Thank you so much Joel,
I understood. Just one more thing please.
How can I include a document against it's highest ranking topic only if it
crosses a threshold?
regards,
Kashyap
On Wed, Apr 29, 2015 at 9:45 AM, Joel Nothman <joel.noth...@gmail.com>
wrote:
> 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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