0.000000\n", + "┃ 1 0.000000\n", + "┃ coef_age010_trn -0.000081\n", + "┃ coef_age1619_da -0.000052\n", + "┃ coef_age16p_sr -0.000041\n", + "┃ ... \n", + "┃ coef_walk_transit_ASC_walk_school 0.000097\n", + "┃ coef_walk_transit_ASC_walk_shopping 0.000045\n", + "┃ coef_walk_transit_ASC_walk_social -0.000075\n", + "┃ coef_walk_transit_ASC_walk_univ 0.000000\n", + "┃ coef_walk_transit_ASC_walk_work -0.000163\n", + "┃ Length: 272, dtype: float64\n", + "┣ nit: 304\n", + "┣ nfev: 317\n", + "┣ njev: 304\n", + "┣ status: 0\n", + "┣ message: 'Optimization terminated successfully'\n", + "┣ success: True\n", + "┣ elapsed_time: datetime.timedelta(seconds=212, microseconds=168906)\n", + "┣ method: 'SLSQP'\n", + "┣ n_cases: 162587\n", + "┣ iteration_number: 304\n", + "┣ loglike: -83882.486683268" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.maximize_loglike(method='SLSQP', options={\"maxiter\": 1000})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estimated coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", " \n", - 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0
 ValueNull Value
Parameter  
-9990.000000e+00-999-999. 0.00
10.000000e+001 1.00 0.00
coef_age010_trn6.536294e-06coef_age010_trn 0.131 0.00
coef_age1619_da-2.022165e-05coef_age1619_da 0.433 0.00
coef_age16p_sr-1.531626e-03coef_age16p_sr-0.403 0.00
coef_bike_ASC_rh0.000000e+00coef_bike_ASC_rh-7.00 0.00
coef_bike_ASC_walk_work4.331337e-05coef_bike_ASC_walk_eatout-1.69 0.00
coef_drive_transit_ASC_commuter_work-4.495517e-05coef_bike_ASC_walk_escort-13.5 0.00
coef_drive_transit_ASC_express_work0.000000e+00coef_bike_ASC_walk_othdiscr-1.32 0.00
coef_drive_transit_ASC_ferry_work0.000000e+00coef_bike_ASC_walk_othmaint-0.712 0.00
coef_drive_transit_ASC_heavyrail_work-4.473961e-10coef_bike_ASC_walk_school-2.41 0.00
coef_drive_transit_ASC_lightrail_work-2.507220e-09coef_bike_ASC_walk_shopping-1.58 0.00
coef_drive_transit_ASC_rh-1.718236e-62coef_bike_ASC_walk_social-13.2 0.00
coef_hhsize1_sr4.378810e-05coef_bike_ASC_walk_univ-0.340 0.00
coef_hhsize2_sr-9.408883e-04coef_bike_ASC_walk_work-2.06 0.00
coef_ivt_work5.462510e-02coef_drive_transit_ASC_commuter_escort 0.544 0.00
coef_joint_auto_ASC_rh_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork 6.90 0.00
coef_joint_auto_ASC_sr2_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_commuter_univ_school 3.47 0.00
coef_joint_auto_ASC_sr3p_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_commuter_work 0.929 0.00
coef_joint_auto_ASC_walk_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_express_escort 0.755 0.00
coef_joint_bike_ASC_rh_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork 0.673 0.00
coef_joint_bike_ASC_walk_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_express_univ_school-0.0910 0.00
coef_joint_drive_transit_ASC_commuter_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_express_work 0.0633 0.00
coef_joint_drive_transit_ASC_express_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_ferry_escort 0.601 0.00
coef_joint_drive_transit_ASC_ferry_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork-1.44 0.00
coef_joint_drive_transit_ASC_heavyrail_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_ferry_univ_school 0.935 0.00
coef_joint_drive_transit_ASC_lightrail_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_ferry_work 0.556 0.00
coef_joint_drive_transit_ASC_rh_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_heavyrail_escort-3.97 0.00
coef_joint_ride_hail_ASC_sr2_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork 0.985 0.00
coef_joint_ride_hail_ASC_sr3p_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_heavyrail_univ_school 0.730 0.00
coef_joint_ride_hail_ASC_taxi_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_heavyrail_work 0.822 0.00
coef_joint_ride_hail_ASC_tnc_shared0.000000e+00coef_drive_transit_ASC_lightrail_escort 2.31 0.00
coef_joint_ride_hail_ASC_tnc_single_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork 0.502 0.00
coef_joint_ride_hail_ASC_walk_transit0.000000e+00coef_drive_transit_ASC_lightrail_univ_school 3.94 0.00
coef_joint_ride_hail_ASC_walk_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_lightrail_work 0.635 0.00
coef_joint_walk_ASC_rh_work_univ_school_escort_atwork0.000000e+00coef_drive_transit_ASC_rh-3.91 0.00
coef_joint_walk_transit_ASC_commuter_work_univ_school_escort_atwork0.000000e+00coef_hhsize1_sr-0.790 0.00
coef_joint_walk_transit_ASC_express_work_univ_school_escort_atwork0.000000e+00coef_hhsize2_sr-0.0583 0.00
coef_joint_walk_transit_ASC_ferry_work_univ_school_escort_atwork0.000000e+00coef_ivt_escort_shopping_eatout_othdiscr_atwork-0.0120 0.00
coef_joint_walk_transit_ASC_heavyrail_work_univ_school_escort_atwork0.000000e+00coef_ivt_othmaint_social-0.00816 0.00
coef_joint_walk_transit_ASC_lightrail_work_univ_school_escort_atwork0.000000e+00coef_ivt_univ_school-0.0162 0.00
coef_joint_walk_transit_ASC_rh_work_univ_school_escort_atwork0.000000e+00coef_ivt_work-0.0150 0.00
coef_joint_walk_transit_ASC_sr2_work_univ_school_escort_atwork0.000000e+00coef_joint_auto_ASC_rh_shopping_eatout_othmaint_social_othdiscr-7.35 0.00
coef_joint_walk_transit_ASC_sr3p_work_univ_school_escort_atwork0.000000e+00coef_joint_auto_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_walk_work_univ_school_escort_atwork0.000000e+00coef_joint_auto_ASC_sr2_eatout_othmaint_social_othdiscr 1.28 0.00
coef_nest_AUTO0.000000e+00coef_joint_auto_ASC_sr2_shopping 2.35 0.00
coef_nest_AUTO_DRIVEALONE0.000000e+00coef_joint_auto_ASC_sr2_work_univ_school_escort_atwork 0.00 0.00
coef_nest_AUTO_SHAREDRIDE20.000000e+00coef_joint_auto_ASC_sr3p_eatout_othmaint_social_othdiscr 1.25 0.00
coef_nest_AUTO_SHAREDRIDE30.000000e+00coef_joint_auto_ASC_sr3p_shopping-19.0 0.00
coef_nest_NONMOTORIZED0.000000e+00coef_joint_auto_ASC_sr3p_work_univ_school_escort_atwork 0.00 0.00
coef_nest_RIDEHAIL0.000000e+00coef_joint_auto_ASC_walk_eatout_othmaint_social_othdiscr-0.0990 0.00
coef_nest_TRANSIT0.000000e+00coef_joint_auto_ASC_walk_shopping-23.5 0.00
coef_nest_TRANSIT_DRIVEACCESS0.000000e+00coef_joint_auto_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_nest_TRANSIT_WALKACCESS0.000000e+00coef_joint_bike_ASC_rh_shopping_eatout_othmaint_social_othdiscr-12.3 0.00
coef_ride_hail_ASC_sr2_work-1.076131e-17coef_joint_bike_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_ride_hail_ASC_sr3p_work_escort_shopping_othmaint_atwork-4.912817e-20coef_joint_bike_ASC_walk_eatout-15.6 0.00
coef_ride_hail_ASC_taxi_work-2.445223e-27coef_joint_bike_ASC_walk_othdiscr-14.4 0.00
coef_ride_hail_ASC_tnc_shared9.512800e-04coef_joint_bike_ASC_walk_othmaint-13.5 0.00
coef_ride_hail_ASC_tnc_single_work-5.210129e-04coef_joint_bike_ASC_walk_shopping-7.05 0.00
coef_ride_hail_ASC_walk_transit-1.264748e-04coef_joint_bike_ASC_walk_social-26.2 0.00
coef_ride_hail_ASC_walk_work-1.549984e-05coef_joint_bike_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_sov_ASC_rh_work_atwork0.000000e+00coef_joint_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr 0.513 0.00
coef_sov_ASC_sr2_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_joint_drive_transit_ASC_commuter_work_univ_school_escort_atwork 0.00 0.00
coef_sov_ASC_sr3p_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_joint_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr-0.806 0.00
coef_sov_ASC_walk_work-1.293137e-04coef_joint_drive_transit_ASC_express_work_univ_school_escort_atwork 0.00 0.00
coef_sr2_ASC_rh_work_school_escort_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_joint_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr 2.03 0.00
coef_sr2_ASC_sr2_work-4.657072e-04coef_joint_drive_transit_ASC_ferry_work_univ_school_escort_atwork 0.00 0.00
coef_sr2_ASC_sr3p0.000000e+00coef_joint_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr 3.19 0.00
coef_sr2_ASC_walk_work-5.813633e-05coef_joint_drive_transit_ASC_heavyrail_work_univ_school_escort_atwork 0.00 0.00
coef_sr3p_ASC_rh_work-8.206902e-30coef_joint_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr 0.539 0.00
coef_sr3p_ASC_sr2_work2.351910e-04coef_joint_drive_transit_ASC_lightrail_work_univ_school_escort_atwork 0.00 0.00
coef_sr3p_ASC_sr3p_work-5.735054e-04coef_joint_drive_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr 2.05 0.00
coef_sr3p_ASC_walk_work3.486286e-05coef_joint_drive_transit_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_walk_ASC_rh0.000000e+00coef_joint_ride_hail_ASC_sr2_shopping_eatout_othmaint_social_othdiscr-6.82 0.00
coef_walk_transit_ASC_commuter_work0.000000e+00coef_joint_ride_hail_ASC_sr2_work_univ_school_escort_atwork 0.00 0.00
coef_walk_transit_ASC_express_work0.000000e+00coef_joint_ride_hail_ASC_sr3p_shopping_eatout_othmaint_social_othdiscr-6.82 0.00
coef_walk_transit_ASC_ferry_work0.000000e+00coef_joint_ride_hail_ASC_sr3p_work_univ_school_escort_atwork 0.00 0.00
coef_walk_transit_ASC_heavyrail_work3.025768e-04coef_joint_ride_hail_ASC_taxi_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_walk_transit_ASC_lightrail_work-1.644943e-03coef_joint_ride_hail_ASC_taxi_work_univ_school_escort_atwork 0.00 0.00
coef_walk_transit_ASC_rh_work-2.191119e-05coef_joint_ride_hail_ASC_tnc_shared-0.346 0.00
coef_walk_transit_ASC_sr2_work5.241777e-05coef_joint_ride_hail_ASC_tnc_single_shopping_eatout_othmaint_social_othdiscr-4.73 0.00
coef_walk_transit_ASC_sr3p_work-2.945579e-05coef_joint_ride_hail_ASC_tnc_single_work_univ_school_escort_atwork 0.00 0.00
coef_walk_transit_ASC_walk_work6.752091e-04coef_joint_ride_hail_ASC_walk_shopping_eatout_othmaint_social_othdiscr-7.01 0.00
coef_bike_ASC_walk_univ0.000000e+00coef_joint_ride_hail_ASC_walk_transit 0.00 0.00
coef_drive_transit_ASC_commuter_univ_school0.000000e+00coef_joint_ride_hail_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_drive_transit_ASC_express_univ_school0.000000e+00coef_joint_walk_ASC_rh_shopping_eatout_othmaint_social_othdiscr-3.05 0.00
coef_drive_transit_ASC_ferry_univ_school0.000000e+00coef_joint_walk_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_drive_transit_ASC_heavyrail_univ_school0.000000e+00coef_joint_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr 0.513 0.00
coef_drive_transit_ASC_lightrail_univ_school0.000000e+00coef_joint_walk_transit_ASC_commuter_work_univ_school_escort_atwork 0.00 0.00
coef_ivt_univ_school-1.050483e-02coef_joint_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr 0.665 0.00
coef_ride_hail_ASC_sr2_univ0.000000e+00coef_joint_walk_transit_ASC_express_work_univ_school_escort_atwork 0.00 0.00
coef_ride_hail_ASC_sr3p_univ0.000000e+00coef_joint_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr 0.647 0.00
coef_ride_hail_ASC_taxi_univ0.000000e+00coef_joint_walk_transit_ASC_ferry_work_univ_school_escort_atwork 0.00 0.00
coef_ride_hail_ASC_tnc_single_univ0.000000e+00coef_joint_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr 2.77 0.00
coef_ride_hail_ASC_walk_univ_escort_shopping_othmaint-2.749099e-18coef_joint_walk_transit_ASC_heavyrail_work_univ_school_escort_atwork 0.00 0.00
coef_sov_ASC_rh_univ0.000000e+00coef_joint_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr 0.728 0.00
coef_sov_ASC_walk_univ0.000000e+00coef_joint_walk_transit_ASC_lightrail_work_univ_school_escort_atwork 0.00 0.00
coef_sr2_ASC_rh_univ0.000000e+00coef_joint_walk_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr 0.0739 0.00
coef_sr2_ASC_sr2_univ0.000000e+00coef_joint_walk_transit_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_sr2_ASC_walk_univ0.000000e+00coef_joint_walk_transit_ASC_sr2_eatout-14.9 0.00
coef_sr3p_ASC_rh_univ_escort_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_joint_walk_transit_ASC_sr2_othdiscr-15.0 0.00
coef_sr3p_ASC_sr2_univ0.000000e+00coef_joint_walk_transit_ASC_sr2_othmaint-5.32 0.00
coef_sr3p_ASC_sr3p_univ0.000000e+00coef_joint_walk_transit_ASC_sr2_shopping-21.6 0.00
coef_sr3p_ASC_walk_univ0.000000e+00coef_joint_walk_transit_ASC_sr2_social-20.5 0.00
coef_walk_transit_ASC_commuter_univ_school0.000000e+00
coef_joint_walk_transit_ASC_sr2_work_univ_school_escort_atwork 0.00 0.00
coef_walk_transit_ASC_express_univ_school0.000000e+00coef_joint_walk_transit_ASC_sr3p_eatout-16.8 0.00
coef_walk_transit_ASC_ferry_univ_school0.000000e+00coef_joint_walk_transit_ASC_sr3p_othdiscr-16.7 0.00
coef_walk_transit_ASC_heavyrail_univ_school-5.322431e-05coef_joint_walk_transit_ASC_sr3p_othmaint-3.97 0.00
coef_walk_transit_ASC_lightrail_univ_school1.268521e-04coef_joint_walk_transit_ASC_sr3p_shopping-25.6 0.00
coef_walk_transit_ASC_rh_univ0.000000e+00coef_joint_walk_transit_ASC_sr3p_social-34.7 0.00
coef_walk_transit_ASC_sr2_univ0.000000e+00coef_joint_walk_transit_ASC_sr3p_work_univ_school_escort_atwork 0.00 0.00
coef_walk_transit_ASC_sr3p_univ0.000000e+00coef_joint_walk_transit_ASC_walk_eatout 3.42 0.00
coef_walk_transit_ASC_walk_univ0.000000e+00coef_joint_walk_transit_ASC_walk_othdiscr-1.02 0.00
coef_bike_ASC_walk_school-2.045778e-57coef_joint_walk_transit_ASC_walk_othmaint 0.283 0.00
coef_ride_hail_ASC_sr2_school-7.168630e-16coef_joint_walk_transit_ASC_walk_shopping 0.717 0.00
coef_ride_hail_ASC_sr3p_school-4.930109e-16coef_joint_walk_transit_ASC_walk_social-2.27 0.00
coef_ride_hail_ASC_taxi_school-4.621869e-27coef_joint_walk_transit_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_ride_hail_ASC_tnc_single_school-1.784036e-04coef_nest_AUTO 0.720 1.00
coef_ride_hail_ASC_walk_school-4.237276e-16coef_nest_AUTO_DRIVEALONE 0.350 1.00
coef_sov_ASC_rh_school-4.635032e-48coef_nest_AUTO_SHAREDRIDE2 0.350 1.00
coef_sov_ASC_walk_school-2.359157e-05coef_nest_AUTO_SHAREDRIDE3 0.350 1.00
coef_sr2_ASC_sr2_school5.082394e-05coef_nest_NONMOTORIZED 0.720 1.00
coef_sr2_ASC_walk_school-3.377311e-05coef_nest_RIDEHAIL 0.360 1.00
coef_sr3p_ASC_rh_school-8.786869e-64coef_nest_TRANSIT 0.720 1.00
coef_sr3p_ASC_sr2_school-1.979710e-04coef_nest_TRANSIT_DRIVEACCESS 0.500 1.00
coef_sr3p_ASC_sr3p_school2.444092e-04coef_nest_TRANSIT_WALKACCESS 0.500 1.00
coef_sr3p_ASC_walk_school-1.610767e-04coef_ride_hail_ASC_sr2_eatout_social_othdiscr-6.47 0.00
coef_walk_transit_ASC_rh_school1.961319e-05coef_ride_hail_ASC_sr2_escort_shopping_othmaint-3.84 0.00
coef_walk_transit_ASC_sr2_school2.811230e-05coef_ride_hail_ASC_sr2_school-1.92 0.00
coef_walk_transit_ASC_sr3p_school-4.190001e-05coef_ride_hail_ASC_sr2_univ-4.34 0.00
coef_walk_transit_ASC_walk_school-1.524977e-05coef_ride_hail_ASC_sr2_work-12.5 0.00
coef_bike_ASC_walk_escort-8.352243e-10coef_ride_hail_ASC_sr3p_eatout_social_othdiscr-6.91 0.00
coef_drive_transit_ASC_commuter_escort0.000000e+00coef_ride_hail_ASC_sr3p_school-8.86 0.00
coef_drive_transit_ASC_express_escort0.000000e+00coef_ride_hail_ASC_sr3p_univ-4.92 0.00
coef_drive_transit_ASC_ferry_escort0.000000e+00coef_ride_hail_ASC_sr3p_work_escort_shopping_othmaint_atwork-10.1 0.00
coef_drive_transit_ASC_heavyrail_escort-1.175371e-11coef_ride_hail_ASC_taxi_eatout_social_othdiscr-2.56 0.00
coef_drive_transit_ASC_lightrail_escort0.000000e+00coef_ride_hail_ASC_taxi_escort_shopping_othmaint-3.67 0.00
coef_ivt_escort_shopping_eatout_othdiscr_atwork-7.640350e-02coef_ride_hail_ASC_taxi_school 0.195 0.00
coef_ride_hail_ASC_sr2_escort_shopping_othmaint-2.138916e-17coef_ride_hail_ASC_taxi_univ-1.60 0.00
coef_ride_hail_ASC_taxi_escort_shopping_othmaint-2.798746e-29coef_ride_hail_ASC_taxi_work-2.42 0.00
coef_ride_hail_ASC_tnc_single_escort_shopping_othmaint9.394624e-05coef_ride_hail_ASC_tnc_shared 0.940 0.00
coef_sov_ASC_rh_escort_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_ride_hail_ASC_tnc_single_eatout_social_othdiscr 3.02 0.00
coef_sov_ASC_sr2_escort0.000000e+00coef_ride_hail_ASC_tnc_single_escort_shopping_othmaint 2.44 0.00
coef_sov_ASC_sr3p_escort0.000000e+00coef_ride_hail_ASC_tnc_single_school 2.04 0.00
coef_sov_ASC_walk_escort0.000000e+00coef_ride_hail_ASC_tnc_single_univ 0.108 0.00
coef_sr2_ASC_sr2_escort-6.160566e-04coef_ride_hail_ASC_tnc_single_work 2.66 0.00
coef_sr2_ASC_walk_escort-1.374539e-08coef_ride_hail_ASC_walk_eatout_social_othdiscr-1.92 0.00
coef_sr3p_ASC_sr2_escort4.939249e-05coef_ride_hail_ASC_walk_school-0.961 0.00
coef_sr3p_ASC_sr3p_escort-5.312809e-05coef_ride_hail_ASC_walk_transit 1.26 0.00
coef_sr3p_ASC_walk_escort7.813745e-07coef_ride_hail_ASC_walk_univ_escort_shopping_othmaint-3.68 0.00
coef_walk_transit_ASC_commuter_escort0.000000e+00coef_ride_hail_ASC_walk_work 1.33 0.00
coef_walk_transit_ASC_express_escort0.000000e+00coef_sov_ASC_rh_escort_shopping_eatout_othmaint_social_othdiscr 0.00 0.00
coef_walk_transit_ASC_ferry_escort0.000000e+00coef_sov_ASC_rh_school-9.21 0.00
coef_walk_transit_ASC_heavyrail_escort1.847275e-05coef_sov_ASC_rh_univ-6.65 0.00
coef_walk_transit_ASC_lightrail_escort-6.175222e-05coef_sov_ASC_rh_work_atwork-7.00 0.00
coef_walk_transit_ASC_rh_escort_shopping_othmaint-3.544690e-05coef_sov_ASC_sr2_escort 0.00 0.00
coef_walk_transit_ASC_sr2_escort-4.425501e-05coef_sov_ASC_sr2_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork-999. 0.00
coef_walk_transit_ASC_sr3p_escort-4.715077e-99coef_sov_ASC_sr3p_escort 0.00 0.00
coef_walk_transit_ASC_walk_escort-5.506864e-35coef_sov_ASC_sr3p_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork-999. 0.00
coef_bike_ASC_walk_shopping-1.404547e-12coef_sov_ASC_walk_eatout-1.88 0.00
coef_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sov_ASC_walk_escort 0.00 0.00
coef_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sov_ASC_walk_othdiscr-1.66 0.00
coef_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sov_ASC_walk_othmaint-1.38 0.00
coef_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sov_ASC_walk_school-2.30 0.00
coef_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sov_ASC_walk_shopping-1.59 0.00
coef_joint_auto_ASC_rh_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sov_ASC_walk_social-1.87 0.00
coef_joint_auto_ASC_sr2_shopping1.199811e-05coef_sov_ASC_walk_univ-1.06 0.00
coef_joint_auto_ASC_sr3p_shopping-5.796288e-07coef_sov_ASC_walk_work-1.17 0.00
coef_joint_auto_ASC_walk_shopping-1.431461e-09coef_sr2_ASC_rh_univ-6.69 0.00
coef_joint_bike_ASC_rh_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_rh_work_school_escort_shopping_eatout_othmaint_social_othdiscr_atwork-7.00 0.00
coef_joint_bike_ASC_walk_shopping0.000000e+00coef_sr2_ASC_sr2_eatout 1.54 0.00
coef_joint_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_escort 0.829 0.00
coef_joint_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_othdiscr 1.16 0.00
coef_joint_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_othmaint 1.50 0.00
coef_joint_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_school-0.206 0.00
coef_joint_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_shopping 1.78 0.00
coef_joint_drive_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_social 0.975 0.00
coef_joint_ride_hail_ASC_sr2_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_univ 0.430 0.00
coef_joint_ride_hail_ASC_sr3p_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr2_work 0.470 0.00
coef_joint_ride_hail_ASC_taxi_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_sr3p-999. 0.00
coef_joint_ride_hail_ASC_tnc_single_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_walk_eatout-0.189 0.00
coef_joint_ride_hail_ASC_walk_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_walk_escort-2.21 0.00
coef_joint_walk_ASC_rh_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_walk_othdiscr-0.449 0.00
coef_joint_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_walk_othmaint-0.603 0.00
coef_joint_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_walk_school-0.134 0.00
coef_joint_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr0.000000e+00coef_sr2_ASC_walk_shopping-0.528 0.00
coef_joint_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr3.150898e-09coef_sr2_ASC_walk_social-0.791 0.00
coef_joint_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr-1.676959e-05coef_sr2_ASC_walk_univ 1.30 0.00
coef_joint_walk_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr-4.853053e-05coef_sr2_ASC_walk_work-0.125 0.00
coef_joint_walk_transit_ASC_sr2_shopping-6.388223e-11coef_sr3p_ASC_rh_school-6.90 0.00
coef_joint_walk_transit_ASC_sr3p_shopping-3.009123e-13coef_sr3p_ASC_rh_univ_escort_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_joint_walk_transit_ASC_walk_shopping-1.854492e-40coef_sr3p_ASC_rh_work-71.9 0.00
coef_sov_ASC_walk_shopping1.979504e-05coef_sr3p_ASC_sr2_eatout 0.0170 0.00
coef_sr2_ASC_sr2_shopping-1.633169e-04coef_sr3p_ASC_sr2_escort 0.132 0.00
coef_sr2_ASC_walk_shopping-2.087183e-05coef_sr3p_ASC_sr2_othdiscr-0.193 0.00
coef_sr3p_ASC_sr2_shopping-2.114611e-05coef_sr3p_ASC_sr2_othmaint 0.0597 0.00
coef_sr3p_ASC_sr3p_shopping-7.777893e-06coef_sr3p_ASC_sr2_school-1.38 0.00
coef_sr3p_ASC_walk_shopping-5.499761e-05coef_sr3p_ASC_sr2_shopping 0.409 0.00
coef_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sr3p_ASC_sr2_social 0.441 0.00
coef_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sr3p_ASC_sr2_univ-0.312 0.00
coef_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork0.000000e+00coef_sr3p_ASC_sr2_work-0.769 0.00
coef_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork-9.355912e-05coef_sr3p_ASC_sr3p_eatout 1.75 0.00
coef_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork1.458827e-04coef_sr3p_ASC_sr3p_escort 1.12 0.00
coef_walk_transit_ASC_sr2_shopping-4.838269e-05coef_sr3p_ASC_sr3p_othdiscr 1.21 0.00
coef_walk_transit_ASC_sr3p_shopping-7.841219e-48coef_sr3p_ASC_sr3p_othmaint 1.48 0.00
coef_walk_transit_ASC_walk_shopping-5.761910e-05coef_sr3p_ASC_sr3p_school-0.385 0.00
coef_bike_ASC_walk_eatout-5.163611e-94coef_sr3p_ASC_sr3p_shopping 1.86 0.00
coef_joint_auto_ASC_sr2_eatout_othmaint_social_othdiscr1.042498e-05coef_sr3p_ASC_sr3p_social 1.38 0.00
coef_joint_auto_ASC_sr3p_eatout_othmaint_social_othdiscr6.920224e-06coef_sr3p_ASC_sr3p_univ 0.629 0.00
coef_joint_auto_ASC_walk_eatout_othmaint_social_othdiscr-5.600401e-05coef_sr3p_ASC_sr3p_work 0.255 0.00
coef_joint_bike_ASC_walk_eatout0.000000e+00coef_sr3p_ASC_walk_eatout-0.240 0.00
coef_joint_walk_transit_ASC_sr2_eatout-2.828607e-08coef_sr3p_ASC_walk_escort-1.30 0.00
coef_joint_walk_transit_ASC_sr3p_eatout-2.226057e-09coef_sr3p_ASC_walk_othdiscr-0.712 0.00
coef_joint_walk_transit_ASC_walk_eatout-1.504477e-05coef_sr3p_ASC_walk_othmaint-0.347 0.00
coef_ride_hail_ASC_sr2_eatout_social_othdiscr-3.869795e-16coef_sr3p_ASC_walk_school-0.603 0.00
coef_ride_hail_ASC_sr3p_eatout_social_othdiscr-1.643875e-13coef_sr3p_ASC_walk_shopping-0.431 0.00
coef_ride_hail_ASC_taxi_eatout_social_othdiscr-2.363466e-28coef_sr3p_ASC_walk_social-0.451 0.00
coef_ride_hail_ASC_tnc_single_eatout_social_othdiscr-2.038351e-04coef_sr3p_ASC_walk_univ 1.68 0.00
coef_ride_hail_ASC_walk_eatout_social_othdiscr-1.208287e-15coef_sr3p_ASC_walk_work-0.120 0.00
coef_sov_ASC_walk_eatout1.478005e-05coef_walk_ASC_rh-7.00 0.00
coef_sr2_ASC_sr2_eatout1.134955e-05coef_walk_transit_ASC_commuter_escort 0.544 0.00
coef_sr2_ASC_walk_eatout-5.387236e-05coef_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork 0.183 0.00
coef_sr3p_ASC_sr2_eatout-4.592690e-05coef_walk_transit_ASC_commuter_univ_school 2.15 0.00
coef_sr3p_ASC_sr3p_eatout8.013239e-05coef_walk_transit_ASC_commuter_work 0.646 0.00
coef_sr3p_ASC_walk_eatout-2.559943e-05coef_walk_transit_ASC_express_escort 0.755 0.00
coef_walk_transit_ASC_rh_eatout_social_othdiscr2.475426e-05coef_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork 1.09 0.00
coef_walk_transit_ASC_sr2_eatout-8.378197e-06coef_walk_transit_ASC_express_univ_school 0.736 0.00
coef_walk_transit_ASC_sr3p_eatout5.928461e-05coef_walk_transit_ASC_express_work-0.116 0.00
coef_walk_transit_ASC_walk_eatout3.351367e-05coef_walk_transit_ASC_ferry_escort 0.601 0.00
coef_bike_ASC_walk_othmaint-1.105637e-05coef_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork-5.31 0.00
coef_ivt_othmaint_social5.370351e-02coef_walk_transit_ASC_ferry_univ_school 0.102 0.00
coef_joint_bike_ASC_walk_othmaint0.000000e+00coef_walk_transit_ASC_ferry_work-0.283 0.00
coef_joint_walk_transit_ASC_sr2_othmaint-1.624906e-06coef_walk_transit_ASC_heavyrail_escort 1.73 0.00
coef_joint_walk_transit_ASC_sr3p_othmaint-7.397451e-24coef_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork 1.19 0.00
coef_joint_walk_transit_ASC_walk_othmaint-3.953263e-30coef_walk_transit_ASC_heavyrail_univ_school 1.50 0.00
coef_sov_ASC_walk_othmaint5.401771e-05coef_walk_transit_ASC_heavyrail_work 0.661 0.00
coef_sr2_ASC_sr2_othmaint-4.666221e-05coef_walk_transit_ASC_lightrail_escort 2.37 0.00
coef_sr2_ASC_walk_othmaint4.186596e-05coef_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork 0.939 0.00
coef_sr3p_ASC_sr2_othmaint7.950676e-05coef_walk_transit_ASC_lightrail_univ_school 1.96 0.00
coef_sr3p_ASC_sr3p_othmaint-8.383183e-05coef_walk_transit_ASC_lightrail_work 0.724 0.00
coef_sr3p_ASC_walk_othmaint1.080875e-04coef_walk_transit_ASC_rh_eatout_social_othdiscr-4.96 0.00
coef_walk_transit_ASC_sr2_othmaint-4.017102e-15coef_walk_transit_ASC_rh_escort_shopping_othmaint-5.01 0.00
coef_walk_transit_ASC_sr3p_othmaint-6.376816e-12coef_walk_transit_ASC_rh_school-7.20 0.00
coef_walk_transit_ASC_walk_othmaint1.941271e-05coef_walk_transit_ASC_rh_univ-4.27 0.00
coef_bike_ASC_walk_social-7.802548e-08coef_walk_transit_ASC_rh_work-4.01 0.00
coef_joint_bike_ASC_walk_social0.000000e+00coef_walk_transit_ASC_sr2_eatout-2.12 0.00
coef_joint_walk_transit_ASC_sr2_social0.000000e+00coef_walk_transit_ASC_sr2_escort-0.579 0.00
coef_joint_walk_transit_ASC_sr3p_social-2.473076e-24coef_walk_transit_ASC_sr2_othdiscr-1.93 0.00
coef_joint_walk_transit_ASC_walk_social-4.316912e-11coef_walk_transit_ASC_sr2_othmaint-34.7 0.00
coef_sov_ASC_walk_social-5.943503e-10coef_walk_transit_ASC_sr2_school-3.46 0.00
coef_sr2_ASC_sr2_social-2.952234e-05coef_walk_transit_ASC_sr2_shopping-2.57 0.00
coef_sr2_ASC_walk_social2.268310e-05coef_walk_transit_ASC_sr2_social-2.27 0.00
coef_sr3p_ASC_sr2_social1.341521e-05coef_walk_transit_ASC_sr2_univ-4.00 0.00
coef_sr3p_ASC_sr3p_social3.699722e-05coef_walk_transit_ASC_sr2_work-2.58 0.00
coef_sr3p_ASC_walk_social1.658572e-05coef_walk_transit_ASC_sr3p_eatout-4.64 0.00
coef_walk_transit_ASC_sr2_social2.083999e-04coef_walk_transit_ASC_sr3p_escort-4.04 0.00
coef_walk_transit_ASC_sr3p_social-4.566934e-12coef_walk_transit_ASC_sr3p_othdiscr-1.89 0.00
coef_walk_transit_ASC_walk_social-1.676922e-04coef_walk_transit_ASC_sr3p_othmaint-29.4 0.00
coef_bike_ASC_walk_othdiscr5.983509e-05coef_walk_transit_ASC_sr3p_school-3.51 0.00
coef_joint_bike_ASC_walk_othdiscr0.000000e+00coef_walk_transit_ASC_sr3p_shopping-2.48 0.00
coef_joint_walk_transit_ASC_sr2_othdiscr0.000000e+00coef_walk_transit_ASC_sr3p_social-21.6 0.00
coef_joint_walk_transit_ASC_sr3p_othdiscr-5.170098e-09coef_walk_transit_ASC_sr3p_univ-28.7 0.00
coef_joint_walk_transit_ASC_walk_othdiscr-8.676475e-14coef_walk_transit_ASC_sr3p_work-3.23 0.00
coef_sov_ASC_walk_othdiscr4.282980e-06coef_walk_transit_ASC_walk_eatout 1.42 0.00
coef_sr2_ASC_sr2_othdiscr-6.122804e-05coef_walk_transit_ASC_walk_escort-1.54 0.00
coef_sr2_ASC_walk_othdiscr-2.831322e-05coef_walk_transit_ASC_walk_othdiscr-0.863 0.00
coef_sr3p_ASC_sr2_othdiscr-4.358718e-05coef_walk_transit_ASC_walk_othmaint 0.213 0.00
coef_sr3p_ASC_sr3p_othdiscr-8.122719e-06coef_walk_transit_ASC_walk_school-1.57 0.00
coef_sr3p_ASC_walk_othdiscr4.957928e-05coef_walk_transit_ASC_walk_shopping-1.25 0.00
coef_walk_transit_ASC_sr2_othdiscr8.887145e-06coef_walk_transit_ASC_walk_social-0.846 0.00
coef_walk_transit_ASC_sr3p_othdiscr2.325113e-05coef_walk_transit_ASC_walk_univ-1.03 0.00
coef_walk_transit_ASC_walk_othdiscr-4.684099e-05coef_walk_transit_ASC_walk_work 0.296 0.00
nit110nfev255njev110status0message'Optimization terminated successfully'successTrueelapsed_time0:02:12.823819method'SLSQP'n_cases13279iteration_number110logloss0.5679149515725046" - ], - "text/plain": [ - "┣ x: -999 -999.000000\n", - "┃ 1 1.000000\n", - "┃ coef_age010_trn 0.360551\n", - "┃ coef_age1619_da 0.344530\n", - "┃ coef_age16p_sr -0.151886\n", - "┃ ... \n", - "┃ coef_sr3p_ASC_sr3p_othdiscr 0.844944\n", - "┃ coef_sr3p_ASC_walk_othdiscr -5.841028\n", - "┃ coef_walk_transit_ASC_sr2_othdiscr -1.232224\n", - "┃ coef_walk_transit_ASC_sr3p_othdiscr -1.199264\n", - "┃ coef_walk_transit_ASC_walk_othdiscr -0.980497\n", - "┃ Length: 272, dtype: float64\n", - "┣ loglike: -7541.342641931288\n", - "┣ d_loglike: -999 0.000000\n", - "┃ 1 0.000000\n", - "┃ coef_age010_trn 0.000007\n", - "┃ coef_age1619_da -0.000020\n", - "┃ coef_age16p_sr -0.001532\n", - "┃ ... \n", - "┃ coef_sr3p_ASC_sr3p_othdiscr -0.000008\n", - "┃ coef_sr3p_ASC_walk_othdiscr 0.000050\n", - "┃ coef_walk_transit_ASC_sr2_othdiscr 0.000009\n", - "┃ coef_walk_transit_ASC_sr3p_othdiscr 0.000023\n", - "┃ coef_walk_transit_ASC_walk_othdiscr -0.000047\n", - "┃ Length: 272, dtype: float64\n", - "┣ nit: 110\n", - "┣ nfev: 255\n", - "┣ njev: 110\n", - "┣ status: 0\n", - "┣ message: 'Optimization terminated successfully'\n", - "┣ success: True\n", - "┣ elapsed_time: datetime.timedelta(seconds=132, microseconds=823819)\n", - "┣ method: 'SLSQP'\n", - "┣ n_cases: 13279\n", - "┣ iteration_number: 110\n", - "┣ logloss: 0.5679149515725046" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.maximize_loglike(method='SLSQP', options={\"maxiter\": 1000})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Estimated coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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Value Null Value
-999-999.-999.00
1 1.00 1.00
coef_age010_trn 0.361 0.00
coef_age1619_da 0.345 0.00
coef_age16p_sr-0.152 0.00
coef_bike_ASC_rh-7.00 0.00
coef_bike_ASC_walk_work-2.66 0.00
coef_drive_transit_ASC_commuter_work 0.427 0.00
coef_drive_transit_ASC_express_work-0.355 0.00
coef_drive_transit_ASC_ferry_work 0.528 0.00
coef_drive_transit_ASC_heavyrail_work-12.0 0.00
coef_drive_transit_ASC_lightrail_work-10.3 0.00
coef_drive_transit_ASC_rh-162. 0.00
coef_hhsize1_sr-0.891 0.00
coef_hhsize2_sr 0.0397 0.00
coef_ivt_work-0.0102 0.00
coef_joint_auto_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_joint_auto_ASC_sr2_work_univ_school_escort_atwork 0.00 0.00
coef_joint_auto_ASC_sr3p_work_univ_school_escort_atwork 0.00 0.00
coef_joint_auto_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_joint_bike_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_joint_bike_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_joint_drive_transit_ASC_commuter_work_univ_school_escort_atwork 0.00 0.00
coef_joint_drive_transit_ASC_express_work_univ_school_escort_atwork 0.00 0.00
coef_joint_drive_transit_ASC_ferry_work_univ_school_escort_atwork 0.00 0.00
coef_joint_drive_transit_ASC_heavyrail_work_univ_school_escort_atwork 0.00 0.00
coef_joint_drive_transit_ASC_lightrail_work_univ_school_escort_atwork 0.00 0.00
coef_joint_drive_transit_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_joint_ride_hail_ASC_sr2_work_univ_school_escort_atwork 0.00 0.00
coef_joint_ride_hail_ASC_sr3p_work_univ_school_escort_atwork 0.00 0.00
coef_joint_ride_hail_ASC_taxi_work_univ_school_escort_atwork 0.00 0.00
coef_joint_ride_hail_ASC_tnc_shared 0.00 0.00
coef_joint_ride_hail_ASC_tnc_single_work_univ_school_escort_atwork 0.00 0.00
coef_joint_ride_hail_ASC_walk_transit 0.00 0.00
coef_joint_ride_hail_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_commuter_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_express_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_ferry_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_heavyrail_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_lightrail_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_rh_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_sr2_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_sr3p_work_univ_school_escort_atwork 0.00 0.00
coef_joint_walk_transit_ASC_walk_work_univ_school_escort_atwork 0.00 0.00
coef_nest_AUTO 0.720 1.00
coef_nest_AUTO_DRIVEALONE 0.350 1.00
coef_nest_AUTO_SHAREDRIDE2 0.350 1.00
coef_nest_AUTO_SHAREDRIDE3 0.350 1.00
coef_nest_NONMOTORIZED 0.720 1.00
coef_nest_RIDEHAIL 0.360 1.00
coef_nest_TRANSIT 0.720 1.00
coef_nest_TRANSIT_DRIVEACCESS 0.500 1.00
coef_nest_TRANSIT_WALKACCESS 0.500 1.00
coef_ride_hail_ASC_sr2_work-25.0 0.00
coef_ride_hail_ASC_sr3p_work_escort_shopping_othmaint_atwork-29.7 0.00
coef_ride_hail_ASC_taxi_work-2.52 0.00
coef_ride_hail_ASC_tnc_shared 23.3 0.00
coef_ride_hail_ASC_tnc_single_work 24.0 0.00
coef_ride_hail_ASC_walk_transit 22.1 0.00
coef_ride_hail_ASC_walk_work 21.8 0.00
coef_sov_ASC_rh_work_atwork-7.00 0.00
coef_sov_ASC_sr2_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork-999. 0.00
coef_sov_ASC_sr3p_work_univ_school_shopping_eatout_othmaint_social_othdiscr_atwork-999. 0.00
coef_sov_ASC_walk_work-3.01 0.00
coef_sr2_ASC_rh_work_school_escort_shopping_eatout_othmaint_social_othdiscr_atwork-7.00 0.00
coef_sr2_ASC_sr2_work 0.197 0.00
coef_sr2_ASC_sr3p-999. 0.00
coef_sr2_ASC_walk_work-2.22 0.00
coef_sr3p_ASC_rh_work-71.9 0.00
coef_sr3p_ASC_sr2_work-0.954 0.00
coef_sr3p_ASC_sr3p_work 0.00228 0.00
coef_sr3p_ASC_walk_work-2.36 0.00
coef_walk_ASC_rh-7.00 0.00
coef_walk_transit_ASC_commuter_work 0.401 0.00
coef_walk_transit_ASC_express_work-0.355 0.00
coef_walk_transit_ASC_ferry_work 0.528 0.00
coef_walk_transit_ASC_heavyrail_work 0.503 0.00
coef_walk_transit_ASC_lightrail_work 0.633 0.00
coef_walk_transit_ASC_rh_work-2.87 0.00
coef_walk_transit_ASC_sr2_work-2.26 0.00
coef_walk_transit_ASC_sr3p_work-2.65 0.00
coef_walk_transit_ASC_walk_work-0.391 0.00
coef_bike_ASC_walk_univ-0.340 0.00
coef_drive_transit_ASC_commuter_univ_school 0.908 0.00
coef_drive_transit_ASC_express_univ_school 0.322 0.00
coef_drive_transit_ASC_ferry_univ_school 1.72 0.00
coef_drive_transit_ASC_heavyrail_univ_school 0.849 0.00
coef_drive_transit_ASC_lightrail_univ_school 1.44 0.00
coef_ivt_univ_school-0.00827 0.00
coef_ride_hail_ASC_sr2_univ-4.34 0.00
coef_ride_hail_ASC_sr3p_univ-4.92 0.00
coef_ride_hail_ASC_taxi_univ-1.60 0.00
coef_ride_hail_ASC_tnc_single_univ 0.108 0.00
coef_ride_hail_ASC_walk_univ_escort_shopping_othmaint-26.3 0.00
coef_sov_ASC_rh_univ-6.65 0.00
coef_sov_ASC_walk_univ-1.06 0.00
coef_sr2_ASC_rh_univ-6.69 0.00
coef_sr2_ASC_sr2_univ 0.430 0.00
coef_sr2_ASC_walk_univ 1.30 0.00
coef_sr3p_ASC_rh_univ_escort_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_sr3p_ASC_sr2_univ-0.312 0.00
coef_sr3p_ASC_sr3p_univ 0.629 0.00
coef_sr3p_ASC_walk_univ 1.68 0.00
coef_walk_transit_ASC_commuter_univ_school 0.908 0.00
coef_walk_transit_ASC_express_univ_school 0.322 0.00
coef_walk_transit_ASC_ferry_univ_school 1.72 0.00
coef_walk_transit_ASC_heavyrail_univ_school 0.867 0.00
coef_walk_transit_ASC_lightrail_univ_school 1.57 0.00
coef_walk_transit_ASC_rh_univ-4.27 0.00
coef_walk_transit_ASC_sr2_univ-4.00 0.00
coef_walk_transit_ASC_sr3p_univ-28.7 0.00
coef_walk_transit_ASC_walk_univ-1.03 0.00
coef_bike_ASC_walk_school-106. 0.00
coef_ride_hail_ASC_sr2_school-17.6 0.00
coef_ride_hail_ASC_sr3p_school-17.9 0.00
coef_ride_hail_ASC_taxi_school-1.64 0.00
coef_ride_hail_ASC_tnc_single_school 23.7 0.00
coef_ride_hail_ASC_walk_school-17.9 0.00
coef_sov_ASC_rh_school-126. 0.00
coef_sov_ASC_walk_school-3.12 0.00
coef_sr2_ASC_sr2_school-0.541 0.00
coef_sr2_ASC_walk_school-2.04 0.00
coef_sr3p_ASC_rh_school-168. 0.00
coef_sr3p_ASC_sr2_school-1.02 0.00
coef_sr3p_ASC_sr3p_school 0.193 0.00
coef_sr3p_ASC_walk_school-2.44 0.00
coef_walk_transit_ASC_rh_school-4.36 0.00
coef_walk_transit_ASC_sr2_school-2.27 0.00
coef_walk_transit_ASC_sr3p_school-2.41 0.00
coef_walk_transit_ASC_walk_school-1.50 0.00
coef_bike_ASC_walk_escort-13.5 0.00
coef_drive_transit_ASC_commuter_escort 0.544 0.00
coef_drive_transit_ASC_express_escort 0.755 0.00
coef_drive_transit_ASC_ferry_escort 0.601 0.00
coef_drive_transit_ASC_heavyrail_escort-14.2 0.00
coef_drive_transit_ASC_lightrail_escort 0.525 0.00
coef_ivt_escort_shopping_eatout_othdiscr_atwork-0.0119 0.00
coef_ride_hail_ASC_sr2_escort_shopping_othmaint-23.9 0.00
coef_ride_hail_ASC_taxi_escort_shopping_othmaint-3.66 0.00
coef_ride_hail_ASC_tnc_single_escort_shopping_othmaint 24.2 0.00
coef_sov_ASC_rh_escort_shopping_eatout_othmaint_social_othdiscr 0.00 0.00
coef_sov_ASC_sr2_escort 0.00 0.00
coef_sov_ASC_sr3p_escort 0.00 0.00
coef_sov_ASC_walk_escort 0.00 0.00
coef_sr2_ASC_sr2_escort 0.488 0.00
coef_sr2_ASC_walk_escort-25.5 0.00
coef_sr3p_ASC_sr2_escort-0.207 0.00
coef_sr3p_ASC_sr3p_escort 0.896 0.00
coef_sr3p_ASC_walk_escort-3.42 0.00
coef_walk_transit_ASC_commuter_escort 0.544 0.00
coef_walk_transit_ASC_express_escort 0.755 0.00
coef_walk_transit_ASC_ferry_escort 0.601 0.00
coef_walk_transit_ASC_heavyrail_escort 0.850 0.00
coef_walk_transit_ASC_lightrail_escort 1.13 0.00
coef_walk_transit_ASC_rh_escort_shopping_othmaint-2.36 0.00
coef_walk_transit_ASC_sr2_escort-1.02 0.00
coef_walk_transit_ASC_sr3p_escort-185. 0.00
coef_walk_transit_ASC_walk_escort-88.1 0.00
coef_bike_ASC_walk_shopping-23.2 0.00
coef_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork 0.513 0.00
coef_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork 0.665 0.00
coef_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork 0.647 0.00
coef_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork 0.540 0.00
coef_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork 0.539 0.00
coef_joint_auto_ASC_rh_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_joint_auto_ASC_sr2_shopping 2.78 0.00
coef_joint_auto_ASC_sr3p_shopping-19.1 0.00
coef_joint_auto_ASC_walk_shopping-23.5 0.00
coef_joint_bike_ASC_rh_shopping_eatout_othmaint_social_othdiscr-12.3 0.00
coef_joint_bike_ASC_walk_shopping-7.05 0.00
coef_joint_drive_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr 0.513 0.00
coef_joint_drive_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr 0.665 0.00
coef_joint_drive_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr 0.647 0.00
coef_joint_drive_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr 0.540 0.00
coef_joint_drive_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr 0.539 0.00
coef_joint_drive_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr 4.61 0.00
coef_joint_ride_hail_ASC_sr2_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_joint_ride_hail_ASC_sr3p_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_joint_ride_hail_ASC_taxi_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_joint_ride_hail_ASC_tnc_single_shopping_eatout_othmaint_social_othdiscr-4.73 0.00
coef_joint_ride_hail_ASC_walk_shopping_eatout_othmaint_social_othdiscr-7.00 0.00
coef_joint_walk_ASC_rh_shopping_eatout_othmaint_social_othdiscr-3.04 0.00
coef_joint_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr 0.513 0.00
coef_joint_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr 0.665 0.00
coef_joint_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr 0.647 0.00
coef_joint_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr 21.6 0.00
coef_joint_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr 0.924 0.00
coef_joint_walk_transit_ASC_rh_shopping_eatout_othmaint_social_othdiscr 3.13 0.00
coef_joint_walk_transit_ASC_sr2_shopping-21.6 0.00
coef_joint_walk_transit_ASC_sr3p_shopping-25.6 0.00
coef_joint_walk_transit_ASC_walk_shopping-102. 0.00
coef_sov_ASC_walk_shopping-3.79 0.00
coef_sr2_ASC_sr2_shopping 1.75 0.00
coef_sr2_ASC_walk_shopping-3.65 0.00
coef_sr3p_ASC_sr2_shopping 0.336 0.00
coef_sr3p_ASC_sr3p_shopping 1.64 0.00
coef_sr3p_ASC_walk_shopping-3.92 0.00
coef_walk_transit_ASC_commuter_shopping_eatout_othmaint_social_othdiscr_atwork 0.513 0.00
coef_walk_transit_ASC_express_shopping_eatout_othmaint_social_othdiscr_atwork 0.665 0.00
coef_walk_transit_ASC_ferry_shopping_eatout_othmaint_social_othdiscr_atwork 0.647 0.00
coef_walk_transit_ASC_heavyrail_shopping_eatout_othmaint_social_othdiscr_atwork 0.926 0.00
coef_walk_transit_ASC_lightrail_shopping_eatout_othmaint_social_othdiscr_atwork 0.831 0.00
coef_walk_transit_ASC_sr2_shopping-2.12 0.00
coef_walk_transit_ASC_sr3p_shopping-91.1 0.00
coef_walk_transit_ASC_walk_shopping-0.814 0.00
coef_bike_ASC_walk_eatout-175. 0.00
coef_joint_auto_ASC_sr2_eatout_othmaint_social_othdiscr 0.381 0.00
coef_joint_auto_ASC_sr3p_eatout_othmaint_social_othdiscr 0.389 0.00
coef_joint_auto_ASC_walk_eatout_othmaint_social_othdiscr-4.38 0.00
coef_joint_bike_ASC_walk_eatout-15.6 0.00
coef_joint_walk_transit_ASC_sr2_eatout-14.9 0.00
coef_joint_walk_transit_ASC_sr3p_eatout-16.8 0.00
coef_joint_walk_transit_ASC_walk_eatout 1.10 0.00
coef_ride_hail_ASC_sr2_eatout_social_othdiscr-16.9 0.00
coef_ride_hail_ASC_sr3p_eatout_social_othdiscr-11.9 0.00
coef_ride_hail_ASC_taxi_eatout_social_othdiscr-2.56 0.00
coef_ride_hail_ASC_tnc_single_eatout_social_othdiscr 24.0 0.00
coef_ride_hail_ASC_walk_eatout_social_othdiscr-17.7 0.00
coef_sov_ASC_walk_eatout-2.63 0.00
coef_sr2_ASC_sr2_eatout 1.10 0.00
coef_sr2_ASC_walk_eatout-4.55 0.00
coef_sr3p_ASC_sr2_eatout-0.213 0.00
coef_sr3p_ASC_sr3p_eatout 1.47 0.00
coef_sr3p_ASC_walk_eatout-2.51 0.00
coef_walk_transit_ASC_rh_eatout_social_othdiscr-2.66 0.00
coef_walk_transit_ASC_sr2_eatout-2.01 0.00
coef_walk_transit_ASC_sr3p_eatout-2.52 0.00
coef_walk_transit_ASC_walk_eatout 0.166 0.00
coef_bike_ASC_walk_othmaint-0.784 0.00
coef_ivt_othmaint_social-0.0121 0.00
coef_joint_bike_ASC_walk_othmaint-13.5 0.00
coef_joint_walk_transit_ASC_sr2_othmaint-11.3 0.00
coef_joint_walk_transit_ASC_sr3p_othmaint-44.0 0.00
coef_joint_walk_transit_ASC_walk_othmaint-73.6 0.00
coef_sov_ASC_walk_othmaint-4.43 0.00
coef_sr2_ASC_sr2_othmaint 0.935 0.00
coef_sr2_ASC_walk_othmaint-3.14 0.00
coef_sr3p_ASC_sr2_othmaint 0.0421 0.00
coef_sr3p_ASC_sr3p_othmaint 1.13 0.00
coef_sr3p_ASC_walk_othmaint-5.07 0.00
coef_walk_transit_ASC_sr2_othmaint-34.7 0.00
coef_walk_transit_ASC_sr3p_othmaint-29.4 0.00
coef_walk_transit_ASC_walk_othmaint-0.288 0.00
coef_bike_ASC_walk_social-13.2 0.00
coef_joint_bike_ASC_walk_social-26.2 0.00
coef_joint_walk_transit_ASC_sr2_social-20.5 0.00
coef_joint_walk_transit_ASC_sr3p_social-34.7 0.00
coef_joint_walk_transit_ASC_walk_social-2.39 0.00
coef_sov_ASC_walk_social-25.3 0.00
coef_sr2_ASC_sr2_social 0.768 0.00
coef_sr2_ASC_walk_social-2.85 0.00
coef_sr3p_ASC_sr2_social-0.122 0.00
coef_sr3p_ASC_sr3p_social 1.21 0.00
coef_sr3p_ASC_walk_social-3.08 0.00
coef_walk_transit_ASC_sr2_social-2.06 0.00
coef_walk_transit_ASC_sr3p_social-21.6 0.00
coef_walk_transit_ASC_walk_social-2.31 0.00
coef_bike_ASC_walk_othdiscr-1.40 0.00
coef_joint_bike_ASC_walk_othdiscr-14.4 0.00
coef_joint_walk_transit_ASC_sr2_othdiscr-15.0 0.00
coef_joint_walk_transit_ASC_sr3p_othdiscr-16.7 0.00
coef_joint_walk_transit_ASC_walk_othdiscr-25.9 0.00
coef_sov_ASC_walk_othdiscr-4.94 0.00
coef_sr2_ASC_sr2_othdiscr 0.856 0.00
coef_sr2_ASC_walk_othdiscr-2.47 0.00
coef_sr3p_ASC_sr2_othdiscr-0.418 0.00
coef_sr3p_ASC_sr3p_othdiscr 0.845 0.00
coef_sr3p_ASC_walk_othdiscr-5.84 0.00
coef_walk_transit_ASC_sr2_othdiscr-1.23 0.00
coef_walk_transit_ASC_sr3p_othdiscr-1.20 0.00
coef_walk_transit_ASC_walk_othdiscr-0.980 0.00
" + "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -5168,7 +5189,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -5189,7 +5210,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -5211,7 +5232,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -5280,7 +5301,7 @@ " \n", " 309\n", " coef_walk_transit_ASC_walk_eatout\n", - " 0.166429\n", + " 1.418767\n", " F\n", " \n", " \n", @@ -5320,7 +5341,7 @@ "3 coef_nest_AUTO_DRIVEALONE 0.350000 T\n", "4 coef_nest_AUTO_SHAREDRIDE2 0.350000 T\n", ".. ... ... ...\n", - "309 coef_walk_transit_ASC_walk_eatout 0.166429 F\n", + "309 coef_walk_transit_ASC_walk_eatout 1.418767 F\n", "310 walk_express_penalty 10.000000 T\n", "311 adjust_tnc_shared 30.000000 T\n", "312 coef_origin_density_applied_work_univ_school 0.000000 T\n", @@ -5329,7 +5350,7 @@ "[314 rows x 3 columns]" ] }, - "execution_count": 19, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -5346,7 +5367,7 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "ESTER", "language": "python", "name": "python3" }, @@ -5360,7 +5381,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.10.15" }, "toc": { "base_numbering": 1, diff --git a/activitysim/examples/example_estimation/notebooks/est_mode_setup.py b/activitysim/examples/example_estimation/notebooks/est_mode_setup.py new file mode 100644 index 0000000000..af0546cbe2 --- /dev/null +++ b/activitysim/examples/example_estimation/notebooks/est_mode_setup.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +import argparse +import os +import shutil +import sys +import warnings +from pathlib import Path + +# suppress RuntimeWarning from xarray +warnings.filterwarnings( + "ignore", + category=RuntimeWarning, + module="xarray", +) + + +def prepare( + household_sample_size: int = 20_000, subdir: str = "test-estimation-data" +) -> Path: + """Prepare the example for estimation. + + This function prepares the example for estimation by downloading the example data and + setting up the working directory. The current working directory is then set to the + created example directory. + + Parameters + ---------- + household_sample_size : int, optional + The number of households to sample from the synthetic population. The default is 20_000. + subdir : str, optional + The subdirectory to store the example data. The default is "test-estimation-data". + + Returns + ------- + Path + The path to the created example directory. + """ + root_dir = Path(__file__).parent.parent.resolve() + sys.path.insert(0, str(root_dir)) + + try: + from build_full_mtc_example import as_needed + except ImportError: + print( + "Please run this script from the " + "activitysim/examples/example_estimation/notebooks directory." + ) + raise + + as_needed(root_dir / "notebooks" / subdir, household_sample_size) + relative_path = os.path.relpath( + root_dir / "notebooks" / subdir / "activitysim-prototype-mtc-extended" + ) + os.chdir(relative_path) + return Path(relative_path) + + +def backup(filename: str | os.PathLike): + """Create or restore from a backup copy of a file.""" + backup_filename = f"{filename}.bak" + if Path(backup_filename).exists(): + shutil.copy(backup_filename, filename) + else: + shutil.copy(filename, backup_filename) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Prepare the example for estimation.") + parser.add_argument( + "--household_sample_size", + type=int, + default=20_000, + help="The number of households to sample from the synthetic population.", + ) + parser.add_argument( + "--subdir", + type=str, + default="test-estimation-data", + help="The subdirectory to store the example data.", + ) + args = parser.parse_args() + prepare(args.household_sample_size, args.subdir) diff --git a/activitysim/examples/example_estimation/scripts/infer.py b/activitysim/examples/example_estimation/scripts/infer.py index 6b6991992f..94869cffb2 100644 --- a/activitysim/examples/example_estimation/scripts/infer.py +++ b/activitysim/examples/example_estimation/scripts/infer.py @@ -1,6 +1,8 @@ # ActivitySim # See full license in LICENSE.txt. +from __future__ import annotations + import logging import os import sys @@ -165,6 +167,8 @@ def read_alts(): alts = read_alts() tour_types = list(alts.columns.values) + if "tot_tours" in tour_types: + tour_types.remove("tot_tours") # tour_frequency is index in alts table alts["alt_id"] = alts.index @@ -806,7 +810,7 @@ def infer(state: workflow.State, configs_dir, input_dir, output_dir): assert skip_controls or check_controls("joint_tour_participants", "index") # patch_tour_ids - trips = patch_trip_ids(tours, trips) + trips = patch_trip_ids(state, tours, trips) survey_tables["trips"]["table"] = trips # so we can check_controls assert skip_controls or check_controls("trips", "index") @@ -846,18 +850,27 @@ def infer(state: workflow.State, configs_dir, input_dir, output_dir): # python infer.py data args = sys.argv[1:] -assert len(args) == 2, "usage: python infer.py " +assert len(args) == 3, "usage: python infer.py " data_dir = args[0] configs_dir = args[1] +output_dir = args[2] with open(os.path.join(configs_dir, "constants.yaml")) as stream: CONSTANTS = yaml.load(stream, Loader=yaml.SafeLoader) input_dir = os.path.join(data_dir, "survey_data/") -output_dir = input_dir if apply_controls: read_tables(input_dir, control_tables) +state = ( + workflow.State() + .initialize_filesystem( + configs_dir=(configs_dir,), + output_dir=output_dir, + data_dir=(data_dir,), + ) + .load_settings() +) infer(state, configs_dir, input_dir, output_dir) diff --git a/activitysim/examples/example_manifest.yaml b/activitysim/examples/example_manifest.yaml index 769dad925a..c3e8ededa7 100644 --- a/activitysim/examples/example_manifest.yaml +++ b/activitysim/examples/example_manifest.yaml @@ -508,6 +508,7 @@ include: - production_semcog/extensions - production_semcog/data + - production_semcog/data_model - production_semcog/configs - production_semcog/configs_mp - production_semcog/output @@ -618,220 +619,6 @@ data/land_use.csv 36383b4c9dacdd7dd10c84b4479aa9f53ced23e155b6d354c468decc6710c80e -- name: placeholder_sandag_1_zone - description: 90-TAZ (includes univ) 1-zone test example for the SANDAG region - # activitysim create -e placeholder_sandag_1_zone -d test_placeholder_sandag_1_zone - # cd test_placeholder_sandag_1_zone - # activitysim run -c configs_1_zone -c prototype_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml - # cd .. - include: - - placeholder_sandag/../prototype_mtc/configs - prototype_mtc - - placeholder_sandag/configs_1_zone - - placeholder_sandag/data_1 - - placeholder_sandag/output_1 - -- name: placeholder_sandag_1_zone_full - description: full 1-zone example for the SANDAG region - # activitysim create -e placeholder_sandag_1_zone_full -d test_placeholder_sandag_1_zone_full - # cd test_placeholder_sandag_1_zone_full - # activitysim run -c configs_1_zone -c prototype_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml - # cd .. - include: - - placeholder_sandag/../prototype_mtc/configs - prototype_mtc - - placeholder_sandag/configs_1_zone - # placeholder_sandag/data_1 # load data from activitysim_resources instead - - placeholder_sandag/output_1 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/households.csv - data_1/households.csv - 2b0b19a1a0b00901e80a533fc6418a4dbeb3d0b017d9feadae71324f9145dcec - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/persons.csv - data_1/persons.csv - 7864364ef70ab3f490570768da12984593da81921a0cd3f8e4b0d724ae436f7a - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/land_use.csv - data_1/land_use.csv - 2088fd1375a6a5ee253cc11da5f1b9171a9e0b788896589197ecd41ded35edd0 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/skims1.omx - data_1/skims1.omx - 33cd691c51b205e0c2bd09dba5612cd05862127f3c8ffc83c4fd533e83a0817b - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/skims2.omx - data_1/skims2.omx - e650d70aeb932a1452f636088af599abe6e66dd0843a045130d8e683601b5a82 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/skims3.omx - data_1/skims3.omx - 9940974c5cde572ad88e1e1d3db7ba7e7954084599f65aac1ebe575233cde68d - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/skims4.omx - data_1/skims4.omx - 586cb610b15d698e5ebbb6ba02847c405b5ed6b65c24a01cd812684cf665631c - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/skims5.omx - data_1/skims5.omx - c3584e8c413d63ee38847ece0cc665c969a2152480bd4ca71dfbe9e91b59b2bd - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/skims6.omx - data_1/skims6.omx - e3f7fb1e0b334fc83dc69985dce8b925e3660149c5de5635c7d8464e306074b9 - -- name: placeholder_sandag_2_zone - description: 90-TAZ 609-MAZ (includes univ) 2-zone test example for the SANDAG region - # activitysim create -e placeholder_sandag_2_zone -d test_placeholder_sandag_2_zone - # cd test_placeholder_sandag_2_zone - # activitysim run -c configs_2_zone -c placeholder_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml - # cd .. - include: - - placeholder_sandag/../placeholder_psrc/configs - placeholder_psrc - - placeholder_sandag/configs_2_zone - - placeholder_sandag/data_2 - - placeholder_sandag/data_3/maz_to_maz_bike.csv - data_2/maz_to_maz_bike.csv - # the original data for data_2 was corrupted, but there's no need to store this file twice in the repo - - placeholder_sandag/output_2 - -- name: placeholder_sandag_2_zone_full - description: full 2-zone example for the SANDAG region - # activitysim create -e placeholder_sandag_2_zone_full -d test_placeholder_sandag_2_zone_full - # cd test_placeholder_sandag_2_zone_full - # activitysim run -c configs_2_zone -c placeholder_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml - # cd .. - include: - - placeholder_sandag/../placeholder_psrc/configs - placeholder_psrc - - placeholder_sandag/configs_2_zone - # placeholder_sandag/data_2 # load data from activitysim_resources instead - - placeholder_sandag/output_2 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/households.csv - data_2/households.csv - 4afe40dfbdb89f17cd150fd5b32245364d538cdebb91fd323e18c6f570d1a087 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/land_use.csv - data_2/land_use.csv - e914b54b16000af84d2e49e27697898028a7c007a5792812c7274ce33e1dc064 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/maz.csv - data_2/maz.csv - d9d908adcbe1e56fbb6d50665e767c317cdfbeedeae675dda449206f2a7fb3f1 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/maz_to_maz_bike.csv - data_2/maz_to_maz_bike.csv - c68322fff0ff32a7d487b7cd8dd1e205192d2728621c9cc541e8c41722293cbc - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/maz_to_maz_walk.csv - data_2/maz_to_maz_walk.csv - 76c702d8ded8ae9eb7ae978ffa35c1e74ae6264dd2f90d96cdaa42d49c54684a - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/persons.csv - data_2/persons.csv - 7864364ef70ab3f490570768da12984593da81921a0cd3f8e4b0d724ae436f7a - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/taz.csv - data_2/taz.csv - ac9fa2f5adc13e90b1fa5537444f25b9ea389bdfe56e7e9f1ef3be9eb63014ef - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/skims1.omx - 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data_2/skims6.omx - 77a82afa09ee1f4c48aa7928e96756525d170402ca99da04cdf676e45cb6fbcf - -- name: placeholder_sandag_3_zone - description: 90-TAZ 609-MAZ (includes univ) 3-zone test example for the SANDAG region - # activitysim create -e placeholder_sandag_3_zone -d test_placeholder_sandag_3_zone - # cd test_placeholder_sandag_3_zone - # activitysim run -c configs_3_zone -c prototype_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml - # cd .. - include: - - placeholder_sandag/data_3 - - placeholder_sandag/../prototype_mtc/configs - prototype_mtc - - placeholder_sandag/configs_3_zone - - placeholder_sandag/configs_skip_accessibility - - placeholder_sandag/output_3 - subdirs: - configs_dir: - - configs_3_zone - - prototype_mtc/configs - data_dir: data_3 - output_dir: output_3 - -- name: placeholder_sandag_3_zone_full - description: full 3-zone example for the SANDAG region - # activitysim create -e placeholder_sandag_3_zone_full -d test_placeholder_sandag_3_zone_full - # cd test_placeholder_sandag_3_zone_full - # activitysim run -c configs_3_zone -c prototype_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml - # cd .. - include: - # placeholder_sandag/data_3 # load data from activitysim_resources instead - - placeholder_sandag/../prototype_mtc/configs - prototype_mtc - - placeholder_sandag/configs_3_zone - - placeholder_sandag/configs_skip_accessibility - - placeholder_sandag/output_3 - - placeholder_sandag/data_3/cached_accessibility.csv.gz - data_3/cached_accessibility.csv.gz - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/taz_skims1.omx - data_3/taz_skims1.omx - 5b56d0e79ec671e37f8c71f7fedd741d7bf32d2bced866ab1f03f3973fccce8c - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/taz_skims2.omx - data_3/taz_skims2.omx - fbdd68ba7f8c82914b47da885326da35499293fd9097f5928b5247bbf8f70376 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/taz_skims3.omx - data_3/taz_skims3.omx - 57e2e72e4e5ab19ecccc240df8223fc6433d315ca710dca975f81f32dc25e92d - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/taz_skims4.omx - data_3/taz_skims4.omx - f68cf6cf0b5c8e58a19d8ba9ea6cc39abcce0af417f635d6ccffdd28dae50779 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/tap_skims1.omx - data_3/tap_skims1.omx - a26f38efab2f88a5923938b7572e47badac487a54d4819616bb5ccc5c19a6eb0 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/tap_skims2.omx - data_3/tap_skims2.omx - a0aef494023763572674acbe755d665db100c1d18f664b464c514b30503824d9 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/tap_skims3.omx - data_3/tap_skims3.omx - a33659454e215402618a352259398eed59a8e42991fd8890e267e563a0730853 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/tap_skims4.omx - data_3/tap_skims4.omx - f905398e850ed005df5647de46f13eb976ba3c313f2f7aea1f71c369c9004c7f - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/households.csv - data_3/households.csv - e504d391c19367b86a09074bbabeb9149a419a8e07f98cd7e380f98b399c1cd8 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/persons.csv - data_3/persons.csv - 7864364ef70ab3f490570768da12984593da81921a0cd3f8e4b0d724ae436f7a - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/land_use.csv - data_3/land_use.csv - d773c95d08d24962d53f54929080bacb91993c01925350b0d92a32f18b66d790 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/maz.csv - data_3/maz.csv - c8162a6f38541f62ed706529c062bd0c4158bbcfbc1f65d787b506a5ad218dda - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/taz.csv - data_3/taz.csv - ac9fa2f5adc13e90b1fa5537444f25b9ea389bdfe56e7e9f1ef3be9eb63014ef - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/maz_to_maz_bike.csv - data_3/maz_to_maz_bike.csv - 39199f42334c65f1eadf2c47213ee8a10d83eb0bac4a7447a6fbd165d9065e11 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/maz_to_maz_walk.csv - data_3/maz_to_maz_walk.csv - 8cf6cd05e95ed08a7501229ed8ec515d83fd2904d407ecc350f1867ef2a9b378 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/maz_to_tap_walk.csv - data_3/maz_to_tap_walk.csv - 2d0e4c49f7d94f8d15ee9a5d39d9dc9c9ab10cfe6259a835284053c84cef94d5 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/maz_to_tap_drive.csv - data_3/maz_to_tap_drive.csv - bd4c4ef6a87c0c3db18615f6c2dcf6e863aac62afce48f627ffa142c9c4e9789 - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/tap.csv - data_3/tap.csv - 9b74d18d681a1c61708dd5767d90d7872443a0ef7be189f802c3905592f3c8da - - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/tap_lines.csv - data_3/tap_lines.csv - 0e1b2c532e5e85b48e2ac77b2836be7ec0cc7cba79907c6f5fb11d2ba171230a - - name: prototype_sandag_xborder description: SANDAG cross border travel model # activitysim create -e prototype_sandag_xborder -d test_prototype_sandag_xborder diff --git a/activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py index 7291c65337..1ce3861681 100644 --- a/activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py +++ b/activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py @@ -149,13 +149,15 @@ tap_df.to_csv(os.path.join(output_data, "tap.csv"), index=False) # create taz_z3 and tap skims -with omx.open_file( - os.path.join(input_data, "skims.omx"), "r" -) as ur_skims, omx.open_file( - os.path.join(output_data, "taz_skims.omx"), "w" -) as output_taz_skims_file, omx.open_file( - os.path.join(output_data, "tap_skims.omx"), "w" -) as output_tap_skims_file: +with ( + omx.open_file(os.path.join(input_data, "skims.omx"), "r") as ur_skims, + omx.open_file( + os.path.join(output_data, "taz_skims.omx"), "w" + ) as output_taz_skims_file, + omx.open_file( + os.path.join(output_data, "tap_skims.omx"), "w" + ) as output_tap_skims_file, +): for skim_name in ur_skims.list_matrices(): ur_skim = ur_skims[skim_name][:] new_skim = ur_skim[taz_zone_indexes, :][:, taz_zone_indexes] diff --git a/activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py index a4c6c46fcb..20c7bccf53 100644 --- a/activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py +++ b/activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py @@ -104,11 +104,10 @@ # ### Create taz skims -with omx.open_file( - os.path.join(input_data, "skims.omx"), "r" -) as skims_file, omx.open_file( - os.path.join(output_data, "taz_skims.omx"), "w" -) as output_skims_file: +with ( + omx.open_file(os.path.join(input_data, "skims.omx"), "r") as skims_file, + omx.open_file(os.path.join(output_data, "taz_skims.omx"), "w") as output_skims_file, +): skims = skims_file.list_matrices() num_zones = skims_file.shape()[0] diff --git a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py index 46aa8bf466..3e956301e9 100644 --- a/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py +++ b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py @@ -2,13 +2,13 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess import sys from pathlib import Path import pandas as pd -import pkg_resources import pytest from activitysim.core import test, workflow @@ -16,12 +16,12 @@ def example_path(dirname): resource = os.path.join("examples", "placeholder_multiple_zone", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def mtc_example_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def build_data(): diff --git a/activitysim/examples/placeholder_multiple_zone/three_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/three_zone_example_data.py index 0a43cce220..54224087d6 100644 --- a/activitysim/examples/placeholder_multiple_zone/three_zone_example_data.py +++ b/activitysim/examples/placeholder_multiple_zone/three_zone_example_data.py @@ -143,13 +143,15 @@ tap_df.to_csv(os.path.join(output_data, "tap.csv"), index=False) # create taz_z3 and tap skims -with omx.open_file( - os.path.join(input_data, "skims.omx"), "r" -) as ur_skims, omx.open_file( - os.path.join(output_data, "taz_skims.omx"), "w" -) as output_taz_skims_file, omx.open_file( - os.path.join(output_data, "tap_skims.omx"), "w" -) as output_tap_skims_file: +with ( + omx.open_file(os.path.join(input_data, "skims.omx"), "r") as ur_skims, + omx.open_file( + os.path.join(output_data, "taz_skims.omx"), "w" + ) as output_taz_skims_file, + omx.open_file( + os.path.join(output_data, "tap_skims.omx"), "w" + ) as output_tap_skims_file, +): for skim_name in ur_skims.list_matrices(): ur_skim = ur_skims[skim_name][:] new_skim = ur_skim[taz_zone_indexes, :][:, taz_zone_indexes] diff --git a/activitysim/examples/placeholder_multiple_zone/two_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/two_zone_example_data.py index fbb26e2aaa..a6f5750a0f 100644 --- a/activitysim/examples/placeholder_multiple_zone/two_zone_example_data.py +++ b/activitysim/examples/placeholder_multiple_zone/two_zone_example_data.py @@ -101,11 +101,10 @@ # ### Create taz skims -with omx.open_file( - os.path.join(input_data, "skims.omx"), "r" -) as skims_file, omx.open_file( - os.path.join(output_data, "taz_skims.omx"), "w" -) as output_skims_file: +with ( + omx.open_file(os.path.join(input_data, "skims.omx"), "r") as skims_file, + omx.open_file(os.path.join(output_data, "taz_skims.omx"), "w") as output_skims_file, +): skims = skims_file.list_matrices() num_zones = skims_file.shape()[0] diff --git a/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv index 8cc5fb59e8..039fa9e66d 100755 --- a/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv @@ -33,7 +33,7 @@ util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interact util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) * _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early -util_departure_constants_am_peak_1 _6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_1_6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 util_departure_constants_am_peak_2_7,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 util_departure_constants_am_peak_3_8,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 util_departure_constants_am_peak_4_9,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 diff --git a/activitysim/examples/placeholder_sandag/test/test_sandag.py b/activitysim/examples/placeholder_sandag/test/test_sandag.py deleted file mode 100644 index cda0d08725..0000000000 --- a/activitysim/examples/placeholder_sandag/test/test_sandag.py +++ /dev/null @@ -1,383 +0,0 @@ -from __future__ import annotations - -# ActivitySim -# See full license in LICENSE.txt. -import os -import shutil -import subprocess -import sys -from pathlib import Path - -import pandas as pd -import pkg_resources -import pytest - -from activitysim.core import configuration, test, workflow - - -def example_path(dirname): - resource = os.path.join("examples", "placeholder_sandag", dirname) - return pkg_resources.resource_filename("activitysim", resource) - - -def mtc_example_path(dirname): - resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) - - -def psrc_example_path(dirname): - resource = os.path.join("examples", "placeholder_psrc", dirname) - return pkg_resources.resource_filename("activitysim", resource) - - -def build_data(): - shutil.copy( - example_path(os.path.join("data_3", "maz_to_maz_bike.csv")), - example_path(os.path.join("data_2", "maz_to_maz_bike.csv")), - ) - - -@pytest.fixture(scope="module") -def data(): - build_data() - - -def run_test(zone, multiprocess=False, sharrow=False, recode=True): - def test_path(dirname): - return os.path.join(os.path.dirname(__file__), dirname) - - def regress(zone): - # ## regress tours - if sharrow and os.path.isfile( - test_path(f"regress/final_{zone}_zone_tours_sh.csv") - ): - regress_tours_df = pd.read_csv( - test_path(f"regress/final_{zone}_zone_tours_sh.csv") - ) - else: - regress_tours_df = pd.read_csv( - test_path(f"regress/final_{zone}_zone_tours.csv") - ) - tours_df = pd.read_csv(test_path(f"output_{zone}/final_{zone}_zone_tours.csv")) - tours_df.to_csv( - test_path(f"regress/final_{zone}_zone_tours_last_run.csv"), index=False - ) - print("regress tours") - test.assert_frame_substantively_equal( - tours_df, regress_tours_df, rtol=1e-03, check_dtype=False - ) - - # ## regress trips - if sharrow and os.path.isfile( - test_path(f"regress/final_{zone}_zone_trips_sh.csv") - ): - regress_trips_df = pd.read_csv( - test_path(f"regress/final_{zone}_zone_trips_sh.csv") - ) - else: - regress_trips_df = pd.read_csv( - test_path(f"regress/final_{zone}_zone_trips.csv") - ) - trips_df = pd.read_csv(test_path(f"output_{zone}/final_{zone}_zone_trips.csv")) - trips_df.to_csv( - test_path(f"regress/final_{zone}_zone_trips_last_run.csv"), index=False - ) - print("regress trips") - test.assert_frame_substantively_equal( - trips_df, regress_trips_df, rtol=1e-03, check_dtype=False - ) - - if zone == "2": - # also test accessibility for the 2-zone system - regress_accessibility_df = pd.read_csv( - test_path( - f"regress/final_{zone}_zone_proto_disaggregate_accessibility.csv" - ) - ) - final_accessibility_df = pd.read_csv( - test_path( - f"output_{zone}/final_{zone}_zone_proto_disaggregate_accessibility.csv" - ) - ) - final_accessibility_df = final_accessibility_df[ - [ - c - for c in final_accessibility_df.columns - if not c.startswith("_original_") - ] - ] - test.assert_frame_substantively_equal( - final_accessibility_df, - regress_accessibility_df, - check_dtype=False, - ) - - # run test - file_path = os.path.join(os.path.dirname(__file__), "simulation.py") - - if zone == "2": - base_configs = psrc_example_path("configs") - else: - base_configs = mtc_example_path("configs") - - run_args = [ - "-c", - test_path(f"configs_{zone}_zone"), - "-c", - example_path(f"configs_{zone}_zone"), - "-c", - base_configs, - "-d", - example_path(f"data_{zone}"), - "-o", - test_path(f"output_{zone}"), - ] - - if multiprocess: - run_args = run_args + ["-s", "settings_mp.yaml"] - elif not recode: - run_args = run_args + ["-s", "settings_no_recode.yaml"] - - if sharrow: - run_args = ["-c", test_path(f"configs_{zone}_sharrow")] + run_args - - try: - subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) - except FileNotFoundError: - subprocess.run([sys.executable, file_path] + run_args, check=True) - from tempfile import TemporaryFile - from time import sleep - - with TemporaryFile() as outputstream: - env = os.environ.copy() - pythonpath = env.pop("PYTHONPATH", None) - process = subprocess.Popen( - args=[sys.executable, file_path] + run_args, - shell=True, - stdout=outputstream, - stderr=subprocess.STDOUT, - cwd=os.getcwd(), - env=env, - ) - while process.poll() is None: - where = outputstream.tell() - lines = outputstream.read() - if not lines: - # Adjust the sleep interval to your needs - sleep(0.25) - # make sure pointing to the last place we read - outputstream.seek(where) - else: - # Windows adds an extra carriage return and then chokes on - # it when displaying (or, as it were, not displaying) the - # output. So we give Windows a little helping hand. - print(lines.decode().replace("\r\n", "\n"), end="") - - regress(zone) - - -def test_1_zone(data): - run_test(zone="1", multiprocess=False) - - -def test_1_zone_mp(data): - run_test(zone="1", multiprocess=True) - - -def test_1_zone_sharrow(data): - # Run both single and MP in one test function - # guarantees that compile happens in single - run_test(zone="1", multiprocess=False, sharrow=True) - run_test(zone="1", multiprocess=True, sharrow=True) - - -def test_2_zone(data): - run_test(zone="2", multiprocess=False) - - -def test_2_zone_local_compute(data): - def _test_path(dirname): - return os.path.join(os.path.dirname(__file__), dirname) - - import activitysim.abm # register components # noqa: F401 - - state = workflow.State.make_default( - data_dir=example_path("data_2"), - configs_dir=( - _test_path("configs_2_zone"), - example_path("configs_2_zone"), - psrc_example_path("configs"), - ), - output_dir=_test_path("output_2"), - ) - state.run.all(resume_after=None) - # ## regress tours - regress_tours_df = pd.read_csv(_test_path(f"regress/final_2_zone_tours.csv")) - tours_df = pd.read_csv(_test_path(f"output_2/final_2_zone_tours.csv")) - tours_df.to_csv( - _test_path(f"regress/final_2_zone_tours_last_run_localcompute.csv"), index=False - ) - test.assert_frame_substantively_equal( - tours_df, regress_tours_df, rtol=1e-03, check_dtype=False - ) - - # ## regress trips - regress_trips_df = pd.read_csv(_test_path(f"regress/final_2_zone_trips.csv")) - trips_df = pd.read_csv(_test_path(f"output_2/final_2_zone_trips.csv")) - trips_df.to_csv(_test_path(f"regress/final_2_zone_trips_last_run.csv"), index=False) - test.assert_frame_substantively_equal( - trips_df, regress_trips_df, rtol=1e-03, check_dtype=False - ) - - # also test accessibility for the 2-zone system - regress_accessibility_df = pd.read_csv( - _test_path(f"regress/final_2_zone_proto_disaggregate_accessibility.csv") - ) - final_accessibility_df = pd.read_csv( - _test_path(f"output_2/final_2_zone_proto_disaggregate_accessibility.csv") - ) - final_accessibility_df = final_accessibility_df[ - [c for c in final_accessibility_df.columns if not c.startswith("_original_")] - ] - test.assert_frame_substantively_equal( - final_accessibility_df, - regress_accessibility_df, - check_dtype=False, - ) - - -def test_2_zone_norecode(data): - run_test(zone="2", multiprocess=False, recode=False) - - -def test_2_zone_mp(data): - run_test(zone="2", multiprocess=True) - - -def test_2_zone_sharrow(data): - # Run both single and MP in one test function - # guarantees that compile happens in single - run_test(zone="2", multiprocess=False, sharrow=True) - run_test(zone="2", multiprocess=True, sharrow=True) - - -def test_3_zone(data): - run_test(zone="3", multiprocess=False) - - -def test_3_zone_mp(data): - run_test(zone="3", multiprocess=True) - - -def test_3_zone_sharrow(data): - # Run both single and MP in one test function - # guarantees that compile happens in single - run_test(zone="3", multiprocess=False, sharrow=True) - run_test(zone="3", multiprocess=True, sharrow=True) - - -EXPECTED_MODELS_3_ZONE = [ - "initialize_landuse", - "initialize_households", - "compute_accessibility", - "initialize_los", - "initialize_tvpb", - "school_location", - "workplace_location", - "auto_ownership_simulate", - "free_parking", - "cdap_simulate", - "mandatory_tour_frequency", - "mandatory_tour_scheduling", - "joint_tour_frequency", - "joint_tour_composition", - "joint_tour_participation", - "joint_tour_destination", - "joint_tour_scheduling", - "non_mandatory_tour_frequency", - "non_mandatory_tour_destination", - "non_mandatory_tour_scheduling", - "tour_mode_choice_simulate", - "atwork_subtour_frequency", - "atwork_subtour_destination", - "atwork_subtour_scheduling", - "atwork_subtour_mode_choice", - "stop_frequency", - "trip_purpose", - "trip_destination", - "trip_purpose_and_destination", - "trip_scheduling", - "trip_mode_choice", - "write_data_dictionary", - "track_skim_usage", - "write_trip_matrices", - "write_tables", -] - - -@test.run_if_exists("placeholder_sandag_3_zone_reference_pipeline.zip") -def test_3_zone_progressive(): - import activitysim.abm # register components - - state = workflow.create_example( - "placeholder_sandag_3_zone", directory="/tmp/placeholder_sandag_3_zone" - ) - - assert state.settings.models == EXPECTED_MODELS_3_ZONE - assert state.settings.chunk_size == 0 - assert state.settings.sharrow == False - - state.settings.recode_pipeline_columns = True - state.settings.treat_warnings_as_errors = False - state.settings.households_sample_size = 30 - state.settings.use_shadow_pricing = False - state.settings.want_dest_choice_sample_tables = False - state.settings.want_dest_choice_presampling = True - state.settings.cleanup_pipeline_after_run = True - state.settings.output_tables = configuration.OutputTables( - h5_store=False, - action="include", - prefix="final_3_zone_", - sort=True, - tables=["trips", "tours"], - ) - from activitysim.abm.tables.skims import network_los_preload - - state.get(network_los_preload) - state.network_settings.read_skim_cache = False - state.network_settings.write_skim_cache = False - state.network_settings.rebuild_tvpb_cache = False - - for step_name in EXPECTED_MODELS_3_ZONE: - state.run.by_name(step_name) - try: - state.checkpoint.check_against( - Path(__file__).parent.joinpath( - "placeholder_sandag_3_zone_reference_pipeline.zip" - ), - checkpoint_name=step_name, - ) - except Exception: - print(f"> placeholder_sandag_3_zone {step_name}: ERROR") - raise - else: - print(f"> placeholder_sandag_3_zone {step_name}: ok") - - -if __name__ == "__main__": - # call each test explicitly so we get a pass/fail for each - build_data() - run_test(zone="1", multiprocess=False) - run_test(zone="1", multiprocess=True) - run_test(zone="1", multiprocess=False, sharrow=True) - run_test(zone="1", multiprocess=True, sharrow=True) - - run_test(zone="2", multiprocess=False) - run_test(zone="2", multiprocess=True) - run_test(zone="2", multiprocess=False, sharrow=True) - run_test(zone="2", multiprocess=True, sharrow=True) - - run_test(zone="3", multiprocess=False) - run_test(zone="3", multiprocess=True) - run_test(zone="3", multiprocess=False, sharrow=True) - run_test(zone="3", multiprocess=True, sharrow=True) diff --git a/activitysim/examples/production_semcog/data_model/enums.py b/activitysim/examples/production_semcog/data_model/enums.py index 71c4369248..8ae8f9eb4c 100644 --- a/activitysim/examples/production_semcog/data_model/enums.py +++ b/activitysim/examples/production_semcog/data_model/enums.py @@ -3,6 +3,7 @@ Instructions: modify these enumerated variables as needed for your ActivitySim implementation. """ + from enum import IntEnum diff --git a/activitysim/examples/production_semcog/data_model/input_checks.py b/activitysim/examples/production_semcog/data_model/input_checks.py index b9b1f338b1..ce93a86e0c 100644 --- a/activitysim/examples/production_semcog/data_model/input_checks.py +++ b/activitysim/examples/production_semcog/data_model/input_checks.py @@ -3,6 +3,7 @@ Instructions: customize these example values for your own ActivitySim implementation """ + from typing import List, Optional import os, sys, logging diff --git a/activitysim/examples/production_semcog/test/test_semcog.py b/activitysim/examples/production_semcog/test/test_semcog.py index 10651d4453..e247fd6451 100644 --- a/activitysim/examples/production_semcog/test/test_semcog.py +++ b/activitysim/examples/production_semcog/test/test_semcog.py @@ -2,11 +2,11 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess import pandas as pd -import pkg_resources from activitysim.core.test._tools import assert_frame_substantively_equal @@ -14,7 +14,7 @@ def run_test_semcog(multiprocess=False): def example_path(dirname): resource = os.path.join("examples", "production_semcog", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) diff --git a/activitysim/examples/prototype_arc/configs/settings.yaml b/activitysim/examples/prototype_arc/configs/settings.yaml index 6ff25b94e0..185f67a103 100644 --- a/activitysim/examples/prototype_arc/configs/settings.yaml +++ b/activitysim/examples/prototype_arc/configs/settings.yaml @@ -98,6 +98,10 @@ input_table_list: # assume enough RAM to not chunk chunk_training_mode: disabled +households_sample_size: 100 + +check_model_settings: False + models: - initialize_landuse - initialize_households @@ -129,14 +133,15 @@ models: - trip_scheduling_choice - trip_departure_choice - trip_mode_choice - - parking_location +# - parking_location - write_data_dictionary - track_skim_usage - write_trip_matrices - write_tables -# resume_after: +#resume_after: trip_purpose_and_destination +num_processes: 2 multiprocess: False fail_fast: True diff --git a/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.yaml b/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.yaml index 3d1231f0a9..a0e4f1ee75 100644 --- a/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.yaml +++ b/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.yaml @@ -9,12 +9,16 @@ # - start_period # - end_period -SPECIFICATION: trip_scheduling_choice.csv +SPEC: trip_scheduling_choice.csv #COEFFICIENTS: trip_scheduling_choice_coeff.csv #SAMPLE_ALTERNATIVES: trip_departure_sample_patterns.csv -PREPROCESSOR: +compute_settings: + protect_columns: + - schedule_id + +preprocessor: SPEC: trip_scheduling_choice_preprocessor DF: tours TABLES: diff --git a/activitysim/examples/prototype_arc/test/output/.gitignore b/activitysim/examples/prototype_arc/test/output/.gitignore deleted file mode 100644 index bf5bf15e3e..0000000000 --- a/activitysim/examples/prototype_arc/test/output/.gitignore +++ /dev/null @@ -1,7 +0,0 @@ -*.csv -*.log -*.prof -*.h5 -*.txt -*.yaml -*.omx diff --git a/activitysim/examples/prototype_arc/test/output/cache/.gitignore b/activitysim/examples/prototype_arc/test/output/cache/.gitignore deleted file mode 100644 index 1d085cacc9..0000000000 --- a/activitysim/examples/prototype_arc/test/output/cache/.gitignore +++ /dev/null @@ -1 +0,0 @@ -** diff --git a/activitysim/examples/prototype_arc/test/output/trace/.gitignore b/activitysim/examples/prototype_arc/test/output/trace/.gitignore deleted file mode 100644 index 8edb806780..0000000000 --- a/activitysim/examples/prototype_arc/test/output/trace/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -*.csv -*.log -*.txt diff --git a/activitysim/examples/prototype_arc/test/simulation.py b/activitysim/examples/prototype_arc/test/simulation.py old mode 100755 new mode 100644 diff --git a/activitysim/examples/prototype_arc/test/test_arc.py b/activitysim/examples/prototype_arc/test/test_arc.py index 54462ec958..3e637289c5 100644 --- a/activitysim/examples/prototype_arc/test/test_arc.py +++ b/activitysim/examples/prototype_arc/test/test_arc.py @@ -2,13 +2,13 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess import sys import pandas as pd import pandas.testing as pdt -import pkg_resources from activitysim.core.test import assert_frame_substantively_equal @@ -16,7 +16,7 @@ def _test_arc(recode=False, sharrow=False): def example_path(dirname): resource = os.path.join("examples", "prototype_arc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) diff --git a/activitysim/examples/prototype_marin/test/test_marin.py b/activitysim/examples/prototype_marin/test/test_marin.py index 4de5028f19..11f5cd88f1 100644 --- a/activitysim/examples/prototype_marin/test/test_marin.py +++ b/activitysim/examples/prototype_marin/test/test_marin.py @@ -2,20 +2,20 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess from pathlib import Path import pandas as pd import pandas.testing as pdt -import pkg_resources from activitysim.core import test, workflow def example_path(dirname): resource = os.path.join("examples", "prototype_marin", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def _test_path(dirname): diff --git a/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv index 6c735c4237..779109503a 100644 --- a/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv @@ -32,7 +32,7 @@ util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours #,,, util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early -util_departure_constants_am_peak_1 _6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_1_6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 util_departure_constants_am_peak_2_7,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 util_departure_constants_am_peak_3_8,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 util_departure_constants_am_peak_4_9,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 diff --git a/activitysim/examples/prototype_mtc/test/test_mtc.py b/activitysim/examples/prototype_mtc/test/test_mtc.py index b72ac5c495..06878f4f85 100644 --- a/activitysim/examples/prototype_mtc/test/test_mtc.py +++ b/activitysim/examples/prototype_mtc/test/test_mtc.py @@ -2,6 +2,7 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess import sys @@ -9,7 +10,6 @@ import pandas as pd import pandas.testing as pdt -import pkg_resources from activitysim.core import test, workflow @@ -17,7 +17,7 @@ def run_test_mtc(multiprocess=False, chunkless=False, recode=False, sharrow=False): def example_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) diff --git a/activitysim/examples/prototype_mtc_extended/data_model/enums.py b/activitysim/examples/prototype_mtc_extended/data_model/enums.py index 124647e5db..b28960501a 100644 --- a/activitysim/examples/prototype_mtc_extended/data_model/enums.py +++ b/activitysim/examples/prototype_mtc_extended/data_model/enums.py @@ -3,6 +3,7 @@ Instructions: modify these enumerated variables as needed for your ActivitySim implementation. """ + from enum import IntEnum diff --git a/activitysim/examples/prototype_mtc_extended/data_model/input_checks.py b/activitysim/examples/prototype_mtc_extended/data_model/input_checks.py index 1cc63e5836..f87767ab69 100644 --- a/activitysim/examples/prototype_mtc_extended/data_model/input_checks.py +++ b/activitysim/examples/prototype_mtc_extended/data_model/input_checks.py @@ -3,6 +3,7 @@ Instructions: customize these example values for your own ActivitySim implementation """ + from __future__ import annotations import csv diff --git a/activitysim/examples/prototype_mtc_extended/data_model/input_checks_pydantic_dev.py b/activitysim/examples/prototype_mtc_extended/data_model/input_checks_pydantic_dev.py index b171119066..f2c3ddb997 100644 --- a/activitysim/examples/prototype_mtc_extended/data_model/input_checks_pydantic_dev.py +++ b/activitysim/examples/prototype_mtc_extended/data_model/input_checks_pydantic_dev.py @@ -3,6 +3,7 @@ Instructions: customize these example values for your own ActivitySim implementation """ + from typing import List, Optional import os, sys, logging diff --git a/activitysim/examples/prototype_mtc_extended/sampling_scenarios.py b/activitysim/examples/prototype_mtc_extended/sampling_scenarios.py index f718f4745d..ed6e352c48 100644 --- a/activitysim/examples/prototype_mtc_extended/sampling_scenarios.py +++ b/activitysim/examples/prototype_mtc_extended/sampling_scenarios.py @@ -1,11 +1,11 @@ from __future__ import annotations import argparse +import importlib.resources import os import shutil import pandas as pd -import pkg_resources import yaml from activitysim.cli.run import add_run_args, run @@ -35,12 +35,12 @@ def integer_params(params): def base_path(dirname): resource = os.path.join("examples", "placeholder_sandag_2_zone", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def extended_path(dirname): resource = os.path.join("examples", "placeholder_sandag_2_zone_extended", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def run_model(): diff --git a/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py b/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py index f78b7f8b99..ae74051737 100644 --- a/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py +++ b/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py @@ -2,6 +2,7 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess import sys @@ -9,7 +10,6 @@ import pandas as pd import pandas.testing as pdt -import pkg_resources import pytest from activitysim.core import configuration, test, workflow @@ -23,11 +23,11 @@ def _test_prototype_mtc_extended( ): def example_path(dirname): resource = os.path.join("examples", "prototype_mtc_extended", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def example_mtc_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) diff --git a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling.yaml index 34b5a9a8e2..0411943461 100644 --- a/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling.yaml +++ b/activitysim/examples/prototype_mwcog/configs/non_mandatory_tour_scheduling.yaml @@ -1,5 +1,8 @@ -SPEC: tour_scheduling_nonmandatory.csv -COEFFICIENTS: tour_scheduling_nonmandatory_coeffs.csv +# Should not specify both general SPEC and SPEC_SEGMENTS.SPEC +# SPEC: tour_scheduling_nonmandatory.csv + +# Should not specify both COEFFICENTS and SPEC_SEGMENTS +# COEFFICIENTS: tour_scheduling_nonmandatory_coeffs.csv LOGIT_TYPE: MNL diff --git a/activitysim/examples/prototype_mwcog/test/test_mwcog.py b/activitysim/examples/prototype_mwcog/test/test_mwcog.py index 8978d269d0..9bc6cae753 100644 --- a/activitysim/examples/prototype_mwcog/test/test_mwcog.py +++ b/activitysim/examples/prototype_mwcog/test/test_mwcog.py @@ -2,13 +2,13 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess import sys import pandas as pd import pandas.testing as pdt -import pkg_resources from activitysim.core import test @@ -16,7 +16,7 @@ def _test_mwcog(sharrow=False): def example_path(dirname): resource = os.path.join("examples", "prototype_mwcog", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) diff --git a/activitysim/examples/prototype_sandag_xborder/configs/estimation.yaml b/activitysim/examples/prototype_sandag_xborder/configs/estimation.yaml index bbe11f7fc8..fc857fbb98 100644 --- a/activitysim/examples/prototype_sandag_xborder/configs/estimation.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/estimation.yaml @@ -29,55 +29,7 @@ survey_tables: file_name: trips_survey.csv index_col: trip_id -estimation_table_recipes: - - interaction_sample_simulate: - omnibus_tables: - choosers_combined: - - choices - - override_choices - - choosers - alternatives_combined: - - interaction_sample_alternatives - - interaction_expression_values - omnibus_tables_append_columns: [choosers_combined] - - interaction_simulate: - omnibus_tables: - choosers_combined: - - choices - - override_choices - - choosers - omnibus_tables_append_columns: [choosers_combined] - - simple_simulate: - omnibus_tables: - values_combined: - - choices - - override_choices - - expression_values - - choosers - omnibus_tables_append_columns: [values_combined] - - cdap_simulate: - omnibus_tables: - values_combined: - - choices - - override_choices - - choosers - omnibus_tables_append_columns: [values_combined] - - simple_probabilistic: - omnibus_tables: - values_combined: - - choices - - override_choices - - choosers - - probs - omnibus_tables_append_columns: [values_combined] - - -model_estimation_table_types: +estimation_table_types: tour_scheduling_probabilistic: simple_probabilistic tour_od_choice: interaction_sample_simulate school_location: interaction_sample_simulate diff --git a/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py b/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py index e9d538cde8..608e430763 100644 --- a/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py +++ b/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py @@ -2,6 +2,7 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess import sys @@ -9,7 +10,6 @@ import pandas as pd import pandas.testing as pdt -import pkg_resources from activitysim.core import workflow from activitysim.core.test import run_if_exists @@ -18,7 +18,7 @@ def _test_sandag_xborder(sharrow=False, mp=True): def example_path(dirname): resource = os.path.join("examples", "prototype_sandag_xborder", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) diff --git a/activitysim/workflows/steps/cmd/__init__.py b/activitysim/workflows/steps/cmd/__init__.py index e62b19e851..1463609e30 100644 --- a/activitysim/workflows/steps/cmd/__init__.py +++ b/activitysim/workflows/steps/cmd/__init__.py @@ -4,6 +4,7 @@ environment,variable expansion, and expansion of ~ to a user’s home directory. """ + import logging from .dsl import CmdStep diff --git a/activitysim/workflows/steps/cmd/dsl.py b/activitysim/workflows/steps/cmd/dsl.py index 1d05a7d3b6..7337eab3ca 100644 --- a/activitysim/workflows/steps/cmd/dsl.py +++ b/activitysim/workflows/steps/cmd/dsl.py @@ -1,4 +1,5 @@ """pypyr step yaml definition for commands - domain specific language.""" + import logging import os import shlex diff --git a/activitysim/workflows/steps/contrast/data_inventory.py b/activitysim/workflows/steps/contrast/data_inventory.py index a3ff172a31..70b5f4a104 100644 --- a/activitysim/workflows/steps/contrast/data_inventory.py +++ b/activitysim/workflows/steps/contrast/data_inventory.py @@ -63,7 +63,7 @@ def run_step(context: Context) -> None: 1 ), index=dtypes_table.index, - ).apply(lambda x: "" if x else "\u2B05") + ).apply(lambda x: "" if x else "\u2b05") report << dtypes_table with report: diff --git a/activitysim/workflows/steps/main.py b/activitysim/workflows/steps/main.py index ccefaa825b..9859254d2b 100644 --- a/activitysim/workflows/steps/main.py +++ b/activitysim/workflows/steps/main.py @@ -1,4 +1,5 @@ """Naive custom loader without any error handling.""" + from __future__ import annotations import os diff --git a/activitysim/workflows/steps/progression.py b/activitysim/workflows/steps/progression.py index c0832dc46c..d77513bfa8 100644 --- a/activitysim/workflows/steps/progression.py +++ b/activitysim/workflows/steps/progression.py @@ -96,7 +96,7 @@ def update_progress_overall(description, formatting=""): def reset_progress_step(*args, description="", prefix="", **kwargs): if not os.environ.get("NO_RICH", False): - print(f"\u23F1 {time.strftime('%I:%M:%S %p')} - {description}") + print(f"\u23f1 {time.strftime('%I:%M:%S %p')} - {description}") progress.reset(progress_step, *args, description=prefix + description, **kwargs) else: print("╭" + "─" * (len(description) + 2) + "╮") diff --git a/conda-environments/activitysim-dev-base.yml b/conda-environments/activitysim-dev-base.yml index 74df60783d..e69de29bb2 100644 --- a/conda-environments/activitysim-dev-base.yml +++ b/conda-environments/activitysim-dev-base.yml @@ -1,81 +0,0 @@ -# Environment for Advanced Development -# This is a set of recommended dependencies for ActivitySim developers. -# It includes a variety of tools and packages not necessary for actually -# running models, but useful for writing code, running tests and -# experiments, etc. -# -# This file does not install ActivitySim or Sharrow, you must do that yourself -# (so that you can install a preferred branch or fork) -# -# usage: $ mamba env create --file=activitysim-dev-base.yml -n ASIM-DEV - -channels: -- conda-forge -- nodefaults -dependencies: -- python=3.10 -- pip -- altair -- asv # for benchmarking -- black >= 22.0,<23 -- bump2version # for making a release -- coveralls -- cytoolz = 0.12.* -- dask = 2023.11.* -- descartes -- filelock -- fsspec -- geopandas -- gh -- git -- ipykernel # so this env will appear in jupyter as a selection -- isort -- jupyterlab -- larch = 5.7.* -- matplotlib -- multimethod <2.0 -- myst-parser # allows markdown in sphinx -- nbconvert -- nbformat -- nbmake -- numba = 0.57.* -- numexpr -- numpy = 1.24.* -- numpydoc -- openmatrix = 0.3.* -- orca = 1.8 -- pandas = 2.2.* -- pandera >= 0.15, <0.18.1 -- platformdirs = 3.2.* -- pre-commit -- psutil = 5.9.* -- pyarrow = 11.* -- pycodestyle -- pydantic = 2.6.0 -- pydata-sphinx-theme -- pyinstrument = 4.4 -- pypyr = 5.8.* -- pytables >=3.9 -- pytest = 7.2.* -- pytest-cov -- pytest-regressions -- pyyaml = 6.* -- requests = 2.28.* -- rich = 13.3.* -- ruby # required for benchmarking pre-commit hooks -- ruff -- setuptools_scm -- scikit-learn = 1.2.* -- simwrapper > 1.7 -- snakeviz # for profiling -- sparse -- sphinx = 6.1.* -- sphinx_rtd_theme = 1.2.* -- sphinx-argparse = 0.4.* -- xarray = 2025.01.* -- xmle -- zarr>=2,<3 -- zstandard - -- pip: - - autodoc_pydantic diff --git a/conda-environments/activitysim-dev.yml b/conda-environments/activitysim-dev.yml index 50d67cfc2a..e69de29bb2 100644 --- a/conda-environments/activitysim-dev.yml +++ b/conda-environments/activitysim-dev.yml @@ -1,79 +0,0 @@ -# Environment for development -# This is a set of recommended dependencies for ActivitySim developers. -# It includes a variety of tools and packages not necessary for actually -# running models, but useful for writing code, running tests and -# experiments, etc. -# -# usage: $ mamba env create --file=activitysim-dev.yml -n ASIM-SH -channels: -- conda-forge -- nodefaults -dependencies: -- python=3.10 -- pip -- altair -- asv # for benchmarking -- black >= 22.0,<23 -- bump2version # for making a release -- coveralls -- cytoolz = 0.12.* -- dask = 2023.11.* -- descartes -- filelock -- fsspec -- geopandas -- gh -- git -- ipykernel # so this env will appear in jupyter as a selection -- isort -- jupyterlab -- larch = 5.7.* -- matplotlib -- multimethod <2.0 -- myst-parser # allows markdown in sphinx -- nbconvert -- nbformat -- nbmake -- numba = 0.57.* -- numexpr -- numpy = 1.24.* -- numpydoc -- openmatrix = 0.3.* -- orca = 1.8 -- pandas = 2.2.* -- pandera >= 0.15, <0.18.1 -- platformdirs = 3.2.* -- pre-commit -- psutil = 5.9.* -- pyarrow = 11.* -- pycodestyle -- pydantic = 2.6.* -- pydata-sphinx-theme -- pyinstrument = 4.4 -- pypyr = 5.8.* -- pytables >=3.9 -- pytest = 7.2.* -- pytest-cov -- pytest-regressions -- pyyaml = 6.* -- requests = 2.28.* -- rich = 13.3.* -- ruby # required for benchmarking pre-commit hooks -- ruff -- setuptools_scm -- scikit-learn = 1.2.* -- sharrow >= 2.9.1 -- simwrapper > 1.7 -- snakeviz # for profiling -- sparse -- sphinx = 6.1.* -- sphinx_rtd_theme = 1.2.* -- sphinx-argparse = 0.4.* -- xarray = 2025.01.* -- xmle -- zarr>=2,<3 -- zstandard - -- pip: - - autodoc_pydantic - - -e .. diff --git a/conda-environments/docbuild.yml b/conda-environments/docbuild.yml index 275709c4e3..e69de29bb2 100644 --- a/conda-environments/docbuild.yml +++ b/conda-environments/docbuild.yml @@ -1,62 +0,0 @@ -# Environment for building docs -# -# This conda environment is used to build the documentation. It includes a -# number of dependencies needed for the documentation only, and not to run or -# test ActivitySim itself. Note that ActivitySim itself is *not* installed -# in this environment, you must install it separately after using this recipe, -# which allows you to install the specific version you want. - -name: docbuild -channels: -- conda-forge -dependencies: -- python=3.10 -- pip -- altair -- black >= 22.0,<23 -- bump2version -- coveralls -- cytoolz >= 0.8.1 -- descartes -- geopandas -- gh -- git -- jupyter-book -- jupyterlab -- larch >=5.5.3 -- matplotlib -- multimethod <2.0 -- myst-nb -- myst-parser -- numba >= 0.57 -- numpy >= 1.16.1, <2 -- numpydoc -- openmatrix >= 0.3.4.1 -- pandas = 2.2.* -- pandera >= 0.15, <0.18.1 -- platformdirs -- psutil >= 4.1 -- pyarrow >= 2.0,<19 -- pydantic = 2.6.* -- pypyr >= 5.3 -- pytables >=3.9 -- pytest -- pytest-cov -- pytest-regressions -- pyyaml >= 5.1 -- requests >= 2.7 -- scikit-learn >= 1.1 -- sharrow >= 2.9.1 -- simwrapper > 1.7 -- sparse -- sphinx-argparse -- sphinx-autosummary-accessors -- sphinx-copybutton -- sphinx-remove-toctrees -- sphinx_rtd_theme -- xarray = 2025.01.* -- zarr>=2,<3 - -- pip: - - autodoc_pydantic - - -e .. diff --git a/conda-environments/github-actions-tests.yml b/conda-environments/github-actions-tests.yml index 019bcccda3..e69de29bb2 100644 --- a/conda-environments/github-actions-tests.yml +++ b/conda-environments/github-actions-tests.yml @@ -1,41 +0,0 @@ -# Environment for testing in GitHub Actions -# This environment contains a minimal set of dependencies needed to run most tests. -# It does not install ActivitySim itself (which is done by the test scripts) and -# is not meant for use outside the CI tools. -name: asim-test -channels: -- conda-forge -dependencies: -- pip -- black = 22.12.0 -- coveralls = 3.3.1 -- cytoolz = 0.12.2 -- dask = 2023.11.* -- isort = 5.12.0 -- multimethod <2.0 -- nbmake = 1.4.6 -- numba = 0.57.* -- numpy = 1.24.* -- openmatrix = 0.3.5.0 -- orca = 1.8 -- pandera >= 0.15, <0.18.1 -- pandas = 2.2.* -- platformdirs = 3.2.* -- psutil = 5.9.* -- pyarrow = 11.* -- pydantic = 2.6.* -- pypyr = 5.8.* -- pytables >= 3.9 -- pytest = 7.2.* -- pytest-cov -- pytest-regressions -- pyyaml = 6.* -- requests = 2.28.* -- ruff -- scikit-learn = 1.2.* -- sharrow >= 2.9.1 -- simwrapper > 1.7 -- sparse -- xarray = 2025.01.* -- zarr>=2,<3 -- zstandard diff --git a/docs/Makefile b/docs/Makefile index ba6e4b5159..877005ed39 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -3,15 +3,10 @@ # You can set these variables from the command line. SPHINXOPTS = -SPHINXBUILD = sphinx-build +SPHINXBUILD = uv run --project .. sphinx-build PAPER = BUILDDIR = _build -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) -endif - # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter @@ -53,7 +48,7 @@ html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." - python add_image_map.py + uv run --project .. add_image_map.py dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @@ -64,7 +59,7 @@ singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." - python add_image_map.py + uv run --project .. add_image_map.py pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle diff --git a/docs/_static/theme_overrides.css b/docs/_static/theme_overrides.css index 231ec5701a..772d0e7ebe 100644 --- a/docs/_static/theme_overrides.css +++ b/docs/_static/theme_overrides.css @@ -8,3 +8,7 @@ div.sd-card-header { font-weight: 800; var(--pst-font-family-base); } + +div.sd-card { + margin-bottom: 20px; +} diff --git a/docs/dev-guide/build-docs.md b/docs/dev-guide/build-docs.md index dc8c5d67d3..d59bee572b 100644 --- a/docs/dev-guide/build-docs.md +++ b/docs/dev-guide/build-docs.md @@ -15,21 +15,16 @@ them into html as well. ActivitySim's docstrings are written in ## Building the Documentation Developers who want to test a build of the ActivitySim documentation locally can -do so using `sphinx`. A pre-packaged conda environment is available to simplify this -process. On the command line, starting from the `activitysim` directory that constitutes the -main repository (i.e. you should see subdirectories including `activitysim`, -`conda-environments`, `docs`, and a few others) run these commands: +do so using `sphinx`. The *uv* `dev` environment includes the required dependencies. On the command line, starting from the `activitysim` directory that constitutes the +main repository (i.e. you should see subdirectories including `activitysim`, `docs`, and a few others) run these commands: ```bash -mkdir -p ../.env -mamba env update -p ../.env/DOCBUILD -f conda-environments/docbuild.yml -conda activate ../.env/DOCBUILD cd docs make clean make html ``` -This will build the docs in the `docs/_build/html` directory. They can be viewed +This will build the docs in the `docs/_build/html` directory. They can be viewed in a web browser using the `file:///` protocol, or by double-clicking on the `index.html` file (or any other .html file in that directory). diff --git a/docs/dev-guide/changes.md b/docs/dev-guide/changes.md index e8a94effd4..2cd584b3c4 100644 --- a/docs/dev-guide/changes.md +++ b/docs/dev-guide/changes.md @@ -5,6 +5,118 @@ major new features that may require modifications to existing model configuratio or code to utilize, as well as breaking changes that may cause existing model configurations or code to fail to run correctly. + +## Upcoming Changes + +This section describes changes that are implemented in current development +branch (i.e., the main branch on GitHub), but not yet released in a stable version +of ActivitySim. See below under the various version headings for changes in +released versions. + + +## v1.5.1 + +This release includes a handful of minor updates and fixes, as well as enhancements +to the ActivtySim documentation. Users should generally not expect any breaking +changes relative to v1.5.0, except that when running a simulation there will be a +significant reduction in logging messages displayed on screen and written to the run log. + + +## v1.5 + +This release includes most of the new features and enhancements developed as part +of the Phase 10 work. + +### Preprocessing & Annotation + +We have expanded preprocessing & annotation functionality, which is now standardized +in formatting and available on most model components. Existing model implementations +may need to make minor upgrades to model configuration files to conform with the new +standardized formatting. + +### Estimation Mode + +Estimation mode has been updated to work with Larch v6. This new version of Larch +is modernized and more stable across platforms, and is more consistent with ActivitySim +spec files (as both are now built on Sharrow). The overall workflow for re-estimating +model parameters is very similar to before, but users will need to use Larch v6 instead +of Larch v5. In addition, some new capabilities have been added for modifying model +specifications in Larch (instead of re-running ActivitySim). + +### Using UV for Dependency Management + +Beginning with version 1.5, ActivitySim uses [UV](https://uv.dev/) for dependency +management. UV is a modern dependency management tool that is designed to be +simple to use and easy to understand. See [Installing ActivitySim](Installing-ActivitySim) +for details on how to install ActivitySim using UV. + +### Skim Naming Conflict Resolution + +The SkimDataset structure (required when using sharrow, optional in legacy mode) +requires every skim variable to have a unique name. It also merges OMX variables +based on time period, so that e.g. `BIKETIME__AM` and `BIKETIME__PM`, which would +be 2-d arrays in the OMX file, become just two different parts of a 3-d array +called `BIKETIME` in the SkimDataset. This is problematic when the skims also +contain a 2-d array called `BIKETIME`, as that has no temporal dimension, and it +gets loaded into a 2-d array in the SkimDataset, with the same name as the 3-d array, +and thus one is overwritten and lost. + +ActivitySim now includes a skims input check to identify this overwriting condition, +and raise an error if it is happening, so that the user can correct the condition +via (1) the `omx_ignore_patterns` setting, (2) revising the skim generation process +to not create the overlapping named skims in the file in the first place, +or (3) renaming one or both skims if the users actually wants both skims variables +in the model. The error message generated includes a link to instructions and +discussion of these alternatives. + +### Settings Checker + +A new settings checker has been added to validate model configuration files +before running the model. This tool checks for common configuration errors, +such as missing required settings, incorrect data types, and invalid values. +This can help users identify and fix configuration issues before running +the model, which can save time and effort. In prior versions, configuration +errors would often only be discovered when the model was run, which could +lead to long run times before the error was encountered. + +### Expression Profiling (legacy mode only) + +ActivitySim now includes a new performance profiling feature for expression evaluation +in the ActivitySim framework. The feature allows developers to track and log the +runtime of individual expressions, providing insights into potential bottlenecks +in complex models. Key changes include the integration of a performance timer, +updates to various core functions to support profiling, and new configuration +settings for controlling profiling behavior. +See [Expression Profiling](Expression-Profiling) for details. + +### Telecommute Status Model + +A new telecommute status model component has been added to ActivitySim. This component +models the telecommute status of workers, which can be used to determine +whether a worker telecommutes full-time, part-time, or not at all. A simple +implementation of the telecommute status model can be based on the worker's telecommute +frequency. For example, if a worker telecommutes 4 days a week, then there is +a 80% probability for them to telecommute on the simulation day. The telecommute +status model software can accommodate more complex model forms if needed. An example +telecommute status model specification can be found in +[ActivitySim/sandag-abm3-example#30](https://github.com/ActivitySim/sandag-abm3-example/pull/30). + + +## v1.4 + +### Improved Estimation Mode + +Version 1.4 includes several improvements to the estimation mode, including: + +- The ability to run estimation mode in parallel, which can significantly + speed up the estimation process for large models. +- The ability to modify the model specification and coefficients file(s) for + the estimated submodels without re-running ActivitySim, which allows for more + flexibility in the estimation process. +- Other improvements to the estimation mode workflow, including better error + handling, logging, and model evaluation tools. + + ## v1.3 ### New Canonical Examples diff --git a/docs/dev-guide/components/telecommute_status.md b/docs/dev-guide/components/telecommute_status.md new file mode 100644 index 0000000000..c889f7e32e --- /dev/null +++ b/docs/dev-guide/components/telecommute_status.md @@ -0,0 +1,66 @@ +(component-telecommute_status)= +# Telecommute Status + +```{eval-rst} +.. currentmodule:: activitysim.abm.models.telecommute_status +``` + +ActivitySim telecommute representation consists of two long term submodels - +a person [work_from_home](work_from_home) model and +a person [telecommute_frequency](telecommute_frequency) model. +The work from home model predicts if a worker works exclusively from home, +whereas the telecommute frequency model predicts number of days in a week a worker telecommutes, +if they do not exclusively work from home. +However, neither of them predicts whether a worker telecommutes or not on the simulation day. +This telecommute status model extends the previous two models to predict for all workers whether +they telecommute on the simulation day. + +A simple implementation of the telecommute status model can be based on the worker's telecommute frequency. +For example, if a worker telecommutes 4 days a week, then there is a 80% probability for them +to telecommute on the simulation day. +The telecommute status model software can accommodate more complex model forms if needed. + +There have been discussions about where to place the telecommute status model within the model sequence, +particularly regarding its interation with the Coordinated Daily Activity Pattern (CDAP) model. +Some have proposed expanding the CDAP definition of the "Mandatory" day pattern to include commuting, telecommuting and working from home, +and then applying the telecommute status model to workers with a "Mandatory" day pattern. +While this idea had merit, it would require re-defining and re-estimating CDAP for many regions, which presents practical challenges. + +During Phase 9B development, the Consortium collaboratively reached a consensus on a preferred design for explicitly modeling telecommuting. +It was decided that the existing CDAP definitions would remain unchanged. The new design introduces the ability +to model hybrid workers—those who work both in-home and out-of-home on the simulation day. To support this, +the Consortium recommended adding two new models: a Telecommute Arrangement model and an In-Home Work Activity Duration model. + +As of August 2025, these two models remain at the design stage and have not yet been implemented. Once deployed, +they will supersede the current telecommute status model, which will no longer be needed. In the interim, +the telecommute status model can be used to flag telecommuters in the simulation. + +The main interface to the telecommute status model is the +[telecommute_status](activitysim.abm.models.telecommute_status) function. This +function is registered as an Inject step in the example Pipeline. + +## Structure + +- *Configuration File*: `telecommute_status.yaml` +- *Core Table*: `persons` +- *Result Table*: `is_telecommuting` + + +## Configuration + +```{eval-rst} +.. autopydantic_model:: TelecommuteStatusSettings + :inherited-members: BaseModel, PydanticReadable + :show-inheritance: +``` + +### Examples + +- [Example SANDAG ABM3](https://github.com/ActivitySim/sandag-abm3-example/tree/main/configs/resident/telecommute_status.yaml) + + +## Implementation + +```{eval-rst} +.. autofunction:: telecommute_status +``` diff --git a/docs/dev-guide/install.md b/docs/dev-guide/install.md index 8f4b07bd8e..d4492f0978 100644 --- a/docs/dev-guide/install.md +++ b/docs/dev-guide/install.md @@ -1,73 +1,87 @@ (developer-installation)= # Developer Installation -Installing ActivitySim as a developer is almost as easy as just using it, -but making some tweaks to the processes enables live code development and -testing. - -## Package Manager - -ActivitySim has a lot of dependencies. It's easiest and fastest to install -them using a package manager like conda, or its faster and free sibling -[Mambaforge](https://github.com/conda-forge/miniforge#mambaforge). -Depending on your security settings, you might need to install in a -container like docker, instructions for that are coming soon. - -Note that if you are installing `mamba`, you only should install `mamba` -in the *base* environment. If you install `mamba` itself in other environments, -it will not function correctly. If you've got an existing conda installation -and you want to install mamba into it, you can install mamba into the *base* -environment like this: - -```sh -conda update conda -n base -conda install -n base -c conda-forge mamba +Installing ActivitySim as a developer is as easy as just using it with *uv*. +The easiest wat to make sure you are up-to-date is to run `uv sync --locked`, +which will make sure that all the dependencies are loaded correctly. + +Depending on what you are working on, you may want to check out a branch +other than the default `main`. To do so, you can use a `git switch` command +to any other existing branch name. If you want to start an new +branch, first create it with `git branch cool-new-feature` and then switch +to it with `git switch cool-new-feature`. + +By default, *uv* installs projects in editable mode, such that changes to the +source code are immediately reflected in the environment. + +```{important} +If you add to the ActivitySim dependencies during development or remove, make +sure to use the `uv add` and `uv remove` commands so that the `pyproject.toml` +and `uv.lock` files are updated correctly and your virtual environment is +updated. ``` -While you are at it, if you are a Jupyter user you might want to also install -`nb_conda_kernels` in your base conda environment alongside any other `jupyter` -libraries: +## Cloning from GitHub + +Developers who want to work on ActivitySim should clone the repository from GitHub. +If you have the [GitHub command line tool](https://cli.github.com) installed, you +*could* run this command to clone the consortium's repository: -```sh -mamba install -n base nb_conda_kernels -c conda-forge +```{sh} +gh repo clone ActivitySim/activitysim ``` -This will ensure your development environments are selectable as kernels in -Jupyter Notebook/Lab/Etc. +Usually, you'll actually not want to do this. Instead, **you should work on your +own fork of the repository.** You can create a fork on GitHub using the web interface +or the command line tool's [fork](https://cli.github.com/manual/gh_repo_fork) command. +Then you would clone the repository by referencing your fork: + +```{sh} +gh repo clone MyUsernameOrOrganization/activitysim +``` -## Environment +This way, you can make whatever changes you want in your fork and store them locally +on your computer or push them to GitHub, and they definitely won't step on anyone +else's work. When you're ready to share your awesome new features, you can open a +pull request to do so. -It's convenient to start from a completely clean conda environment -and git repository. Assuming you have `mamba` installed, and you -want to install in a new directory called "workspace" run: +Also, you may notice that the consortium repository has a huge history and includes +a lot of older data files that you probably don't need. To reduce your download time +and disk space usage, you can tell Git that you don't want that whole history. If you +just want to have access to the current code and a couple of years history, you can +cut off the older history using the `shallow-since` option: -```sh -mkdir workspace -cd workspace -mamba env create -p ASIM-ENV --file https://raw.githubusercontent.com/ActivitySim/activitysim/main/conda-environments/activitysim-dev-base.yml -conda activate ./ASIM-ENV -git clone https://github.com/ActivitySim/sharrow.git -python -m pip install -e ./sharrow -git clone https://github.com/ActivitySim/activitysim.git -python -m pip install -e ./activitysim +```{sh} +gh repo clone MyUsernameOrOrganization/activitysim -- --shallow-since="2025-01-01" ``` -Note the above commands will create an environment with all the -necessary dependencies, clone both ActivitySim and sharrow from GitHub, -and `pip install` each of these libraries in editable mode, which -will allow your code changes to be reflected when running ActivitySim -in this environment. +This can reduce the cloning download and disk usage by several gigabytes! By default +you won't see all your branches appear in this more limited clone, just the `main` +branch. -Depending on what you are working on, you may want to check out a branch -other than `develop`. To do so, you can point the `git switch` command -above to any other existing branch name. If you want to start an new -branch, first create it with `git branch cool-new-feature` and then switch -to it with `git switch cool-new-feature`. +### Fetching a Specific Branch -Now your environment should be ready to use. Happy coding! +This is easily solved by downloading individual branches specifically by +name. -```{important} -If you add to the ActivitySim dependencies, make sure to also update -the environments in `conda-environments`, which are used for testing -and development. +```{sh} +# change into the git repo's directory if not already there +cd activitysim + +# Fetch the specific branch named "patch-67" +git fetch origin refs/heads/patch-67:refs/remotes/origin/patch-67 + +# Check out the branch as a local branch to work on +git checkout -b patch-67 origin/patch-67 +``` + +### Fetching All Branches in a Fork + +If you want to work more expansively, you may want to fetch all the branches in +your fork, rather than getting specific branches one at a time. You can change the +repo settings to do so by adding to the Git config file: + +```{sh} +git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" +git fetch --shallow-since="2025-01-01" origin ``` diff --git a/docs/dev-guide/logging.md b/docs/dev-guide/logging.md index f2a5a3db7c..b00451c463 100644 --- a/docs/dev-guide/logging.md +++ b/docs/dev-guide/logging.md @@ -69,3 +69,67 @@ handlers: if_sub_task: WARNING if_not_sub_task: NOTSET ``` + +## Logging levels +Python's built-in `logging` module that includes five levels of logging, which are (in order +of increasing severity): `DEBUG`, `INFO`, `WARNING`, `ERROR`, and `CRITICAL`. One can set the +minimum level to display messages in both the console window as well as the output logfile +within `logging.yaml` in the model settings. For example, if the block of code below were +inside the `logging.yaml` file, than the console window and output activitysim.log file would +print every logging message at the level of `INFO` and above: + +```yaml +loggers: + activitysim: + level: INFO + handlers: [console, logfile] + propogate: false +``` + +However, if a model run were to crash and the user wanted to print all of the `DEBUG` messages +in order to diagnose what was causing the crash, they would need to change the `level` within +the logging settings: + +```yaml +loggers: + activitysim: + level: DEBUG + handlers: [console, logfile] + propogate: false +``` + +The following guidelines demonstrate how each level is used within ActivitySim: + +### Debug (Level 10) +The `DEBUG` message indicates detailed information that would be of interest to a user while +debugging a model. The information reported at this level can include: +- Runtimes of specific steps of model components, such as the time to run each of sampling, + logsum computation, and simulation in destination choice +- Table attributes at various stages of processing, such as the size or columns +- Evaluations of preprocessor or specification expressions +- General repetitive messages that can be used to narrow down exactly where an error is occuring + +### Info (Level 20) +The `INFO` message gives reports general information about how the status of the model run, +particularly where in the model flow the system is at. The information reported at this level +can include: +- Beginning and ending of a model step +- Intermediate stages of a longer step. For example, in trip destination, the trip number and + segment will be reported at this level. + +### Warning (Level 30) +The `WARNING` message notifies the user of a potential issue that they should be aware of, +but doesn't result in the model system failing. The information reported at this level can include: +- Future changes to dependencies +- ActivitySim needing to force certain travel behavior due to such behavior not working + +### Error (Level 40) +The `ERROR` message gives the user information that is causing an error in a model step. The +information reported at this level can include: +- More detailed issues on what could be causing an error message that wouldn't be shown in the + traceback message + +## Critical (Level 50) +The `CRITICAL` message gives the user information that is causing a critical error in a model step. +The information reported at this level can include: +- Reporting to the user on the teardown of a subprocess \ No newline at end of file diff --git a/docs/development.rst b/docs/development.rst index 8ba941f24a..b71373f7c7 100644 --- a/docs/development.rst +++ b/docs/development.rst @@ -322,7 +322,7 @@ Generally speaking, there are two types of ActivitySim examples: test examples a examples are :ref:`prototype_mtc`, :ref:`example_estimation`, :ref:`placeholder_multiple_zone`, and :ref:`prototype_marin`. These examples are owned and maintained by the project. * Agency examples - these are agency partner model implementations currently being setup. The current agency examples - are :ref:`prototype_arc`, :ref:`prototype_semcog`, :ref:`placeholder_psrc`, :ref:`placeholder_sandag`, and :ref:`prototype_sandag_xborder`. These examples can be + are :ref:`prototype_arc`, :ref:`prototype_semcog`, :ref:`placeholder_psrc`, and :ref:`prototype_sandag_xborder`. These examples can be configured in ways different from the test examples, include new inputs and expressions, and may include new planned software components for contribution to ActivitySim. These examples are owned by the agency. diff --git a/docs/index.rst b/docs/index.rst index 421d776e78..6641997c83 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -33,6 +33,40 @@ the development and management of the ActivitySim is on the `project site `__, + our one-zone system prototype. This example is originally based on MTC's Travel Model One (TM1), + but has evolved to be a slightly different model. + - The `SANDAG Example `__, + our two-zone system model. Some effort has been made to keep it aligned + with SANDAG's model, but it is not an exact copy of SANDAG's production model. + + .. grid-item-card:: + + :fa:`square-arrow-up-right` |nbsp| |nbsp| Member Agency Models + + ^^^ + + Several consortium member agencies have open-sourced their ActivitySim + implementations. These open models may or may not be complete calibrated + tools. Unless clearly marked, users should not assume that mlinked models are + "official" implementations used for policy analysis; public agencies often + publish in-progress model development to foster collaboration and transparency. + Contact the agencies directly with questions. + + - `Puget Sound Regional Commission `__ (Seattle) + - `Atlanta Regional Commission `__ + - `Metropolitan Council `__ (Minneapolis-St. Paul) + - `Oregon Modeling Statewide Collaborative `__ + .. toctree:: :hidden: diff --git a/docs/users-guide/estimation-mode/asim-est.md b/docs/users-guide/estimation-mode/asim-est.md new file mode 100644 index 0000000000..77ef1e65aa --- /dev/null +++ b/docs/users-guide/estimation-mode/asim-est.md @@ -0,0 +1,34 @@ +# Running ActivitySim in Estimation Mode + +ActivitySim is run in estimation mode to read ActivitySim-format travel survey +files, and apply the ActivitySim submodels to write estimation data bundles (EDBs) +that contains the model utility specifications, coefficients, chooser data, and +alternatives data for each submodel. These EDBs are then used with Larch to +re-estimate the model parameters. + +ActivitySim can be run in estimation mode by including a few extra settings in +the `estimation.yaml` config file. The key setting in this file is the `enabled` +setting, which must be set to `True` in order to run in estimation mode. The +default value for this setting is `False`, so if it is not explicitly set to +`True`, ActivitySim will run in normal simulation mode, and everything else +in the `estimation.yaml` config file will be ignored. These settings are +documented below. After running ActivitySim in estimation mode, the EDBs will be +written to disk, and can be used with Larch to re-estimate the model parameters. + +```{eval-rst} +.. currentmodule:: activitysim.core.estimation +``` + +## Configuration Settings + +```{eval-rst} +.. autopydantic_model:: EstimationConfig + :inherited-members: PydanticReadable + :show-inheritance: +``` + +## Survey Table Settings + +```{eval-rst} +.. autopydantic_model:: SurveyTableConfig +``` diff --git a/docs/users-guide/estimation-mode/index.md b/docs/users-guide/estimation-mode/index.md new file mode 100644 index 0000000000..4593a5a42c --- /dev/null +++ b/docs/users-guide/estimation-mode/index.md @@ -0,0 +1,58 @@ + +# Estimation Mode + +ActivitySim includes the ability to re-estimate submodels using choice model estimation +tools. It is possible to output the data needed for estimation and then use more or less +any parameter estimation tool to find the best-fitting parameters for each model, but +ActivitySim has a built-in integration with the [`larch`](https://larch.driftless.xyz) +package, which is an open source Python package for estimating discrete choice models. + +## Estimation Workflow, Summarized + +The general workflow for estimating models is shown in the following figures and +explained in more detail below. + +![estimation workflow](https://activitysim.github.io/activitysim/develop/_images/estimation_tools.jpg) + +First, the user converts their household travel survey into ActivitySim-format +households, persons, tours, joint tour participants, and trip tables. The +households and persons tables must have the same fields as the synthetic population +input tables since the surveyed households and persons will be run through the same +set of submodels as the simulated households and persons. + +The ActivitySim estimation example [``scripts\infer.py``](https://github.com/ActivitySim/activitysim/blob/main/activitysim/examples/example_estimation/scripts/infer.py) +module reads the ActivitySim-format household travel survey files and checks for +inconsistencies in the input tables versus the model design, and calculates +additional fields such as the household joint tour frequency based on the trips +and joint tour participants table. Survey households and persons observed choices +much match the model design (i.e. a person cannot have more work tours than the model +allows). + +ActivitySim is then run in estimation mode to read the ActivitySim-format +travel survey files, and apply the ActivitySim submodels to write estimation data bundles +(EDBs) that contains the model utility specifications, coefficients, chooser data, +and alternatives data for each submodel. + +The relevant EDBs are read and transformed into the format required by the model +estimation tool (i.e. larch) and then the coefficients are re-estimated. The +``activitysim.estimation.larch`` library is included for integration with larch +and there is a Jupyter Notebook estimation example for most core submodels. +Certain kinds of changes to the model specification are allowed during the estimation +process, as long as the required data fields are present in the EDB. For example, +the user can add new expressions that transform existing data, such as converting +a continuous variable into a categorical variable, a polynomial transform, or a +piecewise linear form. More intensive changes to the model specification, such as +adding data that is not in the EDB, or adding new alternatives, are generally not +possible without re-running the estimation mode to write a new EDB. + +Based on the results of the estimation, the user can then update the model +specification and coefficients file(s) for the estimated submodel. + +```{eval-rst} +.. toctree:: + :maxdepth: 2 + + Running ActivitySim in Estimation Mode + Using Larch to Re-estimate Models + ActivitySim Larch Tool API +``` diff --git a/docs/users-guide/estimation-mode/larch-api.rst b/docs/users-guide/estimation-mode/larch-api.rst new file mode 100644 index 0000000000..94805cda03 --- /dev/null +++ b/docs/users-guide/estimation-mode/larch-api.rst @@ -0,0 +1,50 @@ +============== +Larch Tool API +============== + +.. currentmodule:: activitysim.estimation.larch + + +activitysim.estimation.larch.general +------------------------------------ + +.. automodule:: activitysim.estimation.larch.general + :members: + + +activitysim.estimation.larch.data_maker +--------------------------------------- + +.. automodule:: activitysim.estimation.larch.data_maker + :members: + +activitysim.estimation.larch.simple_simulate +-------------------------------------------- + +.. automodule:: activitysim.estimation.larch.simple_simulate + :members: + +activitysim.estimation.larch.cdap +--------------------------------- + +.. automodule:: activitysim.estimation.larch.cdap + :members: + +activitysim.estimation.larch.location_choice +-------------------------------------------- + +.. automodule:: activitysim.estimation.larch.location_choice + :members: + +.. automodule:: activitysim.estimation.larch.mode_choice + :members: + +.. automodule:: activitysim.estimation.larch.nonmand_tour_freq + :members: + +.. automodule:: activitysim.estimation.larch.scheduling + :members: + +.. automodule:: activitysim.estimation.larch.stop_frequency + :members: +``` diff --git a/docs/users-guide/estimation-mode/larch.md b/docs/users-guide/estimation-mode/larch.md new file mode 100644 index 0000000000..da029b6365 --- /dev/null +++ b/docs/users-guide/estimation-mode/larch.md @@ -0,0 +1,321 @@ +# Using Larch + +ActivitySim component models are mostly built as discrete choice models. The +parameters for these models typically need to be estimated based on observed +survey data. The estimation process is facilitated by the Larch package, which +is a Python package for estimating discrete choice models. Larch is a +general-purpose package that can be used to estimate a wide variety of discrete +choice models, including the multinomial logit and nested logit models that are +commonly used in ActivitySim. This section highlights some of the features of +Larch, particularly as they relate to ActivitySim, as there are a few subtle +differences between the two packages that users should be aware of when +estimating models. + +## Setting up Larch Models + +ActivitySim includes a number of scripts and tools to set up Larch models for +estimation. The `activitysim.estimation.larch` library includes functions to +read the EDBs written by ActivitySim and convert them into Larch models, +including a generic [`component_model`](activitysim.estimation.larch.component_model) +function that can be used to load the data and set up Larch for any standard +ActivitySim component. This function is demonstrated in the [example notebooks] +(#example-notebooks). + +When given a truthy `return_data` argument, the `component_model` function will +return a 2-tuple of the Larch model and the data used to create it. The data as +the second element of this tuple should be treated as a *copy* of the data used +to create the model, and is provided primarily for the user to review and use in +debugging if needed. If it is necessary to modify the data (e.g. to recreate +temporary variables), the user should modify the `data` attribute of the model +itself (i.e. `model.data` if `model` is the first element of the returned +tuple), not the data returned in the second element of the tuple. + +## Model Specification + +By default, the process of estimating parameters for ActivitySim model +components with Larch is based on the existing model specification files. These +are the CSV files that are used to define the utility function for each logit +component. When running ActivitySim, these files are typically found in the +configs directory, but when running in estimation mode, they are written out to +the EDB as well, which is where the `activitysim.estimation.larch` library +functions look for these input files. + +Users are not limited to using the existing model specification files, however. +The Larch tools for model estimation now allow users to modify the model +specification files, and then re-estimate the model, including existing and new +parameters. The revised model specification files must rely on the same data +that has already been written out to the EDB, but the user can add new +expressions to the specification to transform the data, or to create new +variables. This is particularly useful for creating new piecewise linear +transformations, or for creating new categorical variables from continuous +variables. The user can also add new variables to the specification that are not +in the EDB, but this will require re-running ActivitySim in estimation mode to +write a new EDB. Examples for how to re-specify the model specification files +are included in [selected example notebooks](#examples-that-include-re-specification). + +## Maximum Likelihood Estimation + +The approach used to estimate the parameters of a discrete choice model is +maximum likelihood estimation (MLE). The goal of MLE is to find the set of +parameters that maximize the likelihood of observing the choice data that we +have collected. + +Finding the maximum likelihood estimates of the parameters is a non-linear +optimization problem. To solve this problem, Larch primarily relies on the +widely-used `scipy.optimize` package, which provides a number of +[optimization algorithms](https://docs.scipy.org/doc/scipy/reference/optimize.html#local-multivariate-optimization) +that can be used to find the maximum likelihood estimates. Different algorithms +have different strengths and weaknesses, and the choice of algorithm can have a +significant impact on the speed and accuracy of the estimation process. By +default, when no constraints or bounds are present, Larch uses an +implementation of the [BHHH algorithm](https://en.wikipedia.org/wiki/Berndt–Hall–Hall–Hausman_algorithm), +which is not included in scipy but is usually efficient for simple, well +specified choice models without any constraints. When constraints or bounds are +present, by default Larch uses the `scipy.optimize.minimize` function with the +`SLSQP` algorithm. The `larch.Model.estimate` method allows the user to specify +the optimization algorithm to use via the `method` argument, which can be set to +'BHHH', 'SLSQP', or any other algorithm supported by `scipy.optimize.minimize`. +If you are estimating a model and find the optimization is not converging as +fast as expected (or at all), you may want to try a different optimization +algorithm. + +## Model Evaluation + +The `larch.Model` class includes a number of methods for evaluating the +quality of each estimated model. These tools are explained in +[detail](https://larch.driftless.xyz/v6.0/user-guide/analysis.html) in the +Larch documentation. + +A [simple aggregate analysis](https://larch.driftless.xyz/v6.0/user-guide/analysis.html#choice-and-availability-summary) +of a Larch model data’s choice and availability statistics is available. + +Larch also includes methods to +[analyze model predictions](https://larch.driftless.xyz/v6.0/user-guide/analysis.html#analyze-predictions) +across various dimensions. The `analyze_predictions_co` method can be used to +examine how well the model predicts choices against any available (or +computable) attribute of the chooser. In addition, there are tools to evaluate +[demand elasticity](https://larch.driftless.xyz/v6.0/user-guide/analysis.html#elasticity) with +respect to changes in underlying data. + +## Model Overspecification + +When using ActivitySim for simulation, there are generally few limitations or +requirements on the uniqueness of data elements. For example, it may end up +being confusing for a user, but there is nothing fundamentally wrong with having +two different variables in the model specification that both represent "income" +but have different scales, or with having alternative-specific constants for all +the alternatives. In model estimation, however, this can lead to problems. +Having two data elements that are perfectly correlated (e.g. two different +variables that both represent "income") or having a full set of +alternative-specific values for all the alternatives can lead to numerical +problems in the estimation process, as the log likelihood function will have +flat areas and will not have a unique maximum. This is called "model +overspecification". In Larch, the user is warned if an estimated model appears +to be overspecified, see the Larch documentation +[for details](https://larch.driftless.xyz/v6.0/user-guide/choice-models.html#overspecification). + +## Recreating Temporary Variables + +When writing out estimation data bundles, ActivitySim may omit certain temporary +variables included in a model spec. For example, in the example workplace +location choice model, the spec creates a temporary variable +["_DIST"](https://github.com/ActivitySim/activitysim-prototype-mtc/blob/7da9d6d6deca670cc4701fea749a270ab6fe77aa/configs/workplace_location.csv#L2) +which is then reused in several subsequent expressions. When the model's +estimation data bundle is written out, the "_DIST" variable may not be +included[^1]. This is not a problem when simply re-estimating the parameters of +the current model specification, as all the piecewise linear transformations +that use "_DIST" are included. However, if the user wanted to change those +piecewise linear transformations (e.g. by moving the breakpoints), the absence +of the "_DIST" value will be relevant. + +[^1]: Future versions of ActivitySim may include these values in the EDB output. + +If the missing temporary value can be reconstructed from the data that *is* +included in the EDB, it can be added back into the model's data. For example, +here we reconstitute the total distance by summing up over the piecewise +component parts: + +```{python} +model.data["_DIST"] = ( + model.data.util_dist_0_1 + + model.data.util_dist_1_2 + + model.data.util_dist_2_5 + + model.data.util_dist_5_15 + + model.data.util_dist_15_up +) +``` + +Note in this expression, we are modifying `model.data`, i.e. the data attached +to the model. If you have other raw data available in your estimation notebook, +e.g., from running `model, data = component_model(..., return_data=True)`, it is +not sufficient to manipulate `data` itself; you must manipulate `model.data` or +otherwise re-attach any data changes to the model, or else the changes will not +show up in estimation. + +## Expressing Alternative Availability + +In ActivitySim, the unavailability of alternatives is typically expressed in the +utility function given in the model specification, by including an indicator variable +for unavailable alternatives, which is then attached to a large negative coefficient. +This creates a large negative utility for the unavailable alternative, which will +render it effectively unavailable in the choice model. If *all* the alternatives +are made unavailable in this manner, this can result in a condition where no +alternative can be chosen, and ActivitySim will raise an error. + +When estimating models in Larch for use with ActivitySim, it is totally acceptable and +appropriate to use this approach to express alternative availability, +by embedding it in the utility function. This will greatly simplify the process +of subsequently transferring the resulting model specification and parameters +back to the ActivitySim model. However, it is important to note that this +approach is not the only way to express alternative availability in Larch. + +Larch includes a system to define the availability of alternatives explicitly as a +[separate array of values](https://larch.driftless.xyz/dev/user-guide/choice-models.html#availability), +which is not included in the utility function. This is +more robust in estimation, as the Larch computational engine can (and will) +automatically shift the utility values to avoid numerical underflow or overflow +issues that can arise when some choices are very unlikely but not strictly unavailable. +When using the ActivitySim style of expressing alternative availability, the onus +is entirely on the user to ensure that the utility values are not so large or small +that they cause numerical problems. If this is not checked, it is possible that +the model will appear to be estimating correctly in Larch, but the resulting model +will underflow in ActivitySim, resulting in an error when the model is run. + +The scripts that build Larch models from estimation data bundles +(`activitysim.estimation.larch`) will attempt to identify unavailability flags +in the utility specifications, and when such flags are found it will automatically +convert them to the Larch availability array format. However, since specification +files can be complex, and the unavailability flags can be expressed in many different +ways, it is possible that the automatic detection will not always work as expected. +It is a good idea to check the +[choice and availability summary](https://larch.driftless.xyz/dev/user-guide/analysis.html#choice-and-availability-summary) +on the Larch model to confirm that the availability of alternatives is being +processes as expected. + +## Components that have Related Models + +Within ActivitySim, it is possible for multiple parts of model components +to share a common set of coefficients. It is even possible for completely +separate components to do so. For example, in the MTC example model, +the joint tour destination choice model and the non-mandatory tour destination +choice model share a common set of coefficients written in a single file. +To re-estimate these coefficients, the user must simultaneously work with all +the estimation survey data from both models. + +In Larch, the case of two models sharing coefficients is handled by creating two separate +`Model` objects, one for each model, and then using the `ModelGroup` object to link them +together. The `ModelGroup` object allows the user to specify a set of common parameters +for two or more models, and then estimate them together. In the case of re-estimating +the joint tour destination choice model and the non-mandatory tour destination +choice model, it may be that both models have a similar (or even identical) +utility structure. In other cases, the linked models may have different utility +structures, which share a subset of parameters, but also may have other parameters +that are unique to each model. In either case, when using the `ModelGroup` object, +parameters are identified as being linked across models by having a common name. + +There are also components in ActivitySim where a single component can embed multiple +discrete choice models which share details, but each sub-model can have a different +utility structure or different sets of parameters. For example, the `tour_mode_choice` +component has a coefficient template file, which allows the model developer to +specify a different set of coefficients for each tour purpose, which are otherwise +processed using a common utility function. This is implemented in Larch with the +`ModelGroup` object, where each purpose is represented as a separate `Model` object, +and the `ModelGroup` object is used to link them together. This logic is further +extended by including the at-work subtour mode choice component, which allows for +the joint estimation of the tour mode choice model and the at-work subtour mode +choice model, which very reasonably share numerous parameters, but also have a few +differences. Similarly, the stop frequency and CDAP models are implemented for +Larch estimation as `ModelGroup` objects, segmented on tour purpose and household +size, respectively. + +When estimating a `ModelGroup` object, process for estimating the likelihood +maximizing parameters is the same as for a single model: the log likelihood is computed +for each observation (i.e. chooser) in the data set according to the parameters, model, +and data for that chooser, and the overall log likelihood is the sum of all the +chooser log likelihoods. By using this approach, the rest of the estimation process is +the same as for a single model, including finding parameter estimates, the standard +error of those estimates, and any statistical tests or interpretations that are +desired. + +## Components with Size Terms + +Location choice models in ActivitySim (and in discrete choice modeling +in general) usually include a "size" term. The size term is a measure +of the quantity of the alternative, which in location choice models is +typically a geographic area that contains multiple distinct alternatives. +For example, in a workplace location choice model, the size term might +be the number of jobs in the zone. In practice, the size term is a statistical +approximation of the number of opportunities available in the zone, and +can be composed of multiple components, such as the number of employers, the +number of households, and/or the number of retail establishments. + +The size term is included in the utility function of the model, but it is +expressed differently from other qualitative measures. The typical model +specification for a location choice model will include a utility function +given in a spec file, which will represent the quality of the alternative(s) +that are being considered. Put another way, the "regular" utility function +is a measure of the quality of the alternative, while the size term is a +measure of the quantity of the alternative. + +In ActivitySim, size terms appear not in the utility spec files, but instead +are expressed in a separate "size term" spec file, typically named +"destination_choice_size_terms.csv". This one file contains all the size +terms for all the location choice models. + +When using Larch for model estimation, size terms can be estimated alongside the +other parameters. The `update_size_spec` function in the `activitysim.estimation.larch` library +allows the user to update the size term specification for a model. This function +takes the existing size term specification and updates the appropriate rows +that correspond to the model being re-estimated. The resulting updated size +term output file will also include the (unmodified) size term specification +for all other size-based models. When copying the revised size term specification +to the model configuration, the user should be careful that re-estimation updates +from multiple models are not inadvertently overwriting each other. + +As an alternative, users can choose to *not* re-estimate the size terms, by +providing exogenous size terms in the model specification, and instructing Larch +not to re-estimate these parameters. This is done via the `Model.lock_value` +command, which will fix any given named parameter to a specific value. This command +takes two arguments: the name of the parameter to be fixed, and the value to +fix it to. The `lock_value` command can be used to fix the size term parameters +to the values in the size term specification file, and then the model will be +estimated without re-estimating the size terms. If no re-estimation is desired, +users can also safely ignore the `update_size_spec` function. + +## Example Notebooks + +ActivitySim includes a collection of Jupyter notebooks with interactive +re-estimation examples for many core submodels, which can be found in the GitHub +repository under the [`activitysim/examples/example_estimation/notebooks`](https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples/example_estimation/notebooks) +directory. Most of these notebooks demonstrate the process of re-estimating +model parameters, without changing the model specification, i.e. finding updated +values for coefficients without changing the mathematical form of a model's +utility function. + +### Examples that include Re-Specification + +A selection of these notebooks have also been updated to demonstrate the process +of estimating model parameters and also *changing the model specification*. +These notebooks generally include instructions and a demonstration of how to +modify the model specification, and then re-estimate the model parameters, as +well as how to compare the results of the original and modified models +side-by-side, which can be useful for understanding the impact of the changes +made, and conducting statistical tests to determine if the changes made are +statistically significant. + +The following notebooks include examples of modifying the model specification: + +- [`03_work_location.ipynb`](https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples/example_estimation/notebooks/03_work_location.ipynb): + This notebook includes a demonstration of modification to the SPEC file for a + destination choice model, using the "interact-sample-simulate" type model. +- [`04_auto_ownership.ipynb`](https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples/example_estimation/notebooks/04_auto_ownership.ipynb): + This notebook includes a demonstration of modification to the SPEC file for the + auto ownership model. It shows an example of an edit in the utility function + for a "simple simulate" type model. +- [`06_cdap.ipynb`](https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples/example_estimation/notebooks/06_cdap.ipynb): + This notebook includes a demonstration of modification to the SPEC file for the + CDAP model. This model has a complex structure that is unique among the + ActivitySim component models. +- [`17_tour_mode_choice.ipynb`](https://github.com/ActivitySim/activitysim/tree/main/activitysim/examples/example_estimation/notebooks/17_tour_mode_choice.ipynb): + This notebook includes a demonstration of modification to the spec, coefficients, + and coefficients template file for the tour mode choice model. diff --git a/docs/users-guide/example_models.rst b/docs/users-guide/example_models.rst index fbb727f623..a671302d32 100644 --- a/docs/users-guide/example_models.rst +++ b/docs/users-guide/example_models.rst @@ -2985,25 +2985,24 @@ Running the model To run the example, do the following: -* Activate the correct conda environment if needed * View the list of available examples :: - activitysim create --list + uv run activitysim create --list * Create a local copy of an example folder :: - activitysim create --example prototype_mtc --destination my_test_example + uv run activitysim create --example prototype_mtc --destination my_test_example * Run the example :: cd my_test_example - activitysim run -c configs -d data -o output + uv run activitysim run -c configs -d data -o output * ActivitySim will log progress and write outputs to the output folder. @@ -3019,7 +3018,7 @@ The example should run in a few minutes since it runs a small sample of househol :: - python simulation.py + uv run simulation.py .. _multiprocess_example : @@ -3032,7 +3031,7 @@ include the multiprocessing configuration settings via settings file inheritance :: - activitysim run -c configs_mp -c configs -d data -o output + uv run activitysim run -c configs_mp -c configs -d data -o output The multiprocessing example also writes outputs to the output folder. diff --git a/docs/users-guide/index.rst b/docs/users-guide/index.rst index 35f60622fb..d464a6cd0d 100644 --- a/docs/users-guide/index.rst +++ b/docs/users-guide/index.rst @@ -44,6 +44,7 @@ Contents visualization example_models example_performance + estimation-mode/index .. toctree:: :maxdepth: 1 other_examples diff --git a/docs/users-guide/model_anatomy.rst b/docs/users-guide/model_anatomy.rst index 461991a2e4..a1e0cc2753 100644 --- a/docs/users-guide/model_anatomy.rst +++ b/docs/users-guide/model_anatomy.rst @@ -21,21 +21,21 @@ file and the ``configs\network_los.yaml`` file. The following tables are currently implemented: - * households - household attributes for each household being simulated. Index: ``household_id`` (see ``activitysim.abm.tables.households.py``) - * landuse - zonal land use (such as population and employment) attributes. Index: ``zone_id`` (see ``activitysim.abm.tables.landuse.py``) - * persons - person attributes for each person being simulated. Index: ``person_id`` (see ``activitysim.abm.tables.persons.py``) - * time windows - manages person time windows throughout the simulation. See :ref:`time_windows`. Index: ``person_id`` (see the person_windows table create decorator in ``activitysim.abm.tables.time_windows.py``) - * tours - tour attributes for each tour (mandatory, non-mandatory, joint, and atwork-subtour) being simulated. Index: ``tour_id`` (see ``activitysim.abm.models.util.tour_frequency.py``) - * trips - trip attributes for each trip being simulated. Index: ``trip_id`` (see ``activitysim.abm.models.stop_frequency.py``) +* households - household attributes for each household being simulated. Index: ``household_id`` (see ``activitysim.abm.tables.households.py``) +* landuse - zonal land use (such as population and employment) attributes. Index: ``zone_id`` (see ``activitysim.abm.tables.landuse.py``) +* persons - person attributes for each person being simulated. Index: ``person_id`` (see ``activitysim.abm.tables.persons.py``) +* time windows - manages person time windows throughout the simulation. See :ref:`time_windows`. Index: ``person_id`` (see the person_windows table create decorator in ``activitysim.abm.tables.time_windows.py``) +* tours - tour attributes for each tour (mandatory, non-mandatory, joint, and atwork-subtour) being simulated. Index: ``tour_id`` (see ``activitysim.abm.models.util.tour_frequency.py``) +* trips - trip attributes for each trip being simulated. Index: ``trip_id`` (see ``activitysim.abm.models.stop_frequency.py``) A few additional tables are also used, which are not really tables, but classes: - * input store - reads input data tables from the input data store - * constants - various constants used throughout the model system, such as person type codes - * shadow pricing - shadow price calculator and associated utility methods, see :ref:`shadow_pricing` - * size terms - created by reading the ``destination_choice_size_terms.csv`` input file. Index - ``segment`` (see ``activitysim.abm.tables.size_terms.py``) - * skims - each model runs requires skims, but how the skims are defined can vary significantly depending on the ActivitySim implementation. The skims class defines Inject injectables to access the skim matrices. The skims class reads the skims from the omx_file on disk. - * table dictionary - stores which tables should be registered as random number generator channels for restartability of the pipeline +* input store - reads input data tables from the input data store +* constants - various constants used throughout the model system, such as person type codes +* shadow pricing - shadow price calculator and associated utility methods, see :ref:`shadow_pricing` +* size terms - created by reading the ``destination_choice_size_terms.csv`` input file. Index - ``segment`` (see ``activitysim.abm.tables.size_terms.py``) +* skims - each model runs requires skims, but how the skims are defined can vary significantly depending on the ActivitySim implementation. The skims class defines Inject injectables to access the skim matrices. The skims class reads the skims from the omx_file on disk. +* table dictionary - stores which tables should be registered as random number generator channels for restartability of the pipeline @@ -54,10 +54,10 @@ system for non-motorized travel, and optionally a transit access points (TAPs) z The three versions of multiple zone systems are one-zone, two-zone, and three-zone. - * **One-zone**: This version is based on TM1 and supports only TAZs. All origins and +* **One-zone**: This version is based on TM1 and supports only TAZs. All origins and destinations are represented at the TAZ level, and all skims including auto, transit, and non-motorized times and costs are also represented at the TAZ level. - * **Two-zone**: This version is similar to many DaySim models. It uses microzones (MAZs) +* **Two-zone**: This version is similar to many DaySim models. It uses microzones (MAZs) for origins and destinations, and TAZs for specification of auto and transit times and costs. Impedance for walk or bike all-the-way from the origin to the destination can be specified at the MAZ level for close together origins and destinations, and at @@ -65,7 +65,7 @@ The three versions of multiple zone systems are one-zone, two-zone, and three-zo walk access and egress times with times specified in the MAZ file by transit mode. Careful pre-calculation of the assumed transit walk access and egress time by MAZ and transit mode is required depending on the network scenario. - * **Three-zone**: This version is based on the SANDAG generation of CT-RAMP models. +* **Three-zone**: This version is based on the SANDAG generation of CT-RAMP models. Origins and destinations are represented at the MAZ level. Impedance for walk or bike all-the-way from the origin to the destination can be specified at the MAZ level for close together origins and destinations, and at the TAZ level for further @@ -84,8 +84,15 @@ The three versions of multiple zone systems are one-zone, two-zone, and three-zo combinations of nearby boarding and alighting TAPs for each origin destination MAZ pair. -Regions that have an interest in more precise transit forecasts may wish to adopt the -three-zone approach, while other regions may adopt the one or two-zone approach. The +.. caution:: + The ActivitySim consortium is moving away from the three-zone approach, in favor of + to the one- or two-zone approaches. The code for the three-zone approach remains + available for users who have already implemented it, but it is recommended that + users consider the one- or two-zone approaches for new implementations. + The three-zone system may be formally deprecated and removed in the future. + +Regions that have an interest in more precise transit and non-motorized forecasts +may wish to adopt the two-zone approach, while other regions may adopt the one or two-zone approach. The microzone version requires coding households and land use at the microzone level. Typically an all-streets network is used for representation of non-motorized impedances. This requires a routable all-streets network, with centroids and connectors for @@ -103,23 +110,88 @@ transit modeling. initial development, these examples were insufficient for validation and performance testing of the new software. As a result, the :ref:`prototype_marin` example was created. -Example simple test configurations and inputs for two and three-zone system models are described below. + +.. _omx_skims : + +Skims +~~~~~ + +The basic level-of-service data that represents the transportation system is +made available to ActivitySim via one or more sets of "skims". Skims are +essentially matrices of travel times, costs, and other level of service attributes, +calculated between various zones in the system. This skim data is made available +to ActivitySim using files in the`openmatrix `__ +(OMX) format. All of the skim data can be provided in a single OMX file, or +multiple OMX files can be used (this is typical for larger models, to keep file +sizes manageable). If multiple files are used, the content of those files is +simply concatenated together into a single notional bucket of skim data when +the model is run. Within that bucket, each skim variable is identified by a unique name. +For skim variables that vary across model time periods, the time period is +appended to the skim name, separated by a double underscore (e.g. ``BUS_IVT__AM``). + +.. caution:: + When using "legacy" mode for ActivitySim, it is possible (but not recommended) + to have a skim variable that has both a time period agnostic value as well as + a set of time period dependent values, e.g. "WALK_TIME" and "WALK_TIME__AM". + If you have conflicting names like this, a warning message will be issued, which + will look like this in an ActivitySim log file: + + .. code-block:: text + + WARNING: activitysim/core/skim_dict_factory.py:212: + UserWarning: some skims have both time-dependent and time-agnostic versions: + - BIKE_LOGSUM + - BIKE_TIME + + This is a warning, not an error, and the model will run if not using sharrow. + However, if "sharrow" mode is activated, this will result in an error once the + skims are actually loaded, unless instructions are included in the settings file + to resolve the conflict. The error message will look like this: + + .. code-block:: text + + ERROR: skims ['BIKE_TIME'] are present in both time-dependent and time-agnostic formats. + Please add ignore rules to the omx_ignore_patterns setting to resolve this issue. + To ignore the time dependent skims, add the following to your settings file: + + omx_ignore_patterns: + - '^BIKE_TIME__.+' + + To ignore the time agnostic skims, add the following to your settings file: + + omx_ignore_patterns: + - '^BIKE_TIME$' + + You can also do some variation or combination of the two, as long as you resolve + the conflict(s). In addition, note that minor edits to model spec files may be + needed to accommodate these changes in how skim data is represented (e.g. changing + `odt_skims` to `od_skims`, or similar modifications wherever the offending variable + names are used). Alternatively, you can modify the skim data in the source files to + remove the naming conflicts, which is typically done upstream of ActivitySim in + whatever tool you are using to create the skims in the first place. + + It should be relatively simple to resolve the conflict by following the instructions + in the error message. The cleaner and more reliable solution is to ensure each skim + variable has a unique name, e.g. by changing the name on the time period agnostic + value, so that instead of "BIKE_TIME" it is "BIKE_TIME_BASE". This may also require + minor edits to the model spec files to accommodate the new skim name. + Examples ~~~~~~~~ +Example simple test configurations and inputs for two and three-zone system models are described below. To run the two zone and three zone system examples, do the following: -* Activate the correct conda environment if needed * Create a local copy of the example :: # simple two zone example - activitysim create -e placeholder_2_zone -d test_placeholder_2_zone + uv run activitysim create -e placeholder_2_zone -d test_placeholder_2_zone # simple three zone example - activitysim create -e placeholder_3_zone -d test_placeholder_3_zone + uv run activitysim create -e placeholder_3_zone -d test_placeholder_3_zone * Change to the example directory @@ -128,11 +200,11 @@ To run the two zone and three zone system examples, do the following: :: # simple two zone example - activitysim run -c configs_2_zone -c configs -d data_2 -o output_2 + uv run activitysim run -c configs_2_zone -c configs -d data_2 -o output_2 # simple three zone example, single process and multiprocess (and makes use of settings file inheritance for running) - activitysim run -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_static.yaml - activitysim run -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_mp.yaml + uv run activitysim run -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_static.yaml + uv run activitysim run -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_mp.yaml Settings ~~~~~~~~ @@ -421,7 +493,7 @@ alternatives, coefficients, constants and other settings for each model. For mor Outputs ------- -The key output of ActivitySIm is the HDF5 data pipeline file ``output\pipeline.h5``. This datastore by default contains +The key output of ActivitySim is the HDF5 data pipeline file ``output\pipeline.h5``. This datastore by default contains a copy of each data table after each model step in which the table was modified. The exact fields for each set of outputs will be different for various implementations of ActivitySim. diff --git a/docs/users-guide/model_dev.rst b/docs/users-guide/model_dev.rst index b9fbc8572f..dd63911c9d 100644 --- a/docs/users-guide/model_dev.rst +++ b/docs/users-guide/model_dev.rst @@ -11,30 +11,54 @@ Input Data Preparation Estimation ---------- -ActivitySim includes the ability to re-estimate submodels using choice model estimation tools -such as `larch `__. To do so, ActivitySim adopts the concept of an estimation -data bundle (EDB), which is a collection of the necessary data to re-estimate a submodel. For example, for the auto ownership submodel, +ActivitySim includes the ability to re-estimate submodels using choice model estimation +tools such as `larch `__. To do so, ActivitySim adopts +the concept of an estimation data bundle (EDB), which is a collection of the necessary +data to re-estimate a submodel. For example, for the auto ownership submodel, the EDB consists of the following files: -* model settings - the auto_ownership_model_settings.yaml file -* coefficients - the auto_ownership_coefficients.csv file with each coefficient name, value, and constrain set to True or False if the coefficient is estimatable -* utilities specification - the auto_ownership_SPEC.csv utility expressions file -* chooser and alternatives data - the auto_ownership_values_combined.csv file with all chooser and alternatives data such as household information, land use information, and the utility data components for each alternative +* model settings - the auto_ownership_model_settings.yaml file -ActivitySim also includes Jupyter :ref:`estimation_example_notebooks` for estimating submodels with larch, as well as an ``activitysim.estimation.larch`` submodule that transforms EDBs into larch models. Additional estimation software translators can be added later if desired. +* coefficients - the auto_ownership_coefficients.csv file with each coefficient + name, value, and constrain set to True or False if the coefficient is estimatable -The combination of writing an EDB for a submodel + a larch estimation notebook means users can easily re-estimate submodels. This -combination of functionality means: +* utilities specification - the auto_ownership_SPEC.csv utility expressions file -* There is no duplication of model specifications. ActivitySim owns the specification and larch pivots off of it. Users code model specifications and utility expressions in ActivitySim so as to facilitate ease of use and eliminate inconsistencies and errors between the code used to estimate the models and the code used to apply the models. -* The EDB includes all the data and model structure information and the ``activitysim.estimation.larch`` submodule used by the example notebooks transforms the EDB to larch's data model for estimation. -* Users are able to add zones, alternatives, new chooser data, new taz data, new modes, new coefficients, revise utilities, and revise nesting structures in ActivitySim and larch responds accordingly. -* Eventually it may be desirable for ActivitySim to automatically write larch estimators (or other types of estimators), but for now the integration is loosely coupled rather than tightly coupled in order to provide flexibility. +* chooser and alternatives data - the auto_ownership_values_combined.csv file with + all chooser and alternatives data such as household information, land use + information, and the utility data components for each alternative + +ActivitySim also includes Jupyter :ref:`estimation_example_notebooks` for estimating +submodels with larch, as well as an ``activitysim.estimation.larch`` submodule that +transforms EDBs into larch models. Additional estimation software translators can +be added later if desired. + +The combination of writing an EDB for a submodel + a larch estimation notebook +means users can easily re-estimate submodels. This combination of functionality means: + +* There is no duplication of model specifications. ActivitySim owns the specification + and larch pivots off of it. Users code model specifications and utility expressions + in ActivitySim so as to facilitate ease of use and eliminate inconsistencies and + errors between the code used to estimate the models and the code used to apply the + models. + +* The EDB includes all the data and model structure information and the + ``activitysim.estimation.larch`` submodule used by the example notebooks transforms + the EDB to larch's data model for estimation. + +* Users are able to add zones, alternatives, new chooser data, new taz data, new + modes, new coefficients, revise utilities, and revise nesting structures in + ActivitySim and larch responds accordingly. + +* In the future ActivitySim may be more tightly coupled to Larch, but for now the + integration is loosely coupled to provide flexibility. Users preferring a different + estimation tool can write their own translator. Workflow ~~~~~~~~ -The general workflow for estimating models is shown in the following figures and explained in more detail below. +The general workflow for estimating models is shown in the following figures and +explained in more detail below. .. image:: ../images/estimation_tools.jpg @@ -57,19 +81,18 @@ Example To run the estimation example, do the following: -* Activate the correct conda environment if needed * Create a local copy of the estimation example folder :: - activitysim create -e example_estimation_sf -d test_example_estimation_sf + uv run activitysim create -e example_estimation_sf -d test_example_estimation_sf * Run the example :: cd test_example_estimation_sf - activitysim run -c configs_estimation/configs -c configs -o output -d data_sf + uv run activitysim run -c configs_estimation/configs -c configs -o output -d data_sf * ActivitySim should log some information and write outputs to the output folder, including EDBs for each submodel. The estimation example runs for about 15 minutes and writes EDBs for 2000 households. @@ -92,13 +115,11 @@ Additional settings for running ActivitySim in estimation mode are specified in Estimation Notebooks ~~~~~~~~~~~~~~~~~~~~ -ActivitySim includes a `Jupyter Notebook `__ recipe book with interactive re-estimation examples for each estimatable submodel. To run a Jupyter notebook, do the following: +ActivitySim includes a `Jupyter Notebook `__ recipe book with interactive examples. -* Open a conda prompt and activate the conda environment with ActivitySim installed -* If needed, ``conda install jupyterlab`` so you can run jupyter notebooks -* Type ``jupyter notebook`` to launch the web-based notebook manager -* Navigate to the ``examples/examples_estimaton/notebooks`` folder and select a notebook from the table below -* Save the updated coefficient file(s) to the configs folder and run the model in simulation mode +* To start JupyterLab, from the ActivitySim project directory run ``uv run jupyter lab``. This will start the JupyterLab server and pop up a browser window with the interactive development environment. +* Navigate to the ``examples/examples_estimaton/notebooks`` folder and select a notebook from the table below. +* Save the updated coefficient file(s) to the configs folder and run the model in simulation mode. +-------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Example | Notebook | @@ -193,4 +214,4 @@ Models API Calibration ----------- - \ No newline at end of file + diff --git a/docs/users-guide/modelsetup.rst b/docs/users-guide/modelsetup.rst index 2a64830625..a3293fdbdd 100644 --- a/docs/users-guide/modelsetup.rst +++ b/docs/users-guide/modelsetup.rst @@ -16,7 +16,7 @@ assumes the user is more experienced in running travel demand models and profici used ActivitySim or has not used recent versions of ActivitySim. More detailed instructions for installing and running ActivitySim are also available in this Users Guide. -* Use the :ref:`Pre-packaged Installer` +* Use the :ref:`pre-packaged installer` * :ref:`Run the Primary Example` * Placeholder (Edit model input files, configs, as needed) @@ -63,9 +63,9 @@ ActivitySim has features that makes it possible to customize model runs or impro * :ref:`Sharrow ` is a Python library designed to decrease run-time for ActivitySim models by creating an optimized compiled version of the model. This can also be turned on/off. * :ref:`Tracing ` allows the user to access information throughout the model run for a specified number of households/persons/zones. Enabling this feature will increase run-time and memory usage. It is recommended that this feature be turned off for typical model application. * Optimization of data types including: - + Converting string variables to pandas categoricals. ActivitySim releases ** and higher have this capability. - + Converting higher byte integer variables to lower byte integer variables (such as reducing ‘num tours’ from int64 to int8). - + Converting higher byte float variables to lower bytes. ActivitySim releases X.X.X and higher have this capability as a switch and defaults to turning this feature off. + + Converting string variables to pandas categoricals. ActivitySim releases 1.3.0 and higher have this capability. + + Converting higher byte integer variables to lower byte integer variables (such as reducing ‘num tours’ from int64 to int8). ActivitySim releases 1.3.0 and higher have this capability as a switch and defaults to turning this feature off. + + Converting higher byte float variables to lower bytes. ActivitySim releases 1.3.0 and higher have this capability as a switch and defaults to turning this feature off. Steps for enabling/disabling these options are included in the :ref:`Advanced Configuration` sub-section, under :ref:`Ways to Run the Model` page of this Users’ Guide. @@ -80,51 +80,27 @@ Steps for enabling/disabling these options are included in the :ref:`Advanced Co Software ________ -Activitysim is implemented in the Python programming language. It also uses several open source Python packages such as pandas, numpy, pytables, openmatrix etc. Hence it is recommended that you install and use a *conda* package manager for your system. -One easy way to do so is by using -`Mambaforge `__. -Mamba is a free open source cross-platform package manager that runs on -Windows, OS X and Linux and is fully compatible with conda packages. It is -also usually substantially faster than conda itself. Instructions to install mambaforge can be found `here `__. Installers for different Operating Systems can be found `here `__. - -Alternatively, if you prefer a package installer backed by corporate tech -support available (for a fee) as necessary, you can install -`Anaconda 64bit Python 3 `__, -although you should consult the `terms of service `__ -for this product and ensure you qualify since businesses and -governments with over 200 employees do not qualify for free usage. -If you're using `conda` instead of `mamba`, just replace every call to -`mamba` below with `conda`, as they share the same user interface and most -command formats. - -If you access the internet from behind a firewall, then you may need to -configure your proxy server. To do so, create a `.condarc` file in your -home installation folder, such as: - -:: - - proxy_servers: - http: http://myproxy.org:8080 - https: https://myproxy.org:8080 - ssl_verify: false - +Activitysim is implemented in the Python programming language. It uses several open source Python packages such as pandas, numpy, pytables, openmatrix etc. Installing ActivitySim ---------------------- -There are multiple ways to install the ActivitySim codebase: +There are two recommended ways to install ActivitySim: + +1. Using a :ref:`pre-packaged installer` -1. Using a :ref:`Pre-packaged Installer` (recommended for users who do not need to change the Python code) +2. Using the :ref:`UV Package and Project Manager` -2. Using a :ref:`Python package manager like mamba ` (recommended for users who need to change/customize the Python code) +The first is recommended for users who are new to Python and use Windows, do not actively create and manage Python virtual environments, +and do not need to change the ActivitySim code. The second is recommended for users who actively create and manage Python virtual environments, +and/or want to change/customize the ActivitySim code. -3. Using :ref:`pip - Python's standard package manager ` Pre-packaged Installer ______________________ -Begining with version 1.2, ActivitySim is now available for Windows via a +Beginning with version 1.2, ActivitySim is now available for Windows via a pre-packaged installer. This installer provides everything you need to run ActivitySim, including Python, all the necessary supporting packages, and ActivitySim itself. You should only choose this installation process if you @@ -146,127 +122,332 @@ Once the install is complete, ActivitySim can be run directly from any command prompt by running `/Scripts/ActivitySim.exe`. -Using *mamba* package manager -_____________________________ +UV Package and Project Manager +______________________________________ + +This method is recommended for ActivitySim users who are familiar with +Python, create and manage ActivitySim Python virtual environments, and optionally wish to customize ActivitySim code to run their models. +UV is a free open source cross-platform package and project manager that runs +on Windows, OS X, and Linux. It is 10-100x faster than conda, and pip itself, which is +the standard Python package manager. The *uv* features include automatic +environment management including installation and management of Python +versions and dependency locking. + +Install UV +^^^^^^^^^^^^^^ + +We recommend installing UV as an independent tool on your machine, separate from any existing package managers you may have such as conda or pip. + +For Windows users, run the following command in PowerShell to install *uv*. It does not require administrator privileges and installs *uv* for the current user only. +By default, uv is installed to ``~/.local/bin`` directory. Usually, this is ``C:/Users//.local/bin``. + +:: + + # Run the installer. Please review the printed message after installation. + powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" + + # Add uv to PATH + $env:PATH = "$env:USERPROFILE\.local\bin;$env:Path" + +If an agency wants to install *uv* globally for all users on Windows, run PowerShell as Administrator and run the following command. + +:: + + # Run the installer with a custom install directory (e.g., C:\shared\uv) that is accessible to all users + powershell -ExecutionPolicy ByPass -c {$env:UV_INSTALL_DIR = "C:\shared\uv";irm https://astral.sh/uv/install.ps1 | iex} + + # Add uv to PATH for all users (requires administrator privileges) + [Environment]::SetEnvironmentVariable("PATH", $env:PATH + ";C:\shared\uv", [EnvironmentVariableTarget]::Machine) + +For more instructions on installing *uv* on Windows, MacOS, or Linux, please visit https://docs.astral.sh/uv/getting-started/installation/. + +To verify that *uv* is installed correctly, open a new Command Prompt (not Anaconda Prompt) and run the following command. + +:: -This method is recommended for ActivitySim users who also wish to customize the Python code to run their models. The steps involved are described as follows: + uv --version -1. Install the *mamba* package manager as described in the :ref:`Software Requirements ` subsection. +.. note:: + If you already have *uv* installed from an older project and you encounter errors + such as + + :: -2. Create a conda environment (basically a Python install just for this project) -using mambaforge prompt or conda prompt depending on the package manager you use (on Windows) or the terminal (macOS or Linux):: + error: Failed to parse uv.lock... missing field version... + + later in the process, you may need to update *uv* to the latest version by reinstalling it via the official + installation script: https://docs.astral.sh/uv/getting-started/installation/#standalone-installer. + You can check the version of *uv* you have installed by running + + :: - mamba create -n asim python=3.10 activitysim -c conda-forge --override-channels + uv --version -This command will create the environment and install all the dependencies -required for running ActivitySim. It is only necessary to create the environment -once per machine, you do not need to (re)create the environment for each session. -If you would also like to install other tools or optional dependencies, it is -possible to do so by adding additional libraries to this command. For example:: - mamba create -n asim python=3.10 activitysim jupyterlab larch -c conda-forge --override-channels -This example installs a specific version of Python, version 3.9. A similar -approach can be used to install specific versions of other libraries as well, -including ActivitySim, itself. For example:: +Install ActivitySim with UV +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - mamba create -n asim python=3.9 activitysim=1.0.2 -c conda-forge --override-channels +There are two options to install ActivitySim using *uv*. -Additional libraries can also be installed later. You may want to consider these -tools for certain development tasks:: +The first is to use *uv* to install an official ActivitySim release from the Python Package Index (PyPI). +The second is to use *uv* to install ActivitySim from the source code repository and use the dependency lockfile. - # packages for testing - mamba install pytest pytest-cov coveralls black flake8 pytest-regressions -c conda-forge --override-channels -n asim +.. note:: + The first option (:ref:`Option 1: From PyPI`) is the quickest way to install ActivitySim from an official release and is recommended for users who do not wish to change the Python code. + However, they may end up using different deep dependencies than those tested by the developers. + The second option (:ref:`Option 2: From Source with Lockfile`) is recommended for users who may want to customize the Python code, and/or who want to run ActivitySim + exactly as it was tested by the developers using the dependency lockfile which results in the exact same deep dependencies. - # packages for building documentation - mamba install sphinx numpydoc sphinx_rtd_theme==0.5.2 -c conda-forge --override-channels -n asim +The steps involved are described as follows. - # packages for estimation integration - mamba install larch -c conda-forge --override-channels -n asim +Option 1: From PyPI +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - # packages for example notebooks - mamba install jupyterlab matplotlib geopandas descartes -c conda-forge --override-channels -n asim +You will use *uv* to create a project and virtual environment to work from and add ActivitySim. -To create an environment containing all these optional dependencies at once, you -can run the shortcut command +Open Command Prompt (not Anaconda Prompt), and run the following commands. :: - mamba env create activitysim/ASIM -n asim + # create a new project directory and cd into it + mkdir asim_project + cd asim_project + + # initialize a virtual environment + # This sets the Python version to 3.10, which is currently fully tested for ActivitySim development + uv init --python 3.10 + + # add ActivitySim package from the latest release on PyPI + uv add activitysim + +*uv* will create a new virtual environment within the ``asim_project`` project folder +and install ActivitySim and its dependencies. The virtual environment is a hidden folder +within the ``asim_project`` directory called ``.venv`` and operates the same way as Python's classic *venv*. You will notice +two new files created in the ``asim_project`` directory: ``pyproject.toml`` and ``uv.lock``. These files +are automatically created, updated, and used by *uv* to manage your ``asim_project`` project and its dependencies. +You can share these files with others to recreate the same environment for your ``asim_project`` project. For more guidance on sharing your working environment, +see the Common Q&A :ref:`How to share my working environment with others?` section below. -3. To use the **asim** environment, you need to activate it +By running the command ``uv add activitysim``, you install the official release of ActivitySim from PyPI and its direct dependencies +listed in ActivitySim's ``pyproject.toml`` file. This approach is the quickest +for getting started but it does not rely on ActivitySim's own lockfile to install deep dependencies so you may +end up with different versions of deep dependencies than those tested by ActivitySim developers. +If you want to ensure exact versions of ActivitySim's deep dependencies, you should install ActivitySim using Option 2: From Source with Lockfile. + +Option 2: From Source with Lockfile +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To install dependencies from the lockfile and run ActivitySim exactly how +its developers tested it, after installing *uv*, open Command Prompt, clone the ActivitySim project using Git. (If Git is not installed, +instructions can be found `here `_.) :: - conda activate asim -The activation of the correct environment needs to be done every time you -start a new session (e.g. opening a new conda Prompt window). + git clone https://github.com/ActivitySim/activitysim.git + cd activitysim -.. note:: +Run the ``uv sync --locked`` command to create a virtual environment using the lockfile. It will initialize a virtual environment within the ``activitysim`` directory +and install ActivitySim and all its dependencies exactly as specified in the ``uv.lock`` file. +The virtual environment is a hidden folder within the current directory called +``.venv`` and operates the same way as Python's classic *venv*. + +:: - The *activate* and *deactivate* commands to start and stop using environments - are called as `conda` even if you are otherwise using `mamba`. mamba is a drop-in replacement and uses the same commands and configuration options as conda. - You can swap almost all commands between conda & mamba. For more details, refer to `the mamba user guide `__. + uv sync --locked + # or uv sync --locked --no-editable -Using *pip* - Python's standard package manager -_______________________________________________ +It is worth pointing out that by default, *uv* installs projects in +editable mode, such that changes to the source code are immediately reflected +in the environment. ``uv sync`` accepts a ``--no-editable`` +flag, which instructs *uv* to install the project in non-editable mode, +removing any dependency on the source code. -If you prefer to install ActivitySim without a package manager like *mamba* or *conda*, it is possible to -do so with pip, although you may find it more difficult to get all of the -required dependencies installed correctly. If you can use conda for -the dependencies, you can get most of the libraries you need from there:: +Also, ``uv sync`` automatically installs the dependencies listed in ``pyproject.toml`` +under ``dependencies`` under ``[project]``, and it also installs those listed +under ``dev`` under ``[dependency-groups]``. If you want to +skip the dependency groups entirely with a *uv* install (and only install those +that would install via ``pip`` from ``pypi``), use the ``--no-default-groups`` flag +with ``uv sync``. - # required packages for running ActivitySim - mamba install cytoolz numpy pandas psutil pyarrow numba pytables pyyaml openmatrix requests -c conda-forge - # required for ActivitySim version 1.0.1 and earlier - pip install zbox +Which Option Should I Use? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -And then simply install activitysim with pip. ++--------------------------------------------------------------------------------+-----------+---------------------------+ +| If I want to ... | From PyPI | From Source with Lockfile | ++================================================================================+===========+===========================+ +| Install an official release of ActivitySim. | Yes | | ++--------------------------------------------------------------------------------+-----------+---------------------------+ +| Install a development version of ActivitySim. | | Yes | ++--------------------------------------------------------------------------------+-----------+---------------------------+ +| Install ActivitySim quickly to run models without changing the code. | Yes | | ++--------------------------------------------------------------------------------+-----------+---------------------------+ +| Do ActivitySim code development. | | Yes | ++--------------------------------------------------------------------------------+-----------+---------------------------+ +| Run ActivitySim with deep dependencies exactly as tested by the developers. | | Yes | ++--------------------------------------------------------------------------------+-----------+---------------------------+ + + +Run ActivitySim with UV +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Activate the virtual environment created by *uv*. This option is similar to using Python's classic venv or Conda env. :: - python -m pip install activitysim + # cd into the project directory if not already there + ## if you used the From PyPI option + cd asim_project + ## if you used the From Source with Lockfile option + cd activitysim + + # Activate the virtual environment + .venv\Scripts\activate + +Once the virtual environment is activated, you can run ActivitySim commands directly using the ``activitysim`` command. +For example, run the ActivitySim commandline using the following. More information about the commandline interface is available in +the :ref:`Ways to Run the Model` section. -If you are using a firewall you may need to add ``--trusted-host pypi.python.org --proxy=myproxy.org:8080`` to this command. +:: -For development work, can also install ActivitySim directly from source. Clone -the ActivitySim repository, and then from within that directory run:: + activitysim run -c configs -o output -d data - python -m pip install . -e +Alternatively, you can run ActivitySim commands directly using *uv* without activating the virtual environment. -The "-e" will install in editable mode, so any changes you make to the ActivitySim -code will also be reflected in your installation. +:: -Installing from source is easier if you have all the necessary dependencies already -installed in a development conda environment. Developers can create an -environment that has all the optional dependencies preinstalled by running:: + uv run activitysim run -c configs -o output -d data - mamba env create activitysim/ASIM-DEV +Common Q&A +^^^^^^^^^^^^^^^^ +My travel demand model requires additional Python packages not included with ActivitySim. How do I add them? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +You can add additional packages to your *uv* project by using the ``uv add`` command. For example, to add the ``geopandas`` package, +run the following command within your existing *uv* project directory. -If you prefer to use a different environment name than `ASIM-DEV`, just -append `--name OTHERNAME` to the command. Then all that's left to do is install -ActivitySim itself in editable mode as described above. +:: -.. note:: + # cd into your project directory + cd asim_project + + # Add geopandas package + uv add geopandas - ActivitySim is a 64bit Python 3 library that uses a number of packages from the - scientific Python ecosystem, most notably `pandas `__ - and `numpy `__. +This will add the package to your virtual environment and update the ``pyproject.toml`` and the ``uv.lock`` file to include the new package and its dependencies. + +If you envision having a version of Python packages that is different from the one used by ActivitySim, e.g., you need pandas 1.x for visualization (for some reason), +we recommend creating a separate *uv* project for your custom packages and managing them independently from ActivitySim. + +:: + + # Open Command Prompt + mkdir viz_project + cd viz_project + uv init + uv add pandas==1.5.3 + +Many agencies use commercial software that have Python APIs and dependencies that may conflict with ActivitySim dependencies. +In such cases, we also recommend creating a separate *uv* project for the commercial software and managing them independently from ActivitySim. + +:: + + # Open Command Prompt + mkdir emme_project + cd emme_project + uv init --python 2.7 + # Then copy the emme.pth file (provides EMME API handshakes) from the Emme installation directory to emme_project/.venv/Lib/site-packages/ + +When having multiple *uv* projects, you can switch between them by activating the respective virtual environments. + +:: + + # Activate visualization project + # Open Command Prompt + cd path\to\viz_project + .venv\Scripts\activate + + # Deactivate visualization project + deactivate + +How to share my working environment with others? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +You can share your working environment with others by sharing the ``uv.lock`` file and the ``pyproject.toml`` file located in your project directory (and ``.python-version`` file if it exists). +The ``uv.lock`` file contains the exact versions of all packages and dependencies used in your project. +Others can recreate the same environment by running the ``uv sync --locked`` command in a new project directory containing the shared files. + +:: + + # Initialize a new project directory + mkdir new_asim_project + cd new_asim_project + + # Copy .python-version file to new project directory (if exists) + copy path\to\shared\.python-version . + # Copy pyproject.toml file to new project directory + copy path\to\shared\pyproject.toml . + # Copy uv.lock file to new project directory + copy path\to\shared\uv.lock . + + # Recreate the same environment + uv sync --locked + +Can other users on the same server or machine use my already created virtual environment? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +This is doable but it requires additional setup and admin rights - therefore we do not generally recommend it. We recommend following the practice in :ref:`How to share my working environment with others?`. + +If you'd still like to proceed, here are the recommended steps to follow (proceed with caution!): + +1. Ensure that you installed UV globally (requires admin rights) on the server/machine for all users. :ref:`Install UV` section above provides instructions on how to do this. + +2. Assuming you installed UV globally in ``C:\shared\uv\``, ensure that all users have read and execute permissions to this directory. + +3. Create a directory under ``C:\shared\uv\`` to install Python globally for UV. For example, open Command Prompt, create a directory named ``uv_python`` under ``C:\shared\uv\``. + +:: + + cd C:\shared\uv\ + mkdir uv_python + +4. Under Environment Variables > System variables (requires Admin), create a new system environment variable named ``UV_PYTHON_INSTALL_DIR`` and set its value to the Python directory created in step 3 ``C:\shared\uv\uv_python\``. + +5. Run the following command to install Python globally for UV. This should install Python executables globally in the ``UV_PYTHON_INSTALL_DIR`` directory. + +:: + + uv python install 3.10 + +6. Under Environment Variables > System variables (requires Admin), create a new system environment variable named ``UV_PYTHON`` and set its value to the ``python.exe`` created in step 5. + +7. Create a directory to host UV projects under ``C:\shared\uv\`` + +:: + + cd C:\shared\uv\ + mkdir uv_projects + cd uv_projects + +8. Create a new *uv* project and install ActivitySim using either :ref:`Option 1: From PyPI` or :ref:`Option 2: From Source with Lockfile` as described above. + +9. Ensure that all users have read and execute permissions to the shared uv directory. + +:: - As mentioned above, the recommended way to get your own scientific Python installation is to - install 64 bit Anaconda, which contains many of the libraries upon which - ActivitySim depends + some handy Python installation management tools. + icacls C:\shared\uv /reset /T - Anaconda includes the ``conda`` command line tool, which does a number of useful - things, including creating `environments `__ - (i.e. stand-alone Python installations/instances/sandboxes) that are the recommended - way to work with multiple versions of Python on one machine. Using conda - environments keeps multiple Python setups from conflicting with one another. +If I use the From PyPI option to install ActivitySim, would I run into dependency issues? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Using the :ref:`Option 1: From PyPI` option to install ActivitySim may result in different versions of deep dependencies than those tested by ActivitySim developers. +This is because the :ref:`Option 1: From PyPI` option installs only the direct dependencies listed in ActivitySim's ``pyproject.toml`` file, +and relies on *uv* to resolve and install the deep dependencies. It is likely that a newer version of ActivitySim deep dependencies +may cause compatibility issues. For example, see this recent update with ``numexpr``: https://github.com/pydata/numexpr/issues/540 - You need to activate the activitysim environment each time you start a new command - session. You can remove an environment with ``conda remove -n asim --all`` and - check the current active environment with ``conda info -e``. +When that happens, we recommend using the :ref:`Option 2: From Source with Lockfile` option to install ActivitySim, which ensures that +you are using the exact same deep dependencies as those tested by ActivitySim developers. In the meantime, you can also +report the compatibility issues to the ActivitySim development team via GitHub Issues, so that they can address them in future releases. - For more information on Anaconda, see Anaconda's `getting started - `__ guide. +If I want to use ``uv run`` to run ActivitySim commands, do I still need to activate the virtual environment? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +No, if you use ``uv run`` to run ActivitySim commands, you do not need to activate the virtual environment first. +However, you will need to call ``uv run`` in the project directory where the virtual environment is located. Also, like ``uv sync``, +``uv run`` automatically updates the lockfile and installs any missing dependencies before running the command. diff --git a/docs/users-guide/other_examples.rst b/docs/users-guide/other_examples.rst index 321b4882ba..662c82bc22 100644 --- a/docs/users-guide/other_examples.rst +++ b/docs/users-guide/other_examples.rst @@ -50,8 +50,6 @@ Some available examples include those listed in the table below. +-------------------------------------------+-----------------------------------------------------------+--------------+----------------------+ | placeholder_psrc | PSRC agency example | 2 | Future development | +-------------------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| placeholder_sandag | SANDAG agency example | 3 | Future development | -+-------------------------------------------+-----------------------------------------------------------+--------------+----------------------+ | prototype_sandag_xborder | SANDAG agency example | 3 | In development | +-------------------------------------------+-----------------------------------------------------------+--------------+----------------------+ | prototype_mwcog | MWCOG agency example | 2 | In development | @@ -100,10 +98,8 @@ has essentially the same configuration as the simpler three zone example above. *Example* - To run prototype_marin, do the following: -* Activate the correct conda environment if needed * Create a local copy of the example :: @@ -117,7 +113,7 @@ To run prototype_marin, do the following: :: # Marin TM2 work tour mode choice for the MTC region - activitysim run -c configs -d data -o output -s settings_mp.yaml + uv run activitysim run -c configs -d data -o output -s settings_mp.yaml * For optimal performance, configure multiprocessing and chunk_size based on machine hardware. @@ -197,25 +193,6 @@ prototype_mtc model design. It uses PSRC zones, land use, synthetic population, See example commands in `example_manifest.yaml `_ for running placeholder_psrc. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. -.. _placeholder_sandag : - -**placeholder_sandag** - - -.. note:: - - This example is in development - - -The placeholder_sandag is a multi-part model, containing one-, two-, and three- zone system (MAZs, TAZs, and TAPs) implementation of the -prototype_mtc model design. It uses SANDAG zones, land use, synthetic population, and network LOS (skims). - -*Example* - - -See example commands in `example_manifest.yaml `_ -for running placeholder_sandag. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. - .. _prototype_sandag_xborder : **prototype_sandag_xborder** diff --git a/docs/users-guide/performance/expr-profiling.md b/docs/users-guide/performance/expr-profiling.md new file mode 100644 index 0000000000..f510be64df --- /dev/null +++ b/docs/users-guide/performance/expr-profiling.md @@ -0,0 +1,107 @@ +# Expression Profiling + +Part of the appeal of ActivitySim is in its flexibility: it is possible to craft +a massive variety of mathematical forms and relationships through creative use +of the expressions found in most component spec files. But as we have all learned +from Spider-Man, with great power comes great responsibility. Users can write +arbitrary code in spec files, and the runtime performance of ActivitySim will +depend on the parsimony and efficiency of that code. + +Sometimes these spec files can be large, and it may be difficult to determine +simply by inspection which expressions in a given spec file are faster or slower. +ActivitySim now offers an expression-level profiling tool to assist in diagnosing +performance problems that arise from inefficient spec files. + +```{important} +At this time, +expression profiling only works for the evaluation of expressions in "legacy" mode. +It does not work in "sharrow" mode, as the compiled expressions run with sharrow +are not run in a serial fashion and are not able to be profiled in the same way. +``` + +## Profiling an Entire Model Run + +The simplest way to use the expression profiler is to set the +[`expression_profile`](activitysim.core.configuration.Settings.expression_profile) +configuration setting in the top level model settings (typically `settings.yaml`): + +```yaml +expression_profile: true +``` + +This will cause the profiler to be activated for all expressions in the model, +across all components. This includes expressions in the spec files, as well as +expressions in all preprocessors and annotators. Each time the expressions in +any spec file are evaluated, the profiler will record the time taken to evaluate +each expression. An "expr-performance" subdirectory will be created in the model's +logging directory, and a new log file will be created each time the expressions in +any spec file are evaluated. The file is named according to the `trace_label` found +where the expressions are being evaluated. It will include a list of all the evaluated +expressions from the spec file, along with the time taken to evaluate each expression. +For multi-processed models, each subprocess will create its own log file directory, +similar to the logging directory structure for the other model components. + +## Summary Outputs + +At the end of a model run where the `expression_profile` setting is active, +ActivitySim will also create a pair of summary files in the "expr-performance" +subdirectory. The first is named "expression-timing-subcomponents.html", +and contains a simple concatenation of the runtimes of +expressions in the various subcomponents stored in the log files, +filtered to only include expressions that tool a notable amount of time. +By default, this is set to 0.1 seconds, but can be changed by setting the +[`expression_profile_cutoff`](activitysim.core.configuration.Settings.expression_profile_cutoff) +configuration setting in the model settings. + +The second file, "expression-timing-components.html", shows an aggregate +summary of the runtimes for each expression, +aggregated across all the log files. The aggregation is by model component and +expression, so that this summary includes the total time taken to evaluate each +expression within each model component, recognizing that identical expressions +may be evaluated multiple times in different model subcomponents (e.g. across +different purposes, or tour numbers, etc.). This more aggregated summary is +typically the one that will be most useful for identifying expressions that +provide the most overall potential for performance improvement via streamlining. + +Users should note that the expression profiler is not a substitute for good coding +practices. It will not necessarily identify all performance problems, and it is not +able to suggest improvements to the expressions. It is simply a tool to help users +identify which expressions are taking the most time to evaluate, and therefore +which expressions are the best candidates for improvement. + +Also, users should understand that the expression profiler is not directly measuring the +computational complexity of the expressions, but rather the time taken to evaluate +the expressions. This time can be affected by a number of factors, including the +complexity of the expression, the size of the data being processed, and whether +there are other processes running on the machine at the same time competing for +resources. For multiprocessing model runs, those other processes may include +other the subprocesses of ActivitySim, which may lead to surprising results. + +There is also no adjustment made for parallelization of the expression evaluations. +For example, if the same expression is evaluated in parallel across 8 processes on +a machine with 8 cores, and each process takes 0.1 seconds to evaluate the expression, +the profiler will still show that the expression took 0.8 seconds to evaluate, even +though the total wall clock time taken to evaluate the expression across all processes +was only 0.1 seconds. + +Profiling expressions also adds some overhead to the model run, increasing the +total runtime of the model by a modest but noticeable amount. In consortium +[experiments](https://github.com/ActivitySim/activitysim/pull/936#issuecomment-3165410169) +with this tool, runtime for the full-scale SANDAG model was found to +increase by approximately 12.5% when the profiler was enabled, adding more than +13 minutes to a model run that already took 105 minutes. Users should thus +be careful about using the profiler in production runs. It is recommended turn off +the profiler in production runs, and only use it for debugging and development. + +## Profiling Individual Components + +The expression profiler can also be used to profile individual components, rather +than the entire model. This is done by setting the `compute_settings.performance_log` +attribute for the component in the model settings. This attribute can be set to the +filename where the profiler log file should be written, which will override +the default behavior of writing the log file to the "expr-performance" subdirectory. +This feature only works for components that are run in a single process, and which +have a `compute_settings` attribute. It is generally not recommended to use this +feature unless a specific component is suspected of having atypical performance +problems, as it will not provide the same summary reporting as profiling the entire +model. diff --git a/docs/users-guide/performance/index.md b/docs/users-guide/performance/index.md index 2f722f621d..ab807125e1 100644 --- a/docs/users-guide/performance/index.md +++ b/docs/users-guide/performance/index.md @@ -23,6 +23,7 @@ These techniques are the focus of this section. Chunking to Reduce Peak Memory Usage Compiling with Sharrow Skim Data Format + Expression Profiling ``` ## Checklist for Performance Tuning diff --git a/docs/users-guide/run_primary_example.rst b/docs/users-guide/run_primary_example.rst index 898b354041..d45e0eb4bf 100644 --- a/docs/users-guide/run_primary_example.rst +++ b/docs/users-guide/run_primary_example.rst @@ -3,22 +3,18 @@ Run the Primary Example To setup and run the primary example (see :ref:`examples`) from the command line interface, do the following: -* Open the mamba or conda command prompt -* If you installed ActivitySim using conda environments, activate the conda - environment with ActivitySim installed (i.e. ``conda activate asim``) +* Type ``uv run activitysim create -e prototype_mtc -d test_prototype_mtc`` to copy + the very small prototype_mtc example to a new test_prototype_mtc directory +* Change to the test_prototype_mtc directory ``cd test_prototype_mtc`` +* Type ``uv run activitysim run -c configs -o output -d data`` to run the example +* Review the outputs in the output directory +* ActivitySim will log progress and write outputs to the output folder. .. note:: - Most ``conda`` and ``mamba`` commands can be used interchangeably in the **mamba command prompt**. In the above step, you could instead use ``mamba activate asim`` in the mamba command prompt. -* Or, if you used the :ref:`pre-packaged installer`, + Or, if you used the :ref:`pre-packaged installer`, replace all the commands below that call ``activitysim ...`` with the complete path to your installed location, which is probably something like ``c:\programdata\activitysim\scripts\activitysim.exe``. -* Type ``activitysim create -e prototype_mtc -d test_prototype_mtc`` to copy - the very small prototype_mtc example to a new test_prototype_mtc directory -* Change to the test_prototype_mtc directory ``cd test_prototype_mtc`` -* Type ``activitysim run -c configs -o output -d data`` to run the example -* Review the outputs in the output directory -* ActivitySim will log progress and write outputs to the output folder. The example should run in a few minutes since it runs a small sample of households. diff --git a/docs/users-guide/visualization.rst b/docs/users-guide/visualization.rst index 2a930d337c..3da5a7fc0b 100644 --- a/docs/users-guide/visualization.rst +++ b/docs/users-guide/visualization.rst @@ -129,9 +129,13 @@ Trip-level skim data are also made available in the preprocessing stage by attac Install and Run Simwrapper ~~~~~~~~~~~~~~~~~~~~~~~~~~ -The SimWrapper Python package, which contains convenience functions for initiating the SimWrapper app in the browser and a local file server for accessing summary tables from this app, is automatically installed as a dependency of ActivitySim. However, you can also use SimWrapper independent of ActivitySim to, for example, visualize summaries on a different workstation. SimWrapper is available on both conda-forge and pip: +The SimWrapper Python package, which contains convenience functions for initiating the SimWrapper app in the browser and a local file server for accessing summary tables from this app, is automatically installed as a dependency of ActivitySim. However, you can also use SimWrapper independent of ActivitySim to, for example, visualize summaries on a different workstation. SimWrapper is available on both conda-forge and pypi: :: + > uv add simwrapper + +or + > conda install -c conda-forge simwrapper or diff --git a/docs/users-guide/ways_to_run.rst b/docs/users-guide/ways_to_run.rst index 98c1f09e8b..1b21221078 100644 --- a/docs/users-guide/ways_to_run.rst +++ b/docs/users-guide/ways_to_run.rst @@ -80,11 +80,9 @@ Refer to the :ref:`Run the Primary Example` section to learn how to run the prim Using Jupyter Notebook ______________________ -ActivitySim includes a `Jupyter Notebook `__ recipe book with interactive examples. To run a Jupyter notebook, do the following: +ActivitySim includes a `Jupyter Notebook `__ recipe book with interactive examples. -* Open a conda prompt and activate the conda environment with ActivitySim installed -* If needed, ``conda install jupyterlab`` so you can run jupyter notebooks -* Type ``jupyter notebook`` to launch the web-based notebook manager +* To start JupyterLab, from the ActivitySim project directory run ``uv run jupyter lab``. This will start the JupyterLab server and pop up a browser window with the interactive development environment. * Navigate to the ``examples/prototype_mtc/notebooks`` folder and select a notebook to learn more: * `Getting started `__ diff --git a/pyproject.toml b/pyproject.toml index 6a76cacb26..a8fef9948e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,11 +11,11 @@ name = "activitysim" dynamic = ["version"] dependencies = [ "cytoolz >= 0.8.1", - "multimethod <2.0", + "multimethod < 2.0", "numba >= 0.57", - "numpy >= 1.16.1, <2", + "numpy >= 1.16.1, <1.26", "openmatrix >= 0.3.4.1", - "pandas >= 2.2", + "pandas >= 2", "pandera >=0.15, <0.18.1", "platformdirs", "psutil >= 4.1", @@ -24,11 +24,11 @@ dependencies = [ "pypyr >= 5.3", "pyyaml >= 5.1", "requests >= 2.7", - "scikit-learn >= 1.1", - "sharrow >= 2.9.1", - "simwrapper > 1.7", + "scikit-learn >= 1.2", + "setuptools>=80.9.0", + "sharrow>=2.15", "sparse", - "tables >= 3.9", + "tables >= 3.9", # pytables is tables in pypi "xarray >= 2024.05", ] readme = "README.md" @@ -76,7 +76,6 @@ log_cli = true tb = "native" [tool.ruff] -select = ["E", "F", "B", "UP", "TID"] line-length = 140 exclude = [ ".git", @@ -88,12 +87,73 @@ exclude = [ "sandbox/" ] -[tool.ruff.isort] +[tool.ruff.lint] +select = ["E", "F", "B", "UP", "TID"] + +[tool.ruff.lint.isort] known-first-party = ["activitysim"] required-imports = ["from __future__ import annotations"] -[tool.ruff.pydocstyle] +[tool.ruff.lint.pydocstyle] convention = "numpy" -[tool.ruff.per-file-ignores] +[tool.ruff.lint.per-file-ignores] "__init__.py" = ["E402", "F401"] # Ignore import violations in all `__init__.py` files + +[dependency-groups] +dev = [ + "altair", + "autodoc_pydantic", + "asv", # for benchmarking + "black==22.12.0", + "bump2version", # for making a release + "coveralls>=3.3.1", + "dask==2023.11", + "descartes", + "filelock", + "fsspec", + "geopandas", + "gh", + "ipykernel", # so this env will appear in jupyter as a selection + "isort==5.12.0", + "jupyter-book", + "jupyterlab", + "larch>=6.0.42", + "matplotlib", + "myst-nb", + "myst-parser", # allows markdown in sphinx + "nbconvert", + "nbformat", + "nbmake==1.4.6", + "numexpr", + "numpydoc", + "pre-commit", + "pycodestyle", + "pydata-sphinx-theme", + "pydot>=4.0.1", + "pyinstrument==4.4", + "pytest==7.2", + "pytest-cov", + "pytest-regressions", + "requests>=2.32.3", + "rich==13.3", + "ruby", # for benchmarking pre-commit hooks + "ruff", + "setuptools_scm", + "simwrapper > 1.7", + "snakeviz", # for profiling + "sphinx==6.1", + "sphinx-argparse", + "sphinx-autosummary-accessors", + "sphinx-copybutton", + "sphinx-remove-toctrees", + "sphinx_rtd_theme==1.2", + "sphinx-argparse==0.4", + "xlsxwriter==3.2.5", + "xmle", + "zarr>=2,<3", + "zstandard" +] + +[tool.uv] +default-groups = ["dev"] diff --git a/test/cdap/test_cdap.py b/test/cdap/test_cdap.py index 38e2657769..b26078c904 100644 --- a/test/cdap/test_cdap.py +++ b/test/cdap/test_cdap.py @@ -162,9 +162,9 @@ def test_cdap_from_pipeline(reconnect_pipeline: workflow.State, caplog): lambda x: x[:-1].upper() if x.endswith("0") else x.upper() ) household_df["cdap_activity"] = household_df.apply( - lambda x: x["cdap_activity"] + "J" - if x["has_joint_tour"] == 1 - else x["cdap_activity"], + lambda x: ( + x["cdap_activity"] + "J" if x["has_joint_tour"] == 1 else x["cdap_activity"] + ), axis=1, ) diff --git a/test/random_seed/test_random_seed.py b/test/random_seed/test_random_seed.py index 6f4997f62d..d9a33f2bf1 100644 --- a/test/random_seed/test_random_seed.py +++ b/test/random_seed/test_random_seed.py @@ -2,13 +2,13 @@ # ActivitySim # See full license in LICENSE.txt. +import importlib.resources import os import subprocess from shutil import copytree import pandas as pd import pandas.testing as pdt -import pkg_resources import yaml @@ -35,7 +35,7 @@ def run_test_random_seed(): def example_path(dirname): resource = os.path.join("examples", "prototype_mtc", dirname) - return pkg_resources.resource_filename("activitysim", resource) + return str(importlib.resources.files("activitysim").joinpath(resource)) def test_path(dirname): return os.path.join(os.path.dirname(__file__), dirname) diff --git a/test/test_expression_profiling.py b/test/test_expression_profiling.py new file mode 100644 index 0000000000..f3b61a6fb2 --- /dev/null +++ b/test/test_expression_profiling.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +import argparse +import os +from contextlib import contextmanager + +from activitysim.cli.run import add_run_args, run +from activitysim.core import timing, workflow + + +@contextmanager +def change_directory(path): + """ + Context manager to temporarily change the current working directory. + """ + old_cwd = os.getcwd() # Save the current working directory + try: + os.chdir(path) # Change to the new directory + yield # Execute the code within the 'with' block + finally: + os.chdir(old_cwd) # Revert to the original directory + + +def test_expression_profiling_mtc(): + state = workflow.create_example("prototype_mtc", temp=True) + + state.settings.expression_profile = True + state.run.all() + + # generate a summary of slower expression evaluation times + # across all models and write to a file + analyze = timing.AnalyzeEvalTiming(state) + analyze.component_report(style=state.settings.expression_profile_style) + analyze.subcomponent_report(style=state.settings.expression_profile_style) + + workdir = state.filesystem.working_dir + outdir = workdir.joinpath(state.filesystem.output_dir) + timestamp = state.get("run_timestamp", "unknown") + assert timestamp != "unknown", "Run timestamp should not be 'unknown'" + assert outdir.joinpath( + f"log/expr-performance/{timestamp}/expression-timing-subcomponents.html" + ).exists() + assert outdir.joinpath( + f"log/expr-performance/{timestamp}/expression-timing-components.html" + ).exists() + assert outdir.joinpath( + f"log/expr-performance/{timestamp}/tour_mode_choice.work.simple_simulate.eval_nl.eval_utils.log" + ).exists() + + +def test_expression_profiling_semcog(): + # testing a two zone system model + state = workflow.create_example("production_semcog", temp=True) + + state.settings.expression_profile = True + + print("state.filesystem.working_dir=", state.filesystem.working_dir) + # import the extensions module, which is located in the working directory + with change_directory(state.filesystem.working_dir): + import sys + + sys.path.insert(0, ".") + import extensions # noqa: F401 + + sys.path.pop(0) + + state.run.all() + + # generate a summary of slower expression evaluation times + # across all models and write to a file + analyze = timing.AnalyzeEvalTiming(state) + analyze.component_report(style=state.settings.expression_profile_style) + analyze.subcomponent_report(style=state.settings.expression_profile_style) + + workdir = state.filesystem.working_dir + outdir = workdir.joinpath(state.filesystem.output_dir) + + timestamp = state.get("run_timestamp", "unknown") + assert timestamp != "unknown", "Run timestamp should not be 'unknown'" + + assert outdir.joinpath( + f"expr-performance/{timestamp}/expression-timing-subcomponents.html" + ).exists() + assert outdir.joinpath( + f"expr-performance/{timestamp}/expression-timing-components.html" + ).exists() + assert outdir.joinpath( + f"expr-performance/{timestamp}/trip_destination.trip_num_1.atwork.compute_logsums.dp.preprocessor.trip_mode_choice_annotate_trips_preprocessor.log" + ).exists() + + +def test_expression_profiling_mtc_mp(): + state = workflow.create_example("prototype_mtc", temp=True) + # state = workflow.create_example("prototype_mtc", "/tmp/exprprof5") + with state.filesystem.working_dir.joinpath("configs_mp", "settings.yaml").open( + mode="a" + ) as f: + f.write("\n\nexpression_profile: true\n") + + args = [ + # "-c", + # str(state.filesystem.working_dir.joinpath("configs-override")), + "-c", + str(state.filesystem.working_dir.joinpath("configs_mp")), + "-c", + str(state.filesystem.working_dir.joinpath("configs")), + "-d", + str(state.filesystem.working_dir.joinpath("data")), + "-o", + str(state.filesystem.working_dir.joinpath("output")), + ] + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args(args) + run(args) + + ep_dir = state.filesystem.working_dir.joinpath("output", "log", "expr-performance") + + # list all the subdirectories in the output directory + subdirectories = [item for item in ep_dir.iterdir() if item.is_dir()] + + # should only be one subdirectory with a timestamp + assert ( + len(subdirectories) == 1 + ), "There should be exactly one subdirectory with a timestamp" + + timestamp = subdirectories[0].name + base_dir = ep_dir.joinpath(timestamp) + + assert base_dir.joinpath("expression-timing-subcomponents.html").exists() + assert base_dir.joinpath("expression-timing-components.html").exists() + assert base_dir.joinpath( + "mp_households_0-trip_destination.trip_num_1.atwork.compute_logsums.dp.preprocessor.trip_mode_choice_annotate_trips_preprocessor.log" + ).exists() diff --git a/test/test_skim_name_conflicts.py b/test/test_skim_name_conflicts.py new file mode 100644 index 0000000000..eafd5ee426 --- /dev/null +++ b/test/test_skim_name_conflicts.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +import os +import shutil +from importlib.resources import files +from pathlib import Path + +import openmatrix +import pytest + +import activitysim.abm # noqa: F401 +from activitysim.core import workflow + + +def example_path(dirname): + resource = files("activitysim.examples.placeholder_sandag").joinpath(dirname) + return str(resource) + + +def mtc_example_path(dirname): + resource = files("activitysim.examples.prototype_mtc").joinpath(dirname) + return str(resource) + + +def psrc_example_path(dirname): + resource = files("activitysim.examples.placeholder_psrc").joinpath(dirname) + return str(resource) + + +@pytest.fixture(scope="session") +def example_data_dir(tmp_path_factory) -> Path: + """Fixture to provide the path to the example data directory.""" + td = tmp_path_factory.mktemp("skim-conflict-data") + shutil.copytree(example_path("data_2"), td.joinpath("data_2")) + shutil.copy( + example_path(os.path.join("data_3", "maz_to_maz_bike.csv")), + td.joinpath("data_2"), + ) + + # add extra skims to OMX to create a conflict + + with openmatrix.open_file(td.joinpath("data_2").joinpath("skims1.omx"), "a") as omx: + for t in ["EA", "AM", "MD", "PM", "EV"]: + # Create a new matrix for each time period + omx.createMatrix(f"DISTBIKE__{t}", obj=omx["DISTBIKE"][:]) + + return td.joinpath("data_2") + + +def test_skim_name_conflicts(example_data_dir, tmp_path_factory): + # when sharrow is required, the run should fail due to conflicting skim names + state = workflow.State.make_default( + data_dir=example_data_dir, + configs_dir=( + example_path("configs_2_zone"), + psrc_example_path("configs"), + ), + output_dir=tmp_path_factory.mktemp("out-fail"), + settings={ + "households_sample_size": 20, + "sharrow": "require", + "disable_zarr": True, + }, + ) + with pytest.raises(ValueError): + state.run( + [ + "initialize_landuse", + "initialize_households", + ] + ) + + +def test_skim_name_conflicts_no_sharrow(example_data_dir, tmp_path_factory): + # when sharrow is disabled, the run should warn about conflicting skim names but not fail + state = workflow.State.make_default( + data_dir=example_data_dir, + configs_dir=( + example_path("configs_2_zone"), + psrc_example_path("configs"), + ), + output_dir=tmp_path_factory.mktemp("out-pass"), + settings={ + "households_sample_size": 20, + "sharrow": False, + "disable_zarr": True, + }, + ) + # Run the beginning workflow with the modified settings, should only warn about the conflict + with pytest.warns( + UserWarning, + match="some skims have both time-dependent and time-agnostic versions", + ): + state.run( + [ + "initialize_landuse", + "initialize_households", + ] + ) + + +@pytest.mark.parametrize("solution", ["^DISTBIKE$", "^DISTBIKE__.+"]) +def test_skim_name_conflicts_ok(example_data_dir, tmp_path_factory, solution): + # when sharrow is required, and omx_ignore_patterns is set correctly, + # the run should work without raising an error + state = workflow.State.make_default( + data_dir=example_data_dir, + configs_dir=( + example_path("configs_2_zone"), + psrc_example_path("configs"), + ), + output_dir=tmp_path_factory.mktemp("out-solved"), + settings={ + "households_sample_size": 20, + "sharrow": "require", + "disable_zarr": True, + "omx_ignore_patterns": [solution], + }, + ) + state.run( + [ + "initialize_landuse", + "initialize_households", + ] + ) diff --git a/test/trace_id/.gitignore b/test/trace_id/.gitignore new file mode 100644 index 0000000000..67176c62d1 --- /dev/null +++ b/test/trace_id/.gitignore @@ -0,0 +1,2 @@ +configs*/ +output/ \ No newline at end of file diff --git a/test/trace_id/simulation.py b/test/trace_id/simulation.py new file mode 100644 index 0000000000..70cf3457fd --- /dev/null +++ b/test/trace_id/simulation.py @@ -0,0 +1,16 @@ +# ActivitySim +# See full license in LICENSE.txt. + +from __future__ import annotations + +import argparse +import sys + +from activitysim.cli.run import add_run_args, run + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/test/trace_id/test_trace_id.py b/test/trace_id/test_trace_id.py new file mode 100644 index 0000000000..4d678759e6 --- /dev/null +++ b/test/trace_id/test_trace_id.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +# ActivitySim +# See full license in LICENSE.txt. +import importlib.resources +import os +import subprocess +from shutil import copytree + +import pandas as pd +import pandas.testing as pdt +import yaml + + +def update_settings(settings_file, key, value): + with open(settings_file, "r") as f: + settings = yaml.safe_load(f) + f.close() + + settings[key] = value + + with open(settings_file, "w") as f: + yaml.safe_dump(settings, f) + f.close() + + +def test_trace_ids_have_same_hash(): + def example_path(dirname): + resource = os.path.join("examples", "prototype_mtc", dirname) + return str(importlib.resources.files("activitysim").joinpath(resource)) + + def test_path(dirname): + return os.path.join(os.path.dirname(__file__), dirname) + + new_configs_dir = test_path("configs") + new_mp_configs_dir = test_path("configs_mp") + new_settings_file = os.path.join(new_configs_dir, "settings.yaml") + copytree(example_path("configs"), new_configs_dir) + copytree(example_path("configs_mp"), new_mp_configs_dir) + + update_settings( + new_settings_file, "trace_hh_id", 1932009 + ) # Household in the prototype_mtc example with 11 people + + def check_csv_suffix(directory): + suffix = None + mismatched_files = [] + for root, dirs, files in os.walk(directory): + for filename in files: + if filename.lower().endswith(".csv"): + file_suffix = filename[-10:] + if suffix is None: + suffix = file_suffix + elif file_suffix != suffix: + mismatched_files.append(os.path.join(root, filename)) + if mismatched_files: + raise AssertionError( + f"CSV files with mismatched suffixes: {mismatched_files}" + ) + + file_path = os.path.join(os.path.dirname(__file__), "simulation.py") + + run_args = [ + "-c", + test_path("configs_mp"), + "-c", + test_path("configs"), + "-d", + example_path("data"), + "-o", + test_path("output"), + ] + + try: + os.mkdir(test_path("output")) + except FileExistsError: + pass + + subprocess.run(["coverage", "run", "-a", file_path] + run_args, check=True) + + check_csv_suffix(os.path.join(test_path("output"), "trace")) + + +if __name__ == "__main__": + test_trace_ids_have_same_hash() diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000000..f1ffb126df --- /dev/null +++ b/uv.lock @@ -0,0 +1,6139 @@ +version = 1 +revision = 2 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"2024-05-10T11:23:08.421Z" }, +] + +[[package]] +name = "activitysim" +source = { editable = "." } +dependencies = [ + { name = "cytoolz" }, + { name = "multimethod" }, + { name = "numba" }, + { name = "numpy" }, + { name = "openmatrix" }, + { name = "pandas", version = "2.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, + { name = "pandas", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" }, + { name = "pandera" }, + { name = "platformdirs" }, + { name = "psutil" }, + { name = "pyarrow" }, + { name = "pydantic" }, + { name = "pypyr" }, + { name = "pyyaml" }, + { name = "requests" }, + { name = "scikit-learn" }, + { name = "setuptools" }, + { name = "sharrow" }, + { name = "sparse" }, + { name = "tables", version = "3.10.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "tables", version = "3.10.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "xarray" }, +] + +[package.dev-dependencies] +dev = [ + { name = "altair" }, + { name = "asv" }, + { name = "autodoc-pydantic" }, + { name = "black" }, + { name = "bump2version" }, + { name = "coveralls" }, + { name = "dask" }, + { name = "descartes" }, + { name = "filelock" }, + { name = "fsspec" }, + { name = "geopandas" }, + { name = "gh" }, + { name = "ipykernel" }, + { name = "isort" }, + { name = "jupyter-book" }, + { name = "jupyterlab" }, + { name = "larch" }, + { name = "matplotlib" }, + { name = "myst-nb" }, + { name = "myst-parser" }, + { name = "nbconvert" }, + { name = "nbformat" }, + { name = "nbmake" }, + { name = "numexpr" }, + { name = "numpydoc" }, + { name = "pre-commit" }, + { name = "pycodestyle" }, + { name = "pydata-sphinx-theme" }, + { name = "pydot" }, + { name = "pyinstrument" }, + { name = "pytest" }, + { name = "pytest-cov" }, + { name = "pytest-regressions" }, + { name = "requests" }, + { name = "rich" }, + { name = "ruby" }, + { name = "ruff" }, + { name = "setuptools-scm" }, + { name = "simwrapper" }, + { name = "snakeviz" }, + { name = "sphinx" }, + { name = "sphinx-argparse" }, + { name = "sphinx-autosummary-accessors" }, + { name = "sphinx-copybutton" }, + { name = "sphinx-remove-toctrees" }, + { name = "sphinx-rtd-theme" }, + { name = "xlsxwriter" }, + { name = "xmle" }, + { name = "zarr", version = "2.18.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "zarr", version = "2.18.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "zstandard" }, +] + +[package.metadata] +requires-dist = [ + { name = "cytoolz", specifier = ">=0.8.1" }, + { name = "multimethod", specifier = "<2.0" }, + { name = "numba", specifier = ">=0.57" }, + { name = "numpy", specifier = ">=1.16.1,<1.26" }, + { name = "openmatrix", specifier = ">=0.3.4.1" }, 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