Add LightGBM Regressor and Classifier test function

[ZohaibHassan16]

Description

This PR introduces LightGBM-based test functions, which includes both a Regressor and a Classifier.

Related Issues

Fixes #6

Type of Change

• [ X] [ENH] - New feature (non-breaking change adding functionality)

How was this solved?

Here is what I did:

1. Created the Classes: Built LightGBMRegressorFunction and LightGBMClassifierFunction classes.
2. Hyperparameters & Stuff: Set up a search space with 9 params, used a closure in objective func and made sure model works with library's surrogate system.
3. Registered Everything: I updated the __init__.py files in both folders

Checklist

Required

• [ x] PR title includes appropriate tag: [ENH]
• [ x] Code passes make check (lint, format, isort)

Tests

• [ x] No tests needed (briefly explain why: The implementation follows established, verified temps by using existing models like RandomForest and DecisionTree, I just adhered to their convention)

Documentation

• [ x] Documentation added/updated (docstrings, user guide, examples)

Note: New features ([ENH]) typically require both tests and documentation.
Bug fixes ([BUG]) should include a regression test when possible.

Testing

Verification can be performed by instantiating the classes and checking the search space or objective function:
from surfaces.test_functions.machine_learning.hyperparameter_optimization.tabular.regression import LightGBMRegressorFunction lgbm_f = LightGBMRegressorFunction(dataset="diabetes", cv=5) print(lgbm_f.search_space) lgbm_f._create_objective_function() print(f"Pure objective function ready: {lgbm_f.pure_objective_function is not None}")

[SimonBlanke]

Thanks for opening the PR. I'll look into this. In the meantime you should also add this new feature to the docs: https://github.com/SimonBlanke/Surfaces/tree/main/docs/source

[ZohaibHassan16]

Hey @SimonBlanke , just did and also fixed a few module export issues which I stubbed into while testing docs in terminal. Let me know if you need anything else.

[SimonBlanke]

There are some test fails, because the package is not installed in the CI. This needs to be corrected.
It is also necessary to add a simple test for this function in the same style as existing tests.

[ZohaibHassan16]

Hey @SimonBlanke,

I've finished up the LightGBM integration !!

Here is what I’ve updated:

Testing: I updated tests/full/smoke/test_lightgbm.py to be a full execution test.

Verification Results: I ran a manual logic check and the official smoke tests. Both confirmed that the models are up running and training correctly on real datasets (digits for Classification and diabetes for Regression).

Manual Logic Test:

Official Pytest Run:

Let me know if there’s anything else you’d like me to tweak!