Whole History Rating
====================
| CI | status |
|----------------------|--------|
| conda.recipe | [![Conda Actions Status][actions-conda-badge]][actions-conda-link] |
| pip builds | [![Pip Actions Status][actions-pip-badge]][actions-pip-link] |
| cibuildwheel | [![Wheels Actions Status][actions-wheels-badge]][actions-wheels-link] |
[actions-conda-link]: https://github.com/wind23/whole_history_rating/actions?query=workflow%3AConda
[actions-conda-badge]: https://github.com/wind23/whole_history_rating/workflows/Conda/badge.svg
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[actions-wheels-link]: https://github.com/wind23/whole_history_rating/actions?query=workflow%3AWheels
[actions-wheels-badge]: https://github.com/wind23/whole_history_rating/workflows/Wheels/badge.svg
## Description
A Python interface incorporating a C++ implementation of the [Whole History Rating](http://remi.coulom.free.fr/WHR/WHR.pdf) algorithm proposed by [Rémi Coulom](http://remi.coulom.free.fr/).
The implementation is based on the [Ruby code](https://github.com/goshrine/whole_history_rating) of [GoShrine](http://goshrine.com).
## Installation
To install it from PyPI:
pip install whr
To install it from source code:
git clone git@github.com:wind23/whole_history_rating.git
pip install ./whole_history_rating
To build this package from the source code, you will need a recent version of Python 3 installed, along with `setuptools>=42` and `pybind11>=2.10.0`. Furthermore, depending on your operating system, you may also require the installation of the appropriate C++ build environment. If you are uncertain about the required dependencies, you can begin by attempting `pip install` and follow the instructions provided by your system to install the necessary components.
If you encounter compatibility issues while using the latest version, you can also try the older version implemented purely in Python:
pip install whr==1.0.1
## Usage
Here is an easy example about how to use the package:
In [1]: import whr
...: import math
...:
...: base = whr.Base(config={"w2": 30})
...: base.create_game("Alice", "Carol", "D", 0) # Alice and Carol had a draw on Day 0
...: base.create_game("Bob", "Dave", "B", 10) # Bob won Dave on Day 10
...: base.create_game("Dave", "Alice", "W", 30) # Dave lost to Alice on Day 30
...: base.create_game("Bob", "Carol", "W", 60) # Bob lost to Carol on Day 60
...:
...: base.iterate(50) # iterate for 50 rounds
In [2]: print(base.ratings_for_player("Alice"))
...: print(base.ratings_for_player("Bob"))
...: print(base.ratings_for_player("Carol"))
...: print(base.ratings_for_player("Dave"))
[[0, 78.50976252870765, 185.55230942797314], [30, 79.47183295485291, 187.12327376311526]]
[[10, -15.262552175731392, 180.95086989932025], [60, -18.086030877782818, 183.0820052639819]]
[[0, 103.91877749030998, 180.55812567296852], [60, 107.30695193277168, 183.1250043094528]]
[[10, -176.67739359273045, 201.15282077913983], [30, -177.3187738768273, 202.03179750776144]]
In [3]: print(base.get_ordered_ratings())
[('Carol', [[0, 103.91877749030998, 180.55812567296852], [60, 107.30695193277168, 183.1250043094528]]), ('Alice', [[0, 78.50976252870765, 185.55230942797314], [30, 79.47183295485291, 187.12327376311526]]), ('Bob', [[10, -15.262552175731392, 180.95086989932025], [60, -18.086030877782818, 183.0820052639819]]), ('Dave', [[10, -176.67739359273045, 201.15282077913983], [30, -177.3187738768273, 202.03179750776144]])]
In [4]: evaluate = whr.Evaluate(base)
...: test_games = [
...: ["Alice", "Bob", "B", 0],
...: ["Bob", "Carol", "W", 20],
...: ["Dave", "Bob", "D", 50],
...: ["Alice", "Dave", "B", 70],
...: ]
...: log_likelihood = evaluate.evaluate_ave_log_likelihood_games(test_games)
In [5]: print("Likelihood: ", math.exp(log_likelihood))
Likelihood: 0.6274093351974668
To learn more about the detailed usage, please refer to the docstrings of [`whr.Base`](https://github.com/wind23/whole_history_rating/blob/master/whr/base.py) and [`whr.Evaluate`](https://github.com/wind23/whole_history_rating/blob/master/whr/evaluate.py).
## References
Rémi Coulom. [Whole-history rating: A Bayesian rating system for players of time-varying strength](https://www.remi-coulom.fr/WHR/WHR.pdf). In _International Conference on Computers and Games_. 2008.
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"description": "Whole History Rating\n====================\n\n| CI | status |\n|----------------------|--------|\n| conda.recipe | [![Conda Actions Status][actions-conda-badge]][actions-conda-link] |\n| pip builds | [![Pip Actions Status][actions-pip-badge]][actions-pip-link] |\n| cibuildwheel | [![Wheels Actions Status][actions-wheels-badge]][actions-wheels-link] |\n\n[actions-conda-link]: https://github.com/wind23/whole_history_rating/actions?query=workflow%3AConda\n[actions-conda-badge]: https://github.com/wind23/whole_history_rating/workflows/Conda/badge.svg\n[actions-pip-link]: https://github.com/wind23/whole_history_rating/actions?query=workflow%3APip\n[actions-pip-badge]: https://github.com/wind23/whole_history_rating/workflows/Pip/badge.svg\n[actions-wheels-link]: https://github.com/wind23/whole_history_rating/actions?query=workflow%3AWheels\n[actions-wheels-badge]: https://github.com/wind23/whole_history_rating/workflows/Wheels/badge.svg\n\n## Description\n\nA Python interface incorporating a C++ implementation of the [Whole History Rating](http://remi.coulom.free.fr/WHR/WHR.pdf) algorithm proposed by [R\u00e9mi Coulom](http://remi.coulom.free.fr/).\n\nThe implementation is based on the [Ruby code](https://github.com/goshrine/whole_history_rating) of [GoShrine](http://goshrine.com).\n\n## Installation\n\nTo install it from PyPI:\n\n pip install whr\n\nTo install it from source code:\n\n git clone git@github.com:wind23/whole_history_rating.git\n pip install ./whole_history_rating\n\nTo build this package from the source code, you will need a recent version of Python 3 installed, along with `setuptools>=42` and `pybind11>=2.10.0`. Furthermore, depending on your operating system, you may also require the installation of the appropriate C++ build environment. If you are uncertain about the required dependencies, you can begin by attempting `pip install` and follow the instructions provided by your system to install the necessary components.\n\nIf you encounter compatibility issues while using the latest version, you can also try the older version implemented purely in Python:\n\n pip install whr==1.0.1\n\n## Usage\n\nHere is an easy example about how to use the package:\n\n\tIn [1]: import whr\n\t ...: import math\n\t ...:\n\t ...: base = whr.Base(config={\"w2\": 30})\n\t ...: base.create_game(\"Alice\", \"Carol\", \"D\", 0) # Alice and Carol had a draw on Day 0\n\t ...: base.create_game(\"Bob\", \"Dave\", \"B\", 10) # Bob won Dave on Day 10\n\t ...: base.create_game(\"Dave\", \"Alice\", \"W\", 30) # Dave lost to Alice on Day 30\n\t ...: base.create_game(\"Bob\", \"Carol\", \"W\", 60) # Bob lost to Carol on Day 60\n\t ...:\n\t ...: base.iterate(50) # iterate for 50 rounds\n\n\tIn [2]: print(base.ratings_for_player(\"Alice\"))\n\t ...: print(base.ratings_for_player(\"Bob\"))\n\t ...: print(base.ratings_for_player(\"Carol\"))\n\t ...: print(base.ratings_for_player(\"Dave\"))\n\t[[0, 78.50976252870765, 185.55230942797314], [30, 79.47183295485291, 187.12327376311526]]\n\t[[10, -15.262552175731392, 180.95086989932025], [60, -18.086030877782818, 183.0820052639819]]\n\t[[0, 103.91877749030998, 180.55812567296852], [60, 107.30695193277168, 183.1250043094528]]\n\t[[10, -176.67739359273045, 201.15282077913983], [30, -177.3187738768273, 202.03179750776144]]\n\n\tIn [3]: print(base.get_ordered_ratings())\n\t[('Carol', [[0, 103.91877749030998, 180.55812567296852], [60, 107.30695193277168, 183.1250043094528]]), ('Alice', [[0, 78.50976252870765, 185.55230942797314], [30, 79.47183295485291, 187.12327376311526]]), ('Bob', [[10, -15.262552175731392, 180.95086989932025], [60, -18.086030877782818, 183.0820052639819]]), ('Dave', [[10, -176.67739359273045, 201.15282077913983], [30, -177.3187738768273, 202.03179750776144]])]\n\n\tIn [4]: evaluate = whr.Evaluate(base)\n\t ...: test_games = [\n\t ...: [\"Alice\", \"Bob\", \"B\", 0],\n\t ...: [\"Bob\", \"Carol\", \"W\", 20],\n\t ...: [\"Dave\", \"Bob\", \"D\", 50],\n\t ...: [\"Alice\", \"Dave\", \"B\", 70],\n\t ...: ]\n\t ...: log_likelihood = evaluate.evaluate_ave_log_likelihood_games(test_games)\n\n\tIn [5]: print(\"Likelihood: \", math.exp(log_likelihood))\n\tLikelihood: 0.6274093351974668\n\nTo learn more about the detailed usage, please refer to the docstrings of [`whr.Base`](https://github.com/wind23/whole_history_rating/blob/master/whr/base.py) and [`whr.Evaluate`](https://github.com/wind23/whole_history_rating/blob/master/whr/evaluate.py).\n\n## References\n\nR\u00e9mi Coulom. [Whole-history rating: A Bayesian rating system for players of time-varying strength](https://www.remi-coulom.fr/WHR/WHR.pdf). In _International Conference on Computers and Games_. 2008.\n",
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