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 svm

from 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)

    #SVM

    clf = 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)