torch_eval_util#

Source code: sensai/torch/torch_eval_util.py

class TorchVectorRegressionModelEvaluationUtil(io_data: InputOutputData, evaluator_params: Optional[Union[RegressionEvaluatorParams, Dict[str, Any]]] = None, cross_validator_params: Optional[Union[VectorModelCrossValidatorParams, Dict[str, Any]]] = None, test_io_data: Optional[InputOutputData] = None)[source]#

Bases: RegressionModelEvaluation

Parameters:

  • io_data – the data set to use for evaluation. For evaluation purposes, this dataset usually will be split into training and test data according to the rules specified by evaluator_params. However, if test_io_data is specified, then this is taken to be the training data and test_io_data is taken to be the test data when creating evaluators for simple (single-split) evaluation.

  • evaluator_params – parameters with which to instantiate evaluators

  • cross_validator_params – parameters with which to instantiate cross-validators

  • test_io_data – optional test data (see io_data)