File size: 1,645 Bytes
fe8d248 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
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', default='', type=str, required=False)
parser.add_argument('--name', default='', type=str, required=False)
parser.add_argument('--answer_file', default='/Users/tencentintern/PycharmProjects/DSBench/kaggle_data/data_filted_csv/answers/playground-series-s3e13/test_answer.csv', type=str, required=False)
parser.add_argument('--predict_file', default='/Users/tencentintern/PycharmProjects/DSBench/kaggle_data/data_filted_csv/answers/playground-series-s3e13/test_answer.csv', type=str, required=False)
parser.add_argument('--value', type=str, default="prognosis")
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 mpa_at_3(actual, predictions):
"""
Calculate Mean Percentage Agreement at 3 (MPA@3).
Parameters:
actual (list): List of actual prognosis values.
predictions (list of lists): List of lists containing up to 3 predicted prognosis values.
Returns:
float: The MPA@3 score.
"""
total = len(actual)
score = 0.0
for act, preds in zip(actual, predictions):
preds = preds.split()
if act in preds[:3]:
score += 1
return score / total
# 计算平均错误率
performance = mpa_at_3(actual[args.value], submission[args.value])
print(performance)
with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
f.write(str(performance))
|