Context

class Context

Contains all the data + properties the user has passed to a check/suite, and validates it seamlessly.

Parameters

trainVisionData , default: None

Dataset or DataFrame object, representing data an estimator was fitted on

testVisionData , default: None

Dataset or DataFrame object, representing data an estimator predicts on

modelBasicModel , default: None

A scikit-learn-compatible fitted estimator instance

model_name: str , default: ‘’

The name of the model

scorersMapping[str, Metric] , default: None

dict of scorers names to a Metric

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: ‘cpu’

processing unit for use

random_stateint

A seed to set for pseudo-random functions

n_samplesint, default: None

__init__(train: Optional[VisionData] = None, test: Optional[VisionData] = None, 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] = None, train_predictions: Optional[Union[List[Tensor], Tensor]] = None, test_predictions: Optional[Union[List[Tensor], Tensor]] = None)

__new__(*args, **kwargs)

Attributes

Context.device	Return device specified by the user.
Context.model	Return & validate model if model exists, otherwise raise error.
Context.model_name	Return model name.
Context.static_predictions	Return the static_predictions.
Context.test	Return test if exists, otherwise raise error.
Context.train	Return train if exists, otherwise raise error.

Methods

Context.assert_predictions_valid([kind])	Assert that for given DatasetKind the model & dataset infer_on_batch return predictions in right format.
Context.assert_task_type(*expected_types)	Assert task_type matching given types.
Context.get_data_by_kind(kind)	Return the relevant VisionData by given kind.
Context.get_is_sampled_footnote([kind])	Get footnote to display when the datasets are sampled.
Context.have_test()	Return whether there is test dataset defined.

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DetectionData.validate_shared_label

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Context.device