engine/search.py

"""
Nexus-Core Search Engine
Efficient alpha-beta with essential optimizations
- Basic transposition table
- Simple move ordering (MVV-LVA)
- Quiescence search
"""

import onnxruntime as ort
import numpy as np
import chess
import time
import logging
from pathlib import Path
from typing import Optional, Dict, Tuple, List

logger = logging.getLogger(__name__)


class NexusCoreEngine:
    """
    Lightweight chess engine for Nexus-Core
    Optimized for speed over strength
    """
    
    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 = 2):
        """Initialize engine"""
        
        self.model_path = Path(model_path)
        if not self.model_path.exists():
            raise FileNotFoundError(f"Model not found: {model_path}")
        
        # Load ONNX model
        sess_options = ort.SessionOptions()
        sess_options.intra_op_num_threads = num_threads
        sess_options.inter_op_num_threads = num_threads
        sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
        
        logger.info(f"Loading Nexus-Core from {model_path}...")
        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
        
        # Simple transposition table (dict-based, 100K entries)
        self.tt_cache = {}
        self.max_tt_size = 100000
        
        # Statistics
        self.nodes_evaluated = 0
        
        logger.info("✅ Nexus-Core engine ready")
    
    def fen_to_tensor(self, fen: str) -> np.ndarray:
        """Convert FEN to 12-channel tensor"""
        board = chess.Board(fen)
        tensor = np.zeros((1, 12, 8, 8), dtype=np.float32)
        
        piece_to_channel = {
            chess.PAWN: 0, chess.KNIGHT: 1, chess.BISHOP: 2,
            chess.ROOK: 3, chess.QUEEN: 4, chess.KING: 5
        }
        
        for square, piece in board.piece_map().items():
            rank, file = divmod(square, 8)
            channel = piece_to_channel[piece.piece_type]
            if piece.color == chess.BLACK:
                channel += 6
            tensor[0, channel, rank, file] = 1.0
        
        return tensor
    
    def evaluate(self, board: chess.Board) -> float:
        """Neural network evaluation"""
        self.nodes_evaluated += 1
        
        # Check cache (simple FEN-based)
        fen_key = board.fen().split(' ')[0]
        if fen_key in self.tt_cache:
            return self.tt_cache[fen_key]
        
        # Run inference
        input_tensor = self.fen_to_tensor(board.fen())
        output = self.session.run([self.output_name], {self.input_name: input_tensor})
        
        # Value output (tanh normalized)
        eval_score = float(output[0][0][0]) * 400.0  # Scale to centipawns
        
        # Flip for black
        if board.turn == chess.BLACK:
            eval_score = -eval_score
        
        # Cache result
        if len(self.tt_cache) < self.max_tt_size:
            self.tt_cache[fen_key] = eval_score
        
        return eval_score
    
    def order_moves(self, board: chess.Board, moves: List[chess.Move]) -> List[chess.Move]:
        """
        Simple move ordering
        1. Captures (MVV-LVA)
        2. Checks
        3. Other moves
        """
        scored_moves = []
        
        for move in moves:
            score = 0
            
            # Captures
            if board.is_capture(move):
                victim = board.piece_at(move.to_square)
                attacker = board.piece_at(move.from_square)
                if victim and attacker:
                    victim_val = self.PIECE_VALUES.get(victim.piece_type, 0)
                    attacker_val = self.PIECE_VALUES.get(attacker.piece_type, 1)
                    score = (victim_val * 10 - attacker_val) * 100
            
            # Promotions
            if move.promotion == chess.QUEEN:
                score += 9000
            
            # Checks
            board.push(move)
            if board.is_check():
                score += 5000
            board.pop()
            
            scored_moves.append((score, move))
        
        scored_moves.sort(key=lambda x: x[0], reverse=True)
        return [move for _, move in scored_moves]
    
    def quiescence(self, board: chess.Board, alpha: float, beta: float, depth: int = 2) -> float:
        """Quiescence search (captures only)"""
        
        stand_pat = self.evaluate(board)
        
        if stand_pat >= beta:
            return beta
        if alpha < stand_pat:
            alpha = stand_pat
        
        if depth == 0:
            return stand_pat
        
        # Only captures
        captures = [m for m in board.legal_moves if board.is_capture(m)]
        if not captures:
            return stand_pat
        
        captures = self.order_moves(board, captures)
        
        for move in captures:
            board.push(move)
            score = -self.quiescence(board, -beta, -alpha, depth - 1)
            board.pop()
            
            if score >= beta:
                return beta
            if score > alpha:
                alpha = score
        
        return alpha
    
    def alpha_beta(
        self,
        board: chess.Board,
        depth: int,
        alpha: float,
        beta: float,
        start_time: float,
        time_limit: float
    ) -> Tuple[float, Optional[chess.Move]]:
        """Alpha-beta search"""
        
        # Time check
        if time.time() - start_time > time_limit:
            return self.evaluate(board), None
        
        # Terminal nodes
        if board.is_game_over():
            if board.is_checkmate():
                return -10000, None
            return 0, None
        
        # Leaf nodes
        if depth == 0:
            return self.quiescence(board, alpha, beta), None
        
        legal_moves = list(board.legal_moves)
        if not legal_moves:
            return 0, None
        
        ordered_moves = self.order_moves(board, legal_moves)
        
        best_move = ordered_moves[0]
        best_score = float('-inf')
        
        for move in ordered_moves:
            board.push(move)
            score, _ = self.alpha_beta(board, depth - 1, -beta, -alpha, start_time, time_limit)
            score = -score
            board.pop()
            
            if score > best_score:
                best_score = score
                best_move = move
            
            alpha = max(alpha, score)
            if alpha >= beta:
                break
        
        return best_score, best_move
    
    def get_best_move(self, fen: str, depth: int = 4, time_limit: int = 3000) -> Dict:
        """Main search entry"""
        
        board = chess.Board(fen)
        self.nodes_evaluated = 0
        
        time_limit_sec = time_limit / 1000.0
        start_time = time.time()
        
        # Special cases
        legal_moves = list(board.legal_moves)
        if len(legal_moves) == 0:
            return {'best_move': '0000', 'evaluation': 0.0, 'depth_searched': 0, 'nodes_evaluated': 0}
        
        if len(legal_moves) == 1:
            return {
                'best_move': legal_moves[0].uci(),
                'evaluation': round(self.evaluate(board) / 100.0, 2),
                'depth_searched': 0,
                'nodes_evaluated': 1,
                'time_taken': 0
            }
        
        # Iterative deepening
        best_move = legal_moves[0]
        best_score = float('-inf')
        
        for current_depth in range(1, depth + 1):
            if time.time() - start_time > time_limit_sec * 0.9:
                break
            
            try:
                score, move = self.alpha_beta(
                    board, current_depth,
                    float('-inf'), float('inf'),
                    start_time, time_limit_sec
                )
                
                if move:
                    best_move = move
                    best_score = score
                
            except Exception as e:
                logger.warning(f"Search error: {e}")
                break
        
        time_taken = int((time.time() - start_time) * 1000)
        
        return {
            'best_move': best_move.uci(),
            'evaluation': round(best_score / 100.0, 2),
            'depth_searched': current_depth,
            'nodes_evaluated': self.nodes_evaluated,
            'time_taken': time_taken
        }
    
    def validate_fen(self, fen: str) -> bool:
        try:
            chess.Board(fen)
            return True
        except:
            return False
    
    def get_model_size(self) -> float:
        return self.model_path.stat().st_size / (1024 * 1024)