Context#

class Context[source]#

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

Parameters
trainUnion[Dataset, pd.DataFrame] , default: None

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

testUnion[Dataset, pd.DataFrame] , 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

features_importance: pd.Series , default: None

pass manual features importance

feature_importance_force_permutationbool , default: False

force calculation of permutation features importance

feature_importance_timeoutint , default: 120

timeout in second for the permutation features importance calculation

scorersMapping[str, Union[str, Callable]] , default: None

dict of scorers names to scorer sklearn_name/function

scorers_per_classMapping[str, Union[str, Callable]] , 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>

__init__(train: Optional[Union[Dataset, DataFrame]] = None, test: Optional[Union[Dataset, DataFrame]] = None, model: Optional[BasicModel] = None, model_name: str = '', features_importance: Optional[Series] = None, feature_importance_force_permutation: bool = False, feature_importance_timeout: int = 120, scorers: Optional[Mapping[str, Union[str, Callable]]] = None, scorers_per_class: Optional[Mapping[str, Union[str, Callable]]] = None)[source]#
__new__(*args, **kwargs)#

Attributes

Context.features_importance

Return features importance, or None if not possible.

Context.features_importance_type

Return feature importance type if feature importance is available, else None.

Context.model

Return & validate model if model exists, otherwise raise error.

Context.model_name

Return model name.

Context.task_type

Return task type if model & train & label exists.

Context.test

Return test if exists, otherwise raise error.

Context.train

Return train if exists, otherwise raise error.

Methods

Context.assert_classification_task()

Assert the task_type is classification.

Context.assert_regression_task()

Assert the task type is regression.

Context.assert_task_type(*expected_types)

Assert task_type matching given types.

Context.get_data_by_kind(kind)

Return the relevant Dataset by given kind.

Context.get_is_sampled_footnote(n_samples[, ...])

Get footnote to display when the datasets are sampled.

Context.get_scorers([alternative_scorers, ...])

Return initialized & validated scorers in a given priority.

Context.get_single_scorer([...])

Return initialized & validated single scorer in a given priority.

Context.have_test()

Return whether there is test dataset defined.