class CategoryMismatchTrainTest[source]#

Find new categories in the test set.

columnsUnion[Hashable, List[Hashable]] , default: None

Columns to check, if none are given checks all columns except ignored ones.

ignore_columnsUnion[Hashable, List[Hashable]] , default: None

Columns to ignore, if none given checks based on columns variable.

max_features_to_showint , default: 5

maximum features with new categories to show

max_new_categories_to_showint , default: 5

maximum new categories to show in feature

aggregation_method: str, default: ‘max’

argument for the reduce_output functionality, decides how to aggregate the drift scores for a collective score. The collective score value is between 0 and 1 for all methods other than l2_combination. Possible values are: ‘l2_weighted’: L2 norm over the combination of drift scores and feature importance, minus the L2 norm of feature importance alone, specifically, ||FI + DRIFT|| - ||FI||. This method returns a value between 0 and sqrt(n_features). ‘weighted’: Weighted mean based on feature importance, provides a robust estimation on how much the drift will affect the model’s performance. ‘mean’: Mean of all drift scores. ‘max’: Maximum of all the features drift scores. ‘none’: No averaging. Return a dict with a drift score for each feature. ‘top_5’ No averaging. Return a dict with a drift score for top 5 features based on feature importance.

n_samplesint , default: 10_000_000

number of samples to use for this check.

random_stateint, default: 42

random seed for all check internals.

__init__(columns: Optional[Union[Hashable, List[Hashable]]] = None, ignore_columns: Optional[Union[Hashable, List[Hashable]]] = None, max_features_to_show: int = 5, max_new_categories_to_show: int = 5, aggregation_method='max', n_samples: int = 10000000, random_state: int = 42, **kwargs)[source]#
__new__(*args, **kwargs)#



Add new condition function to the check.


Add condition - require column's number of different new categories to be less or equal to threshold.


Add condition - require column's ratio of instances with new categories to be less or equal to threshold.


Remove all conditions from this check instance.


Run conditions on given result.


Return check configuration (conditions' configuration not yet supported).


Return an aggregated drift score based on aggregation method defined.


Return check object from a CheckConfig object.

CategoryMismatchTrainTest.from_json(conf[, ...])

Deserialize check instance from JSON string.


Return check metadata.

Name of class in split camel case.


Return parameters to show when printing the check.


Return an aggregated drift score based on aggregation method defined.


Remove given condition by index., ...)

Run check.


Run check.


Serialize check instance to JSON string.