熵：H(D)=-Plog2(P)

info(A)=Info(D)-Info_A(D)

打開CSV文件:

分析：屬性 ：age income student credit_rating 類：buys_computer

共14人

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import tree
from sklearn import preprocessing #數據的預處理
from sklearn.externals.six import StringIO
# 打開CSV文件

#分階段展示結果：

featureList=[] #featureList是屬性列表
labelList=[] # labelList是類列表
for row in reader: # 對每行進行循環遍歷
labelList.append(row[len(row)-1])

結果：[{‘age‘: ‘youth‘, ‘income‘: ‘high‘, ‘student‘: ‘no‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘youth‘, ‘income‘: ‘high‘, ‘student‘: ‘no‘, ‘credit_rating‘: ‘excellent‘},

{‘age‘: ‘middle_aged‘, ‘income‘: ‘high‘, ‘student‘: ‘no‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘senior‘, ‘income‘: ‘medium‘, ‘student‘: ‘no‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘senior‘, ‘income‘: ‘low‘, ‘student‘: ‘yes‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘senior‘, ‘income‘: ‘low‘, ‘student‘: ‘yes‘, ‘credit_rating‘: ‘excellent‘},

{‘age‘: ‘middle_aged‘, ‘income‘: ‘low‘, ‘student‘: ‘yes‘, ‘credit_rating‘: ‘excellent‘},

{‘age‘: ‘youth‘, ‘income‘: ‘medium‘, ‘student‘: ‘no‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘youth‘, ‘income‘: ‘low‘, ‘student‘: ‘yes‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘senior‘, ‘income‘: ‘medium‘, ‘student‘: ‘yes‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘youth‘, ‘income‘: ‘medium‘, ‘student‘: ‘yes‘, ‘credit_rating‘: ‘excellent‘},

{‘age‘: ‘middle_aged‘, ‘income‘: ‘medium‘, ‘student‘: ‘no‘, ‘credit_rating‘: ‘excellent‘},

{‘age‘: ‘middle_aged‘, ‘income‘: ‘high‘, ‘student‘: ‘yes‘, ‘credit_rating‘: ‘fair‘},

{‘age‘: ‘senior‘, ‘income‘: ‘medium‘, ‘student‘: ‘no‘, ‘credit_rating‘: ‘excellent‘}]

將14個數據初步轉換成14行列表的形式：便於下一步利用vec.fit_transform(feature).toarray

#Vetorize features
vec=DictVectorizer()
dummyX=vec.fit_transform(featureList).toarray()　　#轉換0，1，scikit庫可以識別
print("dummyX:"+str(dummyX))

print(vec.get_feature_names())
[‘age=middle_aged‘, ‘age=senior‘, ‘age=youth‘, ‘credit_rating=excellent‘, ‘credit_rating=fair‘, ‘income=high‘, ‘income=low‘, ‘income=medium‘, ‘student=no‘, ‘student=yes‘]


# labelList存放類

print("labelList:"+str(labelList))
lb=preprocessing.LabelBinarizer()
dummY=lb.fit_transform(labelList)
print("dummyY:"+str(dummY)
#選擇器
clf=tree.DecisionTreeClassifier()
clf=tree.DecisionTreeClassifier(criterion=‘entropy‘)  #熵 entropy
clf=clf.fit(dummyX,dummY)  
print("clf:"+str(clf))

# Visualize model
with open("allElectronicInformationGainOri.dot", ‘w‘) as f:
f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file=f)

oneRowX = dummyX[0, :].reshape(1,-1)
print("oneRowX: " + str(oneRowX))

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

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

註意維度的變換

決策樹算法學習