\!hDSSKrSSKJr "SS\5r"SS\5rg)N)train_test_splitc6\rSrSrSrSSjrS SjrS SjrSrg) RandomHoldoutSplit aTrain/Validation set splitter for sklearn's GridSearchCV etc. Provides train/validation set indices to split a dataset into train/validation sets using random indices. Parameters ---------- valid_size : float (default: 0.5) Proportion of examples that being assigned as validation examples. 1-`valid_size` will then automatically be assigned as training set examples. random_seed : int (default: None) The random seed for splitting the data into training and validation set partitions. stratify : bool (default: False) True or False, whether to perform a stratified split or not Examples ----------- For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/RandomHoldoutSplit/ Nc(XlX lX0lgN) valid_size random_seedstratify)selfr r r s kC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\mlxtend/evaluate/holdout.py__init__RandomHoldoutSplit.__init__(s$& c #<# [R"URS5nUR(a'[ UUUR SUUR S9upV nO&[ UUUR SUUR S9upV n[S5HnXV4v M g7f)ahGenerate indices to split data into training and test set. Parameters ---------- X : array-like, shape (num_examples, num_features) Training data, where num_examples is the number of training examples and num_features is the number of features. y : array-like, shape (num_examples,) The target variable for supervised learning problems. Stratification is done based on the y labels. groups : object Always ignored, exists for compatibility. Yields ------ train_index : ndarray The training set indices for that split. valid_index : ndarray The validation set indices for that split. rT) test_sizeshuffler random_stateN)nparangeshaper rr r range) r Xygroupsind train_index valid_index_is r splitRandomHoldoutSplit.split-s0ii # ==-=//!-- . *Ka.>//!-- . *KaqA* *sBBcgaReturns the number of splitting iterations in the cross-validator Parameters ---------- X : object Always ignored, exists for compatibility. y : object Always ignored, exists for compatibility. groups : object Always ignored, exists for compatibility. Returns ------- n_splits : 1 Returns the number of splitting iterations in the cross-validator. Always returns 1. rr rrrs r get_n_splitsRandomHoldoutSplit.get_n_splits](r)r r r )g?NFrNNN __name__ __module__ __qualname____firstlineno____doc__rr"r(__static_attributes__r&rr rr s4! .+`rrc2\rSrSrSrSrSSjrS SjrSrg) PredefinedHoldoutSplittaJTrain/Validation set splitter for sklearn's GridSearchCV etc. Uses user-specified train/validation set indices to split a dataset into train/validation sets using user-defined or random indices. Parameters ---------- valid_indices : array-like, shape (num_examples,) Indices of the training examples in the training set to be used for validation. All other indices in the training set are used to for a training subset for model fitting. Examples ----------- For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/evaluate/PredefinedHoldoutSplit/ cXlgr valid_indices)r r8s r rPredefinedHoldoutSplit.__init__s*rNc#6# [R"URS5n[R"URS[RS9nSXPR '[R "USS5n[S5H nXEXF4v M g7f)a_Generate indices to split data into training and test set. Parameters ---------- X : array-like, shape (num_examples, num_features) Training data, where num_examples is the number of examples and num_features is the number of features. y : array-like, shape (num_examples,) The target variable for supervised learning problems. Stratification is done based on the y labels. groups : object Always ignored, exists for compatibility. Yields ------ train_index : ndarray The training set indices for that split. valid_index : ndarray The validation set indices for that split. r)dtypeFTrN)rrronesbool_r8wherer)r rrrr train_mask valid_maskr!s r r"PredefinedHoldoutSplit.splitsv2ii #WWQWWQZrxx8 ). %%&XXj%6 qA/3?2 2sBBcgr%r&r's r r(#PredefinedHoldoutSplit.get_n_splitsr*rr7rr+r,r&rr r4r4ts*+3Brr4)numpyrsklearn.model_selectionrobjectrr4r&rr rGs*4ddNNVNr