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RCO アドテク部勉強会 2014/11/12
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Kenji Sugiki
November 20, 2014
Technology
1.9k
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RCO アドテク部勉強会 2014/11/12
エンジニアのための機械学習入門
Kenji Sugiki
November 20, 2014
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Transcript
Τ ϯ δχ Ξ ͷ ͨ Ί ͷ ػ ց
ֶ श ೖ ਿ ݈ ೋ @ 2 0 1 4 / 1 1 / 1 2 Ξ υ ς Ϋ ෦ ษ ڧ ձ
· ͣ ʢ ؆ ୯ ͳ ʣ ࣗ
ݾ հ ͔ Β ʂ
࣮ ɺ σ ʔ λ αΠΤ ϯ ςΟε τ
Λ ۀ ͱ ͠ ͯ ॳ Ίͯ ͔ Β 3 ͷ ۦ ͚ ग़ ͠Ͱ ͢ɻ ʢ େ ֶ ͷ ͱ ͖ ݚ ڀ ͱ ͯ͠ গ ͠ ͬͯ · ͠ ͨ ʣ ձ ࣾ ੈ ͷ த ͷ χʔζ ʹ ߹ Θ ͤ ͯɺ W E B Ξ ϓ Ϧ ։ ൃ ͔ Β ੳ Τ ϯ δ ϯ ։ ൃ ʹ ͩ Μ ͩ Μ γ ϑ τ ͯ͠ · ͠ ͨ ɻ
ʮ ػ ց ֶ श ʯ ͱ ͍ ͏ ͱ
͢͠ ͗ ͯ ࣗ ʹ Ͱ ͖ ͳ ͍ ͱ ࢥ ͬͯ ͠ · ͏ ਓ ͍ Δ ͔ ͱ ࢥ ͍ · ͢ɻ Ͱ ɺ ͦ Μ ͳ ͜ ͱ ͳ ͍ Α ɺ ͱ ͍ ͏ Λ ࠓ ͠ Α ͏ ͱ ࢥ ͍ · ͢ɻ http://shop.oreilly.com/product/0636920018483.do "Machine Learning for Hackers - Case Studies and Algorithms to Get You Started" Drew Conway, John Myles White, O'reilly ↑ػցֶशͷ࣮ྫͱ͍ํΛΔʹΦεεϝ
ϑϧε λ ο Ϋ Τ ϯ δχ Ξ άϩʔεϋοΧʔͬͯྲྀߦͬ
ͯ · ͢ Ͷʁ ػ ց ֶ श ͥ ͻ Ҿ ͖ ग़ ͠ ͱ ͯ͠ Ճ ͑ͯ Β ͑Δ ͱ ɻ
Ͱ ɺ ػ ց ֶ श ͬͯ
͠ ͍ ΜͰ ͠ ΐʁ
ػ ց ֶ श ʹ ࣜ ػ ց ֶ
श ʹ Ξϧ ΰ Ϧζ Ϝ ͱࢥ͏ͱɺΤϯδχΞʹೃછΈ͕ਂ͘ ϋʔυϧ͕͍ͩͿԼ͕Γ·͢Ͷɻ
k-meansͷΞϧΰϦζϜʢٙࣅίʔυʴࣜʣ ͳΜ͔ͦ͠͏!ʂ http://stanford.edu/~cpiech/cs221/handouts/kmeans.html
ͨͩͷforϧʔϓͷ ͳ͔Ͱͯ͠Δͩ ͚ ΛҾ͍ͯೋ͠ ͯɺҰ൪খ͍͞൪ ߸Λฦͯ͠Δ͚ͩ ϥϯμϜʹkݸબͿ ͚ͩ ಡΈํ͓͚͑ͬͯ͞ɺϧʔϓͱؔͷΈ߹ΘͤͳͷͰ ͦΜͳʹ͘͠ͳ͍Ͱ͢ɻ
ຊޠʹ͢Δͱɺ σʔλɿμϒϧྻ [σʔλ1..N][ಛͷॏΈ] ॳظԽɿ 1. ϥϯμϜʹΫϥελத৺ʢηϯτϩΠυʣΛkݸબͿ ෮ɿ 2. Nݸͷσʔλʹରͯ͠ɺkݸͷηϯτϩΠυͱڑ͕Ұ൪͍ۙΫϥελ ൪߸ΛৼΔɻ
3. ͯ͢ͷΫϥελkʹରͯ͠ɺ൪߸ৼͬͨσʔλͷฏۉΛͱͬͯɺͦΕ ΛηϯτϩΠυͱ͢Δɻ ߋ৽͕ͳ͘ͳΔ·Ͱ2, 3Λ܁Γฦ͢ɻ ྻͱforϧʔϓͱൺֱΦϖϨʔλΛ͑ҙ֎ͱ؆୯ʹͰ͖·͢ɻ
ຖ ճ ࣮ Ί Μ Ͳ ͍ ͳ
ɻ ɻ
ͪ Ζ Μ ศ ར ͳ ϥ Π ϒ
ϥ Ϧ ͕ ఏ ڙ ͞ Εͯ ͍ · ͢Α ʂ
Ϋ ϥε ϝ ι ο υ ͱ ೖ
ग़ ྗ ͷ σ ʔ λ ϑ Υ ʔϚ ο τ ͞ ͑ ͓ ͞ ͑ͯ ͓ ͚ ɺ Έ ͳ ͞ ΜͰ ͖ · ͢ Ͷ ^ ^
Numpy, Scipy: ઢܗϥΠϒϥϦ Scikit-Learn: ػցֶशϥΠϒϥϦ Statsmodels: ճؼੳϥΠϒϥϦ Pandas: σʔλૢ࡞ܥϥΠϒϥϦ Matplotlib:
άϥϑඳࣸܥϥΠϒϥϦ Ipython (Notebook): ରࣜΠϯλϑΣʔε PythonϥΠϒϥϦͱͯ͜͠Μͳͷ͕͋Γ·͢ ͜Ε͚ͩ͑܅σʔλαΠΤϯςΟετʹʂʂ
I P Y T H O N N O T
E B O O K 1. ϒϥβͰίʔυ͕ॻ͚Δ 2. MarkdownͰυΩϡϝϯτ ΛͤΔʢࣜରԠʣ 3. ରࣜʹίʔυ͕ॻ͚Δ 4. ίʔυิϔϧϓදࣔ 5. σʔλ݁ՌΛਤࣔ http://ipython.org/notebook.html
1. CVSSQLͰඞཁͳσʔλΛ४උ͢Δ 2. PandasͰσʔλΛಡΈࠐΉ 3. ػցֶशͰಡΊΔΑ͏ʹσʔλΛܗ͢Δ 4. scikit-learnstatsmodelͷػցֶशϞσϧΛݺͼग़͢(fit) 5. ݁ՌΛςʔϒϧͰදࣔͨ͠ΓɺmatplotlibͰਤࣔͯ͠ධՁ͢Δ
6. ݁Ռ͕Α͚ΕͦͷϞσϧΛద༻͢Δ (predict) df = pd.read_csv(…) df[‘device’].apply({‘pc’:0, ‘sp’:1}} clf = RandomForestClassifier(n_estimators=10) clf.fit(df[‘తม’], df.ix[:, ’આ໌ม1’:’આ໌มn’]) clf.feature_importances_ # ॏཁੑͷධՁ ༧ଌ݁Ռ = clf.predict(new_df[‘આ໌ม1’:’આ໌มn’])
ػցֶशͷೖྗσʔλͷϑΥʔϚοτʢ͍͍͍ͭͩͨಉ͡ʣ తมyʢ୯Ұʣ આ໌มXʢෳʣ ࣈorϑϥά ΧςΰϦorࣈorϑϥά ※ิɿΫϥελϦϯάͷ࣌తม͕ͳ͍ʹ͕͑ͳ͍ɹϨίʔυʹσʔλΛ͍͍ײ͡ʹ͚ͯ͘ΕΔ ༧ଌ͍ͨ͑͠KPI நग़ͨ͠ଐੑσʔλ جຊCSVϑΝΠϧͱಉ͡ߏʢߦྻʣͰɺઆ໌ม͕ଟ͍࣌มIDͱͷΩʔό ϦϡʔͰ࣋ͭͱޮతʢม͕ͳ͍͜ͱ͕ଟ͍ͷͰʣ
આ໌ม ≒ ΦϒδΣΫτΫϥεͷϝϯόม y = w_1 * x_1 + w_2 * x_2 + …
ྫʣϢʔβ͕ͦͷࠂΛΫϦοΫ͢Δ͔Λ༧ଌ͍ͨ͠ʁ ΫϦοΫͨ͠/͠ͳ͍ ϢʔβͷσϞάϥใ ࠂͷଐੑσʔλ తมʢ୯Ұʣ આ໌มʢෳʣ ֶशσʔλʢ͜Ε·Ͱͷ݁Ռͷσʔλʣ ςετσʔλʢ༧ଌ͍ͨ͠σʔλʣ ෆ໌ తมʢ୯Ұʣ
ϢʔβͷσϞάϥใ ࠂͷଐੑσʔλ આ໌มʢෳʣ ͜͜Λػց͕উखʹຒΊͯ͘ΕΔ
ද త ͳ ख ๏ - ྨɾճؼੳ - ΫϥελϦϯά
- ࣌ܥྻੳ - ΞιγΤʔγϣϯੳʗόεέοτੳ ৗۀϨϕϧͳΒɺ ؔͱͦͷ͍ํ͓͚͑ͬͯ͞ ଟ͘ͷਓ͖ͬͱͰ͖·͢ɻ http://en.wikipedia.org/wiki/Regression_analysis http://en.wikipedia.org/wiki/K-means_clustering http://webdocs.cs.ualberta.ca/~zaiane/courses/cmput499/slides/Lect10/sld054.htm http://www.dtreg.com/TimeSeries.htm
ৄࡉඌ࡚͞ΜͷຊϒϩάͰʂ ʮۜ࠲Ͱಇ͘Data Scientistͷϒϩάʯ http://tjo.hatenablog.com/ ͱΓ͋͑ͣɺखΛಈ͔ͯͬͯ͠ΈΕͳΜͱ͔ͳΓ·͢ʂ ʮखΛಈ͔͠ͳ͕ΒֶͿ Ϗδωεʹ׆͔͢σʔλϚΠχϯάʯ http://gihyo.jp/book/2014/978-4-7741-6674-2
S C I K I T- L E A R
N A L G O R I T H M C H E AT- S H E E T Ұ෦Ͱ͕͢ɺ ͲͷϞσϧΛ બ͍͍͔ Θ͔Γ͍͢ http://scikit-learn.org/stable/tutorial/machine_learning_map/
σ ʔ λ ྔ ͕ े Χ ς ΰ
Ϧ ྨ ͠ ͨ ͍ ਖ਼ ղ σ ʔ λ ͋ Γ σ ʔ λ ྔ ͦ Μ ͳ ʹ ଟ ͘ ͳ ͍ ઢ ܗ S V M ( S V C )
͞ ͖ ΄ Ͳ ೖ ྗ σ ʔ λ Λ
४ උ ͢ Δ ͱ ͍ ͏ ͕ ͋ Γ · ͠ ͨ ͕
࣮ લ ॲ ཧ ͱ ͍ ͏ ࣄ
ͷ ΄ ͏ ͕ େ ม ͜ Ε ʹ ಛ Խ ͠ ͨ ਓ Λ Ϛ Τ γ ϣ Ϧε τ ͱ ݺ Ϳ ɻ PythonͰͷલॲཧͷΦεεϝຊ http://www.oreilly.co.jp/books/9784873116556/ ʮPythonʹΑΔσʔλੳೖʕNumPyɺpandasΛͬͨσʔλॲཧʯ O'reilly, Wes McKinney΄͔
࣮ ట ष ͍ ɺ ੜ ϩ ά
D B Λ ू ܭ ɾ ݁ ߹ ɾ ܗ ͯ͠ɺ ཉ ͠ ͍ ಛ σ ʔ λ Λ ࡞ Δ ͓ ࣄ ͕ ۀ ͷ େ ෦ Λ క Ίͯ · ͢ ʢ ʙ 9 ׂ ʣ
Τ ϯ δχ Ξ ͷ Έ ͳ ͞ Μ
ͳ Β ૾ ͭ ͘ ͱ ࢥ ͏ ͷ Ͱ ׂ Ѫ
Έ ͳ ͞ Μ ɺ ͥ ͻ Ծ આ
Λ ཱ ͯ ͯɺ σ ʔ λ Λ ४ උ ͯ͠ɺ τ ϥ Π ͯ͠ Έ ͯ͘ ͩ ͞ ͍ ɻ
ͪ ͳ Έ ʹ ɺ ࠷ ۙ ͷ ࣗ
ͷ ͓ ࣄ ϩάσʔλՃɿ͜Ε͕Ϗοάσʔλ DBͷσʔλՃɿ͋ΕɺDBAʁ σʔλͷ݁߹ɿͦΕ୯ʹJOINͯ͠1ͭͷςʔϒϧΛ࡞ͬͯΔͷͶ ػցֶशɿ͋͋ɺྫͷϥΠϒϥϦ͏ͷͶ σʔλੳɿͪΐͬͽ͠ σʔλαΠΤϯςΟετʹੳΔਓ ͡Όͳͯ͘ɺݩʑΤϯδχΞدΓͷ৬ۀͰɺ ෯͍εΩϧ͕ٻΊΒΕ·͢ɻ "Ϗοάσʔλʴۀཧղʴػցֶशʴੳʴ࣮"
Έ ͳ ͞ Μ ͷ ۀ σ ʔ
λ αΠΤ ϯ ςΟε τ ͷ Ұ ෦ Λ ୲ ͬͯ ͍ · ͢ ʂ ͥ ͻ ͜ ͷ ػ ձ ʹ ɺ ػ ց ֶ श ʹ τ ϥ Π ͠ ͯɺάϩʔεϋοΫྗΛ৳ͯ͠Έͯʁ σ ʔ λ αΠΤ ϯ ςΟε τ ʹ Ұ ਓ ͷ εʔύʔϚ ϯ σ ʔ λ αΠΤ ϯ ςΟε τ ʹ ν ʔϜ
͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠ɻ