ElbowClassSum

class ElbowClassSum(distance: str = 'euclidean', prototype_type: str = 'mean', mean_center: bool = False)

Elbow Class Sum (ECS) transformer to select a subset of channels/variables.

Overview: From the input of multivariate time series data, create a distance matrix [1, 2] by calculating the distance between each class prototype. The ECS selects the subset of channels using the elbow method, which maximizes the distance between the class centroids by aggregating the distance for every class pair across each channel.

Note: Channels, variables, dimensions, features are used interchangeably in literature. E.g., channel selection = variable selection.

Parameters:

distancestr

Distance metric to use for creating the class prototype. Default: ‘euclidean’

prototype_typestr

Type of class prototype to use for representing a class. Default: ‘mean’

mean_centerbool

If True, mean centering is applied to the class prototype. Default: False

Attributes:

prototypeDataFrame

A multivariate time series representation for entire dataset.

distance_frameDataFrame

Distance matrix for each class pair. shape = [n_channels, n_class_pairs]

channels_selected_list

List of selected channels.

rank: list

Rank of channels based on the distance between class prototypes.

Notes

More details on class prototype can be found in [1] and [2]. Original repository: 1. https://github.com/mlgig/Channel-Selection-MTSC 2. https://github.com/mlgig/ChannelSelectionMTSC

References

..[1]: Bhaskar Dhariyal et al. “Fast Channel Selection for Scalable Multivariate Time Series Classification.” AALTD, ECML-PKDD, Springer, 2021 ..[2]: Bhaskar Dhariyal et al. “Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification”, DAMI, ECML, Springer, 2023

Examples

>>> from aeon.transformations.collection.channel_selection import ElbowClassSum
>>> import numpy as np
>>> X = np.random.random((20,6,30))
>>> y = np.array([1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2])
>>> cs = ElbowClassSum()
>>> cs.fit(X, y)
ElbowClassSum()
>>> Xt = cs.transform(X)

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X[, y])	Fit transformer to X, optionally using y if supervised.
fit_transform(X[, y])	Fit to data, then transform 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_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.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
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.
transform(X[, y])	Transform X and return a transformed version.

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)

Fit transformer to X, optionally using y if supervised.

State change:

Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, 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.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

selfa fitted instance of the estimator

fit_transform(X, y=None)

Fit to data, then transform it.

Fits the transformer to X and y and returns a transformed version of X.

State change:

Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, 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.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

transformed version of X

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.

inverse_transform(X, y=None)

Inverse transform X and return an inverse transformed version.

Currently it is assumed that only transformers with tags

“input_data_type”=”Series”, “output_data_type”=”Series”,

can have an inverse_transform.

State required:

Requires state to be “fitted”.

Accesses in self:

_is_fitted : must be True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, 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.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

inverse transformed version of X

of the same type as X

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.

transform(X, y=None)

Transform X and return a transformed version.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, 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.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

transformed version of X