如何在Gensim Topic建模上预测测试数据

Question

我已经使用Gensim LDAMallet进行主题建模，但是我们可以通过哪种方式预测样本段落并使用预训练模型来获取其主题模型。

# Build the bigram and trigram models
bigram = gensim.models.Phrases(t_preprocess(dataset.data), min_count=5, threshold=100)
bigram_mod = gensim.models.phrases.Phraser(bigram) 

def make_bigrams(texts):
   return [bigram_mod[doc] for doc in texts]

data_words_bigrams = make_bigrams(t_preprocess(dataset.data))

# Create Dictionary
id2word = corpora.Dictionary(data_words_bigrams)

# Create Corpus
texts = data_words_bigrams

# Term Document Frequency
corpus = [id2word.doc2bow(text) for text in texts]

mallet_path='/home/riteshjain/anaconda3/mallet/mallet2.0.8/bin/mallet' 
ldamallet = gensim.models.wrappers.LdaMallet(mallet_path,corpus=corpus, num_topics=12, id2word=id2word, random_seed = 0)

coherence_model_ldamallet = CoherenceModel(model=ldamallet, texts=texts, dictionary=id2word, coherence='c_v')

a = "When Honda builds a hybrid, you've got to be sure it’s a marvel. And an Accord Hybrid is when technology surpasses the known and takes a leap of faith into tomorrow. This is the next generation Accord, the ninth generation to be precise."

如何使用此文本（a）从预训练的模型中获取其主题。 请帮忙。

Answer 1

您将要像处理经过训练的集合一样处理“ a”：

 # import a new data set to be passed through the pre-trained LDA data_new = pd.read_csv('YourNew.csv', encoding = "ISO-8859-1"); data_new = data_new.dropna() data_text_new = data_new[['Your Target Column']] data_text_new['index'] = data_text_new.index documents_new = data_text_new # process the new data set through the lemmatization, and stopwork functions def preprocess(text): result = [] for token in gensim.utils.simple_preprocess(text): if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3: nltk.bigrams(token) result.append(lemmatize_stemming(token)) return result processed_docs_new = documents_new['Your Target Column'].map(preprocess) # create a dictionary of individual words and filter the dictionary dictionary_new = gensim.corpora.Dictionary(processed_docs_new[:]) dictionary_new.filter_extremes(no_below=15, no_above=0.5, keep_n=100000) # define the bow_corpus bow_corpus_new = [dictionary_new.doc2bow(doc) for doc in processed_docs_new] 

然后，您可以将其作为一个函数传递：

 a = ldamallet[bow_corpus_new[:len(bow_corpus_new)]] b = data_text_new topic_0=[] topic_1=[] topic_2=[] for i in a: topic_0.append(i[0][1]) topic_1.append(i[1][1]) topic_2.append(i[2][1]) d = {'Your Target Column': b['Your Target Column'].tolist(), 'topic_0': topic_0, 'topic_1': topic_1, 'topic_2': topic_2} df = pd.DataFrame(data=d) df.to_csv("YourAllocated.csv", index=True, mode = 'a') 

我希望这有帮助 ：）