Test the best hybrid partition generated by hierarchical community detection methods wiht threshold sparsification using clus framework

This code is part of my doctoral research at PPG-CC/DC/UFSCar. Test Hybrid Partitions + Threshold Sparsification + Hierarchical Community Detection Methods + Clus Classifier

How to cite

@misc{Gatto2022, author = {Gatto, E. C.}, title = {Test Hybrid Partitions from Hierarchical Community Detection Methods with Treshold Sparcification and Clus Classifier}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/cissagatto/TCP-TR-H-Clus}}}

Flowchart

Preparing your experiment

STEP 1

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.

** Click here to get explanation about each property.

STEP 2

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 tcp.R script. The dataset will be loaded from there.

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!

STEP 4

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

Step-5

To run this code you will need the partitions generated from the following code. Please, read the instructions there.

• Jaccard, Cosine, Roger-Tanimoto and Russel-Rao: Click here to download

• Random: Click here to download

STEP 6

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 (b)
Partitions_Path	Absolute path to the folder where partitions are store
Validation	Choose which one to run: 1-silhouette, 2-macrof1, or 3-microf1 (a)
Similarity	Choose which one to run: jaccard, rogers, etc.
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

(a) The experiment can be run with 3 different validation methods to choose the best partition:

1 - using silhouette coefficient (quality clustering measure);

2 - using the macrof-1 assessement multilabel measure;

3 - using the microf-1 assessement multilabel measure.

(b) 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.

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

To run the code, open the terminal, enter the ~/TCP-TR-H-Clus/R folder, and type

Rscript tcp.R [absolute_path_to_config_file]

Example:

Rscript tcp.R "~/TCP-TR-H-Clus/config-files/jaccard/Macro-F1/ctjma-GpositiveGO.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

elainececiliagatto@gmail.com

Links

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Thanks