Apply nose2pytest @pep8speaks

imblearn/combine/tests/test_smote_enn.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from sklearn.utils.testing import (assert_allclose, assert_array_equal,
-                                   assert_raises_regex)
+from sklearn.utils.testing import assert_allclose, assert_array_equal
+from sklearn.utils.testing import assert_raises_regex
 
 from imblearn.combine import SMOTEENN
 from imblearn.under_sampling import EditedNearestNeighbours

imblearn/combine/tests/test_smote_tomek.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from sklearn.utils.testing import (assert_allclose, assert_array_equal,
-                                   assert_raises_regex)
+from sklearn.utils.testing import assert_allclose, assert_array_equal
+from sklearn.utils.testing import assert_raises_regex
 
 from imblearn.combine import SMOTETomek
 from imblearn.over_sampling import SMOTE

imblearn/datasets/tests/test_imbalance.py

@@ -11,8 +11,8 @@
 import numpy as np
 
 from sklearn.datasets import load_iris
-from sklearn.utils.testing import (assert_equal, assert_raises_regex,
-                                   assert_warns_message)
+from sklearn.utils.testing import assert_raises_regex
+from sklearn.utils.testing import assert_warns_message
 
 from imblearn.datasets import make_imbalance
 
@@ -46,17 +46,17 @@ def test_make_imbalance_float():
     X_, y_ = assert_warns_message(DeprecationWarning,
                                   "'ratio' being a float is deprecated",
                                   make_imbalance, X, Y, ratio=0.5, min_c_=1)
-    assert_equal(Counter(y_), {0: 50, 1: 25, 2: 50})
+    assert Counter(y_) == {0: 50, 1: 25, 2: 50}
     # resample without using min_c_
     X_, y_ = make_imbalance(X_, y_, ratio=0.25, min_c_=None)
-    assert_equal(Counter(y_), {0: 50, 1: 12, 2: 50})
+    assert Counter(y_) == {0: 50, 1: 12, 2: 50}
 
 
 def test_make_imbalance_dict():
     ratio = {0: 10, 1: 20, 2: 30}
     X_, y_ = make_imbalance(X, Y, ratio=ratio)
-    assert_equal(Counter(y_), ratio)
+    assert Counter(y_) == ratio
 
     ratio = {0: 10, 1: 20}
     X_, y_ = make_imbalance(X, Y, ratio=ratio)
-    assert_equal(Counter(y_), {0: 10, 1: 20, 2: 50})
+    assert Counter(y_) == {0: 10, 1: 20, 2: 50}

imblearn/datasets/tests/test_zenodo.py

@@ -7,8 +7,8 @@
 # License: MIT
 
 from imblearn.datasets import fetch_datasets
-from sklearn.utils.testing import (assert_equal, assert_allclose,
-                                   assert_raises_regex, SkipTest)
+from sklearn.utils.testing import SkipTest, assert_allclose
+from sklearn.utils.testing import assert_raises_regex
 
 DATASET_SHAPE = {'ecoli': (336, 7),
                  'optical_digits': (5620, 64),
@@ -54,12 +54,12 @@ def test_fetch():
     for k in DATASET_SHAPE.keys():
 
         X1, X2 = datasets1[k].data, datasets2[k].data
-        assert_equal(DATASET_SHAPE[k], X1.shape)
-        assert_equal(X1.shape, X2.shape)
+        assert DATASET_SHAPE[k] == X1.shape
+        assert X1.shape == X2.shape
 
         y1, y2 = datasets1[k].target, datasets2[k].target
-        assert_equal((X1.shape[0],), y1.shape)
-        assert_equal((X1.shape[0],), y2.shape)
+        assert (X1.shape[0],) == y1.shape
+        assert (X1.shape[0],) == y2.shape
 
 
 def test_fetch_filter():
@@ -73,14 +73,14 @@ def test_fetch_filter():
                       random_state=37)
 
     X1, X2 = datasets1['ecoli'].data, datasets2['ecoli'].data
-    assert_equal(DATASET_SHAPE['ecoli'], X1.shape)
-    assert_equal(X1.shape, X2.shape)
+    assert DATASET_SHAPE['ecoli'] == X1.shape
+    assert X1.shape == X2.shape
 
     assert_allclose(X1.sum(), X2.sum())
 
     y1, y2 = datasets1['ecoli'].target, datasets2['ecoli'].target
-    assert_equal((X1.shape[0],), y1.shape)
-    assert_equal((X1.shape[0],), y2.shape)
+    assert (X1.shape[0],) == y1.shape
+    assert (X1.shape[0],) == y2.shape
 
 
 def test_fetch_error():

imblearn/ensemble/tests/test_balance_cascade.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from sklearn.utils.testing import (assert_array_equal, assert_raises,
-                                   assert_raises_regex)
+from sklearn.utils.testing import assert_array_equal, assert_raises
+from sklearn.utils.testing import assert_raises_regex
 from sklearn.ensemble import RandomForestClassifier
 
 from imblearn.ensemble import BalanceCascade

imblearn/ensemble/tests/test_easy_ensemble.py

@@ -6,7 +6,7 @@
 from __future__ import print_function
 
 import numpy as np
-from sklearn.utils.testing import assert_array_equal, assert_equal
+from sklearn.utils.testing import assert_array_equal
 
 from imblearn.ensemble import EasyEnsemble
 
@@ -25,10 +25,10 @@ def test_ee_init():
     ratio = 1.
     ee = EasyEnsemble(ratio=ratio, random_state=RND_SEED)
 
-    assert_equal(ee.ratio, ratio)
-    assert_equal(ee.replacement, False)
-    assert_equal(ee.n_subsets, 10)
-    assert_equal(ee.random_state, RND_SEED)
+    assert ee.ratio == ratio
+    assert ee.replacement is False
+    assert ee.n_subsets == 10
+    assert ee.random_state == RND_SEED
 
 
 def test_fit_sample_auto():

imblearn/metrics/tests/test_classification.py

@@ -16,15 +16,14 @@
 from sklearn.preprocessing import label_binarize
 from sklearn.utils.fixes import np_version
 from sklearn.utils.validation import check_random_state
-from sklearn.utils.testing import (assert_allclose, assert_array_equal,
-                                   assert_no_warnings, assert_equal,
-                                   assert_raises, assert_warns_message,
-                                   ignore_warnings, assert_not_equal,
-                                   assert_raise_message)
-from sklearn.metrics import (accuracy_score, average_precision_score,
-                             brier_score_loss, cohen_kappa_score,
-                             jaccard_similarity_score, precision_score,
-                             recall_score, roc_auc_score)
+from sklearn.utils.testing import assert_allclose, assert_array_equal
+from sklearn.utils.testing import assert_no_warnings, assert_raises
+from sklearn.utils.testing import assert_warns_message, ignore_warnings
+from sklearn.utils.testing import assert_raise_message
+from sklearn.metrics import accuracy_score, average_precision_score
+from sklearn.metrics import brier_score_loss, cohen_kappa_score
+from sklearn.metrics import jaccard_similarity_score, precision_score
+from sklearn.metrics import recall_score, roc_auc_score
 
 from imblearn.metrics import sensitivity_specificity_support
 from imblearn.metrics import sensitivity_score
@@ -33,6 +32,8 @@
 from imblearn.metrics import make_index_balanced_accuracy
 from imblearn.metrics import classification_report_imbalanced
 
+from pytest import approx
+
 RND_SEED = 42
 R_TOL = 1e-2
 
@@ -113,11 +114,11 @@ def test_sensitivity_specificity_score_binary():
 
 def test_sensitivity_specificity_f_binary_single_class():
     # Such a case may occur with non-stratified cross-validation
-    assert_equal(1., sensitivity_score([1, 1], [1, 1]))
-    assert_equal(0., specificity_score([1, 1], [1, 1]))
+    assert sensitivity_score([1, 1], [1, 1]) == 1.
+    assert specificity_score([1, 1], [1, 1]) == 0.
 
-    assert_equal(0., sensitivity_score([-1, -1], [-1, -1]))
-    assert_equal(0., specificity_score([-1, -1], [-1, -1]))
+    assert sensitivity_score([-1, -1], [-1, -1]) == 0.
+    assert specificity_score([-1, -1], [-1, -1]) == 0.
 
 
 @ignore_warnings
@@ -166,9 +167,8 @@ def test_sensitivity_specificity_ignored_labels():
                     rtol=R_TOL)
 
     # ensure the above were meaningful tests:
-    for average in ['macro', 'weighted', 'micro']:
-        assert_not_equal(
-            specificity_13(average=average), specificity_all(average=average))
+    for each in ['macro', 'weighted', 'micro']:
+        assert specificity_13(average=each) != specificity_all(average=each)
 
 
 def test_sensitivity_specificity_error_multilabels():
@@ -333,15 +333,15 @@ def test_classification_report_imbalanced_multiclass():
         y_pred,
         labels=np.arange(len(iris.target_names)),
         target_names=iris.target_names)
-    assert_equal(_format_report(report), expected_report)
+    assert _format_report(report) == expected_report
     # print classification report with label detection
     expected_report = ('pre rec spe f1 geo iba sup 0 0.83 0.79 0.92 0.81 '
                        '0.86 0.74 24 1 0.33 0.10 0.86 0.15 0.44 0.19 31 2 '
                        '0.42 0.90 0.55 0.57 0.63 0.37 20 avg / total 0.51 '
                        '0.53 0.80 0.47 0.62 0.41 75')
 
     report = classification_report_imbalanced(y_true, y_pred)
-    assert_equal(_format_report(report), expected_report)
+    assert _format_report(report) == expected_report
 
 
 def test_classification_report_imbalanced_multiclass_with_digits():
@@ -361,14 +361,14 @@ def test_classification_report_imbalanced_multiclass_with_digits():
         labels=np.arange(len(iris.target_names)),
         target_names=iris.target_names,
         digits=5)
-    assert_equal(_format_report(report), expected_report)
+    assert _format_report(report) == expected_report
     # print classification report with label detection
     expected_report = ('pre rec spe f1 geo iba sup 0 0.83 0.79 0.92 0.81 '
                        '0.86 0.74 24 1 0.33 0.10 0.86 0.15 0.44 0.19 31 2 '
                        '0.42 0.90 0.55 0.57 0.63 0.37 20 avg / total 0.51 '
                        '0.53 0.80 0.47 0.62 0.41 75')
     report = classification_report_imbalanced(y_true, y_pred)
-    assert_equal(_format_report(report), expected_report)
+    assert _format_report(report) == expected_report
 
 
 def test_classification_report_imbalanced_multiclass_with_string_label():
@@ -382,15 +382,15 @@ def test_classification_report_imbalanced_multiclass_with_string_label():
                        '0.19 31 red 0.42 0.90 0.55 0.57 0.63 0.37 20 '
                        'avg / total 0.51 0.53 0.80 0.47 0.62 0.41 75')
     report = classification_report_imbalanced(y_true, y_pred)
-    assert_equal(_format_report(report), expected_report)
+    assert _format_report(report) == expected_report
 
     expected_report = ('pre rec spe f1 geo iba sup a 0.83 0.79 0.92 0.81 '
                        '0.86 0.74 24 b 0.33 0.10 0.86 0.15 0.44 0.19 31 '
                        'c 0.42 0.90 0.55 0.57 0.63 0.37 20 avg / total '
                        '0.51 0.53 0.80 0.47 0.62 0.41 75')
     report = classification_report_imbalanced(
         y_true, y_pred, target_names=["a", "b", "c"])
-    assert_equal(_format_report(report), expected_report)
+    assert _format_report(report) == expected_report
 
 
 def test_classification_report_imbalanced_multiclass_with_unicode_label():
@@ -411,7 +411,7 @@ def test_classification_report_imbalanced_multiclass_with_unicode_label():
                              classification_report_imbalanced, y_true, y_pred)
     else:
         report = classification_report_imbalanced(y_true, y_pred)
-        assert_equal(_format_report(report), expected_report)
+        assert _format_report(report) == expected_report
 
 
 def test_classification_report_imbalanced_multiclass_with_long_string_label():
@@ -427,7 +427,7 @@ def test_classification_report_imbalanced_multiclass_with_long_string_label():
                        '0.37 20 avg / total 0.51 0.53 0.80 0.47 0.62 0.41 75')
 
     report = classification_report_imbalanced(y_true, y_pred)
-    assert_equal(_format_report(report), expected_report)
+    assert _format_report(report) == expected_report
 
 
 def test_iba_sklearn_metrics():
@@ -436,22 +436,22 @@ def test_iba_sklearn_metrics():
     acc = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         accuracy_score)
     score = acc(y_true, y_pred)
-    assert_equal(score, 0.54756)
+    assert score == approx(0.54756)
 
     jss = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         jaccard_similarity_score)
     score = jss(y_true, y_pred)
-    assert_equal(score, 0.54756)
+    assert score == approx(0.54756)
 
     pre = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         precision_score)
     score = pre(y_true, y_pred)
-    assert_equal(score, 0.65025)
+    assert score == approx(0.65025)
 
     rec = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         recall_score)
     score = rec(y_true, y_pred)
-    assert_equal(score, 0.41616000000000009)
+    assert score == approx(0.41616000000000009)
 
 
 def test_iba_error_y_score_prob():

imblearn/over_sampling/tests/test_adasyn.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from sklearn.utils.testing import (assert_allclose, assert_array_equal,
-                                   assert_equal, assert_raises_regex)
+from sklearn.utils.testing import assert_allclose, assert_array_equal
+from sklearn.utils.testing import assert_raises_regex
 from sklearn.neighbors import NearestNeighbors
 
 from imblearn.over_sampling import ADASYN
@@ -30,13 +30,13 @@
 def test_ada_init():
     ratio = 'auto'
     ada = ADASYN(ratio=ratio, random_state=RND_SEED)
-    assert_equal(ada.random_state, RND_SEED)
+    assert ada.random_state == RND_SEED
 
 
 def test_ada_fit():
     ada = ADASYN(random_state=RND_SEED)
     ada.fit(X, Y)
-    assert_equal(ada.ratio_, {0: 4, 1: 0})
+    assert ada.ratio_ == {0: 4, 1: 0}
 
 
 def test_ada_fit_sample():

imblearn/over_sampling/tests/test_random_over_sampler.py

@@ -8,7 +8,7 @@
 from collections import Counter
 
 import numpy as np
-from sklearn.utils.testing import assert_array_equal, assert_equal
+from sklearn.utils.testing import assert_array_equal
 
 from imblearn.over_sampling import RandomOverSampler
 
@@ -24,7 +24,7 @@
 def test_ros_init():
     ratio = 'auto'
     ros = RandomOverSampler(ratio=ratio, random_state=RND_SEED)
-    assert_equal(ros.random_state, RND_SEED)
+    assert ros.random_state == RND_SEED
 
 
 def test_ros_fit_sample():
@@ -75,6 +75,6 @@ def test_multiclass_fit_sample():
     ros = RandomOverSampler(random_state=RND_SEED)
     X_resampled, y_resampled = ros.fit_sample(X, y)
     count_y_res = Counter(y_resampled)
-    assert_equal(count_y_res[0], 5)
-    assert_equal(count_y_res[1], 5)
-    assert_equal(count_y_res[2], 5)
+    assert count_y_res[0] == 5
+    assert count_y_res[1] == 5
+    assert count_y_res[2] == 5

imblearn/over_sampling/tests/test_smote.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from sklearn.utils.testing import (assert_allclose, assert_array_equal,
-                                   assert_raises_regex)
+from sklearn.utils.testing import assert_allclose, assert_array_equal
+from sklearn.utils.testing import assert_raises_regex
 from sklearn.neighbors import NearestNeighbors
 from sklearn.svm import SVC
 

imblearn/tests/test_common.py

@@ -3,8 +3,6 @@
 #          Christos Aridas
 # License: MIT
 
-from sklearn.utils.testing import assert_greater
-from sklearn.utils.testing import assert_false
 from sklearn.utils.testing import _named_check
 
 from imblearn.utils.estimator_checks import check_estimator, _yield_all_checks
@@ -16,12 +14,12 @@ def test_all_estimator_no_base_class():
     for name, Estimator in all_estimators():
         msg = ("Base estimators such as {0} should not be included"
                " in all_estimators").format(name)
-        assert_false(name.lower().startswith('base'), msg=msg)
+        assert not name.lower().startswith('base'), msg
 
 
 def test_all_estimators():
     estimators = all_estimators(include_meta_estimators=True)
-    assert_greater(len(estimators), 0)
+    assert len(estimators) > 0
     for name, Estimator in estimators:
         # some can just not be sensibly default constructed
         yield (_named_check(check_estimator, name),