IdentifierLabelCorrelation#

class IdentifierLabelCorrelation[source]#

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)[source]#
__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#