A Closer Look at SPOT's Tasks

In Sect. 4, SPOT was run as an automatic tuner. Steps from the automatic mode can be used in an interactive manner. SPOT can be started with the command

spot (<configurationfile>,  <task>)

where configurationfile is the name of the SPOT configuration file (String) and task is a STRING and can be one of the tasks init, seq, run, rep or auto. SPOT can also be run in a meta mode to perform tuning over a set of problem instances.

SPOT provides tools to perform the following tasks:

1. Initialize. An initial design is generated. This is usually the first step during experimentation. The employed parameter region (ROI) and the constant algorithm parameters (APD) have to be provided by the user. SPOT's parameters are specified in the CONF file. Although it is recommended to use the same basename for CONF, ROI, and APD files in order to define a project, this is not mandatory. SPOT allows a flexible combination of different filenames, e.g., one APD file can be used for different projects.
2. Run. This is usually the second step. The optimization algorithm is started with configurations of the generated design. Additionally information about the algorithms problem design are used in this step. The algorithm writes its results to the result file.
3. Sequential step. A new design, based on information from the result file, is generated. A prediction model is used in this step. Several generic prediction models are available in SPOT by default. To perform an efficient analysis, especially in situations when only few algorithms runs are possible, user-specified prediction models can easily be integrated into SPOT.
4. Report. An analysis, based on information from the result file, is generated. Since all data flow is stored in files, new report facilities can be added very easily. SPOT contains some scripts to perform a basic regression analysis and plots such as histograms, scatter plots, plots of the residuals, etc.
5. Automatic mode. In the automatic mode, the steps run and sequential are performed after an initialization for a predetermined number of times.
6. Meta mode. In the meta mode, the tuning process is repeated for several configurations. For example, tuning can be performed for different starting points $\ensuremath{\vec{x}}\xspace _0$, several dimensions, or randomly chosen problem instances.