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