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#!/usr/bin/env python3
"""Build a larger cross-sectional position pool (successor to
tests/cross_sectional_200k.ep_) out of the full twic.pgn +
lichess_elite*.pgn corpus.
Unlike sample_eval_calibration_positions.py's random-byte-offset
sampling (one position per game, uniform-random ply), this does an
exhaustive streaming pass over every game and pulls 5 positions per
qualifying game at fixed fractional points in the game
(early/early-mid/mid/mid-late/late) -- Scott's own framing. Fractional-
ply sampling structurally balances game-stage representation (every
kept game contributes exactly one position to each of the 5 stage
buckets), which random-ply sampling does not.
Deliberately does NOT filter for quiescence -- these positions are
`go` roots for the engine's own search (which resolves tactics via its
own qsearch at every node), not Texel-tuning eval-only positions. See
sample_eval_calibration_positions.py's own docstring for the same
reasoning: a cross-sectional pool should look like "all positions a
real search tree touches", not just the quiet subset.
Ply fractions: 5 samples come from 6 equal segments (1/6..5/6 of the
game's ply count), not literal 1/5..5/5 -- 5/5 is the final move of
the game, frequently checkmate/stalemate/resignation-adjacent, which
`go` can't search meaningfully. Interior boundaries only.
"""
import argparse
import collections
import io
import sys
from pathlib import Path
import chess
import chess.pgn
STAGE_NAMES = ["early", "early_mid", "mid", "mid_late", "late"]
FRACTIONS = [1 / 6, 2 / 6, 3 / 6, 4 / 6, 5 / 6]
PIECE_VALUES = {chess.KNIGHT: 3, chess.BISHOP: 3, chess.ROOK: 5, chess.QUEEN: 9}
def material_bucket(board):
"""Same bucketing as eval.c's _MaterialBucket / the calibration
script's own copy: combined non-pawn army value (pawn units) / 8,
capped at 7 (0 = bare kings, 7 = full army)."""
total = 0
for pt, v in PIECE_VALUES.items():
total += v * len(board.pieces(pt, chess.WHITE))
total += v * len(board.pieces(pt, chess.BLACK))
return min(7, total // 8)
def iter_game_texts(pgn_path):
"""Stream raw PGN text blocks, one game at a time, without loading
the whole file -- these corpora run into the GB range (twic.pgn
alone is 3.5GB)."""
with open(pgn_path, "r", encoding="utf-8", errors="replace") as f:
header_lines = []
movetext_lines = []
in_headers = True
for line in f:
if line.startswith("["):
if not in_headers and (header_lines or movetext_lines):
yield "".join(header_lines) + "\n" + "".join(movetext_lines)
header_lines, movetext_lines = [], []
header_lines.append(line)
in_headers = True
elif line.strip() == "":
if header_lines and not movetext_lines:
in_headers = False
else:
in_headers = False
movetext_lines.append(line)
if header_lines or movetext_lines:
yield "".join(header_lines) + "\n" + "".join(movetext_lines)
def normalized_fen_key(fen):
"""Board+side-to-move+castling+ep, dropping halfmove/fullmove
counters -- used only for dedup accounting, never written out."""
parts = fen.split()
return " ".join(parts[:4])
def process_pgn(path, min_elo, min_ply, stats, seen_keys, out_f):
kept_games = 0
scanned_games = 0
for raw in iter_game_texts(path):
scanned_games += 1
game = chess.pgn.read_game(io.StringIO(raw))
if game is None:
continue
headers = game.headers
try:
we = int(headers.get("WhiteElo", "0"))
be = int(headers.get("BlackElo", "0"))
except ValueError:
we = be = 0
if min_elo and (we < min_elo or be < min_elo):
stats["rejected_elo"] += 1
continue
moves = list(game.mainline_moves())
if len(moves) < min_ply:
stats["rejected_short"] += 1
continue
result = headers.get("Result", "*")
stats["games_by_result"][result] += 1
stats["source_games"][path.name] += 1
kept_games += 1
board = game.board()
move_idx = 0
boundaries = [round(f * len(moves)) for f in FRACTIONS]
# de-dup consecutive identical boundaries (very short-but-kept
# games can round two fractions to the same ply)
seen_boundaries = set()
bi = 0
for target_ply in boundaries:
target_ply = max(1, min(target_ply, len(moves) - 1))
while move_idx < target_ply:
board.push(moves[move_idx])
move_idx += 1
stage = STAGE_NAMES[bi]
bi += 1
if target_ply in seen_boundaries:
stats["skipped_duplicate_boundary"] += 1
continue
seen_boundaries.add(target_ply)
if board.is_game_over():
stats["skipped_game_over"] += 1
continue
fen = board.fen()
key = normalized_fen_key(fen)
stats["stage_total"][stage] += 1
stats["material_bucket"][material_bucket(board)] += 1
stats["side_to_move"]["white" if board.turn else "black"] += 1
if key in seen_keys:
stats["duplicate_fens"] += 1
stats["stage_duplicates"][stage] += 1
continue
seen_keys.add(key)
out_f.write(f"setboard {fen}\n")
out_f.write("go\n")
stats["written"] += 1
stats["stage_written"][stage] += 1
stats["scanned_games"] += scanned_games
stats["kept_games"] += kept_games
return kept_games
def main():
ap = argparse.ArgumentParser()
ap.add_argument("pgn_paths", nargs="+")
ap.add_argument("--min-elo", type=int, default=2000)
ap.add_argument("--min-ply", type=int, default=30,
help="minimum game length in plies to be sampled "
"at all -- filters short draws/miniatures")
ap.add_argument("--out", required=True)
ap.add_argument("--progress-every", type=int, default=50000)
args = ap.parse_args()
stats = {
"scanned_games": 0,
"kept_games": 0,
"rejected_elo": 0,
"rejected_short": 0,
"skipped_game_over": 0,
"skipped_duplicate_boundary": 0,
"duplicate_fens": 0,
"written": 0,
"games_by_result": collections.Counter(),
"source_games": collections.Counter(),
"stage_total": collections.Counter(),
"stage_written": collections.Counter(),
"stage_duplicates": collections.Counter(),
"material_bucket": collections.Counter(),
"side_to_move": collections.Counter(),
}
seen_keys = set()
with open(args.out, "w") as out_f:
for p in args.pgn_paths:
path = Path(p)
print(f"processing {path.name}...", file=sys.stderr)
before = stats["kept_games"]
process_pgn(path, args.min_elo, args.min_ply, stats, seen_keys, out_f)
print(f" kept {stats['kept_games'] - before} games "
f"(running total written: {stats['written']})", file=sys.stderr)
print("\n=== summary ===")
print(f"games scanned: {stats['scanned_games']:,}")
print(f"games kept: {stats['kept_games']:,}")
print(f" rejected (elo): {stats['rejected_elo']:,}")
print(f" rejected (too short): {stats['rejected_short']:,}")
print(f"positions written: {stats['written']:,}")
print(f"duplicate fens skipped: {stats['duplicate_fens']:,} "
f"({100.0*stats['duplicate_fens']/max(1,stats['written']+stats['duplicate_fens']):.1f}%)")
print(f"game-over boundaries skipped: {stats['skipped_game_over']:,}")
print()
print("by result:")
for r, c in stats["games_by_result"].most_common():
print(f" {r:>8}: {c:,}")
print()
print("by source file:")
for s, c in stats["source_games"].most_common():
print(f" {s}: {c:,}")
print()
print("by game stage (written / total-before-dedup):")
for stage in STAGE_NAMES:
w = stats["stage_written"][stage]
t = stats["stage_total"][stage]
d = stats["stage_duplicates"][stage]
print(f" {stage:>10}: written={w:,} dup_rate={100.0*d/max(1,t):.1f}%")
print()
print("by material bucket (0=bare kings .. 7=full army):")
for b in range(8):
print(f" bucket {b}: {stats['material_bucket'][b]:,}")
print()
print("by side to move:")
for k, c in stats["side_to_move"].items():
print(f" {k}: {c:,}")
if __name__ == "__main__":
main()
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