change assert_raise for raises(xxx)

Author: Joan Massich

imblearn/ensemble/tests/test_balance_cascade.py

@@ -6,12 +6,14 @@
 from __future__ import print_function
 
 import numpy as np
-from sklearn.utils.testing import assert_array_equal, assert_raises
+from sklearn.utils.testing import assert_array_equal
 from sklearn.utils.testing import assert_raises_regex
 from sklearn.ensemble import RandomForestClassifier
 
 from imblearn.ensemble import BalanceCascade
 
+from pytest import raises
+
 RND_SEED = 0
 X = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
               [1.25192108, -0.22367336], [0.53366841, -0.30312976],
@@ -299,7 +301,8 @@ def test_fit_sample_auto_linear_svm():
 def test_init_wrong_classifier():
     classifier = 'rnd'
     bc = BalanceCascade(classifier=classifier)
-    assert_raises(NotImplementedError, bc.fit_sample, X, Y)
+    with raises(NotImplementedError):
+        bc.fit_sample(X, Y)
 
 
 def test_fit_sample_auto_early_stop():

imblearn/metrics/tests/test_classification.py

@@ -17,7 +17,7 @@
 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
-from sklearn.utils.testing import assert_no_warnings, assert_raises
+from sklearn.utils.testing import assert_no_warnings
 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
@@ -32,7 +32,7 @@
 from imblearn.metrics import make_index_balanced_accuracy
 from imblearn.metrics import classification_report_imbalanced
 
-from pytest import approx
+from pytest import approx, raises
 
 RND_SEED = 42
 R_TOL = 1e-2
@@ -177,27 +177,22 @@ def test_sensitivity_specificity_error_multilabels():
     y_true_bin = label_binarize(y_true, classes=np.arange(5))
     y_pred_bin = label_binarize(y_pred, classes=np.arange(5))
 
-    assert_raises(ValueError, sensitivity_score, y_true_bin, y_pred_bin)
+    with raises(ValueError):
+        sensitivity_score(y_true_bin, y_pred_bin)
 
 
 @ignore_warnings
 def test_sensitivity_specificity_support_errors():
     y_true, y_pred, _ = make_prediction(binary=True)
 
     # Bad pos_label
-    assert_raises(
-        ValueError,
-        sensitivity_specificity_support,
-        y_true,
-        y_pred,
-        pos_label=2,
-        average='binary')
+    with raises(ValueError):
+        sensitivity_specificity_support(y_true, y_pred, pos_label=2,
+                                        average='binary')
 
     # Bad average option
-    assert_raises(
-        ValueError,
-        sensitivity_specificity_support, [0, 1, 2], [1, 2, 0],
-        average='mega')
+    with raises(ValueError):
+        sensitivity_specificity_support([0, 1, 2], [1, 2, 0], average='mega')
 
 
 def test_sensitivity_specificity_unused_pos_label():
@@ -459,16 +454,20 @@ def test_iba_error_y_score_prob():
 
     aps = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         average_precision_score)
-    assert_raises(AttributeError, aps, y_true, y_pred)
+    with raises(AttributeError):
+        aps(y_true, y_pred)
 
     brier = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         brier_score_loss)
-    assert_raises(AttributeError, brier, y_true, y_pred)
+    with raises(AttributeError):
+        brier(y_true, y_pred)
 
     kappa = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         cohen_kappa_score)
-    assert_raises(AttributeError, kappa, y_true, y_pred)
+    with raises(AttributeError):
+        kappa(y_true, y_pred)
 
     ras = make_index_balanced_accuracy(alpha=0.5, squared=True)(
         roc_auc_score)
-    assert_raises(AttributeError, ras, y_true, y_pred)
+    with raises(AttributeError):
+        ras(y_true, y_pred)

imblearn/tests/test_pipeline.py

@@ -11,7 +11,6 @@
 import time
 
 import numpy as np
-from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_raises_regex
 from sklearn.utils.testing import assert_raise_message
 from sklearn.utils.testing import assert_array_equal
@@ -33,6 +32,8 @@
 from imblearn.under_sampling import (RandomUnderSampler,
                                      EditedNearestNeighbours as ENN)
 
+from pytest import raises
+
 JUNK_FOOD_DOCS = (
     "the pizza pizza beer copyright",
     "the pizza burger beer copyright",
@@ -176,7 +177,8 @@ def transform(self, X, y=None):
 
 def test_pipeline_init():
     # Test the various init parameters of the pipeline.
-    assert_raises(TypeError, Pipeline)
+    with raises(TypeError):
+        Pipeline()
     # Check that we can't instantiate pipelines with objects without fit
     # method
     assert_raises_regex(TypeError,
@@ -215,7 +217,8 @@ def test_pipeline_init():
     repr(pipe)
 
     # Check that params are not set when naming them wrong
-    assert_raises(ValueError, pipe.set_params, anova__C=0.1)
+    with raises(ValueError):
+        pipe.set_params(anova__C=0.1)
 
     # Test clone
     pipe2 = clone(pipe)
@@ -471,8 +474,10 @@ def test_set_pipeline_steps():
 
     # With invalid data
     pipeline.set_params(steps=[('junk', ())])
-    assert_raises(TypeError, pipeline.fit, [[1]], [1])
-    assert_raises(TypeError, pipeline.fit_transform, [[1]], [1])
+    with raises(TypeError):
+        pipeline.fit([[1]], [1])
+    with raises(TypeError):
+        pipeline.fit_transform([[1]], [1])
 
 
 def test_set_pipeline_step_none():
@@ -595,10 +600,12 @@ def test_classes_property():
 
     reg = make_pipeline(SelectKBest(k=1), LinearRegression())
     reg.fit(X, y)
-    assert_raises(AttributeError, getattr, reg, "classes_")
+    with raises(AttributeError):
+        getattr(reg, "classes_")
 
     clf = make_pipeline(SelectKBest(k=1), LogisticRegression(random_state=0))
-    assert_raises(AttributeError, getattr, clf, "classes_")
+    with raises(AttributeError):
+        getattr(clf, "classes_")
     clf.fit(X, y)
     assert_array_equal(clf.classes_, np.unique(y))
 
@@ -1018,9 +1025,8 @@ def test_pipeline_with_step_that_implements_both_sample_and_transform():
         random_state=0)
 
     clf = LogisticRegression()
-    assert_raises(TypeError, Pipeline, [('step', FitTransformSample()),
-                                        ('logistic', clf)])
-    # assert_raises(TypeError, lambda x: [][0])
+    with raises(TypeError):
+        Pipeline([('step', FitTransformSample()), ('logistic', clf)])
 
 
 def test_pipeline_with_step_that_it_is_pipeline():
@@ -1041,7 +1047,8 @@ def test_pipeline_with_step_that_it_is_pipeline():
     rus = RandomUnderSampler(random_state=0)
     filter1 = SelectKBest(f_classif, k=2)
     pipe1 = Pipeline([('rus', rus), ('anova', filter1)])
-    assert_raises(TypeError, Pipeline, [('pipe1', pipe1), ('logistic', clf)])
+    with raises(TypeError):
+        Pipeline([('pipe1', pipe1), ('logistic', clf)])
 
 
 def test_pipeline_fit_then_sample_with_sampler_last_estimator():

imblearn/under_sampling/prototype_selection/tests/test_allknn.py

@@ -7,12 +7,13 @@
 
 import numpy as np
 from sklearn.utils.testing import assert_allclose, assert_array_equal
-from sklearn.utils.testing import assert_raises
 from sklearn.neighbors import NearestNeighbors
 from sklearn.datasets import make_classification
 
 from imblearn.under_sampling import AllKNN
 
+from pytest import raises
+
 RND_SEED = 0
 X = np.array([[-0.12840393, 0.66446571], [1.32319756, -0.13181616],
               [0.04296502, -0.37981873], [0.83631853, 0.18569783],
@@ -171,4 +172,5 @@ def test_allknn_fit_sample_with_nn_object():
 def test_alknn_not_good_object():
     nn = 'rnd'
     allknn = AllKNN(n_neighbors=nn, random_state=RND_SEED, kind_sel='mode')
-    assert_raises(ValueError, allknn.fit_sample, X, Y)
+    with raises(ValueError):
+        allknn.fit_sample(X, Y)

imblearn/under_sampling/prototype_selection/tests/test_instance_hardness_threshold.py

@@ -7,12 +7,12 @@
 
 import numpy as np
 from sklearn.utils.testing import assert_array_equal
-from sklearn.utils.testing import assert_raises
-from sklearn.utils.testing import assert_raises_regex
 from sklearn.ensemble import GradientBoostingClassifier
 
 from imblearn.under_sampling import InstanceHardnessThreshold
 
+from pytest import raises
+
 RND_SEED = 0
 X = np.array([[-0.3879569, 0.6894251], [-0.09322739, 1.28177189],
               [-0.77740357, 0.74097941], [0.91542919, -0.65453327],
@@ -31,7 +31,8 @@ def test_iht_wrong_estimator():
     est = 'rnd'
     iht = InstanceHardnessThreshold(
         estimator=est, ratio=ratio, random_state=RND_SEED)
-    assert_raises(NotImplementedError, iht.fit_sample, X, Y)
+    with raises(NotImplementedError):
+        iht.fit_sample(X, Y)
 
 
 def test_iht_init():

imblearn/under_sampling/prototype_selection/tests/test_repeated_edited_nearest_neighbours.py

@@ -7,12 +7,13 @@
 
 import numpy as np
 from sklearn.utils.testing import assert_array_equal
-from sklearn.utils.testing import assert_raises
 
 from sklearn.neighbors import NearestNeighbors
 
 from imblearn.under_sampling import RepeatedEditedNearestNeighbours
 
+from pytest import raises
+
 RND_SEED = 0
 X = np.array([[-0.12840393, 0.66446571], [1.32319756, -0.13181616],
               [0.04296502, -0.37981873], [0.83631853, 0.18569783],
@@ -53,7 +54,8 @@ def test_renn_iter_wrong():
     max_iter = -1
     renn = RepeatedEditedNearestNeighbours(
         max_iter=max_iter, random_state=RND_SEED)
-    assert_raises(ValueError, renn.fit_sample, X, Y)
+    with raises(ValueError):
+        renn.fit_sample(X, Y)
 
 
 def test_renn_fit_sample():
@@ -177,4 +179,5 @@ def test_renn_not_good_object():
     nn = 'rnd'
     renn = RepeatedEditedNearestNeighbours(
         n_neighbors=nn, random_state=RND_SEED, kind_sel='mode')
-    assert_raises(ValueError, renn.fit_sample, X, Y)
+    with raises(ValueError):
+        renn.fit_sample(X, Y)