DilationMappingTransformer

DilationMappingTransformer

class DilationMappingTransformer(dilation=2)[source]

Dilation mapping transformer.

A transformer for applying an index grid dilation mapping to time series data, in the terminology of [1].

This transformation is motivated by kernel dilation, it reorders the timesteps of a time series to simulate the effect of dilation. For instance, in a pipeline, it enables a dilation-like effect for downstream models that do not inherently support such a feature.

Mathematically, the mapping operates on sequences \(x_1, \dots, x_k\). The dilation with factor \(d\) is defined as the sequence \(x_1, x_{1+d}, x_{1+2d}, \dots, x_2, x_{2+d}, x_{2+2d}, \dots, x_d, x_{2d}, \dots\), where the subsequences with grid spacing \(d\) are maximal.

The resulting sequence is of equal length to the input sequence.

This transformer reorders the values, and resets the sequence index to a RangeIndex, if the mtype is pandas based.

Parameters:

dilationint, default=2

The dilation factor. Determines the spacing between original data points in the transformed series. Must be an integer greater than 0. A dilation of 1 means no change, while higher values increase the spacing.

Attributes:

is_fitted

Whether fit has been called.

References

[1]

Patrick Schäfer and Ulf Leser, “WEASEL 2.0–A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification”, 2023, arXiv preprint arXiv:2301.10194.

Examples

>>> from sktime.transformations.dilation_mapping import \
...     DilationMappingTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> y_transform = DilationMappingTransformer(dilation=2).fit_transform(y)

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.