test_ridge.py 文件源码

def test_ridgecv_sample_weight():
    rng = np.random.RandomState(0)
    alphas = (0.1, 1.0, 10.0)

    # There are different algorithms for n_samples > n_features
    # and the opposite, so test them both.
    for n_samples, n_features in ((6, 5), (5, 10)):
        y = rng.randn(n_samples)
        X = rng.randn(n_samples, n_features)
        sample_weight = 1.0 + rng.rand(n_samples)

        cv = KFold(5)
        ridgecv = RidgeCV(alphas=alphas, cv=cv)
        ridgecv.fit(X, y, sample_weight=sample_weight)

        # Check using GridSearchCV directly
        parameters = {'alpha': alphas}
        fit_params = {'sample_weight': sample_weight}
        gs = GridSearchCV(Ridge(), parameters, fit_params=fit_params,
                          cv=cv)
        gs.fit(X, y)

        assert_equal(ridgecv.alpha_, gs.best_estimator_.alpha)
        assert_array_almost_equal(ridgecv.coef_, gs.best_estimator_.coef_)