Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification

This repository holds all the necessary code to run the very-same experiments described in the paper "Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification".

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References

If you use our work to fulfill any of your needs, please cite us:

@misc{derosa2022comparative,
      title={Comparative Study Between Distance Measures On Supervised Optimum-Path Forest Classification}, 
      author={Gustavo Henrique de Rosa and Mateus Roder and João Paulo Papa},
      year={2022},
      eprint={2202.03854},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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Structure

• data: Folder that holds the input dataset files, already in OPF file format;
• outputs: Folder that holds the output files, such as .npy, .pkl and .txt;
• utils
  • loader.py: Utility to load datasets and split them into training, validation and testing sets.

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Package Guidelines

Installation

Install all the pre-needed requirements using:

pip install -r requirements.txt

Data configuration

Please download the datasets in the OPF file format and then put them on a data/ folder.

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Usage

Classify with Optimum-Path Forest

The first step is to classify the data using an OPF-based classifier with a particular distance measure. To accomplish such a step, one needs to use the following script:

python classify_with_opf.py -h

Note that -h invokes the script helper, which assists users in employing the appropriate parameters.

Process Classification Report

After conducting the classification task, one needs to process its report into readable outputs. Please, use the following script to accomplish such a procedure:

python process_report.py -h

Note that this script converts the .pkl reports into readable .txt outputs.

(Optinal) Plot Confusion Matrix

After performing the classification task, there is an optinal script that allows to plot its confusion matrix. Please, use the following script to accomplish such a procedure:

python plot_confusion_matrix.py -h

Bash Script

Instead of invoking every script to conduct the experiments, it is also possible to use the provided shell script, as follows:

./pipeline.sh

Such a script will conduct every step needed to accomplish the experimentation used throughout this paper. Furthermore, one can change any input argument that is defined in the script.

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Support

We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository.

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