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.
Phillip
On Fri, Jan 29, 2021 at 1:47 PM Sean Owen <sro...@gmail.com> 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 <londonjava...@gmail.com>
> 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]
>> https://www.oreilly.com/library/view/evaluating-machine-learning/9781492048756/ch04.html
>>
>
