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()