This code is part of my PhD at PPG-CC/DC/UFSCar. The aim is generate a specific type of random partition for multilabel classification.
@misc{Gatto2022, author = {Gatto, E. C.}, title = {Generate Random Partitions Version 2 for Multilabel Classification}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/cissagatto/Generate-Partitions-Random2}}}
This code source is composed of the project R to be used in RStudio IDE and also the following scripts R:
- libraries.R
- utils.R
- generateR1.R
- run.R
- random2.R
- gpr2_config_files.R
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.
To run this experiment you need the X-Fold Cross-Validation files and they must be compacted in tar.gz format. You can download these files, with 10-folds, ready for multiple multilabel dataset by clicking here. For a new dataset, in addition to including it in the datasets-original.csv file, you must also run this code here. In the repository in question you will find all the instructions needed to generate the files in the format required for this experiment. The tar.gz file can be placed on any folder on your computer or cluster. The absolute path of the file should be passed as a parameter in the configuration file that will be read by exhaustive.R script. The dataset will be loaded from there.
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 |
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
To run this code you will need a configuration file saved in csv format and with the following information:
| Config | Value |
|---|---|
| Dataset_Path | Absolute path to the folder where the dataset's tar.gz is stored |
| Temporary_Path | Absolute path to the folder where temporary processing will be performed* |
| dataset_name | Dataset name according to datasets-original.csv file |
| number_dataset | Dataset number according to datasets-original.csv file |
| number_folds | Number of folds used in cross validation |
| number_cores | Number of cores for parallel processing |
- Use folders 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.
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).
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.
The results stored in the folder OUTPUT it will be used in the next phase: Best-Partition-Silhoute, Best-Partition-MacroF1 or Best-Partition-MicroF1. The result for a dataset must be put in the folder PARTITIONS in the respective code.
To run the code, open the terminal, enter the ~/Generate-Partitions-Random2/R folder, and type
Rscript random2.R [absolute_path_to_config_file]
Example:
Rscript random2.R "~/Generate-Partitions-Random2/R2-Config-Files/R2-GpositiveGO.csv"
The results are stored in a folder called reports in the project root.
- 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.
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