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TFIDF.py
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from departing_and_reducing_stopwords import Departing_word as dwd
from sklearn.feature_extraction.text import TfidfTransformer, TfidfVectorizer, CountVectorizer
from sklearn import feature_extraction
import numpy as np
from pprint import pprint
'''
#np.set_printoptions(threshold=np.inf)
word_raw = dwd('stopwords.txt', 'verse.txt', 'out.txt') # 测试文本
word_raw.departing()
words = word_raw.get_words()
string = " ".join(words)
words_text = []
words_text.append(string)
corpus_raw = dwd('stopwords.txt', 'corpus.txt', 'corpus_out.txt') # 自制小语料库测试
corpus_raw.departing()
corpus = corpus_raw.get_words()
# 构建tfidf模型
#print(voc_list)
tfidf_vec = TfidfVectorizer(token_pattern=r"(?u)\b\w\w+\b",max_df=0.15,min_df=0.002)
#tfidf_model = tfidf_vec.fit(corpus)
#words_text.extend(corpus_text)
#print(words_text)
#tfidf_matrix = tfidf_vec.fit_transform(words)
tfidf_vec.fit(corpus)
tfidf_matrix = tfidf_vec.transform(words)
#print(tfidf_vec.inverse_transform(tfidf_matrix))
for i in tfidf_matrix.toarray():
print(i)
print(tfidf_matrix.toarray())
# print(tfidf_vec.get_feature_names())
# 词表
wordlist = tfidf_vec.get_feature_names()
#print(tfidf_vec.vocabulary_)
#print(tfidf_matrix.toarray())
print(type(wordlist))
#print(wordlist)
#print(len(wordlist))
# 权值表
weightlist = tfidf_matrix.toarray()
#print(len(weightlist))
print(type(weightlist))
# tf-idf矩阵中 元素a[i][j]表示j词在i类文本中的tfidf权重
# 写入文本
with open("tfidf_word.txt", 'w', encoding='utf-8')as f:
for i in range(len(weightlist)):
f.write("第"+str(i)+"段文本："+"\n")
# print("------第",i,'段文本词语的tfidf权重')
for j in range(len(wordlist)):
#print(wordlist[j],weightlist[i][j])
f.write(wordlist[j]+" "+str(weightlist[i][j])+"\n")
dict_final = {}
# count2=0
for i in range(len(weightlist)):
for j in range(len(wordlist)):
try:
dict_final[wordlist[j]]+=weightlist[i][j]
except:
#print(wordlist[j])
# count2+=1
dict_final[wordlist[j]]=0
dict_final[wordlist[j]] += weightlist[i][j]
#print(wordlist[j],weightlist[i][j])
#print('?')
#dict_final[wordlist[j]] = weightlist[i][j]
dict_final_sort = sorted(dict_final.items(), key=lambda x: x[1], reverse=True)
#print(dict_final_sort)
#print(dict(dict_final_sort))
with open("main_txt.txt",'w')as f:
for i in list(dict_final_sort):
every=str(i)
# print(every)
f.write(every+'\n')
'''
class TextVectorizer:
def __init__(self,corpus_name,text_name,stopwords,corpus_output,text_output,max_df,min_df):
self.tfidf_vec = TfidfVectorizer(token_pattern=r"(?u)\b\w\w+\b",max_df=max_df,min_df=min_df)
self.corpus_name = corpus_name
self.text_name = text_name
self.stopwords = stopwords
self.corpus_output = corpus_output
self.text_output = text_output
def init_corpus_and_text(self):
corpus_raw = dwd(self.stopwords, self.corpus_name, self.corpus_output) # 自制小语料库测试
corpus_raw.departing()
corpus = corpus_raw.get_words()
self.corpus = corpus
word_raw = dwd(self.stopwords, self.text_name, self.text_output) # 测试文本
word_raw.departing()
words = word_raw.get_words()
self.text = words
def fitting(self):
self.tfidf_vec.fit(self.corpus)
def transforming(self):
self.tfidf_matrix = self.tfidf_vec.transform(self.text)
def getWordList(self):
return self.tfidf_vec.get_feature_names()
def getWeightList(self):
return self.tfidf_matrix.toarray()
def writeInFile(self,filename):
weightlist = self.getWeightList()
wordlist = self.getWordList()
with open(filename, 'w', encoding='utf-8')as f:
for i in range(len(weightlist)):
f.write("第"+str(i)+"段文本："+"\n")
# print("------第",i,'段文本词语的tfidf权重')
for j in range(len(wordlist)):
#print(wordlist[j],weightlist[i][j])
f.write(wordlist[j]+" "+str(weightlist[i][j])+"\n")
def getDict(self):
dict_final = {}
weightlist = self.getWeightList()
wordlist = self.getWordList()
for i in range(len(weightlist)):
for j in range(len(wordlist)):
try:
dict_final[wordlist[j]]+=weightlist[i][j]
except:
dict_final[wordlist[j]]=0
dict_final[wordlist[j]] += weightlist[i][j]
dict_final_sort = sorted(dict_final.items(), key=lambda x: x[1], reverse=True)
return dict_final_sort
def writeTfidfInFileSorted(self,filename):
dict_ = self.getDict()
with open(filename,'w')as f:
for i in list(dict_):
every=str(i)
# print(every)
f.write(every+'\n')
def getTextVector(self):
feature_name = []
feature_tfidf = []
textvector = []
dict_ = self.getDict()
for i in dict_:
#tuple_ = ()
if i[1] > 0:
feature_name.append(i[0])
feature_tfidf.append(i[1])
#tuple_ = (i)
textvector.append(i)
return textvector,feature_name,feature_tfidf
'''
corpus_raw = dwd('stopwords.txt', 'corpus.txt', 'corpus_out.txt') # 自制小语料库测试
corpus_raw.departing()
corpus = corpus_raw.get_words()
word_raw = dwd('stopwords.txt', 'verse.txt', 'out.txt') # 测试文本
word_raw.departing()
words = word_raw.get_words()
'''
if __name__ == "__main__":
'''
tv = TextVectorizer('corpus.txt','verse.txt','stopwords.txt','corpus_output.txt','text_output.txt',max_df=0.15,min_df=0.0002)
tv.init_corpus_and_text()
tv.fitting()
tv.transforming()
wordlist = tv.getWordList()
weightlist = tv.getWeightList()
dict_ = tv.getDict()
tv.writeInFile("file1.txt")
tv.writeTfidfInFileSorted("file2.txt")
textvector,feature_name,feature_tfidf = tv.getTextVector()
print(textvector)
print(feature_name)
print(feature_tfidf)
#print(wordlist)
#for i in weightlist:
#print(i)
#print(dict_)
#print(feature_name)
#print(len(feature_name))
'''
pass