FAQ

General

What is a global surrogate model?

A global surrogate model is a mathematical model that mimics the behavior of a computationally expensive simulation code over the complete parameter space as accurately as possible, using as little data points as possible. So note that optimization is not the primary goal, although it can be done as a post-processing step. Global surrogate models are useful for:

• design space exploration, to get a feel of how the different parameters behave
• sensitivity analysis
• what-if analysis
• prototyping
• ...

In addition they are a cheap way to model large scale systems, multiple global surrogate models can be chained together in a model cascade.

The SUMO Toolbox is primarily concerned with global surrogate modeling, though surrogate driven optimization is supported as well.

What about surrogate driven optimization?

See the Surrogate Driven Optimization section.

How can I stay up to date with the latest news

To stay up to date with the latest news and releases, we also recommend subscribing to our mailinglist here. Traffic will be kept to a minimum and you can unsubscribe at any time. (Note: due to technical reasons you will not be able to post on the mailing list)

Upgrading

How do I upgrade to a newer version?

Delete your old toolbox directory and replace it by the new one.

Using

I want to model my own problem

See the Adding an example page.

I want to contribute some data/patch/documentation/...

See the Contributing page.

How do I interface with the toolbox?

See the Interfacing with the toolbox page.

Can I give a model created by the toolbox to somebody else?

See Running#Portability

Why are the Neural Networks so slow?

You are probably using the CrossValidation measure. CrossValidation is used by default if you have not defined a measure yourself. Since you need to train them, neural nets will always be slower than the other models. Using crossvalidation will slow things down much much more (5-times slower by default). Therefore, when using one of the neural network model types, please use a different measure such as ValidationSet or SampleError. See the comments in default.xml for examples.

Note: Starting from version 5.0, two new neural network backends will be available as well (based on FANN and NNSYSID). These are a lot faster than the default backend based on the Matlab Neural Network Toolbox. However, the accuracy it not as good.

How can I speed things up?

There are a number of things you can do to speed things up:

• Disable some, or all of the profilers or disable the output handlers that draw charts
• Turn off the plotting of models in ContextConfig, you can always generate plots from the saved mat files
• If you have a multi-core/multi-cpu machine, set the threadCount variable in LocalSampleEvaluator equal to the number of cores/CPUs
• Upgrade to Matlab 7.4 or later which has better multi-threaded support

How do I turn off adaptive sampling (run the toolbox for a fixed set of samples)

See Adaptive Modeling Mode.

How do I change the error function (relative error, RMS, ...)?

The <Measure> tag specifies the algorithm to use to assign models a score, e.g., cross validation. It is also possible to specify which error function to use, in the measure. Say you want to use cross validation with the maximum absolute error, then you would put:

<Measure type="CrossValidation" target="0.001" errorFcn="maxAbsoluteError"/>

On the other hand, if you wanted to use the TestSamples measure with a relative root-mean-square error you would put:

<Measure type="ValidationSet" target="0.001" errorFcn="relativeRms"/>

The default error function is "rootRelativeSquareError" These error functions can be found in the src/matlab/tools/errorFunctions directory. You are free to modify them and add your own.

How do I enable more profilers

Go to the profiler tag and put "*" as the regular expression. See also the next question.

What regular expressions can I use to filter Profilers?

See the syntax here.

How can I ensure deterministic results?

See the Random state page.

How do I get the closed from (symbolic expression) of a model?

Use the getExpression(..) function, see Using a model.

Troubleshooting

I have a problem and I want to report it

See the Reporting problems page.

I sometimes get flat models when using rational functions

First make sure the model is indeed flat, and does not just appear so on the plot. You can verify this by looking at the output axis range and making sure it's within reasonable bounds. When there are poles in the model, the axis range is sometimes stretched to make it possible to plot the high values around the pole, causing the rest of the model to appear flat. If the model contains poles, refer to the next question for the solution.

The PolynomialModel tries to do a least squares fit, based on which monomials are allowed in numerator and denominator. We have experienced that some models just find a flat model as the best least squares fit. There are two causes for this:

1. The number of sample points is few, and the model parameters (as explained here and here) force the model to use only a very small set of degrees of freedom. The solution in this case is to increase the minimum percentage bound in the xxxPolynomialInterface section of your configuration file: change the percentageBounds option to "60,100", "80,100", or even "100,100". A setting of "100,100" will force the polynomial models to always exactly interpolate. However, note that this does not scale very well with the number of samples. If, after increasing the percentage bounds you still get weird, spiky, models you simply need more samples or you should switch to a different model type.
2. Another possibility is that given a set of monomial degrees, the flat function is just the best possible least squares fit. In that case you simply need to wait for more samples.

When using rational functions I sometimes get 'spikes' (poles) in my model

When the denominator polynomial of a rational model has zeros inside the domain, the model will tend to infinity near these points. In most cases these models will only be recognized as being `the best' for a short period of time. As more samples get selected these models get replaced by better ones and the spikes should disappear.

However, since even the Cross-Validation measure (which is the most accurate one available) is not capable of detecting all poles, it is possible that a model which does contain some poles will be considered the best model by the toolbox. This may or may not be an issue, depending on what you want to use the model for. If it doesn't matter that the model is very inaccurate at one particular, small spot (near the pole), you can use the model with the pole and it should perform properly.

However, if the model should have a reasonable error on the entire domain, several methods are available to reduce the chance of getting poles or remove the possibility altogether. The possible solutions are:

• Simply wait for more data, usually spikes disappear but not always.
• Lower the percentageBounds option in the configuration file. For example, say you have 500 data points and if the percentageBounds option is set to 100 percent it means the degrees of the polinomials in the rational function can go up to 500. If you set the percentage bounds to 10, on the other hand, the maximum degree is set at 50 (= 10 percent of 500).
• If you roughly know the output range your data should have, an easy way to elimiante poles is to use the Min-Max Measure together with your current measure (Crossvalidation by default). This will cause models whose response falls outside the min-max bounds to be penalized extra, thus spikes should disappear. See the Combining measures and MinMax Measure pages for more information.
• Use a different model type (RBF, ANN, SVM,...), spikes are a typical problem of rational functions.
• Try using the RationalPoleSampleSelector, it was designed to get rid of this problem more quickly, but it only selects one sample at the time and therefore probably needs updating.

There is no noise in my data yet the rational functions don't interpolate

See this question.

When loading a model from disk I get "Warning: Class ':all:' is an unknown object class. Object 'model' of this class has been converted to a structure."

You are trying to load a model file without the toolbox in your matlab path. Make sure the toolbox is in your matlab path. In short: Start matlab, run <SUMO-Toolbox-directory>/startup.m (to ensure the toolbox is in your path) and then try to load your model.

When loading a model from disk I get something like "Warning: Element(s) of array 'foo' do not match the current constructor definition for class 'bar'. The element(s) have been converted to structures."

This is a silly bug in the code. If you are using a toolbox version prior to 4.0 please upgrade to the latest release or latest snapshot, your problems should be fixed. If you are using a current version (>= 4.0) please report it as a bug.

When trying to run the toolbox you get an error like "No component with id 'ann' of type 'adaptive model builder' found in config file."

This means you have specified to use a component with a certain id (in this case an AdaptiveModelBuilder component with id 'ann') but a component with that id does not exist further down in the configuration file (in this particular case 'ann' does not exist but 'anngenetic' does, as a quick search through the configuration file will show). So make sure you only declare components which have a definition lower down. So see which components are available, simply scroll down the configuration file and see which id's are specified. Please also refer to the Declarations and Definitions page.

When using NANN models I sometimes get "Runtime error in matrix library, Choldc failed. Matrix not positive definite"

This is a problem in the mex implementation of the NNSYSID toolbox. Simply delete the mex files, the matlab implementation will be used and this will not cause any problems.

When using FANN models I sometimes get "Invalid MEX-file createFann.mexa64, libfann.so.2: cannot open shared object file: No such file or directory."

This means Matlab cannot find the FANN library itself to link to dynamically. Make sure it is in your library path, ie, on unix systems, make sure it is inlcuded in LD_LIBRARY_PATH.