scdynomics.utils.data.splitter
Data splitter for scDynOmics datasets
author: jy
Functions
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Splits the dataset into training, validation, and testing sets using K-Fold cross-validation. |
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Randomly splits a dataset into training, validation, and test subsets. |
- scdynomics.utils.data.splitter.kfold_random_split(dataset, n_splits: int = 1, valid_fraction: float = 0.1, stratified_test: bool = False, stratified_valid: bool = True, random_seed: int = None) tuple
Splits the dataset into training, validation, and testing sets using K-Fold cross-validation.
- Parameters:
- dataset:
scdynomics.Multimodal_Corpus The input Dataset to be split.
- n_splits:
int(default:1) The total number of folds for cross-validation.
- valid_fraction:
float(default:0.1) Fraction of the fold’s training data held out for validation.
- stratified_test:
bool(default:False) If True, utilizes StratifiedKFold instead of standard KFold for the test splits.
- stratified_valid:
bool(default:True) If True, the validation split within the fold is stratified.
- random_seed:
int(default:None) Random seed for reproducibility.
- dataset:
- Returns:
tuple A tuple of lists (train_list, valid_list, test_list) containing the respective subsets for each fold.
- scdynomics.utils.data.splitter.random_split(dataset, test_fraction: float = None, valid_fraction: float = 0.1, stratified_test: bool = False, stratified_valid: bool = False, random_seed: int = None) tuple
Randomly splits a dataset into training, validation, and test subsets.
- Parameters:
- dataset:
scdynomics.Multimodal_Corpus The input Dataset to be split.
- test_fraction:
float(default:None) The proportion of data allocated to the test set.
- valid_fraction:
float(default:0.1) The proportion of the remaining training data allocated to the validation set.
- stratified_test:
bool(default:False) If True, splits the test set stratifying according to the dataset’s label_key.
- stratified_valid:
bool(default:False) If True, splits the validation set stratifying according to the dataset’s label_key.
- random_seed:
int(default:None) Controls the randomness of the split for reproducibility.
- dataset:
- Returns:
tuple A tuple containing lists of (train_list, valid_list, test_list) representing the subset DataLoaders.