334bc313b0
- Use piece_sq * 6 + piece_type encoding - 32 active features for 32 pieces on board - Simplified from FullThreats (60,720) to HalfKAv2_hm only - All tests passing (11 tests)
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
"""Verify HalfKAv2_hm features match Stockfish NNUE exactly"""
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import chess
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from python.model.feature_extractor import fen_to_features
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from python.stockfish_wrapper import NNUEEvaluator
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from python.constants import HALF_KA_V2_HM
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def get_stockfish_evaluation(fen: str) -> float:
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"""Get Stockfish NNUE evaluation in centipawns"""
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evaluator = NNUEEvaluator()
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eval = evaluator.evaluate(fen)
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evaluator.close()
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return eval
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def get_our_evaluation(fen: str) -> float:
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"""Get our model's evaluation"""
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import torch
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from python.model.nnue_linear import LinearEval
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features = fen_to_features(fen)
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features_tensor = torch.tensor([features], dtype=torch.float32)
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model = LinearEval()
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with torch.no_grad():
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eval = model(features_tensor)[0, 0].item()
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return eval
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# Test positions
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test_positions = [
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"rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1", # Starting
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"rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR b KQkq - 0 1", # Black to move
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"8/8/8/8/8/8/8/8 w KQkq - 0 1", # Empty board
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]
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print("Position\t\t\t\tStockfish\t\tOur Model\tDiff")
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print("-" * 80)
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for fen in test_positions:
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try:
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stockfish_eval = get_stockfish_evaluation(fen)
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our_eval = get_our_evaluation(fen)
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diff = abs(stockfish_eval - our_eval)
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print(f"{fen[:25]:25}\t{stockfish_eval:10.2f}\t{our_eval:10.2f}\t{diff:.2f}")
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except Exception as e:
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print(f"{fen[:25]:25}\tERROR: {e}")
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