FYI, I've used sklearn's LogisticRegression in an online/real-time text
classification app without having to dig into the internals and gotten
~2.5ms response time (including vectorizing; vocab size ~200k).
On 23 September 2013 06:37, Peter Prettenhofer <peter.prettenho...@gmail.com
> wrote:
> We don't have a PMML interface yet [1] - so you need to write custom code
> to extract internal state each individual classifier.
>
> What do you mean by performance critical (<1ms, <<1ms)? Do you make
> predictions per sample or can you buffer samples and make predictions for
> batches?
> In general, what kills performance is the overhead of python function
> calls - its usually way larger than the actual prediction (which usually
> happens in C-land).
>
> [1] http://en.wikipedia.org/wiki/Predictive_Model_Markup_Language
>
>
> 2013/9/23 Fred Baba <fred.b...@gmail.com>
>
>> I'd like to use classifiers trained via sklearn in a real-time
>> application, performance critical application. How do I access the internal
>> representation of trained classifiers?
>>
>> For linear classifiers/regressions, I can simply store the coefficients
>> and generate the linear combination myself. For tree regressions, I can use
>> sklearn.tree.export_graphviz. Ideally there would be an export facility for
>> all classifiers (particularly for examining the structure of generated
>> models). Is there a general solution way to do this?
>>
>>
>>
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>
>
> --
> Peter Prettenhofer
>
>
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