修改稀疏矩阵读取方式,提高建立索引速度,前一次提交复杂度太高
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@ -12,6 +12,7 @@ from sklearn import feature_extraction
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from sklearn.feature_extraction.text import TfidfTransformer
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from sklearn.feature_extraction.text import CountVectorizer
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from tools.show import show
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import numpy as np
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class InverseIndex:
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@ -82,7 +83,10 @@ class InverseIndex:
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#calculate tf-idf
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def CalcTFIDF(self):
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docArray = self.loadDataFromCutFile(100)
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sh = show()
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count = sh.showcount()
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docArray = self.loadDataFromCutFile(count)
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#docArray = self.loadDataFromCutFile(10)
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vectorizer = CountVectorizer()
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transformer = TfidfTransformer()
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tfidf = transformer.fit_transform(vectorizer.fit_transform(docArray))
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@ -99,18 +103,29 @@ class InverseIndex:
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f.close()
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colnum = tfidf.shape[1]
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row = tfidf.shape[0]
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#for i in range(0,colnum):
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# filename = Global.inverse_dir+str(i/Global.filesize)+'.txt'
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# f = open(filename,'a')
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# idx_list = dict()
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# for j in range(0,row):
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# val = tfidf[j,i]
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# if val > 0:
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# idx_list[j+1] = val
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# f.write(json.dumps(idx_list)+'\n')
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# f.close()
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#i表示词项的编号,row表示非零文档所在的行
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for i in range(0,colnum):
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filename = Global.inverse_dir+str(i/Global.filesize)+'.txt'
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coldata = tfidf.getcol(i)
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col_nonzero_index = np.nonzero(coldata)
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item_weight_dict = dict()
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for row in col_nonzero_index[0]:
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item_weight_dict[row+1] = coldata[row][0].data[0]
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f = open(filename,'a')
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idx_list = dict()
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for j in range(0,row):
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val = tfidf[j,i]
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if val > 0:
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idx_list[j+1] = val
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f.write(json.dumps(idx_list)+'\n')
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f.write(json.dumps(item_weight_dict)+'\n')
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f.close()
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print 'item ',i,'calculate done'
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def WriteInverseIndex(self,mat):
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pass
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11
ml/Search.py
11
ml/Search.py
@ -19,7 +19,7 @@ class Search:
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return kw_id
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def getQueryItem(self,searchWord):
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def QuerySingle(self,searchWord):
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idx = self.kw_id[searchWord.decode('utf-8')]
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cut = Cut()
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ii_line = cut.getInverseIndexRow(idx,Global.inverse_dir,Global.filesize)
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@ -29,6 +29,15 @@ class Search:
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data = json.loads(line)
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print data['title'],'\n',data['time'],'\n',data['content'],'\n'
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def getQueryItem(self,searchWord):
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idx = self.kw_id[searchWord.decode('utf-8')]
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cut = Cut()
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ii_line = cut.getInverseIndexRow(idx,Global.inverse_dir,Global.filesize)
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record =json.loads(ii_line)
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for rec in record:
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line = cut.getRow(int(rec),Global.cutnews_origin_dir,Global.filesize)
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data = json.loads(line)
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print data['title'],'\n',data['time'],'\n',data['content'],'\n'
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def getInverseRecord(self,item):
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@ -7,9 +7,9 @@ from ml.Cut import Cut
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s = show()
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#s.showcount()
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#s.shownews(1)
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#s.showKeyWord()
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s.showKeyWord()
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#s.showitem(2608)
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c = Cut()
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line = c.getRow(50,Global.cutnews_origin_dir,Global.filesize)
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s.showitem(line)
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#c = Cut()
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#line = c.getRow(50,Global.cutnews_origin_dir,Global.filesize)
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#s.showitem(line)
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@ -30,6 +30,7 @@ class show:
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c+=1
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f.close()
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print c
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return c
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def showitem(self,line):
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data = json.loads(line)
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@ -42,7 +43,7 @@ class show:
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data = json.loads(line)
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print 'load keyword done.'
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print type(data)
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for k in data.keys():
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print k,data[k]
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print len(data)
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# for k in data.keys():
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# print k,data[k]
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tools/show.pyc
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