Installbuilder choiceparameter value full#
I put the full stack trace at #710 (comment) since it also had the stack trace for the categorical variables. Is the application here essentially to select and ordered subset of elements from some ordered list? it seems that in such cases with rather complex constraints it might make sense to think about a more custom way of optimizing this rather than trying to express it in the existing parameter & constraint interface.Ĭc see now that indeed, Parameter Constraints not supported for ChoiceParameter ( Reproducer via Google Colab). You could try to do sth like you're suggesting above, but that would require using integer parameters. In general, once a parameter is defined as an ordinal categorical parameter, I do not believe we allow imposing linear constraints across it and another parameter - simply b/c designating it as categorical means that the values are not directly comparable (within the parameter, values are completely ordered by definition). So regardless whether you have an ordinal parameter with values or values, both of then will be normalized to the representation. So if we represent ordered choices internally we normalize them to equidistant numbers in (this may not be true for the Service API, I will need to check), regardless of their numerical value. ax_client.create_experiment() or the other APIs? Is this the case? How do I replace this with a Hamming distance instead? Is this a flag that could be incorporated into e.g. It seems like ordinal variables will use a Matérn-5/2 kernel by default, in which case I'd assume the numeric choices of the ordinal parameters to play a significant role. While this is likely to result in better optimization performance, it may lead to slow optimization of the acquisition function when there are many categorical variables. Rather than optimizing the acquisition function in a continuously relaxed space, we optimize it separately over each combination of the categorical variables. This approach can be combined with the idea automatic relevance determination (ARD) where each categorical variable has its own lengthscale. A natural choice is to set the distance is 0 if two categories are equal and 1 otherwise, similar to the idea of Hamming distances. K(x,y)=kcat(xcat,ycat)×kord(xord,yord)+kcat(xcat,ycat)+kord(xord,yord)įor the ordinal variables we can use a standard kernel such as Matérn-5/2, but for the categorical variables we need a way to compute distances between the different categories. In particular, we use a kernel of the form: Our new approach uses separate kernels for the categorical and ordinal (continuous/integer) variables. In addition, the acquisition function is often optimized in the corresponding continuous space and the final candidate is selected by rounding back to the original space, which may result in selecting sub-optimal points according to the acquisition function. While this is a convenient choice, it can drastically increase the dimensionality of the search space. In this setting, a categorical variable with categories is represented by three new variables (one for each category). The most common way of dealing with categorical variables in Bayesian optimization is to one-hot encode the categories to allow fitting a GP model in a continuous space.
Installbuilder choiceparameter value password#
You can use the parameterString to ask for the username and password, and using a validationActionList to check the username and password provided with are correct.Docs suggest ordinal parameters use Matern 5/2 kernelīased on Support for mixed search spaces and categorical variables (docs):
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