Hi Christof.
I gave some comments on the google doc.

Andy

On 03/19/2015 05:12 PM, Christof Angermueller wrote:
> Hi All,
>
> you can find my proposal for the hyperparameter optimization topic here:
> * http://goo.gl/XHuav8
> *
> https://docs.google.com/document/d/1bAWdiu6hZ6-FhSOlhgH-7x3weTluxRfouw9op9bHBxs/edit?usp=sharing
>
> Please give feedback!
>
> Cheers,
> Christof
>
>
> On 20150310 15:27, Sturla Molden wrote:
>> Andreas Mueller <t3k...@gmail.com> wrote:
>>> Does emcee implement Bayesian optimization?
>>> What is the distribution you assume? GPs?
>>> I thought emcee was a sampler. I need to check in with Dan ;)
>> Just pick the mode :-)
>>
>> The distribution is whatever you want it to be.
>>
>> Sturla
>>
>>
>>
>>
>>> On 03/09/2015 09:27 AM, Sturla Molden wrote:
>>>> For Bayesian optimization with MCMC (which I believe spearmint also
>>>> does) I have found that emcee is very nice:
>>>>
>>>> http://dan.iel.fm/emcee/current/
>>>>
>>>> It is much faster than naïve MCMC methods and all we need to do is
>>>> compute a callback that computes the loglikelihood given the parameter
>>>> set (which can just as well be hyperparameters).
>>>>
>>>> To do this computation in parallel one can simply evaluate the walkers
>>>> in parallel and do a barrier synchronization after each step. The
>>>> contention due to the barrier can be reduced by increasing the number of
>>>> walkers as needed. Also one should use something like DCMT for random
>>>> numbers to make sure there are no contention for the PRNG and to ensure
>>>> that each thread (or process) gets an independent stream of random numbers.
>>>>
>>>> emcee implements this kind of optimization using multiprocessing, but it
>>>> passes parameter sets around using pickle and is therefore not very
>>>> efficient compared to just storing the current parameter for each walker
>>>> in shared memory. So there is a lot of room for improvement here.
>>>>
>>>>
>>>> Sturla
>>>>
>>>>
>>>>
>>>> On 07/03/15 15:06, Kyle Kastner wrote:
>>>>> I think finding one method is indeed the goal. Even if it is not the
>>>>> best every time, a 90% solution for 10% of the complexity would be
>>>>> awesome. I think GPs with parameter space warping are *probably* the
>>>>> best solution but only a good implementation will show for sure.
>>>>>
>>>>> Spearmint and hyperopt exist and work for more complex stuff but with
>>>>> far more moving parts and complexity. Having a tool which is easy to use
>>>>> as the grid search and random search modules currently are would be a
>>>>> big benefit.
>>>>>
>>>>> My .02c
>>>>>
>>>>> Kyle
>>>>>
>>>>> On Mar 7, 2015 7:48 AM, "Christof Angermueller"
>>>>> <c.angermuel...@gmail.com
>>>>> <mailto:c.angermuel...@gmail.com>> wrote:
>>>>>
>>>>>        Hi Andreas (and others),
>>>>>
>>>>>        I am a PhD student in Bioinformatics at the University of 
>>>>> Cambridge,
>>>>>        (EBI/EMBL), supervised by Oliver Stegle and Zoubin Ghahramani. In 
>>>>> my
>>>>>        PhD, I apply and develop different machine learning algorithms for
>>>>>        analyzing biological data.
>>>>>
>>>>>        There are different approaches for hyperparameter optimization, 
>>>>> some
>>>>>        of which you mentioned on the topics page:
>>>>>        * Sequential Model-Based Global Optimization (SMBO) ->
>>>>>        http://www.cs.ubc.ca/labs/beta/Projects/SMAC/
>>>>>        * Gaussian Processes (GP) -> Spearmint;
>>>>>        https://github.com/JasperSnoek/spearmint
>>>>>        * Tree-structured Parzen Estimator Approach (TPE) -> Hyperopt:
>>>>>        http://hyperopt.github.io/hyperopt/
>>>>>
>>>>>        And more recent approaches based on neural networks:
>>>>>        * Deep Networks for Global Optimization (DNGO) ->
>>>>>        http://arxiv.org/abs/1502.05700
>>>>>
>>>>>        The idea is to implement ONE of this approaches, right?
>>>>>
>>>>>        Do you prefer a particular approach due to theoretical or practical
>>>>>        reasons?
>>>>>
>>>>>        Spearmint also supports distributing jobs on a cluster (SGE). I
>>>>>        imagine that this requires platform specific code, which could be
>>>>>        difficult to maintain. What do you think?
>>>>>
>>>>>        Spearmint and hyperopt are already established python packages.
>>>>>        Another sklearn implementation might be considered as redundant, 
>>>>> are
>>>>>        hard to establish. Do you have a particular new feature in mind?
>>>>>
>>>>>
>>>>>        Cheers,
>>>>>        Christof
>>>>>
>>>>>        --
>>>>>        Christof Angermueller
>>>>>        cangermuel...@gmail.com
>>>>> <mailto:cangermuel...@gmail.com>
>>>>>        http://cangermueller.com
>>>>>
>>>>>
>>>>>        
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