[mlpack] Cross-validation and hyper-parameter tuning infrastructure

[Kirill Mishchenko]

Hi Ryan.

> My suggestion is to add another overload:
> 
>  HyperParameterOptimizer<...> h(data, datasetInfo, labels);
> 
> This is because I consider the dataset information, which encodes the
> types of dimensions, to be a part of the dataset.  Not all machine
> learning methods support a DatasetInfo object; I believe that it is only
> DecisionTree and HoeffdingTree at the moment (maybe there is one more I
> forgot).

There are pros and cons of such design. Advantage: for some users it can be more natural to pass datasetInfo into the constructor rather than into the method Optimize. Disadvantages: 1) we need to double the amount of constructors for HyperParameterOptimizer, as well as for the cross-validation classes KFoldCV and SimpleCV (4 in total - weighted/non-weighted learning + presence/absence of datasetInfo parameter) ; 2) we need to double the amount of considered cases in the implementation of the method Evaluate of cross-validation classes (4 in total again - weighted/non-weighted learning + presence/absence of datasetInfo parameter); 3) I’m not sure it can be refactored in some way, so the same probably will be true for new cross-validation classes.

> But now, we have C++11
> and rvalue references, so we can do a redesign here to work around at
> least the first issue: we can have the optimizers hold 'FunctionType',
> and allow the user to pass in a 'FunctionType&&' and then use the move
> constructor.

I’m not sure it’s possible since we don’t know the type of the template parameter FunctionType until we initialize it in the body of the method Optimize.

> Thanks again for the discussion,

My pleasure.

Best regards,

Kirill Mishchenko

> On 26 Apr 2017, at 20:17, Ryan Curtin <ryan at ratml.org> wrote:
> 
> On Wed, Apr 26, 2017 at 11:24:18AM +0500, Kirill Mishchenko wrote:
>> Hi Ryan.
>> 
>>> The key problem, like you said, is that we don't know what AuxType
>>> should be so we can't call its constructor.  But maybe we can adapt
>>> things a little bit:
>>> 
>>> template<typename AuxType, typename... Args>
>>> struct Holder /* needs a better name */
>>> {
>>> // This typedef allows us access to the type we need to construct.
>>> typedef AuxType Aux;
>>> 
>>> // These are the parameters we will use.
>>> std::tuple<Args...> args;
>>> 
>>> Holder(Args... argsIn) { /* put argsIn into args */ }
>>> };
>>> 
>>> Then we could use this in addition with the Bind() class when calling an
>>> optimizer:
>>> 
>>> std::array<double, 3> param3s = { 1.0, 2.0 4.0 };
>>> std::array<double, 2> auxParam1s = { 1.0, 3.0 };
>>> std::array<double, 4> auxParam2s = { 4.0, 5.0, 6.0, 8.0 };
>>> auto results = tuner.Optimize<GridSearch>(Bind(param1), Bind(param2),
>>>     param3s, Holder<AuxType>(auxParam1s, auxParam2s));
>>> 
>>> Like most of my other code ideas, this is a very basic sketchup, but I
>>> think it can work.  Let me know what you think or if there is some
>>> detail I did not think about enough that will make the idea fail. :)
>> 
>> I think this approach is quite implementable. Moreover, we should be
>> able to provide support of Bind for aux parameters:
>> 
>>  std::array<double, 3> param3s = { 1.0, 2.0, 4.0 };
>>  double auxParam1 = 1.0;
>>  std::array<double, 4> auxParam2s = { 4.0, 5.0, 6.0, 8.0 };
>>  auto results = tuner.Optimize<GridSearch>(Bind(param1), Bind(param2),
>>     param3s, Holder<AuxType>(Bind(auxParam1), auxParam2s));
> 
> Yeah, that seems like it will work.  It might be worth spending some
> time thinking about what would be the easiest for the user to
> understand, but in either case the general implementation will be the
> same.
> 
>>> Sure; I think maybe we should allow the user to pass in a DatasetInfo
>>> with the training data and labels, to keep things simple.
>> 
>> Can you clarify a bit more what you mean here?
> 
> Yeah, my impression is that the user creates the hyperparameter
> optimizer like this:
> 
>  HyperParameterOptimizer<...> h(data, labels);
> 
> My suggestion is to add another overload:
> 
>  HyperParameterOptimizer<...> h(data, datasetInfo, labels);
> 
> This is because I consider the dataset information, which encodes the
> types of dimensions, to be a part of the dataset.  Not all machine
> learning methods support a DatasetInfo object; I believe that it is only
> DecisionTree and HoeffdingTree at the moment (maybe there is one more I
> forgot).
> 
>>> // move optimizer type to class template parameter
>>> HyperParameterOptimizer<SoftmaxRegression<>, Accuracy, KFoldCV, SA> h;
>>> 
>>> h.Optimizer().Tolerance() = 1e-5;
>>> h.Optimizer().MoveCtrlSweep() = 3;
>>> 
>>> h.Optimize(…);
>> 
>> In this approach we need to construct an optimizer before the method
>> Optimize (of HyperParamOptimizer(Tuner) in the example above) is
>> called, and it can be very problematic because of two reasons.
>> 
>> 1. We don’t know what FunctionType object (which wraps cross
>> validation) to optimize since it depends on what we pass to the method
>> Optimize (in particular, it depends on whether or not we bind some
>> arguments).
>> 
>> 2. In the case of GridSearch we also don’t know sets of values for
>> parameters before calling the method Optimize. Recall that we pass
>> these sets of values during construction of an GridSearch object.
> 
> Right, I see what you mean.  At the current time the mlpack optimizers
> expect a 'FunctionType&' to be passed to the optimizer, and this
> reference is held internally.  However, that design decision was made
> before C++11 and was intended to avoid copies.  But now, we have C++11
> and rvalue references, so we can do a redesign here to work around at
> least the first issue: we can have the optimizers hold 'FunctionType',
> and allow the user to pass in a 'FunctionType&&' and then use the move
> constructor.
> 
> In that way, you could create an optimizer without having access to the
> instantiated FunctionType.
> 
> I can see a few ways to solve the second issue after that change is
> done.  But in either case, the goal from my end would be to avoid a big
> long call to Optimize() that has both Bind(), Holder<>(), and
> OptimizerArg() types all in it.  I think the idea of passing optimizer
> arguments after the arguments to the machine learning algorithm and
> marking them all with OptimizerArg() might be confusing for users, and
> it's easier if they can directly modify the parameters of the optimizer.
> 
>>> If that's correct, then it might be nice to implement some additional
>>> idea such as when the user passes a 'math::Range<double> lambda', the
>>> search will be over all possible values of lambda within the given
>>> range.  (One can simply modify the objective value to be DBL_MAX when
>>> outside the bounds of the given lambda, or we can consider visiting how
>>> optimizers can work in a constrained context.)
>> 
>> I think this behaviour should be handled by optimizers since we
>> suppose to call them only once. I guess we already have touched this
>> feature in the discussion about simulated annealing.
> 
> I agree; at the current time we don't have any support for constrained
> optimizers though.  Whatever you end up implementing for GridSearch
> might be a good start, since technically grid search is a special case
> of constrained optimization.
> 
>> In the light of what we have discussed recently I think it is worth to
>> revisit what and when can be implemented as a GSoC project. <...>
> 
> I agree with the changes that you have proposed.
> 
> Thanks again for the discussion, I think the ideas here are getting
> really mature.  I think that there is some cool functionality that will
> be possible with these modules that isn't possible in any other machine
> learning library.  For instance, even just hyperparameter search over
> continuous variables isn't very well supported by other toolkits, and
> would be a really nice thing to showcase for mlpack.
> 
> Ryan
> 
> -- 
> Ryan Curtin    | "You can think about it... but don't do it."
> ryan at ratml.org <mailto:ryan at ratml.org> |   - Sheriff Justice

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