Parameter tuning based on response surface models An update on work in progress

1. Parameter tuning based on response surface modelsAn update on work in progress EARG, Feb 27th, 2008 Presenter: Frank Hutter

2. Motivation • Parameter tuning is important • Recent approaches (ParamILS, racing, CALIBRA) “only” return the best parameter configuration • Extra information would be nice, e.g. • The most important parameter is X • The effect of parameters X and Y is largely independent • For parameter X options 1 and 2 are bad, 3 is best, 4 is decent • ANOVA is one tool for that, but has limitations (e.g. discretization of parameters, linear model)

3. More motivation • Support the actual design process by providing feedback about parameters • E.g. parameter X should always be i (code gets simpler!!) • Predictive models of runtime are widely applicable • Prediction can be updated based on new information (such as “the algorithm has been unsuccessfully running for X seconds”) • (True) portfolios of algorithms • Once we can learn a function f:Q! runtime, learning a function g:Q, X! runtime should be a simply extension (X=inst. charac., Lin learns h: X! runtime)

4. The problem setting • For now: static algorithm configuration, i. e. find the best fixed parameter setting across instances • But as mentioned above this approach extends to PIAC (per instance algorithm configuration) • Randomized algorithms: variance for a single instance (runtime distributions) • High inter-instance variance in hardness • We focus on minimizing runtime • But the approach also applies to other objectives • (Special treatment of censoring and cost for gathering a data point is then simply not necessary) • We focus on optimizing averages across instances • Generalization to other objectives may not be straight-forward

5. Learning a predictive model • Supervised learning problem, regression • Given training data (x1, o1), …, (xn, on), learn function f such that f(xi) ¼ oi • What is a data point xi ? • 1) Predictive model of average cost • Average of how many instances/runs ? • Not too many data points, but each one very costly • Doesn’t have to be average cost, could be anything • 2) Predictive model of single costs, get average cost by aggregation • Have to deal with ten thousands of data points • If predictions are Gaussian, the aggregates are Gaussian (means and variances add)

6. Desired properties of model • 1) Discrete and continuous inputs • Parameters are discrete/continuous • Instances features (so far) all continuous • 2) Censoring • When a run times out we only have a lower bound on its true runtime • 3) Scalability: tens of thousands of points • 4) Explicit predictive uncertainties • 5) Accuracy of predictions • Considered models: • Linear regression (basis functions? especially for discrete inputs) • regression trees (no uncertainty estimates) • Gaussian processes (4&5 ok, 1 done, 2 almost done, hopefully 3)

7. Coming up • 1) Implemented: model average runtimes, optimize based on that model • Censoring “almost” integrated • 2) Further TODOs: • Active learning criterion under noise • Scaling: Bayesian committee machine

8. Active learning for function optimization • EGO [Jones, Schonlau & Welch, 1998] • Assumes deterministic functions • Here: averages over 100 instances • Start with a Latin hypercube design • Run the algorithm, get (xi,oi) pairs • While not terminate • Fit the model (kernel parameter optimization, all continuous) • Find best point to sample (optimization in the space of parameter configurations) • Run the algorithm at that point, add new (x,y) pair

9. Active learning criterion • EGO uses maximum expected improvement • EI(x) = s p(y|mx, s2x) max(0, f_min-y) dy • Easy to evaluate (can be solved in closed form) • Problem in EGO: sometimes not the actual runtime y is modeled, but a transformation, e.g. log(y) • Expected improvement then needs to be adapted: • EI(x) = s p(y|mx, s2x) max(0, f_min-exp(y)) dy • Easy to evaluate (can still be solved in closed form) • Take into account cost of sample: • EI(x) = s p(y|mx, s2x) 1/exp(y) max(0, f_min-exp(y)) dy • Easy to evaluate (can still be solved in closed form) • Not implemented yet (the others are implemented)

10. Which kernel to use? • Kernel: distance measure between two data points • Low distance ! high correlation • Squared exponential, Matern, etc: • SE: k(x, x’) = ss exp(- å li(xi-xi’)2 ) • For discrete parameters: new Hamming distance kernel • ss epx(- å li(xi ¹ xi’) ) • Positive definite by reduction to String kernels • “Automatic relevance determination” • One length scale parameter li per dimension • Many kernel parameters lead to • Problems with overfitting • Very long runtimes for kernel parameter optimization • For CPLEX: 60 extra parameters, about 15h for a single kernel parameter optimization using DIRECT, without any improvement • Thus: no length scale parameters.Only two parameters: noise sn, and overall variability of the signal, ss

11. How to optimize kernel parameters? • Objective • Standard: maximizing marginal likelihood • Doesn’t work under censoring • Alternative: maximizing likelihood of unseen data using cross-validation • Efficient when not too many folds k are used: • Marginal likelihood requires inversion of N by N matrix • Cross validation with k=2 requires inversions of two N/2 by N/2 matrices. In practice still quite a bit slower (some algebra tricks may help) • Algorithm • Using DIRECT (DIviding RECTangles), global sampling-based method (does not scale to high dim)

12. How to optimize exp. improvement? • Currently only 3 algorithms to be tuned: • SAPS (4 continuous params) • SPEAR(26 parameters, about half of them discrete) • For now continuous ones are discretized • CPLEX(60 params, 50 of them discrete) • For now continuous ones are discretized • Purely continuous/purely discrete optimization • DIRECT / multiple restart local search

13. TODO: integrate censoring • Learning with censored data almost done • (needs solid testing since it’ll be central later) • Active selection of censoring threshold ? • Something simple might suffice, such as picking cutoff equal to predicted runtime or to the best runtime so far • Integration bounds in expected improvement would change, but nothing else • Runtime • With censoring about 3 times slower than without (Newton’s method takes about 3 steps) • „Good“ scaling • 42 points: 19 seconds; 402 points: 143 seconds • Maybe Newton does not need as many steps with more points

14. Treat as “completed at threshold”, 4s

15. Don’t use censored data, 4s

16. Laplace approximation to posterior, 10s

17. Schmee & Hahn, 21 iterations, 36s

18. Anecdotal: Lin’s original implementation of Schmee & Hahn, on my machine – beware of normpdf

19. A counterintuitive example from practice(same hyperparameters in same rows)

20. Preliminary results and demo • Experiments with noise-free kernel • Great cross-validation results for SPEAR & CPLEX • Poor cross-validation results for SAPS • Explanation • Even when averaging 100 instances, the response is NOT noise-free • SAPS is continuous: • can pick configurations arbitrarily close to each other • if results differ substantially SE kernel must have huge variance ! very poor results • Matern kernel works better for SAPS

21. TODOs • Finish censoring • Consider noise (even possible when averaging over instances), change active learning criterion • Scaling • Efficiency of implementation: reusing work for multiple predictions

22. TODO: Active learning under noise • [Williams, Santner, and Notz, 2000] • Very heavy on notation • But there is good stuff in there • 1) Actively choose a parameter setting • Best setting so far is not known ! fmin is now a random variable • take joint samples of performance from predictive distributions for all settings tried so far, take min of those samples, compute expected improvement as if that min was the deterministic fmin • Average the exp. improvements computed for 100 independent samples • 2) Actively choose an instance to run for that parameter setting: minimizing posterior variance

23. TODO: scaling • Bayesian committee machine • More or less a mixture of GPs, each of them on a small subset of data (cluster data ahead of time) • Fairly straight-forward wrapper around GP code (or really any code that provides Gaussian predictions) • Maximizing cross-validated performance is easy • In principle could update by just updating one component at a time • But in practice once we re-optimize hyperparameters we’re changing every component anyways • Likewise we can do rank-1 updates for the basic GPs, but a single matrix inversion is really not the expensive part (rather the 1000s of matrix inversions for kernel parameter optimization)

24. Future work • We can get main effects and interaction effects, much like in ANOVA • The integrals seem to be solvable in closed form • We can get plots of predicted mean and variance as one parameter is varied, marginalized over all others • Similarly as two or three are varied • This allows for plots of interactions