Suite.run#
- Suite.run(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]] = 'cpu', random_state: int = 42, n_samples: Optional[int] = 10000) SuiteResult[source]#
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
- scorersMapping[str, Metric] , default None
dict of scorers names to scorer sklearn_name/function
- scorers_per_classMapping[str, Metric], default None
dict of scorers for classification without averaging of the classes See <a href= “https://scikit-learn.org/stable/modules/model_evaluation.html#from-binary-to-multiclass-and-multilabel”> scikit-learn docs</a>
- deviceUnion[str, torch.device], default: None
processing unit for use
- random_stateint
A seed to set for pseudo-random functions
- n_samplesint, default: 10,000
number of samples to draw from the dataset.
- Returns
- ——-
- SuiteResult
All results by all initialized checks