FIX remove max_samples in RandomTreesEmbedding (scikit-learn#15693)

Author: glemaitre

doc/whats_new/v0.22.rst

@@ -326,13 +326,10 @@ Changelog
 
 - |Enhancement| Addition of ``max_samples`` argument allows limiting
   size of bootstrap samples to be less than size of dataset. Added to
-  :class:`ensemble.ForestClassifier`,
-  :class:`ensemble.ForestRegressor`,
   :class:`ensemble.RandomForestClassifier`,
   :class:`ensemble.RandomForestRegressor`,
   :class:`ensemble.ExtraTreesClassifier`,
-  :class:`ensemble.ExtraTreesRegressor`,
-  :class:`ensemble.RandomTreesEmbedding`. :pr:`14682` by
+  :class:`ensemble.ExtraTreesRegressor`. :pr:`14682` by
   :user:`Matt Hancock <notmatthancock>` and
   :pr:`5963` by :user:`Pablo Duboue <DrDub>`.
 

sklearn/ensemble/_forest.py

@@ -2112,17 +2112,6 @@ class RandomTreesEmbedding(BaseForest):
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest. See :term:`the Glossary <warm_start>`.
 
-    max_samples : int or float, default=None
-        If bootstrap is True, the number of samples to draw from X
-        to train each base estimator.
-
-        - If None (default), then draw `X.shape[0]` samples.
-        - If int, then draw `max_samples` samples.
-        - If float, then draw `max_samples * X.shape[0]` samples. Thus,
-          `max_samples` should be in the interval `(0, 1)`.
-
-        .. versionadded:: 0.22
-
     Attributes
     ----------
     estimators_ : list of DecisionTreeClassifier
@@ -2154,8 +2143,7 @@ def __init__(self,
                  n_jobs=None,
                  random_state=None,
                  verbose=0,
-                 warm_start=False,
-                 max_samples=None):
+                 warm_start=False):
         super().__init__(
             base_estimator=ExtraTreeRegressor(),
             n_estimators=n_estimators,
@@ -2170,7 +2158,7 @@ def __init__(self,
             random_state=random_state,
             verbose=verbose,
             warm_start=warm_start,
-            max_samples=max_samples)
+            max_samples=None)
 
         self.max_depth = max_depth
         self.min_samples_split = min_samples_split