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import os.path
import numpy as np
import pandas as pd
import argparse
from sklearn.metrics import roc_auc_score
from sklearn.metrics import mean_squared_error
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="MedHouseVal")
args = parser.parse_args()
# 定义 RMSLE 计算函数
def rmsle(y_true, y_pred):
return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_true)) ** 2))
actual = pd.read_csv( args.answer_file)
submission = pd.read_csv( args.predict_file)
performance = np.sqrt(mean_squared_error(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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