KASBA

class KASBA(n_clusters: int = 8, distance: str | Callable = 'msm', ba_subset_size: float = 0.5, initial_step_size: float = 0.05, max_iter: int = 300, tol: float = 1e-06, distance_params: dict | None = None, decay_rate: float = 0.1, verbose: bool = False, random_state: int | RandomState | None = None)

KASBA clusterer [1].

KASBA is a $k$-means clustering algorithm designed for use with the MSM distance metric [2] however, it can be used with any elastic distance that is a metric. KASBA finds initial clusters using an adapted form of kmeans++ to use elastic distances, a fast assignment step that exploits the metric property to avoid distance calculations in assignment, and an adapted elastic barycentre average that uses a stochastic gradient descent to find the barycentre averages.

Parameters:

n_clustersint, default=8

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

distancestr or callable, default=’msm’

The distance metric to use. If a string, must be one of the following: ‘msm’, ‘twe’. The distance measure use MUST be a metric.

ba_subset_sizefloat, default=0.5

The proportion of the data to use in the barycentre average step. For the first iteration all the data will be used however, on subsequent iterations a subset of the data will be used. This will be a % of the data passed (e.g. 0.5 = 50%). If there are less than 10 data points, all the available data will be used every iteration.

initial_step_sizefloat, default=0.05

The initial step size for the stochastic gradient descent in the barycentre average step.

max_iterint, default=300

Maximum number of iterations of the k-means algorithm before it is forcibly stopped.

tolfloat, default=1e-6

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

distance_paramsdict, default=None

Dictionary containing kwargs for the distance being used. For example if you wanted to specify a cost for MSM you would pass distance_params={“c”: 0.2}. See documentation of aeon.distances for more details.

decay_ratefloat, default=0.1

The decay rate for the step size in the barycentre average step. The initial_step_size will be multiplied by np.exp(-decay_rate * i) every iteration where i is the current iteration.

verbosebool, default=False

Verbosity mode.

random_stateint, np.random.RandomState instance or None, default=None

Determines random number generation for centroid initialization. If int, random_state is the seed used by the random number generator; If np.random.RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

cluster_centers_3d np.ndarray

Array of shape (n_clusters, n_channels, n_timepoints)) Time series that represent each of the cluster centers.

labels_1d np.ndarray

1d array of shape (n_case,) Labels that is the index each time series belongs to.

inertia_float

Sum of squared distances of samples to their closest cluster center.

n_iter_int

Number of iterations run.

Notes

Capabilities

Missing Values	No
Multithreading	No
Univariate	Yes
Multivariate	Yes
Unequal Length	No

References

[1]

Holder, Christopher & Bagnall, Anthony. (2024). Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering. 10.48550/arXiv.2411.17838.

[2]

Stefan A., Athitsos V., Das G.: The Move-Split-Merge metric for time

series. IEEE Transactions on Knowledge and Data Engineering 25(6), 2013.

Examples

>>> import numpy as np
>>> from aeon.clustering import KASBA
>>> X = np.random.random(size=(10,2,20))
>>> clst= KASBA(distance="msm",n_clusters=2)
>>> clst.fit(X)
KASBA(n_clusters=2)
>>> preds = clst.predict(X)

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
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[, raise_error, ...])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_fitted_params([deep])	Get fitted parameters.
get_metadata_routing()	Sklearn metadata routing.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
predict(X)	Predict the closest cluster each sample in X belongs to.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset([keep])	Reset the object to a clean post-init state.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone(random_state=None)

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None

Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject

Instance of type(self), clone of self (see above)

fit(X, y=None) → BaseCollectionEstimator

Fit time series clusterer to training data.

Parameters:

X3D np.ndarray (any number of channels, equal length series)

of shape (n_cases, n_channels, n_timepoints)

or 2D np.array (univariate, equal length series)

of shape (n_cases, n_timepoints)

or list of numpy arrays (any number of channels, unequal length series)

of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i

other types are allowed and converted into one of the above.

y: ignored, exists for API consistency reasons.

Returns:

self:

Fitted estimator.

fit_predict(X, y=None) → ndarray

Compute cluster centers and predict cluster index for each time series.

Convenience method; equivalent of calling fit(X) followed by predict(X)

Parameters:

Xnp.ndarray (2d or 3d array of shape (n_cases, n_timepoints) or shape

(n_cases, n_channels, n_timepoints)). Time series instances to train clusterer and then have indexes each belong to return.

y: ignored, exists for API consistency reasons.

Returns:

np.ndarray (1d array of shape (n_cases,))

Index of the cluster each time series in X belongs to.

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

get_metadata_routing()

Sklearn metadata routing.

Not supported by aeon estimators.

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.

get_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

predict(X) → ndarray

Predict the closest cluster each sample in X belongs to.

Parameters:

X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Returns:

np.array

shape ``(n_cases)`, index of the cluster each time series in X. belongs to.

predict_proba(X) → ndarray

Predicts labels probabilities for sequences in X.

Default behaviour is to call _predict and set the predicted class probability to 1, other class probabilities to 0. Override if better estimates are obtainable.

Parameters:

X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Returns:

y2D array of shape [n_cases, n_classes] - predicted class probabilities

1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

reset(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:

hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None

If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

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.

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:

selfobject

Reference to self.