python使用sklearn實現決策樹的方法示例

阿新 • • 發佈：2020-01-09

1. 基本環境

安裝 anaconda 環境，由於國內登陸不了他的官網 https://www.continuum.io/downloads,不過可以使用國內的映象站點： https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/

新增繪圖工具 Graphviz http://www.graphviz.org/Download_windows.php

安裝後，將bin 目錄內容新增到環境變數path 即可

參考blog ： https://www.jb51.net/article/169878.htm

官網技術文件： http://scikit-learn.org/stable/modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart

2. 遇到的一些問題

csv 檔案讀取 https://docs.python.org/3.5/library/csv.html?highlight=csv#module-csv

https://docs.python.org/2/library/csv.html?highlight=csv#module-csv

3. 實現

資料檔案：

這裡寫圖片描述

這是一個給定 4 個屬性， age， income， student,credit_rating 以及一個標記屬性 class_buys_computer 的資料集，我們需要根據這個資料集進行分析並構建一顆決策樹

程式碼實現：

核心就是呼叫 tree 的 DecisionTreeClassifier 方法對資料進行訓練得到一顆決策樹

# -*- coding: utf-8 -*-
"""
Created on Sun Dec 25 11:25:40 2016

@author: Administrator
"""

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import tree
from sklearn import preprocessing
from sklearn.externals.six import StringIO
import pydotplus
from IPython.display import Image

# Read in the csv file and put features into list of dict and list of class label
allElectornicsData = open('AllElectronics.csv','r')
reader = csv.reader(allElectornicsData)
# headers = reader.next()  python2.7 supported  本質獲取csv 檔案的第一行資料
#headers = reader.__next__()  python 3.5.2 
headers = next(reader)

print(headers)

featureList = []
labelList = []

for row in reader:
  labelList.append(row[len(row) - 1])
  rowDict = {}
  for i in range(1,len(row) - 1):
    rowDict[headers[i]] = row[i]
  featureList.append(rowDict)

print(featureList)
print(labelList)

# Vetorize features
vec = DictVectorizer()
dummyX = vec.fit_transform(featureList).toarray()

print("dummyX: " + str(dummyX))
print(vec.get_feature_names())
print("labelList: " + str(labelList))

# vectorize class labels
lb = preprocessing.LabelBinarizer()
dummyY = lb.fit_transform(labelList)
print("dummyY: ",str(dummyY))

# Using decision tree for classification    ===========【此處呼叫為演算法核心】============
#clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = tree.DecisionTreeClassifier(criterion='gini')
clf = clf.fit(dummyX,dummyY)
print("clf: ",str(clf))

# Visualize model
# dot -Tpdf iris.dot -o ouput.pdf
with open("allElectronicInformationGainOri.dot",'w') as f:
  f = tree.export_graphviz(clf,feature_names = vec.get_feature_names(),out_file = f)


# predict
oneRowX = dummyX[0,:]
print("oneRowX: " + str(oneRowX))

newRowX = oneRowX
newRowX[0] = 1
newRowX[2] = 0
print("newRowX: " + str(newRowX))

predictedY = clf.predict(newRowX)
print("predictedY: " + str(predictedY))

輸出結果：

ID3 演算法

這裡寫圖片描述