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Commit 3e45aee

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LSturtewLSturtew
authored
TST Remove assert warn from preprocessing tests (#19691)
1 parent 0892a98 commit 3e45aee
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‎sklearn/preprocessing/tests/test_data.py

Copy file name to clipboardExpand all lines: sklearn/preprocessing/tests/test_data.py
+23-14Lines changed: 23 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -19,8 +19,6 @@
1919
from sklearn.utils._testing import assert_array_almost_equal
2020
from sklearn.utils._testing import assert_array_equal
2121
from sklearn.utils._testing import assert_array_less
22-
from sklearn.utils._testing import assert_warns_message
23-
from sklearn.utils._testing import assert_no_warnings
2422
from sklearn.utils._testing import assert_allclose
2523
from sklearn.utils._testing import assert_allclose_dense_sparse
2624
from sklearn.utils._testing import skip_if_32bit
@@ -291,28 +289,37 @@ def test_standard_scaler_numerical_stability():
291289
x = np.full(8, np.log(1e-5), dtype=np.float64)
292290
# This does not raise a warning as the number of samples is too low
293291
# to trigger the problem in recent numpy
294-
x_scaled = assert_no_warnings(scale, x)
292+
with pytest.warns(None) as record:
293+
scale(x)
294+
assert len(record) == 0
295295
assert_array_almost_equal(scale(x), np.zeros(8))
296296

297297
# with 2 more samples, the std computation run into numerical issues:
298298
x = np.full(10, np.log(1e-5), dtype=np.float64)
299-
w = "standard deviation of the data is probably very close to 0"
300-
x_scaled = assert_warns_message(UserWarning, w, scale, x)
299+
warning_message = (
300+
"standard deviation of the data is probably very close to 0"
301+
)
302+
with pytest.warns(UserWarning, match=warning_message):
303+
x_scaled = scale(x)
301304
assert_array_almost_equal(x_scaled, np.zeros(10))
302305

303306
x = np.full(10, 1e-100, dtype=np.float64)
304-
x_small_scaled = assert_no_warnings(scale, x)
307+
with pytest.warns(None) as record:
308+
x_small_scaled = scale(x)
309+
assert len(record) == 0
305310
assert_array_almost_equal(x_small_scaled, np.zeros(10))
306311

307312
# Large values can cause (often recoverable) numerical stability issues:
308313
x_big = np.full(10, 1e100, dtype=np.float64)
309-
w = "Dataset may contain too large values"
310-
x_big_scaled = assert_warns_message(UserWarning, w, scale, x_big)
314+
warning_message = (
315+
"Dataset may contain too large values"
316+
)
317+
with pytest.warns(UserWarning, match=warning_message):
318+
x_big_scaled = scale(x_big)
311319
assert_array_almost_equal(x_big_scaled, np.zeros(10))
312320
assert_array_almost_equal(x_big_scaled, x_small_scaled)
313-
314-
x_big_centered = assert_warns_message(UserWarning, w, scale, x_big,
315-
with_std=False)
321+
with pytest.warns(UserWarning, match=warning_message):
322+
x_big_centered = scale(x_big, with_std=False)
316323
assert_array_almost_equal(x_big_centered, np.zeros(10))
317324
assert_array_almost_equal(x_big_centered, x_small_scaled)
318325

@@ -1239,9 +1246,11 @@ def test_quantile_transform_sparse_ignore_zeros():
12391246
n_quantiles=5)
12401247

12411248
# dense case -> warning raise
1242-
assert_warns_message(UserWarning, "'ignore_implicit_zeros' takes effect"
1243-
" only with sparse matrix. This parameter has no"
1244-
" effect.", transformer.fit, X)
1249+
warning_message = ("'ignore_implicit_zeros' takes effect"
1250+
" only with sparse matrix. This parameter has no"
1251+
" effect.")
1252+
with pytest.warns(UserWarning, match=warning_message):
1253+
transformer.fit(X)
12451254

12461255
X_expected = np.array([[0, 0],
12471256
[0, 0],

‎sklearn/preprocessing/tests/test_discretization.py

Copy file name to clipboardExpand all lines: sklearn/preprocessing/tests/test_discretization.py
+11-10Lines changed: 11 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,6 @@
99
from sklearn.utils._testing import (
1010
assert_array_almost_equal,
1111
assert_array_equal,
12-
assert_warns_message,
1312
assert_allclose_dense_sparse
1413
)
1514

@@ -109,9 +108,10 @@ def test_same_min_max(strategy):
109108
[1, 0],
110109
[1, 1]])
111110
est = KBinsDiscretizer(strategy=strategy, n_bins=3, encode='ordinal')
112-
assert_warns_message(UserWarning,
113-
"Feature 0 is constant and will be replaced "
114-
"with 0.", est.fit, X)
111+
warning_message = ("Feature 0 is constant and will be replaced "
112+
"with 0.")
113+
with pytest.warns(UserWarning, match=warning_message):
114+
est.fit(X)
115115
assert est.n_bins_[0] == 1
116116
# replace the feature with zeros
117117
Xt = est.transform(X)
@@ -257,9 +257,9 @@ def test_overwrite():
257257
def test_redundant_bins(strategy, expected_bin_edges):
258258
X = [[0], [0], [0], [0], [3], [3]]
259259
kbd = KBinsDiscretizer(n_bins=3, strategy=strategy)
260-
msg = ("Bins whose width are too small (i.e., <= 1e-8) in feature 0 "
261-
"are removed. Consider decreasing the number of bins.")
262-
assert_warns_message(UserWarning, msg, kbd.fit, X)
260+
warning_message = ("Consider decreasing the number of bins.")
261+
with pytest.warns(UserWarning, match=warning_message):
262+
kbd.fit(X)
263263
assert_array_almost_equal(kbd.bin_edges_[0], expected_bin_edges)
264264

265265

@@ -269,9 +269,10 @@ def test_percentile_numeric_stability():
269269
Xt = np.array([0, 0, 4]).reshape(-1, 1)
270270
kbd = KBinsDiscretizer(n_bins=10, encode='ordinal',
271271
strategy='quantile')
272-
msg = ("Bins whose width are too small (i.e., <= 1e-8) in feature 0 "
273-
"are removed. Consider decreasing the number of bins.")
274-
assert_warns_message(UserWarning, msg, kbd.fit, X)
272+
warning_message = ("Consider decreasing the number of bins.")
273+
with pytest.warns(UserWarning, match=warning_message):
274+
kbd.fit(X)
275+
275276
assert_array_almost_equal(kbd.bin_edges_[0], bin_edges)
276277
assert_array_almost_equal(kbd.transform(X), Xt)
277278

‎sklearn/preprocessing/tests/test_function_transformer.py

Copy file name to clipboardExpand all lines: sklearn/preprocessing/tests/test_function_transformer.py
+16-11Lines changed: 16 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -4,8 +4,7 @@
44

55
from sklearn.preprocessing import FunctionTransformer
66
from sklearn.utils._testing import (assert_array_equal,
7-
assert_allclose_dense_sparse)
8-
from sklearn.utils._testing import assert_warns_message, assert_no_warnings
7+
assert_allclose_dense_sparse)
98

109

1110
def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
@@ -127,29 +126,35 @@ def test_check_inverse():
127126
accept_sparse=accept_sparse,
128127
check_inverse=True,
129128
validate=True)
130-
assert_warns_message(UserWarning,
131-
"The provided functions are not strictly"
132-
" inverse of each other. If you are sure you"
133-
" want to proceed regardless, set"
134-
" 'check_inverse=False'.",
135-
trans.fit, X)
129+
warning_message = ("The provided functions are not strictly"
130+
" inverse of each other. If you are sure you"
131+
" want to proceed regardless, set"
132+
" 'check_inverse=False'.")
133+
with pytest.warns(UserWarning, match=warning_message):
134+
trans.fit(X)
136135

137136
trans = FunctionTransformer(func=np.expm1,
138137
inverse_func=np.log1p,
139138
accept_sparse=accept_sparse,
140139
check_inverse=True,
141140
validate=True)
142-
Xt = assert_no_warnings(trans.fit_transform, X)
141+
with pytest.warns(None) as record:
142+
Xt = trans.fit_transform(X)
143+
assert len(record) == 0
143144
assert_allclose_dense_sparse(X, trans.inverse_transform(Xt))
144145

145146
# check that we don't check inverse when one of the func or inverse is not
146147
# provided.
147148
trans = FunctionTransformer(func=np.expm1, inverse_func=None,
148149
check_inverse=True, validate=True)
149-
assert_no_warnings(trans.fit, X_dense)
150+
with pytest.warns(None) as record:
151+
trans.fit(X_dense)
152+
assert len(record) == 0
150153
trans = FunctionTransformer(func=None, inverse_func=np.expm1,
151154
check_inverse=True, validate=True)
152-
assert_no_warnings(trans.fit, X_dense)
155+
with pytest.warns(None) as record:
156+
trans.fit(X_dense)
157+
assert len(record) == 0
153158

154159

155160
def test_function_transformer_frame():

‎sklearn/preprocessing/tests/test_label.py

Copy file name to clipboardExpand all lines: sklearn/preprocessing/tests/test_label.py
+6-8Lines changed: 6 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -12,7 +12,6 @@
1212
from sklearn.utils.multiclass import type_of_target
1313

1414
from sklearn.utils._testing import assert_array_equal
15-
from sklearn.utils._testing import assert_warns_message
1615
from sklearn.utils._testing import ignore_warnings
1716
from sklearn.utils import _to_object_array
1817

@@ -351,15 +350,14 @@ def test_multilabel_binarizer_unknown_class():
351350
mlb = MultiLabelBinarizer()
352351
y = [[1, 2]]
353352
Y = np.array([[1, 0], [0, 1]])
354-
w = 'unknown class(es) [0, 4] will be ignored'
355-
matrix = assert_warns_message(UserWarning, w,
356-
mlb.fit(y).transform, [[4, 1], [2, 0]])
357-
assert_array_equal(matrix, Y)
353+
warning_message = 'unknown class.* will be ignored'
354+
with pytest.warns(UserWarning, match=warning_message):
355+
matrix = mlb.fit(y).transform([[4, 1], [2, 0]])
358356

359357
Y = np.array([[1, 0, 0], [0, 1, 0]])
360358
mlb = MultiLabelBinarizer(classes=[1, 2, 3])
361-
matrix = assert_warns_message(UserWarning, w,
362-
mlb.fit(y).transform, [[4, 1], [2, 0]])
359+
with pytest.warns(UserWarning, match=warning_message):
360+
matrix = mlb.fit(y).transform([[4, 1], [2, 0]])
363361
assert_array_equal(matrix, Y)
364362

365363

@@ -535,7 +533,7 @@ def check_binarized_results(y, classes, pos_label, neg_label, expected):
535533
output_type=y_type,
536534
classes=classes,
537535
threshold=((neg_label +
538-
pos_label) /
536+
pos_label) /
539537
2.))
540538

541539
assert_array_equal(toarray(inversed), toarray(y))

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