Fast Preference Learning README

@page readme FastPL README

Introduction

This work is based on Olivier Sobrie's work on preference learning algorithms (python code : https://github.com/oso/pymcda, thesis: https://tel.archives-ouvertes.fr/tel-01370555/document).

The objective of this repository is to translate the previous code (py-mcda) in C++ and set up the parallelisation tools needed to process a greater amount of entrypoints.

The README contains information on how to run the application and help future developpers to set their environment.

The full description and documentation of the project can be found at https://mostah.github.io/fastPL/.

---

Repository structure

• include : Header files

• src : Sources files

• test : Test files

• extsrc : External sources and dependencies

• data : Data (datasets and models) repository

• doc : Doxygen documentation repository

• .circleci : CircleCi pipelines configuration

---

Run project in Docker (Recommended)

Build Docker image

First thing first, build the docker image. You can find here some documentation on docker.

docker build . -t fastpl

Currently, the build of all dependencies requires ~1h

Run the main app in Docker

The following command will show the run config options (helper):

docker run fastpl ./Main -h

To run the app with on a specific dataset:

docker run fastpl ./Main -d $dataset_path -o $output_path

At the end of the algorithm, the model will be stored in the $output_path

Run the tests in Docker

Run all tests:

docker run fastpl ./Test

Run specified tests:

docker run fastpl ./Test --gtest_filter=TestGeneralName.TestPreciseName   # One specific Test
docker run fastpl ./Test --gtest_filter=TestGeneralName.*                 # All tests of Name1 = GeneralName 

Check the logs

Get the fastpl docker container id by running docker ps

docker run -it $container_id /bin/bash 
cd /home/fastpl/logs

---

Run project locally

The following requires CMake to be installed in your machine.

From the root of the project directory:

Download dependencies

git submodule init
git submodule update

Build C++ files

mkdir build && cd build
cmake .. -DBUILD_DEPS:BOOL=ON -DUSE_SCIP=OFF && make

Currently, the build of all dependencies requires ~1h

Run the main app locally

From the build directory:

The following command will show the run config options (helper):

./Main -h

To run the app on a specified dataset:

./Main -d $dataset_path -o $output_path

At the end of the algorithm, the model will be stored in the $output_path

Run the tests locally

From the build directory:

Run all tests:

./Test

Run specified tests:

./Test --gtest_filter=TestGeneralName.TestPreciseName   # One specific Test
./Test --gtest_filter=TestGeneralName.*                 # All tests of Name1 = GeneralName 

---

Application configuration

The application configuration can be found at: app-config

The application configuration holds the general config of the learning algorithms.

Parameters

• log_level: log filter, values in INFO, ERROR, DEBUG
• log_file: path of the logfile
• data_dir: data directory path. When changed, the args -d and -o passed along with ./Main program is set relatively to the data directory path configured here
• model_batch_size: model population size used in the metaheuristic
• max_iterations: max iteration of the metaheuristic before terminating the application
• n_profile_update: number of iteration of profile update for one weight update

---

CircleCi pipelines

CircleCi pipelines configuration can be found at: .circleci

pipelines

• Build: Try to build all the project
• Tests (requires build): Try to pass all the tests
• Doc generation (requires build): Try to generate the documentation

Documentation

The documentation of this project is generated by doxygen. In order to generate locally or update the online documentation, the following modules must be installed: doxygen, doxygen-doc, doxygen-gui, graphviz.

The documentation configuration and files can be found at: doc

Update online documentation

The online documentation is currently host on GitHub Pages: https://mostah.github.io/fastPL/.

The GitHub Page displays static html files from a specified branch. The files used by GitHub Pages to display the latest documentation are set on the gh-pages branch and must not be changed manually.

The following script updates the gh-pages branch by generating the documentation from the latest master commit.

From root directory:

sh doc_generation.sh

Generate documentation locally

From /doc directory:

cmake . && doxygen Doxyfile.Doxigen

Open the documentation on your browser, from /doc/html directory:

open index.html

---

Profiling

Profiling requires the app to be run on a Docker container. The following assumes the fastpl app has already been built by docker.

Profiling with GPROF

Running the docker container and ssh into it

docker run -it fastpl /bin/bash

Executing the program we want to profile

./$PROGRAM

Creating the profiling data as a callgraph image (Recommended)

gprof $PROGRAM | python3 gprof2dot/gprof2dot.py | dot -Tpng -o analysis.png

Creating the profiling data as a text file

gprof $PROGRAM > analysis.txt

Copy the analysis from the docker container into your machine

Keep this terminal open, and open a new one

Get the $ID of the fastpl container running with docker ps

docker cp $ID:/home/fastPL/build/analysis.{txt or png} $YOUR_PATH

The profiling data should now be in $YOUR_PATH in your machine.