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Shparkley: Scaling Shapley with Apache Spark
The document discusses the implementation of Shapley values in Apache Spark by Cristine Dewar and Xiang Huang to enhance interpretability and fairness in machine learning models at Affirm. It highlights the need for a scalable and efficient method to allocate marginal contributions of features, employing Monte Carlo approximation for performance enhancement. The Shparkley framework demonstrates significant runtime improvements over brute force explanations while maintaining accuracy in feature contribution values.
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Shparkley: Scaling Shapley with Apache Spark
- 2. Shparkley Scaling Shapley valueswith Apache Spark Cristine Dewar and Xiang Huang
- 3. Agenda Introduction What is aShapley value? How did we implement it? How does the algorithm perform?
- 4. Introduction Cristine Dewar is anapplied machine learning scientist on Affirm’s fraud ML team. Xiang Huang is an applied machine learning scientist working on underwriting problems for Affirm.
- 5. Introduction Affirm offers pointof sale loans for our customers. Our applied machine learning team creates models for credit risk decisioning and for fraud detection and builds recommendation systems to personalize a customer’s experience.
- 6. Introduction For both fraudand credit, it is extremely important to be able to have a model that is fair and interpretable.
- 7. Introduction We have millionsof rows of data and hundreds of features. We need a solution that allows us to interpret how our models are impacting individual users at scale and can serve up results quickly.
- 8. What we need Weneed a solution that: ▪ Allows us to interpret the effect of features on individual users ▪ Does so in a timely manner
- 9. What we need Incooperative game theory, there is a problem of how to allocate the surplus of resources generated by the cooperation of the players.
- 10. What we need Wewant the following properties when allocating marginal contribution to players: ▪ Symmetry ▪ Dummy ▪ Additivity ▪ Efficiency
- 11. What we need Symmetry- two players that contribute equally will be paid out equally
- 12. What we need Dummy- a player that does not contribute will have a value of zero
- 13. Additivity - theplayer’s average marginal contribution of each game is the same as evaluating that player on the entire season What we need .3 .2 .25 .25 Avg( ) =
- 14. What we need Efficiency- marginal contributions for each feature summed with the average prediction is that sample’s prediction Average prediction .5 + .3 - .4 - .2 + .1 This user’s prediction .3
- 15. What we need ▪Symmetry ▪ Dummy ▪ Additivity ▪ Efficiency
- 16.
- 17. What is aShapley value? A Shapley value is a way to define payments proportional to each players marginal contribution for all members of the group.
- 18. Four feature modelexample FICO score Number of delinquencies Loan Amount Repaid Affirm
- 19. MATH! marginal contribution of feature j scorewith feature j score prior to adding feature j number of features this term is the fraction of permutations with the features in that order Shapley value equation place in permutation order possible permutation orders for feature j
- 20. Why does permutationorder matter? We are no only trying to see how well a feature works on it’s own We are trying to measure is how well a feature collaborates
- 21.
- 22. Permutations |S| = 1 |S|= 2 |S| = 3 |S| = 4
- 23. MATH! marginal contribution of feature j scorewith feature j score prior to adding feature j number of features Shapley value equation place in permutation order
- 24. Comparing performance Score of Scoreof Score of Score of vs. score of no features vs. score of vs. score of vs. score of
- 25. MATH! marginal contribution of feature j scorewith feature j score prior to adding feature j number of features Shapley value equation place in permutation order
- 26. Approximate Approximate by suppressingthe permuted feature’s contribution by making it noise
- 27. Make sense, soundsgreat A way to get the marginal contribution for individual rows not just a generalized feature importance. Even with approximation, it seems super computationally expensive, how do we deal with that?
- 28.
- 29. Monte-Carlo approximation forShapley value Shparkley Implementation Black Box Model 660 $500 Yes 2 Joe Shapley Value for Fico Score
- 30. Monte-Carlo approximation forShapley value Shparkley Implementation Sampled Order 660 $500 Yes 2 Joe 700 $300 Yes 0 Sally
- 31. Monte-Carlo approximation forShapley value Shparkley Implementation 660 $500 Yes 0 700 $500 Yes 0 From Joe Sampled Order 660 $500 Yes 2 Joe From Sally From Joe From Sally 700 $300 Yes 0 Sally
- 32. Monte-Carlo approximation forShapley value Shparkley Implementation 660 $500 Yes 0 700 $500 Yes 0 From Joe Sampled Order 660 $500 Yes 2 Joe From Sally From Joe From Sally Instance with Joe Fico score Instance without Joe Fico score 700 $300 Yes 0 Sally
- 33. Shparkley Implementation Data ... X X partition ... Sampled backgrounddataset X instance to investigate 600 300 1No broadcast
- 34. Shparkley Implementation X Shapley for Foreach row Row in partition 660 1000 0Yes instance to investigate 600 300 1No
- 35. Shparkley Implementation X Shapley for PermutationOrder Order by Order by Row in partition 660 1000 0Yes 0 660 Yes1000 instance to investigate 600 300 1No 1 600 No300
- 36. Shparkley Implementation X Feature setwith Loan Amount 1 600 300 Yes Shapley for Row in partition 660 1000 0Yes 0 660 Yes1000 Permutation Order Order by Order by instance to investigate 600 300 1No 1 600 No300 Feature set without Loan Amount 1 600 Yes1000
- 37. Shparkley Implementation Marginal Contribution fromthis row 0.8 - 0.7 = 0.1 Output: 0.7 Output: 0.8 Black Box Model Row in partition 660 1000 0Yes instance to investigate 600 300 1No Feature set without Loan Amount 1 600 Yes1000 Feature set with Loan Amount 1 600 300 Yes
- 38. Shparkley Implementation groupby(feature).agg(... feature Shapleyvalue weighted mean: MCFeature Marginal Contribution(MC) ... MC Feature
- 39. Shparkley Implementation ▪ Highlights ○Spark-based implementation that scales with datasets ○ Leverage runtime advantages from batch prediction ○ Reuse predictions to calculate shapley value for all features ○ Shapley Value with weight support
- 40. Runtime and convergence Shparkleyconvergence for monte carlo error
- 41. Runtime and convergence Runtimecomparison with shap BruteForce Explainer vs. Shparkley Feature Name Value Difference(%) Rank Difference Fico Score 3.7% 0 No. of Delinquencies 1.1% Length on credit Report 2.9% No. of inquiries in last six months 5.1% Loan Amount 0.4% User has repaid Affirm 2.5% Merchant Category 0.5% Cluster Config 10 machines (1 master 9 workers) Machine spec r5.4xlarge EC2 Instance (16 Cores, 128GB memory)
- 42. Conclusion Our implementation comparedto a brute force explanation: ▪ improves the runtime by 50-60x ▪ shows minimal difference in shapley values Our open source implementation by Niloy Gupta, Isaac Joseph, Adam Johnston, Xiang Huang, and Cristine Dewar is available at github.com/Affirm/shparkley
- 43.
- 44. References ● Interpretable MachineLearning: Shapley Values ● An Efficient Explanation of Individual Classifications using Game Theory ● SHAP (SHapley Additive exPlanations)