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sdv.metrics.tabular.ContinuousKLDivergence

class sdv.metrics.tabular.ContinuousKLDivergence(column_pairs_metric, **column_pairs_metric_kwargs)[source]

MultiColumnPairsMetric based on ColumnPairs ContinuousKLDivergence.

This approximates the KL divergence by binning the continuous values to turn them into categorical values and then computing the relative entropy. Afterwards normalizes the value applying 1 / (1 + KLD).

name

Name to use when reports about this metric are printed.

Type

str

goal

The goal of this metric.

Type

sdmetrics.goal.Goal

min_value

Minimum value or values that this metric can take.

Type

Union[float, tuple[float]]

max_value

Maximum value or values that this metric can take.

Type

Union[float, tuple[float]]

column_pairs_metric

ColumnPairs ContinuousKLDivergence.

Type

sdmetrics.column_pairs.base.ColumnPairsMetric

field_types

Field types to which the SingleColumn metric will be applied.

Type

dict

__init__(column_pairs_metric, **column_pairs_metric_kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(column_pairs_metric, …)

Initialize self.

compute(real_data, synthetic_data[, metadata])

Compute this metric.

compute_breakdown(real_data, synthetic_data)

Compute the breakdown of this metric.

get_subclasses([include_parents])

Recursively find subclasses of this metric.

normalize(raw_score)

Return the raw_score as is, since it is already normalized.

Attributes

column_pairs_metric_kwargs

field_types

goal

max_value

min_value

name