package exploratory
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class
AggregateBalanceMeasure extends Transformer with DataBalanceParams with ComplexParamsWritable with Wrappable with BasicLogging
This transformer computes a set of aggregated balance measures that represents how balanced the given dataframe is along the given sensitive features.
This transformer computes a set of aggregated balance measures that represents how balanced the given dataframe is along the given sensitive features.
The output is a dataframe that contains one column:
 A struct containing measure names and their values showing higher notions of inequality.
The following measures are computed:
 Atkinson Index  https://en.wikipedia.org/wiki/Atkinson_index
 Theil Index (L and T)  https://en.wikipedia.org/wiki/Theil_index
The output dataframe contains one row.
 Annotations
 @Experimental()
 A struct containing measure names and their values showing higher notions of inequality.
The following measures are computed:
 trait DataBalanceParams extends Params with HasOutputCol

class
DistributionBalanceMeasure extends Transformer with DataBalanceParams with ComplexParamsWritable with Wrappable with BasicLogging
This transformer computes data balance measures based on a reference distribution.
This transformer computes data balance measures based on a reference distribution. For now, we only support a uniform reference distribution.
The output is a dataframe that contains two columns:
 The sensitive feature name.
 A struct containing measure names and their values showing differences between
the observed and reference distributions. The following measures are computed:
 KullbackLeibler Divergence  https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence
 JensenShannon Distance  https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence
 Wasserstein Distance  https://en.wikipedia.org/wiki/Wasserstein_metric
 Infinity Norm Distance  https://en.wikipedia.org/wiki/Chebyshev_distance
 Total Variation Distance  https://en.wikipedia.org/wiki/Total_variation_distance_of_probability_measures
 ChiSquared Test  https://en.wikipedia.org/wiki/Chisquared_test
The output dataframe contains a row per sensitive feature.
 Annotations
 @Experimental()

class
FeatureBalanceMeasure extends Transformer with DataBalanceParams with HasLabelCol with ComplexParamsWritable with Wrappable with BasicLogging
This transformer computes a set of balance measures from the given dataframe and sensitive features.
This transformer computes a set of balance measures from the given dataframe and sensitive features.
The output is a dataframe that contains four columns:
 The sensitive feature name.
 A feature value within the sensitive feature.
 Another feature value within the sensitive feature.
 A struct containing measure names and their values showing parities between the two feature values.
The following measures are computed:
 Demographic Parity  https://en.wikipedia.org/wiki/Fairness_(machine_learning)
 Pointwise Mutual Information  https://en.wikipedia.org/wiki/Pointwise_mutual_information
 SorensenDice Coefficient  https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
 Jaccard Index  https://en.wikipedia.org/wiki/Jaccard_index
 Kendall Rank Correlation  https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient
 LogLikelihood Ratio  https://en.wikipedia.org/wiki/Likelihood_function#Likelihood_ratio
 ttest  https://en.wikipedia.org/wiki/Student's_ttest
The output dataframe contains a row per combination of feature values for each sensitive feature.
 Annotations
 @Experimental()
Value Members
 object AggregateBalanceMeasure extends ComplexParamsReadable[AggregateBalanceMeasure] with Serializable
 object DistributionBalanceMeasure extends ComplexParamsReadable[DistributionBalanceMeasure] with Serializable
 object FeatureBalanceMeasure extends ComplexParamsReadable[FeatureBalanceMeasure] with Serializable