Running

Getting started

If you are just getting started with the toolbox and you have no idea how everything works, this section should help you on your way. First make sure you know what the toolbox is used for, you have finished the toolbox Installation and you have done a successful test run by running the default configuration. If that works you know everything is working correctly. Then:

1. Go through the presentation available here, paying specific attention to the control flow
2. The behavior of the toolbox is fully configured through two XML files. If you do not know what XML is please read FAQ#What is XML? first.
3. Read the toolbox configuration structure section. This is very important. Then print out config/default.xml and take your time to read it through and understand the structure and the way things work.
4. Do the test run again, this time play closer attention to what is happening and see if you understand what is going on. If you still have no idea you can refer to the Understanding the control flow section below.
5. Ok, by now you should have a rough idea how the configuration file is structured and how the control flow works. Now, Change default.xml to run a different example. This is explained below. If you can do that and it works you should have mastered all the basic skills needed to use the toolbox. You can now browse through the rest of the wiki as needed.

If you get stuck or have any problems please let us know.

We are well aware that documentation is not always complete and possibly even out of date in some cases. We try to document everything as best we can but much is limited by available time and manpower. We are are a university research group after all. The most up to date documentation can always be found (if not here) in the default.xml configuration file and, of course, in the source files. If something is unclear please don't hesitate to ask.

Running the default configuration

Once the SUMO Toolbox is installed you can do a simple test run to check if everything is working as expected. This is explained on the installation page.

Running different examples

Prerequisites

This section is about running a different example problem, if you want to model your own problem see Adding an example. Make sure you understand the difference between the simulator configuration file and the toolbox configuration file. You should also have read Toolbox configuration#Structure.

Changing default.xml

The examples/ directory contains many example simulators that you can use to test the toolbox with. These examples range from predefined functions, to datasets from various domains, to native simulation code. If you want to try one of the examples, open config/default.xml and edit the <Simulator> tag to suit your needs.

For example, originally default.xml contains:

<Simulator>Academic2DTwice</Simulator>

This means the toolbox will look in the examples directory for a project directory called Academic2DTwice and load the xml file with the same name inside that directory (in this case: Academic2DTwice/Academic2DTwice.xml).

Now lets say you want to run one of the different example problems, for example, lets say you want to try the Michalewicz example. In this case you would replace the original Simulator tag with:

<Simulator>Michalewicz</Simulator>

In addition you would have to change the <Outputs> tag. The Academic2DTwice example has two outputs (out and outinverse). However, the Michalewicz example has only one (out). Thus telling the SUMO Toolbox to model the outinverse output in that case makes no sense since it does not exist for the Michalewicz example. So the following output configuration suffices:

<Outputs>
   <Output name="out">
</Output>

The rest of default.xml can be kept the same. Then simply run 'go' to run the example (making sure that the toolbox is in your Matlab path of course).

Note that it is also possible to specify an absolute path or refer to a particular xml file directly. For example:

<Simulator>/path/to/your/project/directory</Simulator>

or:

<Simulator>Ackley/Ackley2D.xml</Simulator>

Important notes

Select a matching SampleEvaluator

There is one important caveat. Some examples consist of a fixed data set, some are implemented as a Matlab function, others as a C++ executable, etc. When running a different example you have to tell the SUMO Toolbox how the example is implemented so the toolbox knows how to extract data (eg: should it load a data file or should it call a Matlab function). This is done by specifying the correct SampleEvaluator tag. The default SampleEvaluator is:

<SampleEvaluator>matlab</SampleEvaluator>

So this means that the toolbox expects the example you want to run is implemented as a Matlab function. Thus it is no use running an example that is implemented as a static dataset using the 'matlab' or 'local' sample evaluators. Doing this will result in an error. In this case you should use 'scatteredDataset' (or sometimes griddedDataset).

To see how an example is implemented open the XML file inside the example directory and look at the

<Implementation>

tag. To see which SampleEvaluators are available see Config:SampleEvaluator.

Select an appropriate AdaptiveModelBuilder

Also remember that if you switch to a different example you may also have to change the Config:AdaptiveModelBuilder used. For example, if you are using a spline model (which only works in 2D) and you decide to model a problem with many dimensions (e.g., CompActive or BostonHousing) you will have to switch to a different model type (e.g., any of the SVM or LS-SVM model builders).

Switch off Sample Selection if not needed

If you are modeling a fixed, small size dataset it may make no sense to select samples incrementally. Instead you will probably load all the data at once and only generate models. See Adaptive_Modeling_Mode for how to do this.

Finally the question may remain, what settings should I use for my problem? Well there is no best answer to this question, see General_guidelines.

Running different configuration files

If you just type "go" the SUMO-Toolbox will run using the configuration options in default.xml. However you may want to make a copy of default.xml and play around with that, leaving your original default.xml intact. So the question is, how do you run that file? Lets say your copy is called MyConfigFile.xml. In order to tell SUMO to run that file you would type:

go('/path/to/MyConfigFile.xml')

The path can be an absolute path, or a path relative to the SUMO Toolbox root directory. To see what other options you have when running go type help go.

Remember to always run go from the toolbox root directory.

Merging your configuration

If you know what you are doing, you can merge your own custom configuration with the default configuration by using the '-merge' option. Options or tags that are missing in this custom file will then be filled up with the values from the default configuration. This prevents you from having to duplicate tags in default.xml. However, if you are unfamiliar with XML and not quite sure what you are doing we advise against using it.

Running optimization examples

The SUMO toolbox can also be used for minimizing the simulator in an intelligent way. There are 2 examples in included in config/Optimization. To run these examples is exactly the same as always, e.g. go('config/optimization/Branin.xml'). The only difference is in the sample selector which is specified in the configuration file itself.

• 

The example configuration files are well documented, it is advised to go through them for more detailed information.

Understanding the control flow

The general SUMO-Toolbox control flow

When the toolbox is running you might wonder what exactly is going on. The high level control flow that the toolbox goes through is illustrated in the flow chart and explained in more detail below. You may also refer to the general SUMO presentation.

1. Select samples according to the initial design and execute the Simulator for each of the points
2. Once enough points are available, start the Model builder which will start producing models as it optimizes the model parameters
   1. the number of models generated depends on the AdaptiveModelBuilder used. Usually the AdaptiveModelBuilder tag contains a setting like maxFunEvals or popSize. This indicates to the algorithm that is optimizing the model parameters (and thus generating models) how many models it should maximally generate before stopping. By increasing this number you will generate more models in between sampling iterations, thus have a higher chance of getting a better model, but increasing the computation time. This step is what we refer to as a modeling iteration.
   2. optimization over the model parameters is driven by the Measure(s) that are enabled. Selection of the Measure is thus very important for the modeling process!
   3. each time the model builder generates a model that has a lower measure score than the previous best model, the toolbox will trigger a "New best model found" event, save the model, generate a plot, and trigger all the profilers to update themselves.
   4. so note that by default, you only see something happen when a new best model is found, you do not see all the other models that are being generated in the background. If you want to see those, you must increase the logging granularity (or just look in the log file) or enable more profilers.
3. So the model builder will run until it has completed
4. Then, if the current best model satisfies all the targets in the enabled Measures, it means we have reached the requirements and the toolbox terminates.
5. If not, the SampleSelector selects a new set of samples (= a sampling iteration), they are simulated, and the model building resumes or is restarted according to the configured restart strategy
6. This whole loop continues (thus the toolbox will keep running) until one of the following conditions is true:
   1. the targets specified in the active measure tags have been reached (each Measure has a target value which you can set). Note though, that when you are using multiple measures (see Multi-Objective Modeling) or when using single measures like AIC or LRM, it becomes difficult to set a priori targets since you cant really interpret the scores (in contrast to the simple case with a single measure like CrossValidation where your target is simply the error you require). In those cases you should usually set the targets to 0 and use one of the other criteria below to make sure the toolbox stops.
   2. the maximum running time has been reached (maximumTime property in the Config:SUMO tag)
   3. the maximum number of samples has been reached (maximumTotalSamples property in the Config:SUMO tag)
   4. the maximum number of modeling iterations has been reached (maxModelingIterations property in the Config:SUMO tag)

Note that it is also possible to disable the sample selection loop, see Adaptive Modeling Mode. Also note that while you might think the toolbox is not doing anything, it is actually building models in the background (see above for how to see the details). The toolbox will only inform you (unless configured otherwise) if it finds a model that is better than the previous best model (using that particular measure!!). If not it will continue running until one of the stopping conditions is true.

Output

All output is stored under the directory specified in the Config:ContextConfig section of the configuration file (by default this is set to "output").

Starting from version 6.0 the output directory is always relative to the project directory of your example. Unless you specify an absolute path.

After completion of a SUMO Toolbox run, the following files and directories can be found there (e.g. : in output/<run_name+date+time>/ subdirectory) :

• config.xml: The xml file that was used by this run. Can be used to reproduce the entire modeling process for that run.
• randstate.dat: contains states of the random number generators, so that it becomes possible to deterministically repeat a run (see the Random state page).
• samples.txt: a list of all the samples that were evaluated, and their outputs.
• profilers-dir: contains information and plots about convergence rates, resource usage, and so on.
• best-dir: contains the best models (+ plots) of all outputs that were constructed during the run. This is continuously updated as the modeling progresses.
• models_outputName-dir: contains a history of all intermediate models (+ plots + movie) for each output that was modeled.

If you generated models multi-objectively you will also find the following directory:

• paretoFronts-dir: contains snapshots of the population during multi-objective optimization of the model parameters.

Debugging

Remember to always check the log file first if problems occur! When reporting problems please attach your log file and the xml configuration file you used.

To aid understanding and debugging you should set the console and file logging level to FINE (or even FINER, FINEST) as follows:

Change the level of the ConsoleHandler tag to FINE, FINER or FINEST. Do the same for the FileHandler tag.

<!-- Configure ConsoleHandler instances -->
<ConsoleHandler>
	<Option key="Level" value="FINE"/>
</ConsoleHandler>

Using models

Once you have generated a model, you might wonder what you can do with it. To see how to load, export, and use SUMO generated models see the Using a model page.

Modeling complex outputs

The toolbox supports the modeling of complex valued data. If you do not specify any specific <Output> tags, all outputs will be modeled with complexHandling set to 'complex'. This means that a real output will be modeled as a real value, and a complex output will be modeled as a complex value (with a real and imaginary part). If you don't want this (i.e., you want to model the modulus of a complex output or you want to model real and imaginary parts separately), you explicitly have to set complexHandling to 'modulus', 'real', 'imaginary', or 'split'.

More information on this subject can be found at the Outputs page.

Models with multiple outputs

If multiple Outputs are selected, by default the toolbox will model each output separately using a separate adaptive model builder object. So if you have a system with 3 outputs you will get three different models each with one output. However, sometimes you may want a single model with multiple outputs. For example instead of having a neural network for each component of a complex output (real/imaginary) you might prefer a single network with 2 outputs. To do this simply set the 'combineOutputs' attribute of the <AdaptiveModelBuilder> tag to 'true'. That means that each time that model builder is selected for an output, the same model builder object will be used instead of creating a new one.

Note though, that not all model types support multiple outputs. If they don't you will get an error message.

Also note that you can also generate models with multiple outputs in a multi-objective fashion. For information on this see the page on Multi-Objective Modeling.

Multi-Objective Model generation

See the page on Multi-Objective Modeling.

Interfacing with the SUMO Toolbox

To learn how to interface with the toolbox or model your own problem see the Adding an example and Interfacing with the toolbox pages.

Tips

See the Tips page for various tips and gotchas.