pyrregular.models.rifc

RIFC Pipeline. Baseline classifier that uses RandomIntervalFeatureExtractor to extract simple statistical features from windows.

Module Attributes

rifc_pipeline

This pipeline applies RandomIntervalFeatureClassifier.

Classes

RandomIntervalFeatureClassifier([features, ...])

class pyrregular.models.rifc.RandomIntervalFeatureClassifier(features=(<function nanmean>, <function nanstd>, <function nanmin>, <function nanmax>, <function nanmedian>, <function _nanskew>, <function _nankurtosis>), random_state=None, n_intervals='log')[source]

Bases: BaseEstimator, ClassifierMixin

fit(X, y)[source]
predict(X)[source]
predict_proba(X)[source]
set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RandomIntervalFeatureClassifier

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

pyrregular.models.rifc.rifc_pipeline = Pipeline(steps=[('randomintervalfeatureclassifier',                  RandomIntervalFeatureClassifier())])

This pipeline applies RandomIntervalFeatureClassifier.