<filename>autogl/datasets/utils/_general.py
import numpy as np
import random
import torch
import torch.utils.data
import typing as _typing
from sklearn.model_selection import StratifiedKFold, KFold
from autogl import backend as _backend
from autogl.data import InMemoryDataset


def index_to_mask(index: torch.Tensor, size):
    mask = torch.zeros(size, dtype=torch.bool, device=index.device)
    mask[index] = True
    return mask


def random_splits_mask(
        dataset: InMemoryDataset,
        train_ratio: float = 0.2, val_ratio: float = 0.4,
        seed: _typing.Optional[int] = None
) -> InMemoryDataset:
    r"""If the data has masks for train/val/test, return the splits with specific ratio.

    Parameters
    ----------
    dataset : InMemoryDataset
        graph set
    train_ratio : float
        the portion of data that used for training.

    val_ratio : float
        the portion of data that used for validation.

    seed : int
        random seed for splitting dataset.
    """
    if not train_ratio + val_ratio <= 1:
        raise ValueError("the sum of provided train_ratio and val_ratio is larger than 1")

    def __random_split_masks(
            num_nodes: int
    ) -> _typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        _rng_state: torch.Tensor = torch.get_rng_state()
        if seed is not None and isinstance(seed, int):
            torch.manual_seed(seed)
        perm = torch.randperm(num_nodes)
        train_index = perm[:int(num_nodes * train_ratio)]
        val_index = perm[int(num_nodes * train_ratio): int(num_nodes * (train_ratio + val_ratio))]
        test_index = perm[int(num_nodes * (train_ratio + val_ratio)):]
        torch.set_rng_state(_rng_state)
        return (
            index_to_mask(train_index, num_nodes),
            index_to_mask(val_index, num_nodes),
            index_to_mask(test_index, num_nodes)
        )

    for index in range(len(dataset)):
        for node_type in dataset[index].nodes:
            data_keys = [data_key for data_key in dataset[index].nodes.data]
            if len(data_keys) > 0:
                _num_nodes: int = dataset[index].nodes[node_type].data[data_keys[0]].size(0)
                _masks: _typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor] = (
                    __random_split_masks(_num_nodes)
                )
                dataset[index].nodes[node_type].data["train_mask"] = _masks[0]
                dataset[index].nodes[node_type].data["val_mask"] = _masks[1]
                dataset[index].nodes[node_type].data["test_mask"] = _masks[2]
    return dataset


def random_splits_mask_class(
        dataset: InMemoryDataset,
        num_train_per_class: int = 20,
        num_val_per_class: int = 30,
        total_num_val: _typing.Optional[int] = ...,
        total_num_test: _typing.Optional[int] = ...,
        seed: _typing.Optional[int] = ...
):
    r"""If the data has masks for train/val/test, return the splits with specific number of samples from every class for training as suggested in Pitfalls of graph neural network evaluation [#]_ for semi-supervised learning.

    References
    ----------
    .. [#] <NAME>., <NAME>., <NAME>., & <NAME>. (2018).
        Pitfalls of graph neural network evaluation.
        arXiv preprint arXiv:1811.05868.

    Parameters
    ----------
    dataset: InMemoryDataset
        instance of ``InMemoryDataset``
    num_train_per_class : int
        the number of samples from every class used for training.

    num_val_per_class : int
        the number of samples from every class used for validation.

    total_num_val : int
        the total number of nodes that used for validation as alternative.

    total_num_test : int
        the total number of nodes that used for testing as alternative. The rest of the data will be seleted as test set if num_test set to None.

    seed : int
        random seed for splitting dataset.
    """
    for graph_index in range(len(dataset)):
        for node_type in dataset[graph_index].nodes:
            if (
                    'y' in dataset[graph_index].nodes[node_type].data and
                    'label' in dataset[graph_index].nodes[node_type].data
            ):
                raise ValueError(
                    f"Both 'y' and 'label' data exist "
                    f"for node type [{node_type}] in "
                    f"graph with index [{graph_index}]."
                )
            elif (
                    'y' not in dataset[graph_index].nodes[node_type].data and
                    'label' not in dataset[graph_index].nodes[node_type].data
            ):
                continue
            elif 'y' in dataset[graph_index].nodes[node_type].data:
                label: torch.Tensor = dataset[graph_index].nodes[node_type].data['y']
            elif 'label' in dataset[graph_index].nodes[node_type].data:
                label: torch.Tensor = dataset[graph_index].nodes[node_type].data['label']
            else:
                raise RuntimeError
            num_nodes: int = label.size(0)
            num_classes: int = label.cpu().max().item() + 1

            _rng_state: torch.Tensor = torch.get_rng_state()
            if seed not in (Ellipsis, None) and isinstance(seed, int):
                torch.manual_seed(seed)
            train_mask = torch.zeros(num_nodes, dtype=torch.bool, device=label.device)
            val_mask = torch.zeros(num_nodes, dtype=torch.bool, device=label.device)
            test_mask = torch.zeros(num_nodes, dtype=torch.bool, device=label.device)
            for class_index in range(num_classes):
                idx = (label == class_index).nonzero().view(-1)
                assert num_train_per_class + num_val_per_class < idx.size(0), (
                    f"the total number of samples from every class "
                    f"used for training and validation is larger than "
                    f"the total samples in class [{class_index}] for node type [{node_type}] "
                    f"in graph with index [{graph_index}]"
                )
                randomized_index: torch.Tensor = torch.randperm(idx.size(0))
                train_idx = idx[randomized_index[:num_train_per_class]]
                val_idx = idx[
                    randomized_index[num_train_per_class: (num_train_per_class + num_val_per_class)]
                ]
                train_mask[train_idx] = True
                val_mask[val_idx] = True

            if isinstance(total_num_val, int) and total_num_val > 0:
                remaining = (~train_mask).nonzero().view(-1)
                remaining = remaining[torch.randperm(remaining.size(0))]
                val_mask[remaining[:total_num_val]] = True
                if isinstance(total_num_test, int) and total_num_test > 0:
                    test_mask[remaining[total_num_val: (total_num_val + total_num_test)]] = True
                else:
                    test_mask[remaining[total_num_val:]] = True
            else:
                remaining = (~(train_mask + val_mask)).nonzero().view(-1)
                test_mask[remaining] = True

            torch.set_rng_state(_rng_state)
            dataset[graph_index].nodes[node_type].data["train_mask"] = train_mask
            dataset[graph_index].nodes[node_type].data["val_mask"] = val_mask
            dataset[graph_index].nodes[node_type].data["test_mask"] = test_mask
    return dataset


def graph_cross_validation(
        dataset: InMemoryDataset,
        n_splits: int = 10, shuffle: bool = True,
        random_seed: _typing.Optional[int] = ...,
        stratify: bool = False
) -> InMemoryDataset:
    r"""Cross validation for graph classification data

    Parameters
    ----------
    dataset : InMemoryDataset
        dataset with multiple graphs.

    n_splits : int
        the number of folds to split.

    shuffle : bool
        shuffle or not for sklearn.model_selection.StratifiedKFold

    random_seed : int
        random_state for sklearn.model_selection.StratifiedKFold

    stratify: bool
    """
    if not isinstance(n_splits, int):
        raise TypeError
    elif not n_splits > 0:
        raise ValueError
    if not isinstance(shuffle, bool):
        raise TypeError
    if not (random_seed in (Ellipsis, None) or isinstance(random_seed, int)):
        raise TypeError
    elif isinstance(random_seed, int) and random_seed >= 0:
        _random_seed: int = random_seed
    else:
        _random_seed: int = random.randrange(0, 65536)
    if not isinstance(stratify, bool):
        raise TypeError

    if stratify:
        kf = StratifiedKFold(
            n_splits=n_splits, shuffle=shuffle, random_state=_random_seed
        )
    else:
        kf = KFold(
            n_splits=n_splits, shuffle=shuffle, random_state=_random_seed
        )
    dataset_y = [g.data['y' if 'y' in g.data else 'label'].item() for g in dataset]
    idx_list = [
        (train_index.tolist(), test_index.tolist())
        for train_index, test_index
        in kf.split(np.zeros(len(dataset)), np.array(dataset_y))
    ]

    dataset.folds = idx_list
    dataset.train_index = idx_list[0][0]
    dataset.val_index = idx_list[0][1]
    return dataset


def set_fold(dataset: InMemoryDataset, fold_id: int) -> InMemoryDataset:
    r"""Set fold for graph dataset consist of multiple graphs.

    Parameters
    ----------
    dataset: `autogl.data.InMemoryDataset`
        dataset with multiple graphs.
    fold_id: `int`
        The fold in to use, MUST be in [0, dataset.n_splits)

    Returns
    -------
    `autogl.data.InMemoryDataset`
        The reference of original dataset.
    """
    if not (hasattr(dataset, 'folds') and dataset.folds is not None):
        raise ValueError("Dataset do NOT contain folds")
    if not 0 <= fold_id < len(dataset.folds):
        raise ValueError(
            f"Fold id {fold_id} exceed total cross validation split number {len(dataset.folds)}"
        )
    dataset.train_index = dataset.folds[fold_id].train_index
    dataset.val_index = dataset.folds[fold_id].val_index
    return dataset


def graph_random_splits(
        dataset: InMemoryDataset,
        train_ratio: float = 0.2,
        val_ratio: float = 0.4,
        seed: _typing.Optional[int] = ...
):
    r"""Splitting graph dataset with specific ratio for train/val/test.

    Parameters
    ----------
    dataset: ``InMemoryStaticGraphSet``

    train_ratio : float
        the portion of data that used for training.

    val_ratio : float
        the portion of data that used for validation.

    seed : int
        random seed for splitting dataset.
    """
    _rng_state = torch.get_rng_state()
    if isinstance(seed, int):
        torch.manual_seed(seed)
    perm = torch.randperm(len(dataset))
    train_index = perm[: int(len(dataset) * train_ratio)]
    val_index = (
        perm[int(len(dataset) * train_ratio): int(len(dataset) * (train_ratio + val_ratio))]
    )
    test_index = perm[int(len(dataset) * (train_ratio + val_ratio)):]
    dataset.train_index = train_index.tolist()
    dataset.val_index = val_index.tolist()
    dataset.test_index = test_index.tolist()
    torch.set_rng_state(_rng_state)
    return dataset


def graph_get_split(
        dataset, mask: str = "train",
        is_loader: bool = True, batch_size: int = 128,
        num_workers: int = 0, shuffle: bool = False
) -> _typing.Union[torch.utils.data.DataLoader, _typing.Iterable]:
    r"""Get train/test dataset/dataloader after cross validation.

    Parameters
    ----------
    dataset:
        dataset with multiple graphs.

    mask : str

    is_loader : bool
        return original dataset or data loader

    batch_size : int
        batch_size for generating Dataloader
    num_workers : int
        number of workers parameter for data loader
    shuffle: bool
        whether shuffle the dataloader
    """
    if not isinstance(mask, str):
        raise TypeError
    elif mask.lower() not in ("train", "val", "test"):
        raise ValueError
    if not isinstance(is_loader, bool):
        raise TypeError
    if not isinstance(batch_size, int):
        raise TypeError
    elif not batch_size > 0:
        raise ValueError
    if not isinstance(num_workers, int):
        raise TypeError
    elif not num_workers >= 0:
        raise ValueError

    if mask.lower() not in ("train", "val", "test"):
        raise ValueError
    elif mask.lower() == "train":
        optional_dataset_split = dataset.train_split
        if optional_dataset_split is None:
            raise ValueError(f"Provided dataset do NOT have {mask} split")
        else:
            sub_dataset = InMemoryDataset(
                optional_dataset_split, train_index=list(range(len(optional_dataset_split)))
            )
    elif mask.lower() == "val":
        optional_dataset_split = dataset.val_split
        if optional_dataset_split is None:
            raise ValueError(f"Provided dataset do NOT have {mask} split")
        else:
            sub_dataset = InMemoryDataset(
                optional_dataset_split, val_index=list(range(len(optional_dataset_split)))
            )
    elif mask.lower() == "test":
        optional_dataset_split = dataset.test_split
        if optional_dataset_split is None:
            raise ValueError(f"Provided dataset do NOT have {mask} split")
        else:
            sub_dataset = InMemoryDataset(
                optional_dataset_split, test_index=list(range(len(optional_dataset_split)))
            )
    else:
        raise ValueError(
            f"The provided mask parameter must be a str in ['train', 'val', 'test'], "
            f"illegal provided value is [{mask}]"
        )
    if not is_loader:
        return sub_dataset
    if is_loader:
        if not (_backend.DependentBackend.is_dgl() or _backend.DependentBackend.is_pyg()):
            raise RuntimeError("Unsupported backend")
        elif _backend.DependentBackend.is_dgl():
            from dgl.dataloading.pytorch import GraphDataLoader
            return GraphDataLoader(
                sub_dataset,
                **{"batch_size": batch_size, "num_workers": num_workers},
                shuffle=shuffle
            )
        elif _backend.DependentBackend.is_pyg():
            _sub_dataset: _typing.Any = optional_dataset_split
            import torch_geometric
            if int(torch_geometric.__version__.split('.')[0]) >= 2:
                # version 2.x
                from torch_geometric.loader import DataLoader
            else:
                from torch_geometric.data import DataLoader
            return DataLoader(
                _sub_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle
            )
    else:
        return sub_dataset

<filename>autogl/datasets/utils/_general.py
import numpy as np
import random
import torch
import torch.utils.data
import typing as _typing
from sklearn.model_selection import StratifiedKFold, KFold
from autogl import backend as _backend
from autogl.data import InMemoryDataset


def index_to_mask(index: torch.Tensor, size):
    mask = torch.zeros(size, dtype=torch.bool, device=index.device)
    mask[index] = True
    return mask


def random_splits_mask(
        dataset: InMemoryDataset,
        train_ratio: float = 0.2, val_ratio: float = 0.4,
        seed: _typing.Optional[int] = None
) -> InMemoryDataset:
    r"""If the data has masks for train/val/test, return the splits with specific ratio.

    Parameters
    ----------
    dataset : InMemoryDataset
        graph set
    train_ratio : float
        the portion of data that used for training.

    val_ratio : float
        the portion of data that used for validation.

    seed : int
        random seed for splitting dataset.
    """
    if not train_ratio + val_ratio <= 1:
        raise ValueError("the sum of provided train_ratio and val_ratio is larger than 1")

    def __random_split_masks(
            num_nodes: int
    ) -> _typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        _rng_state: torch.Tensor = torch.get_rng_state()
        if seed is not None and isinstance(seed, int):
            torch.manual_seed(seed)
        perm = torch.randperm(num_nodes)
        train_index = perm[:int(num_nodes * train_ratio)]
        val_index = perm[int(num_nodes * train_ratio): int(num_nodes * (train_ratio + val_ratio))]
        test_index = perm[int(num_nodes * (train_ratio + val_ratio)):]
        torch.set_rng_state(_rng_state)
        return (
            index_to_mask(train_index, num_nodes),
            index_to_mask(val_index, num_nodes),
            index_to_mask(test_index, num_nodes)
        )

    for index in range(len(dataset)):
        for node_type in dataset[index].nodes:
            data_keys = [data_key for data_key in dataset[index].nodes.data]
            if len(data_keys) > 0:
                _num_nodes: int = dataset[index].nodes[node_type].data[data_keys[0]].size(0)
                _masks: _typing.Tuple[torch.Tensor, torch.Tensor, torch.Tensor] = (
                    __random_split_masks(_num_nodes)
                )
                dataset[index].nodes[node_type].data["train_mask"] = _masks[0]
                dataset[index].nodes[node_type].data["val_mask"] = _masks[1]
                dataset[index].nodes[node_type].data["test_mask"] = _masks[2]
    return dataset


def random_splits_mask_class(
        dataset: InMemoryDataset,
        num_train_per_class: int = 20,
        num_val_per_class: int = 30,
        total_num_val: _typing.Optional[int] = ...,
        total_num_test: _typing.Optional[int] = ...,
        seed: _typing.Optional[int] = ...
):
    r"""If the data has masks for train/val/test, return the splits with specific number of samples from every class for training as suggested in Pitfalls of graph neural network evaluation [#]_ for semi-supervised learning.

    References
    ----------
    .. [#] <NAME>., <NAME>., <NAME>., & <NAME>. (2018).
        Pitfalls of graph neural network evaluation.
        arXiv preprint arXiv:1811.05868.

    Parameters
    ----------
    dataset: InMemoryDataset
        instance of ``InMemoryDataset``
    num_train_per_class : int
        the number of samples from every class used for training.

    num_val_per_class : int
        the number of samples from every class used for validation.

    total_num_val : int
        the total number of nodes that used for validation as alternative.

    total_num_test : int
        the total number of nodes that used for testing as alternative. The rest of the data will be seleted as test set if num_test set to None.

    seed : int
        random seed for splitting dataset.
    """
    for graph_index in range(len(dataset)):
        for node_type in dataset[graph_index].nodes:
            if (
                    'y' in dataset[graph_index].nodes[node_type].data and
                    'label' in dataset[graph_index].nodes[node_type].data
            ):
                raise ValueError(
                    f"Both 'y' and 'label' data exist "
                    f"for node type [{node_type}] in "
                    f"graph with index [{graph_index}]."
                )
            elif (
                    'y' not in dataset[graph_index].nodes[node_type].data and
                    'label' not in dataset[graph_index].nodes[node_type].data
            ):
                continue
            elif 'y' in dataset[graph_index].nodes[node_type].data:
                label: torch.Tensor = dataset[graph_index].nodes[node_type].data['y']
            elif 'label' in dataset[graph_index].nodes[node_type].data:
                label: torch.Tensor = dataset[graph_index].nodes[node_type].data['label']
            else:
                raise RuntimeError
            num_nodes: int = label.size(0)
            num_classes: int = label.cpu().max().item() + 1

            _rng_state: torch.Tensor = torch.get_rng_state()
            if seed not in (Ellipsis, None) and isinstance(seed, int):
                torch.manual_seed(seed)
            train_mask = torch.zeros(num_nodes, dtype=torch.bool, device=label.device)
            val_mask = torch.zeros(num_nodes, dtype=torch.bool, device=label.device)
            test_mask = torch.zeros(num_nodes, dtype=torch.bool, device=label.device)
            for class_index in range(num_classes):
                idx = (label == class_index).nonzero().view(-1)
                assert num_train_per_class + num_val_per_class < idx.size(0), (
                    f"the total number of samples from every class "
                    f"used for training and validation is larger than "
                    f"the total samples in class [{class_index}] for node type [{node_type}] "
                    f"in graph with index [{graph_index}]"
                )
                randomized_index: torch.Tensor = torch.randperm(idx.size(0))
                train_idx = idx[randomized_index[:num_train_per_class]]
                val_idx = idx[
                    randomized_index[num_train_per_class: (num_train_per_class + num_val_per_class)]
                ]
                train_mask[train_idx] = True
                val_mask[val_idx] = True

            if isinstance(total_num_val, int) and total_num_val > 0:
                remaining = (~train_mask).nonzero().view(-1)
                remaining = remaining[torch.randperm(remaining.size(0))]
                val_mask[remaining[:total_num_val]] = True
                if isinstance(total_num_test, int) and total_num_test > 0:
                    test_mask[remaining[total_num_val: (total_num_val + total_num_test)]] = True
                else:
                    test_mask[remaining[total_num_val:]] = True
            else:
                remaining = (~(train_mask + val_mask)).nonzero().view(-1)
                test_mask[remaining] = True

            torch.set_rng_state(_rng_state)
            dataset[graph_index].nodes[node_type].data["train_mask"] = train_mask
            dataset[graph_index].nodes[node_type].data["val_mask"] = val_mask
            dataset[graph_index].nodes[node_type].data["test_mask"] = test_mask
    return dataset


def graph_cross_validation(
        dataset: InMemoryDataset,
        n_splits: int = 10, shuffle: bool = True,
        random_seed: _typing.Optional[int] = ...,
        stratify: bool = False
) -> InMemoryDataset:
    r"""Cross validation for graph classification data

    Parameters
    ----------
    dataset : InMemoryDataset
        dataset with multiple graphs.

    n_splits : int
        the number of folds to split.

    shuffle : bool
        shuffle or not for sklearn.model_selection.StratifiedKFold

    random_seed : int
        random_state for sklearn.model_selection.StratifiedKFold

    stratify: bool
    """
    if not isinstance(n_splits, int):
        raise TypeError
    elif not n_splits > 0:
        raise ValueError
    if not isinstance(shuffle, bool):
        raise TypeError
    if not (random_seed in (Ellipsis, None) or isinstance(random_seed, int)):
        raise TypeError
    elif isinstance(random_seed, int) and random_seed >= 0:
        _random_seed: int = random_seed
    else:
        _random_seed: int = random.randrange(0, 65536)
    if not isinstance(stratify, bool):
        raise TypeError

    if stratify:
        kf = StratifiedKFold(
            n_splits=n_splits, shuffle=shuffle, random_state=_random_seed
        )
    else:
        kf = KFold(
            n_splits=n_splits, shuffle=shuffle, random_state=_random_seed
        )
    dataset_y = [g.data['y' if 'y' in g.data else 'label'].item() for g in dataset]
    idx_list = [
        (train_index.tolist(), test_index.tolist())
        for train_index, test_index
        in kf.split(np.zeros(len(dataset)), np.array(dataset_y))
    ]

    dataset.folds = idx_list
    dataset.train_index = idx_list[0][0]
    dataset.val_index = idx_list[0][1]
    return dataset


def set_fold(dataset: InMemoryDataset, fold_id: int) -> InMemoryDataset:
    r"""Set fold for graph dataset consist of multiple graphs.

    Parameters
    ----------
    dataset: `autogl.data.InMemoryDataset`
        dataset with multiple graphs.
    fold_id: `int`
        The fold in to use, MUST be in [0, dataset.n_splits)

    Returns
    -------
    `autogl.data.InMemoryDataset`
        The reference of original dataset.
    """
    if not (hasattr(dataset, 'folds') and dataset.folds is not None):
        raise ValueError("Dataset do NOT contain folds")
    if not 0 <= fold_id < len(dataset.folds):
        raise ValueError(
            f"Fold id {fold_id} exceed total cross validation split number {len(dataset.folds)}"
        )
    dataset.train_index = dataset.folds[fold_id].train_index
    dataset.val_index = dataset.folds[fold_id].val_index
    return dataset


def graph_random_splits(
        dataset: InMemoryDataset,
        train_ratio: float = 0.2,
        val_ratio: float = 0.4,
        seed: _typing.Optional[int] = ...
):
    r"""Splitting graph dataset with specific ratio for train/val/test.

    Parameters
    ----------
    dataset: ``InMemoryStaticGraphSet``

    train_ratio : float
        the portion of data that used for training.

    val_ratio : float
        the portion of data that used for validation.

    seed : int
        random seed for splitting dataset.
    """
    _rng_state = torch.get_rng_state()
    if isinstance(seed, int):
        torch.manual_seed(seed)
    perm = torch.randperm(len(dataset))
    train_index = perm[: int(len(dataset) * train_ratio)]
    val_index = (
        perm[int(len(dataset) * train_ratio): int(len(dataset) * (train_ratio + val_ratio))]
    )
    test_index = perm[int(len(dataset) * (train_ratio + val_ratio)):]
    dataset.train_index = train_index.tolist()
    dataset.val_index = val_index.tolist()
    dataset.test_index = test_index.tolist()
    torch.set_rng_state(_rng_state)
    return dataset


def graph_get_split(
        dataset, mask: str = "train",
        is_loader: bool = True, batch_size: int = 128,
        num_workers: int = 0, shuffle: bool = False
) -> _typing.Union[torch.utils.data.DataLoader, _typing.Iterable]:
    r"""Get train/test dataset/dataloader after cross validation.

    Parameters
    ----------
    dataset:
        dataset with multiple graphs.

    mask : str

    is_loader : bool
        return original dataset or data loader

    batch_size : int
        batch_size for generating Dataloader
    num_workers : int
        number of workers parameter for data loader
    shuffle: bool
        whether shuffle the dataloader
    """
    if not isinstance(mask, str):
        raise TypeError
    elif mask.lower() not in ("train", "val", "test"):
        raise ValueError
    if not isinstance(is_loader, bool):
        raise TypeError
    if not isinstance(batch_size, int):
        raise TypeError
    elif not batch_size > 0:
        raise ValueError
    if not isinstance(num_workers, int):
        raise TypeError
    elif not num_workers >= 0:
        raise ValueError

    if mask.lower() not in ("train", "val", "test"):
        raise ValueError
    elif mask.lower() == "train":
        optional_dataset_split = dataset.train_split
        if optional_dataset_split is None:
            raise ValueError(f"Provided dataset do NOT have {mask} split")
        else:
            sub_dataset = InMemoryDataset(
                optional_dataset_split, train_index=list(range(len(optional_dataset_split)))
            )
    elif mask.lower() == "val":
        optional_dataset_split = dataset.val_split
        if optional_dataset_split is None:
            raise ValueError(f"Provided dataset do NOT have {mask} split")
        else:
            sub_dataset = InMemoryDataset(
                optional_dataset_split, val_index=list(range(len(optional_dataset_split)))
            )
    elif mask.lower() == "test":
        optional_dataset_split = dataset.test_split
        if optional_dataset_split is None:
            raise ValueError(f"Provided dataset do NOT have {mask} split")
        else:
            sub_dataset = InMemoryDataset(
                optional_dataset_split, test_index=list(range(len(optional_dataset_split)))
            )
    else:
        raise ValueError(
            f"The provided mask parameter must be a str in ['train', 'val', 'test'], "
            f"illegal provided value is [{mask}]"
        )
    if not is_loader:
        return sub_dataset
    if is_loader:
        if not (_backend.DependentBackend.is_dgl() or _backend.DependentBackend.is_pyg()):
            raise RuntimeError("Unsupported backend")
        elif _backend.DependentBackend.is_dgl():
            from dgl.dataloading.pytorch import GraphDataLoader
            return GraphDataLoader(
                sub_dataset,
                **{"batch_size": batch_size, "num_workers": num_workers},
                shuffle=shuffle
            )
        elif _backend.DependentBackend.is_pyg():
            _sub_dataset: _typing.Any = optional_dataset_split
            import torch_geometric
            if int(torch_geometric.__version__.split('.')[0]) >= 2:
                # version 2.x
                from torch_geometric.loader import DataLoader
            else:
                from torch_geometric.data import DataLoader
            return DataLoader(
                _sub_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=shuffle
            )
    else:
        return sub_dataset

If the data has masks for train/val/test, return the splits with specific ratio.      Parameters     ----------     dataset : InMemoryDataset         graph set     train_ratio : float         the portion of data that used for training.      val_ratio : float         the portion of data that used for validation.      seed : int         random seed for splitting dataset. If the data has masks for train/val/test, return the splits with specific number of samples from every class for training as suggested in Pitfalls of graph neural network evaluation [#]_ for semi-supervised learning.      References     ----------     .. [#] <NAME>., <NAME>., <NAME>., & <NAME>. (2018).         Pitfalls of graph neural network evaluation.         arXiv preprint arXiv:1811.05868.      Parameters     ----------     dataset: InMemoryDataset         instance of ``InMemoryDataset``     num_train_per_class : int         the number of samples from every class used for training.      num_val_per_class : int         the number of samples from every class used for validation.      total_num_val : int         the total number of nodes that used for validation as alternative.      total_num_test : int         the total number of nodes that used for testing as alternative. The rest of the data will be seleted as test set if num_test set to None.      seed : int         random seed for splitting dataset. Cross validation for graph classification data      Parameters     ----------     dataset : InMemoryDataset         dataset with multiple graphs.      n_splits : int         the number of folds to split.      shuffle : bool         shuffle or not for sklearn.model_selection.StratifiedKFold      random_seed : int         random_state for sklearn.model_selection.StratifiedKFold      stratify: bool Set fold for graph dataset consist of multiple graphs.      Parameters     ----------     dataset: `autogl.data.InMemoryDataset`         dataset with multiple graphs.     fold_id: `int`         The fold in to use, MUST be in [0, dataset.n_splits)      Returns     -------     `autogl.data.InMemoryDataset`         The reference of original dataset. Splitting graph dataset with specific ratio for train/val/test.      Parameters     ----------     dataset: ``InMemoryStaticGraphSet``      train_ratio : float         the portion of data that used for training.      val_ratio : float         the portion of data that used for validation.      seed : int         random seed for splitting dataset. Get train/test dataset/dataloader after cross validation.      Parameters     ----------     dataset:         dataset with multiple graphs.      mask : str      is_loader : bool         return original dataset or data loader      batch_size : int         batch_size for generating Dataloader     num_workers : int         number of workers parameter for data loader     shuffle: bool         whether shuffle the dataloader # version 2.x