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データ分析コンペにおいて 特徴量管理に疲弊している全人類に伝えたい想い

Takanobu Nozawa
November 05, 2019
14k

データ分析コンペにおいて 特徴量管理に疲弊している全人類に伝えたい想い

Takanobu Nozawa

November 05, 2019
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  1. ϚϚͷҰาΛࢧ͑Δ
    σʔλ෼ੳίϯϖʹ͓͍ͯ
    ಛ௃ྔ؅ཧʹർฐ͍ͯ͠Δશਓྨʹ఻͍͑ͨ૝͍
    ʙֶशɾਪ࿦ύΠϓϥΠϯΛఴ͑ͯʙ
    $POOFIJUP*OD໺ᖒ఩র

    $POOFIJUP.BSDIÉWPMʙػցֶशɾσʔλ෼ੳࢢʙ

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  2. ͜Μʹͪ͸ʂ
    ϚϚͷҰาΛࢧ͑Δ

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  3. ͍͖ͳΓͰ͕͢
    ϚϚͷҰาΛࢧ͑Δ

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  4. σʔλ෼ੳίϯϖʢ,BHHMF 4*(/"5&ͳͲʣ
    ฉ͍ͨ͜ͱ͋Δਓʙ!
    ϚϚͷҰาΛࢧ͑Δ

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  5. σʔλ෼ੳίϯϖʹࢀՃͨ͜͠ͱ͋Δਓʙ!
    ϚϚͷҰาΛࢧ͑Δ

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  6. ಛ௃ྔͷ؅ཧͬͯͲ͏ͯ͠·͔͢ʁ
    ʢςʔϒϧσʔλʹ͓͍ͯʣ
    ϚϚͷҰาΛࢧ͑Δ

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  7. Α͋͘Δύλʔϯʢ࣮ମݧʣ
    ϚϚͷҰาΛࢧ͑Δ

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  8. Α͋͘ΔύλʔϯʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    w ಛ௃ྔ࡞Δ
    DPM<Z " # $ %>ˠ<Z " # $ % & '>
    # e.g)
    train['A'] = train['A'].fillna(0)
    train['B'] = np.log1p(train['B'])
    train['E'] = train['A'] + train['B']
    df_group = train.groupby('D')['E'].mean()
    train['F'] = train['D'].map(df_group)
    <>
    ɾ
    ɾ
    ɾ

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  9. Α͋͘ΔύλʔϯʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    w ࢖͏ಛ௃ྔͷΧϥϜ͚ͩࢦఆ͢Δ
    # e.g)
    feat_col = ['A', 'C', 'D', 'E', 'F', 'J']
    x_train = train[feat_col]
    y_train = train['y']
    # e.g)
    clf.fit(x_train, y_train)
    w ֶशͤ͞Δ
    <>
    <>
    ɾ
    ɾ
    ɾ

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  10. Α͋͘ΔύλʔϯʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    w ࢖͏ಛ௃ྔͷΧϥϜ͚ͩࢦఆ͢Δ
    # e.g)
    feat_col = ['A', 'C', 'D', 'E', 'F', 'J']
    x_train = train[feat_col]
    y_train = train['y']
    # e.g)
    clf.fit(x_train, y_train)
    w ֶशͤ͞Δ
    <>
    <>
    ɾ
    ɾ
    ɾ
    ͋Ε
    b'`ͬͯͲΜͳಛ௃ྔ͚ͩͬʁ

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  11. Α͋͘ΔύλʔϯʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    # e.g)
    train['A'] = train['A'].fillna(0)
    train['B'] = np.log1p(train['B'])
    train['E'] = train['A'] + train['B']
    df_group = train.groupby('D')['E'].mean()
    train['F'] = train['D'].map(df_group)
    <>
    # e.g)
    feat_col = ['A', 'C', 'D', 'E', 'F', 'J']
    x_train = train[feat_col]
    y_train = train['y']
    # e.g)
    clf.fit(x_train, y_train)
    <>
    <>

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  12. Α͋͘ΔύλʔϯʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    # e.g)
    train['A'] = train['A'].fillna(0)
    train['B'] = np.log1p(train['B'])
    train['E'] = train['A'] + train['B']
    df_group = train.groupby('D')['E'].mean()
    train['F'] = train['D'].map(df_group)
    <>
    # e.g)
    feat_col = ['A', 'C', 'D', 'E', 'F', 'J']
    x_train = train[feat_col]
    y_train = train['y']
    # e.g)
    clf.fit(x_train, y_train)
    <>
    <>
    ݟ͚ͭͨʂ
    ʢOPUFCPPLͷ্ͷํʣ

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  13. Α͋͘ΔύλʔϯʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    # e.g)
    train['A'] = train['A'].fillna(0)
    train['B'] = np.log1p(train['B'])
    train['E'] = train['A'] + train['B']
    df_group = train.groupby('D')['E'].mean()
    train['F'] = train['D'].map(df_group)
    <>
    # e.g)
    feat_col = ['A', 'C', 'D', 'E', 'F', 'J']
    x_train = train[feat_col]
    y_train = train['y']
    # e.g)
    clf.fit(x_train, y_train)
    <>
    <>
    ݟ͚ͭͨʂ
    ʢOPUFCPPLͷ্ͷํʣ
    ಛ௃ྔ͕গͳ͍৔߹͸·ͩϚγ͕ͩɺ
    ଟ͘ͳͬͯ͘ΔͱͲΜͳܭࢉͰٻΊͨ
    ಛ௃ྔ͔ͩͬͨΛ͍͍ͪͪߟ͑Δʢ୳͢ʣ
    ͷ͸݁ߏେมͩ͠ɺ͕͔͔࣌ؒΔ

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  14. Α͋͘Δύλʔϯͦͷʢ࣮ମݧʣ
    ϚϚͷҰาΛࢧ͑Δ

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  15. Α͋͘ΔύλʔϯͦͷʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    Αͬ͠Ό͊ʂΊͬͪΌྑ͍είΞͰͨͥʙʙʙ
    ͜ͷOPUFCPPLΛ%VQMJDBUFͯ͠ɺ΋ͬͱྑ͍Ϟσϧ࡞ͬͪΌ͏ͧʂ

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  16. ҰํɺOPUFCPPLͷத਎͸ʜ
    ϚϚͷҰาΛࢧ͑Δ

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  17. Α͋͘ΔύλʔϯͦͷʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    <> import numpy as np
    import pandas as pd
    OPUFCPPLͷத਎
    ɾ
    ɾ
    ɾ
    <> submission.to_csv('submission.csv', index=False)

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  18. Α͋͘ΔύλʔϯͦͷʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    <> import numpy as np
    import pandas as pd
    OPUFCPPLͷத਎
    ɾ
    ɾ
    ɾ
    ɾ
    ɾ
    ɾ
    <> submission.to_csv('submission.csv', index=False)

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  19. Α͋͘ΔύλʔϯͦͷʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    <> import numpy as np
    import pandas as pd
    OPUFCPPLͷத਎
    ɾ
    ɾ
    ɾ
    ɾ
    ɾ
    ɾ
    <> submission.to_csv('submission.csv', index=False)

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  20. Α͋͘ΔύλʔϯͦͷʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    <> import numpy as np
    import pandas as pd
    OPUFCPPLͷத਎
    ɾ
    ɾ
    ɾ
    ɾ
    ɾ
    ɾ
    <> submission.to_csv('submission.csv', index=False)
    ಉ͡ܭࢉΛԿ౓΋΍Βͳ͍ͱ͍͚ͳ͍
    ʴ
    ºʢ ʣˠແବ

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  21. Α͋͘ΔύλʔϯͦͷʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    ౓ॏͳΔ%VQMJDBUFʹΑΓɺOPUFCPPL஍ࠈʹؕΔՄೳੑ΋ʜ
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    ʜʜʜʜʜ

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  22. Α͋͘ΔύλʔϯͦͷʢJQZOCʣ
    ϚϚͷҰาΛࢧ͑Δ
    ౓ॏͳΔ%VQMJDBUFʹΑΓɺOPUFCPPL஍ࠈʹؕΔՄೳੑ΋ʜ
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    dOPUFCPPL-JHIU#(.@TDPSF@JQZOC
    ʜʜʜʜʜ

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  23. ϚϚͷҰาΛࢧ͑Δ
    ࠓ೔࿩͢͜ͱ

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  24. ϚϚͷҰาΛࢧ͑Δ
    σʔλ෼ੳίϯϖʹ͓͍ͯ
    ಛ௃ྔ؅ཧʹർฐ͍ͯ͠Δશਓྨʹ఻͍͑ͨ૝͍
    ʙֶशɾਪ࿦ύΠϓϥΠϯΛఴ͑ͯʙ

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  25. ΞδΣϯμ
    ࣗݾ঺հ
    ಛ௃ྔ؅ཧʹ͍ͭͯ
    ‣ ྻ͝ͱʹQJDLMFϑΝΠϧͰಛ௃ྔΛ؅ཧ
    ‣ ಛ௃ྔੜ੒࣌ɺಉ࣌ʹϝϞϑΝΠϧ΋ੜ੒
    ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ‣ ίϚϯυҰൃͰֶशˠ4VCNJUϑΝΠϧ࡞੒·ͰΛ࣮ߦ
    ‣ ֶशʹ࢖༻ͨ͠ಛ௃ྔ΍Ϟσϧύϥϝʔλ͸MPHͱҰॹʹอଘ
    ‣ TIBQΛ༻͍ͯಛ௃ྔͷߩݙ౓ΛՄࢹԽ͠ɺ࣍ճֶश࣌ͷצॴΛݟ͚ͭΔ
    ϚϚͷҰาΛࢧ͑Δ

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  26. ΞδΣϯμ
    ࣗݾ঺հ
    ಛ௃ྔ؅ཧʹ͍ͭͯ
    ‣ ྻ͝ͱʹQJDLMFϑΝΠϧͰಛ௃ྔΛ؅ཧ
    ‣ ಛ௃ྔੜ੒࣌ɺಉ࣌ʹϝϞϑΝΠϧ΋ੜ੒
    ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ‣ ίϚϯυҰൃͰֶशˠ4VCNJUϑΝΠϧ࡞੒·ͰΛ࣮ߦ
    ‣ ֶशʹ࢖༻ͨ͠ಛ௃ྔ΍Ϟσϧύϥϝʔλ͸MPHͱҰॹʹอଘ
    ‣ TIBQΛ༻͍ͯಛ௃ྔͷߩݙ౓ΛՄࢹԽ͠ɺ࣍ճֶश࣌ͷצॴΛݟ͚ͭΔ
    ϚϚͷҰาΛࢧ͑Δ
    ݰਓͷ஌ܙΛ͓आΓͨ͠Β
    ΊͬͪΌΑ͔ͬͨ
    ʢ˞ʣ
    ͍ͬͯ͏࿩Λ͠·͢
    ʢ˞ʣ͋͘·Ͱओ؍Ͱ͢

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  27. ࣗݾ঺հ
    ϚϚͷҰาΛࢧ͑Δ

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  28. ࣗݾ঺հ
    ϚϚͷҰาΛࢧ͑Δ
    ໊લɿ໺ᖒ఩রʢ/P[BXB5BLBOPCVʣ
    ॴଐɿίωώτגࣜձࣾ
    ɹɹɿ͔ͨͺ͍!UBLBQZ
    w ʙίωώτʹ.-ΤϯδχΞͱͯ͠+0*/
    w ػցֶशʢ/-1ɺਪનγεςϜʣΛϝΠϯʹ΍ΓͭͭΠϯϑϥʢ"84ʣ΋ษڧத
    w ,BHHMFͨ͠ΓɺϒϩάʢIUUQTXXXUBLBQZXPSLʣॻ͍ͨΓɺ໺ٿͨ͠Γɺ
    ϥʔϝϯ৯΂ͨΓ͍ͯ͠·͢

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  29. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ˞ԼههࣄΛࢀߟʹ͍͖ͤͯͨͩ͞·ͨ͠ɻ
    ɾ,BHHMFͰ࢖͑Δ'FBUIFSܗࣜΛར༻ͨ͠ಛ௃ྔ؅ཧ๏
    IUUQTBNBMPHIBUFCMPKQFOUSZLBHHMFGFBUVSFNBOBHFNFOU
    ‣ ྻ͝ͱʹQJDLMFϑΝΠϧͰಛ௃ྔΛ؅ཧ
    ‣ ಛ௃ྔੜ੒࣌ɺಉ࣌ʹϝϞϑΝΠϧ΋ੜ੒

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  30. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ˞ԼههࣄΛࢀߟʹ͍͖ͤͯͨͩ͞·ͨ͠ɻ
    ɾ,BHHMFͰ࢖͑Δ'FBUIFSܗࣜΛར༻ͨ͠ಛ௃ྔ؅ཧ๏
    IUUQTBNBMPHIBUFCMPKQFOUSZLBHHMFGFBUVSFNBOBHFNFOU
    ‣ ྻ͝ͱʹQJDLMFϑΝΠϧͰಛ௃ྔΛ؅ཧ
    ‣ ಛ௃ྔੜ੒࣌ɺಉ࣌ʹϝϞϑΝΠϧ΋ੜ੒
    ࠷ॳʹΠϝʔδΛڞ༗͠·͢

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  31. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    lྻ͝ͱzʹಛ௃ྔΛQJDLMFϑΝΠϧͰ؅ཧ͢Δ

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  32. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    4VSWJWFE 1DMBTT 4FY "HF &NCBSLFE
    NBMF 4
    NBMF $
    GFNBMF $
    NBMF $
    GFNBMF $
    GFNBMF 4
    NBMF 4
    lྻ͝ͱzʹಛ௃ྔΛQJDLMFϑΝΠϧͰ؅ཧ͢Δ

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  33. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    4VSWJWFE 1DMBTT 4FY "HF &NCBSLFE
    NBMF 4
    NBMF $
    GFNBMF $
    NBMF $
    GFNBMF $
    GFNBMF 4
    NBMF 4
    TVSWJWFE@USBJOQLM
    QDMBTT@USBJOQLM
    QDMBTT@UFTUQLM
    TFY@USBJOQLM
    TFY@UFTUQLM
    BHF@USBJOQLM
    BHF@UFTUQLM
    FNCBSLFE@USBJOQLM
    FNCBSLFE@UFTUQLM
    lྻ͝ͱzʹಛ௃ྔΛQJDLMFϑΝΠϧͰ؅ཧ͢Δ

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  34. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ಛ௃ྔੜ੒࣌ɺಉ࣌ʹಛ௃ྔϝϞΛ࡞੒͢Δ

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  35. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ಛ௃ྔੜ੒࣌ɺಉ࣌ʹಛ௃ྔϝϞΛ࡞੒͢Δ

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  36. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ಛ௃ྔੜ੒࣌ɺಉ࣌ʹಛ௃ྔϝϞΛ࡞੒͢Δ
    ݁ߏେมͦ͏ɾɾɾ

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  37. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ಛ௃ྔੜ੒࣌ɺಉ࣌ʹಛ௃ྔϝϞΛ࡞੒͢Δ
    Ͱ΋ɺQZUIPOεΫϦϓτΛ࣮ͭߦ͢Δ͚ͩɻ

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  38. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    IPHFQZΛίϚϯυϥΠϯ͔Β࣮ߦ͢Δ͚ͩ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    class Sex(Feature):
    def create_features(self):
    self.train['Sex'] = train['Sex']
    self.test['Sex'] = test['Sex']
    create_memo('Sex','ੑผ')
    class Age(Feature):
    def create_features(self):
    self.train['Age'] = train['Age']
    self.test['Age'] = test['Age']
    create_memo('Age','೥ྸ')
    class Age_mis_val_median(Feature):
    def create_features(self):
    self.train['Age_mis_val_median'] = train['Age'].fillna(train['Age'].median())
    self.test['Age_mis_val_median'] = test['Age'].fillna(test['Age'].median())
    create_memo('Age_mis_val_median','೥ྸͷܽଛ஋Λதԝ஋Ͱิ׬ͨ͠΋ͷ')

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  39. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    class Sex(Feature):
    def create_features(self):
    self.train['Sex'] = train['Sex']
    self.test['Sex'] = test['Sex']
    create_memo('Sex','ੑผ')
    class Age(Feature):
    def create_features(self):
    self.train['Age'] = train['Age']
    self.test['Age'] = test['Age']
    create_memo('Age','೥ྸ')
    class Age_mis_val_median(Feature):
    def create_features(self):
    self.train['Age_mis_val_median'] = train['Age'].fillna(train['Age'].median())
    self.test['Age_mis_val_median'] = test['Age'].fillna(test['Age'].median())
    create_memo('Age_mis_val_median','೥ྸͷܽଛ஋Λதԝ஋Ͱิ׬ͨ͠΋ͷ')
    IPHFQZΛίϚϯυϥΠϯ͔Β࣮ߦ͢Δ͚ͩ

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  40. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    class Sex(Feature):
    def create_features(self):
    self.train['Sex'] = train['Sex']
    self.test['Sex'] = test['Sex']
    create_memo('Sex','ੑผ')
    class Age(Feature):
    def create_features(self):
    self.train['Age'] = train['Age']
    self.test['Age'] = test['Age']
    create_memo('Age','೥ྸ')
    class Age_mis_val_median(Feature):
    def create_features(self):
    self.train['Age_mis_val_median'] = train['Age'].fillna(train['Age'].median())
    self.test['Age_mis_val_median'] = test['Age'].fillna(test['Age'].median())
    create_memo('Age_mis_val_median','೥ྸͷܽଛ஋Λதԝ஋Ͱิ׬ͨ͠΋ͷ')
    ֤ಛ௃ྔ
    IPHFQZΛίϚϯυϥΠϯ͔Β࣮ߦ͢Δ͚ͩ

    View full-size slide

  41. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    class Sex(Feature):
    def create_features(self):
    self.train['Sex'] = train['Sex']
    self.test['Sex'] = test['Sex']
    create_memo('Sex','ੑผ')
    class Age(Feature):
    def create_features(self):
    self.train['Age'] = train['Age']
    self.test['Age'] = test['Age']
    create_memo('Age','೥ྸ')
    class Age_mis_val_median(Feature):
    def create_features(self):
    self.train['Age_mis_val_median'] = train['Age'].fillna(train['Age'].median())
    self.test['Age_mis_val_median'] = test['Age'].fillna(test['Age'].median())
    create_memo('Age_mis_val_median','೥ྸͷܽଛ஋Λதԝ஋Ͱิ׬ͨ͠΋ͷ')
    ಛ௃ྔϝϞϑΝΠϧ
    IPHFQZΛίϚϯυϥΠϯ͔Β࣮ߦ͢Δ͚ͩ

    View full-size slide

  42. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    DSFBUF@NFNPͷॲཧ֓ཁ

    View full-size slide

  43. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    DSFBUF@NFNPͷॲཧ֓ཁ
    # ಛ௃ྔϝϞcsvϑΝΠϧ࡞੒
    def create_memo(col_name, desc):
    file_path = Feature.dir + '/_features_memo.csv'
    if not os.path.isfile(file_path):
    with open(file_path,"w"):pass
    with open(file_path, 'r+') as f:
    lines = f.readlines()
    lines = [line.strip() for line in lines]
    # ॻ͖ࠐ΋͏ͱ͍ͯ͠Δಛ௃ྔ͕͢Ͱʹॻ͖ࠐ·Ε͍ͯͳ͍͔νΣοΫ
    col = [line for line in lines if line.split(',')[0] == col_name]
    if len(col) != 0:return
    writer = csv.writer(f)
    writer.writerow([col_name, desc])

    View full-size slide

  44. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    DSFBUF@NFNPͷॲཧ֓ཁ
    # ಛ௃ྔϝϞcsvϑΝΠϧ࡞੒
    def create_memo(col_name, desc):
    file_path = Feature.dir + '/_features_memo.csv'
    if not os.path.isfile(file_path):
    with open(file_path,"w"):pass
    with open(file_path, 'r+') as f:
    lines = f.readlines()
    lines = [line.strip() for line in lines]
    # ॻ͖ࠐ΋͏ͱ͍ͯ͠Δಛ௃ྔ͕͢Ͱʹॻ͖ࠐ·Ε͍ͯͳ͍͔νΣοΫ
    col = [line for line in lines if line.split(',')[0] == col_name]
    if len(col) != 0:return
    writer = csv.writer(f)
    writer.writerow([col_name, desc])

    View full-size slide

  45. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Pclass(Feature):
    def create_features(self):
    self.train['Pclass'] = train['Pclass']
    self.test['Pclass'] = test['Pclass']
    create_memo('Pclass','νέοτͷΫϥεɻ1st, 2nd, 3rdͷ3छྨ')
    DSFBUF@NFNPͷॲཧ֓ཁ
    # ಛ௃ྔϝϞcsvϑΝΠϧ࡞੒
    def create_memo(col_name, desc):
    file_path = Feature.dir + '/_features_memo.csv'
    if not os.path.isfile(file_path):
    with open(file_path,"w"):pass
    with open(file_path, 'r+') as f:
    lines = f.readlines()
    lines = [line.strip() for line in lines]
    # ॻ͖ࠐ΋͏ͱ͍ͯ͠Δಛ௃ྔ͕͢Ͱʹॻ͖ࠐ·Ε͍ͯͳ͍͔νΣοΫ
    col = [line for line in lines if line.split(',')[0] == col_name]
    if len(col) != 0:return
    writer = csv.writer(f)
    writer.writerow([col_name, desc])
    $47ܗࣜͰอଘ͓ͯ͘͠ͱ(JUIVC͔Βࢀর͠΍͍͢
    ʢ΋ͪΖΜɺ&YDFM΍/VNCFSTͱ͍ͬͨΞϓϦέʔγϣϯ͔ΒͰ΋៉ྷʹݟ͑Δʣ

    View full-size slide

  46. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ৽͍͠ಛ௃ྔΛ࡞੒͢Δ৔߹

    View full-size slide

  47. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Family_Size(Feature):
    def create_features(self):
    self.train['Family_Size'] = train['Parch'] + train['SibSp']
    self.test['Family_Size'] = test['Parch'] + test['SibSp']
    create_memo('Family_Size','Ո଒ͷ૯਺')
    IPHFQZʹ৽͍͠ಛ௃ྔੜ੒ॲཧΛهड़
    ৽͍͠ಛ௃ྔΛ࡞੒͢Δ৔߹

    View full-size slide

  48. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Family_Size(Feature):
    def create_features(self):
    self.train['Family_Size'] = train['Parch'] + train['SibSp']
    self.test['Family_Size'] = test['Parch'] + test['SibSp']
    create_memo('Family_Size','Ո଒ͷ૯਺')
    QZUIPOIPHFQZ
    ৽͍͠ಛ௃ྔΛ࡞੒͢Δ৔߹
    IPHFQZʹ৽͍͠ಛ௃ྔੜ੒ॲཧΛهड़

    View full-size slide

  49. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    class Family_Size(Feature):
    def create_features(self):
    self.train['Family_Size'] = train['Parch'] + train['SibSp']
    self.test['Family_Size'] = test['Parch'] + test['SibSp']
    create_memo('Family_Size','Ո଒ͷ૯਺')
    QZUIPOIPHFQZ
    ৽͍͠ಛ௃ྔͷΈੜ੒
    ৽͍͠ಛ௃ྔΛ࡞੒͢Δ৔߹
    IPHFQZʹ৽͍͠ಛ௃ྔੜ੒ॲཧΛهड़

    View full-size slide

  50. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    σʔλΛಡΈࠐΉࡍ͸ɺಛ௃ྔΛࢦఆͯ͠ϩʔυ͢Δ͚ͩ
    # ಛ௃ྔͷࢦఆ
    features = [
    "age_mis_val_median",
    "family__size",
    "cabin",
    "fare_mis_val_median"
    ]
    df = [pd.read_pickle(FEATURE_DIR_NAME + f’{f}_train.pkl') for f in features]
    df = pd.concat(df, axis=1)

    View full-size slide

  51. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    σʔλΛಡΈࠐΉࡍ͸ɺಛ௃ྔΛࢦఆͯ͠ϩʔυ͢Δ͚ͩ
    # ಛ௃ྔͷࢦఆ
    features = [
    "age_mis_val_median",
    "family__size",
    "cabin",
    "fare_mis_val_median"
    ]
    df = [pd.read_pickle(FEATURE_DIR_NAME + f’{f}_train.pkl') for f in features]
    df = pd.concat(df, axis=1)
    Կ͕خ͔͔ͬͨ͠

    View full-size slide

  52. ಛ௃ྔ؅ཧʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    w ͭͷεΫϦϓτϑΝΠϧʹಛ௃ྔੜ੒Λ·ͱΊΔ͜ͱͰɺಉ͡ܭࢉΛෳ਺
    ճ࣮ߦ͢Δ͜ͱΛආ͚ɺ࣌ؒΛ༗ޮ׆༻Ͱ͖Δɻ
    ɹˠಛ௃ྔͷ࠶ݱੑ΋୲อɻ
    w ಛ௃ྔͷϝϞΛಉ࣌ʹੜ੒͢Δ͜ͱͰʮ͜ͷಛ௃ྔͳΜ͚ͩͬʁʯͱ಄Λ࢖
    ΘͣʹࡁΜͩɻ
    w ಛ௃ྔΛྻ͝ͱʹ؅ཧ͢Δ͜ͱͰऔΓճָ͕͠ʹͳΔɻ
    ɹˠQJDLMFϑΝΠϧͩͱอଘ΋ಡΈࠐΈ΋଎͍ʂ
    ɹˠಛ௃ྔ͕๲େʹͳΔ৔߹͸ɺ͋Δఔ౓ͷ୯ҐͰ·ͱΊͯ؅ཧ͢Δํ͕ྑ͍͔΋ɻ

    View full-size slide

  53. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ˞Լهॻ੶ʹܝࡌ͞Ε͍ͯΔύΠϓϥΠϯΛࢀߟʹ͠·ͨ͠ɻ
    ɾ,BHHMFͰউͭσʔλ෼ੳͷٕज़
    IUUQTHJIZPKQCPPL
    ‣ ίϚϯυҰൃͰֶशˠ4VCNJUϑΝΠϧ࡞੒·ͰΛ࣮ߦ
    ‣ ֶशʹ࢖༻ͨ͠ಛ௃ྔ΍Ϟσϧύϥϝʔλ͸MPHͱҰॹʹอଘ
    ‣ TIBQΛ༻͍ͯಛ௃ྔͷߩݙ౓ΛՄࢹԽ͠ɺ࣍ճֶश࣌ͷצॴΛݟ͚ͭΔ

    View full-size slide

  54. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    SVOQZΛ࣮ߦ͢Δ͜ͱͰɺֶशɾਪ࿦ɾ4VCNJUϑΝΠϧΛ࡞੒
    # ಛ௃ྔͷࢦఆ
    features = [
    "age_mis_val_median",
    "family__size",
    "cabin",
    "fare_mis_val_median"
    ]
    run_name = 'lgb_1102'
    # ࢖༻͢Δಛ௃ྔϦετͷอଘ
    with open(LOG_DIR_NAME + run_name + "_features.txt", 'wt') as f:
    for ele in features:
    f.write(ele+'\n')
    params_lgb = {
    'boosting_type': 'gbdt',
    'objective': 'binary',
    'early_stopping_rounds': 20,
    'verbose': 10,
    'random_state': 99,
    'num_round': 100
    }
    # ࢖༻͢Δύϥϝʔλͷอଘ
    with open(LOG_DIR_NAME + run_name + "_param.txt", 'wt') as f:
    for key,value in sorted(params_lgb.items()):
    f.write(f'{key}:{value}\n')
    runner = Runner(run_name, ModelLGB, features, params_lgb, n_fold, name_prefix)
    runner.run_train_cv() # ֶश
    runner.run_predict_cv() # ਪ࿦
    Submission.create_submission(run_name) # submit࡞੒

    View full-size slide

  55. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    SVOQZΛ࣮ߦ͢Δ͜ͱͰɺֶशɾਪ࿦ɾ4VCNJUϑΝΠϧΛ࡞੒
    # ಛ௃ྔͷࢦఆ
    features = [
    "age_mis_val_median",
    "family__size",
    "cabin",
    "fare_mis_val_median"
    ]
    run_name = 'lgb_1102'
    # ࢖༻͢Δಛ௃ྔϦετͷอଘ
    with open(LOG_DIR_NAME + run_name + "_features.txt", 'wt') as f:
    for ele in features:
    f.write(ele+'\n')
    params_lgb = {
    'boosting_type': 'gbdt',
    'objective': 'binary',
    'early_stopping_rounds': 20,
    'verbose': 10,
    'random_state': 99,
    'num_round': 100
    }
    # ࢖༻͢Δύϥϝʔλͷอଘ
    with open(LOG_DIR_NAME + run_name + "_param.txt", 'wt') as f:
    for key,value in sorted(params_lgb.items()):
    f.write(f'{key}:{value}\n')
    runner = Runner(run_name, ModelLGB, features, params_lgb, n_fold, name_prefix)
    runner.run_train_cv() # ֶश
    runner.run_predict_cv() # ਪ࿦
    Submission.create_submission(run_name) # submit࡞੒
    ͜ͷSVO@OBNFΛQSFpYͱͯ͠ɺϑΝΠϧ΍ϞσϧΛอଘͯ͘͠ΕΔɻ

    w ࢖༻ͨ͠ಛ௃ྔϦετ
    w ࢖༻ͨ͠ϋΠύʔύϥϝʔλ
    w GPMEຖͷϞσϧ
    w ਪ࿦݁Ռ
    w TVCNJUϑΝΠϧ
    w TIBQͷܭࢉ݁ՌΠϝʔδϑΝΠϧͳͲ

    View full-size slide

  56. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    SVOQZΛ࣮ߦ͢Δ͜ͱͰɺֶशɾਪ࿦ɾ4VCNJUϑΝΠϧΛ࡞੒
    # ಛ௃ྔͷࢦఆ
    features = [
    "age_mis_val_median",
    "family__size",
    "cabin",
    "fare_mis_val_median"
    ]
    run_name = 'lgb_1102'
    # ࢖༻͢Δಛ௃ྔϦετͷอଘ
    with open(LOG_DIR_NAME + run_name + "_features.txt", 'wt') as f:
    for ele in features:
    f.write(ele+'\n')
    params_lgb = {
    'boosting_type': 'gbdt',
    'objective': 'binary',
    'early_stopping_rounds': 20,
    'verbose': 10,
    'random_state': 99,
    'num_round': 100
    }
    # ࢖༻͢Δύϥϝʔλͷอଘ
    with open(LOG_DIR_NAME + run_name + "_param.txt", 'wt') as f:
    for key,value in sorted(params_lgb.items()):
    f.write(f'{key}:{value}\n')
    runner = Runner(run_name, ModelLGB, features, params_lgb, n_fold, name_prefix)
    runner.run_train_cv() # ֶश
    runner.run_predict_cv() # ਪ࿦
    Submission.create_submission(run_name) # submit࡞੒
    ੜ੒͞ΕΔϑΝΠϧྫ

    View full-size slide

  57. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ੜ੒͞ΕΔϑΝΠϧͷྫʢϑΥϧμ͸దٓ෼͚͍ͯ·͢ʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@QSFEQLMʢUFTUσʔλͰͷਪ࿦݁Ռʣ
    w MHC@@@TVCNJTTJPODTWʢਪ࿦݁ՌΛLBHHMFʹఏग़Ͱ͖ΔDTWʹม׵ͨ͠΋ͷʣ
    w MHC@@@GFBUVSFTUYUʢࠓճͷֶशʹ࢖༻ͨ͠ಛ௃ྔϦετʣ
    w MHC@@@QBSBNUYUʢࠓճͷֶशʹ࢖༻ͨ͠ϋΠύʔύϥϝʔλʣ
    w MHC@@@TIBQQOHʢTIBQͰܭࢉͨ͠ՄࢹԽΠϝʔδʣ
    w HFOFSBMMPHʢܭࢉϩάϑΝΠϧʣ
    w SFTVMUMPHʢϞσϧͷείΞ͚͕ͩهࡌ͞ΕͨϩάϑΝΠϧʣ

    View full-size slide

  58. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ੜ੒͞ΕΔϑΝΠϧͷྫʢϑΥϧμ͸దٓ෼͚͍ͯ·͢ʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@QSFEQLMʢUFTUσʔλͰͷਪ࿦݁Ռʣ
    w MHC@@@TVCNJTTJPODTWʢਪ࿦݁ՌΛLBHHMFʹఏग़Ͱ͖ΔDTWʹม׵ͨ͠΋ͷʣ
    w MHC@@@GFBUVSFTUYUʢࠓճͷֶशʹ࢖༻ͨ͠ಛ௃ྔϦετʣ
    w MHC@@@QBSBNUYUʢࠓճͷֶशʹ࢖༻ͨ͠ϋΠύʔύϥϝʔλʣ
    w MHC@@@TIBQQOHʢTIBQͰܭࢉͨ͠ՄࢹԽΠϝʔδʣ
    w HFOFSBMMPHʢܭࢉϩάϑΝΠϧʣ
    w SFTVMUMPHʢϞσϧͷείΞ͚͕ͩهࡌ͞ΕͨϩάϑΝΠϧʣ

    View full-size slide

  59. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ੜ੒͞ΕΔϑΝΠϧͷྫʢϑΥϧμ͸దٓ෼͚͍ͯ·͢ʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@QSFEQLMʢUFTUσʔλͰͷਪ࿦݁Ռʣ
    w MHC@@@TVCNJTTJPODTWʢਪ࿦݁ՌΛLBHHMFʹఏग़Ͱ͖ΔDTWʹม׵ͨ͠΋ͷʣ
    w MHC@@@GFBUVSFTUYUʢࠓճͷֶशʹ࢖༻ͨ͠ಛ௃ྔϦετʣ
    w MHC@@@QBSBNUYUʢࠓճͷֶशʹ࢖༻ͨ͠ϋΠύʔύϥϝʔλʣ
    w MHC@@@TIBQQOHʢTIBQͰܭࢉͨ͠ՄࢹԽΠϝʔδʣ
    w HFOFSBMMPHʢܭࢉϩάϑΝΠϧʣ
    w SFTVMUMPHʢϞσϧͷείΞ͚͕ͩهࡌ͞ΕͨϩάϑΝΠϧʣ

    View full-size slide

  60. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ੜ੒͞ΕΔϑΝΠϧͷྫʢϑΥϧμ͸దٓ෼͚͍ͯ·͢ʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@QSFEQLMʢUFTUσʔλͰͷਪ࿦݁Ռʣ
    w MHC@@@TVCNJTTJPODTWʢਪ࿦݁ՌΛLBHHMFʹఏग़Ͱ͖ΔDTWʹม׵ͨ͠΋ͷʣ
    w MHC@@@GFBUVSFTUYUʢࠓճͷֶशʹ࢖༻ͨ͠ಛ௃ྔϦετʣ
    w MHC@@@QBSBNUYUʢࠓճͷֶशʹ࢖༻ͨ͠ϋΠύʔύϥϝʔλʣ
    w MHC@@@TIBQQOHʢTIBQͰܭࢉͨ͠ՄࢹԽΠϝʔδʣ
    w HFOFSBMMPHʢܭࢉϩάϑΝΠϧʣ
    w SFTVMUMPHʢϞσϧͷείΞ͚͕ͩهࡌ͞ΕͨϩάϑΝΠϧʣ

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  61. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ੜ੒͞ΕΔϑΝΠϧͷྫʢϑΥϧμ͸దٓ෼͚͍ͯ·͢ʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@QSFEQLMʢUFTUσʔλͰͷਪ࿦݁Ռʣ
    w MHC@@@TVCNJTTJPODTWʢਪ࿦݁ՌΛLBHHMFʹఏग़Ͱ͖ΔDTWʹม׵ͨ͠΋ͷʣ
    w MHC@@@GFBUVSFTUYUʢࠓճͷֶशʹ࢖༻ͨ͠ಛ௃ྔϦετʣ
    w MHC@@@QBSBNUYUʢࠓճͷֶशʹ࢖༻ͨ͠ϋΠύʔύϥϝʔλʣ
    w MHC@@@TIBQQOHʢTIBQͰܭࢉͨ͠ՄࢹԽΠϝʔδʣ
    w HFOFSBMMPHʢܭࢉϩάϑΝΠϧʣ
    w SFTVMUMPHʢϞσϧͷείΞ͚͕ͩهࡌ͞ΕͨϩάϑΝΠϧʣ

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  62. ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ
    ϚϚͷҰาΛࢧ͑Δ
    ੜ੒͞ΕΔϑΝΠϧͷྫʢϑΥϧμ͸దٓ෼͚͍ͯ·͢ʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@GPMENPEFMʢGPMEͰ࡞੒͞ΕͨϞσϧʣ
    w MHC@@QSFEQLMʢUFTUσʔλͰͷਪ࿦݁Ռʣ
    w MHC@@@TVCNJTTJPODTWʢਪ࿦݁ՌΛLBHHMFʹఏग़Ͱ͖ΔDTWʹม׵ͨ͠΋ͷʣ
    w MHC@@@GFBUVSFTUYUʢࠓճͷֶशʹ࢖༻ͨ͠ಛ௃ྔϦετʣ
    w MHC@@@QBSBNUYUʢࠓճͷֶशʹ࢖༻ͨ͠ϋΠύʔύϥϝʔλʣ
    w MHC@@@TIBQQOHʢTIBQͰܭࢉͨ͠ՄࢹԽΠϝʔδʣ
    w HFOFSBMMPHʢܭࢉϩάϑΝΠϧʣ
    w SFTVMUMPHʢϞσϧͷείΞ͚͕ͩهࡌ͞ΕͨϩάϑΝΠϧʣ
    Կ͕خ͔͔ͬͨ͠

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  63. ϚϚͷҰาΛࢧ͑Δ
    w ʮ͜ͷಛ௃ྔʯͱʮ͜ͷύϥϝʔλʯΛ࢖ֶͬͯशͤͨ͞Ϟσϧ
    ʹؔͯ͠ɺʮ֤λεΫʹཁͨ࣌ؒ͠ʯͱʮ֤GPMEʴ࠷ऴతͳεί
    ΞʯΛҙࣝ͠ͳͯ͘΋؅ཧͰ͖ΔΑ͏ʹɻ
    w TIBQͷܭࢉ݁Ռ΍GFBUVSFJNQPSUBODFΛग़ྗ͓ͯ͘͜͠ͱ
    Ͱɺ࣍ͷֶश࣌ͷצॴ͕௫ΊΔΑ͏ʹɻ
    ֶशɾਪ࿦ύΠϓϥΠϯʹ͍ͭͯ

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  64. ·ͱΊ
    ϚϚͷҰาΛࢧ͑Δ

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  65. ·ͱΊ
    ϚϚͷҰาΛࢧ͑Δ
    w ಛ௃ྔ؅ཧ͍͍ͧʂ
    ͭͷεΫϦϓτϑΝΠϧʹಛ௃ྔੜ੒Λ·ͱΊΔ͜ͱͰɺಉ͡ܭࢉΛෳ਺ճ࣮ߦ͢Δ͜ͱ
    ΛճආͰ͖Δʂ
    ಛ௃ྔͷϝϞΛಉ࣌ʹੜ੒͢Δ͜ͱͰʮ͜ͷಛ௃ྔͳΜ͚ͩͬʁʯͱ಄Λ࢖͏ճ਺͕ݮ
    Δʂ
    ಛ௃ྔΛྻ͝ͱʹ؅ཧ͢Δ͜ͱͰऔΓճָ͕͠ʹͳͬͨʂʢ͕ɺಛ௃ྔ͕๲େͳ৔߹͸͋
    Δఔ౓ͷ·ͱ·ΓͰ؅ཧͨ͠ํ͕ྑ͍͔΋ʣ
    w ύΠϓϥΠϯ͍͍ͧʂ
    ύΠϓϥΠϯΛߏங͢Δ͜ͱͰɺߴ଎ͳ1%$"Λ࣮ݱʂ
    ֶशʹ࢖༻ͨ͠ಛ௃ྔͱύϥϝʔλΛ؅ཧ͢Δ͜ͱͰɺ࠶ݱੑ΋୲อ͞Ε৺ཧత҆શੑ΋

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  66. ϚϚͷҰาΛࢧ͑Δ
    ͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠

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