def test_multiclass_sgd_equivalence(): clf = KernelSGDClassifier(kernel="linear", random_state=0) clf.fit(mult_dense, mult_target) decisions = clf.decision_function(mult_dense) predictions = clf.predict(mult_dense) clf = SGDClassifier(random_state=0) clf.fit(mult_dense, mult_target) decisions2 = clf.decision_function(mult_dense) predictions2 = clf.predict(mult_dense) assert_array_almost_equal(decisions, decisions2) assert_array_almost_equal(predictions, predictions2)
def test_multiclass_natural_kernel_sgd_equivalence(): for loss in ("hinge", "log"): clf = KernelSGDClassifier(kernel="linear", loss=loss, multiclass="natural", random_state=0) clf.fit(mult_dense, mult_target) decisions = clf.decision_function(mult_dense) predictions = clf.predict(mult_dense) clf = SGDClassifier(random_state=0, loss=loss, multiclass="natural") clf.fit(mult_dense, mult_target) decisions2 = clf.decision_function(mult_dense) predictions2 = clf.predict(mult_dense) assert_array_almost_equal(decisions, decisions2) assert_array_almost_equal(predictions, predictions2)