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):

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

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