# pyRankMCDA
## Introduction
**pyRankMCDA** is a Python library designed for rank aggregation in multi-criteria decision analysis (MCDA). It provides implementations of classical and modern rank aggregation methods, allowing users to combine multiple rankings into a single consensus ranking. This is particularly useful in fields like decision science, information retrieval, and any domain where synthesizing different ordered lists is necessary.
## Features
- **Multiple Rank Aggregation Methods**:
- Borda Method
- Copeland Method
- Footrule Rank Aggregation
- Fast Footrule Rank Aggregation
- Kemeny-Young Method
- Fast Kemeny-Young
- Median Rank Aggregation
- PageRank Algorithm
- Plackett-Luce Model Aggregation
- Reciprocal Rank Fusion
- Schulze Method
- **Distance and Correlation Metrics**:
- Cayley Distance
- Footrule Distance
- Kendall Tau Distance
- Kendall Tau Correlation
- Spearman Rank Correlation
- **Visualization Tools**:
- Heatmaps of rankings
- Radar charts comparing rankings from different methods
- Multidimensional Scaling (MDS) plots for visualizing distances between ranking methods
## Usage
1. Install
```bash
pip install pyRankMCDA
```
2. Basic Example
```python
import numpy as np
from pyRankMCDA import rank_aggregation
# Example rankings from different methods
ranks = np.array([
[1, 2, 3],
[2, 1, 3],
[3, 2, 1]
])
# Initialize rank aggregation object
ra = rank_aggregation(ranks)
# Run Borda method
borda_rank = ra.borda_method(verbose = True)
```
3. Running Multiple Methods
```python
# Define the methods to run
methods = ['bd', 'cp', 'fky', 'md', 'pg']
# Run selected methods
df = ra.run_methods(methods)
```
4. Visualization
```python
# Plot heatmap of rankings
ra.plot_ranks_heatmap(df)
# Plot radar chart of rankings
ra.plot_ranks_radar(df)
```
5. Computing Metrics
```python
# Calculate distance and correlation metrics
d_matrix = ra.metrics(df)
# Plot metric comparisons
ra.metrics_plot(d_matrix)
```
6. Try it in **Colab**:
- Example: ([ Colab Demo ](https://colab.research.google.com/drive/1qtS4kRMN_NdG0yer8UcN196bWnTM-dlI?usp=sharing))
7. Others
- [3MOAHP](https://github.com/Valdecy/Method_3MOAHP) - Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods
- [pyDecision](https://github.com/Valdecy/pyDecision) - A library for many MCDA methods
- [pyMissingAHP](https://github.com/Valdecy/pyMissingAHP) - A Method to Infer AHP Missing Pairwise Comparisons
- [ELECTRE-Tree](https://github.com/Valdecy/ELECTRE-Tree) - Algorithm to infer the ELECTRE Tri-B method parameters
- [Ranking-Trees](https://github.com/Valdecy/Ranking-Trees) - Algorithm to infer the ELECTRE II, III, IV and PROMETHEE I, II, III, IV method parameters
- [EC-PROMETHEE](https://github.com/Valdecy/ec_promethee) - A Committee Approach for Outranking Problems
- [MCDM Scheduler](https://github.com/Valdecy/mcdm_scheduler) - A MCDM approach for Scheduling Problems
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