WeakSegmentsPerformance.run#
- WeakSegmentsPerformance.run(dataset: VisionData, model: Optional[Module] = None, model_name: str = '', scorers: Optional[Mapping[str, Metric]] = None, scorers_per_class: Optional[Mapping[str, Metric]] = None, device: Optional[Union[str, device]] = None, random_state: int = 42, n_samples: Optional[int] = 10000, with_display: bool = True, predictions: Optional[Dict[int, Union[Sequence[Tensor], Tensor]]] = None, train_predictions: Optional[Dict[int, Union[Sequence[Tensor], Tensor]]] = None, test_predictions: Optional[Dict[int, Union[Sequence[Tensor], Tensor]]] = None, train_properties: Optional[Dict[int, Dict[PropertiesInputType, Dict[str, Any]]]] = None, test_properties: Optional[Dict[int, Dict[PropertiesInputType, Dict[str, Any]]]] = None) CheckResult[source]#
Run check.
- Parameters
- dataset: VisionData
VisionData object to process
- model: Optional[nn.Module] , default None
pytorch neural network module instance
- model_namestr , default: ‘’
The name of the model
- scorersOptional[Mapping[str, Metric]] , default: None
dict of scorers names to a Metric
- scorers_per_classOptional[Mapping[str, Metric]] , default: None
dict of scorers for classification without averaging of the classes. See scikit-learn docs.
- deviceUnion[str, torch.device], default: ‘cpu’
processing unit for use
- random_stateint
A seed to set for pseudo-random functions
- with_displaybool , default: True
flag that determines if checks will calculate display (redundant in some checks).
- train_predictionsOptional[Dict[int, Union[Sequence[torch.Tensor], torch.Tensor]]] , default None
Dictionary of the model prediction over the train dataset (keys are the indexes).
- test_predictionsOptional[Dict[int, Union[Sequence[torch.Tensor], torch.Tensor]]] , default None
Dictionary of the model prediction over the test dataset (keys are the indexes).