`tslearn.clustering`.TimeSeriesKMeans¶

class tslearn.clustering.TimeSeriesKMeans(n_clusters=3, max_iter=50, tol=1e-06, n_init=1, metric='euclidean', max_iter_barycenter=100, metric_params=None, n_jobs=None, dtw_inertia=False, verbose=0, random_state=None, init='k-means++')[source]¶

K-means clustering for time-series data.

Parameters:

Parameters:	n_clusters : int (default: 3) Number of clusters to form. max_iter : int (default: 50) Maximum number of iterations of the k-means algorithm for a single run. 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-means 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. metric : {“euclidean”, “dtw”, “softdtw”} (default: “euclidean”) Metric to be used for both cluster assignment and barycenter computation. If “dtw”, DBA is used for barycenter computation. max_iter_barycenter : int (default: 100) Number of iterations for the barycenter computation process. Only used if metric=”dtw” or metric=”softdtw”. metric_params : dict or None (default: None) Parameter values for the chosen metric. For metrics that accept parallelization of the cross-distance matrix computations, n_jobs key passed in metric_params is overridden by the n_jobs argument. n_jobs : int or None, optional (default=None) The number of jobs to run in parallel for cross-distance matrix computations. Ignored if the cross-distance matrix cannot be computed using parallelization. `None` means 1 unless in a `joblib.parallel_backend` context. `-1` means using all processors. See scikit-learns’ Glossary for more details. dtw_inertia: bool (default: False) Whether to compute DTW inertia even if DTW is not the chosen metric. verbose : int (default: 0) If nonzero, print information about the inertia while learning the model and joblib progress messages are printed. 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 : {‘k-means++’, ‘random’ or an ndarray} (default: ‘k-means++’) Method for initialization: ‘k-means++’ : use k-means++ heuristic. See scikit-learn’s k_init_ for more. ‘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:	labels_ : numpy.ndarray Labels of each point. cluster_centers_ : numpy.ndarray of shape (n_clusters, sz, d) Cluster centers. sz is the size of the time series used at fit time if the init method is ‘k-means++’ or ‘random’, and the size of the longest initial centroid if those are provided as a numpy array through init parameter. 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: 50): Maximum number of iterations of the k-means algorithm for a single run.
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-means 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.
metric : {“euclidean”, “dtw”, “softdtw”} (default: “euclidean”): Metric to be used for both cluster assignment and barycenter computation. If “dtw”, DBA is used for barycenter computation.
max_iter_barycenter : int (default: 100): Number of iterations for the barycenter computation process. Only used if metric=”dtw” or metric=”softdtw”.
metric_params : dict or None (default: None): Parameter values for the chosen metric. For metrics that accept parallelization of the cross-distance matrix computations, n_jobs key passed in metric_params is overridden by the n_jobs argument.
n_jobs : int or None, optional (default=None): The number of jobs to run in parallel for cross-distance matrix computations. Ignored if the cross-distance matrix cannot be computed using parallelization. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See scikit-learns’ Glossary for more details.
dtw_inertia: bool (default: False): Whether to compute DTW inertia even if DTW is not the chosen metric.
verbose : int (default: 0): If nonzero, print information about the inertia while learning the model and joblib progress messages are printed.
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 : {‘k-means++’, ‘random’ or an ndarray} (default: ‘k-means++’): Method for initialization: ‘k-means++’ : use k-means++ heuristic. See scikit-learn’s k_init_ for more. ‘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:

labels_ : numpy.ndarray: Labels of each point.
cluster_centers_ : numpy.ndarray of shape (n_clusters, sz, d): Cluster centers. sz is the size of the time series used at fit time if the init method is ‘k-means++’ or ‘random’, and the size of the longest initial centroid if those are provided as a numpy array through init parameter.
inertia_ : float: Sum of distances of samples to their closest cluster center.
n_iter_ : int: The number of iterations performed during fit.

Notes

If metric is set to “euclidean”, the algorithm expects a dataset of equal-sized time series.

Examples

>>> from tslearn.generators import random_walks
>>> X = random_walks(n_ts=50, sz=32, d=1)
>>> km = TimeSeriesKMeans(n_clusters=3, metric="euclidean", max_iter=5,
...                       random_state=0).fit(X)
>>> km.cluster_centers_.shape
(3, 32, 1)
>>> km_dba = TimeSeriesKMeans(n_clusters=3, metric="dtw", max_iter=5,
...                           max_iter_barycenter=5,
...                           random_state=0).fit(X)
>>> km_dba.cluster_centers_.shape
(3, 32, 1)
>>> km_sdtw = TimeSeriesKMeans(n_clusters=3, metric="softdtw", max_iter=5,
...                            max_iter_barycenter=5,
...                            metric_params={"gamma": .5},
...                            random_state=0).fit(X)
>>> km_sdtw.cluster_centers_.shape
(3, 32, 1)
>>> X_bis = to_time_series_dataset([[1, 2, 3, 4],
...                                 [1, 2, 3],
...                                 [2, 5, 6, 7, 8, 9]])
>>> km = TimeSeriesKMeans(n_clusters=2, max_iter=5,
...                       metric="dtw", random_state=0).fit(X_bis)
>>> km.cluster_centers_.shape
(2, 6, 1)

Methods

`fit`(X[, y])	Compute k-means clustering.
`fit_predict`(X[, y])	Fit k-means clustering using X and then predict the closest cluster each time series in X belongs to.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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.
`transform`(X)	Transform X to a cluster-distance space.

fit(X, y=None)[source]¶

Compute k-means clustering.

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

fit_predict(X, y=None)[source]¶

Fit k-means 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.

fit_transform(X, y=None, **fit_params)[source]¶

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:	X : array-like of shape (n_samples, n_features) Input samples. y : array-like of shape (n_samples,) or (n_samples, n_outputs), default=None Target values (None for unsupervised transformations). **fit_params : dict Additional fit parameters.
Returns:	X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

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.

transform(X)[source]¶

Transform X to a cluster-distance space.

In the new space, each dimension is the distance to the cluster centers.

Parameters:	X : array-like of shape=(n_ts, sz, d) Time series dataset
Returns:	distances : array of shape=(n_ts, n_clusters) Distances to cluster centers

Examples using `tslearn.clustering.TimeSeriesKMeans`¶

k-means

tslearn.clustering.TimeSeriesKMeans¶

Examples using tslearn.clustering.TimeSeriesKMeans¶

`tslearn.clustering`.TimeSeriesKMeans¶

Examples using `tslearn.clustering.TimeSeriesKMeans`¶