get_optimal_order.py

from itertools import permutations 
import numpy as np
import pandas as pd

def get_optimal_solution(random_seed, answers_df):
    answers = answers_df.values
    names = answers_df.index.values
    N = answers.shape[0]
    # 全パターンの順位を取得 (8!通り)
    all_orders = np.array(list(permutations(range(N))))
    # 全パターンについて幸福度を計算
    happiness = answers[np.arange(N), all_orders].sum(axis = 1)
    min_happiness = np.min(happiness)
    # 幸福を最大化するような順位を選択
    optimal_solutions = all_orders[happiness == min_happiness]

    np.random.seed(random_seed)
    random_idx = np.random.choice(np.arange(optimal_solutions.shape[0]))
    optimal_solution = optimal_solutions[random_idx]
    
    result = names[np.argsort(optimal_solution)]
    scores = answers[np.arange(N), optimal_solution]
    hope_rank = {name : score for (name, score) in zip(names, scores)}
    return list(result), hope_rank
 
if __name__ == '__main__':
    names = ['岩住', '小池', '辻', '岩瀬', '田中', '小林', '中村', '大矢']
    raw_data = pd.DataFrame([[3,4,6,7,2,5,1,8],
                         [3, 6, 2, 7, 1, 8, 4, 5],
                         [5, 6, 4, 3, 7, 2, 8, 1],
                         [0, 0, 0, 0, 0, 0, 0, 0],
                         [4, 5, 6, 3, 7, 2, 8, 1],
                         [2, 3, 4, 5, 6, 1, 7, 8],
                         [5, 3, 1, 2, 4, 6, 7, 8],
                         [1, 2, 3, 4, 5, 6, 7, 8]], index =  names)
    answers = raw_data.apply(lambda x : x.argsort(), axis = 1)
    answers.loc['岩瀬'] = 0
    result, hope_rank = get_optimal_solution(0, answers)
    print(raw_data)
    print('順番: ' + str(result))
    print('希望度: ' + str(hope_rank))