def test_adagrad_hinge_multiclass(): clf = AdaGradClassifier(alpha=1e-2, n_iter=100, loss="hinge", random_state=0) clf.fit(X, y) assert_almost_equal(clf.score(X, y), 0.960, 3)
def test_adagrad_elastic_hinge(): clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, n_iter=10, random_state=0) clf.fit(X_bin, y_bin) assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_adagrad_hinge_multiclass(): clf = AdaGradClassifier(alpha=1e-2, n_iter=100, loss="hinge", random_state=0) clf.fit(X, y) assert not hasattr(clf, "predict_proba") np.testing.assert_almost_equal(clf.score(X, y), 0.940, 3)
def test_adagrad_elastic_hinge(): clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, n_iter=10, random_state=0) clf.fit(X_bin, y_bin) assert not hasattr(clf, "predict_proba") assert clf.score(X_bin, y_bin) == 1.0
def test_adagrad_elastic_log(): clf = AdaGradClassifier(alpha=0.1, l1_ratio=0.85, loss="log", n_iter=10, random_state=0) clf.fit(X_bin, y_bin) assert clf.score(X_bin, y_bin) == 1.0 check_predict_proba(clf, X_bin)
def test_adagrad_elastic_smooth_hinge(bin_train_data): X_bin, y_bin = bin_train_data clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, loss="smooth_hinge", n_iter=10, random_state=0) clf.fit(X_bin, y_bin) assert not hasattr(clf, "predict_proba") assert clf.score(X_bin, y_bin) == 1.0
def test_adagrad_hinge_multiclass(): clf = AdaGradClassifier(alpha=1e-2, n_iter=100, loss="hinge", random_state=0) clf.fit(X, y) assert not hasattr(clf, "predict_proba") assert_almost_equal(clf.score(X, y), 0.960, 3)
def test_adagrad_elastic_log(): clf = AdaGradClassifier(alpha=0.1, l1_ratio=0.85, loss="log", n_iter=10, random_state=0) clf.fit(X_bin, y_bin) assert_equal(clf.score(X_bin, y_bin), 1.0) check_predict_proba(clf, X_bin)
def test_adagrad_elastic_smooth_hinge(): clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, loss="smooth_hinge", n_iter=10, random_state=0) clf.fit(X_bin, y_bin) assert not hasattr(clf, "predict_proba") assert_equal(clf.score(X_bin, y_bin), 1.0)
def test_adagrad_elastic_hinge(): clf = AdaGradClassifier(alpha=0.5, l1_ratio=0.85, n_iter=10, random_state=0) clf.fit(X_bin, y_bin) assert_equal(clf.score(X_bin, y_bin), 1.0)