Solve issue #116 - Create proper RandomState in EasyEnsemble (#117)

Conflicts:
	imblearn/ensemble/easy_ensemble.py

Author: glemaitre

doc/whats_new.rst

@@ -15,6 +15,7 @@ Changelog
 API
 ~~~
 
+- In :class:`ensemble.EasyEnsemble`, correction of the `random_state` generation.
 - First release of the stable API.
 
 New methods

imblearn/ensemble/easy_ensemble.py

@@ -3,11 +3,16 @@
 
 import numpy as np
 
-from ..base import SamplerMixin
+from sklearn.utils import check_random_state
+
+from ..base import BaseMulticlassSampler
 from ..under_sampling import RandomUnderSampler
 
 
-class EasyEnsemble(SamplerMixin):
+MAX_INT = np.iinfo(np.int32).max
+
+
+class EasyEnsemble(BaseMulticlassSampler):
     """Create an ensemble sets by iteratively applying random under-sampling.
 
     This method iteratively select a random subset and make an ensemble of the
@@ -98,19 +103,26 @@ def _sample(self, X, y):
 
         """
 
+        # Check the random state
+        random_state = check_random_state(self.random_state)
+
         X_resampled = []
         y_resampled = []
         if self.return_indices:
             idx_under = []
 
-        for s in range(self.n_subsets):
-            self.logger.debug('Creation of the set #%s', s)
+        self.samplers_ = []
 
-            # Create the object for random under-sampling
+        for _ in range(self.n_subsets):
             rus = RandomUnderSampler(ratio=self.ratio,
                                      return_indices=self.return_indices,
-                                     random_state=self.random_state,
+                                     random_state=random_state.randint(
+                                         MAX_INT),
                                      replacement=self.replacement)
+            self.samplers_.append(rus)
+
+        for rus in self.samplers_:
+
             if self.return_indices:
                 sel_x, sel_y, sel_idx = rus.fit_sample(X, y)
             else:

imblearn/ensemble/tests/data/ee_idx.npy

@@ -161,6 +161,19 @@ def test_fit_sample_half():
     assert_array_equal(y_resampled, y_gt)
 
 
+def test_random_state_none():
+    """Test that the processing is going throw with random state being None."""
+
+    # Define the ratio parameter
+    ratio = 0.5
+
+    # Create the sampling object
+    ee = EasyEnsemble(ratio=ratio, random_state=None)
+
+    # Get the different subset
+    X_resampled, y_resampled = ee.fit_sample(X, Y)
+
+
 def test_sample_wrong_X():
     """Test either if an error is raised when X is different at fitting
     and sampling"""