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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="Strength")
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 calculate_rmse(actual, predicted):
actual = np.array(actual)
predicted = np.array(predicted)
mse = np.mean((actual - predicted) ** 2)
rmse = np.sqrt(mse)
return rmse
# 计算平均错误率
performance = calculate_rmse(actual[args.value], submission[args.value])
with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
f.write(str(performance))
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