Multidimensional hyperparameter search with vw-hyp

问题:

vw-hypersearch is the Vowpal Wabbit wrapper intended to optimize hyperparameters in vw models: regularization rates, learning rates and decays, minibatches, bootstrap sizes etc. In the tutorial for vw-hypersearch there is a following example:

vw-hypersearch  1e-10  5e-4  vw  --l1 %  train.dat

Here % means the parameter to be optimized, 1e-10 5e-4 are the lower and upper bounds for the interval over which to search. The library uses golden section search method to minimize the number of iterations.

But what if I want to search over multiple hyperparameters? From the sources like this github issue discussion, I get a hint that possibly no multidimentional search methods are realized in vw. Thus, the only way out is to write one's own task-specific optimizers. Am I right?

回答1:

Now this can be done with the module vw-hyperopt.py that lives at /vowpal_wabbit/utl/ in the repository.

See my pull-request here: https://github.com/JohnLangford/vowpal_wabbit/pull/867

In the near future this will be better documented.