如何修复LDA模型一致性分数运行时错误？

Question

text='Alice 是一名学生。她喜欢学习。老师布置了很多家庭作业。'

我正在尝试从具有一致性分数的简单文本（如上）中获取主题。这是我的 LDA 模型：

id2word = corpora.Dictionary(data_lemmatized)
texts = data_lemmatized
corpus = [id2word.doc2bow(text) for text in texts]

lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                           id2word=id2word,
                                           num_topics=5, 
                                           random_state=100,
                                           update_every=1,
                                           chunksize=100,
                                           passes=10,
                                           alpha='auto',
                                           per_word_topics=True)
# Print the Keyword in the 10 topics
pprint(lda_model.print_topics())
doc_lda = lda_model[corpus]

当我尝试运行这个一致性模型时：

coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, 
coherence='c_v')
coherence_lda = coherence_model_lda.get_coherence()
print('\nCoherence Score: ', coherence_lda)

我应该得到这个输出之王->一致性分数：0.532947587081

我收到此错误： raise RuntimeError(''' RuntimeError: 在当前进程完成引导阶段之前尝试启动新进程。

    This probably means that you are not using fork to start your
    child processes and you have forgotten to use the proper idiom
    in the main module:

        if __name__ == '__main__':
            freeze_support()
            ...

    The "freeze_support()" line can be omitted if the program
    is not going to be frozen to produce an executable.

我应该怎么做才能解决这个问题？

Answer 1

我也遇到过同样的问题。在 if__name__==" main "中添加“一致性模型”为我解决了这个问题。

if __name__ == "__main__":

     coherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, 
                                                          dictionary=id2word, 
                                                              coherence='c_v')
     coherence_lda = coherence_model_lda.get_coherence()
     print('\nCoherence Score: ', coherence_lda)