基于机器学习的异常流量监测代码实现

import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
import pydotplus
import matplotlib.pyplot as plt
import graphviz
# from sklearn.externals import joblib
from sklearn.metrics import roc_curve, auc, precision_score, recall_score, f1_score, accuracy_score

（1）KNN

clf = KNeighborsClassifier(algorithm='kd_tree', n_neighbors=8)
clf.fit(a, b)

（2）RF

clf = RandomForestClassifier(n_estimators=9, max_depth=7, min_samples_split=3, random_state=0)
clf.fit(a, b)

（3）DT

clf = tree.DecisionTreeClassifier(criterion='gini', max_depth=7, min_samples_leaf=4, min_samples_split=4)
clf.fit(a, b)

（4）SVM

clf = svm.SVC(kernel='rbf', decision_function_shape='ovr', probability=True)
clf.fit(a, b)

检测结果分析TP、FP、ACC、Precision、Recall、F1值等

if __name__ == "__main__":
　　dataSet = pd.read_excel("link_states_train_medium.xlsx")
　　labelsp = list(dataSet.columns.values)

　　labels = labelsp[0:4]

　　clf = fit_model(dataSet, labels)
　　data = pd.read_excel("link_states_test_medium.xlsx")

　　y_true = data.iloc[:, -1]
　　y_pred = clf.predict(data.iloc[:, :-1])
　　prob = clf.predict_proba(data.iloc[:, :-1])[:, 1]
　　print(prob.tolist())

　　tp = sum(y_true & y_pred)
　　fp = sum((y_true == 0) & (y_pred == 1))
　　tn = sum((y_true == 0) & (y_pred == 0))
　　fn = sum((y_true == 1) & (y_pred == 0))
　　tpr = tp / (tp + fn)
　　fpr = fp / (fp + tn)
　　tnr = tn / (fp + tn)
　　fnr = fn / (tp + fn)
　　accuracy = accuracy_score(y_true, y_pred)
　　precision = precision_score(y_true, y_pred)
　　recall = recall_score(y_true, y_pred)
　　f1_score = f1_score(y_true, y_pred)


　　print("TP: " + str(tp))
　　print("FP: " + str(fp))
　　print("TN: " + str(tn))
　　print("FN: " + str(fn))
　　print("TPR: " + str(tpr))
　　print("FPR: " + str(fpr))
　　print("TNR: " + str(tnr))
　　print("FNR: " + str(fnr))
　　print("Accuracy: " + str(accuracy))
　　print("Precision: " + str(precision))
　　print("Recall: " + str(recall))
　　print("F1 Score: " + str(f1_score))

　　print(y_true.tolist())
　　# print(y_pred.tolist())

　　fpr, tpr, thresholds_keras = roc_curve(y_true, prob)
　　auc = auc(fpr, tpr)
　　print("AUC : ", auc)
　　plt.figure()
　　plt.plot([0, 1], [0, 1], 'k--')
　　plt.plot(fpr, tpr, label='SVM (area = {:.3f})'.format(auc))
　　plt.xlabel('False positive rate')
　　plt.ylabel('True positive rate')
　　plt.title('ROC curve')
　　plt.legend(loc='best')
　　# plt.savefig("../images/ROC/ROC_2分类.png")
　　plt.show()