A set of tools for model-based optimization and tuning of algorithms. It includes surrogate models, optimizers, and design of experiment approaches. The main interface is spot, which uses sequentially updated surrogate models for the purpose of efficient optimization. The main goal is to ease the burden of objective function evaluations, when a single evaluation requires a significant amount of resources.
SPOT - Sequential Parameter Optimization Toolbox
Release Notes Version 2.0.3
Release Notes Version 2.0.2
Release Notes Version 2.0.1
Release notes of older Versions:
Release Notes Version 1.1.0
Release Notes Version 1.0.5543 1. SPOT may now handle NA values returned by target function (tuned algorithm) in case a Kriging surrogate model is used. See help of spotRepairMissingValues 2. SPOT can now handle constraints during surrogate-model optimization, by using constraint optimization approaches in nloptr package. Constraint function has to be supplied to spotConfig$alg.constraint.ineq, and a nloptr algorithm has to be used with spotModelOptim. 3. New demo: Tuning RGP with MAMP approach 4. Code cleanup and several minor bugfixes 5. Added source code for java GUI and java 1+1-ES to package 6. Improvements to documentation, fixed links
Release Notes Version 1.0.4184 - Several bugfixes, including compatibility with rgp >= 0.3-4
Release Notes Version 1.0.4045 1. Contour plot can now show all observations as crosses, when spotConfig$report.observations.all is set. 2. The models spotPredictEarth and spotPredictRandomForest will now turn variables marked as "FACTOR" in the ROI into real factors with as.factor() 3. There is now a new report function, for runs with spotPredictEarth only: spotReportEarth 4. spotPredictForrester Models do not have to be rebuild if evaluated with different options 5. Several infill criteria like Expected Improvement added, see spotInfill... 6. Code cleanup and speed fixes 7. Bugfix for multi criteria optimization: A crash has been fixed, that occurred when using sms-emoa for optimizing the surrogate model in certain situations. 8. A R-port of the DACE-Kriging Toolbox was added to SPOT. See: daceBuilder and spotPredictDace 10. SPOT runs with no file output that crashed or were interrupted by the user can now be continued, if spotConfig$spot.catch.error is set to TRUE. Otherwise, only interrupted runs can be continued properly. 11. Contour and 3d surface plots were adapted for multi objective optimization. 13. Several examples of Model-Ensembles were added, e.g., spotPredictEnsemble*** 14. If cmaes is used as an optimizer on the model, it will use the vectorize option where possible. 16. spotPredictLmOptim was removed, see the demo spotDemo14LmOptimSann for replacement functionality 17. Added the Gaussian Landscape Generator to the package for the purpose of test function generation, see spotGlgCreate 18. New easy to use interfaces to train surrogate models, or predict with them: spotModel.train, spotModel.predict and spotModel.func. 19. Plotting tools for surfaces: spotSurf3d and spotSurfContour. 20. By using the spotPredictMCO function one can use different models for each optimized objective, rather than model all objectives with the same model. For instance, for an optimization problem with three objectives, one can select Co-Kriging for the first objective, Kriging for the second and Random Forest for the third. 21. Report functions for performing a Mixed Model analysis have been added, which are spotReportMAMP and spotReportSAMP. Examples are provided in demos 21 and 22.
Release Notes Version 1.0.2662 Removed the dependency on the maptree package and changed the corresponding default report function spotReportDefault
Release Notes Version 1.0.2661 1. Significant speed-up for spotPredictForrester, by factors of 2 or more (depending on dimensionality and number of observations) 2. Fix in spotPredictForrester: Sometimes L-BFGS-B might violate boundaries, this is now checked and controlled by the calling function 3. Better coloring in 3d plots (spotReport3d), both surface plot reports (spotReport3d and spotReportContour) use the same color scheme. 4. Bugfix when using OCBA: RNG seeds are now determined in the right order 5. Multi criteria optimization with SPOT now uses a more appropriate infill criterion: When estimating the hypervolume contribution of points suggested by a model, allready known points will also be considered. 6. The Evolutionary Strategy used as a target algorithm for SPOT can now be called with an optim-like interface: esOptim 7. For stability reasons, the nugget effect in the Kriging model spotPredictDice is now always on.
Release Notes Version 1.0.2257 Version has changed from 0.1.* to 1.0.*, since now one of the most important predictors from the original Matlab package is implemented, as well as methods for Multi Criteria Optimization. More detailed changes are: 1. Fixed auto.loop.nevals stopping criterion (one superfluous step) 2. Added several additional examples of surrogate model interfaces: spotPredictForrester (new, more stable Kriging model, based on Forrester's code as used with original Matlab-SPOT) spotPredictEarth (MARS, based on earth package) spotPredictGausspr (based on kernlab package gausspr) spotPredictKsvm (SVM based on kernlab package) spotPredictPsvm (Penalized SVM) spotPredictQrnn (Quantile regression neural network, from qrnn package) 3. Adaptation for R 2.14.X (Namespace and others) 4. Dependencies changed, removed unused dependencies, moved optional dependencies to suggests 6. MSPOT: Spot can now do multi criteria optimization, see also vignette("MultiCriteriaSPOT"). 7. Updated some demos to use new and better features 9. Changed default settings in SANN and ES algorithm interfaces 10. Bugfix to seed handling in target algorithm/function interfaces: Will now be saved and loaded to make sure that seed of main SPOT processes is undisturbed. 11. spotPredictMlegp can be run with limited number of observations (randomly sampled), to prevent exploding time consumption 12. Documentation updated 14. New demo for tuning symbolic regression (RGP) 15. New report functions which create surface plots: spotReport3d, spotReportContour 16. Changes to spotGetOptions and related functions: - any .path variables in the spotConfig were removed. User should make sure to load function to workspace. - Removed unused parameter settings - Settings of optional modules/functions will now be handled in those very functions, not in spotGetOptions. That means, they will only be in the list of parameters if actually used or set by the user. 17. Region of Interest will now use lower/upper instead of low/high for column names of boundaries. A new constructor can be used to create Region Of Interests: spotROI 18. Some internal functions now use single arguments instead of parameter list (spotWriteDes, spotWriteAroi, spotReadRoi) 19. Updated the spotGui() function to fit to the changes (new version: 1.3) 20. spot() and spotOptim() have now the argument "..." (dot-dot-dot) to pass on additional parameters to a target function.
Release Notes Version 0.1.1550 1. Fixed bugs with regard to problems with handling random number generator seed 2. Changes to spotTask "meta" (spotStepMetaOpt): New structure, not fully documented yet. 3. Sensitivity report will not use the same graphic window as the online plot any-more 4. Changed/added suitable error messages 3. some other smaller bug fixes
Release Notes Version 0.1.1375 1. Important new function: spotOptim: an optim like spot interface spotOcba: used with spotCreateSequentialDesignOcba. It is the new default, but works only with noisy functions. See documentation. 2. Prediction functions should now return spotConfig. Both the model fit (if available, used for the predict interface) and the y values for the large design are returned inside spotConfig. 3. Any model fit that works with the predict interface can now be optimized in a additional function call in the sequential design step. Therefore a new parameter is introduced: spotConfig$seq.predictionOpt.func See also: spotPredictOptMulti 4. Usability changes to the spotGui 5. Updated documentation 6. Setting spot.seed or alg.seed to NULL will result into randomly chosen seeds. 7. spot() and spotOptim() can be started without file input or output. 8. New test functions spotFuncStartMexicanHat spotFuncStartRastrigin spotFuncStartRosenbrock 9. Function handles can be passed to spot or spotOptim, instead of strings, for the used objective function. 10. re-added to the package: spotPredictKrig 11. several bugfixes to predictors and other functions 12. Where possible, verbosity of other packages used by spot is now in line with spot's verbosity
Known major problems: spotpredictLmOptim does not work with interface changes (this also affects the corresponding demo)
Release Notes Version 0.1.1065 1. New parameters in spotConfig: a. spot.fileMode: Boolean, defines if created data is logged in res/des/bst/aroi files If TRUE (default) files will be written, else the results will be written to spotConfig (see the following parameters) b. alg.currentResult: Holds the results of the target algorithm or function that is optimized by spot, as a data frame. (earlier only in .res file) c. alg.aroi: this holds the aroi (earlier only in .aroi file) d. alg.currentBest: This holds a data frame with the best results (earlier only in .bst files) e. alg.currentDesign: This holds a data frame with the design to be evaluated by the next "run" task.(earlier only in .des files)
2. Changes to user-defined functions: spotConfig$alg.func: This function now receives and returns only spotConfig. This means resFileName, apdFileName, and desFileName are no input arguments any more, they need to be read from spotConfig. The function needs to be changed to write the \"result\" data frame to the spotConfig$alg.currentResult. It can also be written to .res file, if wanted by the user. (check for spotConfig$spot.fileMode if needed) spotConfig$report.func The function now also returns spotConfig. To get the best value the following lines are be used now in the default report: ################################# spotConfig=spotWriteBest(mergedData, spotConfig); C1=spotConfig$alg.currentBest[nrow(spotConfig$alg.currentBest),] ################################# In general some spot functions you might use in your user-defined functions will now return spotConfig. 3. spot() can now be called with a list of settings, e.g. spotConfig. example: spot(\"example.conf\",\"auto\",spotConfig=list(auto.loop.nevals=150)). spot() will also return the spotConfig. This can then be feed into the next spot() run. 4. Changes to the spotGui: a. The spotGui layout was changed, a menu bar added. b. New feature: Create your own test functions with the function generator
Release Notes Version 0.1.1016: 1. New function: spotGui() Starts a java gui to work with SPOT 2. New parameters for the "meta" task: - spotConfig$report.meta.func - spotConfig$report.meta.path Define name and path of a report that summarizes a meta experiment 3. Fixed Documentation
Initial version: 0.1.888