topic model:如何按主题模型topic查询文档?

下面我创建了一个完全可复制的示例来计算给定数据帧的主题模型。

import numpy as np  
import pandas as pd

data = pd.DataFrame({'Body': ['Here goes one example sentence that is generic',
                  'My car drives really fast and I have no brakes',
                  'Your car is slow and needs no brakes', 
                  'Your and my vehicle are both not as fast as the airplane']})

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer(lowercase = True, analyzer = 'word')

data_vectorized = vectorizer.fit_transform(data.Body)
lda_model = LatentDirichletAllocation(n_components=4, 
                                      learning_method='online', 
                                      random_state=0,
                                      verbose=1)
lda_topic_matrix = lda_model.fit_transform(data_vectorized)

问题: 如何按主题筛选文档?如果是,文档是否可以有多个主题标记,或者是否需要阈值?

最后,我喜欢用“1”标记每个文档,这取决于它是否具有主题2和主题3的高负载,否则为“0”。