ElbowClassSum

ElbowClassSum

class ElbowClassSum(distance=None)[source]

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] by calculating the distance between each class centroid. 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:

distance: sktime pairwise panel transform, str, or callable, optional, default=None

if panel transform, will be used directly as the distance in the algorithm default None = euclidean distance on flattened series, FlatDist(ScipyDist()) if str, will behave as FlatDist(ScipyDist(distance)) = scipy dist on flat series if callable, must be univariate nested_univ x nested_univ -> 2D float np.array

Attributes:

channels_selected_list of integer

List of variables/channels selected by the estimator integers (iloc reference), referring to variables/channels by order

channels_selected_idx_list of pandas compatible index elements

List of variables/channels selected by the estimator if data are index-less (no channel/var names), identical to channels_selected

distance_frame_DataFrame

distance matrix of the class centroids pair and channels.

shape = [n_channels, n_class_centroids_pairs]

Table 1 provides an illustration in [1].

train_time_int

Time taken to train the ECS.

Notes

Original repository: https://github.com/mlgig/Channel-Selection-MTSC

References

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

Examples

>>> from sktime.transformations.channel_selection import ElbowClassSum
>>> from sktime.utils._testing.panel import make_classification_problem
>>> X, y = make_classification_problem(n_columns=3, n_classes=3, random_state=42)
>>> cs = ElbowClassSum()
>>> cs.fit(X, y)
ElbowClassSum(...)
>>> Xt = cs.transform(X)

Any sktime compatible distance can be used, e.g., DTW distance:

>>> from sktime.dists_kernels import DtwDist
>>>
>>> cs = ElbowClassSum(distance=DtwDist())
>>> cs.fit(X, y)
ElbowClassSum(...)
>>> Xt = cs.transform(X)

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.