Using vw hypersearch

Introduction

vw-hypersearch is a simple wrapper to vw to help in finding lowest-loss hyper-parameters (argmin).

For example: say you want to find the lowest average loss for --l1 (L1-norm regularization) on a train-set called train.dat. You can run:

    $ vw-hypersearch 1e-10 1 vw --l1 % train.dat

vw-hypersearch will train multiple times (but in a efficient way) until it finds the --l1 value resulting in the lowest average training loss.

In the call:

• the % character is a placeholder for the (argmin) parameter we are looking for.
• 1e-10 is the lower-bound for the search range
• 1 is the upper-bound of the search range

The lower & upper bounds are arguments to vw-hypersearch. Anything from vw on, are normal vw arguments exactly as you would use in training. The only change you must apply to the training command is to use % instead of the value of the parameter you're trying to optimize on.

Calling vw-hypersearch without any arguments should provide a Usage message.

More advanced options

Additional arguments can be passed to vw-hypersearch preceding vw itself:

• -L will do a log-space golden-section search instead of a simple golden-section search.
• -t test.dat (note: this must come before the vw argument) will search for the training parameter that results in a minimum loss on test.dat rather than train.dat (ignoring the training errors).
• An optional 3rd numeric parameter will be interpreted as a tolerance parameter directing vw-hypersearch to stop only when a difference in two consecutive run errors is less than tolerance

Examples

# Find the learning-rate resulting in the lowest average loss
# for a logistic loss train-set:
vw-hypersearch 0.1 100 vw --loss_function logistic --learning_rate % train.dat

# Find the bootstrap resulting in the lowest average loss
# vw-hypersearch will automatically search in integer-space
# since --bootstrap expects an integer
vw-hypersearch 2 16 vw --bootstrap % train.dat

Implementation notes

vw-hypersearch conducts a golden-section search search by default. This search method strikes a good balance between safety and efficiency.

Caveats

• Lowest average loss is not necessarily optimal
• Your real goal should always be to find a minimal generalization error, not training error.
• Some parameters do not have a convex loss, for these vw-hypersearch will converge on some local-minimum instead of a global one

More questions?

vw-hypersearch is written in perl and is included with vowpal wabbit (in the utl subdirectory). In case of doubt, look at the source