data_modeling/evaluation/playground-series-s3e19_eval.py

import os.path

import numpy as np
import pandas as pd
import argparse
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.metrics import mean_absolute_error

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="Machine failure")

args = parser.parse_args()

answers = pd.read_csv( args.answer_file)
predictions = pd.read_csv( args.predict_file)

# 提取预测值和实际标签
predicted_values = predictions['num_sold'].values
actual_values = answers['num_sold'].values  # 修改列名为answers


smape = np.mean(2 * np.abs(predicted_values - actual_values) / (np.abs(actual_values) + np.abs(predicted_values)))
performance = smape
with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
    f.write(str(performance))