General guidelines

The default.xml file can be used as a starting point for default behavior for the SUMO Toolbox. If you are a new user, you should initially leave most options at their default values. The default settings were chosen since they produce good results on average.

However, usually the optimal choice of components depends on the problem itself, so that the default settings aren't necessarily the best. This page will give the user general guidelines to decide which component to use for each situation they may encounter. The user is of course free to ignore these rules and experiment with other settings.

Note this list is very brief and incomplete, feel free to Contact us if you have any further questions.

Measures

The default Measure is CrossValidation. Even though this is a very good, accurate, overall measure, there are some considerations to make in the following cases:

• Expensive modelers (ann): If it is relatively expensive to train a model (for example, with neural networks), CrossValidation is also very slow, because it has to train a model for each fold (which is 5 by default). If modeling takes too long, you might want to use a faster alternative, such as ValidationSet.
• ErrorSampleSelector: CrossValidation might give a biased result when combined with the ErrorSampleSelector. This is because the ErrorSampleSelector tends to cluster samples around one point, which will result in very accurate surrogate models for all the points in this cluster (and thus good results with CrossValidation ). So when using CrossValidation and ErrorSampleSelector together, keep in mind that the real accuracy might be slightly lower than the estimated one.
• Rational modeler: When using Rational modeler, you might want to manually add a MinMax measure (if you got a rough estimate of the minimum and maximum values for your outputs) and use it together with CrossValidation. By adding the MinMax measure, you eliminate models which have poles in the design space, because these poles always break the minimum and maximum bounds. This usually results in better models and quicker convergence.

Selecting a good Measure is a very important part of the modeling process! Make sure you also read Multi-Objective Modeling.

Sample Selectors

The default SampleSelector is the LOLASampleSelector combined with the ErrorSampleSelector, with a weight of 0.8 for LOLA and 0.2 for error. This is a very robust sample selector, capable of dealing with most situations. There are, however, some cases in which it is advisable to choose a different one:

• Large-scale problems (1000+ samples): The GradientSampleSelector's time complexity is O(n²) to the number of samples n, so for large-scale experiments in which many samples are taken, the GradientSampleSelector becomes quite slow. Depending on the time it takes to perform one simulation, this may or may not be a problem. If it takes a long time to perform one simulation, the cost for selecting new samples with the GradientSampleSelector might still be negligible.
• Rational modeler: Benchmarks have shown that the gain of using the LOLASampleSelector over the ErrorSampleSelector when using global approximation methods (mainly rational/polynomial) is pretty much zero. It is therefore advisable to use the (much faster) ErrorSampleSelector when using the Rational modeler. This can be done by changing the weights in default.xml to 1.0 for error and 0.0 for LOLA.

When using the ErrorSampleSelector instead of the LOLASampleSelector, it is always a good idea to combine it with the DensitySampleSelector, to combat stability/robustness issues the ErrorSampleSelector often causes. It is a good idea to select about 60% of the samples with the ErrorSampleSelector, and 40% with the DensitySampleSelector. This will ensure that at least the entire design space is covered to a certain degree. This additional sample selector is NOT necessary when using the LOLASampleSelector.

Adaptive Model Builders

The question that always gets asked is Which model type should I use for my data? Unfortunately there is no straightforward since it all depends on your problem: how many dimensions, how many points, is your function rugged, smooth, or both, is there noise, etc, etc. Based on this knowledge it is possible to say which model types are more likely to do well but it remains a heuristic. Best is to try a few and see what happens, or use the heterogenetic model builder to try multiple model types in parallel and automatically try to determine the best type.

Howeve, since this question keeps coming up, some very rough intuition is the following:

1. The models SVM, RBF, DACE, Kriging, RBFNN, GaussianProcess all belong to the same family, thus their general performance with respect to the data distribution will also be similar
2. SVM and LS-SVM perform pretty much the same, though LS-SVM is faster
3. The SVM models are usually the best to use for a high number of dimensions. They become slower to use if the number of datapoints increases though (> 1000).
4. The SVM models also tend to converge quite quickly. You will quickly get a smooth fit, but for high accuracy you often need a lot of datapoints.
5. If your function is uniformly smooth pretty much any model type will do well with a nice spread out data distribution
6. If your function is uniformly rugged ('bumpy') the SVM/RBF/Kriging/... type models will tend to do quite well
7. If your function is smooth but with some sharp non-linearities, the SVM/RBF/Kriging/... family tend to need quite a lot of samples to get the accuracy low enough. In this case the ANN models perform much better.
8. The rational models can behave very erratic and are not recommended for for difficult bumpy problems or if the dimension exceeds 3.
9. The ANN models generally perfom very well across all problems but are very slow to use. Also if the function is uniformly rugged the Kriging/RBF/... models will give a better fit with much less points (eg. ackley function).
10. The FANN and NANN models are much faster than the ANN models, but usually the accuracy of the ANN models is much better

Note this is just some very rough intuition gained from our experience with different datasets, your mileage may vary! If you have any suggestions let us know