def test_make_nn_regression(): X, y, w = make_nn_regression(n_samples=10, n_features=50, n_informative=5) assert_equal(X.shape[0], 10) assert_equal(X.shape[1], 50) assert_equal(y.shape[0], 10) assert_equal(w.shape[0], 50) assert_equal(np.sum(X.data != 0), 10 * 5) X, y, w = make_nn_regression(n_samples=10, n_features=50, n_informative=50) assert_equal(np.sum(X.data != 0), 10 * 50)
def test_regression_squared_loss_nn_l2(): X, y, _ = make_nn_regression(n_samples=100, n_features=10, n_informative=8, random_state=0) reg = SGDRegressor(loss="squared", penalty="nnl2", learning_rate="constant", eta0=1e-1, alpha=1e-4, random_state=0) reg.fit(X, y) pred = reg.predict(X) assert_almost_equal(np.mean((pred - y) ** 2), 0.033, 3) assert_almost_equal(reg.coef_.sum(), 2.131, 3) assert_false((reg.coef_ < 0).any())
def test_regression_squared_loss_nn_l2(): X, y, _ = make_nn_regression(n_samples=100, n_features=10, n_informative=8, random_state=0) reg = SGDRegressor(loss="squared", penalty="nnl2", learning_rate="constant", eta0=1e-1, alpha=1e-4, random_state=0) reg.fit(X, y) pred = reg.predict(X) assert_almost_equal(np.mean((pred - y)**2), 0.033, 3) assert_almost_equal(reg.coef_.sum(), 2.131, 3) assert_false((reg.coef_ < 0).any())