def test_fista_regression_simplex(): rng = np.random.RandomState(0) w = project_simplex(rng.rand(10)) X = rng.randn(1000, 10) y = np.dot(X, w) reg = FistaRegressor(penalty="simplex", max_iter=100, verbose=0) reg.fit(X, y) y_pred = reg.predict(X) error = np.sqrt(np.mean((y - y_pred)**2)) assert_almost_equal(error, 0.000, 3) assert_true(np.all(reg.coef_ >= 0)) assert_almost_equal(np.sum(reg.coef_), 1.0, 3)
def test_fista_regression_simplex(): rng = np.random.RandomState(0) w = project_simplex(rng.rand(10)) X = rng.randn(1000, 10) y = np.dot(X, w) reg = FistaRegressor(penalty="simplex", max_iter=100, verbose=0) reg.fit(X, y) y_pred = reg.predict(X) error = np.sqrt(np.mean((y - y_pred) ** 2)) assert_almost_equal(error, 0.000, 3) assert_true(np.all(reg.coef_ >= 0)) assert_almost_equal(np.sum(reg.coef_), 1.0, 3)
def test_fista_regression_l1_ball(): rng = np.random.RandomState(0) alpha = 5.0 w = project_simplex(rng.randn(10), alpha) X = rng.randn(1000, 10) y = np.dot(X, w) reg = FistaRegressor(penalty="l1-ball", alpha=alpha, max_iter=100, verbose=0) reg.fit(X, y) y_pred = reg.predict(X) error = np.sqrt(np.mean((y - y_pred) ** 2)) np.testing.assert_almost_equal(error, 0.000, 3) np.testing.assert_almost_equal(np.sum(np.abs(reg.coef_)), alpha, 3)
def test_fista_regression_l1_ball(): rng = np.random.RandomState(0) alpha = 5.0 w = project_simplex(rng.randn(10), alpha) X = rng.randn(1000, 10) y = np.dot(X, w) reg = FistaRegressor(penalty="l1-ball", alpha=alpha, max_iter=100, verbose=0) reg.fit(X, y) y_pred = reg.predict(X) error = np.sqrt(np.mean((y - y_pred) ** 2)) assert_almost_equal(error, 0.000, 3) assert_almost_equal(np.sum(np.abs(reg.coef_)), alpha, 3)
def test_fista_regression_trace(): rng = np.random.RandomState(0) def _make_data(n_samples, n_features, n_tasks, n_components): W = rng.rand(n_tasks, n_features) - 0.5 U, S, V = svd(W, full_matrices=True) S[n_components:] = 0 S = diagsvd(S, U.shape[0], V.shape[0]) W = np.dot(np.dot(U, S), V) X = rng.rand(n_samples, n_features) - 0.5 Y = np.dot(X, W.T) return X, Y, W X, Y, W = _make_data(200, 50,30, 5) reg = FistaRegressor(max_iter=15, verbose=0) reg.fit(X, Y) Y_pred = reg.predict(X) error = (Y_pred - Y).ravel() error = np.dot(error, error) assert_almost_equal(error, 77.45, 2)
def test_fista_regression(): reg = FistaRegressor(max_iter=100, verbose=0) reg.fit(bin_dense, bin_target) y_pred = np.sign(reg.predict(bin_dense)) assert_almost_equal(np.mean(bin_target == y_pred), 0.985)
def test_fista_regression(): reg = FistaRegressor(max_iter=100, verbose=0) reg.fit(bin_dense, bin_target) y_pred = np.sign(reg.predict(bin_dense)) assert_almost_equal(np.mean(bin_target == y_pred), 0.985)
def test_fista_regression(bin_dense_train_data): X, y = bin_dense_train_data reg = FistaRegressor(max_iter=100, verbose=0) reg.fit(X, y) y_pred = np.sign(reg.predict(X)) np.testing.assert_almost_equal(np.mean(y == y_pred), 0.985)