LabelPropertyOutliers.run#

LabelPropertyOutliers.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, 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).