BaseSegmenter

class BaseSegmenter(axis, n_segments=2)

Bases: BaseSeriesEstimator

Base class for segmentation algorithms.

Segmenters take a single time series of length n_timepoints and returns a segmentation. Series can be univariate (single series) or multivariate, with n_channels dimensions. If the segmenter can handle multivariate series, if will have the tag "capability:multivariate" set to True. Multivariate series are segmented along a the axis of time determined by self.axis.

Segmentation representation

Given a time series of 10 points with two change points found in position 4 and 8.

The segmentation can be output in two forms: a) A list of change points (tag "returns_dense" is True).

output example [4,8] for a series length 10 means three segments at positions (0,1,2,3), (4,5,6,7) and (8,9). This dense representation is the default behaviour, as it is the minimal representation. Indicated by tag “return_dense” being set to True. It is assumed to be sorted, and the first segment is assumed to start at position 0. Hence, the first change point must be greater than 0 and the last less than the series length. If the last value is n_timepoints-1 then the last point forms a single segment. An empty list indicates no change points.

b) A list of integers of length m indicating the segment of each time point ( tag "returns_dense" is False).

output [0,0,0,0,1,1,1,1,2,2] or output [0,0,0,1,1,1,1,0,0,0] This sparse representation can be used to indicate shared segments indicating segment 1 is somehow the same (perhaps in generative process) as segment 3. Indicated by tag return_dense being set to False.

Multivariate series are always segmented at the same points. If independent segmentation is required, fit a different segmenter to each channel.

Parameters:

axisint

Axis along which to segment if passed a multivariate series (2D input). If axis is 0, it is assumed each column is a time series and each row is a timepoint. i.e. the shape of the data is (n_timepoints,n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is ``(n_channels, n_timepoints)`. Each segmenter must specify the axis it assumes in the constructor and pass it to the base class.

n_segmentsint, default = 2

Number of segments to split the time series into. If None, then the number of segments needs to be found in fit.

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X[, y, axis])	Fit time series segmenter to X.
fit_predict(X[, y, axis])	Fit segmentation to data and return it.
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[, axis])	Create amd return segmentation of 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.
to_classification(change_points, length)	Convert change point locations to a classification vector.
to_clusters(change_points, length)	Convert change point locations to a clustering vector.

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)

final fit(X, y=None, axis=1)

Fit time series segmenter to X.

If the tag fit_is_empty is true, this just sets the is_fitted tag to true. Otherwise, it checks self can handle X, formats X into the structure required by self then passes X (and possibly y) to _fit.

Parameters:

XOne of VALID_SERIES_INPUT_TYPES

Input time series to fit a segmenter.

yOne of VALID_SERIES_INPUT_TYPES or None, default None

Training time series, a labeled 1D series same length as X for supervised segmentation.

axisint, default = None

Axis along which to segment if passed a multivariate X series (2D input). If axis is 0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

self

Fitted estimator

fit_predict(X, y=None, axis=1)

Fit segmentation to data and return it.

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.

final predict(X, axis=1)

Create amd return segmentation of X.

Parameters:

XOne of VALID_SERIES_INPUT_TYPES

Input time series

axisint, default = None

Axis along which to segment if passed a multivariate series (2D input) with n_channels time series. If axis is 0, it is assumed each row is a time series and each column is a time point. i.e. the shape of the data is (n_timepoints,n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

List

Either a list of indexes of X indicating where each segment begins or a list of integers of len(X) indicating which segment each time point belongs to.

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.

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.

classmethod to_classification(change_points: list[int], length: int)

Convert change point locations to a classification vector.

Change point detection results can be treated as classification with true change point locations marked with 1’s at position of the change point and remaining non-change point locations being 0’s.

For example change points [2, 8] for a time series of length 10 would result in: [0, 0, 1, 0, 0, 0, 0, 0, 1, 0].

classmethod to_clusters(change_points: list[int], length: int)

Convert change point locations to a clustering vector.

Change point detection results can be treated as clustering with each segment separated by change points assigned a distinct dummy label.

For example change points [2, 8] for a time series of length 10 would result in: [0, 0, 1, 1, 1, 1, 1, 1, 2, 2].