test_split.py 文件源码

def test_stratified_kfold_ratios():
    # Check that stratified kfold preserves class ratios in individual splits
    # Repeat with shuffling turned off and on
    n_samples = 1000
    X = np.ones(n_samples)
    y = np.array([4] * int(0.10 * n_samples) +
                 [0] * int(0.89 * n_samples) +
                 [1] * int(0.01 * n_samples))

    for shuffle in (False, True):
        for train, test in StratifiedKFold(5, shuffle=shuffle).split(X, y):
            assert_almost_equal(np.sum(y[train] == 4) / len(train), 0.10, 2)
            assert_almost_equal(np.sum(y[train] == 0) / len(train), 0.89, 2)
            assert_almost_equal(np.sum(y[train] == 1) / len(train), 0.01, 2)
            assert_almost_equal(np.sum(y[test] == 4) / len(test), 0.10, 2)
            assert_almost_equal(np.sum(y[test] == 0) / len(test), 0.89, 2)
            assert_almost_equal(np.sum(y[test] == 1) / len(test), 0.01, 2)