CS246: Mining Massive Datasets Jure Leskovec, Stanford University

Transcription

1 CS246: Mnng Massve Datasets Jure Leskovec, Stanford Unversty

2 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 2 Hgh dm. data Graph data Infnte data Machne learnng Apps Localty senstve hashng PageRank, SmRank Flterng data streams SVM Recommen der systems Clusterng Communty Detecton Web advertsng Decson Trees Assocaton Rules Dmensonal ty reducton Spam Detecton Queres on streams Perceptron, knn Duplcate document detecton

3 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 3 Study of algorthms that mprove ther performance at some task th experence

4 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 4 Gven some data: Learn a functon to map from the nput to the output Gven: Tranng examples xx, yy = ff xx for some unknon functon ff Fnd: A good approxmaton to ff

5 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 5 Would lke to do predcton: estmate a functon f(x) so that y = f(x) Where y can be: Real number: Regresson Categorcal: Classfcaton Complex object: Rankng of tems, Parse tree, etc. Data s labeled: Have many pars {(x, y)} x vector of bnary, categorcal, real valued features y class ({1, 1}, or a real number)

6 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 6 Task: Gven data (X,Y) buld a model f() to predct Y based on X Strategy: Estmate yy = ff xx on (XX, YY). Hope that the same ff(xx) also orks to predct unknon YY The hope s called generalzaton Tranng data Test data Overfttng: If f(x) predcts ell Y but s unable to predct Y We ant to buld a model that generalzes ell to unseen data But Jure, ho can e ell on data e have never seen before?!? X X Y Y

7 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 7 tranng ponts 1) Tranng data s dran ndependently at random accordng to unknon probablty dstrbuton PP(xx, yy) 2) The learnng algorthm analyzes the examples and produces a classfer ff Gven ne data xx, yy dran from PP, the classfer s gven xx and predcts yy = ff(xx) The loss LL(yy, yy) s then measured Goal of the learnng algorthm: Fnd ff that mnmzes expected loss EE PP [LL]

8 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 8 tranng data PP(xx, yy) Tranng set SS test data Learnng algorthm (xx, yy) yy xx ff Why s t hard? We estmate ff on tranng data but ant the ff to ork ell on unseen future (.e., test) data yy yy loss functon L(yy, yy)

9 Goal: Mnmze the expected loss mn EE P[LL] But, e don t have access to PP but only to tranng sample DD: mn EE D[LL] So, e mnmze the average loss on the tranng data: 2/17/2015 mn NN JJ = 1 NN L h(xx), yy =1 Problem: Just memorzng the tranng data gves us a perfect model (th zero loss) Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 9

10 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 10 Gven: A set of N tranng examples {(xx (1), yy (1) ), (xx (2), yy (2) ),, (xx (nn), yy (nn) )} A loss functon LL Fnd: The eght vector that mnmzes the expected loss on the tranng data NN JJ = 1 NN L ssssss xx, yy =1

11 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 11 Problem: Stepse Constant Loss functon Loss *x Dervatve s ether 0 or

12 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 12 Approxmatng the expected loss by a smooth functon Replace the orgnal objectve functon by a surrogate loss functon. E.g., hnge loss: NN JJ = 1 NN max 0, 1 yy xx () =1 When yy = 1:

13 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 13

14 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 14 Mnmze ff by Gradent Descent Start th eght vector (0) Compute gradent JJ (0) = JJ (0) 0, JJ (0) 1,, Compute (1) = (0) ηηηηjj (0) here ηη s a step sze parameter Repeat untl convergence JJ (0) nn

15 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 15 Example: Spam flterng Instance space x X ( X = n data ponts) Bnary or realvalued feature vector x of ord occurrences d features (ords other thngs, d~100,000) Class y Y y: Spam (1), Ham (1)

16 PP(xx, yy): dstrbuton of emal messages xx and ther true labels yy ( spam, ham ) Tranng sample: a set of emal messages that have been labeled by the user Learnng algorthm: What e study! ff: The classfer output by the learnng alg. Test pont: A ne emal xx (th ts true, but hdden, label yy) Loss functon LL(yy, yy): 2/17/2015 predcted label yy true label yy spam ham spam 0 10 not spam 1 0 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 16

17 Idea: Pretend e do not kno the data/labels e actually do kno Tranng set Buld the model f(x) on X Valdaton the tranng data (mnmze J) set See ho ell f(x) does on Test X set the valdaton data If t does ell, then apply t also to X Refnement: Cross valdaton Estmate y = f(x) on X,Y. Hope that the same f(x) also orks on unseen X, Y Splttng nto tranng/valdaton set s brutal Let s splt our data (X,Y) nto 10folds (buckets) Take out 1fold for valdaton, tran on remanng 9 Repeat ths 10 tmes, report average performance 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 17 Y

18 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 19 We ll talk about the follong methods: Support Vector Machnes Decson trees Man queston: Ho to effcently tran (buld a model/fnd model parameters)?

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20 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 21 Want to separate from usng a lne Data: Tranng examples: (x 1, y 1 ) (x n, y n ) Each example : x = ( x (1),, x (d) ) x (j) s real valued y { 1, 1 } Inner product: dd xx = (jj) xx (jj) jj=1 Whch s best lnear separator (defned by )?

21 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 22 A B C Dstance from the separatng hyperplane corresponds to the confdence of predcton Example: We are more sure about the class of A and B than of C

22 Margn γγ: Dstance of closest example from the decson lne/hyperplane The reason e defne margn ths ay s due to theoretcal convenence and exstence of generalzaton error bounds that depend on the value of margn. 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 23

23 Remember: the Dot product AA BB = AA BB cccccc θθ AA cccccccc AA = AA (jj) 22 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 24 dd jj=11

24 Dot product AA BB = AA BB cccccc θθ What s xx 11, xx 22? x 2 x 1 x 2 x 1 x 2 x 1 In ths case γγ In ths case γγ So, γγ roughly corresponds to the margn Bottom lne: Bgger γγ bgger the separaton 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 25

25 Dstance from a pont to a lne A (x A (1), x A (2) ) H L Let: Lne L: xb = (1) x (1) (2) x (2) b=0 = ( (1), (2) ) Pont A = (x A (1), x A (2) ) Note e assume 22 = 11 Pont M on a lne = (x M (1), x M (2) ) (0,0) M (x M (1), x M (2) ) d(a, L) = AH = (AM) = (x A (1) x M (1) ) (1) (x A (2) x M (2) ) (2) = x A (1) (1) x A (2) (2) b = A b Remember x (1) M (1) x (2) M (2) = b snce M belongs to lne L 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 26

26 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 27 Predcton = sgn( x b) Confdence = ( x b) y For th datapont: γγ = xx bb yy Want to solve: mmmmmm mmmmmm Can rerte as maxγ, γ γγ s. t., y ( x b) γ

27 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 28 Maxmze the margn: Good accordng to ntuton, theory (c.f. VC dmenson ) and practce maxγ, γ s. t., y ( x b) γ γ γ γ xb=0 γγ s margn dstance from the separatng hyperplane Maxmzng the margn

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29 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 30 Separatng hyperplane s defned by the support vectors Ponts on / planes from the soluton If you kne these ponts, you could gnore the rest Generally, d1 support vectors (for d dm. data)

30 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 31 Problem: Let xx bb yy = γγ then 22 xx 22bb yy = 22γγ Scalng ncreases margn! Soluton: Work th normalzed : γγ = xx bb yy x 2 x 1 Let s also requre support vectors xx jj to be on the plane defned by: xx jj bb = ±11 dd = (jj) 2 jj=1

31 Want to maxmze margn γγ! What s the relaton beteen x 1 and x 2? xx 11 = xx We also kno: xx 11 bb = 11 xx 22 bb = 11 So: xx 11 bb = 11 xx xx 22 bb bb = 11 = 11 γ = 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 32 x 2 2γ x 1 1 = Note: = 2

32 We started th max, γ γ s. t., y ( x b) arg maxγ = arg max mn s. t., y 2 ( x b) γ But can be arbtrarly large! We normalzed and... Then: = arg mn arg mn Ths s called SVM th hard constrants = 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, x 2 2γ x 1

33 If data s not separable ntroduce penalty: mn 1 2 s. t., y 2 ( x C (#number of b) 1 Mnmze ǁǁ 2 plus the number of tranng mstakes Set C usng cross valdaton Ho to penalze mstakes? All mstakes are not equally bad! mstakes) 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 34

34 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 35 Introduce slack varables ξ mn, b, ξ 0 s. t., 1 2 y 2 ( x If pont x s on the rong sde of the margn then get penalty ξ n C ξ b) = 1 1 ξ ξ ξ j For each data pont: If margn 1, don t care If margn < 1, pay lnear penalty

35 mn s. t., 1 2 y 2 ( x C (#number of b) 1 What s the role of slack penalty C: C= : Only ant to, b that separate the data C=0: Can set ξ to anythng, then =0 (bascally gnores the data) (0,0) mstakes) 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 36 small C good C bg C

36 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 37 SVM n the natural form n arg mn C max, b 1 2 SVM uses Hnge Loss : 0/1 loss { 0,1 y ( x b) } Margn = 1 Emprcal loss L (ho ell e ft tranng data) Regularzaton parameter penalty mn, b 1 2 s. t., y 2 ( x n C ξ = 1 b) 1 ξ Hnge loss: max{0, 1z} z = y ( x b)

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38 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 39 mn, b 1 2 s. t., y ( x C Want to estmate and bb! Standard ay: Use a solver! Solver: softare for fndng solutons to common optmzaton problems Use a quadratc solver: Mnmze quadratc functon Subject to lnear constrants Problem: Solvers are neffcent for bg data! n = 1 b) ξ 1 ξ

39 Want to estmate, b! Alternatve approach: Want to mnmze J(,b): Sde note: Ho to mnmze convex functons gg(zz)? Use gradent descent: mn z g(z) Iterate: z t1 z t η g(z t ) 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 40 = = = n d j j j b x y C b J 1 1 ) ( ) ( 2 1 ) ( 0,1 max ), ( n b b x y s t C ξ ξ = 1 ) (,.. mn 1 2 1, g(z) z

40 Want to mnmze J(,b): Compute the gradent (j).r.t. (j) 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 41 = = = n j j j j y x L C b L J 1 ) ( ) ( ) ( ) ( ), ( ), ( else 1 ) ( f 0 ), ( ) ( ) ( j j x y b x y y x L = = ( ) = = = = n d j j j d j j b x y C b J 1 1 ) ( ) ( 1 2 ) ( 2 1 ) ( 0,1 max ), ( Emprcal loss LL(xx yy )

41 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 42 Gradent descent: Iterate untl convergence: For j = 1 d j) f (, b) Evaluate: J = ( j) Update: (j) (j) η J (j) n ( ( j) L( x =, y ) C ( j) = 1 Problem: Computng J (j) takes O(n) tme! n sze of the tranng dataset η learnng rate parameter C regularzaton parameter

42 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 43 Stochastc Gradent Descent Instead of evaluatng gradent over all examples evaluate t for each ndvdual tranng example j) ( j) L( x, y J ( x ) = C ( j) Stochastc gradent descent: ( ) Iterate untl convergence: For = 1 n For j = 1 d Compute: J (j) (x ) Update: (j) (j) η J (j) (x ) J We just had: n ( j) ( j) L( x, y ) = C ( j) = 1 Notce: no summaton over anymore

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44 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 45 Example by Leon Bottou: Reuters RCV1 document corpus Predct a category of a document One vs. the rest classfcaton n = 781,000 tranng examples (documents) 23,000 test examples d = 50,000 features One feature per ord Remove stopords Remove lo frequency ords

45 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 46 Questons: (1) Is SGD successful at mnmzng J(,b)? (2) Ho quckly does SGD fnd the mn of J(,b)? (3) What s the error on a test set? Standard SVM Fast SVM SGDSVM Tranng tme Value of J(,b) Test error (1) SGDSVM s successful at mnmzng the value of J(,b) (2) SGDSVM s super fast (3) SGDSVM test set error s comparable

46 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 48 SGD SVM Conventonal SVM Optmzaton qualty: J(,b) J ( opt,b opt ) For optmzng J(,b) thn reasonable qualty SGDSVM s super fast

47 SGD on full dataset vs. Conjugate Gradent on a sample of n tranng examples Theory says: Gradent descent converges n lnear tme kk. Conjugate gradent converges n kk. Bottom lne: Dong a smple (but fast) SGD update many tmes s better than dong a complcated (but slo) CG update a fe tmes kk condton number 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 49

48 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 51 Sparse Lnear SVM: Feature vector x s sparse (contans many zeros) Do not do: x = [0,0,0,1,0,0,0,0,5,0,0,0,0,0,0, ] But represent x as a sparse vector x =[(4,1), (9,5), ] Can e do the SGD update more effcently? C η Approxmated n 2 steps: L( x, y ) ηc ( 1 η) ) cheap: x s sparse and so fe coordnates j of ll be updated expensve: s not sparse, all coordnates need to be updated x L, ( y

49 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 52 Soluton 1: = ss vv Represent vector as the product of scalar s and vector v Then the update procedure s: To step update procedure: (1) (2) L( x, y ) ηc ( 1 η) (1) vv = vv ηηηη xx,yy (2) ss = ss(11 ηη) Soluton 2: Perform only step (1) for each tranng example Perform step (2) th loer frequency and hgher η

50 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 53 Stoppng crtera: Ho many teratons of SGD? Early stoppng th cross valdaton Create a valdaton set Montor cost functon on the valdaton set Stop hen loss stops decreasng Early stoppng Extract to (very) small sets of tranng data A and B Tran on A, stop by valdatng on B Number of tranng epochs on A s an estmate of k Tran for k epochs on the full dataset

51 Idea 1: One aganst all Learn 3 classfers vs. {o, } vs. {o, } o vs. {, } Obtan: b, b, o b o Ho to classfy? Return class c arg max c c x b c 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 54

52 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 55 Idea 2: Learn 3 sets of eghts smoultaneously! For each class c estmate c, b c Want the correct class y to have hghest margn: y x b y 1 c x b c c y, (x, y )

53 Optmzaton problem: To obtan parameters c, b c (for each class c) e can use smlar technques as for 2 class SVM SVM s dely perceved a very poerful learnng algorthm 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 56 c c y y n c b b x b x C ξ ξ = 1 mn 1 c 2 2 1, y c 0,, ξ

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55 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 58 Ne settng: Onlne Learnng Allos for modelng problems here e have a contnuous stream of data We ant an algorthm to learn from t and sloly adapt to the changes n data Idea: Do slo updates to the model SGDSVM makes updates f msclassfyng a datapont So: Frst tran the classfer on tranng data. Then for every example from the stream, f e msclassfy, update the model (usng a small learnng rate)

56 Protocol: User comes and tell us orgn and destnaton We offer to shp the package for some money ($10 $50) Based on the prce e offer, sometmes the user uses our servce (y = 1), sometmes they don't (y = 1) Task: Buld an algorthm to optmze hat prce e offer to the users Features x capture: Informaton about user Orgn and destnaton Problem: Wll user accept the prce? 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 59

57 Model hether user ll accept our prce: yy = ff(xx; ) Accept: y =1, Not accept: y=1 Buld ths model th say Perceptron or SVM The ebste that runs contnuously Onlne learnng algorthm ould do somethng lke User comes User s represented as an (x,y) par here x: Feature vector ncludng prce e offer, orgn, destnaton y: If they chose to use our servce or not The algorthm updates usng just the (x,y) par Bascally, e update the parameters every tme e get some ne data 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 60

58 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 61 We dscard ths dea of a data set Instead e have a contnuous stream of data Further comments: For a major ebste here you have a massve stream of data then ths knd of algorthm s pretty reasonable Don t need to deal th all the tranng data If you had a small number of users you could save ther data and then run a normal algorthm on the full dataset Dong multple passes over the data

59 2/17/2015 Jure Leskovec, Stanford CS246: Mnng Massve Datasets, 62 An onlne algorthm can adapt to changng user preferences For example, over tme users may become more prce senstve The algorthm adapts and learns ths So the system s dynamc