如何在Gensim主题建模上预测测试数据

Question

我已经使用Gensim LDAMallet进行主题建模，但是我们可以以何种方式预测示例段落并使用预训练模型获得其主题模型。

# 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( 从预训练模型中获取其主题。请帮忙。

Answer 1

你需要像训练集一样处理'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]

然后你可以把它作为一个函数传递：

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')

我希望这对:)有所帮助