iraceplot

The iraceplot package allows to plot configuration data obtained by configuration process performed by the irace configurator.

https://github.com/auto-optimization/iraceplot

Science Score: 13.0%

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Keywords

cran irace parameter-tuning r-package

Last synced: 6 months ago · JSON representation

Repository

The iraceplot package allows to plot configuration data obtained by configuration process performed by the irace configurator.

Basic Info

Host: GitHub
Owner: auto-optimization
License: other
Language: R
Default Branch: master
Homepage: https://auto-optimization.github.io/iraceplot/
Size: 292 MB

Statistics

Stars: 5
Watchers: 1
Forks: 2
Open Issues: 23
Releases: 0

Topics

cran irace parameter-tuning r-package

Created over 4 years ago · Last pushed about 1 year ago

Metadata Files

Readme Changelog License

The iraceplot package

Maintainers: Leslie Pérez Cáceres, Manuel López-Ibáñez

Creators: Pablo Oñate Marín, Leslie Pérez Cáceres, Manuel López-Ibáñez

Contact: https://groups.google.com/d/forum/irace-package

Introduction

The iraceplot package provides different plots to visualize the data generated by the irace software for automatic algorithm configuration (hyper-parameter optimization).

This package provides visualizations of:

Parameter configurations using parallel coordinates with parallel_coord().
Boxplots of configurations performance (training and testing) with boxplot_training() and boxplot_test().
Sampling distributions with sampling_frequency().
Overview of all performance data generate by a single run of irace with plot_experiments_matrix().

For more details about these functions, please check the user guide of the package and the documentation of the functions implemented in the package.

The package also provides an HTML report, using report(), summarizing relevant information obtained during an execution of irace.

The aim of this package is to provide support for the analysis of the best parameter settings found, the assessment of the parameter space explored by irace and the overall performance of the configuration process. Such analysis might lead to insights about the role of algorithmic components their interactions, or to improve the configuration process itself.

Keywords: automatic configuration, offline tuning, parameter tuning, parameter visualization, irace.

Requisites

R (https://www.r-project.org) is required for running irace and to use iraceplot.

User guide

A user guide comes with the package. The following is a quick-start guide. The user guide gives more detailed instructions.

Installing R

The official instructions are available at https://cran.r-project.org/doc/manuals/r-release/R-admin.html. We give below a quick R installation guide that will work in most cases.

GNU/Linux

You should install R from your package manager. On a Debian/Ubuntu system it will be something like:

$ sudo apt-get install r-base

Once R is installed, you can launch R from the Terminal and from the R prompt install the iraceplot package. See instructions below.

OS X

You can install R directly from a CRAN mirror (https://cran.r-project.org/bin/macosx/).

Alternatively, if you use homebrew, you can just do $ brew install --cask r

(Using brew install r is not recommended because that will build R from source and you will not be able to use any CRAN binary, possibly resulting in annoying build failures).

Once R is installed, you can launch R from the Terminal (or from your Applications), and from the R prompt install the iraceplot package. See instructions below.

Windows

You can install R from a CRAN mirror (https://cran.r-project.org/bin/windows/). Once R is installed, you can launch the R console and install the iraceplot package from it. See instructions below.

Installing the iraceplot package

Stable version

For installing the stable version from CRAN, launch R or Rstudio and evaluate: r install.packages("iraceplot")

Or you may wish to try the development version from GitHub

GitHub (Development version)

If you wish to try the development version, you can install it by executing the following command within the R console:

r install.packages('iraceplot', repos = c('https://auto-optimization.r-universe.dev', 'https://cloud.r-project.org'))

Basic Usage

Load the package in the R console:

r library(iraceplot)

You need the log file generated by irace (irace.Rdata or the filename given by the option logFile of irace). Then, generate a general-purpose report with:

r report("irace.Rdata")

This should create a filename report.html and open it in your browser (if the browser does not open, try to find the file and open it yourself). The result will look something like this report example.

There is a lot more functionality in this package. Check the documentation and the User Guide to identify the plots most suited to your needs.

Owner

Name: auto-optimization
Login: auto-optimization
Kind: organization

Repositories: 2
Profile: https://github.com/auto-optimization

GitHub Events

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Last synced: 6 months ago

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Total issues: 35
Total pull requests: 1
Average time to close issues: 3 months
Average time to close pull requests: 4 days
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Total pull request authors: 1
Average comments per issue: 0.6
Average comments per pull request: 3.0
Merged pull requests: 1
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Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 0
Average time to close issues: 1 day
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Average comments per issue: 1.0
Average comments per pull request: 0
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Top Authors

Issue Authors

MLopez-Ibanez (33)
leslieperez (1)
ndangtt (1)

Pull Request Authors

j-mezger (1)

Top Labels

Issue Labels

enhancement (2)

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Packages

Total packages: 1
Total downloads:
- cran 284 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 6
Total maintainers: 1

cran.r-project.org: iraceplot

Plots for Visualizing the Data Produced by the 'irace' Package

Homepage: https://auto-optimization.github.io/iraceplot/
Documentation: http://cran.r-project.org/web/packages/iraceplot/iraceplot.pdf
License: MIT + file LICENSE
Latest release: 2.1.0
published about 1 year ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 284 Last month

Rankings

Stargazers count: 28.5%

Forks count: 28.8%

Dependent packages count: 29.8%

Average: 32.8%

Dependent repos count: 35.5%

Downloads: 41.6%

Maintainers (1)

manuel.lopez-ibanez@manchester.ac.uk