import xlrd
import jieba
import sys
import importlib
import os         #python内置的包，用于进行文件目录操作，我们将会用到os.listdir函数
import pickle    #导入cPickle包并且取一个别名pickle #持久化类
import random
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pylab import mpl
from sklearn.naive_bayes import MultinomialNB # 导入多项式贝叶斯算法包
from sklearn import svmfrom sklearn import metrics
from sklearn.datasets.base import Bunch
from sklearn.feature_extraction.text import TfidfVectorizer
importlib.reload(sys)#把内容和类别转化成一个向量的形式
trainContentdatasave=[] #存储所有训练和测试数据的分词
testContentdatasave=[]trainContentdata = []
testContentdata = []
trainlabeldata = []
testlabeldata = []#导入文本描述的训练和测试数据
def importTrainContentdata():file = '20180716_train.xls'wb = xlrd.open_workbook(file)ws = wb.sheet_by_name("Sheet1")for r in range(ws.nrows):trainContentdata.append(ws.cell(r, 0).value)def importTestContentdata():file = '20180716_test.xls'wb = xlrd.open_workbook(file)ws = wb.sheet_by_name("Sheet1")for r in range(ws.nrows):testContentdata.append(ws.cell(r, 0).value)   #导入类别的训练和测试数据
def importTrainlabeldata():file = '20180716_train_label.xls'wb = xlrd.open_workbook(file)ws = wb.sheet_by_name("Sheet1")for r in range(ws.nrows):trainlabeldata.append(ws.cell(r, 0).value)def importTestlabeldata():file = '20180716_test_label.xls'wb = xlrd.open_workbook(file)ws = wb.sheet_by_name("Sheet1")for r in range(ws.nrows):testlabeldata.append(ws.cell(r, 0).value)if __name__=="__main__": importTrainContentdata()importTestContentdata()importTrainlabeldata()importTestlabeldata()'''贝叶斯clf = MultinomialNB(alpha=0.052).fit(train_set.tdm, train_set.label)  #clf = svm.SVC(C=0.7, kernel='poly', gamma=10, decision_function_shape='ovr')clf.fit(train_set.tdm, train_set.label)  predicted=clf.predict(test_set.tdm)逻辑回归tv = TfidfVectorizer()train_data = tv.fit_transform(X_train)test_data = tv.transform(X_test)lr = LogisticRegression(C=3)lr.fit(train_set.tdm, train_set.label)predicted=lr.predict(test_set.tdm)print(lr.score(test_set.tdm, test_set.label))#print(test_set.tdm)#SVMclf = SVC(C=1500)clf.fit(train_set.tdm, train_set.label)predicted=clf.predict(test_set.tdm)print(clf.score(test_set.tdm, test_set.label))'''tv = TfidfVectorizer()train_data = tv.fit_transform(trainContentdata)test_data = tv.transform(testContentdata)clf = SVC(C=1500)clf.fit(train_data, trainlabeldata)print(clf.score(test_data, testlabeldata))a=[]b=[]for i in range(len(predicted)):b.append((int)(float(predicted[i])))a.append(int(test_set.label[i][0]))'''f=open('F:/goverment/ArticleMining/predict.txt', 'w')for i in range(len(predicted)):f.write(str(b[i]))f.write('\n')f.write("写好了")f.close()#for i in range(len(predicted)):#print(b[i])'''#metrics_result(a, b)