README.md

metadata

license: mit
source_datasets:
  - BlueSunflower/chess_games_base
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: test
        path: test.jsonl
dataset_info:
  features:
    - name: fen
      dtype: string
    - name: move
      dtype: string
    - name: result
      dtype: string

Dataset Card for stockfish-debug

See my blog post for additional details.

Columns

The datase contain the following columns:

• fen: The FEN string of the board.
• move: The move that was played.
• result: The result of the game (with "-" for unfinished games).

Data details

Pre-processing of the Stockfish games provided by BlueSunflower/chess_games_base.

Code used:

import jsonlines
import chess
import tqdm


def preprocess_games(in_path, out_path):
    with jsonlines.open(in_path) as reader:
        with jsonlines.open(out_path, "w") as writer:
            for obj in tqdm.tqdm(reader):
                state_action = []
                parsed_moves = [m for m in obj["moves"].split() if not m.endswith(".")]
                board = chess.Board()
                for m in parsed_moves:
                    fen = board.fen()
                    move = board.push_san(m)
                    state_action.append({"fen": fen, "move":move.uci()})
                outcome = board.outcome()
                if outcome is None:
                    result = "-"
                else:
                    result = outcome.result()
                writer.write_all([
                    {**sa, "result":result} for sa in state_action
                ])

Use the Dataset

Using basic dataset code:

from datasets import load_dataset


dataset = load_dataset("yp-edu/stockfish-debug")