added random_state=0 to many instances (scikit-learn#7968)

Author: chenhe95

sklearn/linear_model/tests/test_logistic.py

@@ -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)
     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)
@@ -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()
@@ -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
@@ -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)
@@ -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)
 
@@ -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)