SklearnDataSplitter¶

class mastml.data_splitters.SklearnDataSplitter(splitter, **kwargs)[source]¶

Class to wrap any scikit-learn based data splitter, e.g. KFold

Args:

splitter (str): string denoting the name of a sklearn.model_selection object, e.g. ‘KFold’ will draw from sklearn.model_selection.KFold()

kwargs : key word arguments for the sklearn.model_selection object, e.g. n_splits=5 for KFold()

Attributes:

parallel_run: an attribute definining wheteher to run splits with all available computer cores

Methods:

get_n_splits: method to calculate the number of splits to perform

split: method to perform split into train indices and test indices

_setup_savedir: method to create a savedir based on the provided model, splitter, selector names and datetime

Args:

model: (mastml.models.SklearnModel or other estimator object), an estimator, e.g. KernelRidge

selector: (mastml.feature_selectors or other selector object), a selector, e.g. EnsembleModelFeatureSelector

savepath: (str), string designating the savepath

Returns:

splitdir: (str), string containing the new subdirectory to save results to

Methods Summary

`get_n_splits`([X, y, groups])	Returns the number of splitting iterations in the cross-validator
`split`(X[, y, groups])	Generate indices to split data into training and test set.

Methods Documentation

get_n_splits(X=None, y=None, groups=None)[source]¶: Returns the number of splitting iterations in the cross-validator

split(X, y=None, groups=None)[source]¶

Generate indices to split data into training and test set.

Parameters¶

Xarray-like of shape (n_samples, n_features): Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,): The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None: Group labels for the samples used while splitting the dataset into train/test set.