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https://github.com/gugarosa/opf_speedup
📄 Official implementation regarding the technical report "Speeding Up OPFython with Numba".
https://github.com/gugarosa/opf_speedup
implementation numba opfython paper
Last synced: 28 days ago
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📄 Official implementation regarding the technical report "Speeding Up OPFython with Numba".
- Host: GitHub
- URL: https://github.com/gugarosa/opf_speedup
- Owner: gugarosa
- License: mit
- Created: 2021-03-23T13:35:52.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-10-05T12:15:53.000Z (about 2 years ago)
- Last Synced: 2024-10-18T07:39:48.971Z (2 months ago)
- Topics: implementation, numba, opfython, paper
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Speeding Up OPFython with Numba
*This repository holds all the necessary code to run the very-same experiments described in the paper "Speeding Up OPFython with Numba".*
---
## References
If you use our work to fulfill any of your needs, please cite us:
```bibtex
@misc{rosa2021speedup,
title={Speeding Up OPFython with Numba},
author={Gustavo H. de Rosa and João Paulo Papa},
year={2021},
eprint={2106.11828},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
---
## 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.
* `timed_opf.py`: Re-implementation of the SupervisedOPF class to return training and prediction times.
---
## Package Guidelines
### Installation
Install all the pre-needed requirements using:
```Python
pip install -r requirements.txt
```
### Data configuration
Please [download](https://www.recogna.tech/files/opf_speedup/data.tar.gz) the datasets in the OPF file format and then put them on a `data/` folder.
---
## Usage
### Time Benchmark with Supervised Optimum-Path Forest
The first step is to benchmark both training and prediction times using a Supervised OPF classifier with a particular distance measure. To accomplish such a step, one needs to use the following script:
```Python
python benchmark_time.py -h
```
*Note that `-h` invokes the script helper, which assists users in employing the appropriate parameters.*
### Process Benchmark Report
After conducting the benchmarking task, one needs to process its report into readable outputs. Please, use the following script to accomplish such a procedure:
```Python
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:
```Bash
./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.
---
## 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.
---