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 <[email protected]> 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." > [email protected] <mailto:[email protected]> | - Sheriff Justice
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