archetypes.torch.AA#

class archetypes.torch.AA(n_archetypes, max_iter=300, tol=0.0001, init='uniform', init_kwargs=None, save_init=False, verbose=False, device=None, generator=None, method='autogd', method_kwargs=None)#

Archetype Analysis with Torch backend.

Parameters:

n_archetypesint: The number of archetypes to compute.
max_iterint, default=300: Maximum number of iterations of the archetype analysis algorithm for a single run.
tolfloat, default=1e-4: Relative tolerance of two consecutive iterations to declare convergence.
initstr or callable, default=’uniform’: Method used to initialize the archetypes. If a string, it must be one of the following: ‘uniform’, ‘furthest_sum’, ‘furthest_first’ or ‘aa_plus_plus’. See Initialization Methods.
init_kwargsdict, default=None: Additional keyword arguments to pass to the initialization method.
save_initbool, default=False: If True, save the initial coefficients in the attribute B_init_.
method: str, default=’nnls’: The optimization method to use for the archetypes and the coefficients. It must be one of the following: ‘autogd’.
method_kwargsdict, default=None: Additional arguments to pass to the optimization method. See Optimization Methods.
verbosebool, default=False: Verbosity mode.
devicestr or torch.device, default=None: The device to use for the computation. If None, the default device is used.
generatorint, Generator instance or None, default=None: Determines random number generation of coefficients. Use an int to make the randomness deterministic.

Attributes:

archetypes_np.ndarray: The computed archetypes. It has shape (n_archetypes, n_features).
archetypes_init_np.ndarray: The initial archetypes. It is only available if save_init=True.
similarity_degree_np.ndarray: The similarity degree of each sample to each archetype. It has shape (n_samples, n_archetypes).
archetypes_similarity_degree_np.ndarray: The similarity degree of each archetype to each sample. It has shape (n_archetypes, n_samples).
labels_np.ndarray: The label of each sample. It is the index of the closest archetype. It has shape (n_samples,).
loss_list: The loss at each iteration.

Methods

`fit`(X[, y])	Compute Archetype Analysis.
`fit_transform`(X[, y])	Compute the archetypes and transform X to the archetypal space.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform X to the archetypal space.

References

[1]

“Archetypal Analysis” by Cutler, A. and Breiman, L. (1994).

[2]

“Archetypal Analysis for machine learning and data mining” by Morup, M. and Hansen, L. K. (2012).

fit(X, y=None, **fit_params)#

Compute Archetype Analysis.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training instances to compute the archetypes. It must be noted that the data will be converted to C ordering, which will cause a memory copy if the given data is not C-contiguous. If a sparse matrix is passed, a copy will be made if it’s not in CSR format.
yIgnored: Not used, present here for API consistency by convention.

Returns:

selfobject: Fitted estimator.

fit_transform(X, y=None, **fit_params)#

Compute the archetypes and transform X to the archetypal space.

Equivalent to fit(X).transform(X).

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): New data to transform.
yIgnored: Not used, present here for API consistency by convention.

Returns:

X_newndarray of shape (n_samples, n_archetypes): X transformed in the new space.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform(X)#

Transform X to the archetypal space.

In the new space, each dimension is the distance to the archetypes. Note that even if X is sparse, the array returned by transform will typically be dense.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): New data to transform.

Returns:

X_newndarray of shape (n_samples, n_archetypes): X transformed in the new space.

archetypes.torch.AA#

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