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From Sean Owen <>
Subject Re: Hyperparameter Optimization via Randomization
Date Sat, 30 Jan 2021 14:00:09 GMT
I was thinking ParamGridBuilder would have to change to accommodate a
continuous range of values, and that's not hard, though other code wouldn't
understand that type of value, like the existing simple grid builder.
It's all possible just wondering if simply randomly sampling the grid is
enough. That would be a simpler change, just a new method or argument.

Yes part of it is that if you really want to search continuous spaces,
hyperopt is probably even better, so how much do you want to put into
Pyspark - something really simple sure.
Not out of the question to do something more complex if it turns out to
also be pretty simple.

On Sat, Jan 30, 2021 at 4:42 AM Phillip Henry <>

> Hi, Sean.
> Perhaps I don't understand. As I see it, ParamGridBuilder builds an
> Array[ParamMap]. What I am proposing is a new class that also builds an
> Array[ParamMap] via its build() method, so there would be no "change in the
> APIs". This new class would, of course, have methods that defined the
> search space (log, linear, etc) over which random values were chosen.
> Now, if this is too trivial to warrant the work and people prefer
> Hyperopt, then so be it. It might be useful for people not using Python but
> they can just roll-their-own, I guess.
> Anyway, looking forward to hearing what you think.
> Regards,
> Phillip
> On Fri, Jan 29, 2021 at 4:18 PM Sean Owen <> wrote:
>> I think that's a bit orthogonal - right now you can't specify continuous
>> spaces. The straightforward thing is to allow random sampling from a big
>> grid. You can create a geometric series of values to try, of course -
>> 0.001, 0.01, 0.1, etc.
>> Yes I get that if you're randomly choosing, you can randomly choose from
>> a continuous space of many kinds. I don't know if it helps a lot vs the
>> change in APIs (and continuous spaces don't make as much sense for grid
>> search)
>> Of course it helps a lot if you're doing a smarter search over the space,
>> like what hyperopt does. For that, I mean, one can just use hyperopt +
>> Spark ML already if desired.
>> On Fri, Jan 29, 2021 at 9:01 AM Phillip Henry <>
>> wrote:
>>> 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 <> 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
>>>> 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]
>>>>> 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
>>>>> are libraries out there to do it please let me know and I'll shut up
>>>>> Regards,
>>>>> Phillip
>>>>> [1]

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