archetypes.AA#

class archetypes.AA(n_archetypes=4, n_init=1, max_iter=300, tol=0.0001, algorithm_init='auto', verbose=False, random_state=None)#

Archetype Analysis estimator.

Parameters:

n_archetypesint, default=4: The number of archetypes to compute.
n_initint, default=5: Number of time the archetype analysis algorithm will be run with different coefficient initialization. The final results will be the best output of n_init consecutive runs in terms of RSS.
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.
verbosebool, default=False: Verbosity mode.
random_stateint, RandomState instance or None, default=None: Determines random number generation of coefficients. Use an int to make the randomness deterministic.

References

[1]

Adele Cutler, & Leo Breiman (1994). Archetypal analysis. Technometrics, 36, 338-347.

Methods

`fit`(X[, y])	Compute Archetype Analysis.
`fit_transform`(X[, y])	Compute the archetypes and transform X to archetype-distance 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 an archetype-distance space.

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 archetype-distance space.

Equivalent to fit(X).transform(X), but more efficiently implemented.

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”}, default=None

Configure output of transform and fit_transform.

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

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 an archetype-distance 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.