test_split.py 文件源码

def test_stratified_shuffle_split_init():
    X = np.arange(7)
    y = np.asarray([0, 1, 1, 1, 2, 2, 2])
    # Check that error is raised if there is a class with only one sample
    assert_raises(ValueError, next,
                  StratifiedShuffleSplit(3, 0.2).split(X, y))

    # Check that error is raised if the test set size is smaller than n_classes
    assert_raises(ValueError, next, StratifiedShuffleSplit(3, 2).split(X, y))
    # Check that error is raised if the train set size is smaller than
    # n_classes
    assert_raises(ValueError, next,
                  StratifiedShuffleSplit(3, 3, 2).split(X, y))

    X = np.arange(9)
    y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2])
    # Check that errors are raised if there is not enough samples
    assert_raises(ValueError, StratifiedShuffleSplit, 3, 0.5, 0.6)
    assert_raises(ValueError, next,
                  StratifiedShuffleSplit(3, 8, 0.6).split(X, y))
    assert_raises(ValueError, next,
                  StratifiedShuffleSplit(3, 0.6, 8).split(X, y))

    # Train size or test size too small
    assert_raises(ValueError, next,
                  StratifiedShuffleSplit(train_size=2).split(X, y))
    assert_raises(ValueError, next,
                  StratifiedShuffleSplit(test_size=2).split(X, y))