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Fairness Metric Calculation for Spark

Calculate fairness metrics using Spark. This implementation is based on Aequitas and includes the following metrics:

  • Equal Parity
  • Proportional Parity
  • False Positive Rate Parity
  • False Discovery Rate Parity
  • False Negative Rate Parity
  • False Omission Rate Parity

How to build

This project requires sbt (tested against version 0.13.17) and scala (version 2.11.x).

git clone https://github.com/eubr-atmosphere/spark-fairness.git
cd spark-fairness
sbt build

Spark Packages

WIP

You can use the Fairness Metric Calculation in Spark by adding its JAR file (using option --jars) or by using Spark Packages:

spark-shell --conf spark.jars.packages=spark-fairness:spark-fairness:0.1.0-spark2.1-s_2.11

Usage

FairnessEvaluatorTransformer is a Spark transformer, with the following parameters:

  • sensitiveColumn: Name of the column with the sensitive information.
  • labelColumn: Name of the column with the label information (expected result).
  • scoreColumn: Name of the column with the score information (predicted result).
  • baselineValue: Value used as baseline value.
  • tau: Define the range of assessment for the metrics.

Python (WIP)

import ...
df = spark.read.csv('samples/compas.csv')
transformer = FairnessEvaluatorTransformer(
    sensitiveColumn='race', labelColumn='label_value',
    scoreColumn='score', baselineValue='Caucasian',
    tau=0.8)
result = transformer.transform(df)
result.show()