Remove nonzero occurence in ENN

Conflicts:
	imblearn/under_sampling/nearmiss.py

Author: Guillaume Lemaitre

imblearn/under_sampling/edited_nearest_neighbours.py

@@ -124,7 +124,7 @@ def _sample(self, X, y):
 
         # If we need to offer support for the indices
         if self.return_indices:
-            idx_under = np.nonzero(y == self.min_c_)[0]
+            idx_under = np.flatnonzero(y == self.min_c_)
 
         # Create a k-NN to fit the whole data
         nn_obj = NearestNeighbors(n_neighbors=self.size_ngh + 1,
@@ -162,12 +162,12 @@ def _sample(self, X, y):
                 raise NotImplementedError
 
             # Get the samples which agree all together
-            sel_x = np.squeeze(sub_samples_x[np.nonzero(nnhood_bool), :])
-            sel_y = sub_samples_y[np.nonzero(nnhood_bool)]
+            sel_x = sub_samples_x[np.flatnonzero(nnhood_bool), :]
+            sel_y = sub_samples_y[np.flatnonzero(nnhood_bool)]
 
             # If we need to offer support for the indices selected
             if self.return_indices:
-                idx_tmp = np.nonzero(y == key)[0][np.nonzero(nnhood_bool)]
+                idx_tmp = np.flatnonzero(y == key)[np.flatnonzero(nnhood_bool)]
                 idx_under = np.concatenate((idx_under, idx_tmp), axis=0)
 
             X_resampled = np.concatenate((X_resampled, sel_x), axis=0)

imblearn/under_sampling/nearmiss.py

@@ -166,7 +166,7 @@ def _selection_dist_based(self, X, y, dist_vec, num_samples, key,
         sel_idx = sorted_idx[:num_samples]
 
         return (X[y == key][sel_idx], y[y == key][sel_idx],
-                np.nonzero(y == key)[0][sel_idx])
+                np.flatnonzero(y == key)[sel_idx])
 
     def _sample(self, X, y):
         """Resample the dataset.
@@ -195,9 +195,9 @@ def _sample(self, X, y):
 
         # Assign the parameter of the element of this class
         # Check that the version asked is implemented
-        if not (self.version == 1 or self.version == 2 or self.version == 3):
-            raise ValueError('UnbalancedData.NearMiss: there is only 3 '
-                             'versions available with parameter version=1/2/3')
+        if self.version not in [1, 2, 3]:
+            raise ValueError("Parameter 'version' must be 1, 2 or 3, "
+	                     "got {0}".format(self.version))
 
         # Start with the minority class
         X_min = X[y == self.min_c_]
@@ -215,7 +215,7 @@ def _sample(self, X, y):
 
         # If we need to offer support for the indices
         if self.return_indices:
-            idx_under = np.nonzero(y == self.min_c_)[0]
+            idx_under = np.flatnonzero(y == self.min_c_)
 
         # For each element of the current class, find the set of NN
         # of the minority class