def _test_ridge_loo(filter_):
# test that can work with both dense or sparse matrices
n_samples = X_diabetes.shape[0]
ret = []
fit_intercept = filter_ == DENSE_FILTER
ridge_gcv = _RidgeGCV(fit_intercept=fit_intercept)
# check best alpha
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
alpha_ = ridge_gcv.alpha_
ret.append(alpha_)
# check that we get same best alpha with custom loss_func
f = ignore_warnings
scoring = make_scorer(mean_squared_error, greater_is_better=False)
ridge_gcv2 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv2.fit)(filter_(X_diabetes), y_diabetes)
assert ridge_gcv2.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with custom score_func
func = lambda x, y: -mean_squared_error(x, y)
scoring = make_scorer(func)
ridge_gcv3 = RidgeCV(fit_intercept=False, scoring=scoring)
f(ridge_gcv3.fit)(filter_(X_diabetes), y_diabetes)
assert ridge_gcv3.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with a scorer
scorer = get_scorer('neg_mean_squared_error')
ridge_gcv4 = RidgeCV(fit_intercept=False, scoring=scorer)
ridge_gcv4.fit(filter_(X_diabetes), y_diabetes)
assert ridge_gcv4.alpha_ == pytest.approx(alpha_)
# check that we get same best alpha with sample weights
if filter_ == DENSE_FILTER:
ridge_gcv.fit(filter_(X_diabetes),
y_diabetes,
sample_weight=np.ones(n_samples))
assert ridge_gcv.alpha_ == pytest.approx(alpha_)
# simulate several responses
Y = np.vstack((y_diabetes, y_diabetes)).T
ridge_gcv.fit(filter_(X_diabetes), Y)
Y_pred = ridge_gcv.predict(filter_(X_diabetes))
ridge_gcv.fit(filter_(X_diabetes), y_diabetes)
y_pred = ridge_gcv.predict(filter_(X_diabetes))
assert_allclose(np.vstack((y_pred, y_pred)).T, Y_pred, rtol=1e-5)
return ret
def test_compute_covariance(shape, uniform_weights):
rng = np.random.RandomState(0)
X = rng.randn(*shape)
if uniform_weights:
sw = np.ones(X.shape[0])
else:
sw = rng.chisquare(1, shape[0])
sqrt_sw = np.sqrt(sw)
X_mean = np.average(X, axis=0, weights=sw)
X_centered = (X - X_mean) * sqrt_sw[:, None]
true_covariance = X_centered.T.dot(X_centered)
X_sparse = sp.csr_matrix(X * sqrt_sw[:, None])
gcv = _RidgeGCV(fit_intercept=True)
computed_cov, computed_mean = gcv._compute_covariance(X_sparse, sqrt_sw)
assert_allclose(X_mean, computed_mean)
assert_allclose(true_covariance, computed_cov)
