def test_fit_linear_binary_auc(): for data in (bin_dense, bin_csr): for loss in ("l1", "l2"): clf = LinearSVC(loss=loss, criterion="auc", random_state=0, max_iter=25) clf.fit(data, bin_target) assert_equal(clf.score(data, bin_target), 1.0)
def test_fit_linear_binary(data, loss, request): X, y = request.getfixturevalue(data) clf = LinearSVC(loss=loss, random_state=0, max_iter=10) clf.fit(X, y) assert list(clf.classes_) == [0, 1] assert clf.score(X, y) == 1.0 y_pred = clf.decision_function(X).ravel()
def test_warm_start(bin_dense_train_data, C): bin_dense, bin_target = bin_dense_train_data clf = LinearSVC(warm_start=True, loss="l1", random_state=0, max_iter=100) clf.C = C clf.fit(bin_dense, bin_target) acc = clf.score(bin_dense, bin_target) assert acc > 0.99
def test_fit_linear_multi(): for data in (mult_dense, mult_sparse): clf = LinearSVC(random_state=0) clf.fit(data, mult_target) y_pred = clf.predict(data) acc = np.mean(y_pred == mult_target) assert_greater(acc, 0.85)
def test_fit_linear_binary(): for data in (bin_dense, bin_csr): for loss in ("l1", "l2"): clf = LinearSVC(loss=loss, random_state=0, max_iter=10) clf.fit(data, bin_target) assert_equal(clf.score(data, bin_target), 1.0) y_pred = clf.decision_function(data).ravel()
def test_warm_start(): clf = LinearSVC(warm_start=True, loss="l1", random_state=0, max_iter=100) for C in (0.1, 0.2): clf.C = C clf.fit(bin_dense, bin_target) acc = clf.score(bin_dense, bin_target) assert_greater(acc, 0.99)
def test_fit_linear_multi(data, request): X, y = request.getfixturevalue(data) clf = LinearSVC(random_state=0) clf.fit(X, y) assert list(clf.classes_) == [0, 1, 2] y_pred = clf.predict(X) acc = np.mean(y_pred == y) assert acc > 0.85
def test_fit_linear_multi(): for data in (mult_dense, mult_sparse): clf = LinearSVC(random_state=0) clf.fit(data, mult_target) assert list(clf.classes_) == [0, 1, 2] y_pred = clf.predict(data) acc = np.mean(y_pred == mult_target) assert acc > 0.85
def test_fit_linear_binary_auc(data, loss, request): X, y = request.getfixturevalue(data) clf = LinearSVC(loss=loss, criterion="auc", random_state=0, max_iter=25) clf.fit(X, y) assert clf.score(X, y) == 1.0