Priority Queue in python with a custom object

I've come across this because you can use this priority queue in the selection process in the A* algorithm (i.e the process where you choose the node with the lowest f score), to get an O(1) instead of the traditional O(n) solution.

Since we are choosing the node with the lowest score and the priority queue keeps the lowest value at the beginning, we can then achieve this O(1) solution.

Here is the first file
node.py

class Node(object):
    def __init__(self, title, i, j):
        self.title = title
        self.i = str(i)
        self.j = str(j)
        self.f = 0
        self.g = 0
        self.h = 0
        # no neighbors at first
        self.neighbors = []

    def __str__(self):
        return '<' + self.title + '>'

    def __repr__(self):
        character = 'W' if self.title is '#' else 'O'
        return '<' + self.i + ' ' + self.j + ' ' + self.title + ', ' + str(len(self.neighbors))+'>'

Don't mind the unnecessary code.

Here is the second and final file
priority_item.py

from dataclasses import dataclass, field
from typing import Any
from queue import PriorityQueue
from node import Node


@dataclass(order=True)
class PrioritizedItem:
    priority: int
    item: object = field()

q = PriorityQueue()
n = Node('a', 0, 0)
n.f = 1
n2 = Node('b', 0, 0)
n2.f = 0
n3 = Node('c', 0, 0)
n3.f = 2
q.put(PrioritizedItem(n.f, n))
q.put(PrioritizedItem(n2.f, n2))
q.put(PrioritizedItem(n3.f, n3))
print(q.get())

In this file I create a new PrioritizedItem which will encompass my custom object.

Below it I make 3 new node objects just as a test where the second has the lowest f score.

When inserting these custom nodes into the list, you wrap it in the PrioritizedItem and pass the value that you want to be used as the priority as the first argument, and then the priority queue will sort them from lowest to highest. This is because the PriorityQueue uses a min-heap data structure in its underlying implementation.