[ENH] Add LightGBM Experiment integration

src/hyperactive/experiment/integrations/__init__.py

@@ -2,6 +2,7 @@
 # copyright: hyperactive developers, MIT License (see LICENSE file)
 
 from hyperactive.experiment.integrations.sklearn_cv import SklearnCvExperiment
+from hyperactive.experiment.integrations.lightgbm_experiment import LightGBMExperiment
 from hyperactive.experiment.integrations.skpro_probareg import (
     SkproProbaRegExperiment,
 )
@@ -21,4 +22,5 @@
     "SktimeClassificationExperiment",
     "SktimeForecastingExperiment",
     "TorchExperiment",
+    "LightGBMExperiment",
 ]

src/hyperactive/experiment/integrations/lightgbm_experiment.py

@@ -0,0 +1,64 @@
+"""Experiment adapter for LightGBM cross-validation experiments."""
+
+# copyright: hyperactive developers, MIT License (see LICENSE file)
+
+from hyperactive.experiment.integrations.sklearn_cv import SklearnCvExperiment
+
+
+class LightGBMExperiment(SklearnCvExperiment):
+    """Experiment adapter for LightGBM cross-validation experiments.
+
+    Thin wrapper around SklearnCvExperiment for LightGBM estimators.
+
+    LightGBM estimators follow the sklearn API, so this class does not
+    add new functionality beyond SklearnCvExperiment. It exists for
+    discoverability and explicit LightGBM support.
+    """
+
+    _tags = {
+        "python_dependencies": "lightgbm",
+    }
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator."""
+        from skbase.utils.dependencies import _check_soft_dependencies
+
+        if not _check_soft_dependencies("lightgbm", severity="none"):
+            return []
+
+        from sklearn.datasets import load_iris, load_diabetes
+        from lightgbm import LGBMClassifier, LGBMRegressor
+
+        # Classification test case
+        X, y = load_iris(return_X_y=True)
+        params0 = {
+            "estimator": LGBMClassifier(n_estimators=10),
+            "X": X,
+            "y": y,
+            "cv": 2,
+        }
+
+        # Regression test case
+        X, y = load_diabetes(return_X_y=True)
+        params1 = {
+            "estimator": LGBMRegressor(n_estimators=10),
+            "X": X,
+            "y": y,
+            "cv": 2,
+        }
+
+        return [params0, params1]
+
+    @classmethod
+    def _get_score_params(cls):
+        """Return parameter settings for score/evaluate tests."""
+        from skbase.utils.dependencies import _check_soft_dependencies
+
+        if not _check_soft_dependencies("lightgbm", severity="none"):
+            return []
+
+        val0 = {"n_estimators": 5, "max_depth": 2}
+        val1 = {"n_estimators": 5, "max_depth": 2}
+
+        return [val0, val1]