Source code for evalml.preprocessing.data_splitters.training_validation_split

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection._split import BaseCrossValidator


[docs]class TrainingValidationSplit(BaseCrossValidator): """Split the training data into training and validation sets. Arguments: test_size (float): What percentage of data points should be included in the validation set. Defalts to the complement of `train_size` if `train_size` is set, and 0.25 otherwise. train_size (float): What percentage of data points should be included in the training set. Defaults to the complement of `test_size` shuffle (boolean): Whether to shuffle the data before splitting. Defaults to False. stratify (list): Splits the data in a stratified fashion, using this argument as class labels. Defaults to None. random_seed (int): The seed to use for random sampling. Defaults to 0. """ def __init__( self, test_size=None, train_size=None, shuffle=False, stratify=None, random_seed=0, ): self.test_size = test_size self.train_size = train_size self.shuffle = shuffle self.stratify = stratify self.random_seed = random_seed
[docs] @staticmethod def get_n_splits(): """Returns the number of splits of this object""" return 1
[docs] def split(self, X, y=None): """Divides the data into training and testing sets Arguments: X (pd.DataFrame): Dataframe of points to split y (pd.Series): Series of points to split Returns: list: Indices to split data into training and test set """ train, test = train_test_split( np.arange(X.shape[0]), test_size=self.test_size, train_size=self.train_size, shuffle=self.shuffle, stratify=self.stratify, random_state=self.random_seed, ) return iter([(train, test)])