mask with np.max(..., axis=1) > threshold
On 29 April 2015 at 14:35, C K Kashyap <ckkash...@gmail.com> wrote:
> 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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