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International Conference on Learning and Intelligent Optimization

LION 12 2018: Learning and Intelligent Optimization pp 115–130Cite as

CAVE: Configuration Assessment, Visualization and Evaluation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

Abstract

To achieve peak performance of an algorithm (in particular for problems in AI), algorithm configuration is often necessary to determine a well-performing parameter configuration. So far, most studies in algorithm configuration focused on proposing better algorithm configuration procedures or on improving a particular algorithm’s performance. In contrast, we use all the collected empirical performance data gathered during algorithm configuration runs to generate extensive insights into an algorithm, given problem instances and the used configurator. To this end, we provide a tool, called CAVE, that automatically generates comprehensive reports and insightful figures from all available empirical data. CAVE aims to help algorithm and configurator developers to better understand their experimental setup in an automated fashion. We showcase its use by thoroughly analyzing the well studied SAT solver spear on a benchmark of software verification instances and by empirically verifying two long-standing assumptions in algorithm configuration and parameter importance: (i) Parameter importance changes depending on the instance set at hand and (ii) Local and global parameter importance analysis do not necessarily agree with each other.

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Notes

  1. 1.

    https://github.com/automl/CAVE

  2. 2.

    We ignore in this simplified view that several budgets have to be defined, such as, the configuration budget (e.g., time budget or maximal number of algorithm calls) and resource limits of the target algorithm runs (e.g., runtime and memory limits).

  3. 3.

    Typically, the instance set is split into a training and a test set. On the training set, the target algorithm is optimized and on the test set, an unbiased cost estimate of the optimized parameter configuration is obtained.

  4. 4.

    http://ml.informatik.uni-freiburg.de/papers/18-LION12-CAVE.pdf

  5. 5.

    The complete generated report can be found at

    http://ml.informatik.uni-freiburg.de/~biedenka/cave.html

  6. 6.

    In contrast to Xu et al. [20], we normalize the relabelling cost of continuous parameters to [0, 1] since otherwise relabelling of continuous parameters would dominate the similarity metric compared to relabelling of discrete parameters.

  7. 7.

    In capped fANOVA, all cost values to train a marginalized EPM are capped at the cost of the default configuration \(\mathbf {\theta }_\text {def}\): \(c(\mathbf {\theta }) := \min {(c(\mathbf {\theta }_\text {def}),c(\mathbf {\theta }))}\).

  8. 8.

    http://aclib.net/

  9. 9.

    https://github.com/automl/SMAC3

References

  1. Hutter, F., Hoos, H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. JAIR 36, 267–306 (2009)

    Article  Google Scholar 

  2. Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04244-7_14

  3. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello Coello, C.A. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_40

  4. Ansótegui, C., Malitsky, Y., Sellmann, M., Tierney, K.: Model-based genetic algorithms for algorithm configuration. In: Yang, Q., Wooldridge, M. (eds.) Proceedings of IJCAI’15, pp. 733–739 (2015)

    Google Scholar 

  5. López-Ibáñez, M., Dubois-Lacoste, J., Caceres, L.P., Birattari, M., Stützle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)

    Article  MathSciNet  Google Scholar 

  6. Hutter, F., Lindauer, M., Balint, A., Bayless, S., Hoos, H., Leyton-Brown, K.: The configurable SAT solver challenge (CSSC). AIJ 243, 1–25 (2017)

    MathSciNet  MATH  Google Scholar 

  7. Fawcett, C., Helmert, M., Hoos, H., Karpas, E., Roger, G., Seipp, J.: Fd-autotune: domain-specific configuration using fast-downward. In: Helmert, M., Edelkamp, S. (eds.) Proceedings of ICAPS’11 (2011)

    Google Scholar 

  8. Mu, Z., Hoos, H.H., Stützle, T.: The impact of automated algorithm configuration on the scaling behaviour of state-of-the-Art inexact TSP solvers. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) LION 2016. LNCS, vol. 10079, pp. 157–172. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50349-3_11

  9. Wagner, M., Friedrich, T., Lindauer, M.: Improving local search in a minimum vertex cover solver for classes of networks. In: Proceedings of IEEE CEC, pp. 1704–1711. IEEE (2017)

    Google Scholar 

  10. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Automated configuration of mixed integer programming solvers. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 186–202. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_23

  11. Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Proceedings of NIPS’12, pp. 2960–2968 (2012)

    Google Scholar 

  12. Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: Proceedings of ICLR’17 (2017)

    Google Scholar 

  13. Bischl, B., et al.: ASlib: a benchmark library for algorithm selection. AIJ 41–58 (2016)

    Google Scholar 

  14. Smith-Miles, K., Baatar, D., Wreford, B., Lewis, R.: Towards objective measures of algorithm performance across instance space. Comput. OR 45, 12–24 (2014)

    Article  MathSciNet  Google Scholar 

  15. Hutter, F., Hoos, H., Leyton-Brown, K.: Identifying key algorithm parameters and instance features using forward selection. In: Proceedings of LION’13, pp. 364–381 (2013)

    Google Scholar 

  16. Hutter, F., Hoos, H., Leyton-Brown, K.: An efficient approach for assessing hyperparameter importance. In: Proceedings of ICML’14, pp. 754–762 (2014)

    Google Scholar 

  17. Fawcett, C., Hoos, H.: Analysing differences between algorithm configurations through ablation. J. Heuristics 22(4), 431–458 (2016)

    Article  Google Scholar 

  18. Biedenkapp, A., Lindauer, M., Eggensperger, K., Fawcett, C., Hoos, H., Hutter, F.: Efficient parameter importance analysis via ablation with surrogates. In: Proceedings of AAAI’17, pp. 773–779 (2017)

    Google Scholar 

  19. Babić, D., Hutter, F.: Spear theorem prover. Solver description. SAT Competition (2007)

    Google Scholar 

  20. Xu, L., KhudaBukhsh, A.R., Hoos, H.H., Leyton-Brown, K.: Quantifying the similarity of algorithm configurations. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) LION 2016. LNCS, vol. 10079, pp. 203–217. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50349-3_14

  21. Bussieck, M., Drud, A.S., Meeraus, A., Pruessner, A.: Quality assurance and global optimization. In Bliek, C., Jermann, C., Neumaier, A. (eds.) Proceedings of GOCOS. Lecture Notes in Computer Science, vol. 2861. Springer (2003) 223–238

    Google Scholar 

  22. Bussieck, M., Dirkse, S., Vigerske, S.: PAVER 2.0: an open source environment for automated performance analysis of benchmarking data. J. Glob. Optim. 59(2–3), 259–275 (2014)

    Article  Google Scholar 

  23. Rice, J.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)

    Article  Google Scholar 

  24. Nell, C., Fawcett, C., Hoos, H.H., Leyton-Brown, K.: HAL: a framework for the automated analysis and design of high-performance algorithms. In: Coello Coello, C.A. (ed.) LION 2011. LNCS, vol. 6683, pp. 600–615. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_47

  25. Falkner, S., Lindauer, M., Hutter, F.: SpySMAC: automated configuration and performance analysis of SAT solvers. In: Proceedings of SAT’15, pp. 1–8 (2015)

    Google Scholar 

  26. Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., Sculley, D.: Google vizier: a service for black-box optimization. In: Proceedings of KDD, pp. 1487–1495. ACM (2017)

    Google Scholar 

  27. Heinrich, J., Weiskopf, D.: State of the art of parallel coordinates. In: Proceedings of Eurographics, Eurographics Association, pp. 95–116 (2013)

    Google Scholar 

  28. Lloyd, J., Duvenaud, D., Grosse, R., Tenenbaum, J., Ghahramani, Z.: Automatic construction and natural-language description of nonparametric regression models. In: Proceedings of AAAI’14, pp. 1242–1250 (2014)

    Google Scholar 

  29. Hutter, F., Xu, L., Hoos, H., Leyton-Brown, K.: Algorithm runtime prediction: methods and evaluation. AIJ 206, 79–111 (2014)

    MathSciNet  MATH  Google Scholar 

  30. Breimann, L.: Random forests. MLJ 45, 5–32 (2001)

    Google Scholar 

  31. Nudelman, E., Leyton-Brown, K., Hoos, H.H., Devkar, A., Shoham, Y.: Understanding random sat: beyond the clauses-to-variables ratio. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 438–452. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30201-8_33

  32. Eggensperger, K., Lindauer, M., Hoos, H., Hutter, F., Leyton-Brown, K.: Efficient benchmarking of algorithm configuration procedures via model-based surrogates. Mach. Learn. (2018) (To appear)

    Google Scholar 

  33. Hutter, F., et al.: AClib: a benchmark library for algorithm configuration. In: Pardalos, P.M., Resende, M.G.C., Vogiatzis, C., Walteros, J.L. (eds.) LION 2014. LNCS, vol. 8426, pp. 36–40. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09584-4_4

  34. Xu, L., Hutter, F., Hoos, H., Leyton-Brown, K.: SATzilla: Portfolio-based algorithm selection for SAT. JAIR 32, 565–606 (2008)

    Article  Google Scholar 

  35. Schneider, M., Hoos, H.H.: Quantifying homogeneity of instance sets for algorithm configuration. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, pp. 190–204. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34413-8_14

  36. Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: Proceedings of AAAI’10, pp. 210–216 (2010)

    Google Scholar 

  37. Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC - instance-specific algorithm configuration. In: Proceedings of ECAI’10, pp. 751–756 (2010)

    Google Scholar 

  38. Kruskal, J.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1–27 (1964)

    Article  MathSciNet  Google Scholar 

  39. Groenen, P., van de Velden, M.: Multidimensional scaling by majorization: A review. J. Stat. Softw. 73(8) (2016)

    Google Scholar 

  40. Lindauer, M., Hutter, F.: Warmstarting of model-based algorithm configuration. In: Proceedings of the AAAI conference (2018) (To appear)

    Google Scholar 

  41. Gerevini, A., Serina, I.: LPG: a planner based on local search for planning graphs with action costs. In: Proceedings of AIPS’02, pp. 13–22 (2002)

    Google Scholar 

  42. Gebser, M., Kaufmann, B., Schaub, T.: Conflict-driven answer set solving: from theory to practice. AI 187–188, 52–89 (2012)

    Google Scholar 

  43. KhudaBukhsh, A., Xu, L., Hoos, H., Leyton-Brown, K.: SATenstein: automatically building local search SAT solvers from components. In: Proceedings of IJCAI’09, pp. 517–524 (2009)

    Google Scholar 

  44. Balint, A., Schöning, U.: Choosing probability distributions for stochastic local search and the role of make versus break. In: Cimatti, A., Sebastiani, R. (eds.) SAT 2012. LNCS, vol. 7317, pp. 16–29. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31612-8_3

  45. Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm selection and scheduling. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 454–469. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23786-7_35

  46. Meinshausen, N.: Quantile regression forests. JMLR 7, 983–999 (2006)

    Google Scholar 

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Acknowledgments

The authors acknowledge support by the state of Baden-Württemberg through bwHPC and the German Research Foundation (DFG) through grant no INST 39/963-1 FUGG and the Emmy Noether grant HU 1900/2-1.

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Authors and Affiliations

  1. University of Freiburg, Freiburg, Germany

    André Biedenkapp, Joshua Marben, Marius Lindauer & Frank Hutter

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  1. André Biedenkapp
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  2. Joshua Marben
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  3. Marius Lindauer
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  4. Frank Hutter
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Correspondence to André Biedenkapp .

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Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Biedenkapp, A., Marben, J., Lindauer, M., Hutter, F. (2019). CAVE: Configuration Assessment, Visualization and Evaluation. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_10

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