This shows the newsgroup name and highest scoring topic for each doc.
zip(np.take(dataset.target_names, dataset.target),
np.argmax(nmf.transform(tfidf), axis=1))
I think something based on this should be added to the example.
On 29 April 2015 at 07:01, Andreas Mueller <t3k...@gmail.com> wrote:
> Clusters are one per data point, while topics are not. So the model is
> slightly different.
> You can get the list of topics for each sample using
> NMF().fit_transform(X).
>
>
> On 04/28/2015 01:13 PM, C K Kashyap wrote:
>
> Hi everyone,
> I am new to scikit. I only feel sad for not knowing it earlier - it's
> awesome.
>
> I am trying to do the following. Extract topics from a bunch of tweets.
> I tried NMF (from the sample here -
> http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html)
> but I was not able to figure out how to list documents corresponding to the
> extracted topics. Could someone please point me to an example that lists
> the documents under each topic?
>
> When I got stuck with NMF, I thought of using kmeans (min batch). I am
> just wondering though if clustering is a reasonable approach for "topics".
>
> I'd really appreciate any advice here.
>
> Thanks,
> Kashyap
>
>
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