Source code for torcheeg.model_selection.k_fold_per_subject_cross_trial
import logging
import os
import re
from copy import copy
from typing import Dict, List, Tuple, 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')
[docs]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
from torcheeg.model_selection import KFoldPerSubjectCrossTrial
from torcheeg.datasets import DEAPDataset
from torcheeg import transforms
from torcheeg.utils import DataLoader
cv = KFoldPerSubjectCrossTrial(n_splits=5, shuffle=True)
dataset = DEAPDataset(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
from torcheeg.model_selection import KFoldPerSubjectCrossTrial
from torcheeg.datasets import DEAPDataset
from torcheeg import transforms
from torcheeg.utils import DataLoader
cv = KFoldPerSubjectCrossTrial(n_splits=5, shuffle=True)
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()
]))
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. If set to None, a random path will be generated. (default: :obj:`None`)
'''
def __init__(self,
n_splits: int = 5,
shuffle: bool = False,
random_state: Union[float, None] = None,
split_path: Union[None, str] = None):
if split_path is None:
split_path = get_random_dir_path(dir_prefix='model_selection')
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):
log.info(
f'📊 | Create the split of train and test set.'
)
log.info(
f'😊 | Please set \033[92msplit_path\033[0m to \033[92m{self.split_path}\033[0m for the next run, if you want to use the same setting for the experiment.'
)
os.makedirs(self.split_path)
self.split_info_constructor(dataset.info)
else:
log.info(
f'📊 | Detected existing split of train and test set, use existing split from {self.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.'
)
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
