pyrregular.models.grud

GRUD. Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Module Attributes

grud_pipeline

This pipeline applies GRUD.

Classes

GrudWrapper(model, model_params[, random_state])

class pyrregular.models.grud.GrudWrapper(model, model_params, random_state=None)[source]

Bases: PyPOTSWrapper

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → GrudWrapper

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

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.grud.grud_pipeline = GrudWrapper(model=<class 'pypots.classification.grud.model.GRUD'>,             model_params={'batch_size': 32, 'device': None, 'epochs': 1000,                           'num_workers': 0, 'patience': 50,                           'rnn_hidden_size': 256})

This pipeline applies GRUD.