test

.gitea/workflows/test.yaml

@@ -1,5 +1,5 @@
-name: Gitea Actions Test
-run-name: 实验自动评测 Actions 🚀
+name: Gitea Actions Challenge Test
+run-name: 挑战赛自动评测 Actions 🚀
 on: [push]
 
 jobs:
@@ -14,13 +14,13 @@ jobs:
         run: |
           python -m venv ./.venv
           . ./.venv/bin/activate
-          curl -s -o pip_cache.tar.gz ${{ vars.EXP_URL }}/download/pip_cache.tar.gz
+          curl -s -o pip_cache.tar.gz ${{ vars.BACKEND_URL }}/download/pip_cache.tar.gz
           tar -zxf pip_cache.tar.gz
           pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
           rm -rf datingTrainSet2.tsv datingTestSet2.tsv main.py
-          curl -s -o main.py ${{ vars.EXP_URL }}/exp/knn/main.py
-          curl -s -o datingTrainSet2.tsv ${{ vars.EXP_URL }}/exp/knn/datingTrainSet2.tsv
-          curl -s -o datingTestSet2.tsv ${{ vars.EXP_URL }}/exp/knn/datingTestSet2.tsv
+          curl -s -o main.py ${{ vars.BACKEND_URL }}/public/challenge/knn/main.py
+          curl -s -o datingTrainSet2.tsv ${{ vars.BACKEND_URL }}/public/challenge/knn/datingTrainSet2.tsv
+          curl -s -o datingTestSet2.tsv ${{ vars.BACKEND_URL }}/public/challenge/knn/datingTestSet2.tsv
       - name: 评测结果
         run: |
           . ./.venv/bin/activate

function.py

@@ -23,7 +23,7 @@ import numpy as np
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
-id = "202210423xxx"
+id = "202210423229"
 name = "向兴宇"
 
 

main.py

@@ -0,0 +1,168 @@
+import importlib.util
+import numpy as np
+import requests
+import time
+import traceback
+
+EXP_URL = "http://127.0.0.1:880"
+
+
+def load_user_function(file_path, function_name):
+    """
+    动态加载函数。
+    """
+    spec = importlib.util.spec_from_file_location("user_code", file_path)
+    user_code = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(user_code)
+    return getattr(user_code, function_name)
+
+
+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。
+
+    Args:
+    - user_func (function): 学生编写的 KNN 函数。
+    - train_data (numpy array): 训练数据集。
+    - 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。
+    """
+    correct = 0
+    total = len(test_data)
+
+    try:
+        for i in range(total):
+            # 获取测试数据中的每个样本
+            result = user_func(test_data[i], train_data, train_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
+
+
+def load_data(file_path):
+    """
+    加载 TSV 格式数据文件。
+    """
+    data = np.loadtxt(file_path, delimiter="\t")
+    features = data[:, :-1]
+    labels = data[:, -1].astype(int)
+    return features, labels
+
+
+def upload_score(name, id, score_data):
+    """
+    上传成绩到服务器。
+    """
+    url = f"{EXP_URL}/upload_score"
+    try:
+        print("上传成绩中...")
+        # 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.gmtime(time.time() + 8 * 3600)
+                ),
+                "details": score_data["details"],
+                "project": "challenge_knn",
+            },
+        )
+        if response.status_code == 200:
+            print("上传成绩成功!")
+        else:
+            print("上传成绩失败!\n原因:", response.json())
+    except Exception as e:
+        print(f"上传成绩出错: {e}")
+
+
+def main(name, id):
+    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"
+    test_file = "datingTestSet2.tsv"
+
+    train_data, train_labels = load_data(train_file)
+    test_data, test_labels = load_data(test_file)
+
+    # 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:
+        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, log_details
+    )
+
+    # 计算运行时间
+    running_time = time.time() - start_time
+
+    # 输出结果
+    print("")
+    log_details.append(f"得分: {accuracy * 100:.2f}")
+    print(f"得分: {accuracy * 100:.2f}")
+    log_details.append(f"运行时间: {running_time * 1000:.10f}ms")
+    print(f"运行时间: {running_time * 1000:.10f}ms")
+
+    # 上传成绩
+    score_data = {
+        "score": accuracy * 100,
+        "running_time": f"{running_time * 1000:.10f}ms",
+        "details": "<br>".join(log_details),
+    }
+
+    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:
+        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，记录不做提交！",
+        ]
+        print("\n".join(log_details))