From a48cbd82c12f5c41131ccffe3a995370bbaed5a5 Mon Sep 17 00:00:00 2001
From: fang xiaoyu <fangxiaoyu@iie.ac.cn>
Date: Thu, 16 Mar 2023 15:16:42 +0000
Subject: [PATCH] Update Knn_test.py

---
 Knn_test.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/Knn_test.py b/Knn_test.py
index e89f62e..ddc60ee 100644
--- a/Knn_test.py
+++ b/Knn_test.py
@@ -6,21 +6,30 @@ from sklearn.neighbors import KNeighborsClassifier
 from sklearn.metrics import accuracy_score
 from sklearn.metrics import classification_report
 
+#读取数据
 data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
 
-
+#将类别标签转换为数字
 data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
 
 #print(data)
+
+#划分训练集和测试集
 X_train, X_test, y_train, y_test = train_test_split(
     data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42)
 
+#创建KNN分类器
 knn = KNeighborsClassifier(n_neighbors=3)
+
+#训练模型
 knn.fit(X_train, y_train)
 
+#在测试集上测试模型性能
 y_pred = knn.predict(X_test)
-accuracy = accuracy_score(y_test, y_pred)
-print(f"Accuracy: {accuracy}")
+# accuracy = accuracy_score(y_test, y_pred)
+# print(f"Accuracy: {accuracy}")
+
+#输出测试结果
 print(classification_report(y_test, y_pred))
 #Accuracy: 0.8200959488272921
 #               precision    recall  f1-score   support
@@ -30,4 +39,4 @@ print(classification_report(y_test, y_pred))
 #
 #     accuracy                           0.82      3752
 #    macro avg       0.82      0.82      0.82      3752
-# weighted avg       0.82      0.82      0.82      3752
\ No newline at end of file
+# weighted avg       0.82      0.82      0.82      3752