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From Phillip Henry <>
Subject Re: Hyperparameter Optimization via Randomization
Date Fri, 29 Jan 2021 15:01:41 GMT
Thanks, Sean! I hope to offer a PR next week.

Not sure about a dependency on the grid search, though - but happy to hear
your thoughts. I mean, you might want to explore logarithmic space evenly.
For example,  something like "please search 1e-7 to 1e-4" leads to a
reasonably random sample being {3e-7, 2e-6, 9e-5}. These are (roughly)
evenly spaced in logarithmic space but not in linear space. So, saying what
fraction of a grid search to sample wouldn't make sense (unless the grid
was warped, of course).

Does that make sense? It might be better for me to just write the code as I
don't think it would be very complicated.

Happy to hear your thoughts.


On Fri, Jan 29, 2021 at 1:47 PM Sean Owen <> wrote:

> I don't know of anyone working on that. Yes I think it could be useful. I
> think it might be easiest to implement by simply having some parameter to
> the grid search process that says what fraction of all possible
> combinations you want to randomly test.
> On Fri, Jan 29, 2021 at 5:52 AM Phillip Henry <>
> wrote:
>> Hi,
>> I have no work at the moment so I was wondering if anybody would be
>> interested in me contributing code that generates an Array[ParamMap] for
>> random hyperparameters?
>> Apparently, this technique can find a hyperparameter in the top 5% of
>> parameter space in fewer than 60 iterations with 95% confidence [1].
>> I notice that the Spark code base has only the brute force
>> ParamGridBuilder unless I am missing something.
>> Hyperparameter optimization is an area of interest to me but I don't want
>> to re-invent the wheel. So, if this work is already underway or there are
>> libraries out there to do it please let me know and I'll shut up :)
>> Regards,
>> Phillip
>> [1]

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