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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