API Reference

In this section, you will find detailed documentation of all the functions, classes, and methods available in our API.

Whether you are a developer who is new to our API or an experienced user looking for more advanced features, we hope you find this documentation helpful. If you have any questions or feedback, please don’t hesitate to reach out to us.

Algorithms

Scikit-learn

AA([n_archetypes, n_init, max_iter, tol, ...])	Archetype Analysis estimator.
BiAA([n_archetypes, n_init, max_iter, tol, ...])	Bi-Archetype Analysis estimator.

PyTorch

AA(k, m, n[, device])	Archetype analysis implemented in PyTorch.
BiAA(k, m, n[, device])	Biarchetype analysis implemented in PyTorch.
NAA(n_archetypes, shape[, relations, ...])	N-Archetype analysis implemented in PyTorch.

Visualization

simplex(points[, origin, show_points, ...])	A simplex plot of points with multiple optional parameters to obtain a customized visualization.
bisimplex(alphas, archetypes[, ax])	A plot of archetypes with its corresponding alphas in simplex coordinates.
heatmap(data[, labels, n_archetypes, scores, ax])	Plot a heatmap of the data.

Datasets

make_archetypal_dataset(archetypes, shape[, ...])	Generate a dataset from archetypes.
permute_dataset(data[, perms])	Permute a dataset along each dimension.
shuffle_dataset(data[, generator])	Shuffle a dataset along each dimension.
sort_by_archetype_similarity(data, alphas, ...)	Sort a dataset using the archetypal spaces previously computed.
sort_by_labels(data, labels)	Sort a dataset using the labels.

Utils

check_generator([generator])

Check if generator is an int, a Generator or None and return a Generator.