SubsequenceExtractionTransformer

class SubsequenceExtractionTransformer(aggregate_fn, subseq_len, kwargs=None, selector='max')[source]

Extract contiguous subsequences of specified length based on rolling aggregates.

A transformer for the extraction of contiguous subsequences of specified length based on maximal/minimal rolling window aggregates.

Given a sequence \(\\{x_1, x_2, \cdots, x_n \\}\) and subseq_len integer \(k\) such that \(0 < k \leq n\), the transformer’s task is to find index \(i\) satisfying \(1 \leq i \leq i + k - 1 \leq n\) such that for given aggregate_fn \(A: \mathbb{R}^k \longrightarrow \mathbb{R}\):

\(A(x_{i}, \cdots, x_{i+k-1})\) is maximal when selector = 'max', and
\(A(x_{i}, \cdots, x_{i+k-1})\) is minimal when selector = 'min'.

The maximum sum subarray problem is a special case and can be obtained by setting aggregate_fn = np.sum and selector = 'max'.

Parameters:

aggregate_fncallable of signature np.ndarray -> float: Callable function in numpy used to aggregate values in contiguous subsequence to a scalar.
subseq_lenint: Length of the subsequence in .iloc units. Must be less than the lengths of all input series.
kwargsdict, default: None: Dictionary of additional keyword arguments to pass to aggregate_fn.
selector{‘max’, ‘min’}, default: ‘max’: Function used to decide which subsequence to return from the set of scalars or primitives.

Attributes:

is_fitted: Whether fit has been called.

References

Jon Bentley. 1984. Programming pearls: algorithm design techniques. Commun. ACM 27, 9 (Sept. 1984), 865-873. https://doi.org/10.1145/358234.381162

Examples

>>> import numpy as np
>>> from sktime.transformations.subsequence_extraction import (
...     SubsequenceExtractionTransformer
... )
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> X = _make_hierarchical(same_cutoff=False)
>>> subseq_extract = SubsequenceExtractionTransformer(
...     aggregate_fn = np.sum,
...     subseq_len = 3,
... )
>>> subseq_extract.fit(X)
SubsequenceExtractionTransformer(...)
>>> X_transformed = subseq_extract.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.