Create engine/evaluate.py

engine/evaluate.py

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+"""
+Nexus-Nano Evaluator
+Ultra-lightweight 2.8M parameter CNN
+
+Research: MobileNet architecture principles for efficiency
+"""
+
+import onnxruntime as ort
+import numpy as np
+import chess
+import logging
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+class NexusNanoEvaluator:
+    """
+    Lightweight evaluator for Nexus-Nano
+    Optimized for speed over accuracy
+    """
+    
+    PIECE_VALUES = {
+        chess.PAWN: 100,
+        chess.KNIGHT: 320,
+        chess.BISHOP: 330,
+        chess.ROOK: 500,
+        chess.QUEEN: 900,
+        chess.KING: 0
+    }
+    
+    def __init__(self, model_path: str, num_threads: int = 1):
+        """Initialize with single-threaded ONNX session for speed"""
+        
+        self.model_path = Path(model_path)
+        if not self.model_path.exists():
+            raise FileNotFoundError(f"Model not found: {model_path}")
+        
+        # ONNX session (single-threaded for lowest latency)
+        sess_options = ort.SessionOptions()
+        sess_options.intra_op_num_threads = num_threads
+        sess_options.inter_op_num_threads = num_threads
+        sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
+        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
+        
+        logger.info(f"Loading Nexus-Nano model...")
+        self.session = ort.InferenceSession(
+            str(self.model_path),
+            sess_options=sess_options,
+            providers=['CPUExecutionProvider']
+        )
+        
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_name = self.session.get_outputs()[0].name
+        
+        logger.info(f"✅ Model loaded: {self.input_name} -> {self.output_name}")
+    
+    def fen_to_tensor(self, board: chess.Board) -> np.ndarray:
+        """
+        Fast 12-channel tensor conversion
+        Optimized for minimal overhead
+        """
+        tensor = np.zeros((1, 12, 8, 8), dtype=np.float32)
+        
+        # Piece to channel mapping
+        piece_channels = {
+            chess.PAWN: 0, chess.KNIGHT: 1, chess.BISHOP: 2,
+            chess.ROOK: 3, chess.QUEEN: 4, chess.KING: 5
+        }
+        
+        # Fast piece placement
+        for square, piece in board.piece_map().items():
+            rank, file = divmod(square, 8)
+            channel = piece_channels[piece.piece_type]
+            if piece.color == chess.BLACK:
+                channel += 6
+            tensor[0, channel, rank, file] = 1.0
+        
+        return tensor
+    
+    def evaluate_neural(self, board: chess.Board) -> float:
+        """
+        Fast neural evaluation
+        Single forward pass, minimal post-processing
+        """
+        input_tensor = self.fen_to_tensor(board)
+        outputs = self.session.run([self.output_name], {self.input_name: input_tensor})
+        
+        # Raw value (tanh output)
+        raw_value = float(outputs[0][0][0])
+        
+        # Scale to centipawns
+        return raw_value * 300.0  # Slightly lower scale for faster games
+    
+    def evaluate_material(self, board: chess.Board) -> int:
+        """Fast material count"""
+        material = 0
+        
+        for piece_type, value in self.PIECE_VALUES.items():
+            if piece_type == chess.KING:
+                continue
+            white = len(board.pieces(piece_type, chess.WHITE))
+            black = len(board.pieces(piece_type, chess.BLACK))
+            material += (white - black) * value
+        
+        return material
+    
+    def evaluate_hybrid(self, board: chess.Board) -> float:
+        """
+        Fast hybrid: 85% neural + 15% material
+        Higher material weight for stability in fast games
+        """
+        neural = self.evaluate_neural(board)
+        material = self.evaluate_material(board)
+        
+        hybrid = 0.85 * neural + 0.15 * material
+        
+        if board.turn == chess.BLACK:
+            hybrid = -hybrid
+        
+        return hybrid
+    
+    def get_model_size_mb(self) -> float:
+        """Get model size"""
+        return self.model_path.stat().st_size / (1024 * 1024)