Source code for evalml.preprocessing.data_splitters.no_split

"""Empty Data Splitter class."""

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


[docs]class NoSplit(BaseCrossValidator): """Does not split the training data into training and validation sets. All data is passed as the training set, test data is simply an array of `None`. To be used for future unsupervised learning, should not be used in any of the currently supported pipelines. Args: random_seed (int): The seed to use for random sampling. Defaults to 0. Not used. """ def __init__( self, random_seed=0, ): self.random_seed = random_seed
[docs] @staticmethod def get_n_splits(): """Return the number of splits of this object. Returns: int: Always returns 0. """ return 0
@property def is_cv(self): """Returns whether or not the data splitter is a cross-validation data splitter. Returns: bool: If the splitter is a cross-validation data splitter """ return False
[docs] def split(self, X, y=None): """Divide the data into training and testing sets, where the testing set is empty. Args: 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 """ return iter([(np.arange(X.shape[0]), [])])