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https://github.com/nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments
A repo to generate axelrod tournaments
https://github.com/nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments
Last synced: 4 days ago
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A repo to generate axelrod tournaments
- Host: GitHub
- URL: https://github.com/nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments
- Owner: Nikoleta-v3
- License: mit
- Created: 2016-10-27T10:15:32.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-11-03T18:24:56.000Z (about 2 months ago)
- Last Synced: 2024-11-03T19:25:01.970Z (about 2 months ago)
- Language: Jupyter Notebook
- Size: 128 MB
- Stars: 1
- Watchers: 6
- Forks: 1
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
Properties of winning Iterated Prisoner's Dilemma strategies. A meta
tournament analysis.
--------------------------------------
**Authors**: [@Nikoleta-v3](https://github.com/Nikoleta-v3) [@drvinceknight](https://github.com/drvinceknight) [@marcharper](https://github.com/marcharper)
This repository contains the source code and analysis for the paper:
"Properties of winning Iterated Prisoner's Dilemma strategies" which is available at:
[arXiv:2001.05911](https://arxiv.org/abs/2001.05911).
## Software
To clone the repository locally run the following:
```
$ git clone https://github.com/Nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments.git
```
A conda environment specifying all versions of the libraries used is given in
`environment.yml`. To create and activate the environment run:
```
$ conda env create -f environment.yml
$ source activate axlml
```
## Reproducing the results
The raw data containing the summary results of each tournament analysed in the
paper is achieved at: https://zenodo.org/record/3753498
To download and unpack the archived data run the following command whilst the
conda environment is activated:
```
$ inv data
```
To process the raw data run:
```
$ inv process
```
Note that this will take a few minutes.
The `process` task creates four data sets located in the folder `data/`:
- standard_3_processed.csv
- noise_3_processed.csv
- probend_3_processed.csv
- noise_probend_3_processed.csv
These are also available at https://zenodo.org/record/3753565.
To download the processed data sets without carrying out the `process` task run:
```
$ inv prodata
```
## Analysis
Most of the analysis presented in the paper is carried out in the Jupyter notebooks
found in the folder `nbs/`.
The analysis is also carried out the merged data set of the entire data sets
considered in the paper. To generate the merged data set run the command:
```
$ inv merge
```
To carry out the regression and correlation analysis, presented in the notebooks [3. Linear Regression](https://github.com/Nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments/blob/master/nbs/3.%20Linear%20Regression.ipynb)
and [2. Correlation Table and Heatmaps](https://github.com/Nikoleta-v3/meta-analysis-of-prisoners-dilemma-tournaments/blob/master/nbs/2.%20Correlation%20Table%20and%20Heatmaps.ipynb), for the merged
data set run the commands:
```
$ python src/correlation.py
$ python src/regression.py
```
## Tests
Several scripts have been tested with `pytest`. To test run:
```
$ inv test
```
The software is released under an MIT license.