def split_data(
    *inputs,
    splits=[0.5, 0.5],
    shuffle=True,
    stratify_by=None,
    index_only=False,
    seed=None,
):
    """Splits inputs into multiple splits of defined sizes

    Args:
        inputs: correlated tuples/lists/arrays/matrices/tensors to split
        splits: list containing split sizes (fractions or counts);
        shuffle: if True, shuffle the data before splitting
        stratify_by: (None or an input) if not None, use these labels to
            stratify the splits (separating the data into groups by these
            labels and sampling from those, rather than from the population at
            large); overrides shuffle
        index_only: if True, return only the indices of the new splits, not the
            split data itself
        seed: (int) random seed

    Example usage:
        Ls, Xs, Ys = split_data(L, X, Y, splits=[0.8, 0.1, 0.1])
        OR
        assignments = split_data(Y, splits=[0.8, 0.1, 0.1], index_only=True)

    Note: This is very similar to scikit-learn's train_test_split() method,
        but with support for more than two splits.
    """

    def fractions_to_counts(fracs, n):
        """Converts a list of fractions to a list of counts that sum to n"""
        counts = [int(np.round(n * frac)) for frac in fracs]
        # Ensure sum of split counts sums to n
        counts[-1] = n - sum(counts[:-1])
        return counts

    def slice_data(data, indices):
        if isinstance(data, list) or isinstance(data, tuple):
            return [d for i, d in enumerate(data) if i in set(indices)]
        else:
            try:
                # Works for np.ndarray, scipy.sparse, torch.Tensor
                return data[indices]
            except TypeError:
                raise Exception(
                    f"split_data() currently only accepts inputs "
                    f"of type tuple, list, np.ndarray, scipy.sparse, or "
                    f"torch.Tensor; not {type(data)}"
                )

    # Setting random seed
    if seed is not None:
        random.seed(seed)

    try:
        n = len(inputs[0])
    except TypeError:
        n = inputs[0].shape[0]
    num_splits = len(splits)

    # Check splits for validity and convert to fractions
    if all(isinstance(x, int) for x in splits):
        if not sum(splits) == n:
            raise ValueError(
                f"Provided split counts must sum to n ({n}), not {sum(splits)}."
            )
        fracs = [count / n for count in splits]

    elif all(isinstance(x, float) for x in splits):
        if not sum(splits) == 1.0:
            raise ValueError(f"Split fractions must sum to 1.0, not {sum(splits)}.")
        fracs = splits

    else:
        raise ValueError("Splits must contain all ints or all floats.")

    # Make sampling pools
    if stratify_by is None:
        pools = [np.arange(n)]
    else:
        pools = defaultdict(list)
        for i, val in enumerate(stratify_by):
            pools[val].append(i)
        pools = list(pools.values())

    # Make index assignments
    assignments = [[] for _ in range(num_splits)]
    for pool in pools:
        if shuffle or stratify_by is not None:
            random.shuffle(pool)

        counts = fractions_to_counts(fracs, len(pool))
        counts.insert(0, 0)
        cum_counts = np.cumsum(counts)
        for i in range(num_splits):
            assignments[i].extend(pool[cum_counts[i] : cum_counts[i + 1]])

    if index_only:
        return assignments
    else:
        outputs = []
        for data in inputs:
            data_splits = []
            for split in range(num_splits):
                data_splits.append(slice_data(data, assignments[split]))
            outputs.append(data_splits)

        if len(outputs) == 1:
            return outputs[0]
        else:
            return outputs
def split_data ( * inputs , splits = [ 0.5 , 0.5 ] , shuffle = True , stratify_by = None , index_only = False , seed = None , ) : def fractions_to_counts ( fracs , n ) : counts = [ int ( np . round ( n * frac ) ) for frac in fracs ] counts [ - 1 ] = n - sum ( counts [ : - 1 ] ) return counts def slice_data ( data , indices ) : if isinstance ( data , list ) or isinstance ( data , tuple ) : return [ d for i , d in enumerate ( data ) if i in set ( indices ) ] else : try : return data [ indices ] except TypeError : raise Exception ( f"split_data() currently only accepts inputs " f"of type tuple, list, np.ndarray, scipy.sparse, or " f"torch.Tensor; not {type(data)}" ) if seed is not None : random . seed ( seed ) try : n = len ( inputs [ 0 ] ) except TypeError : n = inputs [ 0 ] . shape [ 0 ] num_splits = len ( splits ) if all ( isinstance ( x , int ) for x in splits ) : if not sum ( splits ) == n : raise ValueError ( f"Provided split counts must sum to n ({n}), not {sum(splits)}." ) fracs = [ count / n for count in splits ] elif all ( isinstance ( x , float ) for x in splits ) : if not sum ( splits ) == 1.0 : raise ValueError ( f"Split fractions must sum to 1.0, not {sum(splits)}." ) fracs = splits else : raise ValueError ( "Splits must contain all ints or all floats." ) if stratify_by is None : pools = [ np . arange ( n ) ] else : pools = defaultdict ( list ) for i , val in enumerate ( stratify_by ) : pools [ val ] . append ( i ) pools = list ( pools . values ( ) ) assignments = [ [ ] for _ in range ( num_splits ) ] for pool in pools : if shuffle or stratify_by is not None : random . shuffle ( pool ) counts = fractions_to_counts ( fracs , len ( pool ) ) counts . insert ( 0 , 0 ) cum_counts = np . cumsum ( counts ) for i in range ( num_splits ) : assignments [ i ] . extend ( pool [ cum_counts [ i ] : cum_counts [ i + 1 ] ] ) if index_only : return assignments else : outputs = [ ] for data in inputs : data_splits = [ ] for split in range ( num_splits ) : data_splits . append ( slice_data ( data , assignments [ split ] ) ) outputs . append ( data_splits ) if len ( outputs ) == 1 : return outputs [ 0 ] else : return outputs
Splits inputs into multiple splits of defined sizes

    Args:
        inputs: correlated tuples/lists/arrays/matrices/tensors to split
        splits: list containing split sizes (fractions or counts);
        shuffle: if True, shuffle the data before splitting
        stratify_by: (None or an input) if not None, use these labels to
            stratify the splits (separating the data into groups by these
            labels and sampling from those, rather than from the population at
            large); overrides shuffle
        index_only: if True, return only the indices of the new splits, not the
            split data itself
        seed: (int) random seed

    Example usage:
        Ls, Xs, Ys = split_data(L, X, Y, splits=[0.8, 0.1, 0.1])
        OR
        assignments = split_data(Y, splits=[0.8, 0.1, 0.1], index_only=True)

    Note: This is very similar to scikit-learn's train_test_split() method,
        but with support for more than two splits.