update docstring

imlightgbm/docstring.py

@@ -92,10 +92,12 @@
 Parameters
 ----------
 objective : str
-    Specify the learning objective
-    'binary_focal', 'binary_weighted'.
+    Specify the learning objective.
+    Options are 'binary_focal', 'binary_weighted', 'multiclass focal', and 'multiclas_weighted'.
 alpha: float
+    Parameter used with 'binary_weighted' and 'multiclass_weighted' objective.
 gamma: float
+    Parameter used with 'binary_focal' and 'multiclass_focal' objective.
 boosting_type : str, optional (default='gbdt')
     'gbdt', traditional Gradient Boosting Decision Tree.
     'dart', Dropouts meet Multiple Additive Regression Trees.

imlightgbm/engine.py

@@ -48,6 +48,8 @@ def predict(
         else:
             return softmax(_predict, axis=1)
 
+    predict.__doc__ = lgb.Booster.predict.__doc__
+
 
 @add_docstring("train")
 def train(

imlightgbm/sklearn.py

@@ -19,7 +19,7 @@
 
 
 class ImbalancedLGBMClassifier(LGBMClassifier):
-    """Inbalanced LightGBM classifier."""
+    """Imbalanced LightGBM classifier."""
 
     @add_docstring("classifier")
     def __init__(
@@ -48,19 +48,6 @@ def __init__(
         importance_type: str = "split",
         num_class: int | None = None,
     ) -> None:
-        """Construct a gradient boosting model.
-
-        Parameters
-        ----------
-        objective : str
-            Specify the learning objective. Options are 'binary_focal' and 'binary_weighted'.
-        alpha: float
-            For 'binary_weighted' objective
-        gamma: float
-            For 'binary_focal' objective
-        other parameters:
-            Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more details.
-        """
         self.num_class = num_class
         _objective_enum: Objective = Objective.get(objective)
         self.__alpha_select(objective=_objective_enum, alpha=alpha)
@@ -100,7 +87,6 @@ def predict(
         validate_features: bool = False,
         **kwargs: Any,
     ) -> np.ndarray | spmatrix | list[spmatrix]:
-        """Docstring is inherited from the LGBMClassifier."""
         _predict = super().predict(
             X=X,
             raw_score=raw_score,