tslearn.clustering.KShape

class tslearn.clustering.KShape(n_clusters=3, max_iter=100, tol=1e-06, n_init=1, verbose=False, random_state=None, init='random')

KShape clustering for time series.

KShape was originally presented in [1].

Parameters

n_clustersint (default: 3)

Number of clusters to form.

max_iterint (default: 100)

Maximum number of iterations of the k-Shape algorithm.

tolfloat (default: 1e-6)

Inertia variation threshold. If at some point, inertia varies less than this threshold between two consecutive iterations, the model is considered to have converged and the algorithm stops.

n_initint (default: 1)

Number of time the k-Shape algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia.

verbosebool (default: False)

Whether or not to print information about the inertia while learning the model.

random_stateinteger or numpy.RandomState, optional

Generator used to initialize the centers. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.

init{‘random’ or ndarray} (default: ‘random’)

Method for initialization. ‘random’: choose k observations (rows) at random from data for the initial centroids. If an ndarray is passed, it should be of shape (n_clusters, ts_size, d) and gives the initial centers.

Attributes

cluster_centers_numpy.ndarray of shape (sz, d).

Centroids

labels_numpy.ndarray of integers with shape (n_ts, ).

Labels of each point

inertia_float

Sum of distances of samples to their closest cluster center.

n_iter_int

The number of iterations performed during fit.

Notes

This method requires a dataset of equal-sized time series.

References

1

J. Paparrizos & L. Gravano. k-Shape: Efficient and Accurate Clustering of Time Series. SIGMOD 2015. pp. 1855-1870.

Examples

>>> from tslearn.generators import random_walks
>>> X = random_walks(n_ts=50, sz=32, d=1)
>>> X = TimeSeriesScalerMeanVariance(mu=0., std=1.).fit_transform(X)
>>> ks = KShape(n_clusters=3, n_init=1, random_state=0).fit(X)
>>> ks.cluster_centers_.shape
(3, 32, 1)

Methods

fit(X[, y])	Compute k-Shape clustering.
fit_predict(X[, y])	Fit k-Shape clustering using X and then predict the closest cluster each time series in X belongs to.
from_hdf5(path)	Load model from a HDF5 file.
from_json(path)	Load model from a JSON file.
from_pickle(path)	Load model from a pickle file.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict the closest cluster each time series in X belongs to.
set_params(**params)	Set the parameters of this estimator.
to_hdf5(path)	Save model to a HDF5 file.
to_json(path)	Save model to a JSON file.
to_pickle(path)	Save model to a pickle file.

fit(X, y=None)

Compute k-Shape clustering.

Parameters

Xarray-like of shape=(n_ts, sz, d)

Time series dataset.

y

Ignored

fit_predict(X, y=None)

Fit k-Shape clustering using X and then predict the closest cluster each time series in X belongs to.

It is more efficient to use this method than to sequentially call fit and predict.

Parameters

Xarray-like of shape=(n_ts, sz, d)

Time series dataset to predict.

y

Ignored

Returns

labelsarray of shape=(n_ts, )

Index of the cluster each sample belongs to.

classmethod from_hdf5(path)

Load model from a HDF5 file. Requires h5py http://docs.h5py.org/

Parameters

pathstr

Full path to file.

Returns

Model instance

classmethod from_json(path)

Load model from a JSON file.

Parameters

pathstr

Full path to file.

Returns

Model instance

classmethod from_pickle(path)

Load model from a pickle file.

Parameters

pathstr

Full path to file.

Returns

Model instance

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.

predict(X)

Predict the closest cluster each time series in X belongs to.

Parameters

Xarray-like of shape=(n_ts, sz, d)

Time series dataset to predict.

Returns

labelsarray of shape=(n_ts, )

Index of the cluster each sample belongs to.

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.

to_hdf5(path)

Save model to a HDF5 file. Requires h5py http://docs.h5py.org/

Parameters

pathstr

Full file path. File must not already exist.

Raises

FileExistsError

If a file with the same path already exists.

to_json(path)

Save model to a JSON file.

Parameters

pathstr

Full file path.

to_pickle(path)

Save model to a pickle file.

Parameters

pathstr

Full file path.

Examples using tslearn.clustering.KShape

KShape

KShape

Model Persistence

Model Persistence