add docstring + fix tests

Author: SimonBlanke

src/hyperactive/experiment/integrations/lightgbm_experiment.py

@@ -8,14 +8,52 @@
 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.
+    Thin wrapper around ``SklearnCvExperiment`` for LightGBM estimators.
+    LightGBM's sklearn-compatible API (``LGBMClassifier``, ``LGBMRegressor``)
+    works without adaptation. This class exists for discoverability, explicit
+    soft-dependency tracking via the ``python_dependencies`` tag, and as an
+    extension point for future LightGBM-specific behavior.
+
+    Parameters
+    ----------
+    estimator : LGBMClassifier or LGBMRegressor
+        The LightGBM estimator to evaluate. Any sklearn-compatible estimator
+        is accepted, but LightGBM estimators are the intended use case.
+    X : array-like, shape (n_samples, n_features)
+        Input data.
+    y : array-like, shape (n_samples,)
+        Target values.
+    scoring : callable or str, default=None
+        Scoring function. Defaults follow ``SklearnCvExperiment`` conventions:
+        ``accuracy_score`` for classifiers, ``mean_squared_error`` for
+        regressors.
+    cv : int or cross-validation generator, default=KFold(n_splits=3, shuffle=True)
+        Cross-validation strategy.
+
+    Notes
+    -----
+    LightGBM prints training logs to stdout by default. Pass
+    ``verbosity=-1`` to the estimator constructor to suppress this output.
+
+    For all remaining parameter details see ``SklearnCvExperiment``.
+
+    Examples
+    --------
+    >>> from hyperactive.experiment.integrations import LightGBMExperiment
+    >>> from lightgbm import LGBMClassifier
+    >>> from sklearn.datasets import load_iris
+    >>> X, y = load_iris(return_X_y=True)
+    >>> exp = LightGBMExperiment(
+    ...     estimator=LGBMClassifier(verbosity=-1),
+    ...     X=X,
+    ...     y=y,
+    ... )
+    >>> params = {"n_estimators": 50, "max_depth": 3}
+    >>> score, metadata = exp.score(params)
     """
 
     _tags = {
+        "authors": ["kajal-jotwani"],
         "python_dependencies": "lightgbm",
     }
 
@@ -30,19 +68,17 @@ def get_test_params(cls, parameter_set="default"):
         from lightgbm import LGBMClassifier, LGBMRegressor
         from sklearn.datasets import load_diabetes, load_iris
 
-        # Classification test case
         X, y = load_iris(return_X_y=True)
         params0 = {
-            "estimator": LGBMClassifier(n_estimators=10),
+            "estimator": LGBMClassifier(n_estimators=10, verbosity=-1),
             "X": X,
             "y": y,
             "cv": 2,
         }
 
-        # Regression test case
         X, y = load_diabetes(return_X_y=True)
         params1 = {
-            "estimator": LGBMRegressor(n_estimators=10),
+            "estimator": LGBMRegressor(n_estimators=10, verbosity=-1),
             "X": X,
             "y": y,
             "cv": 2,
@@ -58,7 +94,5 @@ def _get_score_params(cls):
         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]
+        score_params = {"n_estimators": 5, "max_depth": 2}
+        return [score_params, score_params]