Removed unused imports

Author: jrfiedler

sklearn/cluster/dbscan_.py

@@ -13,7 +13,6 @@
 from scipy import sparse
 
 from ..base import BaseEstimator, ClusterMixin
-from ..metrics import pairwise_distances
 from ..utils import check_array, check_consistent_length
 from ..utils.fixes import astype
 from ..neighbors import NearestNeighbors

sklearn/cluster/hierarchical.py

@@ -9,7 +9,6 @@
 """
 from heapq import heapify, heappop, heappush, heappushpop
 import warnings
-import sys
 
 import numpy as np
 from scipy import sparse

sklearn/covariance/outlier_detection.py

@@ -12,7 +12,6 @@
 #
 # License: BSD 3 clause
 
-import warnings
 import numpy as np
 import scipy as sp
 from . import MinCovDet

sklearn/datasets/california_housing.py

@@ -22,7 +22,6 @@
 # License: BSD 3 clause
 
 from io import BytesIO
-import os
 from os.path import exists
 from os import makedirs
 import tarfile

sklearn/feature_selection/tests/test_rfe.py

@@ -1,7 +1,6 @@
 """
 Testing Recursive feature elimination
 """
-import warnings
 import numpy as np
 from numpy.testing import assert_array_almost_equal, assert_array_equal
 from nose.tools import assert_equal, assert_true
@@ -16,7 +15,6 @@
 
 from sklearn.utils import check_random_state
 from sklearn.utils.testing import ignore_warnings
-from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import assert_greater
 
 from sklearn.metrics import make_scorer

sklearn/mixture/gmm.py

@@ -9,7 +9,6 @@
 #         Fabian Pedregosa <[email protected]>
 #         Bertrand Thirion <[email protected]>
 
-import warnings
 import numpy as np
 from scipy import linalg
 from time import time

sklearn/mixture/tests/test_gmm.py

@@ -3,7 +3,6 @@
 import unittest
 import copy
 import sys
-import warnings
 
 from nose.tools import assert_true
 import numpy as np

sklearn/model_selection/_split.py

@@ -14,7 +14,6 @@
 from __future__ import division
 
 import warnings
-import inspect
 from itertools import chain, combinations
 from collections import Iterable
 from math import ceil, floor

sklearn/multiclass.py

@@ -44,7 +44,6 @@
 from .metrics.pairwise import euclidean_distances
 from .utils import check_random_state
 from .utils.validation import _num_samples
-from .utils.validation import check_consistent_length
 from .utils.validation import check_is_fitted
 from .utils.validation import check_X_y
 from .utils.multiclass import (_check_partial_fit_first_call,
@@ -246,7 +245,7 @@ def partial_fit(self, X, y, classes=None):
                                  "method".format(self.estimator))
             self.estimators_ = [clone(self.estimator) for _ in range
                                 (self.n_classes_)]
-        
+
         # A sparse LabelBinarizer, with sparse_output=True, has been shown to
         # outperform or match a dense label binarizer in all cases and has also
         # resulted in less or equal memory consumption in the fit_ovr function
@@ -517,7 +516,7 @@ def partial_fit(self, X, y, classes=None):
         self.estimators_ = Parallel(n_jobs=self.n_jobs)(
             delayed(_partial_fit_ovo_binary)(
                 estimator, X, y, self.classes_[i], self.classes_[j])
-            for estimator, (i, j) in izip(self.estimators_, ((i, j) for i 
+            for estimator, (i, j) in izip(self.estimators_, ((i, j) for i
                                 in range(self.n_classes_) for j in range
                                             (i + 1, self.n_classes_))))
         return self

sklearn/multioutput.py

@@ -16,10 +16,10 @@
 
 import numpy as np
 
-from abc import ABCMeta, abstractmethod
-from .base import BaseEstimator, clone, MetaEstimatorMixin
+from abc import ABCMeta
+from .base import BaseEstimator, clone
 from .base import RegressorMixin, ClassifierMixin
-from .utils import check_array, check_random_state, check_X_y
+from .utils import check_array, check_X_y
 from .utils.fixes import parallel_helper
 from .utils.validation import check_is_fitted, has_fit_parameter
 from .externals.joblib import Parallel, delayed