def test_make_nn_regression():
X, y, w = make_nn_regression(n_samples=10, n_features=50, n_informative=5)
assert X.shape[0] == 10
assert X.shape[1] == 50
assert y.shape[0] == 10
assert w.shape[0] == 50
assert np.sum(X.data != 0) == 10 * 5
X, y, w = make_nn_regression(n_samples=10, n_features=50, n_informative=50)
assert np.sum(X.data != 0) == 10 * 50
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())
def test_regression_squared_loss_nn_l1():
X, y, _ = make_nn_regression(n_samples=100,
n_features=10,
n_informative=8,
random_state=0)
for alpha in (0, 1e-6):
reg = SGDRegressor(loss="squared",
penalty="nn",
learning_rate="constant",
eta0=1e-1,
alpha=alpha,
random_state=0)
reg.fit(X, y)
pred = reg.predict(X)
np.testing.assert_almost_equal(np.mean((pred - y)**2), 0.016, 3)
assert (reg.coef_ >= 0).all()
def reg_nn_train_data():
X, y, _ = make_nn_regression(n_samples=100,
n_features=10,
n_informative=8,
random_state=0)
return X, y
