SklearnToTsmlClusterer

class tsml_eval.estimators.SklearnToTsmlClusterer(clusterer=None, pad_unequal=False, concatenate_channels=False, clone_estimator=True, random_state=None)

Wrapper for sklearn estimators to use the tsml base class.

Methods

fit(X[, y])	Wrap fit.
fit_predict(X[, y])	Perform clustering on X and returns cluster labels.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
get_test_params([parameter_set])	Return unit test parameter settings for the estimator.
predict(X)	Wrap predict.
set_params(**params)	Set the parameters of this estimator.

fit(X, y=None)

Wrap fit.

predict(X) → ndarray

Wrap predict.

fit_predict(X, y=None, **kwargs)

Perform clustering on X and returns cluster labels.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data.

yIgnored

Not used, present for API consistency by convention.

**kwargsdict

Arguments to be passed to fit.

Added in version 1.4.

Returns:

labelsndarray of shape (n_samples,), dtype=np.int64

Cluster labels.

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.

classmethod get_test_params(parameter_set: str | None = None) → dict | List[dict]

Return unit test parameter settings for the estimator.

Parameters:

parameter_setNone or str, default=None

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

paramsdict or list of dict

Parameters to create testing instances of the class.

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.