title: python - sklearn Latent Dirichlet Allocation Transform v. Fittransform
body: <p>I am using sklearn's NMF and LDA sub-modules to analyze unlabeled text. I read the documentation but I am not sure if the transform functions in these modules (NMF and LDA) are the same as the posterior function in R's topicmodels (please see <a href="https://stackoverflow.com/questions/16115102/predicting-lda-topics-for-new-data">Predicting LDA topics for new data</a>). Basically, I am looking for a function that will allow me to predict the topics in test set using the model trained on training set data. I predicted topics on the entire dataset. Then I split the data into train and test sets, trained a model on train set and transformed test set using that model. though it was expected that I would not get the same results, comparing the two runs topics is not assuring me that the transform function serves the same function as R's package. I would appreciate your response.</p>
  
  <p>thank you</p>