import os.path

import numpy as np
import pandas as pd
import argparse
from sklearn.metrics import log_loss

from sklearn.metrics import roc_auc_score
from sklearn.metrics import cohen_kappa_score

parser = argparse.ArgumentParser()

parser.add_argument('--path', type=str, required=True)
parser.add_argument('--name', type=str, required=True)
parser.add_argument('--answer_file', type=str, required=True)
parser.add_argument('--predict_file', type=str, required=True)

parser.add_argument('--value', type=str, default="score")

args = parser.parse_args()

# Compute MAE
def mean_absolute_error(y_true, y_pred):
    return np.mean(np.abs(y_pred - y_true))


actual = pd.read_csv( args.answer_file)
submission = pd.read_csv(args.predict_file)

# 提取实际值和预测值
actual_values = actual[['winner_model_a', 'winner_model_b', 'winner_tie']].values
predicted_values = submission[['winner_model_a', 'winner_model_b', 'winner_tie']].values



performance = log_loss(actual_values, predicted_values)


with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
    f.write(str(performance))