Suite.run#

Suite.run(train_dataset: Optional[Union[Dataset, DataFrame]] = None, test_dataset: Optional[Union[Dataset, DataFrame]] = None, model: Optional[BasicModel] = None, features_importance: Optional[Series] = None, feature_importance_force_permutation: bool = False, feature_importance_timeout: Optional[int] = None, scorers: Optional[Mapping[str, Union[str, Callable]]] = None, scorers_per_class: Optional[Mapping[str, Union[str, Callable]]] = None) SuiteResult[source]#

Run all checks.

Parameters
train_dataset: Optional[Union[Dataset, pd.DataFrame]] , default None

object, representing data an estimator was fitted on

test_datasetOptional[Union[Dataset, pd.DataFrame]] , default None

object, representing data an estimator predicts on

modelBasicModel , default None

A scikit-learn-compatible fitted estimator instance

features_importancepd.Series , default None

pass manual features importance

feature_importance_force_permutationbool , default None

force calculation of permutation features importance

feature_importance_timeoutint , default None

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>

Returns
——-
SuiteResult

All results by all initialized checks