Vowpal Wabbit predictions for multi-label classification

Question

I am sorry, I do feel I am overlooking something really obvious.

But how can the following happen:

$ cat myTrainFile.txt
1:1 |f 1:12 2:13
2:1 |f 3:23 4:234
3:1 |f 5:12 6:34

$ cat myTestFile.txt 
14:1 |f 1:12 2:13
14:1 |f 3:23 4:234
14:1 |f 5:12 6:34

$ vw --csoaa 3 -f myModel.model --compressed < myTrainFile.txt 
final_regressor = myModel.model
...
...

$ vw -t -i myModel.model -p myPred.pred < myTestFile.txt 
only testing
Num weight bits = 18
...
...

$ cat myPred.pred 
14.000000
14.000000
14.000000

So the test file is identical to the train file, but for the labels. Hence, I would expect vw to produce the original labels that it learned from the train file, as it ignores the labels in the test file completely.

However, it seems to reproduce the labels form the test file?!?

Clearly, I am doing something completely wrong here... but what?

Martin Popel Martin Popel · Accepted Answer · 2014-02-14T12:46:30

If you specify just one label in --csoaa (even in the -t test mode), it means that only that label is "available" for this example, so no other label can be predicted. This is another difference from --oaa (where you always specify just the correct label). See https://groups.yahoo.com/neo/groups/vowpal_wabbit/conversations/topics/2949.

If all labels are "available" (possible) for any test example, you must always include all the labels on each line. With -t you do not need to include the costs of the labels if you just want to get the --predictions (if you don't need vw to compute the test loss). So your myTestFile.txt should look like:

1 2 3 |f 1:12 2:13
1 2 3 |f 3:23 4:234
1 2 3 |f 5:12 6:34

and your myTrainFile.txt should look like:

1:0 2:1 3:1 |f 1:12 2:13
1:1 2:0 3:1 |f 3:23 4:234
1:1 2:1 3:0 |f 5:12 6:34

Vowpal Wabbit predictions for multi-label classification

2 Answers