@Fred. It seems very interesting. Could you please provide more information
on how you achieved this?
On Mon, Sep 23, 2013 at 5:46 PM, Fred Mailhot <[email protected]>wrote:
> 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 <
> [email protected]> 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 <[email protected]>
>>
>>> 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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--
Regards
Abhishek Thakur
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