fix gain statistics and dummy learner docstrings

Author: SvenKlaassen

doubleml/utils/dummy_learners.py

@@ -6,30 +6,32 @@ class DMLDummyRegressor(BaseEstimator):
     A dummy regressor that raises an AttributeError when attempting to access
     its fit, predict, or set_params methods.
 
-    Attributes
+    Parameters
     ----------
-    _estimator_type : str
-        Type of the estimator, set to "regressor".
 
-    Methods
-    -------
-    fit(*args)
-        Raises AttributeError: "Accessed fit method of DummyRegressor!"
-    predict(*args)
-        Raises AttributeError: "Accessed predict method of DummyRegressor!"
-    set_params(*args)
-        Raises AttributeError: "Accessed set_params method of DummyRegressor!"
     """
 
     _estimator_type = "regressor"
 
     def fit(*args):
+        """
+        Raises AttributeError: "Accessed fit method of DummyRegressor!"
+        """
+
         raise AttributeError("Accessed fit method of DMLDummyRegressor!")
 
     def predict(*args):
+        """
+        Raises AttributeError: "Accessed predict method of DummyRegressor!"
+        """
+
         raise AttributeError("Accessed predict method of DMLDummyRegressor!")
 
     def set_params(*args):
+        """
+        Raises AttributeError: "Accessed set_params method of DummyRegressor!"
+        """
+
         raise AttributeError("Accessed set_params method of DMLDummyRegressor!")
 
 
@@ -38,33 +40,37 @@ class DMLDummyClassifier(BaseEstimator):
     A dummy classifier that raises an AttributeError when attempting to access
     its fit, predict, set_params, or predict_proba methods.
 
-    Attributes
+    Parameters
     ----------
-    _estimator_type : str
-        Type of the estimator, set to "classifier".
 
-    Methods
-    -------
-    fit(*args)
-        Raises AttributeError: "Accessed fit method of DummyClassifier!"
-    predict(*args)
-        Raises AttributeError: "Accessed predict method of DummyClassifier!"
-    set_params(*args)
-        Raises AttributeError: "Accessed set_params method of DummyClassifier!"
-    predict_proba(*args, **kwargs)
-        Raises AttributeError: "Accessed predict_proba method of DummyClassifier!"
     """
 
     _estimator_type = "classifier"
 
     def fit(*args):
+        """
+        Raises AttributeError: "Accessed fit method of DummyClassifier!"
+        """
+
         raise AttributeError("Accessed fit method of DMLDummyClassifier!")
 
     def predict(*args):
+        """
+        Raises AttributeError: "Accessed predict method of DummyClassifier!"
+        """
+
         raise AttributeError("Accessed predict method of DMLDummyClassifier!")
 
     def set_params(*args):
+        """
+        Raises AttributeError: "Accessed set_params method of DummyClassifier!"
+        """
+
         raise AttributeError("Accessed set_params method of DMLDummyClassifier!")
 
     def predict_proba(*args, **kwargs):
+        """
+        Raises AttributeError: "Accessed predict_proba method of DummyClassifier!"
+        """
+
         raise AttributeError("Accessed predict_proba method of DMLDummyClassifier!")

doubleml/utils/gain_statistics.py

@@ -3,19 +3,21 @@
 
 def gain_statistics(dml_long, dml_short):
     """
-    Compute gain statistics as benchmark values for sensitivity parameters cf_d and cf_y.
+    Compute gain statistics as benchmark values for sensitivity parameters ``cf_d`` and ``cf_y``.
 
-    Parameters:
+    Parameters
     ----------
 
-    dml_long : :class:`doubleml.DoubleML` model including all observed confounders
-    dml_short : :class:`doubleml.DoubleML` model that excludes one or several benchmark confounders
+    dml_long :
+        :class:`doubleml.DoubleML` model including all observed confounders
 
+    dml_short :
+        :class:`doubleml.DoubleML` model that excludes one or several benchmark confounders
 
-    Returns:
+    Returns
     --------
-    Benchmarking dictionary (dict) with values for cf_d, cf_y, rho, and delta_theta.
-
+    benchmark_dict : dict
+        Benchmarking dictionary (dict) with values for ``cf_d``, ``cf_y``, ``rho``, and ``delta_theta``.
     """
     if not isinstance(dml_long.sensitivity_elements, dict):
         raise TypeError("dml_long does not contain the necessary sensitivity elements. "