test_validation.py 文件源码

def test_cross_val_predict():
    boston = load_boston()
    X, y = boston.data, boston.target
    cv = KFold()

    est = Ridge()

    # Naive loop (should be same as cross_val_predict):
    preds2 = np.zeros_like(y)
    for train, test in cv.split(X, y):
        est.fit(X[train], y[train])
        preds2[test] = est.predict(X[test])

    preds = cross_val_predict(est, X, y, cv=cv)
    assert_array_almost_equal(preds, preds2)

    preds = cross_val_predict(est, X, y)
    assert_equal(len(preds), len(y))

    cv = LeaveOneOut()
    preds = cross_val_predict(est, X, y, cv=cv)
    assert_equal(len(preds), len(y))

    Xsp = X.copy()
    Xsp *= (Xsp > np.median(Xsp))
    Xsp = coo_matrix(Xsp)
    preds = cross_val_predict(est, Xsp, y)
    assert_array_almost_equal(len(preds), len(y))

    preds = cross_val_predict(KMeans(), X)
    assert_equal(len(preds), len(y))

    class BadCV():
        def split(self, X, y=None, labels=None):
            for i in range(4):
                yield np.array([0, 1, 2, 3]), np.array([4, 5, 6, 7, 8])

    assert_raises(ValueError, cross_val_predict, est, X, y, cv=BadCV())