python教程分享python sklearn 画出决策树并保存为PDF的实现过程

import numpy as np  import pandas as pd  from sklearn import tree  import graphviz  # x，y是sklearn中需要拟合的数据  x = np.array(exam_train)  y = np.array(classes_train)  clf = tree.decisiontreeclassifier(criterion='entropy', class_weight='balanced', max_depth=25)  clf = clf.fit(x, y)  dot_data = tree.export_graphviz(clf, out_file=none, feature_names=none, filled=true, rounded=true)  # 重要参数可定制  graph = graphviz.source(dot_data)  graph.render(view=true, format="pdf", filename="decisiontree_pdf")

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# -*- coding:utf-8*-  # 将字典 转化为 sklearn 用的数据形式 数据型 矩阵  from sklearn.feature_extraction import dictvectorizer  import csv  from sklearn import preprocessing  from sklearn import tree    allelectrOnicsdata= open('c:/pic/data/tree.csv','rb')  reader = csv.reader(allelectronicsdata)  header = reader.next()  # print header  ## 数据预处理  featurelist = []  labellist = []  for row in reader:      # print row[-1]      labellist.append(row[-1])      # 下面这几步的目的是为了让特征值转化成一种字典的形式，就可以调用sk-learn里面的dictvectorizer，直接将特征的类别值转化成0,1值      rowdict = {}      for i in range(1, len(row) - 1):          rowdict[header[i]] = row[i]      featurelist.append(rowdict)    for each in featurelist:      print each    # vectorize features  vec = dictvectorizer()  dummyx = vec.fit_transform(featurelist).toarray()  print("dummyx:"+str(dummyx))  print(vec.get_feature_names())    # label的转化，直接用preprocessing的labelbinarizer方法  lb = preprocessing.labelbinarizer()  dummyy = lb.fit_transform(labellist)  print("dummyy:"+str(dummyy))  print("labellist:"+str(labellist))    #criterion是选择决策树节点的 标准 ，这里是按照“熵”为标准，即id3算法；默认标准是gini index，即cart算法。  clf = tree.decisiontreeclassifier()  clf = clf.fit(dummyx,dummyy)  print("clf:"+str(clf))  # 导入相关函数，可视化决策树  # 导出的结果是一个dot文件(在系统默认路劲)，需要安装graphviz才能将它住哪华为pdf或png格式  # 输出的dot文件可以使用graphvize软件转为pdf，graphvize安装目录中的bin目录放入到环境变量的path中  # 使用如下命令  #cmd  # dot -tpdf  c:/tree.dot -o c:/tree.pdf  #下载地址：http://www.graphviz.org/download_windows.php  #生成dot文件  with open("c:/tree.dot",'w') as f:      f = tree.export_graphviz(clf, feature_names= vec.get_feature_names(),out_file= f)