Best Partition Silhouette

This code is part of my PhD research at PPG-CC/DC/UFSCar. The aim is select the best partition for multilabel classification using the Silhouette.

How to cite

@misc{Gatto2021, author = {Gatto, E. C.}, title = {Select Best Partition with Silhouette}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/cissagatto/Best-Partition-Silhouette}}}

Source Code

This code source is composed of the project R to be used in RStudio IDE and also the following scripts R:

1. libraries.R
2. utils.R
3. validation.R
4. run.R
5. best.R
6. config-files.R

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.

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 exhaustive.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 will need the previously generated partitions by one of the following codes:

• https://github.com/cissagatto/Generate-Partitions-Rogers
• https://github.com/cissagatto/Generate-Partitions-Jaccard
• https://github.com/cissagatto/Generate-Partitions-Random1
• https://github.com/cissagatto/Generate-Partitions-Random2

You must use the results generated from the OUTPUT folder in those source code. They must be compressed into a TAR.GZ file and placed in a folder on your computer. The absolute path of this folder must be passed as a parameter in the configuration file. Please see the example in the PARTITIONS folder in this source code.

STEP 5

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 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*
Dendrogram	linkage metric used to built the dendrogram. Must be one of the folow:
	"complete", "average" , "single", "mcquitty", "ward.d" or "ward.d2"
Partitions_Path	Absolute path to the folder where partitions are store
similarity	Choose which one to run: jaccard, rogers, random1 and random2
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.

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.

Important: we used the CLUS classifier in this experiment. This implies generating all physical ARFF training, validating, and testing files for each of the generated random partitions. Our code generates the partitions first in memory and then saves them to the HD. However, to avoid memory problems, immediately after saving to HD, the files are validated (or tested) and then deleted. Even so, make sure you have enough space on your HD and RAM for this procedure.

Results

The results stored in the folder OUTPUT it will be used in the next phase: Test-Best-Partition-Silhoute, Test-Best-Partition-MacroF1 or Test-Best-Partition-MicroF1. The result for a dataset must be put in the folder BEST_PARTITIONS in the respective code. Also, must be in "tar.gz" format.

RUN

To run the code, open the terminal, enter the ~/Best-Partition-Silhouette/R folder, and type

Rscript best.R [absolute_path_to_config_file]

Example:

Rscript best.R "~/Best-Partition-Silhouette/config-files/jaccard/ward.d2/sj-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