8000 Revert "TST autoreplace assert_true(...==...) with plain assert (#125… · xhluca/scikit-learn@4b9e331 · GitHub
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Revert "TST autoreplace assert_true(...==...) with plain assert (scikit-learn#12547)"
This reverts commit fb5d3a7.
1 parent ff9344c commit 4b9e331

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‎sklearn/cluster/tests/test_affinity_propagation.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -160,5 +160,5 @@ def test_equal_similarities_and_preferences():
160160
assert_false(_equal_similarities_and_preferences(S, np.array([0, 1])))
161161

162162
# Same preferences
163-
assert _equal_similarities_and_preferences(S, np.array([0, 0]))
164-
assert _equal_similarities_and_preferences(S, np.array(0))
163+
assert_true(_equal_similarities_and_preferences(S, np.array([0, 0])))
164+
assert_true(_equal_similarities_and_preferences(S, np.array(0)))

‎sklearn/cluster/tests/test_bicluster.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -51,7 +51,7 @@ def test_get_submatrix():
5151
submatrix[:] = -1
5252
if issparse(X):
5353
X = X.toarray()
54-
assert np.all(X != -1)
54+
assert_true(np.all(X != -1))
5555

5656

5757
def _test_shape_indices(model):

‎sklearn/cluster/tests/test_feature_agglomeration.py

Lines changed: 10 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -18,24 +18,24 @@ def test_feature_agglomeration():
1818
pooling_func=np.median)
1919
assert_no_warnings(agglo_mean.fit, X)
2020
assert_no_warnings(agglo_median.fit, X)
21-
assert np.size(np.unique(agglo_mean.labels_)) == n_clusters
22-
assert np.size(np.unique(agglo_median.labels_)) == n_clusters
23-
assert np.size(agglo_mean.labels_) == X.shape[1]
24-
assert np.size(agglo_median.labels_) == X.shape[1]
21+
assert_true(np.size(np.unique(agglo_mean.labels_)) == n_clusters)
22+
assert_true(np.size(np.unique(agglo_median.labels_)) == n_clusters)
23+
assert_true(np.size(agglo_mean.labels_) == X.shape[1])
24+
assert_true(np.size(agglo_median.labels_) == X.shape[1])
2525

2626
# Test transform
2727
Xt_mean = agglo_mean.transform(X)
2828
Xt_median = agglo_median.transform(X)
29-
assert Xt_mean.shape[1] == n_clusters
30-
assert Xt_median.shape[1] == n_clusters
31-
assert Xt_mean == np.array([1 / 3.])
32-
assert Xt_median == np.array([0.])
29+
assert_true(Xt_mean.shape[1] == n_clusters)
30+
assert_true(Xt_median.shape[1] == n_clusters)
31+
assert_true(Xt_mean == np.array([1 / 3.]))
32+
assert_true(Xt_median == np.array([0.]))
3333

3434
# Test inverse transform
3535
X_full_mean = agglo_mean.inverse_transform(Xt_mean)
3636
X_full_median = agglo_median.inverse_transform(Xt_median)
37-
assert np.unique(X_full_mean[0]).size == n_clusters
38-
assert np.unique(X_full_median[0]).size == n_clusters
37+
assert_true(np.unique(X_full_mean[0]).size == n_clusters)
38+
assert_true(np.unique(X_full_median[0]).size == n_clusters)
3939

4040
assert_array_almost_equal(agglo_mean.transform(X_full_mean),
4141
Xt_mean)

‎sklearn/cluster/tests/test_hierarchical.py

Lines changed: 8 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -72,7 +72,7 @@ def test_structured_linkage_tree():
7272
children, n_components, n_leaves, parent = \
7373
tree_builder(X.T, connectivity)
7474
n_nodes = 2 * X.shape[1] - 1
75-
assert len(children) + n_leaves == n_nodes
75+
assert_true(len(children) + n_leaves == n_nodes)
7676
# Check that ward_tree raises a ValueError with a connectivity matrix
7777
# of the wrong shape
7878
assert_raises(ValueError,
@@ -114,7 +114,7 @@ def test_height_linkage_tree():
114114
for linkage_func in _TREE_BUILDERS.values():
115115
children, n_nodes, n_leaves, parent = linkage_func(X.T, connectivity)
116116
n_nodes = 2 * X.shape[1] - 1
117-
assert len(children) + n_leaves == n_nodes
117+
assert_true(len(children) + n_leaves == n_nodes)
118118

119119

120120
def test_agglomerative_clustering_wrong_arg_memory():
@@ -152,7 +152,7 @@ def test_agglomerative_clustering():
152152
linkage=linkage)
153153
clustering.fit(X)
154154
labels = clustering.labels_
155-
assert np.size(np.unique(labels)) == 10
155+
assert_true(np.size(np.unique(labels)) == 10)
156156
finally:
157157
shutil.rmtree(tempdir)
158158
# Turn caching off now
@@ -166,7 +166,7 @@ def test_agglomerative_clustering():
166166
labels), 1)
167167
clustering.connectivity = None
168168
clustering.fit(X)
169-
assert np.size(np.unique(clustering.labels_)) == 10
169+
assert_true(np.size(np.unique(clustering.labels_)) == 10)
170170
# Check that we raise a TypeError on dense matrices
171171
clustering = AgglomerativeClustering(
172172
n_clusters=10,
@@ -226,12 +226,12 @@ def test_ward_agglomeration():
226226
connectivity = grid_to_graph(*mask.shape)
227227
agglo = FeatureAgglomeration(n_clusters=5, connectivity=connectivity)
228228
agglo.fit(X)
229-
assert np.size(np.unique(agglo.labels_)) == 5
229+
assert_true(np.size(np.unique(agglo.labels_)) == 5)
230230

231231
X_red = agglo.transform(X)
232-
assert X_red.shape[1] == 5
232+
assert_true(X_red.shape[1] == 5)
233233
X_full = agglo.inverse_transform(X_red)
234-
assert np.unique(X_full[0]).size == 5
234+
assert_true(np.unique(X_full[0]).size == 5)
235235
assert_array_almost_equal(agglo.transform(X_full), X_red)
236236

237237
# Check that fitting with no samples raises a ValueError
@@ -265,7 +265,7 @@ def assess_same_labelling(cut1, cut2):
265265
ecut = np.zeros((n, k))
266266
ecut[np.arange(n), cut] = 1
267267
co_clust.append(np.dot(ecut, ecut.T))
268-
assert (co_clust[0] == co_clust[1]).all()
268+
assert_true((co_clust[0] == co_clust[1]).all())
269269

270270

271271
def test_scikit_vs_scipy():

‎sklearn/cluster/tests/test_k_means.py

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -107,8 +107,8 @@ def test_labels_assignment_and_inertia():
107107
labels_gold[dist < mindist] = center_id
108108
mindist = np.minimum(dist, mindist)
109109
inertia_gold = mindist.sum()
110-
assert (mindist >= 0.0).all()
111-
assert (labels_gold != -1).all()
110+
assert_true((mindist >= 0.0).all())
111+
assert_true((labels_gold != -1).all())
112112

113113
sample_weight = None
114114

@@ -565,9 +565,9 @@ def test_k_means_non_collapsed():
565565
assert_equal(len(np.unique(km.labels_)), 3)
566566

567567
centers = km.cluster_centers_
568-
assert np.linalg.norm(centers[0] - centers[1]) >= 0.1
569-
assert np.linalg.norm(centers[0] - centers[2]) >= 0.1
570-
assert np.linalg.norm(centers[1] - centers[2]) >= 0.1
568+
assert_true(np.linalg.norm(centers[0] - centers[1]) >= 0.1)
569+
assert_true(np.linalg.norm(centers[0] - centers[2]) >= 0.1)
570+
assert_true(np.linalg.norm(centers[1] - centers[2]) >= 0.1)
571571

572572

573573
@pytest.mark.parametrize('algo', ['full', 'elkan'])
@@ -689,7 +689,7 @@ def test_n_init():
689689
failure_msg = ("Inertia %r should be decreasing"
690690
" when n_init is increasing.") % list(inertia)
691691
for i in range(len(n_init_range) - 1):
692-
assert inertia[i] >= inertia[i + 1], failure_msg
692+
assert_true(inertia[i] >= inertia[i + 1], failure_msg)
693693

694694

695695
def test_k_means_function():

‎sklearn/cluster/tests/test_mean_shift.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -31,7 +31,7 @@
3131
def test_estimate_bandwidth():
3232
# Test estimate_bandwidth
3333
bandwidth = estimate_bandwidth(X, n_samples=200)
34-
assert 0.9 <= bandwidth <= 1.5
34+
assert_true(0.9 <= bandwidth <= 1.5)
3535

3636

3737
def test_estimate_bandwidth_1sample():
@@ -125,14 +125,14 @@ def test_bin_seeds():
125125
ground_truth = set([(1., 1.), (2., 1.), (0., 0.)])
126126
test_bins = get_bin_seeds(X, 1, 1)
127127
test_result = set([tuple(p) for p in test_bins])
128-
assert len(ground_truth.symmetric_difference(test_result)) == 0
128+
assert_true(len(ground_truth.symmetric_difference(test_result)) == 0)
129129

130130
# With a bin coarseness of 1.0 and min_bin_freq of 2, 2 bins should be
131131
# found
132132
ground_truth = set([(1., 1.), (2., 1.)])
133133
test_bins = get_bin_seeds(X, 1, 2)
134134
test_result = set([tuple(p) for p in test_bins])
135-
assert len(ground_truth.symmetric_difference(test_result)) == 0
135+
assert_true(len(ground_truth.symmetric_difference(test_result)) == 0)
136136

137137
# With a bin size of 0.01 and min_bin_freq of 1, 6 bins should be found
138138
# we bail and use the whole data here.

‎sklearn/compose/tests/test_column_transformer.py

Lines changed: 11 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -227,7 +227,7 @@ def fit(self, X, y=None):
227227
return self
228228

229229
def transform(self, X, y=None):
230-
assert isinstance(X, (pd.DataFrame, pd.Series))
230+
assert_true(isinstance(X, (pd.DataFrame, pd.Series)))
231231
if isinstance(X, pd.Series):
232232
X = X.to_frame()
233233
return X
@@ -309,15 +309,15 @@ def test_column_transformer_sparse_array():
309309
ct = ColumnTransformer([('trans', Trans(), col)],
310310
remainder=remainder,
311311
sparse_threshold=0.8)
312-
assert sparse.issparse(ct.fit_transform(X_sparse))
312+
assert_true(sparse.issparse(ct.fit_transform(X_sparse)))
313313
assert_allclose_dense_sparse(ct.fit_transform(X_sparse), res)
314314
assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse),
315315
res)
316316

317317
for col in [[0, 1], slice(0, 2)]:
318318
ct = ColumnTransformer([('trans', Trans(), col)],
319319
sparse_threshold=0.8)
320-
assert sparse.issparse(ct.fit_transform(X_sparse))
320+
assert_true(sparse.issparse(ct.fit_transform(X_sparse)))
321321
assert_allclose_dense_sparse(ct.fit_transform(X_sparse), X_res_both)
322322
assert_allclose_dense_sparse(ct.fit(X_sparse).transform(X_sparse),
323323
X_res_both)
@@ -352,7 +352,7 @@ def test_column_transformer_sparse_stacking():
352352
sparse_threshold=0.8)
353353
col_trans.fit(X_array)
354354
X_trans = col_trans.transform(X_array)
355-
assert sparse.issparse(X_trans)
355+
assert_true(sparse.issparse(X_trans))
356356
assert_equal(X_trans.shape, (X_trans.shape[0], X_trans.shape[0] + 1))
357357
assert_array_equal(X_trans.toarray()[:, 1:], np.eye(X_trans.shape[0]))
358358
assert len(col_trans.transformers_) == 2
@@ -597,11 +597,11 @@ def test_column_transformer_named_estimators():
597597
('trans2', StandardScaler(with_std=False), [1])])
598598
assert_false(hasattr(ct, 'transformers_'))
599599
ct.fit(X_array)
600-
assert hasattr(ct, 'transformers_')
601-
assert isinstance(ct.named_transformers_['trans1'], StandardScaler)
602-
assert isinstance(ct.named_transformers_.trans1, StandardScaler)
603-
assert isinstance(ct.named_transformers_['trans2'], StandardScaler)
604-
assert isinstance(ct.named_transformers_.trans2, StandardScaler)
600+
assert_true(hasattr(ct, 'transformers_'))
601+
assert_true(isinstance(ct.named_transformers_['trans1'], StandardScaler))
602+
assert_true(isinstance(ct.named_transformers_.trans1, StandardScaler))
603+
assert_true(isinstance(ct.named_transformers_['trans2'], StandardScaler))
604+
assert_true(isinstance(ct.named_transformers_.trans2, StandardScaler))
605605
assert_false(ct.named_transformers_.trans2.with_std)
606606
# check it are fitted transformers
607607
assert_equal(ct.named_transformers_.trans1.mean_, 1.)
@@ -613,12 +613,12 @@ def test_column_transformer_cloning():
613613
ct = ColumnTransformer([('trans', StandardScaler(), [0])])
614614
ct.fit(X_array)
615615
assert_false(hasattr(ct.transformers[0][1], 'mean_'))
616-
assert hasattr(ct.transformers_[0][1], 'mean_')
616+
assert_true(hasattr(ct.transformers_[0][1], 'mean_'))
617617

618618
ct = ColumnTransformer([('trans', StandardScaler(), [0])])
619619
ct.fit_transform(X_array)
620620
assert_false(hasattr(ct.transformers[0][1], 'mean_'))
621-
assert hasattr(ct.transformers_[0][1], 'mean_')
621+
assert_true(hasattr(ct.transformers_[0][1], 'mean_'))
622622

623623

624624
def test_column_transformer_get_feature_names():

‎sklearn/cross_decomposition/tests/test_pls.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -317,7 +317,7 @@ def test_predict_transform_copy():
317317
assert_array_equal(X_copy, X)
318318
assert_array_equal(Y_copy, Y)
319319
# also check that mean wasn't zero before (to make sure we didn't touch it)
320-
assert np.all(X.mean(axis=0) != 0)
320+
assert_true(np.all(X.mean(axis=0) != 0))
321321

322322

323323
def test_scale_and_stability():

‎sklearn/datasets/tests/test_20news.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -67,14 +67,14 @@ def test_20news_vectorized():
6767

6868
# test subset = train
6969
bunch = datasets.fetch_20newsgroups_vectorized(subset="train")
70-
assert sp.isspmatrix_csr(bunch.data)
70+
assert_true(sp.isspmatrix_csr(bunch.data))
7171
assert_equal(bunch.data.shape, (11314, 130107))
7272
assert_equal(bunch.target.shape[0], 11314)
7373
assert_equal(bunch.data.dtype, np.float64)
7474

7575
# test subset = test
7676
bunch = datasets.fetch_20newsgroups_vectorized(subset="test")
77-
assert sp.isspmatrix_csr(bunch.data)
77+
assert_true(sp.isspmatrix_csr(bunch.data))
7878
assert_equal(bunch.data.shape, (7532, 130107))
7979
assert_equal(bunch.target.shape[0], 7532)
8080
assert_equal(bunch.data.dtype, np.float64)
@@ -85,7 +85,7 @@ def test_20news_vectorized():
8585

8686
# test subset = all
8787
bunch = datasets.fetch_20newsgroups_vectorized(subset='all')
88-
assert sp.isspmatrix_csr(bunch.data)
88+
assert_true(sp.isspmatrix_csr(bunch.data))
8989
assert_equal(bunch.data.shape, (11314 + 7532, 130107))
9090
assert_equal(bunch.target.shape[0], 11314 + 7532)
9191
assert_equal(bunch.data.dtype, np.float64)

‎sklearn/datasets/tests/test_base.py

Lines changed: 16 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -74,15 +74,15 @@ def test_data_home(data_home):
7474
# get_data_home will point to a pre-existing folder
7575
data_home = get_data_home(data_home=data_home)
7676
assert_equal(data_home, data_home)
77-
assert os.path.exists(data_home)
77+
assert_true(os.path.exists(data_home))
7878

7979
# clear_data_home will delete both the content and the folder it-self
8080
clear_data_home(data_home=data_home)
8181
assert_false(os.path.exists(data_home))
8282

8383
# if the folder is missing it will be created again
8484
data_home = get_data_home(data_home=data_home)
85-
assert os.path.exists(data_home)
85+
assert_true(os.path.exists(data_home))
8686

8787

8888
def test_default_empty_load_files(load_files_root):
@@ -126,7 +126,7 @@ def test_load_sample_images():
126126
res = load_sample_images()
127127
assert_equal(len(res.images), 2)
128128
assert_equal(len(res.filenames), 2)
129-
assert res.DESCR
129+
assert_true(res.DESCR)
130130
except ImportError:
131131
warnings.warn("Could not load sample images, PIL is not available.")
132132

@@ -166,9 +166,9 @@ def test_load_missing_sample_image_error():
166166
def test_load_diabetes():
167167
res = load_diabetes()
168168
assert_equal(res.data.shape, (442, 10))
169-
assert res.target.size, 442
169+
assert_true(res.target.size, 442)
170170
assert_equal(len(res.feature_names), 10)
171-
assert res.DESCR
171+
assert_true(res.DESCR)
172172

173173
# test return_X_y option
174174
check_return_X_y(res, partial(load_diabetes))
@@ -179,9 +179,9 @@ def test_load_linnerud():
179179
assert_equal(res.data.shape, (20, 3))
180180
assert_equal(res.target.shape, (20, 3))
181181
assert_equal(len(res.target_names), 3)
182-
assert res.DESCR
183-
assert os.path.exists(res.data_filename)
184-
assert os.path.exists(res.target_filename)
182+
assert_true(res.DESCR)
183+
assert_true(os.path.exists(res.data_filename))
184+
assert_true(os.path.exists(res.target_filename))
185185

186186
# test return_X_y option
187187
check_return_X_y(res, partial(load_linnerud))
@@ -192,8 +192,8 @@ def test_load_iris():
192192
assert_equal(res.data.shape, (150, 4))
193193
assert_equal(res.target.size, 150)
194194
assert_equal(res.target_names.size, 3)
195-
assert res.DESCR
196-
assert os.path.exists(res.filename)
195+
assert_true(res.DESCR)
196+
assert_true(os.path.exists(res.filename))
197197

198198
# test return_X_y option
199199
check_return_X_y(res, partial(load_iris))
@@ -204,7 +204,7 @@ def test_load_wine():
204204
assert_equal(res.data.shape, (178, 13))
205205
assert_equal(res.target.size, 178)
206206
assert_equal(res.target_names.size, 3)
207-
assert res.DESCR
207+
assert_true(res.DESCR)
208208

209209
# test return_X_y option
210210
check_return_X_y(res, partial(load_wine))
215215
assert_equal(res.data.shape, (569, 30))
216216
assert_equal(res.target.size, 569)
217217
assert_equal(res.target_names.size, 2)
218-
assert res.DESCR
219-
assert os.path.exists(res.filename)
218+
assert_true(res.DESCR)
219+
assert_true(os.path.exists(res.filename))
220220

221221
# test return_X_y option
222222
check_return_X_y(res, partial(load_breast_cancer))
@@ -227,8 +227,8 @@ def test_load_boston():
227227
assert_equal(res.data.shape, (506, 13))
228228
assert_equal(res.target.size, 506)
229229
assert_equal(res.feature_names.size, 13)
230-
assert res.DESCR
231-
assert os.path.exists(res.filename)
230+
assert_true(res.DESCR)
231+
assert_true(os.path.exists(res.filename))
232232

233233
# test return_X_y option
234234
check_return_X_y(res, partial(load_boston))
@@ -265,4 +265,4 @@ def test_bunch_pickle_generated_with_0_16_and_read_with_0_17():
265265
def test_bunch_dir():
266266
# check that dir (important for autocomplete) shows attributes
267267
data = load_iris()
268-
assert "data" in dir(data)
268+
assert_true("data" in dir(data))

‎sklearn/datasets/tests/test_rcv1.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -27,8 +27,8 @@ def test_fetch_rcv1():
2727
cat_list, s1 = data1.target_names.tolist(), data1.sample_id
2828

2929
# test sparsity
30-
assert sp.issparse(X1)
31-
assert sp.issparse(Y1)
30+
assert_true(sp.issparse(X1))
31+
assert_true(sp.issparse(Y1))
3232
assert_equal(60915113, X1.data.size)
3333
assert_equal(2606875, Y1.data.size)
3434

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