Python决策树和随机森林算法实例详解

#决策树的helloworld 使用决策树对iris数据集进行分类from sklearn.datasets import load_irisfrom sklearn import treeimport pydotplus#导入iris数据集iris = load_iris()#初始化DecisionTreeClassifierclf = tree.DecisionTreeClassifier()#适配数据clf = clf.fit(iris.data, iris.target)#将决策树以pdf格式可视化dot_data = tree.export_graphviz(clf, out_file=None)graph = pydotplus.graph_from_dot_data(dot_data)graph.write_pdf("iris.pdf")

#使用决策树算法检测POP3暴力破解import reimport matplotlib.pyplot as pltfrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.model_selection import cross_val_scoreimport osfrom sklearn.datasets import load_irisfrom sklearn import treeimport pydotplus#加载kdd数据集def load_kdd99(filename):  X=[]  with open(filename) as f:    for line in f:      line = line.strip('/n')      line = line.split(',')      X.append(line)  return X#找到训练数据集def get_guess_passwdandNormal(x):  v=[]  features=[]  targets=[]  #找到标记为guess-passwd和normal且是POP3协议的数据  for x1 in x:    if ( x1[41] in ['guess_passwd.','normal.'] ) and ( x1[2] == 'pop_3' ):      if x1[41] == 'guess_passwd.':        targets.append(1)      else:        targets.append(0)    #挑选与POP3密码破解相关的网络特征和TCP协议内容的特征作为样本特征      x1 = [x1[0]] + x1[4:8]+x1[22:30]      v.append(x1)  for x1 in v :    v1=[]    for x2 in x1:      v1.append(float(x2))    features.append(v1)  return features,targetsif __name__ == '__main__':  v=load_kdd99("../../data/kddcup99/corrected")  x,y=get_guess_passwdandNormal(v)  clf = tree.DecisionTreeClassifier()  print(cross_val_score(clf, x, y, n_jobs=-1, cv=10))  clf = clf.fit(x, y)  dot_data = tree.export_graphviz(clf, out_file=None)  graph = pydotplus.graph_from_dot_data(dot_data)  graph.write_pdf("POP3Detector.pdf")

from sklearn.model_selection import cross_val_scorefrom sklearn.datasets import make_blobsfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.ensemble import ExtraTreesClassifierfrom sklearn.tree import DecisionTreeClassifierX,y = make_blobs(n_samples = 10000,n_features=10,centers = 100,random_state = 0)clf = DecisionTreeClassifier(max_depth = None,min_samples_split=2,random_state = 0)scores = cross_val_score(clf,X,y)print("决策树准确率；",scores.mean())clf = RandomForestClassifier(n_estimators=10,max_depth = None,min_samples_split=2,random_state = 0)scores = cross_val_score(clf,X,y)print("随机森林准确率：",scores.mean())

# -*- coding:utf-8 -*-#使用随机森林算法检测FTP暴力破解import reimport matplotlib.pyplot as pltfrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.model_selection import cross_val_scoreimport osfrom sklearn import treeimport pydotplusimport numpy as npfrom sklearn.ensemble import RandomForestClassifierdef load_one_flle(filename):  x=[]  with open(filename) as f:    line=f.readline()    line=line.strip('/n')  return linedef load_adfa_training_files(rootdir):  x=[]  y=[]  list = os.listdir(rootdir)  for i in range(0, len(list)):    path = os.path.join(rootdir, list[i])    if os.path.isfile(path):      x.append(load_one_flle(path))      y.append(0)  return x,ydef dirlist(path, allfile):  filelist = os.listdir(path)  for filename in filelist:    filepath = path+filename    if os.path.isdir(filepath):      #处理路径异常      dirlist(filepath+'/', allfile)    else:      allfile.append(filepath)  return allfiledef load_adfa_hydra_ftp_files(rootdir):  x=[]  y=[]  allfile=dirlist(rootdir,[])  for file in allfile:    #正则表达式匹配hydra异常ftp文件    if re.match(r"../../data/ADFA-LD/Attack_Data_Master/Hydra_FTP_/d+/UAD-Hydra-FTP*",file):      x.append(load_one_flle(file))      y.append(1)  return x,yif __name__ == '__main__':  x1,y1=load_adfa_training_files("../../data/ADFA-LD/Training_Data_Master/")  x2,y2=load_adfa_hydra_ftp_files("../../data/ADFA-LD/Attack_Data_Master/")  x=x1+x2  y=y1+y2  vectorizer = CountVectorizer(min_df=1)  x=vectorizer.fit_transform(x)  x=x.toarray()  #clf = tree.DecisionTreeClassifier()  clf = RandomForestClassifier(n_estimators=10, max_depth=None,min_samples_split=2, random_state=0)  clf = clf.fit(x,y)  score = cross_val_score(clf, x, y, n_jobs=-1, cv=10)  print(score)  print('平均正确率为：',np.mean(score))