https://github.com/thomasahle/fastchess

Predicts the best chess move with 27.5% accuracy by a single matrix multiplication
https://github.com/thomasahle/fastchess

ai chess chess-ai chess-engine fasttext machine-learning machinelearning

Last synced: 28 days ago
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Predicts the best chess move with 27.5% accuracy by a single matrix multiplication

• Host: GitHub
• URL: https://github.com/thomasahle/fastchess
• Owner: thomasahle
• License: gpl-3.0
• Created: 2018-02-23T20:47:05.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2021-03-09T21:19:32.000Z (about 4 years ago)
• Last Synced: 2025-03-20T01:06:05.413Z (about 1 month ago)
• Topics: ai, chess, chess-ai, chess-engine, fasttext, machine-learning, machinelearning
• Language: Python
• Homepage:
• Size: 63.8 MB
• Stars: 95
• Watchers: 9
• Forks: 13
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Teaching FastText to play Chess

===============================

FastChess is a chess engine predicting the next move using the http://fastText.cc text classification library.

In other words, it is a simple one-layer + soft-max model, taking the board state as a vector and outputting a vector of probabilities for each possible move.

The project also contains a Monte Carlo Tree Search, following by Alpha Zero, which combines with the simple linear model to provide a higher quality of play.

You can play against FastChess on Lichess: https://lichess.org/@/fastchess-engine (requires log-in).

It's current rating (January 2021) is 2052 Bullet and 1901 Blitz.

Screenshot

==========

![Screenshot](https://raw.githubusercontent.com/thomasahle/fastchess/master/static/screenshot.png)

Run it!

=======

You'll need the following libraries:

    pip install git+https://github.com/facebookresearch/fastText.git

    pip install git+https://github.com/niklasf/python-chess.git

    pip install numpy

Afterwards you can play by

    $ python play_chess.py

    Do you want to be white or black? white

      8 ♜ ♞ ♝ ♛ ♚ ♝ ♞ ♜

      7 ♟ ♟ ♟ ♟ ♟ ♟ ♟ ♟

      6

      5

      4

      3

      2 ♙ ♙ ♙ ♙ ♙ ♙ ♙ ♙

      1 ♖ ♘ ♗ ♕ ♔ ♗ ♘ ♖

        a b c d e f g h

    Your move (e.g. Nf3 or d2d4): e4

To disable MCTS and play directly against the fastText model, add the `-no-mcts` argument.

See `python play_chess.py --help` for more options. 

Train the model

===============

There are two ways to train the model.

The first one is to download a set of pgn files, like https://storage.lczero.org/files/training_pgns/ and run

    python proc.py 'ccrl/**/*.pgn' -test proc.test -train proc.train

    fasttext supervised -input proc.train -output proc.model -t 0 -neg 0 -epoch 1

    fasttext test proc.model.bin proc.test 1

    mv proc.model.bin model.bin

    python play_chess.py -selfplay

The other way is to generate your own data, e.g. using stockfish.

You can train your own model as:

    python make_data.py -games 1000 | shuf > g1000

    /opt/fastText/fasttext supervised -input g1000 -output model -t 0 -neg 0 -epoch 4

And then run the program as:

    python play_chess.py model.bin

You can also generate more data by self-play (as default games are generated by stockfish)

    python make_data.py -games 1000 -selfplay model.bin > games