MAINT: Set n_jobs default to 1 (#187)

Author: glemaitre

imblearn/combine/smote_enn.py

@@ -175,7 +175,7 @@ def _validate_estimator(self):
             if self.kind_smote is None:
                 self.kind_smote = 'regular'
             if self.n_jobs is None:
-                smote_jobs = -1
+                smote_jobs = 1
             else:
                 smote_jobs = self.n_jobs
             warnings.warn('Parameters initialization will be replaced in'
@@ -210,7 +210,7 @@ def _validate_estimator(self):
             if self.kind_enn is None:
                 self.kind_enn = 'all'
             if self.n_jobs is None:
-                self.n_jobs = -1
+                self.n_jobs = 1
             self.enn_ = EditedNearestNeighbours(random_state=self.random_state,
                                                 size_ngh=self.size_ngh,
                                                 n_neighbors=self.n_neighbors,

imblearn/combine/smote_tomek.py

@@ -151,7 +151,7 @@ def _validate_estimator(self):
             if self.kind_smote is None:
                 self.kind_smote = 'regular'
             if self.n_jobs is None:
-                smote_jobs = -1
+                smote_jobs = 1
             else:
                 smote_jobs = self.n_jobs
             self.smote_ = SMOTE(ratio=self.ratio,

imblearn/over_sampling/smote.py

@@ -68,6 +68,9 @@ class SMOTE(BaseBinarySampler):
         The type of SMOTE algorithm to use one of the following options:
         'regular', 'borderline1', 'borderline2', 'svm'.
 
+    n_jobs : int, optional (default=1)
+        The number of threads to open if possible.
+
     Attributes
     ----------
     min_c_ : str or int
@@ -125,7 +128,7 @@ class SMOTE(BaseBinarySampler):
 
     def __init__(self, ratio='auto', random_state=None, k=None, k_neighbors=5,
                  m=None, m_neighbors=10, out_step=0.5, kind='regular',
-                 n_jobs=-1, **kwargs):
+                 n_jobs=1, **kwargs):
         super(SMOTE, self).__init__(ratio=ratio, random_state=random_state)
         self.kind = kind
         self.k = k

imblearn/pipeline.py

@@ -100,8 +100,8 @@ class Pipeline(pipeline.Pipeline):
     >>> pipeline = Pipeline([('smt', smt), ('pca', pca), ('knn', knn)])
     >>> X_train, X_test, y_train, y_test = tts(X, y, random_state=42)
     >>> pipeline.fit(X_train, y_train)
-    Pipeline(steps=[('smt', SMOTE(k=None, k_neighbors=5, kind='regular', m=None, m_neighbors=10,
-       n_jobs=-1, out_step=0.5, random_state=42, ratio='auto')), ('pca', PCA(copy=True, n_components=None, whiten=False)), ('knn', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
+    Pipeline(steps=[('smt', SMOTE(k=None, k_neighbors=5, kind='regular', m=None, m_neighbors=10, n_jobs=1,
+       out_step=0.5, random_state=42, ratio='auto')), ('pca', PCA(copy=True, n_components=None, whiten=False)), ('knn', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
                metric_params=None, n_jobs=1, n_neighbors=5, p=2,
                weights='uniform'))])
     >>> y_hat = pipeline.predict(X_test)

imblearn/under_sampling/cluster_centroids.py

@@ -35,7 +35,7 @@ class ClusterCentroids(BaseMulticlassSampler):
         If None, the random number generator is the RandomState instance used
         by np.random.
 
-    n_jobs : int, optional (default=-1)
+    n_jobs : int, optional (default=1)
         The number of threads to open if possible.
 
     **kwargs : keywords
@@ -79,7 +79,7 @@ class ClusterCentroids(BaseMulticlassSampler):
 
     """
 
-    def __init__(self, ratio='auto', random_state=None, n_jobs=-1, **kwargs):
+    def __init__(self, ratio='auto', random_state=None, n_jobs=1, **kwargs):
         super(ClusterCentroids, self).__init__(ratio=ratio,
                                                random_state=random_state)
         self.n_jobs = n_jobs

imblearn/under_sampling/condensed_nearest_neighbour.py

@@ -43,7 +43,7 @@ class CondensedNearestNeighbour(BaseMulticlassSampler):
     n_seeds_S : int, optional (default=1)
         Number of samples to extract in order to build the set S.
 
-    n_jobs : int, optional (default=-1)
+    n_jobs : int, optional (default=1)
         The number of threads to open if possible.
 
     **kwargs : keywords
@@ -95,7 +95,7 @@ class CondensedNearestNeighbour(BaseMulticlassSampler):
     """
 
     def __init__(self, return_indices=False, random_state=None,
-                 size_ngh=None, n_neighbors=1, n_seeds_S=1, n_jobs=-1,
+                 size_ngh=None, n_neighbors=1, n_seeds_S=1, n_jobs=1,
                  **kwargs):
         super(CondensedNearestNeighbour, self).__init__(
             random_state=random_state)

imblearn/under_sampling/edited_nearest_neighbours.py

@@ -53,7 +53,7 @@ class EditedNearestNeighbours(BaseMulticlassSampler):
         - 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 threads to open if possible.
 
     Attributes
@@ -103,7 +103,7 @@ class EditedNearestNeighbours(BaseMulticlassSampler):
     """
 
     def __init__(self, return_indices=False, random_state=None,
-                 size_ngh=None, n_neighbors=3, kind_sel='all', n_jobs=-1):
+                 size_ngh=None, n_neighbors=3, kind_sel='all', n_jobs=1):
         super(EditedNearestNeighbours, self).__init__(
             random_state=random_state)
         self.return_indices = return_indices

imblearn/under_sampling/instance_hardness_threshold.py

@@ -44,7 +44,7 @@ class InstanceHardnessThreshold(BaseBinarySampler):
     cv : int, optional (default=5)
         Number of folds to be used when estimating samples' instance hardness.
 
-    n_jobs : int, optional (default=-1)
+    n_jobs : int, optional (default=1)
         The number of threads to open if possible.
 
     Attributes
@@ -97,7 +97,7 @@ class InstanceHardnessThreshold(BaseBinarySampler):
     """
 
     def __init__(self, estimator='linear-svm', ratio='auto',
-                 return_indices=False, random_state=None, cv=5, n_jobs=-1,
+                 return_indices=False, random_state=None, cv=5, n_jobs=1,
                  **kwargs):
         super(InstanceHardnessThreshold, self).__init__(
             ratio=ratio,

imblearn/under_sampling/nearmiss.py

@@ -66,7 +66,7 @@ class NearMiss(BaseMulticlassSampler):
         `sklearn.neighbors.base.KNeighborsMixin` that will be used to find
         the k_neighbors.
 
-    n_jobs : int, optional (default=-1)
+    n_jobs : int, optional (default=1)
         The number of threads to open if possible.
 
     Attributes
@@ -117,7 +117,7 @@ class NearMiss(BaseMulticlassSampler):
 
     def __init__(self, ratio='auto', return_indices=False, random_state=None,
                  version=1, size_ngh=None, n_neighbors=3, ver3_samp_ngh=None,
-                 n_neighbors_ver3=3, n_jobs=-1):
+                 n_neighbors_ver3=3, n_jobs=1):
         super(NearMiss, self).__init__(ratio=ratio, random_state=random_state)
         self.return_indices = return_indices
         self.version = version

imblearn/under_sampling/neighbourhood_cleaning_rule.py

@@ -40,7 +40,7 @@ class NeighbourhoodCleaningRule(BaseMulticlassSampler):
         `sklearn.neighbors.base.KNeighborsMixin` that will be used to find
         the k_neighbors.
 
-    n_jobs : int, optional (default=-1)
+    n_jobs : int, optional (default=1)
         The number of threads to open if possible.
 
     Attributes
@@ -87,7 +87,7 @@ class NeighbourhoodCleaningRule(BaseMulticlassSampler):
     """
 
     def __init__(self, return_indices=False, random_state=None,
-                 size_ngh=None, n_neighbors=3, n_jobs=-1):
+                 size_ngh=None, n_neighbors=3, n_jobs=1):
         super(NeighbourhoodCleaningRule, self).__init__(
             random_state=random_state)
         self.return_indices = return_indices