Source code for torcheeg.model_selection.k_fold
import logging
import os
import re
from copy import copy
from typing import Dict, Tuple, Union
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 KFold:
r'''
A tool class for k-fold cross-validations, to divide the training set and the test set. One of the most commonly used data partitioning methods, where the data set is divided into k subsets, with one subset being retained as the test set and the remaining k-1 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.
:obj:`KFold` devides subsets 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:`KFoldPerSubjectGroupbyTrial` or :obj:`KFoldGroupbyTrial`.
.. image:: _static/KFold.png
:alt: The schematic diagram of KFold
:align: center
|
.. code-block:: python
from torcheeg.model_selection import KFold
from torcheeg.datasets import DEAPDataset
from torcheeg import transforms
from torcheeg.utils import DataLoader
cv = KFold(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):
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[None, int] = 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:
for fold_id, (train_index,
test_index) in enumerate(self.k_fold.split(info)):
train_info = info.iloc[train_index]
test_info = info.iloc[test_index]
train_info.to_csv(os.path.join(self.split_path,
f'train_fold_{fold_id}.csv'),
index=False)
test_info.to_csv(os.path.join(self.split_path,
f'test_fold_{fold_id}.csv'),
index=False)
@property
def fold_ids(self):
indice_files = list(os.listdir(self.split_path))
def indice_file_to_fold_id(indice_file):
return int(re.findall(r'fold_(\d*).csv', indice_file)[0])
fold_ids = list(set(map(indice_file_to_fold_id, indice_files)))
fold_ids.sort()
return fold_ids
def split(self, dataset: BaseDataset) -> 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.'
)
fold_ids = self.fold_ids
for fold_id in fold_ids:
train_info = pd.read_csv(
os.path.join(self.split_path, f'train_fold_{fold_id}.csv'))
test_info = pd.read_csv(
os.path.join(self.split_path, f'test_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
