hi sklearners,

FYI Danny Sullivan https://github.com/dsullivan7
will work at Telecom ParisTech with me as a scikit-learn
engineer starting this summer. These topics
(SGD improvements, averaging, SAG etc.) are part
of the roadmap.

I think he will start by setting up a benchmark for online
supervised estimators on sparse and dense data and
then bench the different stochastic variants.

However I am sure that any work done before he starts
this summer will be useful :)

Before Danny actually starts we'll send an email to the list
to detail the plan and get feedback.

Best,
Alex


On Sat, May 3, 2014 at 7:08 PM, Andy <t3k...@gmail.com> wrote:
> On 05/03/2014 05:37 PM, Mathieu Blondel wrote:
>
> Same feeling as Andy. I'd favor implementing averaging instead.
>
> I totally forgot that we don't have averaging yet ^^
> I'd be in favor of geometric averaging: http://arxiv.org/abs/1212.2002
>
>
>
>
> Mathieu
>
>
> On Sun, May 4, 2014 at 12:23 AM, Andy <t3k...@gmail.com> wrote:
>>
>> Hi Sean.
>> For linear classifiers I'm not really aware of benefits in doing
>> mini-batch training, and I don't think it is widely used (someone correct me
>> if I'm wrong).
>> Usually we only like to add features that have a clear benefit for the
>> users, to prevent scikit-learn from becoming bloated.
>>
>> Do you have a particular use-case where it is important?
>>
>> Cheers,
>> Andy
>>
>>
>> On 05/01/2014 02:04 AM, Sean Violante wrote:
>>
>> Hi
>>
>> I was wondering if there is any interest in implementing minibatch/batch
>> for the SGD algorithm. As I understand it, this is not implemented
>>
>> "There is a compromise between the two forms, which is often called
>> "mini-batches", where the true gradient is approximated by a sum over a
>> small number of training examples."
>>
>> http://en.wikipedia.org/wiki/Stochastic_gradient_descent
>>
>>
>>
>> This would be doing a partial_fit (on small number of training examples)
>> but updating the weights only after each epoch rather than after each
>> training sample
>>
>>
>> as far as I can see it would only require a flag in the sgd_fast.pyx code.
>>
>> thanks
>>
>> Sean
>>
>>
>>
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