RandomStratifiedKFold¶
-
class
museotoolbox.cross_validation.
RandomStratifiedKFold
(n_splits=2, n_repeats=False, valid_size=False, random_state=False, verbose=False)[source]¶ Generate a Cross-Validation with full random selection and Stratified K-Fold (same percentange per class).
- Parameters
n_splits (int, optional (default=2)) – Number of splits. 2 means 50% for each class at training and validation.
n_repeats (integer or False, optional (default=False)) – If False, will repeat n_splits once.
valid_size (int or False, optional (default=False)) – If False, valid size is
1 / n_splits
.random_state (integer or None, optional (default=False)) – If int, random_state is the seed used by the random number generator; If None, the random number generator is created with
time.time()
.verbose (integer or False, optional (default=False)) – Controls the verbosity: the higher the value is, the more the messages are detailed.
Example
>>> from museotoolbox.cross_validation import RandomStratifiedKFold >>> from museotoolbox import datasets >>> X,y = datasets.load_historical_data(return_X_y=True) >>> RSK = RandomStratifiedKFold(n_splits=2,random_state=12,verbose=False) >>> for tr,vl in RSK.split(X=X,y=y): print(tr,vl) [ 1600 1601 1605 ..., 9509 9561 10322] [ 3632 1988 11480 ..., 10321 9457 9508] [ 1599 1602 1603 ..., 9508 9560 10321] [ 3948 10928 3490 ..., 10322 9458 9561]
Manage cross-validation methods to generate the duo valid/train samples.
Methods
__init__
([n_splits, n_repeats, valid_size, …])Manage cross-validation methods to generate the duo valid/train samples.
get_n_splits
([X, y, groups])Returns the number of splitting iterations in the cross-validator.
save_to_vector
(vector, field[, group, …])Save to vector files each fold from the cross-validation.
split
(X, y[, groups])Split the vector/array according to y and groups.