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[読み会] TabNet: Attentive Interpretable Tabular Learning
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mei28
January 05, 2021
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[読み会] TabNet: Attentive Interpretable Tabular Learning
読み会資料
TabNet: Attentive Interpretable Tabular Learning(ICLR, 2020, rejected)
mei28
January 05, 2021
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Transcript
TabNet: Attentive Interpretable Tabular Learning ಡΈձ@2021/01/05 ༶໌
• ஶऀ • Sercan O. Arik, Tomas Pfister •
Google Cloud AI • ग़య: ArxivͷPreprint • ICLR 2020ͰϦδΣΫτ͞Εͨจ จใ
• ςʔϒϧσʔλ͚ͷDNNϞσϧ • ܾఆͱNNϞσϧͷ͍͍ͱ͜औΓΛࢦͨ͠ख๏ • ղऍੑ + ਫ਼ ͷ্͕ୡͰ͖ͨɽ
֓ཁ ͲΜͳจʁ
• DNNͷϞσϧ͕ಛʹը૾,ݴޠ,ԻͷͰSOTAͰ͋Δɽ • KaggleͳͲͷੳίϯϖͰॳΊʹܾఆϕʔεͷख๏͕ओྲྀ • ղऍੑ͕ߴ͍͔Β ং ݚڀഎܠ
• ͳΜͰςʔϒϧσʔλʹରͯ͠ɼਂֶशΛऔΓೖΕ͍ͨͷ͔ʁ • େنͳσʔληοτʹ͍ͨͯ͠ɼਂֶशʹΑ্͕ͬͯظͰ͖Δ ͔Β • Deep Learning Scaling
is Predictable, Empirically.(Hestness et al., 2017) ং ݚڀഎܠ
• ςʔϒϧσʔλʹରͯ͠NNϞσϧΛ͏3ͭͷϝϦοτ 1. ෳͷσʔλΛޮΑ͘ΤϯίʔσΟϯάͰ͖Δ 2. ಛྔΤϯδχΞϦϯάͷखؒΛݮΒͤΔ 3. End-to-endͰѻ͏͜ͱ͕Ͱ͖Δɽ ং
ݚڀഎܠ
• σʔλͷલॲཧΛߦΘͣʹend-to-endͰͷֶशΛߦ͑Δɽ • ஞ࣍ҙΛ༻͍Δ͜ͱͰղऍੑͷߴ͍Ϟσϧʹͳ͍ͬͯΔɽ • Local interpretability: ೖྗಛͷॏཁ •
Global interpretability: ֤ಛྔ͕Ϟσϧʹରͯ͠Ͳͷ͘Β͍Өڹ͔ͨ͠ ং ఏҊख๏ͷߩݙ
• DNN+DT • ஞ࣍ҙΛ༻͍ͯɼಛબΛߦ͍ಛΛೖΕࠐΜͰ͍Δɽ • Tree-based learning • ಛબʹDNNΛ༻͍͍ͯΔɽ
• Feature Selection • ίϯύΫτͳදݱ͕Ͱ͖ͨɽ ؔ࿈ݚڀ
• Attentive transformer • ಛྔʹରͯ͠͏MaskͷֶशΛߦ͏ɽ • Feature transformer •
ಛྔͷมɼ࣍εςοϓʹ͏ͷΛܾΊΔɽ ఏҊख๏ ॏཁͳύʔπ
• ͜ΕҎ߱ग़ͯ͘Δ εςοϓ1,2,...ʹରԠ͍ͯ͠Δ i ఏҊख๏ શମͷߏ
• • : աڈͷMͰΘΕ͍ͯΔ͔ʁʹΑͬͯ มΘΔॏΈ(࣮Ͱར༻੍ݶΈ͍ͨͳͷ) • Sparsemax: softmaxʹࣅͨ׆ੑԽؔ M[i]
= sparsemax(P[i] ⋅ hi (a[i − 1])) P[i] ఏҊख๏ Attentive Transformer: ϚεΫͷֶशΛߦ͏ɽ
• SoftmaxΑΓૄʹͳΓ͍͢ ͔ΒɼॏཁͳಛྔΛऔΓग़ ͍͢͠ ίϥϜ SparseMax (Andre et al.,
2016)
• ɼa࣍ͷεςοϓʹճ͞ΕΔ [d[i], a[i]] = fi (M[i] ⋅ f)
ఏҊख๏ Feature Transformer: ೖྗΛม͠ɼ࣍ʹ͏ͷΛܾΊΔ
• ֤εςοϓ Λूܭ ͯ͠࠷ऴతͳ༧ଌʹ ༻͍Δ d[i] ఏҊख๏ ࠷ऴ༧ଌ
• ಛྔͷॏཁϚεΫΛͬͯܭࢉ͢Δ • ؆୯ʹܭࢉ͢ΔͨΊɼϚεΫͰͳ͘ಛྔΛ༻͍Δ ɹɹɹ ɹˠͲͷαϯϓϧ͕ॏཁ͔ʁ • → ಛྔͷॏཁ
ηb [i] = Nd ∑ c ReLU(db,c [i]) Magg−b,j = ∑Ns teps i=1 ηb [i]Mb,j [i] ∑D j=1 ∑Nsteps i=1 ηb [i]Mb,j [i] ఏҊख๏ ղऍੑʹ͍ͭͯ
• Feature selection͕֤εςοϓʹରԠ ఏҊख๏ ಛྔબͷΠϝʔδ
• ֤ϚεΫʹΑͬͯ࡞ΒΕΔಛྔ͕ذʹରԠ͍ͯ͠Δɽ ఏҊख๏ Ͳ͕ܾ͜ఆΆ͍ͷʁ
• ର߅ख๏: • ޯϒʔεςΟϯάܥ: LightGBM, XGBoost, CatBoost • NNϞσϧ
• ͳʹͰൺΔ͔ʁ • ςετσʔλʹର͢Δaccuracy • ϞσϧͷαΠζ ࣮ݧ ࣮ݧઃఆ
• ࣮σʔλ(ForestCoverType)Ͱର߅ख๏ΑΓਫ਼͕ྑ͔ͬͨɽ ࣮ݧ݁Ռ ਫ਼ʹؔͯ͠
• ϞσϧαΠζ͕ܰྔͰਫ਼͕͍͍ɽ ࣮ݧ݁Ռ ϞσϧαΠζʹؔͯ͠
࣮ݧ݁Ռ ղऍੑʹ͍ͭͯ • ͷ݁ՌΛՄࢹԽ • ߦ͕αϯϓϧɼྻ͕ಛྔ • ന͍ͱ͜Ζ͕ಛྔͱͯ͠ॏཁ ͱஅͨ͠ͱ͜Ζ
ηb [i]
• ஞ࣍ҙΛߦ͏͜ͱͰɼॏཁͳಛྔબΛߦͳ͍ͬͯΔɽ • ϚεΫΛ༻͍Δ͜ͱͰղऍੑͷߴ͍Ϟσϧʹͳͬͨɽ • ༷ʑͳྖҬͷςʔϒϧσʔλͰੑೳΛൃشͰ͖Δ͜ͱΛࣔ͠ ͨɽ ·ͱΊ
• Accuracy: 0.81, ROC-AUC: 0.78 ͓·͚ TitanicσʔληοτͰTabNetΛ༡ΜͰΈͨɽ
͓·͚ LightGBM vs NN model vs TabNet LightGBM NN
model • TabNet: Accuracy: 0.81, ROC-AUC: 0.78 https://github.com/mei28/playground_python/blob/main/notebooks/titanic.ipynb ϋΠύϥॳظͷ··Ͱ νϡʔχϯάΛߦͳ͍ͬͯͳ͍