# skmultilearn.model_selection.measures module¶

skmultilearn.model_selection.measures.example_distribution(folds, desired_size)[source]

Examples Distribution (ED) measure

Examples Distribution is a measure of how much a given fold’s size deviates from the desired number of samples in each of the folds.
skmultilearn.model_selection.measures.folds_label_combination_pairs_without_evidence(y, folds, order)[source]

Fold - Label / Label Pair / Label Combination (FLZ, FLPZ, FLCZ) pair count measure

A general implementation of FLZ - the number of pairs of fold and label combination of a given order for which there is no positive evidence in that fold for that combination. With order = 1, it becomes the FLZ measure from Katakis et.al’s original paper, with order = 2, it becomes the FLPZ measure from Szymański et. al.’s paper.

skmultilearn.model_selection.measures.folds_without_evidence_for_at_least_one_label_combination(y, folds, order=1)[source]

Counts the number of folds without evidence for a given Label, Label Pair or Label Combination (FZ, FZLP, FZLC) measure

A general implementation of FZ - the number of folds that contain at least one label combination of order order with no positive examples. With order = 1, it becomes the FZ measure from Katakis et.al’s original paper.

skmultilearn.model_selection.measures.get_combination_wise_output_matrix(y, order)[source]

Returns label combinations of a given order that are assigned to each row

skmultilearn.model_selection.measures.get_indicator_representation(row)[source]

Convert binary indicator to list of assigned labels

skmultilearn.model_selection.measures.get_unique_combinations(combinations_per_row)[source]

Performs set.union on a list of sets

Parameters: combinations_per_row (List[Set[Tuple[int]]]) – list of combination assignments per row all unique label combinations Set[Tuple[int]]
skmultilearn.model_selection.measures.label_combination_distribution(y, folds, order)[source]

Label / Label Pair / Label Combination Distribution (LD, LPD, LCZD) measure

A general implementation of Label / Label Pair / Label Combination Distribution - a measure that evaluates how the proportion of positive evidence for a label / label pair / label combination to the negative evidence for a label (pair/combination) deviates from the same proportion in the entire data set, averaged over all folds and labels.

With order = 1, it becomes the LD measure from Katakis et.al’s original paper, with order = 2, it becomes the LPD measure from Szymański et. al.’s paper.

skmultilearn.model_selection.measures.percentage_of_label_combinations_without_evidence_per_fold(y, folds, order)[source]

Fold - Label / Label Pair / Label Combination (FLZ, FLPZ, FLCZ) pair count measure

A general implementation of FLZ - the number of pairs of fold and label combination of a given order for which there is no positive evidence in that fold for that combination. With order = 1, it becomes the FLZ measure from Katakis et.al’s original paper, with order = 2, it becomes the FLPZ measure from Szymański et. al.’s paper.