PercentOfNulls#
- class PercentOfNulls[source]#
Percent of ‘Null’ values in each column.
- Parameters
- columnsUnion[Hashable, List[Hashable]] , default: None
List of columns to check, if none given checks all columns Except ignored ones.
- ignore_columnsUnion[Hashable, List[Hashable]] , default: None
List of columns to ignore, if none given checks based on columns variable.
- max_features_to_showint , default: 5
maximum features with to show, showing top features based on percent of nulls.
- aggregation_method: str, default: ‘max’
Argument for the reduce_output functionality, decides how to aggregate the vector of per-feature scores into a single aggregate score. The aggregate 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 per-feature scores and feature importance, minus the L2 norm of feature importance alone, specifically, ||FI + PER_FEATURE_SCORES|| - ||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 resulting score will affect the model’s performance. ‘mean’: Mean of all per-feature scores. ‘max’: Maximum of all the per-feature scores. ‘none’: No averaging. Return a dict with a per-feature score for each feature. ‘top_5’ No averaging. Return a dict with a per-feature score for top 5 features based on feature importance.
- n_samplesint , default: 100_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, aggregation_method='max', n_samples: int = 100000, 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 - percent of null values in each column is not greater than the 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 logic. |
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Serialize check instance to JSON string. |
