Yes, this is not a probabilistic method.
On 29 April 2015 at 14:56, C K Kashyap <ckkash...@gmail.com> wrote:
> Works like a charm. Just noticed though that the max value is sometimes
> more than 1.0 .... is that okay?
>
> Regards,
> Kashyap
>
> On Wed, Apr 29, 2015 at 10:12 AM, Joel Nothman <joel.noth...@gmail.com>
> wrote:
>
>> 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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