130 lines
3.6 KiB
Python
Executable File
130 lines
3.6 KiB
Python
Executable File
import requests
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import numpy as np
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import traceback
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import importlib.util
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def load_user_function(file_path, function_name):
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"""
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动态加载函数。
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"""
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spec = importlib.util.spec_from_file_location("user_code", file_path)
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user_code = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(user_code)
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return getattr(user_code, function_name)
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def evaluate_knn(user_func, train_data, train_labels, test_data, test_labels):
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"""
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使用学生编写的 KNN 函数对测试集进行分类，并计算准确率。
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Args:
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- user_func (function): 学生编写的 KNN 函数。
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- train_data (numpy array): 训练数据集。
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- train_labels (numpy array): 训练标签集。
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- test_data (numpy array): 测试数据集。
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- test_labels (numpy array): 测试标签集。
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Returns:
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- float: 分类准确率。
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"""
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correct = 0
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total = len(test_data)
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try:
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for i in range(total):
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# 获取测试数据中的每个样本
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result = user_func(test_data[i], train_data, train_labels)
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if result == test_labels[i]:
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correct += 1
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else:
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print(
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f"第 {i+1} 次测试错误，数据 {test_data[i]} 被分类到 {result} 实际是 {test_labels[i]}"
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)
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accuracy = correct / total
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return accuracy
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except Exception as e:
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print(f"评测期间发生错误: {e}")
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print(traceback.format_exc())
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print("成绩为零！")
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return 0.0
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def load_data(file_path):
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"""
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加载 TSV 格式数据文件。
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"""
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features = []
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labels = []
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with open(file_path, "r") as file:
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for line in file:
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values = line.strip().split("\t")
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features.append([float(x) for x in values[:-1]])
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labels.append(int(values[-1]))
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return np.array(features), np.array(labels)
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def upload_score(name, id, score_data):
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"""
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上传成绩到服务器。
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"""
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url = "http://10.2.1.195:5000/upload_score"
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try:
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print("上传成绩中...")
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print(score_data)
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response = requests.post(
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url,
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json={
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"id": id,
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"name": name,
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"score": score_data["score"],
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"details": score_data["details"],
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},
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)
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if response.status_code == 200:
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print("上传成绩成功")
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else:
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print("上传成绩失败:", response.json())
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except Exception as e:
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print(f"上传成绩出错: {e}")
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def main(name, id):
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user_code_file = "function.py" # 学生代码文件
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user_function_name = "classify0" # 学生需要实现的 KNN 函数
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# 加载训练集和测试集
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train_file = "datingTrainSet2.tsv"
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test_file = "datingTestSet2.tsv"
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train_data, train_labels = load_data(train_file)
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test_data, test_labels = load_data(test_file)
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# 加载学生的 KNN 函数
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try:
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user_func = load_user_function(user_code_file, user_function_name)
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except Exception as e:
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print(f"Error loading user function: {e}")
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return
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# 评估分类准确率
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accuracy = evaluate_knn(user_func, train_data, train_labels, test_data, test_labels)
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# 输出结果
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print(f"得分: {accuracy * 100:.2f}")
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if __name__ == "__main__":
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try:
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from function import name, id
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main(name, id)
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except Exception as e:
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print(f"评测出错: {e}")
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print(traceback.format_exc())
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print("请检查代码和返回值类型是否正确！")
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print("本次评测无效！")
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