def test_multiclass_kernel_sgd(): for fit_intercept in (True, False): clf = KernelSGDClassifier(kernel="rbf", gamma=0.1, fit_intercept=fit_intercept, random_state=0) clf.fit(mult_dense, mult_target) assert_equal(clf.score(mult_dense, mult_target), 1.0)
def test_n_components_multiclass_natural(): for loss in ("hinge", "log"): clf = KernelSGDClassifier(loss=loss, multiclass="natural", kernel="rbf", gamma=0.1, n_components=50, random_state=0) clf.fit(mult_dense, mult_target) assert_equal(clf.n_support_vectors(), 50) assert_greater(clf.score(mult_dense, mult_target), 0.38)
def test_multiclass_hinge_kernel_sgd(): for fit_intercept in (True, False): clf = KernelSGDClassifier(kernel="rbf", gamma=0.1, loss="hinge", multiclass="natural", fit_intercept=fit_intercept, random_state=0) clf.fit(mult_dense, mult_target) assert_greater(clf.score(mult_dense, mult_target), 0.90)
def test_n_components_multiclass(): clf = KernelSGDClassifier(kernel="rbf", gamma=1.0, loss="hinge", random_state=0, n_components=50) clf.fit(mult_dense, mult_target) assert_equal(clf.n_support_vectors(), 55) assert_greater(clf.score(mult_dense, mult_target), 0.3)
def test_n_components_binary(): clf = KernelSGDClassifier(kernel="rbf", gamma=0.1, loss="hinge", random_state=0, n_components=50) clf.fit(bin_dense, bin_target) assert_equal(clf.n_support_vectors(), 50) assert_greater(clf.score(bin_dense, bin_target), 0.6)