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项目作者： gugarosa

项目描述 ：
📄 Official implementation regarding the chapter "Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest".
高级语言： Python
项目地址: git://github.com/gugarosa/opf_siamese.git
创建时间： 2020-07-02T16:52:58Z
项目社区：https://github.com/gugarosa/opf_siamese
开源协议：GNU General Public License v3.0
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Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest

This repository holds all the necessary code to run the very-same experiments described in the chapter “Learning to Weight Similarity Measures with Siamese Networks: A Case Study on Optimum-Path Forest”.

References

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

  1. @incollection{deRosa:22,
  2.   title = {Chapter 7 - Learning to weight similarity measures with Siamese networks: a case study on optimum-path forest☆☆The authors appreciate São Paulo Research Foundation (FAPESP) grants #2013/07375-0, #2014/12236-1, #2017/25908-6, #2018/15597-6, #2018/21934-5 and #2019/02205-5, and CNPq grants 307066/2017-7 and 427968/2018-6.},
  3.   editor = {Alexandre Xavier Falcão and João Paulo Papa},
  4.   booktitle = {Optimum-Path Forest},
  5.   publisher = {Academic Press},
  6.   pages = {155-173},
  7.   year = {2022},
  8.   isbn = {978-0-12-822688-9},
  9.   doi = {https://doi.org/10.1016/B978-0-12-822688-9.00015-3},
  10.   url = {https://www.sciencedirect.com/science/article/pii/B9780128226889000153},
  11.   author = {Gustavo H. {de Rosa} and João Paulo Papa}
  12. }

Structure

  • data: Folder containing the OPF file format datasets;
  • outputs: Folder for saving the output files, such as .npy, .pkl and .txt;
  • utils
    • loader.py: Loads OPF file format datasets;
    • similarity.py: Calculates the similarity between pairs of samples.

Package Guidelines

Installation

Install all the pre-needed requirements using:

  1. pip install -r requirements.txt

Data configuration

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

Usage

Learn a Similarity Function

The first step is to learn a similarity function based on the training data and create the dataset’s squared similarity matrix. To accomplish such a step, one needs to use the following script:

  1. python learn_similarities.py -h

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

Perform the Classification

After learning the similarity, one needs to classify the data using an OPF-based classifier or Scikit-Learn classifiers. Please, use the following scripts to accomplish such a procedure:

  1. python classify_with_opf.py -h

or

  1. python classify_without_opf.py -h

Process Classification Reports

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

  1. python process_report.py -h

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

Bash Script

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

  1. ./pipeline.sh

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

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.