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feat: add running time and submit time log

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LufsX
2024-09-24 18:52:50 +08:00
parent 7854450009
commit 0549d0524c
3 changed files with 67 additions and 20 deletions
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.gitignore
+1
View File
@@ -161,3 +161,4 @@ cython_debug/
#.idea/
uploads
scores.csv
app.py
+10 -2
View File
@@ -48,7 +48,7 @@ def write_to_csv(data):
"""
file_exists = os.path.isfile(csv_file_path)
with open(csv_file_path, mode="a", newline="", encoding="utf-8") as csvfile:
fieldnames = ["id", "name", "score", "details"]
fieldnames = ["time", "id", "name", "score", "running_time", "details"]
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
if not file_exists:
@@ -61,7 +61,15 @@ def write_to_csv(data):
def upload_score():
try:
data = request.json
if "name" in data and "id" in data and "score" in data and "details" in data:
if (
"name" in data
and "id" in data
and "score" in data
and "details" in data
and "running_time" in data
and "time" in data
):
data["details"] = data["details"].replace("\n", "<br>")
write_to_csv(data)
return jsonify({"message": "成绩记录上传成功！"}), 200
else:
exp/knn/main.py
+55 -17
View File
@@ -1,7 +1,8 @@
import requests
import numpy as np
import traceback
import importlib.util
import numpy as np
import requests
import time
import traceback
def load_user_function(file_path, function_name):
@@ -14,7 +15,9 @@ def load_user_function(file_path, function_name):
return getattr(user_code, function_name)
def evaluate_knn(user_func, train_data, train_labels, test_data, test_labels):
def evaluate_knn(
user_func, train_data, train_labels, test_data, test_labels, log_details
):
"""
使用学生编写的 KNN 函数对测试集进行分类，并计算准确率。
@@ -24,6 +27,7 @@ def evaluate_knn(user_func, train_data, train_labels, test_data, test_labels):
- train_labels (numpy array): 训练标签集。
- test_data (numpy array): 测试数据集。
- test_labels (numpy array): 测试标签集。
- log_details (list): 日志记录列表。
Returns:
- float: 分类准确率。
@@ -38,15 +42,22 @@ def evaluate_knn(user_func, train_data, train_labels, test_data, test_labels):
if result == test_labels[i]:
correct += 1
else:
log_details.append(
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]}"
)
print(
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]}"
)
accuracy = correct / total
log_details.append(f"分类准确率: {accuracy * 100:.2f}%")
return accuracy
except Exception as e:
log_details.append(f"评测期间发生错误: {e}")
print(f"评测期间发生错误: {e}")
log_details.append(traceback.format_exc())
print(traceback.format_exc())
log_details.append("成绩为零！")
print("成绩为零！")
return 0.0
@@ -71,21 +82,23 @@ def upload_score(name, id, score_data):
"""
上传成绩到服务器。
"""
url = "http://10.2.1.195:5000/upload_score"
url = "http://10.2.1.195:880/upload_score"
try:
print("上传成绩中...")
print(score_data)
# print(score_data)
response = requests.post(
url,
json={
"id": id,
"name": name,
"score": score_data["score"],
"running_time": score_data["running_time"],
"time": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
"details": score_data["details"],
},
)
if response.status_code == 200:
print("上传成绩成功")
print("上传成绩成功!")
else:
print("上传成绩失败:", response.json())
except Exception as e:
@@ -93,8 +106,8 @@ def upload_score(name, id, score_data):
def main(name, id):
user_code_file = "function.py" # 学生代码文件
user_function_name = "classify0" # 学生需要实现的 KNN 函数
user_code_file = "function.py" # 代码文件
user_function_name = "classify0" # KNN 函数名
# 加载训练集和测试集
train_file = "datingTrainSet2.tsv"
@@ -103,27 +116,52 @@ def main(name, id):
train_data, train_labels = load_data(train_file)
test_data, test_labels = load_data(test_file)
# 加载学生的 KNN 函数
# 加载自定义函数
log_details = []
try:
user_func = load_user_function(user_code_file, user_function_name)
except Exception as e:
print(f"Error loading user function: {e}")
log_details.append(f"Error loading user function: {e}")
print("\n".join(log_details))
return
# 记录评估时间
start_time = time.time()
# 评估分类准确率
accuracy = evaluate_knn(user_func, train_data, train_labels, test_data, test_labels)
accuracy = evaluate_knn(
user_func, train_data, train_labels, test_data, test_labels, log_details
)
# 计算运行时间
running_time = time.time() - start_time
# 输出结果
print(f"得分: {accuracy * 100:.2f}")
log_details.append(f"得分: {accuracy * 100:.2f}%")
log_details.append(f"运行时间: {running_time * 1000:.10f}ms")
print("\n".join(log_details))
# 上传成绩
score_data = {
"score": accuracy,
"running_time": f"{running_time * 1000:.10f}ms",
"details": "\n".join(log_details), # 将日志记录加入 details 中
}
upload_score(name, id, score_data)
if __name__ == "__main__":
try:
from function import name, id
print(f"正在评测 {name}@{id} 的代码...\n")
main(name, id)
except Exception as e:
print(f"评测出错: {e}")
print(traceback.format_exc())
print("请检查代码和返回值类型是否正确！")
print("本次评测无效！")
log_details = [
f"评测出错: {e}",
traceback.format_exc(),
"请检查代码和返回值类型是否正确！",
"本次评测无效！",
]
print("\n".join(log_details))