diff --git a/ChessBot-Dataset.py b/ChessBot-Dataset.py index 29653dd2638ebc9d974482c5c78ebaf7c7dce48f..a011cb8bccf9dadc8ab9297d108dbc3b66de2acc 100644 --- a/ChessBot-Dataset.py +++ b/ChessBot-Dataset.py @@ -1,3 +1,4 @@ +import io import logging from datasets import ( GeneratorBasedBuilder, @@ -10,6 +11,9 @@ from datasets import ( Array2D, ) from adversarial_gym.chess_env import ChessEnv +import zstandard as zstd + + class ChessPGNDataset(GeneratorBasedBuilder): VERSION = "0.0.1" @@ -55,19 +59,23 @@ class ChessPGNDataset(GeneratorBasedBuilder): uid = 0 for path in shards: - with open(path, "r") as f: - while (game := chess.pgn.read_game(f)) is not None: - board = game.board() - base = {"1-0":1,"0-1":-1}.get(game.headers["Result"], 0) - for move in game.mainline_moves(): - state = ChessEnv.get_piece_configuration(board) - state = state if board.turn else -state - action = ChessEnv.move_to_action(move) - result = base * (-1 if board.turn == 0 else 1) - yield uid, { - "state": state.astype("int8"), - "action": int(action), - "result": int(result), - } - uid += 1 - board.push(move) + decompressor = zstd.ZstdDecompressor() + with open(path, "rb") as compressed: + with decompressor.stream_reader(compressed) as reader: + text_stream = io.TextIOWrapper(reader, encoding='utf-8') + while (game := chess.pgn.read_game(text_stream)) is not None: + # while (game := chess.pgn.read_game(f)) is not None: + board = game.board() + base = {"1-0":1,"0-1":-1}.get(game.headers["Result"], 0) + for move in game.mainline_moves(): + state = ChessEnv.get_piece_configuration(board) + state = state if board.turn else -state + action = ChessEnv.move_to_action(move) + result = base * (-1 if board.turn == 0 else 1) + yield uid, { + "state": state.astype("int8"), + "action": int(action), + "result": int(result), + } + uid += 1 + board.push(move) diff --git a/README.md b/README.md index 41bfa3125984c9321cf511c13e5674b804fb2837..cd7ffcd3368f04db5426b590793a24d94bc9a00f 100644 --- a/README.md +++ b/README.md @@ -28,25 +28,19 @@ dataset_info: splits: - name: train num_bytes: 65545518162 - num_examples: 636364254 + num_examples: -1 - name: test num_bytes: 7298385021 - num_examples: 70858107 + num_examples: -1 download_size: 7621976329 dataset_size: 72843903183 --- -This repository contains a gigantic curated chess dataset meant for machine learning. Currently the dataset contains approximately **700 million positions** in **PGN format**, split across 1000 files. Huge credits to the following main sources: +This repository contains a gigantic curated chess dataset meant for machine learning. Currently the dataset contains approximately **2 billion positions** in **PGN format**. Huge credits to the following main sources: - Lumbra's Database (filtered 2600+) -- Lichess Puzzle Database -- Computer Chess: TCEC Database, CCRL - -Some stats (subject to change): -- Games per file: 9808/9809 -- Positions per file: ~700,000 -- Total train positions: 636364254 -- Total test positions: 70858107 +- Lichess Puzzle Database +- Computer Chess: TCEC Database, CCRL, LC0 training data (test80) The supporting dataset code and training platform, known as ChessBot-Battleground, is found at: https://github.com/KeithG33/ChessBot-Battleground/ diff --git a/raw-data.zip b/raw-data.zip deleted file mode 100644 index 71ab921dfc76b69a4f002a65188c9bfc29db19dd..0000000000000000000000000000000000000000 --- a/raw-data.zip +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:95e1030fed8f8fa77cca5879e094ea41103602cf77d49110a09e595d23b430a2 -size 2204952170 diff --git a/raw2dataset.py b/raw2dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d849e107fa954ee4f1fdca2573c1b57b4cbb1ced --- /dev/null +++ b/raw2dataset.py @@ -0,0 +1,244 @@ +from multiprocessing import Pool +import os +import shutil, random, io +from pathlib import Path +from itertools import cycle +import subprocess +import tarfile +import tempfile +import chess.pgn +import zstandard as zstd +from tqdm import tqdm + +RAW_DATA_DIR = Path("raw_data") +LC0_TEST_DIR = RAW_DATA_DIR / "lc0-test" +NUM_TEST_FILES = 1 +TRAIN_DIR = Path("train") +TEST_DIR = Path("test") +ZSTD_LEVEL = 10 +GAMES_PER_SHARD = 100000 # As requested: -#100000 +PROCESS_COUNT = 25 + + +def open_pgn_stream(path: Path): + if path.suffix == '.pgn': + return path.open('r', encoding='utf-8') + elif path.suffix == '.zst': + fb = path.open('rb') + dctx = zstd.ZstdDecompressor() + return io.TextIOWrapper(dctx.stream_reader(fb), encoding='utf-8') + else: + raise ValueError(f"Unsupported extension: {path.suffix}") + + +def open_append_writers(shards, level): + writers = [] + for p in shards: + fb = p.open('ab') + cctx = zstd.ZstdCompressor(level=level) + writers.append(io.TextIOWrapper(cctx.stream_writer(fb), encoding='utf-8')) + return writers + + +def prepare_train_test_dirs(): + TRAIN_DIR.mkdir(exist_ok=True) + TEST_DIR.mkdir(exist_ok=True) + + +def _worker_split_zst_with_pgn_extract(args): + """ + Worker function for the multiprocessing pool. It correctly handles pgn-extract's + behavior by changing the working directory for isolation. + """ + input_path, games_per_shard = args + final_shard_paths = [] + + # Each worker gets a unique temporary directory to work in. + with tempfile.TemporaryDirectory() as temp_dir: + temp_dir_path = Path(temp_dir) + # The decompressed file will live inside this temp directory. + temp_pgn_path = temp_dir_path / "decompressed.pgn" + + # 1. Decompress the source .zst into the temporary directory. + dctx = zstd.ZstdDecompressor() + with input_path.open('rb') as f_in, temp_pgn_path.open('wb') as f_out: + dctx.copy_stream(f_in, f_out) + + # --- CRITICAL SECTION for correct pgn-extract usage --- + original_cwd = Path.cwd() + try: + # 2. Change into the temporary directory. This isolates the pgn-extract output. + os.chdir(temp_dir_path) + + # 3. Run pgn-extract. It will create 1.pgn, 2.pgn, etc. *here*. + # We only need to pass the filename, as we are in its directory. + command = [ + "pgn-extract", + f"-#{games_per_shard}", + temp_pgn_path.name # "decompressed.pgn" + ] + + # This command is now correct according to the documentation. + subprocess.run(command, check=True, capture_output=True, text=True, encoding='latin-1') + except FileNotFoundError: + print("ERROR: 'pgn-extract' command not found. Please install it and ensure it is in your system's PATH.") + raise + except subprocess.CalledProcessError as e: + print(f"--- ERROR during pgn-extract for {input_path.name}: ---\n{e.stderr}\n---") + raise + finally: + os.chdir(original_cwd) + # --- END CRITICAL SECTION --- + + split_pgn_files = sorted( + [p for p in temp_dir_path.glob("*.pgn") if p.stem.isdigit()], + key=lambda p: int(p.stem) + ) + + # 6. Re-compress each shard to a unique .zst file in the original script directory. + cctx = zstd.ZstdCompressor(level=ZSTD_LEVEL) + for pgn_shard_path in split_pgn_files: + final_zst_path = Path(f"{input_path.stem}_part_{pgn_shard_path.stem}.pgn.zst") + with pgn_shard_path.open('rb') as f_in, final_zst_path.open('wb') as f_out: + cctx.copy_stream(f_in, f_out) + final_shard_paths.append(final_zst_path) + + # The temp directory and its contents (decompressed.pgn, 1.pgn, etc.) are now deleted. + return final_shard_paths + + +def resplit_test_files(test_files, games_per_split): + """ + Re-splits each test .pgn.zst file into smaller chunks sequentially. + + Args: + test_files (list of Path): Paths to the original test shards. + games_per_split (int): Number of games per new shard. + + Returns: + List[Path]: List of newly created smaller test shard paths. + """ + print(f"\nResplitting {len(test_files)} test shard(s) into smaller shards...") + + new_test_shards = [] + for test_file in tqdm(test_files, desc="Resplitting test shards"): + smaller_shards = _worker_split_zst_with_pgn_extract((test_file, games_per_split)) + new_test_shards.extend(smaller_shards) + test_file.unlink() # Remove original file + + return new_test_shards + +def split_and_distribute_files(): + """ + Finds all .pgn.zst files, splits them into shards of 100,000 games each, + and distributes the resulting .pgn.zst shards into train/ and test/ folders. + + Returns: + A tuple containing two lists: (train_shard_paths, test_shard_paths) + """ + prepare_train_test_dirs() + + all_zst_inputs = list(LC0_TEST_DIR.rglob("*.zst")) + if not all_zst_inputs: + print(f"No .zst files found in {LC0_TEST_DIR}. Cannot perform Step 1.") + return [], [] + + print(f"Found {len(all_zst_inputs)} source .pgn.zst files. Starting split process...") + + tasks = [(path, GAMES_PER_SHARD) for path in all_zst_inputs] + + all_new_shards = [] + with Pool(processes=PROCESS_COUNT) as pool: + for shards in tqdm(pool.imap(_worker_split_zst_with_pgn_extract, tasks), + total=len(tasks), + desc="Splitting source files"): + all_new_shards.extend(shards) + + print(f"\nCreated a total of {len(all_new_shards)} new shards.") + + # --- Distribution Logic --- + random.shuffle(all_new_shards) + test_selection = all_new_shards[:NUM_TEST_FILES] + train_selection = all_new_shards[NUM_TEST_FILES:] + + print(f"Moving {len(train_selection)} shards to train/ and {len(test_selection)} to test/...") + + for shard in train_selection: + shutil.move(str(shard), TRAIN_DIR / shard.name) + + intermediate_test_paths = [] + for shard in test_selection: + final_path = TEST_DIR / shard.name + shutil.move(str(shard), final_path) + intermediate_test_paths.append(final_path) + + # Final re-split of test file(s) + resplit_test_shards = resplit_test_files(intermediate_test_paths, GAMES_PER_SHARD // 10) + + for shard in resplit_test_shards: + shutil.move(str(shard), TEST_DIR / shard.name) + + final_train_paths = [TRAIN_DIR / s.name for s in train_selection] + final_test_paths = [TEST_DIR / s.name for s in resplit_test_shards] + + return final_train_paths, final_test_paths + + + +def get_pgn_file_paths(input_dir): + return list(Path(input_dir).rglob("*.pgn")) + + +def distribute_games_to_zst_shards(pgn_paths, train_shards, test_shards, split_ratio=0.01): + train_writers = open_append_writers(train_shards, level=ZSTD_LEVEL) + test_writers = open_append_writers(test_shards, level=ZSTD_LEVEL) + + train_cycle = cycle(train_writers) + test_cycle = cycle(test_writers) + + total_games = 0 + for path in pgn_paths: + with open_pgn_stream(path) as f: + games_iter = iter(lambda: chess.pgn.read_game(f), None) + for game in tqdm(games_iter, + desc=f"Distributing games from {path.name}", + unit="game"): + total_games += 1 + if random.random() < split_ratio: + w = next(test_cycle) + else: + w = next(train_cycle) + exporter = chess.pgn.FileExporter(w) + game.accept(exporter) + + for w in train_writers + test_writers: + w.close() + +def compress_raw_data_zst(): + """Creates raw_data.tar.zst with Zstd(level=ZSTD_LEVEL) compression.""" + zst_path = RAW_DATA_DIR.with_suffix('.tar.zst') + cctx = zstd.ZstdCompressor(level=ZSTD_LEVEL) + + with cctx.stream_writer(zst_path.open('wb')) as zst_writer: + # Build a streaming tar over that writer + with tarfile.open(fileobj=zst_writer, mode='w|') as tar: + for path in tqdm(RAW_DATA_DIR.rglob("*"), + desc="Archiving raw_data", + unit="file"): + # Add each file/dir under raw_data into the tar + tar.add(path, arcname=path.relative_to(RAW_DATA_DIR.parent)) + +def main(): + print("Step 1: Split & distribute .pgn.zst ā†’ train/test") + train_shards, test_shards = split_and_distribute_files() + + print("\nStep 2: Distribute remaining .pgn files") + all_pgns = get_pgn_file_paths(RAW_DATA_DIR) + + print(f"Found {len(all_pgns)} .pgn files. Starting distribution process...") + distribute_games_to_zst_shards(all_pgns, train_shards, test_shards) + + print("\nāœ… Pipeline complete.") + +if __name__ == "__main__": + main() diff --git a/raw_data/CC-games/alpha0-VS-stockfish/alphazero_vs_stockfish_all.pgn b/raw_data/CC-games/alpha0-VS-stockfish/alphazero_vs_stockfish_all.pgn new file mode 100644 index 0000000000000000000000000000000000000000..d64ce1ac31c1bd53f94c5337bb4b0d01c748b953 --- /dev/null +++ b/raw_data/CC-games/alpha0-VS-stockfish/alphazero_vs_stockfish_all.pgn @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ff8c16749bdc61b2e1eabacb9f2b2ac2df3616731c0ff1567218431c99623ff +size 198043 diff --git a/raw_data/CC-games/alpha0-VS-stockfish/alphazero_vs_stockfish_tcec_positions_all.pgn b/raw_data/CC-games/alpha0-VS-stockfish/alphazero_vs_stockfish_tcec_positions_all.pgn new file mode 100644 index 0000000000000000000000000000000000000000..0d53ad723b002c2661ac0b4d41c1a9d769dbce40 --- /dev/null +++ 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