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Update Knn_test.py

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fang xiaoyu
2023-03-16 15:16:42 +00:00
parent ff5f5eec82
commit a48cbd82c1
1 changed files with 13 additions and 4 deletions
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Knn_test.py
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@@ -6,21 +6,30 @@ from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report from sklearn.metrics import classification_report
#读取数据
data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv') 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}) data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
#print(data) #print(data)
#划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split( X_train, X_test, y_train, y_test = train_test_split(
data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42) data.iloc[:, :-1], data.iloc[:, -1], test_size=0.2, random_state=42)
#创建KNN分类器
knn = KNeighborsClassifier(n_neighbors=3) knn = KNeighborsClassifier(n_neighbors=3)
#训练模型
knn.fit(X_train, y_train) knn.fit(X_train, y_train)
#在测试集上测试模型性能
y_pred = knn.predict(X_test) y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred) # accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}") # print(f"Accuracy: {accuracy}")
#输出测试结果
print(classification_report(y_test, y_pred)) print(classification_report(y_test, y_pred))
#Accuracy: 0.8200959488272921 #Accuracy: 0.8200959488272921
# precision recall f1-score support # precision recall f1-score support
@@ -30,4 +39,4 @@ print(classification_report(y_test, y_pred))
# #
# accuracy 0.82 3752 # accuracy 0.82 3752
# macro avg 0.82 0.82 0.82 3752 # macro avg 0.82 0.82 0.82 3752
# weighted avg 0.82 0.82 0.82 3752 # weighted avg 0.82 0.82 0.82 3752