econml.sklearn_extensions.model_selection.WeightedKFold

class econml.sklearn_extensions.model_selection.WeightedKFold(n_splits=3, n_trials=10, shuffle=False, random_state=None)

Bases: object

K-Folds cross-validator for weighted data.

Provides train/test indices to split data in train/test sets. Split dataset into k folds of roughly equal size and equal total weight.

The default is to try sklearn.model_selection.KFold a number of trials to find a weight-balanced k-way split. If it cannot find such a split, it will fall back onto a more rigorous weight stratification algorithm.

Parameters

• n_splits (int, default 3) – Number of folds. Must be at least 2.

• n_trials (int, default 10) – Number of times to try sklearn.model_selection.KFold before falling back to another weight stratification algorithm.

• shuffle (bool, optional) – Whether to shuffle the data before splitting into batches.

• random_state (int, RandomState instance, or None, default None) – If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when shuffle == True.

__init__(n_splits=3, n_trials=10, shuffle=False, random_state=None)

Methods

__init__([n_splits, n_trials, shuffle, ...])
get_n_splits(X, y[, groups])	Return the number of splitting iterations in the cross-validator.
split(X, y[, sample_weight])	Generate indices to split data into training and test set.

get_n_splits(X, y, groups=None)

Return 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 – Returns the number of splitting iterations in the cross-validator.

Return type

int

split(X, y, sample_weight=None)

Generate indices to split data into training and test set.

Parameters

• X (array_like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

• y (array_like, shape (n_samples,)) – The target variable for supervised learning problems.

• sample_weight (array_like, shape (n_samples,)) – Weights associated with the training data.