Source code for torcheeg.model_selection.split

import logging
import os
from copy import copy
from typing import Union

import numpy as np
import pandas as pd
from sklearn import model_selection

from torcheeg.datasets.module.base_dataset import BaseDataset

from ..utils import get_random_dir_path

log = logging.getLogger('torcheeg')


def train_test_split(dataset: BaseDataset,
                     test_size: float = 0.2,
                     shuffle: bool = False,
                     random_state: Union[float, None] = None,
                     split_path: Union[None, str] = None):
    r'''
    A tool function for cross-validations, to divide the training set and the test set. It is suitable for experiments with large dataset volume and no need to use k-fold cross-validations. The test samples are sampled according to a certain proportion, and other samples are used as training samples. In most literatures, 20% of the data are sampled for testing.

    :obj:`train_test_split` devides the training set and the test set without grouping. It means that during random sampling, adjacent signal samples may be assigned to the training set and the test set, respectively. When random sampling is not used, some subjects are not included in the training set. If you think these situations shouldn't happen, consider using :obj:`train_test_split_per_subject_groupby_trial` or :obj:`train_test_split_groupby_trial`.

    .. image:: _static/train_test_split.png
        :alt: The schematic diagram of train_test_split
        :align: center

    |

    .. code-block:: python

        from torcheeg.datasets import DEAPDataset
        from torcheeg.model_selection import train_test_split
        from torcheeg import transforms
        from torcheeg.utils import DataLoader

        dataset = DEAPDataset(root_path='./data_preprocessed_python',
                              online_transform=transforms.Compose([
                                  transforms.To2d(),
                                  transforms.ToTensor()
                              ]),
                              label_transform=transforms.Compose([
                                  transforms.Select(['valence', 'arousal']),
                                  transforms.Binary(5.0),
                                  transforms.BinariesToCategory()
                              ]))

        train_dataset, test_dataset = train_test_split(dataset=dataset)

        train_loader = DataLoader(train_dataset)
        test_loader = DataLoader(test_dataset)
        ...

    Args:
        dataset (BaseDataset): Dataset to be divided.
        test_size (int):  If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. (default: :obj:`0.2`)
        shuffle (bool): Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled. (default: :obj:`False`)
        random_state (int, optional): When shuffle is :obj:`True`, :obj:`random_state` affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. (default: :obj:`None`)
        split_path (str): The path to data partition information. If the path exists, read the existing partition from the path. If the path does not exist, the current division method will be saved for next use. If set to None, a random path will be generated. (default: :obj:`None`)
    '''
    if split_path is None:
        split_path = get_random_dir_path(dir_prefix='model_selection')

    if not os.path.exists(split_path):
        log.info(f'📊 | Create the split of train and test set.')
        log.info(
            f'😊 | Please set \033[92msplit_path\033[0m to \033[92m{split_path}\033[0m for the next run, if you want to use the same setting for the experiment.'
        )
        os.makedirs(split_path)
        info = dataset.info

        n_samples = len(dataset)
        indices = np.arange(n_samples)
        train_index, test_index = model_selection.train_test_split(
            indices,
            test_size=test_size,
            random_state=random_state,
            shuffle=shuffle)
        train_info = info.iloc[train_index]
        test_info = info.iloc[test_index]

        train_info.to_csv(os.path.join(split_path, 'train.csv'), index=False)
        test_info.to_csv(os.path.join(split_path, 'test.csv'), index=False)

    else:
        log.info(
            f'📊 | Detected existing split of train and test set, use existing split from {split_path}.'
        )
        log.info(
            f'💡 | If the dataset is re-generated, you need to re-generate the split of the dataset instead of using the previous split.'
        )

    train_info = pd.read_csv(os.path.join(split_path, 'train.csv'))
    test_info = pd.read_csv(os.path.join(split_path, 'test.csv'))

    train_dataset = copy(dataset)
    train_dataset.info = train_info

    test_dataset = copy(dataset)
    test_dataset.info = test_info

    return train_dataset, test_dataset