Counterfactual Explanationsで機械学習モデルを解釈する / TokyoR99

2022年6月4日に行われた、第99回R勉強会@東京（#TokyoR）での発表資料です。
https://tokyor.connpass.com/event/249096/

資料で使っているRコードはこちらになります。
Pythonですが、DiCEを使ったnotebookもあります。
https://github.com/dropout009/TokyoR99

Transcript

1. Counterfactual Explanations
   2022/06/04
   99 R @ #TokyoR
   @dropout009
   

   View Slide

2. TVISION INSIGHTS
   Twitter: @dropout009
   Speaker Deck: dropout009
   Blog: https://dropout009.hatenablog.com/
   

   View Slide

3. Counterfactual Explanations
   

   View Slide

4. •
   • 1,000
   800
   •
   800
   1,000
   

   View Slide

5. • 800 SHapley Additive
   exPlanations SHAP
   • SHAP 𝔼[ #
   𝑓 𝑿 ] 𝑖 #
   𝑓 𝒙!
   • 500 𝑖 800 300
   !
   𝑓 𝒙! = 𝜙" + 𝜙
   !,
   + 𝜙
   !,
   + 𝜙
   !,
   + 𝜙
   !,
   800
   800 +200 +200
   +500 +200 -300
   

   View Slide

6. • 1,000 /
   Counterfactual Explanations CE
   • CE 𝑥!,$ 𝑥$
   ∗
   •
   1,000
   1,000
   

   View Slide

7. • DS
   DE Biz 3 100
   •
   #
   𝑓 𝑋"#, 𝑋"$, 𝑋%!& = 8𝑋"# + 4𝑋"$ + 10𝑋%!&
   • DS 60 DE 30 Biz 20
   •
   #
   𝑓 60,30,20 = 8×60 + 4×30 + 10×20 = 800
   • 1,000
   CE
   (2014.12.10)
   (http://www.datascientist.or.jp/news/2014/pdf/1210.pdf)
   

   View Slide

8. !
   𝑓 𝑋&', 𝑋&(, 𝑋)!* = 8𝑋&' + 4𝑋&( + 10𝑋)!*
   !
   𝑓 60,30,20 = 8×60 + 4×30 + 10×20 = 800
   1,000
   • 1
   • DS 60 85
   • DE 30 80
   • Biz 20 40
   • 2
   • DS 70 DE 60
   CE
   

   View Slide

9. • !
   𝑓 𝑋&', 𝑋&(, 𝑋)!*
   1,000
   • Random Forest Neural Net
   !
   𝑓 𝑋&', 𝑋&(, 𝑋)!*
   • !
   𝑓 𝑋&', 𝑋&(, 𝑋)!*
   1,000
   • 100 0 100 101 3
   101 3 100 3
   CE
   

   View Slide

10. counterfactual counterfactual
    •
    •
    •
    • counterfactual
    counterfactual
    •
    •
    CE
    

    View Slide

11. •
    𝑦
    𝒙∗ = (𝑥"#, 𝑥"$, 𝑥%!&)
    𝒙∗ = argmin
    𝒙
    𝒙 − 𝒙! )
    s. t. #
    𝑓 𝒙 ≥ 𝑦,
    𝒙 ≥ 𝒙!
    • 𝒙! = (𝑥!,"#, 𝑥!,"$, 𝑥!,%!&) 𝑖 𝒙 − 𝒙! ) = ∑+
    𝑥+ − 𝑥!,+
    )
    L2 L1
    • 100
    Biz DS 築
    • 𝒙 ≥ 𝒙!
    CE
    

    View Slide

12. R
    

    View Slide

13. 𝐼𝑛𝑐𝑜𝑚𝑒 = 0.05𝑋&'𝑋&(
    ".,𝑋)!*
    ".-𝜖
    𝑋&'
    ∼ Beta 13, 7 ×100
    𝑋&(
    ∼ Beta 10, 10 ×100
    𝑋)!* ∼ Beta 7, 13 ×100
    𝜖 ∼ 𝒩(1, 0.05.)
    

    View Slide

14. 𝐼𝑛𝑐𝑜𝑚𝑒 = 0.05𝑋&'
    𝑋&(
    ".,𝑋)!*
    ".-𝜖
    

    View Slide

15. 𝐼𝑛𝑐𝑜𝑚𝑒 = 0.05𝑋&'
    𝑋&(
    ".,𝑋)!*
    ".-𝜖
    3 DE
    

    View Slide

16. • Elastic Net Counterfactual
    Neural Net tree
    • 4
    • Elastic Nest L1 L2
    D
    𝜷/0 = argmin
    𝒃
    K
    !23
    4
    𝑦! − 𝒙!
    5𝒃
    .
    + 𝜆 𝜃 𝒃 3 + 1 − 𝜃 𝒃 .
    

    View Slide

17. • DE DS Biz 1,000
    •
    𝑥&'
    ∗ , 𝑥&(
    ∗ , 𝑥)!*
    ∗ = argmin
    6!",6!#,6$%&
    (𝑥&'
    , 𝑥&(
    , 𝑥)!*
    ) − (𝑥!,&'
    , 𝑥!,&(
    , 𝑥!,)!*
    )
    .
    s. t. !
    𝑓 𝑥&', 𝑥&(, 𝑥)!* ≥ 1000
    𝑥&' ≥ 𝑥!,&',
    𝑥&( = 𝑥!,&(
    𝑥)!* ≥ 𝑥!,)!*
    Counterfactual
    

    View Slide

18. •
    Tidymodels
    N <- 2000
    f <- function(ds, de, biz) {
    ds * de^0.6 * biz^0.8 / 20
    }
    df <- tibble(
    ds = rbeta(N, 13, 7) * 100,
    de = rbeta(N, 10, 10) * 100,
    biz = rbeta(N, 7, 13) * 100,
    income = f(ds, de, biz) * rnorm(N, 1, 0.05)
    )
    train_test_split <- rsample::initial_split(df)
    df_train <- rsample::training(train_test_split)
    df_test <- rsample::testing(train_test_split)
    cv_split <- rsample::vfold_cv(df_train, v = 5)
    •
    

    View Slide

19. •
    wf <- workflows::workflow() %>%
    workflows::add_model(model) %>%
    workflows::add_recipe(rec)
    model <- parsnip::linear_reg(
    penalty = tune::tune(),
    mixture = tune::tune()
    ) %>%
    parsnip::set_mode("regression") %>%
    parsnip::set_engine("glmnet")
    rec <- recipes::recipe(income ~ ds + de + biz, data = df) %>%
    recipes::step_interact(terms = ~ ds:de + de:biz + ds:biz + ds:de:biz) %>%
    recipes::step_poly(all_predictors(), degree = 4) %>%
    recipes::step_normalize(all_numeric_predictors())
    •
    •
    

    View Slide

20. •
    bayes_result <- wf %>%
    tune::tune_bayes(
    resamples = cv_split,
    param_info = tune::parameters(
    list(
    penalty = dials::penalty(),
    mixture = dials::mixture()
    )
    ),
    metrics = yardstick::metric_set(rmse),
    initial = 5,
    iter = 30,
    control = tune::control_bayes(verbose = TRUE, no_improve = 5)
    )
    

    View Slide

21. •
    RMSE: 31.3
    R2: 0.974
    #
    best_model <- bayes_result %>%
    tune::select_best()
    #
    wf_final <- wf %>%
    tune::finalize_workflow(best_model)
    #
    final_result <- wf_final %>%
    tune::last_fit(train_test_split)
    #
    final_result %>%
    tune::collect_metrics()
    #
    final_result %>%
    tune::collect_predictions()
    

    View Slide

22. • DE DS Biz 1,000
    Counterfactual Explanations
    counterfactual_explanations <- function(model, current_x, desired_y) {
    as_input <- function(x) tibble(ds = x[1], de = x[2], biz = x[3]) #
    predict_num <- function(model, x) pull(predict(model, as_input(x))) # df numeric
    constraint <- function(x) predict_num(model, x) - desired_y #
    distance <- function(x) norm(current_x - x, type = "2") #
    solution <- Rsolnp::solnp( #
    pars = current_x + 1e-3,
    fun = distance,
    ineqfun = constraint,
    ineqLB = 0,
    ineqUB = 0.1,
    LB = current_x,
    UB = c(100, current_x[2] + 1e-2, 100), # DE
    control = list(tol = 1e-5)
    )
    result <- list(
    current_x = as_input(current_x), #
    current_y = predict_num(model, current_x), #
    desired_y = desired_y, #
    required_x = as_input(solution$pars), #
    predicted_y = predict_num(model, solution$pars) #
    )
    return(result)
    }
    

    View Slide

23. 𝒙!
    = 𝑥!,&'
    , 𝑥!,&(
    , 𝑥!,)!*
    = (66.0, 66.9, 28.9) 607
    𝒙∗ = (77.4, 66.9, 45.8) 1,000
    CE
    

    View Slide

24. View Slide

25. •
    • X
    • Y
    • Counterfactual Explanations CE Y
    SHAP
    •
    Python
    DiCE
    

    View Slide

26. •
    • CE
    •
    • CE
    

    View Slide

27. View Slide

28. • Wachter, Sandra, Brent Mittelstadt, and Chris Russell. "Counterfactual explanations without opening
    the black box: Automated decisions and the GDPR." Harv. JL & Tech. 31 (2017): 841.
    • Mothilal, Ramaravind K., Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers
    through diverse counterfactual explanations." Proceedings of the 2020 Conference on Fairness,
    Accountability, and Transparency. 2020.
    • Molnar, Christoph. "Interpretable machine learning. A Guide for Making Black Box Models Explainable."
    (2019). https://christophm.github.io/interpretable-ml-book/.
    

    View Slide

29. R
    

    View Slide