Context#

class Context[source]#

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

Parameters

train_datasetUnion[TextData, None] , default: None: TextData object, representing data an estimator was fitted on
test_datasetUnion[TextData, None] , default: None: TextData object, representing data an estimator predicts on
with_displaybool , default: True: flag that determines if checks will calculate display (redundant in some checks).
train_predUnion[TTextPred, None] , default: None: predictions on train dataset
test_predUnion[TTextPred, None] , default: None: predictions on test dataset
train_probaUnion[TTextProba, None] , default: None: probabilities on train dataset
test_probaUnion[TTextProba, None] , default: None: probabilities on test dataset
model_classesOptional[List] , default: None: list of classes known to the model
random_state: int, default 42: A seed to set for pseudo-random functions , primarily sampling.

__init__(train_dataset: Optional[TextData] = None, test_dataset: Optional[TextData] = None, with_display: bool = True, train_pred: Optional[Union[Sequence[Union[str, int]], Sequence[Sequence[Union[str, int]]], Sequence[Sequence[Tuple[str, int, int, float]]]]] = None, test_pred: Optional[Union[Sequence[Union[str, int]], Sequence[Sequence[Union[str, int]]], Sequence[Sequence[Tuple[str, int, int, float]]]]] = None, train_proba: Optional[Sequence[Sequence[float]]] = None, test_proba: Optional[Sequence[Sequence[float]]] = None, model_classes: Optional[List] = None, random_state: int = 42)[source]#

Attributes

`Context.model`	Return model if exists, otherwise raise error.
`Context.model_classes`	Return ordered list of possible label classes for classification tasks.
`Context.model_name`	Return the name of the model.
`Context.observed_classes`	Return the observed classes in both train and test.
`Context.task_type`	Return task type if model & train_dataset & label exists.
`Context.test`	Return test if exists, otherwise raise error.
`Context.train`	Return train if exists, otherwise raise error.
`Context.with_display`	Return the with_display flag.

Methods

`Context.assert_metadata`(text_data)	Assert that metadata exists.
`Context.assert_properties`(text_data)	Assert that properties exists.
`Context.assert_task_type`(*expected_types)	Assert task_type matching given types.
`Context.finalize_check_result`(check_result, ...)	Run final processing on a check result which includes validation, conditions processing and sampling footnote.
`Context.get_data_by_kind`(kind)	Return the relevant Dataset by given kind.
`Context.get_scorers`([scorers, use_avg_defaults])	Return initialized & validated scorers if provided or default scorers otherwise.
`Context.get_single_scorer`([scorer, ...])	Return initialized & validated scorer if provided or a default scorer otherwise.
`Context.have_test`()	Return whether there is test_dataset dataset defined.
`Context.infer_task_type`(train_dataset, ...)	Infer the task type.

context

Context.model