TimeSeriesAgglomerative

class TimeSeriesAgglomerative(n_clusters: int | None = 2, distance_threshold: float | None = None, linkage: str = 'single', distance: str | Callable = 'msm', distance_params: dict | None = None, compute_distances: bool = False)

Bases: BaseClusterer

Agglomerative hierarchical clustering for time series.

Hierarchical clustering [1] organises n time series into a nested hierarchy of clusters. Agglomerative hierarchical clustering initially treats each time series as an individual cluster. It then repeatedly merges the two closest clusters until a complete hierarchy is formed. The hierarchy can be cut using either a predefined number of clusters or a distance threshold.

Standard hierarchical clustering commonly relies on Euclidean distance, which may perform poorly when time series contain temporal shifts or local distortions. This implementation combines agglomerative clustering from scikit-learn with pairwise elastic distances provided by aeon. It supports single, average, and complete linkage strategies. Ward linkage is currently excluded [2]. The estimator follows the interface and conventions of aeon.

Parameters:

n_clustersint or None, default=2

The number of clusters to find. It must be None when distance_threshold is not None.

distance_thresholdfloat or None, default=None

Linkage distance threshold used to stop merging clusters. If this is not None, n_clusters must be None and the full tree is computed.

linkage{“complete”, “average”, “single”}, default=”single”

Linkage criterion used to decide which pair of clusters to merge:

• "average" uses the average pairwise distance between the two clusters.

• "complete" uses the maximum pairwise distance between the two clusters.

• "single" uses the minimum pairwise distance between the two clusters.

Ward linkage is not supported because this estimator works with a precomputed time-series distance matrix.

distancestr or Callable, default=”msm”

Distance used by aeon to compute the pairwise time-series distance matrix. Any distance accepted by aeon.distances.pairwise_distance can be used.

distance_paramsdict or None, default=None

Additional keyword arguments for the distance function.

compute_distancesbool, default=False

Whether to store distances between merged clusters. This is useful for inspecting or plotting the hierarchy, but increases memory usage.

Attributes:

labels_np.ndarray of shape (n_cases,)

Cluster labels for each time series.

n_clusters_int

The number of clusters found. If distance_threshold is None, this is equal to n_clusters.

n_leaves_int

Number of leaves in the hierarchical tree.

n_connected_components_int

Number of connected components.

children_np.ndarray of shape (n_cases - 1, 2)

Children of each non-leaf node in the hierarchical tree. Values smaller than n_cases refer to original time series. At merge step i, children_[i, 0] and children_[i, 1] are merged to form node n_cases + i.

distances_np.ndarray of shape (n_cases - 1,)

Linkage distances corresponding to children_. This attribute is available only when distance_threshold is used or compute_distances=True.

distance_matrix_np.ndarray of shape (n_cases, n_cases)

Pairwise time-series distance matrix used for clustering.

estimator_sklearn.cluster.AgglomerativeClustering

The fitted scikit-learn AgglomerativeClustering estimator used internally to perform clustering.

Notes

Capabilities

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

References

[1]

Murtagh, F. and Contreras, P. “Algorithms for hierarchical clustering: an overview.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), 86-97, 2012.

[2]

Murtagh, F. and Legendre, P. “Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?” Journal of Classification, 31(3), 274-295, 2014.

Examples

>>> from aeon.datasets import load_gunpoint
>>> from aeon.clustering import TimeSeriesAgglomerative
>>> X, y = load_gunpoint(split="train")
>>> clusterer = TimeSeriesAgglomerative(
...     n_clusters=2,
...     distance="euclidean",
...     linkage="average",
... )
>>> clusterer.fit(X)
TimeSeriesAgglomerative(distance='euclidean', linkage='average')
>>> clusterer.labels_.shape
(50,)

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])	Fit the clusterer to X and return the cluster index of 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_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.

Clusters the collection X and stores the cluster index of each time series in the labels_ attribute.

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

Fit the clusterer to X and return the cluster index of each time series.

Convenience method; equivalent of calling fit(X) and returning labels_, the cluster indices assigned to the fitted collection. It does not call predict(X), so it is valid for all clusterers, including transductive ones that do not support out-of-sample prediction (capability:predict tag set to False).

Parameters:

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

(n_cases, n_channels, n_timepoints)). Time series instances used to train the clusterer and return their assigned cluster indices.

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_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.

Only clusterers with the capability:predict tag set to True support assigning previously unseen cases to clusters. Transductive clusterers (tag set to False) can only cluster the collection they are fitted on; use fit_predict(X) or inspect labels_ after fit(X) instead.

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 to which each time series in X belongs.

Raises:

NotImplementedError

If the capability:predict tag is False, as the clusterer does not support out-of-sample prediction.

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

Raises:

NotImplementedError

If the capability:predict tag is False, as the clusterer does not support out-of-sample prediction.

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 temporary callback context attached by scikit-learn

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.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

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.