python实现textrank关键词提取

用python写了一个简单版本的textrank，实现提取关键词的功能。

import numpy as np
import jieba
import jieba.posseg as pseg 

class TextRank(object): 

  def __init__(self, sentence, window, alpha, iternum):
    self.sentence = sentence
    self.window = window
    self.alpha = alpha
    self.edge_dict = {} #记录节点的边连接字典
    self.iternum = iternum#迭代次数 

  #对句子进行分词
  def cutSentence(self):
    jieba.load_userdict('user_dict.txt')
    tag_filter = ['a','d','n','v']
    seg_result = pseg.cut(self.sentence)
    self.word_list = [s.word for s in seg_result if s.flag in tag_filter]
    print(self.word_list) 

  #根据窗口，构建每个节点的相邻节点,返回边的集合
  def createNodes(self):
    tmp_list = []
    word_list_len = len(self.word_list)
    for index, word in enumerate(self.word_list):
      if word not in self.edge_dict.keys():
        tmp_list.append(word)
        tmp_set = set()
        left = index - self.window + 1#窗口左边界
        right = index + self.window#窗口右边界
        if left < 0: left = 0
        if right >= word_list_len: right = word_list_len
        for i in range(left, right):
          if i == index:
            continue
          tmp_set.add(self.word_list[i])
        self.edge_dict[word] = tmp_set 

  #根据边的相连关系，构建矩阵
  def createMatrix(self):
    self.matrix = np.zeros([len(set(self.word_list)), len(set(self.word_list))])
    self.word_index = {}#记录词的index
    self.index_dict = {}#记录节点index对应的词 

    for i, v in enumerate(set(self.word_list)):
      self.word_index[v] = i
      self.index_dict[i] = v
    for key in self.edge_dict.keys():
      for w in self.edge_dict[key]:
        self.matrix[self.word_index[key]][self.word_index[w]] = 1
        self.matrix[self.word_index[w]][self.word_index[key]] = 1
    #归一化
    for j in range(self.matrix.shape[1]):
      sum = 0
      for i in range(self.matrix.shape[0]):
        sum += self.matrix[i][j]
      for i in range(self.matrix.shape[0]):
        self.matrix[i][j] /= sum 

  #根据textrank公式计算权重
  def calPR(self):
    self.PR = np.ones([len(set(self.word_list)), 1])
    for i in range(self.iternum):
      self.PR = (1 - self.alpha) + self.alpha * np.dot(self.matrix, self.PR) 

  #输出词和相应的权重
  def printResult(self):
    word_pr = {}
    for i in range(len(self.PR)):
      word_pr[self.index_dict[i]] = self.PR[i][0]
    res = sorted(word_pr.items(), key = lambda x : x[1], reverse=True)
    print(res) 

if __name__ == '__main__':
  s = '程序员(英文Programmer)是从事程序开发、维护的专业人员。一般将程序员分为程序设计人员和程序编码人员，但两者的界限并不非常清楚，特别是在中国。软件从业人员分为初级程序员、高级程序员、系统分析员和项目经理四大类。'
  tr = TextRank(s, 3, 0.85, 700)
  tr.cutSentence()
  tr.createNodes()
  tr.createMatrix()
  tr.calPR()
  tr.printResult() 

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