机器学习----决策树（二）

from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import preprocessing
from sklearn import tree
from sklearn.externals.six import StringIO

allElectronicsData = open("F:\\tree.csv","r")
reader = csv.reader(allElectronicsData)
headers = next(reader)
#第一行数据内容
# 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(labelList)
# print(featureList)

vec = DictVectorizer()
dummyX = vec.fit_transform(featureList).toarray()
print("dummyX:\n"+str(dummyX))
# print(vec.get_feature_names())
# print("labelList:\n" + str(labelList))

lb = preprocessing.LabelBinarizer()
dummyY = lb.fit_transform(labelList)
print("dummyY:\n" + str(dummyY))

#训练分类器
clf = tree.DecisionTreeClassifier(criterion = 'entropy')
clf = clf.fit(dummyX,dummyY)
print("clf:\n" + str(clf))


with open("allElectronicsInformationGainOri.dot","w") as f:
    f = tree.export_graphviz(clf,feature_names = vec.get_feature_names(),out_file=f)

oneRowX = dummyX[0,:]
print("oneRowX:\n" + str(oneRowX))


newRow = oneRowX
newRow[0] = 1
newRow[2] = 0
print("oneRowX:\n" + str(oneRowX))

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