tsfresh.convenience package¶
Submodules¶
tsfresh.convenience.relevant_extraction module¶
-
tsfresh.convenience.relevant_extraction.
extract_relevant_features
(timeseries_container, y, X=None, feature_extraction_settings=None, feature_selection_settings=None, column_id=None, column_sort=None, column_kind=None, column_value=None)[source]¶ High level convenience function to extract time series features from timeseries_container. Then return feature matrix X possibly augmented with relevent features with respect to target vector y.
For more details see the documentation of
extract_features()
andselect_features()
.Examples
>>> from tsfresh.examples import load_robot_execution_failures >>> from tsfresh import extract_relevant_features >>> df, y = load_robot_execution_failures() >>> X = extract_relevant_features(df, y, column_id='id', column_sort='time')
Parameters: - timeseries_container – See parameter timeseries_container in
extract_features()
- y – See parameter y in
select_features()
- X – See parameter X in
select_features()
- column_id – See parameter column_id in
extract_features()
- column_sort – See parameter column_sort in
extract_features()
- column_kind – See parameter column_kind in
extract_features()
- column_value – See parameter column_value in
extract_features()
- feature_extraction_settings – See parameter feature_extraction_settings in
extract_features()
- feature_selection_settings – See parameter feature_selection_settings in
select_features()
Returns: Feature matrix X, possibly extended with relevant time series features.
- timeseries_container – See parameter timeseries_container in
Module contents¶
The convenience
submodule contains methods that allow the user to extract and filter features
conveniently.