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Bayes.py
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Bayes.py
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import random
import re
import traceback
import jieba
import numpy as np
from sklearn.externals import joblib
from sklearn.naive_bayes import MultinomialNB
jieba.load_userdict("train/word.txt")
stop = [line.strip() for line in open('ad/stop.txt', 'r', encoding='utf-8').readlines()] # 停用词
def build_key_word(path): # 通过词频产生特征
d = {}
with open(path, encoding="utf-8") as fp:
for line in fp:
for word in jieba.cut(line.strip()):
p = re.compile(r'\w', re.L)
result = p.sub("", word)
if not result or result == ' ': # 空字符
continue
if len(word) > 1: # 避免大量无意义的词语进入统计范围
d[word] = d.get(word, 0) + 1
kw_list = sorted(d, key=lambda x: d[x], reverse=True)
size = int(len(kw_list) * 0.2) # 取最前的30%
mood = set(kw_list[:size])
return list(mood - set(stop))
def loadDataSet(path): # 返回每条微博的分词与标签
line_cut = []
label = []
with open(path, encoding="utf-8") as fp:
for line in fp:
temp = line.strip()
try:
sentence = temp[2:].lstrip() # 每条微博
label.append(int(temp[:2])) # 获取标注
word_list = []
sentence = str(sentence).replace('\u200b', '')
for word in jieba.cut(sentence.strip()):
p = re.compile(r'\w', re.L)
result = p.sub("", word)
if not result or result == ' ': # 空字符
continue
word_list.append(word)
word_list = list(set(word_list) - set(stop) - set('\u200b')
- set(' ') - set('\u3000') - set('️'))
line_cut.append(word_list)
except Exception:
continue
return line_cut, label # 返回每条微博的分词和标注
def setOfWordsToVecTor(vocabularyList, moodWords): # 每条微博向量化
vocabMarked = [0] * len(vocabularyList)
for smsWord in moodWords:
if smsWord in vocabularyList:
vocabMarked[vocabularyList.index(smsWord)] += 1
return np.array(vocabMarked)
def setOfWordsListToVecTor(vocabularyList, train_mood_array): # 将所有微博准备向量化
vocabMarkedList = []
for i in range(len(train_mood_array)):
vocabMarked = setOfWordsToVecTor(vocabularyList, train_mood_array[i])
vocabMarkedList.append(vocabMarked)
return vocabMarkedList
def trainingNaiveBayes(train_mood_array, label): # 计算先验概率
numTrainDoc = len(train_mood_array)
numWords = len(train_mood_array[0])
prior_Pos, prior_Neg, prior_Neutral = 0.0, 0.0, 0.0
for i in label:
if i == 1:
prior_Pos = prior_Pos + 1
elif i == 2:
prior_Neg = prior_Neg + 1
else:
prior_Neutral = prior_Neutral + 1
prior_Pos = prior_Pos / float(numTrainDoc)
prior_Neg = prior_Neg / float(numTrainDoc)
prior_Neutral = prior_Neutral / float(numTrainDoc)
wordsInPosNum = np.ones(numWords)
wordsInNegNum = np.ones(numWords)
wordsInNeutralNum = np.ones(numWords)
PosWordsNum = 2.0 # 如果一个概率为0，乘积为0，故初始化1，分母2
NegWordsNum = 2.0
NeutralWordsNum = 2.0
for i in range(0, numTrainDoc):
try:
if label[i] == 1:
wordsInPosNum += train_mood_array[i]
PosWordsNum += sum(train_mood_array[i]) # 统计Pos中语料库中词汇出现的总次数
elif label[i] == 2:
wordsInNegNum += train_mood_array[i]
NegWordsNum += sum(train_mood_array[i])
else:
wordsInNeutralNum += train_mood_array[i]
NeutralWordsNum += sum(train_mood_array[i])
except Exception as e:
traceback.print_exc(e)
pWordsPosicity = np.log(wordsInPosNum / PosWordsNum)
pWordsNegy = np.log(wordsInNegNum / NegWordsNum)
pWordsNeutral = np.log(wordsInNeutralNum / NeutralWordsNum)
return pWordsPosicity, pWordsNegy, pWordsNeutral, prior_Pos, prior_Neg, prior_Neutral
def classify(pWordsPosicity, pWordsNegy, pWordsNeutral, prior_Pos, prior_Neg, prior_Neutral,
test_word_arrayMarkedArray):
pP = sum(test_word_arrayMarkedArray * pWordsPosicity) + np.log(prior_Pos)
pN = sum(test_word_arrayMarkedArray * pWordsNegy) + np.log(prior_Neg)
pNeu = sum(test_word_arrayMarkedArray * pWordsNeutral) + np.log(prior_Neutral)
if pP > pN > pNeu or pP > pNeu > pN:
return pP, pN, pNeu, 1
elif pN > pP > pNeu or pN > pNeu > pP:
return pP, pN, pNeu, 2
else:
return pP, pN, pNeu, 3
def predict(test_word_array, test_word_arrayLabel, testCount, PosWords, NegWords, NeutralWords, prior_Pos, prior_Neg,
prior_Neutral):
errorCount = 0
for j in range(testCount):
try:
pP, pN, pNeu, smsType = classify(PosWords, NegWords, NeutralWords, prior_Pos, prior_Neg, prior_Neutral,
test_word_array[j])
if smsType != test_word_arrayLabel[j]:
errorCount += 1
except Exception as e:
traceback.print_exc(e)
print(errorCount / testCount)
if __name__ == '__main__':
for m in range(1,11):
vocabList = build_key_word("train/train.txt")
line_cut, label = loadDataSet("train/train.txt")
train_mood_array = setOfWordsListToVecTor(vocabList, line_cut)
test_word_array = []
test_word_arrayLabel = []
testCount = 100 # 从中随机选取100条用来测试，并删除原来的位置
for i in range(testCount):
try:
randomIndex = int(random.uniform(0, len(train_mood_array)))
test_word_arrayLabel.append(label[randomIndex])
test_word_array.append(train_mood_array[randomIndex])
del (train_mood_array[randomIndex])
del (label[randomIndex])
except Exception as e:
print(e)
multi=MultinomialNB()
multi=multi.fit(train_mood_array,label)
joblib.dump(multi, 'model/gnb.model')
muljob=joblib.load('model/gnb.model')
result=muljob.predict(test_word_array)
count=0
for i in range(len(test_word_array)):
type=result[i]
if type!=test_word_arrayLabel[i]:
count=count+1
# print(test_word_array[i], "----", result[i])
print("mul",count/float(testCount))
PosWords, NegWords, NeutralWords, prior_Pos, prior_Neg, prior_Neutral = \
trainingNaiveBayes(train_mood_array, label)
predict(test_word_array, test_word_arrayLabel, testCount, PosWords, NegWords, NeutralWords, prior_Pos, prior_Neg,
prior_Neutral)