NewCategoryTrainTest#
- class NewCategoryTrainTest[source]#
Find new categories in the test set.
- Parameters
- 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: Optional[str], default: ‘max’
Argument for the reduce_output functionality, decides how to aggregate the vector of per-feature scores into a single aggregated score. The aggregated score value is between 0 and 1 for all methods. Possible values are: ‘l3_weighted’: Default. L3 norm over the ‘per-feature scores’ vector weighted by the feature importance, specifically, sum(FI * PER_FEATURE_SCORES^3)^(1/3). This method takes into account the feature importance yet puts more weight on the per-feature scores. This method is recommended for most cases. ‘l5_weighted’: Similar to ‘l3_weighted’, but with L5 norm. Puts even more emphasis on the per-feature scores and specifically on the largest per-feature scores returning a score closer to the maximum among the per-feature scores. ‘weighted’: Weighted mean of per-feature scores based on feature importance. ‘max’: Maximum of all the per-feature scores. None: No averaging. Return a dict with a per-feature score for each feature.
- 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: Optional[str] = 'max', n_samples: int = 10000000, random_state: int = 42, **kwargs)[source]#
- __new__(*args, **kwargs)#
Methods
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Add new condition function to the check. |
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Add condition - require column's number of different new categories to be less or equal to threshold. |
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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. |
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Run conditions on given result. |
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Return check configuration (conditions' configuration not yet supported). |
Return an aggregated drift score based on aggregation method defined. |
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Return check object from a CheckConfig object. |
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Deserialize check instance from JSON string. |
Return True if the check reduce_output is better when it is greater. |
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Return check metadata. |
Name of class in split camel case. |
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Return parameters to show when printing the check. |
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Return an aggregated drift score based on aggregation method defined. |
Remove given condition by index. |
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Run check. |
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Run check. |
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Serialize check instance to JSON string. |