full_suite

full_suite(**kwargs) → Suite

Create a suite that includes many of the implemented checks, for a quick overview of your model and data.

run(self, train_dataset: Optional[VisionData] = None, test_dataset: Optional[VisionData] = None, model: Optional[Module] = None, scorers: Optional[Mapping[str, Metric]] = None, scorers_per_class: Optional[Mapping[str, Metric]] = None, device: Optional[Union[str, device]] = None, random_state: int = 42, with_display: bool = True, n_samples: Optional[int] = 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, model_name: str = '', run_single_dataset: Optional[str] = None) → SuiteResult

Run all checks.

Parameters

train_dataset: Optional[VisionData] , default None

object, representing data an estimator was fitted on

test_datasetOptional[VisionData] , default None

object, representing data an estimator predicts on

modelnn.Module , default None

A scikit-learn-compatible fitted estimator 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).

run_single_dataset: Optional[str], default None

‘Train’, ‘Test’ , or None to run on both train and test.

Returns

SuiteResult

All results by all initialized checks

previous

model_evaluation

next

datasets