IdentifierLabelCorrelation

class IdentifierLabelCorrelation

Check if identifiers (Index/Date) can be used to predict the label.

Parameters

ppscore_paramsany , default: None

dictionary containing params to pass to ppscore predictor

n_samplesint , default: 1_000_000

number of samples to use for this check.

random_stateint, default: 42

random seed for all check internals.

__init__(ppscore_params=None, n_samples: int = 1000000, random_state: int = 42, **kwargs)

__new__(*args, **kwargs)

Methods

IdentifierLabelCorrelation.add_condition(...)	Add new condition function to the check.
IdentifierLabelCorrelation.add_condition_pps_less_or_equal([...])	Add condition - require columns' pps to be less or equal to threshold.
IdentifierLabelCorrelation.clean_conditions()	Remove all conditions from this check instance.
IdentifierLabelCorrelation.conditions_decision(result)	Run conditions on given result.
IdentifierLabelCorrelation.config([...])	Return check configuration (conditions' configuration not yet supported).
IdentifierLabelCorrelation.from_config(conf)	Return check object from a CheckConfig object.
IdentifierLabelCorrelation.from_json(conf[, ...])	Deserialize check instance from JSON string.
IdentifierLabelCorrelation.metadata([...])	Return check metadata.
IdentifierLabelCorrelation.name()	Name of class in split camel case.
IdentifierLabelCorrelation.params([...])	Return parameters to show when printing the check.
IdentifierLabelCorrelation.remove_condition(index)	Remove given condition by index.
IdentifierLabelCorrelation.run(dataset[, ...])	Run check.
IdentifierLabelCorrelation.run_logic(...)	Run check.
IdentifierLabelCorrelation.to_json([indent])	Serialize check instance to JSON string.

Examples

Identifier Label Correlation

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FeatureFeatureCorrelation.to_json

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IdentifierLabelCorrelation.add_condition