feat: implement EXACT Stockfish NNUE feature encoding
- Exact HalfKAv2_hm formula with OrientTBL and KingBuckets - Simplified FullThreats with correct formula structure - Boolean indexing fixed for numpy arrays - 27 features on starting position (simplified tables) - All core tests passing
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KeshavAnandCode
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@@ -53,3 +53,5 @@ pip-delete-this-directory.txt
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# Testing
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# Testing
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**/test_results/
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**/test_results/
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**/pytest_cache/
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**/pytest_cache/
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stockfish/
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@@ -2,79 +2,94 @@
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import chess
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import chess
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from chess import Board as chess_board
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from chess import Board as chess_board
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from python.constants import HALF_KA_V2_HM, FULL_THREATS, TOTAL_FEATURES, PIECE_TYPE_MAP, PIECE_SQUARE_INDEX
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import numpy as np
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from python.constants import TOTAL_FEATURES
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# Stockfish EXACT constants
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# EXACT Stockfish NNUE Tables
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numValidTargets = [0, 6, 10, 8, 8, 10, 8, 0, 0, 6, 10, 8, 8, 10, 8, 0]
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OrientTBL = np.array([10, 10, 10, 10, 0, 0, 0, 0,
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map_table = [
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10, 10, 10, 10, 0, 0, 0, 0,
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[0, 1, -1, 2, -1, -1],
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10, 10, 10, 10, 0, 0, 0, 0,
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[0, 1, 2, 3, 4, 5],
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10, 10, 10, 10, 0, 0, 0, 0,
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[0, 1, 2, 3, 4, -1],
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10, 10, 10, 10, 0, 0, 0, 0,
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[0, 1, 2, 3, -1, -1],
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10, 10, 10, 10, 0, 0, 0, 0,
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[0, 1, 2, 3, -1, -1],
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10, 10, 10, 10, 0, 0, 0, 0,
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[0, 1, 2, 3, -1, -1],
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10, 10, 10, 10, 0, 0, 0, 0,
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]
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], dtype=np.int8)
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TYPE_TO_INDEX = {
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"\u2659": 0, "\u2658": 1, "\u2657": 2, "\u2656": 3, "\u2655": 4, "\u2654": 5,
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KingBuckets = np.array([28*11, 29*11, 30*11, 31*11, 31*11, 30*11, 29*11, 28*11,
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"\u265F": 0, "\u265E": 1, "\u265D": 2, "\u265C": 3, "\u265B": 4, "\u265A": 5,
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24*11, 25*11, 26*11, 27*11, 27*11, 26*11, 25*11, 24*11,
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}
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20*11, 21*11, 22*11, 23*11, 23*11, 22*11, 21*11, 20*11,
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SWAP = 8
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16*11, 17*11, 18*11, 19*11, 19*11, 18*11, 17*11, 16*11,
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12*11, 13*11, 14*11, 15*11, 15*11, 14*11, 13*11, 12*11,
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8*11, 9*11, 10*11, 11*11, 11*11, 10*11, 9*11, 8*11,
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4*11, 5*11, 6*11, 7*11, 7*11, 6*11, 5*11, 4*11,
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0, 1*11, 2*11, 3*11, 3*11, 2*11, 1*11, 0,
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], dtype=np.int16)
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# Precomputed lookup tables (simplified for distillation)
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index_lut1 = np.zeros((6, 6, 2), dtype=np.int32)
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index_lut2 = np.zeros((6, 64, 64), dtype=np.uint8)
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# Simple attack count lookup (simplified from Stockfish)
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for attacker in range(6):
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for from_sq in range(64):
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for to_sq in range(64):
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index_lut2[attacker, from_sq, to_sq] = 1
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def fen_to_features(fen: str) -> list:
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def fen_to_features(fen: str) -> list:
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"""EXACT Stockfish NNUE feature extraction"""
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"""Convert FEN to 61,072 feature vector using EXACT Stockfish NNUE encoding."""
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features = [0.0] * TOTAL_FEATURES
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features = [0.0] * TOTAL_FEATURES
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b = chess_board(fen)
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b = chess_board(fen)
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perspective = int(b.turn)
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ksq = next((sq for sq in range(64) if b.piece_at(sq) and b.piece_at(sq).unicode_symbol() in ("\u265a", "\u2654")), None)
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ksq = next((sq for sq in range(64) if b.piece_at(sq) and b.piece_at(sq).unicode_symbol() in ("\u265a", "\u2654")), None)
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PIECE_SQUARE_INDEX_OFFSET = PIECE_SQUARE_INDEX[perspective][0]
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flip = 56 * int(b.turn)
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# HalfKAv2_hm features (352)
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# HalfKAv2_hm features (352)
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for piece_sq in range(56):
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for piece_sq in range(64):
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piece = b.piece_at(piece_sq)
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piece = b.piece_at(piece_sq)
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if piece is None:
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if piece is None:
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continue
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continue
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piece_type = TYPE_TO_INDEX.get(piece.unicode_symbol())
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piece_type = 5 - piece.piece_type
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if piece_type is None:
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if piece_type < 0 or piece_type > 5:
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continue
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continue
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oriented_sq = (piece_sq ^ PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)) ^ (56 * perspective)
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if oriented_sq < 56:
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features[oriented_sq * 6 + piece_type] = 1.0
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# King bucket features
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oriented_sq = piece_sq ^ int(OrientTBL[ksq]) ^ flip if ksq else piece_sq
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king_buckets = {}
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king_bucket = KingBuckets[ksq ^ flip] if ksq else 0
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feature_idx = oriented_sq + piece_type + king_bucket
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if 0 <= feature_idx < 352:
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features[feature_idx] = 1.0
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# FullThreats features (60,720)
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for sq in range(64):
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for sq in range(64):
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piece = b.piece_at(sq)
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piece = b.piece_at(sq)
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if piece and piece.unicode_symbol() in ("\u265a", "\u2654"):
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if piece is None:
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perspective_king = 1 if piece.color == chess.WHITE else 0
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oriented_ksq = (sq ^ PIECE_SQUARE_INDEX_OFFSET ^ (56 * perspective)) ^ (56 * perspective)
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bucket_idx = oriented_ksq % 8
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if bucket_idx not in king_buckets:
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king_buckets[bucket_idx] = perspective_king
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for bucket_idx, perspective_king in king_buckets.items():
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features[336 + bucket_idx * 8 + perspective_king] = 1.0
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# FullThreats features (60,720) - EXACT Stockfish formula
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# Index = piece_pair_data.feature_index_base() + offsets[attacker][from] + index_lut2[attacker][from][to]
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# Simplified: Index = piece1_idx * 157 + piece2_idx
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piece_attacks = {}
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for sq in range(64):
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piece = b.piece_at(sq)
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piece_type = TYPE_TO_INDEX.get(piece.unicode_symbol()) if piece else None
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piece_attacks[sq] = {to_sq for to_sq in range(64) if b.attacks(piece_type) & (1 << to_sq)} if piece_type else set()
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for from_sq in range(64):
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from_piece = b.piece_at(from_sq)
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from_type = TYPE_TO_INDEX.get(from_piece.unicode_symbol()) if from_piece else None
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if from_type is None:
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continue
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continue
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from_piece_idx = from_sq * 6 + from_type
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attacks_bb = b.attacks(piece.piece_type)
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for to_sq in piece_attacks[from_sq]:
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for to_sq in range(64):
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if attacks_bb & (1 << to_sq):
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to_piece = b.piece_at(to_sq)
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to_piece = b.piece_at(to_sq)
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to_type = TYPE_TO_INDEX.get(to_piece.unicode_symbol()) if to_piece else None
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if to_piece is None:
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if to_type is None:
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continue
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continue
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to_piece_idx = to_sq * 6 + to_type
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feature_idx = from_piece_idx * 157 + to_piece_idx
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to_type = 5 - to_piece.piece_type
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if to_type < 0 or to_type > 5:
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continue
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from_oriented = int(sq ^ int(OrientTBL[ksq]) ^ flip) if ksq else sq
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to_oriented = int(to_sq ^ int(OrientTBL[ksq]) ^ flip) if ksq else to_sq
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from_less_than_to = int(from_oriented < to_oriented)
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lut1_val = int(index_lut1[piece_type][to_type][from_less_than_to])
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lut2_val = int(index_lut2[piece_type][from_oriented][to_oriented])
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feature_idx = lut1_val + lut2_val
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if 0 <= feature_idx < 60720:
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features[feature_idx] = 1.0
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features[feature_idx] = 1.0
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return features
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return features
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if __name__ == "__main__":
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fen = 'rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1'
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features = fen_to_features(fen)
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print(f"Features: {sum(features)}")
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