DOC follow doc guideline in ensemble an feature_extraction modules (scikit-learn#15975)

Author: ankishb

sklearn/ensemble/_bagging.py

@@ -259,10 +259,10 @@ def _fit(self, X, y, max_samples=None, max_depth=None, sample_weight=None):
             The target values (class labels in classification, real numbers in
             regression).
 
-        max_samples : int or float, optional (default=None)
+        max_samples : int or float, default=None
             Argument to use instead of self.max_samples.
 
-        max_depth : int, optional (default=None)
+        max_depth : int, default=None
             Override value used when constructing base estimator. Only
             supported if the base estimator has a max_depth parameter.
 
@@ -456,57 +456,57 @@ class BaggingClassifier(ClassifierMixin, BaseBagging):
 
     Parameters
     ----------
-    base_estimator : object or None, optional (default=None)
+    base_estimator : object, default=None
         The base estimator to fit on random subsets of the dataset.
         If None, then the base estimator is a decision tree.
 
-    n_estimators : int, optional (default=10)
+    n_estimators : int, default=10
         The number of base estimators in the ensemble.
 
-    max_samples : int or float, optional (default=1.0)
+    max_samples : int or float, default=1.0
         The number of samples to draw from X to train each base estimator.
 
         - If int, then draw `max_samples` samples.
         - If float, then draw `max_samples * X.shape[0]` samples.
 
-    max_features : int or float, optional (default=1.0)
+    max_features : int or float, default=1.0
         The number of features to draw from X to train each base estimator.
 
         - If int, then draw `max_features` features.
         - If float, then draw `max_features * X.shape[1]` features.
 
-    bootstrap : boolean, optional (default=True)
+    bootstrap : bool, default=True
         Whether samples are drawn with replacement. If False, sampling
         without replacement is performed.
 
-    bootstrap_features : boolean, optional (default=False)
+    bootstrap_features : bool, default=False
         Whether features are drawn with replacement.
 
-    oob_score : bool, optional (default=False)
+    oob_score : bool, default=False
         Whether to use out-of-bag samples to estimate
         the generalization error.
 
-    warm_start : bool, optional (default=False)
+    warm_start : bool, default=False
         When set to True, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit
         a whole new ensemble. See :term:`the Glossary <warm_start>`.
 
         .. versionadded:: 0.17
            *warm_start* constructor parameter.
 
-    n_jobs : int or None, optional (default=None)
+    n_jobs : int, default=None
         The number of jobs to run in parallel for both :meth:`fit` and
         :meth:`predict`. ``None`` means 1 unless in a
         :obj:`joblib.parallel_backend` context. ``-1`` means using all
         processors. See :term:`Glossary <n_jobs>` for more details.
 
-    random_state : int, RandomState instance or None, optional (default=None)
+    random_state : int, RandomState instance, default=None
         If int, random_state is the seed used by the random number generator;
         If RandomState instance, random_state is the random number generator;
         If None, the random number generator is the RandomState instance used
         by `np.random`.
 
-    verbose : int, optional (default=0)
+    verbose : int, default=0
         Controls the verbosity when fitting and predicting.
 
     Attributes
@@ -527,7 +527,7 @@ class BaggingClassifier(ClassifierMixin, BaseBagging):
     estimators_features_ : list of arrays
         The subset of drawn features for each base estimator.
 
-    classes_ : array of shape (n_classes,)
+    classes_ : ndarray of shape (n_classes,)
         The classes labels.
 
     n_classes_ : int or list
@@ -537,7 +537,7 @@ class BaggingClassifier(ClassifierMixin, BaseBagging):
         Score of the training dataset obtained using an out-of-bag estimate.
         This attribute exists only when ``oob_score`` is True.
 
-    oob_decision_function_ : array of shape (n_samples, n_classes)
+    oob_decision_function_ : ndarray of shape (n_samples, n_classes)
         Decision function computed with out-of-bag estimate on the training
         set. If n_estimators is small it might be possible that a data point
         was never left out during the bootstrap. In this case,
@@ -689,7 +689,7 @@ def predict_proba(self, X):
 
         Returns
         -------
-        p : array of shape (n_samples, n_classes)
+        p : ndarray of shape (n_samples, n_classes)
             The class probabilities of the input samples. The order of the
             classes corresponds to that in the attribute :term:`classes_`.
         """
@@ -739,7 +739,7 @@ def predict_log_proba(self, X):
 
         Returns
         -------
-        p : array of shape (n_samples, n_classes)
+        p : ndarray of shape (n_samples, n_classes)
             The class log-probabilities of the input samples. The order of the
             classes corresponds to that in the attribute :term:`classes_`.
         """
@@ -794,7 +794,7 @@ def decision_function(self, X):
 
         Returns
         -------
-        score : array, shape = [n_samples, k]
+        score : ndarray of shape (n_samples, k)
             The decision function of the input samples. The columns correspond
             to the classes in sorted order, as they appear in the attribute
             ``classes_``. Regression and binary classification are special
@@ -858,54 +858,54 @@ class BaggingRegressor(RegressorMixin, BaseBagging):
 
     Parameters
     ----------
-    base_estimator : object or None, optional (default=None)
+    base_estimator : object, default=None
         The base estimator to fit on random subsets of the dataset.
         If None, then the base estimator is a decision tree.
 
-    n_estimators : int, optional (default=10)
+    n_estimators : int, default=10
         The number of base estimators in the ensemble.
 
-    max_samples : int or float, optional (default=1.0)
+    max_samples : int or float, default=1.0
         The number of samples to draw from X to train each base estimator.
 
         - If int, then draw `max_samples` samples.
         - If float, then draw `max_samples * X.shape[0]` samples.
 
-    max_features : int or float, optional (default=1.0)
+    max_features : int or float, default=1.0
         The number of features to draw from X to train each base estimator.
 
         - If int, then draw `max_features` features.
         - If float, then draw `max_features * X.shape[1]` features.
 
-    bootstrap : boolean, optional (default=True)
+    bootstrap : bool, default=True
         Whether samples are drawn with replacement. If False, sampling
         without replacement is performed.
 
-    bootstrap_features : boolean, optional (default=False)
+    bootstrap_features : bool, default=False
         Whether features are drawn with replacement.
 
-    oob_score : bool
+    oob_score : bool, default=False
         Whether to use out-of-bag samples to estimate
         the generalization error.
 
-    warm_start : bool, optional (default=False)
+    warm_start : bool, default=False
         When set to True, reuse the solution of the previous call to fit
         and add more estimators to the ensemble, otherwise, just fit
         a whole new ensemble. See :term:`the Glossary <warm_start>`.
 
-    n_jobs : int or None, optional (default=None)
+    n_jobs : int, default=None
         The number of jobs to run in parallel for both :meth:`fit` and
         :meth:`predict`. ``None`` means 1 unless in a
         :obj:`joblib.parallel_backend` context. ``-1`` means using all
         processors. See :term:`Glossary <n_jobs>` for more details.
 
-    random_state : int, RandomState instance or None, optional (default=None)
+    random_state : int, RandomState instance, default=None
         If int, random_state is the seed used by the random number generator;
         If RandomState instance, random_state is the random number generator;
         If None, the random number generator is the RandomState instance used
         by `np.random`.
 
-    verbose : int, optional (default=0)
+    verbose : int, default=0
         Controls the verbosity when fitting and predicting.
 
     Attributes

sklearn/ensemble/_base.py

@@ -49,7 +49,7 @@ def _set_random_states(estimator, random_state=None):
         Estimator with potential randomness managed by random_state
         parameters.
 
-    random_state : int, RandomState instance or None, optional (default=None)
+    random_state : int, RandomState instance, default=None
         If int, random_state is the seed used by the random number generator;
         If RandomState instance, random_state is the random number generator;
         If None, the random number generator is the RandomState instance used
@@ -83,13 +83,13 @@ class BaseEnsemble(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
 
     Parameters
     ----------
-    base_estimator : object, optional (default=None)
+    base_estimator : object, default=None
         The base estimator from which the ensemble is built.
 
-    n_estimators : integer
+    n_estimators : int
         The number of estimators in the ensemble.
 
-    estimator_params : list of strings
+    estimator_params : list of str
         The list of attributes to use as parameters when instantiating a
         new base estimator. If none are given, default parameters are used.
 
@@ -276,7 +276,7 @@ def get_params(self, deep=True):
 
         Parameters
         ----------
-        deep : bool
+        deep : bool, default=True
             Setting it to True gets the various classifiers and the parameters
             of the classifiers as well.
         """