TimeSeriesKMeans

class TimeSeriesKMeans(n_clusters: int = 8, init_algorithm: str | Callable = 'random', metric: str | Callable = 'dtw', n_init: int = 10, max_iter: int = 300, tol: float = 1e-06, verbose: bool = False, random_state: int | RandomState = None, averaging_method: str | Callable[[ndarray], ndarray] = 'mean', distance_params: dict = None, average_params: dict = None)[source]

Time series K-mean implementation.

Parameters:

n_clusters: int, defaults = 8

The number of clusters to form as well as the number of centroids to generate.

init_algorithm: str, np.ndarray (3d array of shape (n_clusters, n_dimensions,

series_length)), defaults = ‘random’ Method for initializing cluster centers or an array of initial cluster centers. If string, any of the following strings are valid:

[‘kmeans++’, ‘random’, ‘forgy’].

If 3D np.ndarray, initializes cluster centers with the provided array. The array: must have shape (n_clusters, n_dimensions, series_length) and the number of clusters in the array must be the same as what is provided to the n_clusters argument.

metric: str or Callable, defaults = ‘dtw’

Distance metric to compute similarity between time series. Any of the following are valid: [‘dtw’, ‘euclidean’, ‘erp’, ‘edr’, ‘lcss’, ‘squared’, ‘ddtw’, ‘wdtw’, ‘wddtw’]

n_init: int, defaults = 10

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

max_iter: int, defaults = 300

Maximum number of iterations of the k-means algorithm for a single run.

tol: float, defaults = 1e-6

Relative tolerance with regards to Frobenius norm of the difference in the cluster centers of two consecutive iterations to declare convergence.

verbose: bool, defaults = False

Verbosity mode.

random_state: int or np.random.RandomState instance or None, defaults = None

Determines random number generation for centroid initialization.

averaging_method: str or Callable, defaults = ‘mean’

Averaging method to compute the average of a cluster. Any of the following strings are valid: [‘mean’, ‘dba’]. If a Callable is provided must take the form Callable[[np.ndarray], np.ndarray].

average_params: dict, defaults = None = no parameters

Dictionary containing kwargs for averaging_method.

distance_params: dict, defaults = None = no parameters

Dictionary containing kwargs for the distance metric being used.

Attributes:

cluster_centers_: np.ndarray (3d array of shape (n_clusters, n_dimensions,: series_length)) Time series that represent each of the cluster centers. If the algorithm stops before fully converging these will not be consistent with labels_.
labels_: np.ndarray (1d array of shape (n_instance,)): Labels that is the index each time series belongs to.
inertia_: float: Sum of squared distances of samples to their closest cluster center, weighted by the sample weights if provided.
n_iter_: int: Number of iterations run.

Examples

>>> from sktime.datasets import load_arrow_head
>>> from sktime.clustering.k_means import TimeSeriesKMeans
>>> X_train, y_train = load_arrow_head(split="train")
>>> X_test, y_test = load_arrow_head(split="test")
>>> clusterer = TimeSeriesKMeans(n_clusters=3)
>>> clusterer.fit(X_train)
TimeSeriesKMeans(n_clusters=3)
>>> y_pred = clusterer.predict(X_test)

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.