Metadata-Version: 2.4
Name: GRating
Version: 0.0.2
Summary: Algorithm ranking library
Author: Oscar A. Gonzalez Sanchez
Project-URL: Homepage, https://github.com/OscarAGonzalezSanchez/GRating
Keywords: optimization,ranking,metaheuristics
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: matplotlib
Requires-Dist: squarify
Requires-Dist: seaborn
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scipy

# GRating

**GRating** is a Python library for ranking optimization algorithms using a pairwise-comparison framework based on the Bradley–Terry model. It transforms benchmark results into win–loss statistics, estimates algorithm strengths, and provides visual tools for analyzing and comparing rankings.

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## Features

- Bradley–Terry rating estimation from benchmark results.
- Automatic generation of win–loss matrices.
- Support for:
- Full permutation comparisons.
- Random subsampling comparisons.
- Friedman Mean Rank computation.
- Statistical comparison between independent GRating studies.
- Wilcoxon significance testing.
- Treemap visualization of rankings.
- Scatter plot visualization of rankings.
- Export rankings to Excel.

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## Installation

Pip installation with `pip install grating`

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## Data Format

GRating expects one CSV file per algorithm.

Example:

```plaintext
results/
├── PSO.csv
├── GA.csv
├── DE.csv
└── AEO.csv
```

Each CSV must contain benchmark functions as columns and independent runs as rows:

|  F1  |  F2  |  F3  |
|  --  |  --  |  --  |
| 0.15 | 1.24 | 3.12 |
| 0.11 | 1.10 | 2.94 |
| 0.14 | 1.30 | 3.05 |

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## Quick Start
