EAggloSegmenter

class EAggloSegmenter(member=None, alpha=1.0, penalty=None)[source]

Hierarchical agglomerative estimation of multiple change points.

E-Agglo is a non-parametric clustering approach for multivariate timeseries[R3a0a2f9eddee-1]_, where neighboring segments are sequentially merged_ to maximize a goodness-of-fit statistic. Unlike most general purpose agglomerative clustering algorithms, this procedure preserves the time ordering of the observations.

This method can detect distributional change within an independent sequence, and does not make any distributional assumptions (beyond the existence of an alpha-th moment). Estimation is performed in a manner that simultaneously identifies both the number and locations of change points.

Parameters:
memberarray_like (default=None)

Assigns points to the initial cluster membership, therefore the first dimension should be the same as for data. If None it will be initialized to dummy vector where each point is assigned to separate cluster.

alphafloat (default=1.0)

Fixed constant alpha in (0, 2] used in the divergence measure, as the alpha-th absolute moment, see equation (4) in [1].

penaltystr or callable or None (default=None)

Function that defines a penalization of the sequence of goodness-of-fit statistic, when overfitting is a concern. If None not penalty is applied. Could also be an existing penalty name, either len_penalty or mean_diff_penalty.

Attributes:
merged_array_like

2D array_like outlining which clusters were merged_ at each step.

gof_float

goodness-of-fit statistic for current clsutering.

cluster_array_like

1D array_like specifying which cluster each row of input data X belongs to.

Notes

Based on the work from [1].

References

[1] (1,2)

Matteson, David S., and Nicholas A. James. “A nonparametric approach for

multiple change point analysis of multivariate data.” Journal of the American Statistical Association 109.505 (2014): 334-345.

[2]

James, Nicholas A., and David S. Matteson. “ecp: An R package for

nonparametric multiple change point analysis of multivariate data.” arXiv preprint arXiv:1309.3295 (2013).

Examples

>>> from aeon.testing.utils.data_gen import piecewise_normal_multivariate
>>> from aeon.segmentation import EAggloSegmenter
>>> X = piecewise_normal_multivariate(means=[[1, 3], [4, 5]], lengths=[3, 4],
... random_state = 10)
>>> model = EAggloSegmenter()
>>> model.fit_predict(X)
array([0, 0, 0, 1, 1, 1, 1])

Methods

check_is_fitted()

Check if the estimator has been fitted.

clone()

Obtain a clone of the object with same hyper-parameters.

clone_tags(estimator[, tag_names])

Clone/mirror tags from another estimator as dynamic override.

create_test_instance([parameter_set])

Construct Estimator instance if possible.

create_test_instances_and_names([parameter_set])

Create list of all test instances and a list of names for them.

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[, tag_value_default])

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()

Get metadata routing of this object.

get_param_defaults()

Get parameter defaults for the object.

get_param_names()

Get parameter names for the object.

get_params([deep])

Get parameters for this estimator.

get_tag(tag_name[, tag_value_default, ...])

Get tag value from estimator class.

get_tags()

Get tags from estimator class.

get_test_params()

Test parameters.

is_composite()

Check if the object is composite.

load_from_path(serial)

Load object from file location.

load_from_serial(serial)

Load object from serialized memory container.

predict(X[, axis])

Create amd return segmentation of X.

reset()

Reset the object to a clean post-init state.

save([path])

Save serialized self to bytes-like object or to (.zip) file.

set_fit_request(*[, axis])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this object.

set_predict_request(*[, axis])

Request metadata passed to the predict method.

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.
classmethod get_test_params() → List[Dict][source]

Test parameters.

check_is_fitted()[source]

Check if the estimator has been fitted.

Raises:
NotFittedError

If the estimator has not been fitted yet.

clone()[source]

Obtain a clone of the object with same hyper-parameters.

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)).

Returns:
instance of type(self), clone of self (see above)
clone_tags(estimator, tag_names=None)[source]

Clone/mirror tags from another estimator as dynamic override.

Parameters:
estimatorobject

Estimator inheriting from :class:BaseEstimator.

tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:
Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]

Construct Estimator instance if possible.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:
instanceinstance of the class with default parameters.

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]

Create list of all test instances and a list of names for them.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:
objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i]).

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}.

parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

fit(X, y=None, axis=None)[source]

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_INPUT_TYPES

Input time series

yOne of VALID_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=None)[source]

Fit segmentation to data and return it.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]

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

Parameters:
tag_namestr

Name of tag value.

tag_value_defaultany type

Default/fallback value if tag is not found.

Returns:
tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

See also

get_tag

Get a single tag from an object.

get_tags

Get all tags from an object.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True
classmethod get_class_tags()[source]

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

Returns:
collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_fitted_params(deep=True)[source]

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:
deepbool, default=True

Whether to return fitted parameters of components.

  • If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).

  • If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:
fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

  • always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object

  • if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

  • if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc.

get_metadata_routing()[source]

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

classmethod get_param_defaults()[source]

Get parameter defaults for the object.

Returns:
default_dict: dict with str keys

keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__.

classmethod get_param_names()[source]

Get parameter names for the object.

Returns:
param_names: list of str, alphabetically sorted list of parameter names of cls
get_params(deep=True)[source]

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, tag_value_default=None, raise_error=True)[source]

Get tag value from estimator class.

Uses dynamic tag overrides.

Parameters:
tag_namestr

Name of tag to be retrieved.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found.

raise_errorbool

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

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 i.e. if tag_name is not in self.get_tags(
).keys()

See also

get_tags

Get all tags from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

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

Get tags from estimator class.

Includes the dynamic tag overrides.

Returns:
dict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

See also

get_tag

Get a single tag from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()
is_composite()[source]

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:
composite: bool

Whether self contains a parameter which is BaseObject.

property is_fitted[source]

Whether fit has been called.

classmethod load_from_path(serial)[source]

Load object from file location.

Parameters:
serialobject

Result of ZipFile(path).open(“object).

Returns:
deserialized self resulting in output at path, of cls.save(path)
classmethod load_from_serial(serial)[source]

Load object from serialized memory container.

Parameters:
serialobject

First element of output of cls.save(None).

Returns:
deserialized self resulting in output serial, of cls.save(None).
predict(X, axis=None)[source]

Create amd return segmentation of X.

Parameters:
XOne of VALID_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()[source]

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail 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 hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[source]

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:
pathNone or file location (str or Path).

if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:
if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file.
set_fit_request(*, axis: bool | None | str = '$UNCHANGED$') → EAggloSegmenter[source]

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)[source]

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

BaseObject parameters

Returns:
selfreference to self (after parameters have been set)
set_predict_request(*, axis: bool | None | str = '$UNCHANGED$') → EAggloSegmenter[source]

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in predict.

Returns:
selfobject

The updated object.

set_tags(**tag_dict)[source]

Set dynamic tags to given values.

Parameters:
**tag_dictdict

Dictionary of tag name : tag value pairs.

Returns:
Self

Reference to self.

Notes

Changes object state by setting tag values in tag_dict as dynamic tags in self.

classmethod to_classification(change_points: List[int], length: int)[source]

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)[source]

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].