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import os.path
import numpy as np
import pandas as pd
import argparse
from sklearn.metrics import roc_auc_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="quality")
args = parser.parse_args()
actual = pd.read_csv(args.answer_file)
submission = pd.read_csv(args.predict_file)
actual.sort_values(by=['Id'])
submission.sort_values(by=['Id'])
def quadratic_weighted_kappa(actual, predicted, N):
O = np.zeros((N, N), dtype=int)
for a, p in zip(actual, predicted):
O[a][p] += 1
w = np.zeros((N, N))
for i in range(N):
for j in range(N):
w[i][j] = ((i - j) ** 2) / ((N - 1) ** 2)
actual_hist = np.zeros(N)
for a in actual:
actual_hist[a] += 1
pred_hist = np.zeros(N)
for p in predicted:
pred_hist[p] += 1
E = np.outer(actual_hist, pred_hist)
E = E / E.sum() * O.sum()
num = (w * O).sum()
den = (w * E).sum()
return 1 - num / den
# 计算平均错误率
performance = quadratic_weighted_kappa(actual[args.value], submission[args.value], 10)
with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
f.write(str(performance))
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