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Commit 3217999

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zitorelovazitorelova
authored
TST Replace the use of assert_warns messages in cluster/tests/ module (#19437)
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‎sklearn/cluster/tests/test_affinity_propagation.py

Copy file name to clipboardExpand all lines: sklearn/cluster/tests/test_affinity_propagation.py
+19-14Lines changed: 19 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -8,9 +8,7 @@
88
from scipy.sparse import csr_matrix
99

1010
from sklearn.exceptions import ConvergenceWarning
11-
from sklearn.utils._testing import (
12-
assert_array_equal, assert_warns,
13-
assert_warns_message, assert_no_warnings)
11+
from sklearn.utils._testing import assert_array_equal
1412

1513
from sklearn.cluster import AffinityPropagation
1614
from sklearn.cluster._affinity_propagation import (
@@ -72,6 +70,7 @@ def test_affinity_propagation():
7270
with pytest.raises(TypeError):
7371
af_2.fit(csr_matrix((3, 3)))
7472

73+
7574
def test_affinity_propagation_predict():
7675
# Test AffinityPropagation.predict
7776
af = AffinityPropagation(affinity="euclidean", random_state=63)
@@ -104,7 +103,8 @@ def test_affinity_propagation_fit_non_convergence():
104103
# Force non-convergence by allowing only a single iteration
105104
af = AffinityPropagation(preference=-10, max_iter=1, random_state=82)
106105

107-
assert_warns(ConvergenceWarning, af.fit, X)
106+
with pytest.warns(ConvergenceWarning):
107+
af.fit(X)
108108
assert_array_equal(np.empty((0, 2)), af.cluster_centers_)
109109
assert_array_equal(np.array([-1, -1, -1]), af.labels_)
110110

@@ -114,24 +114,28 @@ def test_affinity_propagation_equal_mutual_similarities():
114114
S = -euclidean_distances(X, squared=True)
115115

116116
# setting preference > similarity
117-
cluster_center_indices, labels = assert_warns_message(
118-
UserWarning, "mutually equal", affinity_propagation, S, preference=0)
117+
with pytest.warns(UserWarning, match="mutually equal"):
118+
cluster_center_indices, labels = affinity_propagation(
119+
S, preference=0)
119120

120121
# expect every sample to become an exemplar
121122
assert_array_equal([0, 1], cluster_center_indices)
122123
assert_array_equal([0, 1], labels)
123124

124125
# setting preference < similarity
125-
cluster_center_indices, labels = assert_warns_message(
126-
UserWarning, "mutually equal", affinity_propagation, S, preference=-10)
126+
with pytest.warns(UserWarning, match="mutually equal"):
127+
cluster_center_indices, labels = affinity_propagation(
128+
S, preference=-10)
127129

128130
# expect one cluster, with arbitrary (first) sample as exemplar
129131
assert_array_equal([0], cluster_center_indices)
130132
assert_array_equal([0, 0], labels)
131133

132134
# setting different preferences
133-
cluster_center_indices, labels = assert_no_warnings(
134-
affinity_propagation, S, preference=[-20, -10], random_state=37)
135+
with pytest.warns(None) as record:
136+
cluster_center_indices, labels = affinity_propagation(
137+
S, preference=[-20, -10], random_state=37)
138+
assert not len(record)
135139

136140
# expect one cluster, with highest-preference sample as exemplar
137141
assert_array_equal([1], cluster_center_indices)
@@ -144,14 +148,15 @@ def test_affinity_propagation_predict_non_convergence():
144148
X = np.array([[0, 0], [1, 1], [-2, -2]])
145149

146150
# Force non-convergence by allowing only a single iteration
147-
af = assert_warns(ConvergenceWarning,
148-
AffinityPropagation(preference=-10,
149-
max_iter=1, random_state=75).fit, X)
151+
with pytest.warns(ConvergenceWarning):
152+
af = AffinityPropagation(preference=-10,
153+
max_iter=1, random_state=75).fit(X)
150154

151155
# At prediction time, consider new samples as noise since there are no
152156
# clusters
153157
to_predict = np.array([[2, 2], [3, 3], [4, 4]])
154-
y = assert_warns(ConvergenceWarning, af.predict, to_predict)
158+
with pytest.warns(ConvergenceWarning):
159+
y = af.predict(to_predict)
155160
assert_array_equal(np.array([-1, -1, -1]), y)
156161

157162

‎sklearn/cluster/tests/test_birch.py

Copy file name to clipboardExpand all lines: sklearn/cluster/tests/test_birch.py
+2-2Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -17,7 +17,6 @@
1717
from sklearn.utils._testing import assert_almost_equal
1818
from sklearn.utils._testing import assert_array_equal
1919
from sklearn.utils._testing import assert_array_almost_equal
20-
from sklearn.utils._testing import assert_warns
2120

2221

2322
def test_n_samples_leaves_roots():
@@ -92,7 +91,8 @@ def test_n_clusters():
9291

9392
# Test that a small number of clusters raises a warning.
9493
brc4 = Birch(threshold=10000.)
95-
assert_warns(ConvergenceWarning, brc4.fit, X)
94+
with pytest.warns(ConvergenceWarning):
95+
brc4.fit(X)
9696

9797

9898
def test_sparse_X():

‎sklearn/cluster/tests/test_feature_agglomeration.py

Copy file name to clipboardExpand all lines: sklearn/cluster/tests/test_feature_agglomeration.py
+7-3Lines changed: 7 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -3,8 +3,8 @@
33
"""
44
# Authors: Sergul Aydore 2017
55
import numpy as np
6+
import pytest
67
from sklearn.cluster import FeatureAgglomeration
7-
from sklearn.utils._testing import assert_no_warnings
88
from sklearn.utils._testing import assert_array_almost_equal
99

1010

@@ -16,8 +16,12 @@ def test_feature_agglomeration():
1616
pooling_func=np.mean)
1717
agglo_median = FeatureAgglomeration(n_clusters=n_clusters,
1818
pooling_func=np.median)
19-
assert_no_warnings(agglo_mean.fit, X)
20-
assert_no_warnings(agglo_median.fit, X)
19+
with pytest.warns(None) as record:
20+
agglo_mean.fit(X)
21+
assert not len(record)
22+
with pytest.warns(None) as record:
23+
agglo_median.fit(X)
24+
assert not len(record)
2125
assert np.size(np.unique(agglo_mean.labels_)) == n_clusters
2226
assert np.size(np.unique(agglo_median.labels_)) == n_clusters
2327
assert np.size(agglo_mean.labels_) == X.shape[1]

‎sklearn/cluster/tests/test_hierarchical.py

Copy file name to clipboardExpand all lines: sklearn/cluster/tests/test_hierarchical.py
+8-7Lines changed: 8 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,6 @@
3333
from sklearn.cluster._hierarchical_fast import average_merge, max_merge
3434
from sklearn.utils._fast_dict import IntFloatDict
3535
from sklearn.utils._testing import assert_array_equal
36-
from sklearn.utils._testing import assert_warns
3736
from sklearn.datasets import make_moons, make_circles
3837

3938

@@ -94,17 +93,18 @@ def test_unstructured_linkage_tree():
9493
# With specified a number of clusters just for the sake of
9594
# raising a warning and testing the warning code
9695
with ignore_warnings():
97-
children, n_nodes, n_leaves, parent = assert_warns(
98-
UserWarning, ward_tree, this_X.T, n_clusters=10)
96+
with pytest.warns(UserWarning):
97+
children, n_nodes, n_leaves, parent = ward_tree(
98+
this_X.T, n_clusters=10)
9999
n_nodes = 2 * X.shape[1] - 1
100100
assert len(children) + n_leaves == n_nodes
101101

102102
for tree_builder in _TREE_BUILDERS.values():
103103
for this_X in (X, X[0]):
104104
with ignore_warnings():
105-
children, n_nodes, n_leaves, parent = assert_warns(
106-
UserWarning, tree_builder, this_X.T, n_clusters=10)
107-
105+
with pytest.warns(UserWarning):
106+
children, n_nodes, n_leaves, parent = tree_builder(
107+
this_X.T, n_clusters=10)
108108
n_nodes = 2 * X.shape[1] - 1
109109
assert len(children) + n_leaves == n_nodes
110110

@@ -550,7 +550,8 @@ def test_connectivity_fixing_non_lil():
550550
m = np.array([[True, False], [False, True]])
551551
c = grid_to_graph(n_x=2, n_y=2, mask=m)
552552
w = AgglomerativeClustering(connectivity=c, linkage='ward')
553-
assert_warns(UserWarning, w.fit, x)
553+
with pytest.warns(UserWarning):
554+
w.fit(x)
554555

555556

556557
def test_int_float_dict():

‎sklearn/cluster/tests/test_spectral.py

Copy file name to clipboardExpand all lines: sklearn/cluster/tests/test_spectral.py
+2-2Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,6 @@
1010

1111
from sklearn.utils import check_random_state
1212
from sklearn.utils._testing import assert_array_equal
13-
from sklearn.utils._testing import assert_warns_message
1413

1514
from sklearn.cluster import SpectralClustering, spectral_clustering
1615
from sklearn.cluster._spectral import discretize
@@ -132,7 +131,8 @@ def test_affinities():
132131
# nearest neighbors affinity
133132
sp = SpectralClustering(n_clusters=2, affinity='nearest_neighbors',
134133
random_state=0)
135-
assert_warns_message(UserWarning, 'not fully connected', sp.fit, X)
134+
with pytest.warns(UserWarning, match='not fully connected'):
135+
sp.fit(X)
136136
assert adjusted_rand_score(y, sp.labels_) == 1
137137

138138
sp = SpectralClustering(n_clusters=2, gamma=2, random_state=0)

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