Highest ranking topic for each doc is just np.argmax(nmf.transform(tfidf),
axis=1).
This is because nmf.transform
<http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html#sklearn.decomposition.NMF.transform>(tfidf)
returns a matrix of shape (num samples, num components / topics) scoring
each topic per sample. An argmax over axis 1 indicates the highest scoring
topic per sample.
On 29 April 2015 at 11:44, C K Kashyap <ckkash...@gmail.com> wrote:
> Thanks Joel and Andreas,
>
> Joel, I think "highest ranking topic for each doc" is exactly what I am
> looking for. Could you elaborate on the code please?
>
> What would be dataset.target_names and dataset.target in my case -
> http://lpaste.net/131649
>
> Regards,
> Kashyap
>
> On Wed, Apr 29, 2015 at 3:08 AM, Joel Nothman <joel.noth...@gmail.com>
> wrote:
>
>> 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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