five modes duplication

Randomforest_test.py

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+# Name:fang xiaoyu
+# Time: 2023/3/11 23:18
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, confusion_matrix , precision_score, recall_score, f1_score
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+# 读取数据
+data = pd.read_csv('sufshark_openvpn_tcp+youdao_header.csv')
+
+# 将类别转换为数字标签
+data["class1"] = data["class1"].replace({"VPN": 1, "Non-VPN": 0})
+
+# 提取特征和标签
+X = data.drop('class1', axis=1)
+y = data['class1']
+
+
+# 划分训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# 创建随机森林分类器
+rfc = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=10, min_samples_split=2, min_samples_leaf=1,
+                              max_features='auto', bootstrap=True, random_state=42)
+
+# 训练模型
+rfc.fit(X_train, y_train)
+
+# 预测测试集
+y_pred = rfc.predict(X_test)
+
+# 计算准确率
+accuracy = accuracy_score(y_test, y_pred)
+precision = precision_score(y_test,y_pred)
+recall = recall_score(y_test,y_pred)
+f1 = f1_score(y_test,y_pred)
+
+print('Accuracy:', accuracy)
+print("Precision:",precision)
+print("Recall:",recall)
+print("f1_score:",f1)
+print(classification_report(y_test, y_pred))
+# Accuracy: 0.8909026297085999
+# Precision: 0.8626424391746227
+# Recall: 0.9434152913438868
+# f1_score: 0.9012226512226512
+
+# 混淆矩阵可视化
+conf_mat = confusion_matrix(y_test, y_pred)
+sns.heatmap(conf_mat, annot=True, cmap='Blues', fmt='g')
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.show()