FIX fix n_jobs to 1 for RENN and AllKNN

Author: glemaitre

imblearn/combine/tests/test_smote_enn.py

@@ -91,7 +91,7 @@ def test_validate_estimator_default():
 
 
 def test_validate_estimator_deprecation():
-    smt = SMOTEENN(random_state=RND_SEED, n_jobs=-1)
+    smt = SMOTEENN(random_state=RND_SEED)
     X_resampled, y_resampled = smt.fit_sample(X, Y)
     X_gt = np.array([[1.52091956, -0.49283504],
                      [0.84976473, -0.15570176],

imblearn/combine/tests/test_smote_tomek.py

@@ -125,7 +125,7 @@ def test_validate_estimator_default():
 
 
 def test_validate_estimator_deprecation():
-    smt = SMOTETomek(random_state=RND_SEED, n_jobs=-1)
+    smt = SMOTETomek(random_state=RND_SEED)
     X_resampled, y_resampled = smt.fit_sample(X, Y)
     X_gt = np.array([[0.68481731, 0.51935141],
                      [1.34192108, -0.13367336],

imblearn/under_sampling/prototype_selection/edited_nearest_neighbours.py

@@ -286,7 +286,7 @@ class RepeatedEditedNearestNeighbours(BaseCleaningSampler):
         - If ``'mode'``, the majority vote of the neighbours will be used in
           order to exclude a sample.
 
-    n_jobs : int, optional (default=-1)
+    n_jobs : int, optional (default=1)
         The number of thread to open when it is possible.
 
     Notes
@@ -337,7 +337,7 @@ def __init__(self,
                  n_neighbors=3,
                  max_iter=100,
                  kind_sel='all',
-                 n_jobs=-1):
+                 n_jobs=1):
         super(RepeatedEditedNearestNeighbours, self).__init__(
             ratio=ratio, random_state=random_state)
         self.return_indices = return_indices
@@ -512,7 +512,7 @@ class without early stopping.
 
         .. versionadded:: 0.3
 
-    n_jobs : int, optional (default=-1)
+    n_jobs : int, optional (default=1)
         The number of thread to open when it is possible.
 
     Notes
@@ -562,7 +562,7 @@ def __init__(self,
                  n_neighbors=3,
                  kind_sel='all',
                  allow_minority=False,
-                 n_jobs=-1):
+                 n_jobs=1):
         super(AllKNN, self).__init__(ratio=ratio, random_state=random_state)
         self.return_indices = return_indices
         self.size_ngh = size_ngh

imblearn/under_sampling/prototype_selection/tests/test_repeated_edited_nearest_neighbours.py

@@ -45,7 +45,7 @@ def test_renn_init():
 
     assert_equal(renn.n_neighbors, 3)
     assert_equal(renn.kind_sel, 'all')
-    assert_equal(renn.n_jobs, -1)
+    assert_equal(renn.n_jobs, 1)
     assert_equal(renn.random_state, RND_SEED)