Context

class Context

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

Parameters

train: Union[Dataset, pd.DataFrame] , default: None

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

test: Union[Dataset, pd.DataFrame] , default: None

Dataset or DataFrame object, representing data an estimator predicts on

model: BasicModel , 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>

y_pred_train: np.ndarray , default: None

Array of the model prediction over the train dataset.

y_pred_test: np.ndarray , default: None

Array of the model prediction over the test dataset.

y_proba_train: np.ndarray , default: None

Array of the model prediction probabilities over the train dataset.

y_proba_test: np.ndarray , default: None

Array of the model prediction probabilities over the test dataset.

__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, y_pred_train: Optional[ndarray] = None, y_pred_test: Optional[ndarray] = None, y_proba_train: Optional[ndarray] = None, y_proba_test: Optional[ndarray] = None)

__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.

previous

Dataset.train_test_split

next

Context.features_importance