Use scikit-learn TfIdf with gensim LDA

Question

I've used various versions of TFIDF in scikit learn to model some text data. ``` vectorizer = TfidfVectorizer(min_df=1,stop_words='english') ``` The resulting data X is in this format: ``` ' with xyz stored elements in Compressed Sparse Row format> ``` I wanted to experiment with LDA as a way to do reduce dimensionality of my sparse matrix. Is there a simple way to feed the NumPy sparse matrix X into a gensim LDA model? ``` lda = models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=100) ``` I can ignore scikit and go the way the gensim tutorial outlines, but I like the simplicity of the scikit vectorizers and all of its parameters.

Accepted Answer

http://radimrehurek.com/gensim/matutils.html

```
class gensim.matutils.Sparse2Corpus(sparse, documents_columns=True)

Convert a matrix in scipy.sparse format into a streaming gensim corpus.
```

Use scikit-learn TfIdf with gensim LDA

Solution

Problem