How to obtain reproducible but distinct instances of GroupKFold

• KFold is only randomized if shuffle=True. Some datasets should not be shuffled.
• GroupKFold is not randomized at all. Hence the random_state=None.
• GroupShuffleSplit may be closer to what you're looking for.

A comparison of the group-based splitters:

• In GroupKFold, the test sets form a complete partition of all the data.
• LeavePGroupsOut leaves all possible subsets of P groups out, combinatorially; test sets will overlap for P > 1. Since this means P ** n_groups splits altogether, often you want a small P, and most often want LeaveOneGroupOut which is basically the same as GroupKFold with k=1.
• GroupShuffleSplit makes no statement about the relationship between successive test sets; each train/test split is performed independently.

As an aside, Dmytro Lituiev has proposed an alternative GroupShuffleSplit algorithm which is better at getting the right number of samples (not merely the right number of groups) in the test set for a specified test_size.

Inspired by user0's answer (can't comment) but faster:

def RandomGroupKFold_split(groups, n, seed=None):  # noqa: N802
    """
    Random analogous of sklearn.model_selection.GroupKFold.split.

    :return: list of (train, test) indices
    """
    groups = pd.Series(groups)
    ix = np.arange(len(groups))
    unique = np.unique(groups)
    np.random.RandomState(seed).shuffle(unique)
    result = []
    for split in np.array_split(unique, n):
        mask = groups.isin(split)
        train, test = ix[~mask], ix[mask]
        result.append((train, test))

    return result