update dataset with lc0 data

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)

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/
 

raw-data.zip

@@ -1,3 +0,0 @@
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-oid sha256:95e1030fed8f8fa77cca5879e094ea41103602cf77d49110a09e595d23b430a2
-size 2204952170

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 → 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("\n✅ Pipeline complete.")
+
+if __name__ == "__main__":
+    main()

raw_data/CC-games/alpha0-VS-stockfish/alphazero_vs_stockfish_all.pgn

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raw_data/CC-games/alpha0-VS-stockfish/alphazero_vs_stockfish_tcec_positions_all.pgn

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