self-tuning-lam-experiments

This repository contains code to reproduce the experiments, and analysis of experimental data, from the following paper:

Vincent A. Cicirello. 2021. Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving. Applied Sciences, 11, 21, Article 9828 (November 2021). https://doi.org/10.3390/app11219828

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Requirements to Build and Run the Experiments

To build and run the experiments on your own machine, you will need the following:

JDK 11: I used OpenJDK 11, but you should be fine with Oracle's JDK as well. Technically, there is nothing in the code that strictly requires Java 11, so you should be able to build and run with JDK 8 or later. However, the Maven pom.xml provided in the repository assumes Java 11. Also, if you want to recreate the experiments in as similar an environment as used in the reported results, then you should use Java 11.
Apache Maven: In the root of the repository, there is a pom.xml for building the Java programs for the experiments. Using this pom.xml, Maven will take care of downloading the exact version of the Chips-n-Salsa library that was used in the experiments (release 2.12.1), as well as Chips-n-Salsa's dependencies.
Python 3: The repository contains Python programs that were used to compute summary statistics, statistical significance tests, and to generate graphs for the figures of the paper. If you want to run the Python programs, you will need Python 3. I specifically used Python 3.9.6. You also need scipy and matplotlib installed.
Make: The repository contains a Makefile to simplify running the build, running the experiment's Java programs, and running the Python program to analyze the data. If you are familiar with using the Maven build tool, and running Python programs, then you can just run these directly, although the Makefile may be useful to see the specific commands needed.

Building the Java Programs (Option 1)

The source code of the Java programs, implementing the experiments is in the src/main directory. You can build the experiment programs in one of the following ways.

Using Maven: Execute the following from the root of the repository.

mvn clean package

Using Make: Or, you can execute the following from the root of the repository.

make build

This produces a jar file containing 10 Java programs for running different parts of the experiments. The jar also contains all dependencies, including the Chips-n-Salsa library and its dependencies. If you are unfamiliar with the usual structure of the directories of a Java project built with Maven, the .class files, the .jar file, etc will be found in a target directory that is created by the build process.

Downloading a prebuilt jar (Option 2)

As an alternative to building the jar (see above), you can choose to instead download a prebuilt jar of the experiments from the Maven Central repository. The Makefile contains a target that will do this for you, provided that you have curl installed on your system. To download the jar of the precompiled code of the experiments, run the following from the root of the repository:

make download

The jar that it downloads contains the compiled code of the experiments as well as all dependencies, which would include the version of Chips-n-Salsa originally used for the paper, as well as its dependencies, all within a single jar file.

Running the Experiments

You must first either follow the build instructions or download a prebuilt jar (see above sections). Once the jar of the experiments is either built or downloaded, you can then run the experiments with the following executed at the root of the repository:

make experiments

This will run each of the experiment programs in sequence, with the results piped to text files in the root of the project. The output from my runs are found in the /data directory. Be aware that running all of the experiments will take quite a bit of time.

There are also several other targets in the Makefile if you wish to run only some of the experiments from the paper. See the Makefile for details.

Analyzing the Experimental Data

To run the Python program that I used to generate summary statistics, run significance tests, and generate the graphs for the figures frmo the paper, you need Python 3 installed. The source code of the Python programs is found in the src/analysis directory. To run the analysis execute the following at the root of the repository:

make analysis

This will analyze the data from my runs in the /data directory. If you want to analyze the data from your runs instead, then change the variable pathToDataFiles = "" in the Makefile. This make command will also take care of installing any required Python packages if you don't already have them installed, such as matplotlib and scipy.

Other Files in the Repository

There are a few other files, potentially of interest, in the repository, which include:

system-stats.txt: This file contains details of the system I used to run the experiments, such as operating system, processor specs, Java JDK and VM. It is in the /data directory.