Source code for torcheeg.model_selection.k_fold_per_subject_cross_trial

import os
import re
from copy import copy
from typing import List, Tuple, Union, Dict

import numpy as np
import pandas as pd
from sklearn import model_selection
from torcheeg.datasets.module.base_dataset import BaseDataset


class KFoldPerSubjectCrossTrial:
    r'''
    A tool class for k-fold cross-validations, to divide the training set and the test set, commonly used to study model performance in the case of subject dependent experiments. Experiments were performed separately for each subject, where the data set is divided into k subsets of trials, with one subset trials being retained as the test set and the remaining k-1 subset trials being used as training data. In most of the literature, K is chosen as 5 or 10 according to the size of the data set.

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

    |

    .. code-block:: python

        cv = KFoldPerSubjectCrossTrial(n_splits=5, shuffle=True, split_path='./split')
        dataset = DEAPDataset(io_path=f'./deap',
                              root_path='./data_preprocessed_python',
                              online_transform=transforms.Compose([
                                  transforms.ToTensor(),
                                  transforms.To2d()
                              ]),
                              label_transform=transforms.Compose([
                                  transforms.Select(['valence', 'arousal']),
                                  transforms.Binary(5.0),
                                  transforms.BinariesToCategory()
                              ]))

        for train_dataset, test_dataset in cv.split(dataset):
            # The total number of experiments is the number subjects multiplied by K
            train_loader = DataLoader(train_dataset)
            test_loader = DataLoader(test_dataset)
            ...
    
    :obj:`KFoldPerSubjectCrossTrial` allows the user to specify the index of the subject of interest, when the user need to report the performance on each subject.

    .. code-block:: python

        cv = KFoldPerSubjectCrossTrial(n_splits=5, shuffle=True, split_path='./split')
        dataset = DEAPDataset(io_path=f'./deap',
                              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()
                              ]))

        for train_dataset, test_dataset in cv.split(dataset, subject=1):
            # k-fold cross-validation for subject 1
            train_loader = DataLoader(train_dataset)
            test_loader = DataLoader(test_dataset)
            ...

    Args:
        n_splits (int): Number of folds. Must be at least 2. (default: :obj:`5`)
        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. (default: :obj:`./split/k_fold_dataset`)
    '''
    def __init__(self,
                 n_splits: int = 5,
                 shuffle: bool = False,
                 random_state: Union[float, None] = None,
                 split_path: str = './split/k_fold_trial_per_subject'):
        self.n_splits = n_splits
        self.shuffle = shuffle
        self.random_state = random_state
        self.split_path = split_path

        self.k_fold = model_selection.KFold(n_splits=n_splits,
                                            shuffle=shuffle,
                                            random_state=random_state)

    def split_info_constructor(self, info: pd.DataFrame) -> None:
        subjects = list(set(info['subject_id']))

        for subject in subjects:
            subject_info = info[info['subject_id'] == subject]
            trial_ids = list(set(subject_info['trial_id']))

            for fold_id, (train_index_trial_ids,
                          test_index_trial_ids) in enumerate(
                              self.k_fold.split(trial_ids)):
                if len(train_index_trial_ids) == 0 or len(test_index_trial_ids) == 0:
                    raise ValueError(
                        f'The number of training or testing trials for subject {subject} is zero.'
                    )

                train_trial_ids = np.array(
                    trial_ids)[train_index_trial_ids].tolist()
                test_trial_ids = np.array(
                    trial_ids)[test_index_trial_ids].tolist()

                train_info = []
                for train_trial_id in train_trial_ids:
                    train_info.append(subject_info[subject_info['trial_id'] ==
                                                   train_trial_id])
                train_info = pd.concat(train_info, ignore_index=True)

                test_info = []
                for test_trial_id in test_trial_ids:
                    test_info.append(
                        subject_info[subject_info['trial_id'] == test_trial_id])
                test_info = pd.concat(test_info, ignore_index=True)

                train_info.to_csv(os.path.join(
                    self.split_path,
                    f'train_subject_{subject}_fold_{fold_id}.csv'),
                                  index=False)
                test_info.to_csv(os.path.join(
                    self.split_path,
                    f'test_subject_{subject}_fold_{fold_id}.csv'),
                                 index=False)

    @property
    def subjects(self) -> List:
        indice_files = list(os.listdir(self.split_path))

        def indice_file_to_subject(indice_file):
            return re.findall(r'subject_(.*)_fold_(\d*).csv',
                              indice_file)[0][0]

        subjects = list(set(map(indice_file_to_subject, indice_files)))
        subjects.sort()
        return subjects

    @property
    def fold_ids(self) -> List:
        indice_files = list(os.listdir(self.split_path))

        def indice_file_to_fold_id(indice_file):
            return int(
                re.findall(r'subject_(.*)_fold_(\d*).csv', indice_file)[0][1])

        fold_ids = list(set(map(indice_file_to_fold_id, indice_files)))
        fold_ids.sort()
        return fold_ids

    def split(
            self,
            dataset: BaseDataset,
            subject: Union[int,
                           None] = None) -> Tuple[BaseDataset, BaseDataset]:
        if not os.path.exists(self.split_path):
            os.makedirs(self.split_path)
            self.split_info_constructor(dataset.info)

        subjects = self.subjects
        fold_ids = self.fold_ids

        if not subject is None:
            assert subject in subjects, f'The subject should be in the subject list {subjects}.'

        for local_subject in subjects:
            if (not subject is None) and (local_subject != subject):
                continue

            for fold_id in fold_ids:
                train_info = pd.read_csv(
                    os.path.join(
                        self.split_path,
                        f'train_subject_{local_subject}_fold_{fold_id}.csv'))
                test_info = pd.read_csv(
                    os.path.join(
                        self.split_path,
                        f'test_subject_{local_subject}_fold_{fold_id}.csv'))

                train_dataset = copy(dataset)
                train_dataset.info = train_info

                test_dataset = copy(dataset)
                test_dataset.info = test_info

                yield train_dataset, test_dataset

    @property
    def repr_body(self) -> Dict:
        return {
            'n_splits': self.n_splits,
            'shuffle': self.shuffle,
            'random_state': self.random_state,
            'split_path': self.split_path
        }

    def __repr__(self) -> str:
        # init info
        format_string = self.__class__.__name__ + '('
        for i, (k, v) in enumerate(self.repr_body.items()):
            # line end
            if i:
                format_string += ', '
            # str param
            if isinstance(v, str):
                format_string += f"{k}='{v}'"
            else:
                format_string += f"{k}={v}"
        format_string += ')'
        return format_string