LIME

@Tokyo.R 66
https://atnd.org/events/92993

Transcript

1. LIME Masaaki Horikoshi @ ARISE analytics
2. ࣗݾ঺հ • R • ύοέʔδ։ൃͳͲ • Git Awards ࠃ಺1Ґ • Python • http://git-awards.com/users/search?login=sinhrks
3. Α͋͘Δ͜ͱ ΤʔΞΠͰ͍͍ײ͡ʹ΍ͬͱ͍ͯΑʂ
   ݁Ռ͕ྑ͚Ε͹த਎͸ؾʹ͠ͳ͍Αʂʂ Ͱɺ͜Εͬͯ݁ہͲ͏͍͏͜ͱͳͷʁ
   த਎͕Θ͔Βͳ͍΋ͷ͸࢖͑ͳ͍Αʂʂʂ ͑Β͍ਓ ˞΁ʔγϟͰ͸ͳ͍ ݁Ռ͕ग़Δͱʜ
4. Interpretability ղऍՄೳੑ
5. ղऍͷͨΊͷΞϓϩʔν 1. આ໌͠΍͍͢ػցֶशख๏ΛબͿ • ਫ਼౓͕ෆे෼ͳ৔߹͕͋Δ 2. ػցֶशख๏ʹΑΒͳ͍ղऍख๏Λ࢖͏
6. ղऍՄೳੑ • Global Interpretability • Ϟσϧ΍σʔλશମͷ܏޲Λղऍ • ۙࣅ΍ཁ໿౷ܭྔΛར༻ => ہॴతʹ͸ෆਖ਼֬ͳ৔߹΋ • Local Interpretability • Ϟσϧ΍σʔλͷݶΒΕͨྖҬΛղऍ • ΑΓਖ਼֬ͳઆ໌͕Մೳ
7. ղऍՄೳੑ • ద੾ͳख๏͸ʮԿΛʯղऍ͍͔ͨ͠ʹґଘ .PEFM
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8. LIMEͱ͸ʁ
9. Local Interpretable Model-agnostic Explanations
10. LIME • “Why Should I Trust You?” Explaining the Predictions of Any Classifier (2016) • Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
11. LIME • LIME͸ҎԼͷؔ਺Λ΋ͱʹσʔλ x ͷղऍΛಘΔ • G: ղऍ༻ͷֶशثͷू߹ • L: ղऍֶ͍ͨ͠शثͱղऍ༻ͷֶशثͷ ΠxͷݩͰͷࠩ • f: ղऍֶ͍ͨ͠शث • Πx: σʔλ x ͱͷྨࣅ౓ • Ω: ղऍ༻ͷֶशثͷෳࡶ͞ʹର͢Δേଇ߲ • ۩ମతखஈ͸υϝΠϯʹґଘ
12. ςʔϒϧσʔλɾ෼ྨ໰୊ͷྫ • σʔλ x ͷपลͰαϯϓϦϯά • طఆͰ5,000 • αϯϓϦϯάํ๏͸ม਺ͷछྨʹґଘ • Exponential KernelͰॏΈ෇͚ • ม਺બ୒ • Forward/Backward, LARSͳͲ • RidgeճؼͳͲ ˞1ZUIPO࣮૷ ޙड़ ʹ΋ͱͮ͘
13. ύοέʔδ • Python • ࿦จஶऀ࡞ • https://github.com/marcotcr/lime • R • ্هͷϙʔςΟϯά • https://github.com/thomasp85/lime install.packages(‘lime’)
14. LIME (R) • αϯϓϧ library(caret) library(lime) model <- train(iris[-5], iris[[5]], method = 'rf') explainer <- lime(iris[-5], model) explanations <- explain(iris[1, -5], explainer, n_labels = 1, n_features = 2) explanations model_type case label label_prob model_r2 model_intercept 1 classification 1 setosa 1 0.3776584 0.2544468 2 classification 1 setosa 1 0.3776584 0.2544468 model_prediction feature feature_value feature_weight feature_desc 1 0.7113922 Sepal.Width 3.5 0.02101138 3.3 < Sepal.Width 2 0.7113922 Petal.Length 1.4 0.43593404 Petal.Length <= 1.60 data prediction 1 5.1, 3.5, 1.4, 0.2 1, 0, 0 2 5.1, 3.5, 1.4, 0.2 1, 0, 0 ֶशثΛ܇࿅ ղऍ༻ͷΫϥεΛ࡞੒ ղऍΛग़ྗ
15. LIME (R) plot_features(explanations) ղऍΛϓϩοτ
16. Enjoy!