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[MRG + 1] Added a random_state=0 to many make_classification tests #7968

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Dec 2, 2016
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[MRG + 1] Added a random_state=0 to many make_classification tests #7968

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13 changes: 7 additions & 6 deletions sklearn/linear_model/tests/test_logistic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -278,7 +278,7 @@ def test_consistency_path():

def test_liblinear_dual_random_state():
# random_state is relevant for liblinear solver only if dual=True
X, y = make_classification(n_samples=20)
X, y = make_classification(n_samples=20, random_state=0)
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42 would have made @agramfort happier ;)

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@amueller amueller Dec 2, 2016 •
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As @chenhe95 is new to the project I think it's ok to be allow it. Now he knows better ;)

Thanks for the detective work and fix!

lr1 = LogisticRegression(random_state=0, dual=True, max_iter=1, tol=1e-15)
lr1.fit(X, y)
lr2 = LogisticRegression(random_state=0, dual=True, max_iter=1, tol=1e-15)
Expand All @@ -295,7 +295,7 @@ def test_liblinear_dual_random_state():


def test_logistic_loss_and_grad():
X_ref, y = make_classification(n_samples=20)
X_ref, y = make_classification(n_samples=20, random_state=0)
n_features = X_ref.shape[1]

X_sp = X_ref.copy()
Expand Down Expand Up @@ -403,7 +403,8 @@ def test_multinomial_logistic_regression_string_inputs():
# Test with string labels for LogisticRegression(CV)
n_samples, n_features, n_classes = 50, 5, 3
X_ref, y = make_classification(n_samples=n_samples, n_features=n_features,
n_classes=n_classes, n_informative=3)
n_classes=n_classes, n_informative=3,
random_state=0)
y_str = LabelEncoder().fit(['bar', 'baz', 'foo']).inverse_transform(y)
# For numerical labels, let y values be taken from set (-1, 0, 1)
y = np.array(y) - 1
Expand Down Expand Up @@ -745,7 +746,7 @@ def test_multinomial_logistic_regression_with_classweight_auto():
def test_logistic_regression_convergence_warnings():
# Test that warnings are raised if model does not converge

X, y = make_classification(n_samples=20, n_features=20)
X, y = make_classification(n_samples=20, n_features=20, random_state=0)
clf_lib = LogisticRegression(solver='liblinear', max_iter=2, verbose=1)
assert_warns(ConvergenceWarning, clf_lib.fit, X, y)
assert_equal(clf_lib.n_iter_, 2)
Expand Down Expand Up @@ -834,7 +835,7 @@ def test_liblinear_decision_function_zero():
# are zero. This is a test to verify that we do not do the same.
# See Issue: https://github.com/scikit-learn/scikit-learn/issues/3600
# and the PR https://github.com/scikit-learn/scikit-learn/pull/3623
X, y = make_classification(n_samples=5, n_features=5)
X, y = make_classification(n_samples=5, n_features=5, random_state=0)
clf = LogisticRegression(fit_intercept=False)
clf.fit(X, y)

Expand All @@ -846,7 +847,7 @@ def test_liblinear_decision_function_zero():
def test_liblinear_logregcv_sparse():
# Test LogRegCV with solver='liblinear' works for sparse matrices

X, y = make_classification(n_samples=10, n_features=5)
X, y = make_classification(n_samples=10, n_features=5, random_state=0)
clf = LogisticRegressionCV(solver='liblinear')
clf.fit(sparse.csr_matrix(X), y)

Expand Down