TSFELTransformer

TSFELTransformer

class TSFELTransformer(features=None, fs=None, window_size=None, overlap=0, verbose=1, kwargs=None)[source]

TSFEL transformer to extract features by domain or specific feature names.

This transformer uses features parameter to extract features in following ways:

1. By domain: Pass a domain (‘statistical’, ‘temporal’, ‘spectral’, ‘fractal’)

2. By feature names: Pass feature function names (e.g., ‘abs_energy’, ‘auc’)

3. Mixed: Pass a list containing both domain strings and feature names

For domain-based extraction, uses TSFEL’s time_series_features_extractor. For individual features, calls the feature functions directly from tsfel.feature_extraction.features.

See tsfel documentation for available options for features and parameters. https://tsfel.readthedocs.io/en/latest/descriptions/feature_list.html

Parameters:

- featuresstr, list of str, or None, optional (default=None)

Features to extract. Can be:

• A domain string: ‘statistical’, ‘temporal’, ‘spectral’, ‘fractal’

• A list of feature function names: [‘abs_energy’, ‘auc’, ‘autocorr’]

• A list mixing domains and features: [‘statistical’, ‘abs_energy’]

• None: extract all features from all domains

- fsfloat, sampling frequency

- window_sizeint, size of windows for feature extraction

- overlapfloat, overlap between windows (0-1)

- verboseint, verbosity level (0 or 1)

kwargsdict, optional (default=None)

Dictionary of additional keyword arguments that will be forwarded to TSFEL’s underlying feature extraction functions. Use this parameter to specify feature-specific options (e.g., “percentile” for “ecdf_percentile_count”). Refer to TSFEL’s documentation for allowed options for each feature and domain. If not provided, defaults from TSFEL functions will apply.

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> from sktime.transformations.tsfel import TSFELTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> # Extract all statistical domain features
>>> transformer = TSFELTransformer(
...     features="statistical", verbose=0
... )
>>> features = transformer.fit_transform(y)
>>> # Access TSFEL output for feature
>>> transformer['statistical'].iloc[0]
>>> # Extract feature with custom parameters
>>> transformer = TSFELTransformer(
...     features=["ecdf_percentile_count"],
...     verbose=0,
...     kwargs={"percentile": [0.6, 0.9, 1.0]}
... )
>>> features = transformer.fit_transform(y)
>>> transformer['ecdf_percentile_count'].iloc[0]

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.