full_suite#
- full_suite(**kwargs) Suite[source]#
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, model_name: str = '') 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_name: str , 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 <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: ‘cpu’
processing unit for use
- random_stateint
A seed to set for pseudo-random functions
- n_samplesOptional[int], default: None
number of samples
- with_displaybool , default: True
flag that determines if checks will calculate display (redundant in some checks).
- train_predictions: Optional[Dict[int, Union[Sequence[torch.Tensor], torch.Tensor]]] , default None
Dictionary of the model prediction over the train dataset (keys are the indexes).
- test_predictions: Optional[Dict[int, Union[Sequence[torch.Tensor], torch.Tensor]]] , default None
Dictionary of the model prediction over the test dataset (keys are the indexes).
- Returns
- SuiteResult
All results by all initialized checks