def split_dataset(dataset, frac_list=None, shuffle=False, random_state=None):
    """Split dataset into training, validation and test set.

    Parameters
    ----------
    dataset
        We assume ``len(dataset)`` gives the number of datapoints and ``dataset[i]``
        gives the ith datapoint.
    frac_list : list or None, optional
        A list of length 3 containing the fraction to use for training,
        validation and test. If None, we will use [0.8, 0.1, 0.1].
    shuffle : bool, optional
        By default we perform a consecutive split of the dataset. If True,
        we will first randomly shuffle the dataset.
    random_state : None, int or array_like, optional
        Random seed used to initialize the pseudo-random number generator.
        Can be any integer between 0 and 2**32 - 1 inclusive, an array
        (or other sequence) of such integers, or None (the default).
        If seed is None, then RandomState will try to read data from /dev/urandom
        (or the Windows analogue) if available or seed from the clock otherwise.

    Returns
    -------
    list of length 3
        Subsets for training, validation and test.
    """
    from itertools import accumulate

    if frac_list is None:
        frac_list = [0.8, 0.1, 0.1]
    frac_list = np.asarray(frac_list)
    assert np.allclose(
        np.sum(frac_list), 1.0
    ), "Expect frac_list sum to 1, got {:.4f}".format(np.sum(frac_list))
    num_data = len(dataset)
    lengths = (num_data * frac_list).astype(int)
    lengths[-1] = num_data - np.sum(lengths[:-1])
    if shuffle:
        indices = np.random.RandomState(seed=random_state).permutation(num_data)
    else:
        indices = np.arange(num_data)
    return [
        Subset(dataset, indices[offset - length : offset])
        for offset, length in zip(accumulate(lengths), lengths)
    ]
Split dataset into training, validation and test set.

    Parameters
    ----------
    dataset
        We assume ``len(dataset)`` gives the number of datapoints and ``dataset[i]``
        gives the ith datapoint.
    frac_list : list or None, optional
        A list of length 3 containing the fraction to use for training,
        validation and test. If None, we will use [0.8, 0.1, 0.1].
    shuffle : bool, optional
        By default we perform a consecutive split of the dataset. If True,
        we will first randomly shuffle the dataset.
    random_state : None, int or array_like, optional
        Random seed used to initialize the pseudo-random number generator.
        Can be any integer between 0 and 2**32 - 1 inclusive, an array
        (or other sequence) of such integers, or None (the default).
        If seed is None, then RandomState will try to read data from /dev/urandom
        (or the Windows analogue) if available or seed from the clock otherwise.

    Returns
    -------
    list of length 3
        Subsets for training, validation and test.
    
python/dgl/data/utils.py