8000 MNT Catches scipy 1.3.0 for using deprecated tostring method by thomasjpfan · Pull Request #18755 · scikit-learn/scikit-learn · GitHub
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MNT Catches scipy 1.3.0 for using deprecated tostring method #18755

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Merged
merged 4 commits into from
Nov 6, 2020
Merged

MNT Catches scipy 1.3.0 for using deprecated tostring method #18755

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5541597
TST Fixes warnings for scipy 1.3.0
thomasjpfan Nov 4, 2020
573c5f8
Merge remote-tracking branch 'upstream/master' into fix_deprecation_s…
thomasjpfan Nov 4, 2020
14dd1bf
REV Places back record check
thomasjpfan Nov 4, 2020
7e6b813
TST Always catches warnings
thomasjpfan Nov 4, 2020
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18 changes: 15 additions & 3 deletions sklearn/ensemble/tests/test_voting.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
"""Testing for the VotingClassifier and VotingRegressor"""

import warnings
import pytest
import re
import numpy as np
Expand Down Expand Up @@ -370,7 +371,11 @@ def test_set_estimator_drop():
('nb', clf3)],
voting='hard', weights=[1, 1, 0.5])
with pytest.warns(None) as record:
eclf2.set_params(rf='drop').fit(X, y)
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
eclf2.set_params(rf='drop').fit(X, y)

assert not record
assert_array_equal(eclf1.predict(X), eclf2.predict(X))

Expand All @@ -382,7 +387,11 @@ def test_set_estimator_drop():

eclf1.set_params(voting='soft').fit(X, y)
with pytest.warns(None) as record:
eclf2.set_params(voting='soft').fit(X, y)
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
eclf2.set_params(voting='soft').fit(X, y)

assert not record
assert_array_equal(eclf1.predict(X), eclf2.predict(X))
assert_array_almost_equal(eclf1.predict_proba(X), eclf2.predict_proba(X))
Expand All @@ -403,7 +412,10 @@ def test_set_estimator_drop():
voting='soft', weights=[1, 0.5],
flatten_transform=False)
with pytest.warns(None) as record:
eclf2.set_params(rf='drop').fit(X1, y1)
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
eclf2.set_params(rf='drop').fit(X1, y1)
assert not record
assert_array_almost_equal(eclf1.transform(X1),
np.array([[[0.7, 0.3], [0.3, 0.7]],
Expand Down
11 changes: 9 additions & 2 deletions sklearn/gaussian_process/tests/test_gpc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause

import warnings
import numpy as np

from scipy.optimize import approx_fprime
Expand Down Expand Up @@ -201,7 +202,10 @@ def test_warning_bounds():
RBF(length_scale_bounds=[1e3, 1e5]))
gpc_sum = GaussianProcessClassifier(kernel=kernel_sum)
with pytest.warns(None) as record:
gpc_sum.fit(X, y)
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
gpc_sum.fit(X, y)

assert len(record) == 2
assert record[0].message.args[0] == ("The optimal value found for "
Expand All @@ -224,7 +228,10 @@ def test_warning_bounds():
gpc_dims = GaussianProcessClassifier(kernel=kernel_dims)

with pytest.warns(None) as record:
gpc_dims.fit(X_tile, y)
with warnings.catch_warnings():
8000 # scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
gpc_dims.fit(X_tile, y)

assert len(record) == 2
assert record[0].message.args[0] == ("The optimal value found for "
Expand Down
11 changes: 9 additions & 2 deletions sklearn/gaussian_process/tests/test_gpr.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@

import sys
import numpy as np
import warnings

from scipy.optimize import approx_fprime

Expand Down Expand Up @@ -486,7 +487,10 @@ def test_warning_bounds():
RBF(length_scale_bounds=[1e3, 1e5]))
gpr_sum = GaussianProcessRegressor(kernel=kernel_sum)
with pytest.warns(None) as record:
gpr_sum.fit(X, y)
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
gpr_sum.fit(X, y)

assert len(record) == 2
assert record[0].message.args[0] == ("The optimal value found for "
Expand All @@ -509,7 +513,10 @@ def test_warning_bounds():
gpr_dims = GaussianProcessRegressor(kernel=kernel_dims)

with pytest.warns(None) as record:
gpr_dims.fit(X_tile, y)
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
gpr_dims.fit(X_tile, y)

assert len(record) == 2
assert record[0].message.args[0] == ("The optimal value found for "
Expand Down
8 changes: 6 additions & 2 deletions sklearn/linear_model/tests/test_logistic.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import os
import sys
import warnings
import numpy as np
from numpy.testing import assert_allclose, assert_almost_equal
from numpy.testing import assert_array_almost_equal, assert_array_equal
Expand Down Expand Up @@ -391,8 +392,11 @@ def test_logistic_regression_path_convergence_fail():
# advice (scaling the data) and to the logistic regression specific
# documentation that includes hints on the solver configuration.
with pytest.warns(ConvergenceWarning) as record:
_logistic_regression_path(
X, y, Cs=Cs, tol=0., max_iter=1, random_state=0, verbose=0)
with warnings.catch_warnings():
# scipy 1.3.0 uses tostring which is deprecated in numpy
warnings.filterwarnings("ignore", "tostring", DeprecationWarning)
_logistic_regression_path(
X, y, Cs=Cs, tol=0., max_iter=1, random_state=0, verbose=0)

assert len(record) == 1
warn_msg = record[0].message.args[0]
Expand Down
0