Classifier Chains runing with R and Python

This code is part of my PhD research at PPG-CC/DC/UFSCar in colaboration with Katholieke Universiteit Leuven Campus Kulak Kortrijk Belgium. The R script runs in parallel the CC made in python.

How to Cite 📑

If you use this code in your research, please cite the following:

@misc{Gatto2025,
  author = {Gatto, E. C.},
  title = {Classifier Chains with R and Python},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/cissagatto/HPML.CC}}
}

🗂️ Project Structure

The codebase includes R and Python scripts that must be used together.

R Scripts (in /R folder):

• config-files.R
• cc.R
• run-python.R
• cc-python.R
• utils.R
• libraries.R

Python Scripts (in /Python folder):

• main.py
• confusion_matrix.py
• measures.py
• evaluation.py

⚙️ How to Reproduce the Experiment

Step 1 – Prepare the Dataset Metadata File

A file called datasets-original.csv must be in the root project directory. This file is used to read information about the datasets and they are used in the code. We have 90 multilabel datasets in this .csv file. If you want to use another dataset, please, add the following information about the dataset in the file:

Parameter	Status	Description
Id	mandatory	Integer number to identify the dataset
Name	mandatory	Dataset name (please follow the benchmark)
Domain	optional	Dataset domain
Instances	mandatory	Total number of dataset instances
Attributes	mandatory	Total number of dataset attributes
Labels	mandatory	Total number of labels in the label space
Inputs	mandatory	Total number of dataset input attributes
Cardinality	optional	**
Density	optional	**
Labelsets	optional	**
Single	optional	**
Max.freq	optional	**
Mean.IR	optional	**
Scumble	optional	**
TCS	optional	**
AttStart	mandatory	Column number where the attribute space begins * 1
AttEnd	mandatory	Column number where the attribute space ends
LabelStart	mandatory	Column number where the label space begins
LabelEnd	mandatory	Column number where the label space ends
Distinct	optional	** 2
xn	mandatory	Value for Dimension X of the Kohonen map
yn	mandatory	Value for Dimension Y of the Kohonen map
gridn	mandatory	X times Y value. Kohonen's map must be square
max.neigbors	mandatory	The maximum number of neighbors is given by LABELS -1

1 - Because it is the first column the number is always 1.

2 - Click here to get explanation about each property.

STEP 2: Cross-Validation Files

The experiment requires pre-processed cross-validation files in .tar.gz format. You can download the 10-fold files for multilabel datasets here.

For new datasets, you can generate these files by following the instructions in this repository. After generating the files, place the .tar.gz archive in any directory, and provide the absolute path in the configuration file for the global.R script.

STEP 3

You need to have installed all the Java, Python and R packages required to execute this code on your machine or server. This code does not provide any type of automatic package installation!

You can use the Conda Environment that I created to perform this experiment. Below are the links to download the files. Try to use the command below to extract the environment to your computer:

conda env create -file AmbienteTeste.yaml

See more information about Conda environments here

You can also run this code using the AppTainer container that I'm using to run this code in a SLURM cluster. Please, check this tutorial (in portuguese) to see how to do that.

STEP 4

To run this code you will need a configuration file saved in csv format and with the following information:

Config	Value
FolderScripts	Absolute path to the R folder scripts
Dataset_Path	Absolute path to the directory where the dataset's tar.gz is stored
Temporary_Path	Absolute path to the directory where temporary processing will be performed * 1
Partitions_Path	Absolute path to the directory where the best partitions are
Implementation	Must be "clus", "mulan", "python" or "utiml"
Dataset_Name	Dataset name according to dataset-original.csv file
Number_Dataset	Dataset number according to dataset-original.csv file
Number_Folds	Number of folds used in cross validation
Number_Cores	Number of cores for parallel processing

1 - Use directorys like /dev/shm, tmp or scratch here.

You can save configuration files wherever you want. The absolute path will be passed as a command line argument.

🛠️ Software Requirements

This code was develop in RStudio 2024.12.0+467 "Kousa Dogwood" Release (cf37a3e5488c937207f992226d255be71f5e3f41, 2024-12-11) for Ubuntu Jammy Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) rstudio/2024.12.0+467 Chrome/126.0.6478.234 Electron/31.7.6 Safari/537.36, Quarto 1.5.57

• R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six", Copyright (C) 2025 The R Foundation for Statistical Computing, Platform: x86_64-pc-linux-gnu
• Python 3.10
• Conda 24.11.3

💻 Hardware Recommendations

This code may or may not be executed in parallel, however, it is highly recommended that you run it in parallel. The number of cores can be configured via the command line (number_cores). If number_cores = 1 the code will run sequentially. In our experiments, we used 10 cores. For reproducibility, we recommend that you also use ten cores. This code was tested with the emotions dataset in the following machine:

• Linux 6.11.0-26-generic #26~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC x86_64 x86_64 x86_64 GNU/Linux
• Distributor ID: Ubuntu, Description: Ubuntu 24.04.2 LTS, Release: 24.04, Codename: noble
• Manufacturer: Acer, Product Name: Nitro ANV15-51, Version: V1.16, Wake-up Type: Power Switch, Family: Acer Nitro V 15

Then the experiment was executed in a cluster at UFSC (Federal University of Santa Catarina Campus Blumenau).

Results

The results are stored in the REPORTS directory in a tar.gz format.

RUN

To run the code, open the terminal, enter the ~/HPML.CC/R directory, and type:

Rscript cc.R [absolute_path_to_config_file]

Example:

Rscript cc.R "~/HPML.CC/config-files/cc-emotions.csv"

DOWNLOAD RESULTS

[Click here]

Acknowledgment

• This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
• This study was financed in part by the Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPQ) - Process number 200371/2022-3.
• The authors also thank the Brazilian research agencies FAPESP financial support.

📞 Contact

Elaine Cecília Gatto ✉️ elainececiliagatto@gmail.com

Links

| Site | Post-Graduate Program in Computer Science | Computer Department | Biomal | CNPQ | Ku Leuven | Embarcados | Read Prensa | Linkedin Company | Linkedin Profile | Instagram | Facebook | Twitter | Twitch | Youtube |

Thanks