def test_regression_squared_loss():
X, y = make_regression(n_samples=100, n_features=10, n_informative=8, random_state=0)
reg = SGDRegressor(loss="squared", penalty="l2", learning_rate="constant", eta0=1e-2, random_state=0)
reg.fit(X, y)
pred = reg.predict(X)
assert_almost_equal(np.mean((pred - y) ** 2), 4.913, 3)
def test_regression_squared_loss():
X, y = make_regression(n_samples=100, n_features=10, n_informative=8,
random_state=0)
reg = SGDRegressor(loss="squared", penalty="l2", learning_rate="constant",
eta0=1e-2, random_state=0)
reg.fit(X, y)
pred = reg.predict(X)
assert_almost_equal(np.mean((pred - y) ** 2), 4.913, 3)
def test_regression_squared_loss_multiple_output():
X, y = make_regression(n_samples=100, n_features=10, n_informative=8, random_state=0)
reg = SGDRegressor(loss="squared", penalty="l2", learning_rate="constant", eta0=1e-2, random_state=0, max_iter=10)
Y = np.zeros((len(y), 2))
Y[:, 0] = y
Y[:, 1] = y
reg.fit(X, Y)
pred = reg.predict(X)
assert_almost_equal(np.mean((pred - Y) ** 2), 4.541, 3)
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(reg_train_data):
X, y = reg_train_data
reg = SGDRegressor(loss="squared",
penalty="l2",
learning_rate="constant",
eta0=1e-2,
random_state=0)
reg.fit(X, y)
pred = reg.predict(X)
np.testing.assert_almost_equal(np.mean((pred - y)**2), 4.749, 3)
def test_regression_squared_loss_multiple_output():
X, y = make_regression(n_samples=100, n_features=10, n_informative=8,
random_state=0)
reg = SGDRegressor(loss="squared", penalty="l2", learning_rate="constant",
eta0=1e-2, random_state=0, max_iter=10)
Y = np.zeros((len(y), 2))
Y[:, 0] = y
Y[:, 1] = y
reg.fit(X, Y)
pred = reg.predict(X)
assert_almost_equal(np.mean((pred - Y) ** 2), 4.541, 3)
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(reg_nn_train_data, alpha):
X, y = reg_nn_train_data
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 test_regression_squared_loss_multiple_output(reg_train_data):
X, y = reg_train_data
reg = SGDRegressor(loss="squared",
penalty="l2",
learning_rate="constant",
eta0=1e-2,
random_state=0,
max_iter=10)
Y = np.zeros((len(y), 2))
Y[:, 0] = y
Y[:, 1] = y
reg.fit(X, Y)
pred = reg.predict(X)
np.testing.assert_almost_equal(np.mean((pred - Y)**2), 4.397, 3)
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()
