機械学習と解釈可能性 / Machine Learning and Interpretability

吉永尊洸（LINE株式会社 Data Labs）

ソフトウェアジャパン2019（2019/2/5）での発表資料です。
https://www.ipsj.or.jp/event/sj/sj2019/Bigdata.html

Transcript

1. 機械学習と解釈可能性 ソフトウェアジャパン 2019 38 Slides LINE株式会社 DataLabs Takahiro Yoshinaga
2. 本⽇の内容 2 • ⾃⼰紹介 • Introduction • 機械学習モデルと結果を解釈する⽅法 • どの特徴量が重要か • 各特徴量が予測にどう影響するか • 予測に対して特徴量がどう寄与するか • Summary
3. 3 ⾃⼰紹介
4. ⾃⼰紹介 4 • 吉永 尊洸（よしなが たかひろ） ü @t_yoshinaga0106 • 所属 : LINE株式会社 DataLabs • 経歴 ü ~2015.03, Ph.D. : 東⼤（素粒⼦論：SUSY現象論） ü ~2018.01 : データ分析の専⾨会社 ü 2018.02~ : 現職 • 最近の趣味 ü 機械学習の解釈性、息⼦の強化学習
5. 5 Introduction
6. Introduction 6 機械学習の解釈性の重要性が⾼まっている • 現在の機械学習 : ブラックボックスになりがち • ⾼精度の予測は得意だが根拠の説明は苦⼿ • 今後、導⼊を拡⼤していくためには、根拠が説明で きる（≒ ⼈間が解釈できる）ことが重要 • 社会的な背景 • サービス提供側に説明責任が求められている ü ⽇本 : AI利活⽤原則案（総務省, 2018） ü EU : ⼀般データ保護規則（GDPR） ü US : 説明可能AI（XAIプロジェクト）
7. Introduction 7 研究界でも解釈性の論⽂が増えている https://www.ai-gakkai.or.jp/my-bookmark_vol33-no3/ 近年の研究動向は、原先⽣のまとめ参照
8. 本⽇の内容 8 実務的な観点で解釈性がどう活⽤できるか 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++
9. 9 機械学習モデルと 結果を解釈する⽅法
10. 機械学習モデルと結果を解釈する⽅法 10 ⼤きくは、以下の3つの説明ができること 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ （モデルの説明 : 1, 2 結果の説明 : 3）
11. 機械学習モデルと結果を解釈する⽅法 11 ⼤きくは、以下の3つの説明ができること 1. どの特徴量が重要か p モデルが重要視している要因がわかる 2. 各特徴量が予測にどう影響するか p 特徴量を変化させたときの予測の傾向がわかる 3. ある予測結果に対して特徴量がどう寄与するか p なぜその予測値だったか要因がわかる （モデルの説明 : 1, 2 結果の説明 : 3）
12. 機械学習モデルと結果を解釈する⽅法 12 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ ⼤きくは、以下の3つの説明ができること （モデルの説明 : 1, 2 結果の説明 : 3）
13. Feature Importance 13 モデルに使⽤する特徴量の重要度を数値化したもの • Model dependent • RandomForest : 評価指標をより改善する特徴量が重要 ü 分類 : OOB（Out-of-Bag）の誤差率、ジニ係数 ü 回帰 : MSE, RSS • XGBoost ü gain, cover, weight (frequency) • … • Model independent • Permutation Importance ü 値をランダムにしたときに精度が落ちる = 重要度が⾼い モデルによって詳細が異なるので注意
14. Feature Importance : R実装 14 • 汎⽤的なものはmlrの標準関数として搭載 • mlr : Machine Learning in R ü Rの汎⽤型Machine Learningパッケージ (*) • データセット : ボストンの家賃価格 • ブラックボックスモデル : RandomForest https://mlr.mlr-org.com/ (*) 今後はmlrよりもtidymodelsのほうがスタンダードになるかもしれません
15. Feature Importance : R実装 15 • 学習の流れ • Task（データセットと⽬的変数）を定義 • Leaner（回帰/分類、機械学習モデル）を定義 • （ハイパーパラメータ探索：モデル依存） • TaskとLearnerを⽤いて学習 https://mlr.mlr-org.com/
16. Feature Importance : R実装 16 • Feature Importance • generateFeatureImportanceData() ü モデルに依存しない重要度を計算 重要度 より重要 重要な特徴量が わかる https://mlr.mlr-org.com/
17. 機械学習モデルの結果を解釈する⽅法 17 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ ⼤きくは、以下の3つの説明ができること （モデルの説明 : 1, 2 結果の説明 : 3）
18. Partial Dependence 18 特徴量を変化させたときの出⼒の平均的な変化 ˆ f(xS) = ExC [ ˆ f(xS, xC)] = Z ˆ f(xS, xC)dP(xC) <latexit sha1_base64="ld57xGQN1MTgwhUah4SWdr2pOLo=">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</latexit> <latexit 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19. Partial Dependence : R実装 19 • Partial Dependence Plot • generatePartialDependenceData()に 学習済モデルとtaskとFeatureの名前を⼊⼒ • plotPartialDependence()でplot RandomForest 重要度が⾼い順 平均以外も選択可能 https://mlr.mlr-org.com/
20. Partial Dependence : R実装 20 • Partial Dependence Plot • 横軸：特徴量 • 縦軸：特徴量を動かしたときの出⼒の平均的な動き https://mlr.mlr-org.com/
21. Partial Dependence : R実装 21 • Partial Dependence Plot • 横軸：特徴量 • 縦軸：特徴量を動かしたときの出⼒の平均的な動き 重要なだけでなく どう重要かがわかる(*) https://mlr.mlr-org.com/ （*）特徴量間の相関が強い場合、結果は信頼できない ( cf) Accumulated Local Effect)
22. 機械学習モデルの結果を解釈する⽅法 22 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ ⼤きくは、以下の3つの説明ができること （モデルの説明 : 1, 2 結果の説明 : 3）
23. LIME, SHAP 23 説明したいデータ付近でモデルを近似する [Rebeiro+, 16] 局所的な分類器 モデルが負と 判定する領域 モデルが正と 判定する領域 正例 負例 説明したい データ
24. LIME, SHAP 24 説明したいデータ付近でモデルを近似する [Lundberg, Lee, 17] x <latexit sha1_base64="JtZVMwUxIaWGzzkAWchy8W7SiJ4=">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</latexit> <latexit 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25. LIME, SHAP 25 貢献度は満たすべき条件から考える [Lundberg, Lee, 17] 1. 局所的正確性 : local accuracy p データ点の直上では出⼒が⼀致 2. 値がゼロの変数の無影響性 : missingness p データ点で値がゼロの変数は貢献度ゼロ 3. 複数モデル⽐較での無⽭盾性 : consistency p 簡単化の前後でモデル間の貢献度の⼤⼩関係が同じ f(x) = g(x0) <latexit sha1_base64="VSXQjNvRp03gMfdTWGzOLJbuCHE=">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</latexit> <latexit 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26. LIME, SHAP 26 [Lundberg, Lee, 17] p SHAP（SHapley Additive exPlanation）value ü 条件をすべて満たす貢献度の組 ü 協⼒ゲーム理論ではShapley valueとして知られる p LIME ü 貢献度は最適化問題を解くことで算出 i(f, x) = X z0✓x0 |z0|!(M |z0| 1)! M! [fx(z0) fx(z0 \ i)] <latexit sha1_base64="pV2Q9YHdXutEA9pF7o3tS0/+YfU=">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</latexit> <latexit 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27. SHAP : Python実装 27 • SHAP value • 著者によるPython実装がある ü Shapley valueの計算ならRでもいくつかライブラリがある • ブラックボックスモデルを作成 ü データセット : ボストンの家賃価格 ü モデル : RandomForest https://github.com/slundberg/shap
28. SHAP : Python実装 28 • SHAP value • Explaner : SHAP valueを使って予測を⾏う ü モデルの種類（treeベースなど）に特化したmethodが実装 予測値 バーの⻑さ : 特徴量の貢献分 ⾚ : 正の寄与, ⻘ : 負の寄与 https://github.com/slundberg/shap
29. 機械学習モデルの結果を解釈する⽅法 29 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ ⼤きくは、以下の3つの説明ができること （モデルの説明 : 1, 2 結果の説明 : 3）
30. Grad-CAM, Grad-CAM++ 30 DNNに対して予測への影響が⼤きい箇所を可視化 • Class Activation Mapping (CAM) : 特定のクラスに反応する領域を可視化 • Grad-CAM : 勾配ベース(Back-propagation)のCAM ⽝と猫が⼀緒 にいる画像 「猫」クラスの 予測で重要 「⽝」クラスの 予測で重要 [Selvaraju+, 16]
31. Grad-CAM, Grad-CAM++ 31 DNNに対して予測への影響が⼤きい箇所を可視化 [Chattopadhyay+, 17]
32. Grad-CAM, Grad-CAM++ 32 DNNに対して予測への影響が⼤きい箇所を可視化 [Chattopadhyay+, 17] 2. 最終層をマップに利⽤ 1. 学習済みモデルを⽤意 k : channel（フィルタ数） i,j : ピクセルの位置 3. 各フィルタに重みをつける 4. ⾜し上げる Grad-CAM : 微分をピクセル内平均 Grad-CAM++ : 2, 3階微分も考慮
33. Grad-CAM : Python実装 33 • Keras Visualization Toolkit (Keras-vis)に搭載 • Keras : 深層学習のライブラリの⼀つ ü TensorflowやTheano上で動く（ラッパー） • ⽐較的簡単にDNNの実装ができる！ • 例 : バグとロシアンブルーの分類と可視化 https://github.com/raghakot/keras-vis http://marubon-ds.blogspot.com/2018/03/object-detection-by-cam-with-keras.html
34. Grad-CAM : Python実装 34 • 転移学習で2値分類のモデルを作成 • ImageNetを学習したVGG16の重みを利⽤ • 最終層だけ変えて学習 https://github.com/raghakot/keras-vis
35. Grad-CAM : Python実装 35 • Grad-CAMでヒートマップ作成 • visualize_cam()で評価 https://github.com/raghakot/keras-vis もとの図とヒートマップの⽐較するための関数を⽤意
36. Grad-CAM : Python実装 36 • Grad-CAMでヒートマップ作成 • クラス予測の寄与が⼤きい部分がより⾚くなる • モデルがうまく構築できているかの確認に使える https://github.com/raghakot/keras-vis 顔のなかでも⼝の部分 が予測に貢献している 顔のなかでも⽿の部分 が予測に貢献している
37. 37 Summary
38. Summary 38 • 機械学習モデルと結果を解釈する⽅法を紹介 • どの特徴量が重要か • 各特徴量が予測にどう影響するか • 予測に対して特徴量がどう寄与するか • 研究、道具⽴ては進んできている • が、どういった場合に有効/使えないといった実際の 知⾒はあまりない • 今後はこれらを活⽤して実務側 → 研究側へのフィー ドバックをすることが重要になると考えている
39. 39 Appendix
40. 機械学習モデルと結果を解釈する⽅法 40 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ ⼤きくは、以下の3つの説明ができること （モデルの説明 : 1, 2 結果の説明 : 3）
41. Partial Dependence : Python実装 41 • PDPboxが複数のモデルに対応したライブラリ https://github.com/SauceCat/PDPbox
42. 機械学習モデルと結果を解釈する⽅法 42 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ ⼤きくは、以下の3つの説明ができること （モデルの説明 : 1, 2 結果の説明 : 3）
43. Surrogate Model 43 ブラックボックスモデルをまねる・代理モデルを作る x <latexit sha1_base64="JtZVMwUxIaWGzzkAWchy8W7SiJ4=">AAACZHichVHLSsNAFD2Nr1qrrRZBEKRYKq5kIoLiqujGZR/2AbWUJE5raJqEJC3W4g/oVnHhSkFE/Aw3/oCL/oAgLiu4ceFtGhAt6h1m5syZe+6cmZFNTbUdxjo+YWh4ZHTMPx6YCE5OhcLTMznbaFgKzyqGZlgFWbK5puo866iOxgumxaW6rPG8XNvu7eeb3LJVQ991WiYv1aWqrlZURXKISh2WwzG2wtyIDgLRAzF4kTTCt9jDPgwoaKAODh0OYQ0SbGpFiGAwiSuhTZxFSHX3OY4RIG2DsjhlSMTWaKzSquixOq17NW1XrdApGnWLlFHE2RO7Y132yO7ZC/v4tVbbrdHz0qJZ7mu5WQ6dzGXe/1XVaXZw8KX607ODCjZcryp5N12mdwulr28eXXQzm+l4e4lds1fyf8U67IFuoDfflJsUT18iQB8g/nzuQZBbXREJp9ZiiS3vK/yYxyKW6b3XkcAOksjSuRynOMO571kIChFhtp8q+DxNBN9CWPgE9k6J+A==</latexit> <latexit 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44. Surrogate Modelの例： defragTrees 44 [Hara and Hayashi, 16] アンサンブルモデル → シンプルな⽊構造のモデル • 簡略化 = モデル選択
45. Surrogate Modelの例： defragTrees 45 [Hara and Hayashi, 16] 論⽂の⼯夫 • （⽊ベースの）アンサンブルモデルを確率モデルとみなす • Factorized Asymptotic Bayesian (FAB) 推定を使って近 似解を求める テスト誤差は抑えつつ 必要なルールの数も抑える（上限は⼿で指定）
46. defragTrees：Python実装 46 • データセット : 乳がんのデータセット • アンサンブルモデル : XGBoost https://github.com/sato9hara/defragTrees TestデータのAUCは0.96程度
47. defragTrees：Python実装 47 • Surrogate Model • ルール⽣成 (errorは10%くらい) https://github.com/sato9hara/defragTrees
48. defragTrees：Python実装 48 • Surrogate Model • ルール⽣成 (errorは10%くらい) https://github.com/sato9hara/defragTrees 精度をあまり落とさずにシンプルなモデルで表現できた （実際には結果が安定しない場合があるため、試⾏錯誤が必要）
49. 機械学習モデルの結果を解釈する⽅法 49 1. どの特徴量が重要か p Feature Importance 2. 各特徴量が予測にどう影響するか p Partial Dependence p Surrogate Model : Appendix 3. ある予測結果に対して特徴量がどう寄与するか p LIME, SHAP p Grad-CAM, Grad-CAM++ ⼤きくは、以下の3つの説明ができること （モデルの説明 : 1, 2 結果の説明 : 3）
50. LIME, SHAP 50 [Lundberg, Lee, 17] • ゲーム理論 p お互いの選択しだいで利得が変わるゲーム p 最終的な利益への貢献度を公正にエージェントへ分配 • 機械学習の解釈性 p お互いの値しだいで出⼒が変わるモデル p 最終的な出⼒への貢献度を公正に変数へ分配 Shapley valueは協⼒ゲーム理論で知られている
51. SHAP : Python実装 51 • SHAP value • 複数の予測値の傾向の可視化も可能 • Feature ImportanceやPartial Dependenceに近い 使い⽅も可能 https://github.com/slundberg/shap 各データの|SHAP|の平均 特徴量の値の⼤きさとSHAP value