BellPartitionsMultiLabel

This code generates partitions based on bell numbers for multilabel classification. This code is part of my doctoral research. Important: when executing in Linux, this code uses the folder "/dev/shm" to reduce I/O process.

How to cite

@misc{Gatto2021, author = {Gatto, E. C.}, title = {Bell Partitions for Multilabel Classification}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/cissagatto/BellPartitionsMultiLabel/}}}

Scripts

This source code consists of an R project for R Studio and the following R scripts:

1. libraries
2. utils
3. BellPartitionsMultiLabel
4. run
5. bpm

Multi-Label Datasets

You can download the multi-label datasets at this link: https://cometa.ujaen.es/datasets/

Preparing your experiment

Step-1

Put the datasets in the correct folder

Step-2

A file called datasets-original.csv must be in the root project folder. 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*
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
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

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

STEP 3

You need to have installed all the R packages required to execute this code on your machine. Check out which are needed in the file libraries.R. 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.

| download txt | download yml | download yaml |

Folder Structure

Software Requirements

This code was develop in RStudio Version 1.4.1106 © 2009-2021 RStudio, PBC "Tiger Daylily" (2389bc24, 2021-02-11) for Ubuntu Bionic Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) QtWebEngine/5.12.8 Chrome/69.0.3497.128 Safari/537.36. The R Language version was: R version 4.1.0 (2021-05-18) -- "Camp Pontanezen" Copyright (C) 2021 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit).

Hardware Requirements

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 birds dataset in the following machine:

System:

Host: bionote | Kernel: 5.8.0-53-generic | x86_64 bits: 64 | Desktop: Gnome 3.36.7 | Distro: Ubuntu 20.04.2 LTS (Focal Fossa)

CPU:

Topology: 6-Core | model: Intel Core i7-10750H | bits: 64 | type: MT MCP | L2 cache: 12.0 MiB | Speed: 800 MHz | min/max: 800/5000 MHz Core speeds (MHz): | 1: 800 | 2: 800 | 3: 800 | 4: 800 | 5: 800 | 6: 800 | 7: 800 | 8: 800 | 9: 800 | 10: 800 | 11: 800 | 12: 800 |

Then the experiment was executed in a cluster at UFSCar.

Run

Rscript bpm.R [number_dataset] [tem_folder]

Example:

Rscript bpm.R 1 "/dev/shm/res"

Results

Three files are generated as result:

[dataset_name]-countPartitions.csv: Returns the frequency of groups. For example group 1 appears only 1 time while group 3 appears 6 times.

groups	total
1	1
2	7
3	6
4	1

[dataset_name]-groupsPerPartitions.csv: Returns the number of groups for each partition. For example partition 2, 3 and 4 has 2 groups while partition 1 only one.

part	totalGroups
1	1
2	2
3	2
4	2

[dataset_name]-partitions.csv: Returns the label distributions in each partition. For example: in partition 12 the Label1 and Label2 are located in group 1 while Label3 and Label4 are in group 2.

id	part	group	id_labels	labels
1	12	1	1	"Label1"
2	12	1	2	"Label2"
3	12	2	3	"Label3"
4	12	2	4	"Label4"

The results are store in the Reports folder, like the follow image

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

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