如何优化我的 BFS 实施？

Question

我是一名 python 初学者，正在尝试解决 CS50 的一些项目。我做了 this 个项目。这是 IMDB 中一些电影数据的 BFS 实现。我成功解决了。但是我的代码对于一些非常远的 inputs.By 远意味着对于图中一些非常远的节点需要 5 分钟以上的时间。这是我的代码。忽略加载数据部分，关注 shortest_path 方法。

import csv
import sys


class Node():
    def __init__(self, state, parent, action):
        self.state = state
        self.parent = parent
        self.action = action
    def __str__(self):
        print(self.state)


# Maps names to a set of corresponding person_ids
names = {}

# Maps person_ids to a dictionary of: name, birth, movies (a set of movie_ids)
people = {}

# Maps movie_ids to a dictionary of: title, year, stars (a set of person_ids)
movies = {}


def load_data(directory):
    """
    Load data from CSV files into memory.
    """
    # Load people
    with open(f"{directory}/people.csv", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        for row in reader:
            people[row["id"]] = {
                "name": row["name"],
                "birth": row["birth"],
                "movies": set()
            }
            if row["name"].lower() not in names:
                names[row["name"].lower()] = {row["id"]}
            else:
                names[row["name"].lower()].add(row["id"])

    # Load movies
    with open(f"{directory}/movies.csv", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        for row in reader:
            movies[row["id"]] = {
                "title": row["title"],
                "year": row["year"],
                "stars": set()
            }

    # Load stars
    with open(f"{directory}/stars.csv", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        for row in reader:
            try:
                people[row["person_id"]]["movies"].add(row["movie_id"])
                movies[row["movie_id"]]["stars"].add(row["person_id"])
            except KeyError:
                pass


def main():
    if len(sys.argv) > 2:
        sys.exit("Usage: python degrees.py [directory]")
    directory = sys.argv[1] if len(sys.argv) == 2 else "large"

    # Load data from files into memory
    print("Loading data...")
    load_data(directory)
    print("Data loaded.")

    source = person_id_for_name(input("Name: "))
    if source is None:
        sys.exit("Person not found.")
    target = person_id_for_name(input("Name: "))
    if target is None:
        sys.exit("Person not found.")

    path = shortest_path(source, target)

    if path is None:
        print("Not connected.")
    else:
        degrees = len(path)
        print(f"{degrees} degrees of separation.")
        path = [(None, source)] + path
        for i in range(degrees):
            person1 = people[path[i][1]]["name"]
            person2 = people[path[i + 1][1]]["name"]
            movie = movies[path[i + 1][0]]["title"]
            print(f"{i + 1}: {person1} and {person2} starred in {movie}")


def shortest_path(source, target):
    """
    Returns the shortest list of (movie_id, person_id) pairs
    that connect the source to the target.

    If no possible path, returns None.
    """
    source = Node(source,None,None)
    queue = list(((Node(neighbor,source,None) for neighbor in neighbors_for_person(source.state) )))
    explored = list()
    path = list()
    while queue:
        current_node = queue.pop(0)
        if current_node.state not in explored:
            if current_node.state[1] == target:
                print('Goal reached')
                goal = current_node
                while goal:
                    path.append(goal.state)
                    goal = goal.parent
                return path[:-1][::-1]
            explored.append(current_node.state)
        queue.extend((Node(neighbor,current_node,None) for neighbor in neighbors_for_person(current_node.state[1])))





def person_id_for_name(name):
    """
    Returns the IMDB id for a person's name,
    resolving ambiguities as needed.
    """
    person_ids = list(names.get(name.lower(), set()))
    if len(person_ids) == 0:
        return None
    elif len(person_ids) > 1:
        print(f"Which '{name}'?")
        for person_id in person_ids:
            person = people[person_id]
            name = person["name"]
            birth = person["birth"]
            print(f"ID: {person_id}, Name: {name}, Birth: {birth}")
        try:
            person_id = input("Intended Person ID: ")
            if person_id in person_ids:
                return person_id
        except ValueError:
            pass
        return None
    else:
        return person_ids[0]


def neighbors_for_person(person_id):
    """
    Returns (movie_id, person_id) pairs for people
    who starred with a given person.
    """
    movie_ids = people[person_id]["movies"]
    neighbors = set()
    for movie_id in movie_ids:
        for person_id in movies[movie_id]["stars"]:
            neighbors.add((movie_id, person_id))
    return neighbors


if __name__ == "__main__":
    main()

我应该怎样做才能运行快？

Answer 1

一些提示：

not in explored 的时间复杂度为 O(n)（其中 n 是大小explored), 因为 explored 是一个列表。而是将其设为一个集合，以便 not in explored 在恒定时间内执行：
```
explored = set()
# ...
if current_node.state not in explored:
# ...
explored.add(current_node.state)
```

queue.pop(0) 的时间复杂度为 O(n)（其中 n 是大小queue），因为 queue 是一个列表。而是将其设为 deque，以便 queue.popleft() 在恒定时间内执行：

from collections import deque
queue = deque()
# ...
current_node = queue.popleft()
# ...
queue.extend((Node(neighbor,current_node,None) for neighbor in neighbors_for_person(current_node.state[1])))

如何优化我的 BFS 实施？

How can my BFS implementation be optimized?

python

algorithm

optimization

graph-algorithm