Lab 1

1. Lab 1 Getting started with Basic Learning Machines and the Overfitting Problem

2. Lab 1 Polynomial regression

3. Matlab: POLY_GUI • The code implements the ridge regression algorithm: w=argmin Si (1-yi f(xi))2 + g|| w ||2 f(x) = w1 x + w2 x2 + … + wn xn = wxT x = [x, x2, … , xn] wT = X+Y X+= XT(XXT+g)-1=(XTX+ g)-1XT X=[x(1); x(2); … x(p)] (matrix (p, n)) • The leave-one-out error (LOO) is obtained with PRESS statistic (Predicted REsidual Sums of Squares.): • LOO error = (1/p) Sk[ rk/1-(XX+)kk ]2

4. Matlab: POLY_GUI

5. Matlab: POLY_GUI • At the prompt type: poly_gui; • Vary the parameters. Refrain from hitting “CV”. Explain what happens in the following situations: • Sample num. << Target degree (small noise) • Large noise, small sample num • Target degree << Model degree • Why is the LOO error sometimes larger than the training and test error? • Are there local minima in the LOO error? Is the LOO error flat near the optimum? • Propose ways of getting a better solution.

6. CLOP Data Objects The poly_gui emulates CLOP objects of type “data”: • X = rand(10,5) • Y = rand(10,1) • D = data(X,Y) % constructor • methods(D) • get_X(D) • get_Y(D) • plot(D);

7. CLOP Model Objects poly_ridge is a “model” object. • P = poly_ridge; h = plot(P); • D = gene(P); plot(D, h); • Dt = gene(P); • [resu, fittedP] = train(P, D); • mse(resu) • tresu = test(fittedP, Dt); • mse(tresu) • plot(P, h);

8. Lab 1 Support Vector Machines

9. Support Vector Classifier x2 f(x)<0 f(x)>0 f(x) = S aiyi k(x, xi) k  SV x=[x1,x2] f(x)=0 x1 Boser-Guyon-Vapnik-1992

10. Matlab: SVC_GUI • At the prompt type: svc_gui; • The code implements the Support Vector Machine algorithm with kernel k(s, t) = (1 + s t)q exp -g||s-t||2 • Regularization similar to ridge regression: Hinge loss: L(xi)=max(0, 1-yi f(xi))b Empirical risk: Si L(xi) w=argmin (1/C)||w||2 + Si L(xi) shrinkage

11. Lab 1 More loss functions…

12. Loss Functions L(y, f(x)) Decision boundary Margin SVC loss, b=2 max(0, (1- z))2 Adaboost loss e-z logistic loss log(1+e-z) square loss (1- z)2 SVC loss, b=1 max(0, 1-z) 0/1 loss Perceptron loss max(0, -z) z=y f(x) missclassified well classified

13. Exercise: Gradient Descent • Linear discriminant f(x) = Sj wj xj • Functional margin z=y f(x), y=1 • Compute z/ wj • Derive the learning rules Dwj=-h L/wj corresponding to the following loss functions: SVC loss max(0, 1-z) Adaboost loss e-z square loss (1- z)2 logistic loss log(1+e-z) Perceptron loss max(0, -z)

14. Exercise: Dual Algorithms • From the Dwj derive the Dw • w = Siaixi • From the Dw, derive the Dai of the dual algorithms.

15. Summary • Modern ML algorithms optimize a penalized risk functional:

16. Lab 2 Getting started with CLOP

17. Lab 2 CLOP tutorial

18. What is CLOP? • CLOP=Challenge Learning Object Package. • Based on the Spider developed at the Max Planck Institute. • Two basic abstractions: • Data object • Model object • Put the CLOP directory in your path. • At the prompt type: use_spider_clop; • If you have used before poly_gui… type clear classes

19. CLOP Data Objects At the Matlab prompt: • addpath(<clop_dir>); • use_spider_clop; • X=rand(10,8); • Y=[1 1 1 1 1 -1 -1 -1 -1 -1]'; • D=data(X,Y); % constructor • [p,n]=get_dim(D) • get_x(D) • get_y(D)

20. CLOP Model Objects D is a data object previously defined. • model = kridge; % constructor • [resu, model] = train(model, D); • resu, model.W, model.b0 • Yhat = D.X*model.W' + model.b0 • testD = data(rand(3,8), [-1 -1 1]'); • tresu = test(model, testD); • balanced_errate(tresu.X, tresu.Y)

21. Hyperparameters and Chains A model often has hyperparameters: • default(kridge) • hyper = {'degree=3', 'shrinkage=0.1'}; • model = kridge(hyper); • model = chain({standardize,kridge(hyper)}); • [resu, model] = train(model, D); • tresu = test(model, testD); • balanced_errate(tresu.X, tresu.Y) Models can be chained:

22. Hyper-parameters • Kernel methods: kridge and svc: k(x, y) = (coef0 + xy)degree exp(-gamma ||x - y||2) kij = k(xi, xj) kii kii + shrinkage • Naïve Bayes: naive: none • Neural network: neural units, shrinkage, maxiter • Random Forest: rf (windows only) mtry

23. Exercise • Here some the pattern recognition CLOP objects: @rf @naive [use spider @svm] @svc @neural @gentleboost @lssvm @gkridge @kridge @klogistic @logitboost • Try at the prompt example(neural) • Try other pattern recognition objects • Try different sets of hyperparameters, e.g., example(kridge({'gamma=1', 'shrinkage=0.001'})) • Remember: use default(method) to get the HP.

24. Lab 2 Example: Digit Recognition Subset of the MNIST data of LeCun and Cortes used for the NIPS2003 challenge

25. data(X, Y) % Go to the Gisette directory: • cd('GISETTE') % Load “validation” data: • Xt=load('gisette_valid.data'); • Yt=load('gisette_valid.labels'); % Create a data object % and examine it: • Dt=data(Xt, Yt); • browse(Dt, 2); % Load “training” data (longer): • X=load('gisette_train.data'); • Y=load('gisette_train.labels'); • [p, n]=get_dim(Dt); • D=train(subsample(['p_max=' num2str(p)]), data(X, Y)); • clear X Y Xt Yt % Save for later use: • save('gisette', 'D', 'Dt');

26. model(hyperparam) % Define some hyperparameters: • hyper = {'degree=3', 'shrinkage=0.1'}; % Create a kernel ridge % regression model: • model = kridge(hyper); % Train it and test it: • [resu, Model] = train(model, D); • tresu = test(Model, Dt); % Visualize the results: • roc(tresu); • idx=find(tresu.X.*tresu.Y<0); • browse(get(D, idx), 2);

27. Exercise • Here are some pattern recognition CLOP objects: @rf @naive @gentleboost @svc @neural @logitboost @kridge @lssvm @klogistic • Instanciate a model with some hyperparameters (use default(method) to get the HP) • Vary the HP and the number of training examples (Hint: use get(D, 1:n) to restrict the data to n examples).

28. chain({model1, model2,…}) % Combine preprocessing and kernel ridge regression: • my_prepro=normalize; • model = chain({my_prepro,kridge(hyper)}); % Combine replicas of a base learner: • for k=1:10 • base_model{k}=neural; • end • model=ensemble(base_model); ensemble({model1, model2,…})

29. Exercise • Here are some preprocessing CLOP objects: @normalize @standardize @fourier • Chain a preprocessing and a model, e.g., • model=chain({fourier, kridge('degree=3')}); • my_classif=svc({'coef0=1', 'degree=4', 'gamma=0', 'shrinkage=0.1'}); • model=chain({normalize, my_classif}); • Train, test, visualize the results. Hint: you can browse the preprocessed data: • browse(train(standardize, D), 2);

30. Summary % After creating your complex model, just one command: train • model=ensemble({chain({standardize,kridge(hyper)}),chain({normalize,naive})}); • [resu, Model] = train(model, D); % After training your complex model, just one command: test • tresu = test(Model, Dt); % You can use a “cv” object to perform cross-validation: • cv_model=cv(model); • [resu, Model] = train(cv_model, D); • roc(resu);

31. Lab 3 Getting started with Feature Selection

32. POLY_GUI again… • clear classes • poly_gui; • Check the “Multiplicative updates” (MU) box. • Play with the parameters. • Try CV • Compare with no MU

33. Lab 3 Exploring feature selection methods

34. Re-load the GISETTE data % Start CLOP: • clear classes • use_spider_clop; % Go to the Gisette directory: • cd('GISETTE') • load('gisette');

35. Visualization 1) Create a heatmap of the data matrix or a subset: show(D); show(get(D,1:10, 1:2:500)); 2) Look at individual patterns: browse(D); browse(D, 2); % For 2d data % Display feature positions: browse(D, 2, [212, 463, 429, 239]); 3) Make a scatter plot of a few features:scatter(D, [212, 463, 429, 239]);

36. Example • my_classif=svc({'coef0=1', 'degree=3', 'gamma=0', 'shrinkage=1'}); • my_classif=svm('optimizer=''andre'''); • my_classif.algorithm.use_signed_output=0; • model=chain({normalize, s2n('f_max=100'), my_classif}); • [resu, Model] = train(model, D); • tresu = test(Model, Dt); • roc(tresu); % Show the misclassified first • [s,idx]=sort(tresu.X.*tresu.Y); • browse(get(Dt, idx), 2, Model{2});

37. Some Filters in CLOP Univariate: • @s2n (Signal to noise ratio.) • @Ttest (T statistic; similar to s2n.) • @Pearson (Uses Matlab corrcoef. Gives the same results as Ttest, classes are balanced.) • @aucfs (ranksum test) Multivariate: • @relief (no elimination of redundancy) • @gs (Gram-Schmidt orthogonalization; complementary features)

38. Exercise • Change the feature selection algorithm • Visualize the features • What can you say of the various methods? • Which one gives the best results for 2, 10, 100 features? • Can you improve by changing the preprocessing? (Hint: try @pc_extract)

39. Lab 3 Feature significance

40. T-test m- m+ P(Xi|Y=1) P(Xi|Y=-1) -1 xi s- s+ • Normally distributed classes, equal variance s2 unknown; estimated from data as s2within. • Null hypothesis H0: m+ = m- • T statistic: If H0 is true, • t= (m+ - m-)/(swithin1/m++1/m-) Student(m++m--2 d.f.)

41. Evalution of pval and FDR • Ttest object: • computes pval analytically • FDR~pval*nsc/n • probe object: • takes any feature ranking object as an argument (e.g. s2n, relief, Ttest) • pval~nsp/np • FDR~pval*nsc/n

42. Analytic vs. probe 1 0.9 0.8 0.7 0.6 FDR 0.5 0.4 0.3 0.2 0.1 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 rank

43. Example • [resu, FS] = train(Ttest, D); • [resu, PFS] = train(probe(Ttest), D); • figure('Name', 'pvalue'); • plot(get_pval(FS, 1), 'r'); • hold on; plot(get_pval(PFS, 1)); • figure('Name', 'FDR'); • plot(get_fdr(FS, 1), 'r'); • hold on; plot(get_pval(PFS, 1));

44. Exercise • What could explain differences between the pvalue and fdr with the analytic and probe method? • Replace Ttest with chain({rmconst('w_min=0'), Ttest}) • Recompute the pvalue and fdr curves. What do you notice? • Choose an optimum number fnum of features based on pvalue or FDR. Visualize with browse(D, 2,FS, fnum); • Create a model with fnum. Is fnum optimal? Do you get something better with CV?

45. Lab 3 Local feature selection

46. Exercise Consider the 1 nearest neighbor algorithm. We define the following score: Where s(k) (resp. d(k)) is the index of the nearest neighbor of xk belonging to the same class (resp. different class) as xk.

47. Exercise • Motivate the choice of such a cost function to approximate the generalization error (qualitative answer) • How would you derive an embedded method to perform feature selection for 1 nearest neighbor using this functional? • Motivate your choice (what makes your method an ‘embedded method’ and not a ‘wrapper’ method)

48. Relief Relief=<Dmiss/Dhit> Local_Relief= Dmiss/Dhit nearest hit Dhit Dmiss nearest miss Dhit Dmiss

49. Exercise • [resu, FS] = train(relief, D); • browse(D, 2,FS, 20); • [resu, LFS] = train(local_relief,D); • browse(D, 2,LFS, 20); • Propose a modification to the nearest neighbor algorithm that uses features relevant to individual patterns (like those provided by “local_relief”). • Do you anticipate such an algorithm to perform better than the non-local version using “relief”?

50. Epilogue Becoming a pro and playing with other datasets