def test_correlated_w_true(density):
n_features = 50
X, y, w_true = make_correlated_data(n_features=n_features,
density=density,
random_state=42)
assert len(w_true.nonzero()[0]) == int(density * n_features)
X, y, w_new = make_correlated_data(n_features=n_features,
density=density,
w_true=w_true)
assert all(w_new == w_true)
def test_correlated_validation_check(param_name, p_range):
min_val, max_val = p_range
pattern = f'{param_name}.* should be chosen in .*{min_val}, {max_val}'
if min_val == 0:
with pytest.raises(ValueError, match=pattern):
kwargs = {param_name: -1}
make_correlated_data(**kwargs)
if max_val == 1:
with pytest.raises(ValueError, match=pattern):
kwargs = {param_name: 2}
make_correlated_data(**kwargs)
def test_correlated():
n_features = 52
n_samples = 101
X, y, w_true = make_correlated_data(n_samples=n_samples,
n_features=n_features,
random_state=42)
assert X.shape == (n_samples, n_features)
assert y.shape == (n_samples, )
assert w_true.shape == (n_features, )
def get_data(self):
rng = np.random.RandomState(self.random_state)
X, y, _ = make_correlated_data(self.n_samples,
self.n_features,
rho=self.rho,
random_state=rng)
data = dict(X=X, y=y)
return self.n_features, data
def test_correlated_snr(snr):
n_features = 50
X, y, w_true = make_correlated_data(n_samples=10000,
n_features=n_features,
snr=snr,
random_state=42)
y_pred = X @ w_true
if snr == 0:
assert abs(np.corrcoef(y_pred, y)[0, 1]) < 1e-2
elif snr == np.inf:
np.testing.assert_allclose(y, y_pred)
else:
np.testing.assert_allclose(snr, norm(y_pred) / norm(y - y_pred))