shapiq_benchmark.setups.UnsupervisedDataSetup¶

class shapiq_benchmark.setups.UnsupervisedDataSetup(*, dataset, random_state=42, dataset_params=<factory>, n_samples=None, n_bins=20)[source]¶

Bases: DatasetSetup

An UnsupervisedData game on (a seeded sample of) a dataset.

Variables:

n_samples – Use a seeded sample of this many rows (None for all).
n_bins – The number of equal-width bins per feature. Defaults to 20.

Examples

>>> UnsupervisedDataSetup(dataset="xor").build().n_players
4

build()[source]¶

Sample the rows and build the game.

Return type:: UnsupervisedData

load()¶

Load the dataset.

Return type:: Dataset

sample_rows(n_rows, n)¶

Return n sorted row indices out of n_rows, drawn with random_state.

All rows are returned if n is None or at least n_rows.

Return type:: ndarray

stratified_rows(y, n, task)¶

Return n sorted row indices, drawn with random_state and stratified by class.

The rows are stratified for classification where every class has enough rows. All rows are returned if n is None or at least the number of rows.

Return type:: GenericAlias[integer]

to_dict()¶

Return the setup as a JSON-serializable dictionary (the inverse of setup_from_dict()).

Return type:: dict[str, Any]

dataset¶

dataset_params¶

property key¶

A stable identifier of the game this setup builds, used as its cache key.

It is a digest of the setup’s name, version, RECIPE_VERSION, and fields (runtime fields excluded), so it is the same across processes and machines.

n_bins = 20¶

n_samples = None¶

name = 'unsupervised_data'¶: The registered name of the setup.

random_state = 42¶

tasks = ('classification', 'regression')¶: The tasks of the datasets the setup accepts.

version = 1¶: The version of build(), part of key.