def _check_debiased_coefs(self,
X,
y,
sample_weight,
expected_coefs,
expected_intercept=0,
params={}):
debiased_lasso = MultiOutputDebiasedLasso(
) if np.ndim(y) > 1 else DebiasedLasso()
debiased_lasso.set_params(**params)
debiased_lasso.fit(X, y, sample_weight)
all_params = debiased_lasso.get_params()
# Check coeffcients and intercept are the same within tolerance
if np.ndim(y) > 1:
for i in range(y.shape[1]):
np.testing.assert_allclose(debiased_lasso.coef_[i],
expected_coefs[i],
atol=5e-2)
if all_params["fit_intercept"]:
self.assertAlmostEqual(debiased_lasso.intercept_[i],
expected_intercept[i],
delta=1e-2)
else:
np.testing.assert_allclose(debiased_lasso.coef_,
expected_coefs,
atol=5e-2)
if all_params["fit_intercept"]:
self.assertAlmostEqual(debiased_lasso.intercept_,
expected_intercept,
delta=1e-2)
return debiased_lasso.coef_
def _check_debiased_CI_2D(self,
X,
y,
sample_weight,
expected_coefs,
expected_intercept=0,
n_experiments=200,
params={}):
# Unit vectors
X_test = np.eye(TestLassoExtensions.n_dim)
y_test_mean = expected_intercept + expected_coefs.T
is_in_interval = np.zeros(
(y.shape[1], n_experiments, TestLassoExtensions.n_dim))
for i in range(n_experiments):
np.random.seed(i)
X_exp = np.random.normal(size=X.shape)
err = np.random.normal(scale=TestLassoExtensions.error_sd,
size=(X.shape[0], y.shape[1]))
y_exp = expected_intercept + np.dot(X_exp, expected_coefs.T) + err
debiased_lasso = MultiOutputDebiasedLasso()
debiased_lasso.set_params(**params)
debiased_lasso.fit(X_exp, y_exp, sample_weight)
y_lower, y_upper = debiased_lasso.predict_interval(X_test,
alpha=0.1)
for j in range(y.shape[1]):
is_in_interval[j,
i, :] = ((y_test_mean[:, j] >= y_lower[:, j]) &
(y_test_mean[:, j] <= y_upper[:, j]))
for i in range(y.shape[1]):
CI_coverage = np.mean(is_in_interval[i], axis=0)
self.assertTrue(all(CI_coverage >= 0.85))