Python trivial endgame engine is not working as expected

[max22]

I have detected where roughly is a problem. It is in this function which gets processed for node "1" only, see the output section below: (I'm trying to put the node 6109 into A[1]['sequence'] but I cannot) Can someone help to me fix this function def evaluate_and_partition(A, node_id=1):

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def evaluate_and_partition(A, node_id=1):     node = A[node_id]     # Debugging line to track the function calls     print(f"Evaluating Node ID: {node_id}")       # Return previously calculated result if this node has been processed     if node['processed']:         print(f"Returning processed Node ID: {node_id} with result: {node['result']}, moves to mate: {node['moves_to_mate']}")         return node['result'], node['moves_to_mate']       # Initialize variables to store the best result and path found so far     optimal_result = -float('inf')  # Assume worst case scenario     optimal_moves_to_mate = None  # Initialize as None, to be updated     optimal_child_id = None  # Keep track of which child leads to the optimal result       # Iterate through each child of the current node     for child_id in node['children']:         print(child_id)         # Recursively evaluate each child         child_result, child_moves_to_mate = evaluate_and_partition(A, child_id)                   # Check if the current child's result improves upon the best result found so far         if child_result is not None and (             child_result > optimal_result or             (child_result == optimal_result and optimal_moves_to_mate is not None and              child_moves_to_mate is not None and child_moves_to_mate < optimal_moves_to_mate)):             # Update optimal values based on the current child's results             optimal_result = child_result             optimal_moves_to_mate = child_moves_to_mate             optimal_child_id = child_id       # After evaluating all children, update the current node with the best result and path found     node['result'] = optimal_result     if optimal_moves_to_mate is not None:         # If a path to victory or draw is found, increment moves to mate as we move up the tree         node['moves_to_mate'] = optimal_moves_to_mate + 1     else:         # If no path improves the position, moves to mate remains None         node['moves_to_mate'] = None     node['sequence'] = [optimal_child_id] if optimal_child_id is not None else []     node['processed'] = True  # Mark the node as processed       # Return the best result and path found for the current node     return node['result'], node['moves_to_mate']

END OF THE WRONG FUNCTION, the code follows

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import chess   def initialize_game_tree(initial_fen):     """Initializes the game tree with the root node based on the initial FEN."""     return {         1: {             'fen': initial_fen,             'moves_to_mate': None,             'parent': None,             'color': chess.WHITE if initial_fen.split(' ')[1] == 'w' else chess.BLACK,             'result': None,             'processed': False,             'sequence': [],             'children': []         }     }   def add_descendants(node_id, depth, A):     """Recursively adds descendant nodes to the game tree up to a specified depth."""     if depth == 0:         return     board = chess.Board(A[node_id]['fen'])     for move in board.legal_moves:         board.push(move)         new_node_id = len(A) + 1         A[new_node_id] = {             'fen': board.fen(),             'moves_to_mate': None,             'parent': node_id,             'color': chess.WHITE if board.turn else chess.BLACK,             'result': None,             'processed': False,             'sequence': [],             'children': []         }         A[node_id]['children'].append(new_node_id)         add_descendants(new_node_id, depth - 1, A)         board.pop()     def evaluate_and_partition(A, node_id=1):     node = A[node_id]     # Debugging line to track the function calls     print(f"Evaluating Node ID: {node_id}")       # Return previously calculated result if this node has been processed     if node['processed']:         print(f"Returning processed Node ID: {node_id} with result: {node['result']}, moves to mate: {node['moves_to_mate']}")         return node['result'], node['moves_to_mate']       # Initialize variables to store the best result and path found so far     optimal_result = -float('inf')  # Assume worst case scenario     optimal_moves_to_mate = None  # Initialize as None, to be updated     optimal_child_id = None  # Keep track of which child leads to the optimal result       # Iterate through each child of the current node     for child_id in node['children']:         print(child_id)         # Recursively evaluate each child         child_result, child_moves_to_mate = evaluate_and_partition(A, child_id)                   # Check if the current child's result improves upon the best result found so far         if child_result is not None and (             child_result > optimal_result or             (child_result == optimal_result and optimal_moves_to_mate is not None and              child_moves_to_mate is not None and child_moves_to_mate < optimal_moves_to_mate)):             # Update optimal values based on the current child's results             optimal_result = child_result             optimal_moves_to_mate = child_moves_to_mate             optimal_child_id = child_id       # After evaluating all children, update the current node with the best result and path found     node['result'] = optimal_result     if optimal_moves_to_mate is not None:         # If a path to victory or draw is found, increment moves to mate as we move up the tree         node['moves_to_mate'] = optimal_moves_to_mate + 1     else:         # If no path improves the position, moves to mate remains None         node['moves_to_mate'] = None     node['sequence'] = [optimal_child_id] if optimal_child_id is not None else []     node['processed'] = True  # Mark the node as processed       # Return the best result and path found for the current node     return node['result'], node['moves_to_mate']     # Main execution block initial_fen = "4k3/8/8/3K2Q1/8/8/8/8 w - - 6 4" A = initialize_game_tree(initial_fen) add_descendants(1, 5, A)  # Adjust the depth as needed evaluate_terminal_positions(A) evaluate_and_partition(A, 1)   # Print the root node to see the analysis result print(A[1])   # Function to print boards for children of a given node def print_boards_for_children(A, parent_key):     children_keys = A[parent_key].get('children', [])     for key in children_keys:         if key in A:             fen = A[key]['fen']             board = chess.Board(fen)             print(f"Board for child {key}:\n{board}\n")   # Display the initial board and boards for first-level children print(chess.Board(initial_fen)) print_boards_for_children(A, 1)

The output is like this, the node 6109 below is my target to be included to A[1]['sequence']

Output:
Evaluating Node ID: 1
Returning processed Node ID: 1 with result: None, moves to mate: None
{'fen': '4k3/8/8/3K2Q1/8/8/8/8 w - - 6 4', 'moves_to_mate': None, 'parent': None, 'color': True, 'result': None, 'processed': True, 'sequence': [], 'children': [2, 3697, 6109, 8685, 9041, 20864, 32046, 34967, 44777, 50093, 60983, 70685, 83639, 94018, 111578, 126490, 142939, 161014, 178208, 196205, 197783, 202648, 209235, 216611, 225572, 236906, 248111]}
. . . . k . . .
. . . . . . . .
. . . . . . . .
. . . K . . Q .
. . . . . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .
Board for child 2:
. . . . k . Q .
. . . . . . . .
. . . . . . . .
. . . K . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .

Board for child 3697:
. . . Q k . . .
. . . . . . . .
. . . . . . . .
. . . K . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .

Board for child 6109:
. . . . k . . .
. . . . . . Q .
. . . . . . . .
. . . K . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .
. . . . . . . .

buran write Feb-24-2024, 08:29 PM:
please, use python tags when post code, not output