`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')[source]¶

KShape clustering for time series.

KShape was originally presented in [1].

Parameters:

Parameters:	n_clusters : int (default: 3) Number of clusters to form. max_iter : int (default: 100) Maximum number of iterations of the k-Shape algorithm. tol : float (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_init : int (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. verbose : bool (default: False) Whether or not to print information about the inertia while learning the model. random_state : integer 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.

n_clusters : int (default: 3): Number of clusters to form.
max_iter : int (default: 100): Maximum number of iterations of the k-Shape algorithm.
tol : float (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_init : int (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.
verbose : bool (default: False): Whether or not to print information about the inertia while learning the model.
random_state : integer 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)[source]¶

Compute k-Shape clustering.

Parameters:	X : array-like of shape=(n_ts, sz, d) Time series dataset. y Ignored

fit_predict(X, y=None)[source]¶

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:	X : array-like of shape=(n_ts, sz, d) Time series dataset to predict. y Ignored
Returns:	labels : array of shape=(n_ts, ) Index of the cluster each sample belongs to.

classmethod from_hdf5(path)[source]¶

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

Parameters:	path : str Full path to file.
Returns:	Model instance

classmethod from_json(path)[source]¶

Load model from a JSON file.

Parameters:	path : str Full path to file.
Returns:	Model instance

classmethod from_pickle(path)[source]¶

Load model from a pickle file.

Parameters:	path : str Full path to file.
Returns:	Model instance

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:	deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : dict Parameter names mapped to their values.

predict(X)[source]¶

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

Parameters:	X : array-like of shape=(n_ts, sz, d) Time series dataset to predict.
Returns:	labels : array of shape=(n_ts, ) Index of the cluster each sample belongs to.

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:	**params : dict Estimator parameters.
Returns:	self : estimator instance Estimator instance.

to_hdf5(path)[source]¶

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

Parameters:	path : str Full file path. File must not already exist.
Raises:	FileExistsError If a file with the same path already exists.

to_json(path)[source]¶

Save model to a JSON file.

Parameters:	path : str Full file path.

to_pickle(path)[source]¶

Save model to a pickle file.

Parameters:	path : str Full file path.

Examples using `tslearn.clustering.KShape`¶

KShape

Model Persistence

tslearn.clustering.KShape¶

Examples using tslearn.clustering.KShape¶

`tslearn.clustering`.KShape¶

Examples using `tslearn.clustering.KShape`¶