SimpleVote¶

class tsml_eval.estimators.clustering.consensus.SimpleVote(clusterers=None, n_clusters=8, random_state=None)[source]¶

SimpleVote clustering ensemble.

SimpleVote is a clustering ensemble that uses a voting scheme to combine the results of multiple clusterers.

Parameters:

clustererslist of clusterers, default=None: A list of clusterers to use in the ensemble. If None, defaults to 5 KMeans clusterers.
n_clustersint, default=8: The number of clusters to form.
random_stateint, default=None: The seed for random number generation.

Examples

>>> from tsml_eval.estimators.clustering.consensus.simple_vote import SimpleVote
>>> from sklearn.datasets import load_iris
>>> from sklearn.metrics import rand_score
>>> iris = load_iris()
>>> sv = SimpleVote(n_clusters=3, random_state=0)
>>> sv.fit(iris.data)
SimpleVote(...)
>>> rand_score(iris.target, sv.labels_)
0.8737360178970918

Attributes:

labels_ndarray of shape (n_instances,): Labels of each point from the last fit.

Methods

`fit`(X[, y])	Fit model to X using a simple vote ensemble.
`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.
`predict`(X)	Predict cluster labels for X.
`predict_proba`(X)	Predict cluster probabilities for X.
`set_params`(**params)	Set the parameters of this estimator.

fit(X, y=None)[source]¶: Fit model to X using a simple vote ensemble.

predict(X)[source]¶: Predict cluster labels for X.

predict_proba(X)[source]¶: Predict cluster probabilities for X.

fit_predict(X, y=None, **kwargs)[source]¶

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()[source]¶

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)[source]¶

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_params(**params)[source]¶

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.