add docs and tests

Author: kajal-jotwani

docs/source/_snippets/user_guide/integrations.py

@@ -226,6 +226,41 @@ def configure_optimizers(self):
 best_params = optimizer.solve()
 # [end:pytorch_lightning]
 
+# [start:lightgbm_experiment]
+from lightgbm import LGBMClassifier
+from sklearn.datasets import load_iris
+
+from hyperactive.experiment.integrations import LightGBMExperiment
+from hyperactive.opt.gfo import BayesianOptimizer
+
+# Load data
+X, y = load_iris(return_X_y=True)
+
+# Create the experiment
+experiment = LightGBMExperiment(
+    estimator=LGBMClassifier(),
+    X=X,
+    y=y,
+    cv=3,
+)
+
+# Define search space
+search_space = {
+    "n_estimators": [50, 100, 200],
+    "max_depth": [3, 5, 7, -1],
+    "learning_rate": [0.01, 0.05, 0.1, 0.2],
+}
+
+# Optimize
+optimizer = BayesianOptimizer(
+    search_space=search_space,
+    n_iter=10,
+    experiment=experiment,
+)
+best_params = optimizer.solve()
+print(f"Best parameters: {best_params}")
+# [end:lightgbm_experiment]
+
 
 # --- Runnable test code below ---
 if __name__ == "__main__":

docs/source/api_reference/experiments_integrations.rst

@@ -7,7 +7,7 @@ The :mod:`hyperactive.experiment.integrations` module contains experiment classe
 for integration with machine learning frameworks.
 
 These experiments provide seamless hyperparameter optimization for scikit-learn,
-sktime, skpro, and PyTorch Lightning models.
+sktime, skpro, PyTorch Lightning, and LightGBM models.
 
 Scikit-Learn
 ------------
@@ -55,3 +55,14 @@ Experiments for PyTorch Lightning models.
     :template: class.rst
 
     TorchExperiment
+
+LightGBM
+--------
+
+Cross-validation experiments for LightGBM estimators.
+
+.. autosummary::
+    :toctree: auto_generated/
+    :template: class.rst
+
+    LightGBMExperiment

docs/source/user_guide/integrations.rst

@@ -7,7 +7,7 @@ Framework Integrations
 Hyperactive integrates with popular ML frameworks, providing drop-in replacements
 for tools like ``GridSearchCV``. Each ML framework has its own conventions for training and evaluation. The integration
 classes handle cross-validation setup, scoring metrics, and parameter translation, so
-you can use any optimizer with scikit-learn, sktime, skpro, or PyTorch models.
+you can use any optimizer with scikit-learn, sktime, skpro, PyTorch, or LightGBM models.
 
 ----
 
@@ -53,6 +53,15 @@ Supported Frameworks
 
       Deep learning models
 
+   .. grid-item-card:: LightGBM
+      :class-card: sd-border-info
+      :link: #lightgbm-integration
+      :link-type: url
+
+      **LightGBMExperiment**
+
+      Gradient boosting models
+
 ----
 
 Quick Reference
@@ -86,6 +95,10 @@ Quick Reference
      - ``TorchExperiment``
      - Deep learning models
      - ``[all_extras]``
+   * - LightGBM
+     - ``LightGBMExperiment``
+     - Classification, regression
+     - ``[lightgbm]``
 
 ----
 
@@ -237,6 +250,34 @@ For deep learning hyperparameter optimization with PyTorch Lightning:
 
 ----
 
+LightGBM Integration
+--------------------
+
+For gradient boosting hyperparameter optimization with LightGBM:
+
+.. note::
+
+   Requires ``pip install lightgbm``
+
+.. grid:: 1
+   :gutter: 0
+
+   .. grid-item::
+      :class: sd-bg-light sd-pt-3 sd-pb-1 sd-ps-3 sd-pe-3 sd-rounded-3
+
+      **Key Features**
+
+      - Optimize LightGBM classifiers and regressors
+      - LightGBM follows the sklearn API, so cross-validation works out of the box
+      - Supports all LightGBM hyperparameters (``n_estimators``, ``max_depth``, ``learning_rate``, etc.)
+
+.. literalinclude:: ../_snippets/user_guide/integrations.py
+   :language: python
+   :start-after: # [start:lightgbm_experiment]
+   :end-before: # [end:lightgbm_experiment]
+
+----
+
 Tips
 ----
 

src/hyperactive/base/tests/test_lightgbm.py

@@ -0,0 +1,49 @@
+"""Integration test for end-to-end usage of optimizer with LightGBM experiment."""
+# copyright: hyperactive developers, MIT License (see LICENSE file)
+
+
+
+def test_endtoend_lightgbm():
+    """Test end-to-end usage of HillClimbing optimizer with LightGBM experiment."""
+    from skbase.utils.dependencies import _check_soft_dependencies
+
+    if not _check_soft_dependencies("lightgbm", severity="none"):
+        return None
+
+    # define the experiment
+    from lightgbm import LGBMClassifier
+    from sklearn.datasets import load_iris
+
+    from hyperactive.experiment.integrations import LightGBMExperiment
+
+    X, y = load_iris(return_X_y=True)
+
+    lgbm_exp = LightGBMExperiment(
+        estimator=LGBMClassifier(n_estimators=10, verbosity=-1),
+        X=X,
+        y=y,
+        cv=2,
+    )
+
+    # set up the HillClimbing optimizer
+    import numpy as np
+
+    from hyperactive.opt import HillClimbing
+
+    hillclimbing_config = {
+        "search_space": {
+            "n_estimators": np.array([5, 10, 20]),
+            "max_depth": np.array([2, 3, 5]),
+        },
+        "n_iter": 10,
+    }
+    hill_climbing = HillClimbing(**hillclimbing_config, experiment=lgbm_exp)
+
+    # run the HillClimbing optimizer
+    hill_climbing.solve()
+
+    best_params = hill_climbing.best_params_
+    assert best_params is not None, "Best parameters should not be None"
+    assert isinstance(best_params, dict), "Best parameters should be a dictionary"
+    assert "n_estimators" in best_params, "Best parameters should contain 'n_estimators'"
+    assert "max_depth" in best_params, "Best parameters should contain 'max_depth'"