scikit-learn · jnothman · Jan 8, 2019 · Jan 3, 2019 · Jan 3, 2019 · Jan 5, 2019
diff --git a/sklearn/covariance/graph_lasso_.py b/sklearn/covariance/graph_lasso_.py
@@ -5,6 +5,7 @@
 # Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
 # License: BSD 3 clause
 # Copyright: INRIA
+from collections.abc import Sequence
 import warnings
 import operator
 import sys
@@ -19,7 +20,6 @@
 from ..exceptions import ConvergenceWarning
 from ..utils.validation import check_random_state, check_array
 from ..utils import deprecated
-from ..utils.fixes import _Sequence as Sequence
 from ..linear_model import lars_path
 from ..linear_model import cd_fast
 from ..model_selection import check_cv, cross_val_score

diff --git a/sklearn/datasets/samples_generator.py b/sklearn/datasets/samples_generator.py
@@ -8,14 +8,15 @@
 
 import numbers
 import array
+from collections.abc import Iterable
+
 import numpy as np
 from scipy import linalg
 import scipy.sparse as sp
 
 from ..preprocessing import MultiLabelBinarizer
 from ..utils import check_array, check_random_state
 from ..utils import shuffle as util_shuffle
-from ..utils.fixes import _Iterable as Iterable
 from ..utils.random import sample_without_replacement
 
 

diff --git a/sklearn/feature_extraction/dict_vectorizer.py b/sklearn/feature_extraction/dict_vectorizer.py
@@ -3,14 +3,14 @@
 # License: BSD 3 clause
 
 from array import array
+from collections.abc import Mapping
 from operator import itemgetter
 
 import numpy as np
 import scipy.sparse as sp
 
 from ..base import BaseEstimator, TransformerMixin
 from ..utils import check_array, tosequence
-from ..utils.fixes import _Mapping as Mapping
 
 
 def _tosequence(X):

diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 from __future__ import unicode_literals
+from collections.abc import Mapping
 import re
 import warnings
 
@@ -36,7 +37,6 @@
                                    clean_warning_registry, ignore_warnings,
                                    SkipTest, assert_raises, assert_no_warnings,
                                    fails_if_pypy, assert_allclose_dense_sparse)
-from sklearn.utils.fixes import _Mapping as Mapping
 from collections import defaultdict
 from functools import partial
 import pickle

diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
@@ -15,6 +15,7 @@
 
 import array
 from collections import defaultdict
+from collections.abc import Mapping
 import numbers
 from operator import itemgetter
 import re
@@ -30,7 +31,6 @@
 from .stop_words import ENGLISH_STOP_WORDS
 from ..utils.validation import check_is_fitted, check_array, FLOAT_DTYPES
 from ..utils.fixes import sp_version
-from ..utils.fixes import _Mapping as Mapping  # noqa
 
 
 __all__ = ['HashingVectorizer',

diff --git a/sklearn/metrics/scorer.py b/sklearn/metrics/scorer.py
@@ -19,6 +19,7 @@
 # License: Simplified BSD
 
 from abc import ABCMeta
+from collections.abc import Iterable
 
 import numpy as np
 
@@ -39,7 +40,6 @@
 from .cluster import fowlkes_mallows_score
 
 from ..utils.multiclass import type_of_target
-from ..utils.fixes import _Iterable as Iterable
 from ..base import is_regressor
 
 

diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py
@@ -14,6 +14,7 @@
 
 from abc import ABCMeta, abstractmethod
 from collections import defaultdict
+from collections.abc import Mapping, Sequence, Iterable
 from functools import partial, reduce
 from itertools import product
 import operator
@@ -32,8 +33,6 @@
 from ..utils._joblib import Parallel, delayed
 from ..utils import check_random_state
 from ..utils.fixes import MaskedArray
-from ..utils.fixes import _Mapping as Mapping, _Sequence as Sequence
-from ..utils.fixes import _Iterable as Iterable
 from ..utils.random import sample_without_replacement
 from ..utils.validation import indexable, check_is_fitted
 from ..utils.metaestimators import if_delegate_has_method

diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py
@@ -13,6 +13,7 @@
 from __future__ import print_function
 from __future__ import division
 
+from collections.abc import Iterable
 import warnings
 from itertools import chain, combinations
 from math import ceil, floor
@@ -27,7 +28,6 @@
 from ..utils.validation import check_array
 from ..utils.multiclass import type_of_target
 from ..utils.fixes import comb
-from ..utils.fixes import _Iterable as Iterable
 from ..base import _pprint
 
 __all__ = ['BaseCrossValidator',

diff --git a/sklearn/model_selection/tests/test_search.py b/sklearn/model_selection/tests/test_search.py
@@ -1,5 +1,6 @@
 """Test the search module"""
 
+from collections.abc import Iterable, Sized
 from io import StringIO
 from itertools import chain, product
 import pickle
@@ -12,7 +13,6 @@
 import pytest
 
 from sklearn.utils.fixes import sp_version
-from sklearn.utils.fixes import _Iterable as Iterable, _Sized as Sized
 from sklearn.utils.testing import assert_equal
 from sklearn.utils.testing import assert_not_equal
 from sklearn.utils.testing import assert_raises

diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
@@ -17,12 +17,12 @@
 from scipy import sparse
 from scipy import stats
 from scipy import optimize
+from scipy.special import boxcox
 
 from ..base import BaseEstimator, TransformerMixin
 from ..utils import check_array
 from ..utils.extmath import row_norms
 from ..utils.extmath import _incremental_mean_and_var
-from ..utils.fixes import boxcox, nanpercentile, nanmedian
 from ..utils.sparsefuncs_fast import (inplace_csr_row_normalize_l1,
                                       inplace_csr_row_normalize_l2)
 from ..utils.sparsefuncs import (inplace_column_scale,
@@ -1138,7 +1138,7 @@ def fit(self, X, y=None):
                 raise ValueError(
                     "Cannot center sparse matrices: use `with_centering=False`"
                     " instead. See docstring for motivation and alternatives.")
-            self.center_ = nanmedian(X, axis=0)
+            self.center_ = np.nanmedian(X, axis=0)
         else:
             self.center_ = None
 
@@ -1153,8 +1153,8 @@ def fit(self, X, y=None):
                 else:
                     column_data = X[:, feature_idx]
 
-                quantiles.append(nanpercentile(column_data,
-                                               self.quantile_range))
+                quantiles.append(np.nanpercentile(column_data,
+                                                  self.quantile_range))
 
             quantiles = np.transpose(quantiles)
 
@@ -2101,7 +2101,7 @@ def _dense_fit(self, X, random_state):
                                                     size=self.subsample,
                                                     replace=False)
                 col = col.take(subsample_idx, mode='clip')
-            self.quantiles_.append(nanpercentile(col, references))
+            self.quantiles_.append(np.nanpercentile(col, references))
         self.quantiles_ = np.transpose(self.quantiles_)
 
     def _sparse_fit(self, X, random_state):
@@ -2143,7 +2143,8 @@ def _sparse_fit(self, X, random_state):
                 # quantiles. Force the quantiles to be zeros.
                 self.quantiles_.append([0] * len(references))
             else:
-                self.quantiles_.append(nanpercentile(column_data, references))
+                self.quantiles_.append(
+                        np.nanpercentile(column_data, references))
         self.quantiles_ = np.transpose(self.quantiles_)
 
     def fit(self, X, y=None):

diff --git a/sklearn/utils/__init__.py b/sklearn/utils/__init__.py
@@ -1,6 +1,7 @@
 """
 The :mod:`sklearn.utils` module includes various utilities.
 """
+from collections.abc import Sequence
 import numbers
 import platform
 import struct
@@ -13,7 +14,6 @@
 from .class_weight import compute_class_weight, compute_sample_weight
 from . import _joblib
 from ..exceptions import DataConversionWarning
-from .fixes import _Sequence as Sequence
 from .deprecation import deprecated
 from .validation import (as_float_array,
                          assert_all_finite,

diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
@@ -12,18 +12,10 @@
 
 from distutils.version import LooseVersion
 
-from collections.abc import Sequence as _Sequence  # noqa
-from collections.abc import Iterable as _Iterable  # noqa
-from collections.abc import Mapping as _Mapping  # noqa
-from collections.abc import Sized as _Sized  # noqa
-
 import numpy as np
 import scipy.sparse as sp
 import scipy
-from scipy.special import boxcox  # noqa
 from scipy.sparse.linalg import lsqr as sparse_lsqr  # noqa
-from numpy import nanpercentile  # noqa
-from numpy import nanmedian  # noqa
 
 
 def _parse_version(version_string):

diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py
@@ -6,7 +6,7 @@
 ==========================================
 
 """
-from __future__ import division
+from collections.abc import Sequence
 from itertools import chain
 
 from scipy.sparse import issparse
@@ -16,7 +16,6 @@
 
 import numpy as np
 
-from ..utils.fixes import _Sequence as Sequence
 from .validation import check_array
 
 

diff --git a/sklearn/utils/tests/test_fixes.py b/sklearn/utils/tests/test_fixes.py
@@ -12,8 +12,6 @@
 from sklearn.utils.testing import assert_allclose
 
 from sklearn.utils.fixes import MaskedArray
-from sklearn.utils.fixes import nanmedian
-from sklearn.utils.fixes import nanpercentile
 from sklearn.utils.fixes import _joblib_parallel_args
 from sklearn.utils.fixes import _object_dtype_isnan
 
@@ -28,33 +26,6 @@ def test_masked_array_obj_dtype_pickleable():
         assert_array_equal(marr.mask, marr_pickled.mask)
 
 
-@pytest.mark.parametrize(
-    "axis, expected_median",
-    [(None, 4.0),
-     (0, np.array([1., 3.5, 3.5, 4., 7., np.nan])),
-     (1, np.array([1., 6.]))]
-)
-def test_nanmedian(axis, expected_median):
-    X = np.array([[1, 1, 1, 2, np.nan, np.nan],
-                  [np.nan, 6, 6, 6, 7, np.nan]])
-    median = nanmedian(X, axis=axis)
-    if axis is None:
-        assert median == pytest.approx(expected_median)
-    else:
-        assert_allclose(median, expected_median)
-
-
-@pytest.mark.parametrize(
-    "a, q, expected_percentile",
-    [(np.array([1, 2, 3, np.nan]), [0, 50, 100], np.array([1., 2., 3.])),
-     (np.array([1, 2, 3, np.nan]), 50, 2.),
-     (np.array([np.nan, np.nan]), [0, 50], np.array([np.nan, np.nan]))]
-)
-def test_nanpercentile(a, q, expected_percentile):
-    percentile = nanpercentile(a, q)
-    assert_allclose(percentile, expected_percentile)
-
-
 @pytest.mark.parametrize('joblib_version', ('0.11', '0.12.0'))
 def test_joblib_parallel_args(monkeypatch, joblib_version):
     import sklearn.utils._joblib