Vowpal Wabbit inverted_hash option produces empty output, but why?

Question

I'm trying to get a vowpal wabbit model saved with inverted hashes. I have a valid model produced with the following:

vw --oaa 2 -b 24 -d mydata.vw --readable_model mymodel.readable

which produces a model file like this:

Version 7.7.0
Min label:-1.000000
Max label:1.000000
bits:24
0 pairs: 
0 triples: 
rank:0
lda:0
0 ngram: 
0 skip: 
options: --oaa 2
:0
66:0.016244
67:-0.016241
80:0.026017
81:-0.026020
84:0.015005
85:-0.015007
104:-0.053924
105:0.053905
112:-0.015402
113:0.015412
122:-0.025704
123:0.025704
...

(and so on for many thousands more features). However, to be more useful, I need to see the feature names. Seemed like a fairly obvious thing, but I did

vw --oaa 2 -b 24 -d mydata.vw --invert_hash mymodel.inverted

and it produced a model file like this (no weights are produced):

Version 7.7.0
Min label:-1.000000
Max label:1.000000
bits:24
0 pairs: 
0 triples: 
rank:0
lda:0
0 ngram: 
0 skip: 
options: --oaa 2
:0

It feels like I've obviously done something wrong, but I think I'm using the options in the documented way:

--invert_hash is similar to --readable_model, but the model is output in a more human readable format with feature names followed by weights, instead of hash indexes and weights.

Does anyone see why my second command is failing to produce any output?

Answer 1

This is caused by a bug in VW which was fixed recently (on account of this question), see https://github.com/JohnLangford/vowpal_wabbit/issues/337.

By the way, it does not make sense to use --oaa 2. If you want binary classification (aka logistic regression), use --loss_function=logistic (and make sure your labels are 1 and -1). OAA makes sense only for N>2 number of classes (and it is recommended to use --loss_function=logistic with --oaa).

Also note that learning with --invert_hash is much slower (and requires more memory, of course). The recommended way how to create inverted-hash model, especially with multiple passes, is to learn a usual binary model and then convert it to inverted hash using one pass over the training data with -t:

vw -d mytrain.data -c --passes 4 -oaa 3 -f model.binary
vw -d mytrain.data -t -i model.binary --invert_hash model.humanreadable