Hi Jan.
That sounds great!
Please share early version :) [not that I'd have time to review them :-/]
I think breaking backward-compatibility will be necessary, and we should 
think of how we should go about that.

Cheers,
Andy


On 02/13/2015 12:34 AM, Jan Hendrik Metzen wrote:
> Having Bayesian optimization in sklearn would be great +1
>
> I was working recently on a sklearn-compatible rewrite of Gaussian
> processes.  Main features are gradient-based hyperparameter
> optimization, kernel engineering and Gaussian process classification.
> The downside is that it is not completely downward compatible with
> sklearn's current GP interface. I will create a PR in the next days
> where we can discuss the further proceeding (going for merge versus
> adding it to the sklearn-extensions).
>
> Best,
> Jan
>
> On 13.02.2015 00:10, Andy wrote:
>> Sorry, I was using a possibly confusing idiom. The problem with our GP
>> is not so much speed as interface and flexibility.
>> Also, we are not using gradient based parameter optimization.
>>
>> On 02/12/2015 05:48 PM, Artem wrote:
>>> Do you have any particular ideas on how one could speedup GPs,
>>> besides reimplementing it in Cython? Looks like spearmint is
>>> completely pythonic, so they either as slow (or slower), or use
>>> different algorithm (I'm not very familiar with approaches to GPs).
>>>
>>> On Fri, Feb 13, 2015 at 12:41 AM, Andy <t3k...@gmail.com
>>> <mailto:t3k...@gmail.com>> wrote:
>>>
>>>
>>>      On 02/12/2015 04:47 AM, Artem wrote:
>>>>      There are several packages (spearmint, hyperopt, MOE) offering
>>>>      Bayesian Optimization to the problem of choosing
>>>>      hyperparameters. Wouldn't it be nice to add such *Search[CV] to
>>>>      sklearn?
>>>      Yes. I haven't really looked much into the spearmint approach,
>>>      but before we could do anything with GPs I am afraid we need to
>>>      get our GP up to speed.
>>>
>>>      
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