TimeSeriesKMeansTslearn

class TimeSeriesKMeansTslearn(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='random')[source]

K-means clustering for time-series data, from tslearn.

Direct interface to tslearn.clustering.TimeSeriesKMeans.

Parameters:

n_clustersint (default: 3): Number of clusters to form.
max_iterint (default: 50): Maximum number of iterations of the k-means algorithm for a single run.
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-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_barycenterint (default: 100): Number of iterations for the barycenter computation process. Only used if metric="dtw" or metric="softdtw".
metric_paramsdict 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_jobsint 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.
verboseint (default: 0): If nonzero, print information about the inertia while learning the model and joblib progress messages are printed.
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{‘k-means++’, ‘random’ or an ndarray} (default: ‘random’): 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.

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit time series clusterer to training data.
`fit_predict`(X[, y])	Compute cluster centers and predict cluster index for each time series.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X[, y])	Predict the closest cluster each sample in X belongs to.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X[, y])	Score the quality of the clusterer.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.