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MAINT: n_jobs=-1 replaced with n_jobs=4 in tests #13644

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Merged
merged 1 commit into from
Apr 15, 2019
Merged

MAINT: n_jobs=-1 replaced with n_jobs=4 in tests #13644

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6 changes: 3 additions & 3 deletions sklearn/decomposition/tests/test_dict_learning.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,10 +106,10 @@ def test_dict_learning_reconstruction():


def test_dict_learning_reconstruction_parallel():
# regression test that parallel reconstruction works with n_jobs=-1
# regression test that parallel reconstruction works with n_jobs>1
n_components = 12
dico = DictionaryLearning(n_components, transform_algorithm='omp',
transform_alpha=0.001, random_state=0, n_jobs=-1)
transform_alpha=0.001, random_state=0, n_jobs=4)
code = dico.fit(X).transform(X)
assert_array_almost_equal(np.dot(code, dico.components_), X)

Expand All @@ -123,7 +123,7 @@ def test_dict_learning_lassocd_readonly_data():
with TempMemmap(X) as X_read_only:
dico = DictionaryLearning(n_components, transform_algorithm='lasso_cd',
transform_alpha=0.001, random_state=0,
n_jobs=-1)
n_jobs=4)
with ignore_warnings(category=ConvergenceWarning):
code = dico.fit(X_read_only).transform(X_read_only)
assert_array_almost_equal(np.dot(code, dico.components_), X_read_only,
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2 changes: 1 addition & 1 deletion sklearn/linear_model/tests/test_theil_sen.py
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Expand Up @@ -259,7 +259,7 @@ def test_theil_sen_parallel():
lstq = LinearRegression().fit(X, y)
assert_greater(norm(lstq.coef_ - w), 1.0)
# Check that Theil-Sen works
theil_sen = TheilSenRegressor(n_jobs=-1,
theil_sen = TheilSenRegressor(n_jobs=4,
random_state=0,
max_subpopulation=2e3).fit(X, y)
assert_array_almost_equal(theil_sen.coef_, w, 1)
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2 changes: 1 addition & 1 deletion sklearn/tests/test_multioutput.py
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Expand Up @@ -166,7 +166,7 @@ def test_multi_target_sample_weights():

def test_multi_output_classification_partial_fit_parallelism():
sgd_linear_clf = SGDClassifier(loss='log', random_state=1, max_iter=5)
mor = MultiOutputClassifier(sgd_linear_clf, n_jobs=-1)
mor = MultiOutputClassifier(sgd_linear_clf, n_jobs=4)
mor.partial_fit(X, y, classes)
est1 = mor.estimators_[0]
mor.partial_fit(X, y)
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