pyrregular.models.raindrop

Raindrop. GNN-based model for time series classification.

Module Attributes

raindrop_pipeline

This pipeline applies Raindrop.

Classes

RaindropWrapper(model, model_params[, ...])

class pyrregular.models.raindrop.RaindropWrapper(model, model_params, random_state=None)[source]

Bases: PyPOTSWrapper

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

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.raindrop.raindrop_pipeline = RaindropWrapper(model=<class 'pypots.classification.raindrop.model.Raindrop'>,                 model_params={'batch_size': 32, 'd_ffn': 256, 'device': None,                               'dropout': 0.3, 'epochs': 1000, 'n_heads': 2,                               'n_layers': 2, 'num_workers': 0, 'patience': 50})

This pipeline applies Raindrop.