FIX add class_weight="balanced_subsample" to the forests to keep back…

…ward compatibility to 0.16

sklearn/ensemble/forest.py

@@ -41,7 +41,9 @@ class calls the ``fit`` method of each sub-estimator on random samples
 
 from __future__ import division
 
+import warnings
 from warnings import warn
+
 from abc import ABCMeta, abstractmethod
 
 import numpy as np
@@ -89,6 +91,10 @@ def _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees,
         curr_sample_weight *= sample_counts
 
         if class_weight == 'subsample':
+            with warnings.catch_warnings():
+                warnings.simplefilter('ignore', DeprecationWarning)
+                curr_sample_weight *= compute_sample_weight('auto', y, indices)
+        elif class_weight == 'balanced_subsample':
             curr_sample_weight *= compute_sample_weight('balanced', y, indices)
 
         tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
@@ -414,30 +420,40 @@ def _validate_y_class_weight(self, y):
             self.n_classes_.append(classes_k.shape[0])
 
         if self.class_weight is not None:
-            valid_presets = ('auto', 'balanced', 'subsample', 'auto')
+            valid_presets = ('auto', 'balanced', 'balanced_subsample', 'subsample', 'auto')
             if isinstance(self.class_weight, six.string_types):
                 if self.class_weight not in valid_presets:
                     raise ValueError('Valid presets for class_weight include '
-                                     '"balanced" and "subsample". Given "%s".'
+                                     '"balanced" and "balanced_subsample". Given "%s".'
                                      % self.class_weight)
+                if self.class_weight == "subsample":
+                    warn("class_weight='subsample' is deprecated and will be removed in 0.18."
+                         " It was replaced by class_weight='balanced_subsample' "
+                         "using the balanced strategy.", DeprecationWarning)
                 if self.warm_start:
-                    warn('class_weight presets "balanced" or "subsample" are '
+                    warn('class_weight presets "balanced" or "balanced_subsample" are '
                          'not recommended for warm_start if the fitted data '
                          'differs from the full dataset. In order to use '
-                         '"auto" weights, use compute_class_weight("balanced", '
+                         '"balanced" weights, use compute_class_weight("balanced", '
                          'classes, y). In place of y you can use a large '
                          'enough sample of the full training set target to '
                          'properly estimate the class frequency '
                          'distributions. Pass the resulting weights as the '
                          'class_weight parameter.')
 
-            if self.class_weight != 'subsample' or not self.bootstrap:
+            if (self.class_weight not in ['subsample', 'balanced_subsample'] or
+                    not self.bootstrap):
                 if self.class_weight == 'subsample':
-                    class_weight = 'balanced'
+                    class_weight = 'auto'
+                elif self.class_weight == "balanced_subsample":
+                    class_weight = "balanced"
                 else:
                     class_weight = self.class_weight
-                expanded_class_weight = compute_sample_weight(class_weight,
-                                                              y_original)
+                with warnings.catch_warnings():
+                    if class_weight == "auto":
+                        warnings.simplefilter('ignore', DeprecationWarning)
+                    expanded_class_weight = compute_sample_weight(class_weight,
+                                                                  y_original)
 
         return y, expanded_class_weight
 
@@ -758,7 +774,7 @@ class RandomForestClassifier(ForestClassifier):
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest.
 
-    class_weight : dict, list of dicts, "balanced", "subsample" or None, optional
+    class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional
 
         Weights associated with classes in the form ``{class_label: weight}``.
         If not given, all classes are supposed to have weight one. For
@@ -769,7 +785,7 @@ class RandomForestClassifier(ForestClassifier):
         weights inversely proportional to class frequencies in the input data
         as ``n_samples / (n_classes * np.bincount(y))``
 
-        The "subsample" mode is the same as "balanced" except that weights are
+        The "balanced_subsample" mode is the same as "balanced" except that weights are
         computed based on the bootstrap sample for every tree grown.
 
         For multi-output, the weights of each column of y will be multiplied.
@@ -1101,7 +1117,7 @@ class ExtraTreesClassifier(ForestClassifier):
         and add more estimators to the ensemble, otherwise, just fit a whole
         new forest.
 
-    class_weight : dict, list of dicts, "balanced", "subsample" or None, optional
+    class_weight : dict, list of dicts, "balanced", "balanced_subsample" or None, optional
 
         Weights associated with classes in the form ``{class_label: weight}``.
         If not given, all classes are supposed to have weight one. For
@@ -1112,7 +1128,7 @@ class ExtraTreesClassifier(ForestClassifier):
         weights inversely proportional to class frequencies in the input data
         as ``n_samples / (n_classes * np.bincount(y))``
 
-        The "subsample" mode is the same as "balanced" except that weights are
+        The "balanced_subsample" mode is the same as "balanced" except that weights are
         computed based on the bootstrap sample for every tree grown.
 
         For multi-output, the weights of each column of y will be multiplied.

sklearn/ensemble/tests/test_forest.py

@@ -24,6 +24,7 @@
 from sklearn.utils.testing import assert_greater_equal
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_warns
+from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import ignore_warnings
 
 from sklearn import datasets
@@ -802,7 +803,11 @@ def check_class_weight_balanced_and_bootstrap_multi_output(name):
     clf = ForestClassifier(class_weight=[{-1: 0.5, 1: 1.}, {-2: 1., 2: 1.}],
                            random_state=0)
     clf.fit(X, _y)
+    # smoke test for subsample and balanced subsample
+    clf = ForestClassifier(class_weight='balanced_subsample', random_state=0)
+    clf.fit(X, _y)
     clf = ForestClassifier(class_weight='subsample', random_state=0)
+    #assert_warns_message(DeprecationWarning, "balanced_subsample", clf.fit, X, _y)
     clf.fit(X, _y)