Why average loss goes up when training using Vowpal Wabbit - vowpalwabbit

Question

I tried to use VW to train a regression model on a small set of examples (about 3112). I think I'm doing it correctly, yet it showed me weird results. Dug around but didn't find anything helpful.
$ cat sh600000.feat | vw --l1 1e-8 --l2 1e-8 --readable_model model -b 24 --passes 10 --cache_file cache
using l1 regularization = 1e-08
using l2 regularization = 1e-08
Num weight bits = 24
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
using cache_file = cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.040000 0.040000 1 1.0 -0.2000 0.0000 79
0.051155 0.062310 2 2.0 0.2000 -0.0496 79
0.046606 0.042056 4 4.0 0.4100 0.1482 79
0.052160 0.057715 8 8.0 0.0200 0.0021 78
0.064936 0.077711 16 16.0 -0.1800 0.0547 77
0.060507 0.056079 32 32.0 0.0000 0.3164 79
0.136933 0.213358 64 64.0 -0.5900 -0.0850 79
0.151692 0.166452 128 128.0 0.0700 0.0060 79
0.133965 0.116238 256 256.0 0.0900 -0.0446 78
0.179995 0.226024 512 512.0 0.3700 -0.0217 79
0.109296 0.038597 1024 1024.0 0.1200 -0.0728 79
0.579360 1.049425 2048 2048.0 -0.3700 -0.0084 79
0.485389 0.485389 4096 4096.0 1.9600 0.3934 79 h
0.517748 0.550036 8192 8192.0 0.0700 0.0334 79 h
finished run
number of examples per pass = 2847
passes used = 5
weighted example sum = 14236
weighted label sum = -155.98
average loss = 0.490685 h
best constant = -0.0109567
total feature number = 1121506
$ wc model
41 48 657 model
Questions:
Why is the number of features in the output (readable) model less than the number of actual features? I counted that the training data contains 78 features (plus the bias that's 79 as shown during the training). The number of feature bits is 24, which should be far more than enough to avoid collision.
Why does the average loss actually go up in the training as you can see in the above example?
(Minor) I tried to increase the number of feature bits to 32, and it output an empty model. Why?
EDIT:
I tried to shuffle the input file, as well as using --holdout_off, as suggested. But the result is still almost the same - the average loss go up.
$ cat sh600000.feat.shuf | vw --l1 1e-8 --l2 1e-8 --readable_model model -b 24 --passes 10 --cache_file cache --holdout_off
using l1 regularization = 1e-08
using l2 regularization = 1e-08
Num weight bits = 24
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
using cache_file = cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.040000 0.040000 1 1.0 -0.2000 0.0000 79
0.051155 0.062310 2 2.0 0.2000 -0.0496 79
0.046606 0.042056 4 4.0 0.4100 0.1482 79
0.052160 0.057715 8 8.0 0.0200 0.0021 78
0.071332 0.090504 16 16.0 0.0300 0.1203 79
0.043720 0.016108 32 32.0 -0.2200 -0.1971 78
0.142895 0.242071 64 64.0 0.0100 -0.1531 79
0.158564 0.174232 128 128.0 0.0500 -0.0439 79
0.150691 0.142818 256 256.0 0.3200 0.1466 79
0.197050 0.243408 512 512.0 0.2300 -0.0459 79
0.117398 0.037747 1024 1024.0 0.0400 0.0284 79
0.636949 1.156501 2048 2048.0 1.2500 -0.0152 79
0.363364 0.089779 4096 4096.0 0.1800 0.0071 79
0.477569 0.591774 8192 8192.0 -0.4800 0.0065 79
0.411068 0.344567 16384 16384.0 0.0700 0.0450 77
finished run
number of examples per pass = 3112
passes used = 10
weighted example sum = 31120
weighted label sum = -105.5
average loss = 0.423404
best constant = -0.0033901
total feature number = 2451800
The training examples are unique to each other so I doubt there is over-fitting problem (which, as I understand it, usually happens when the number of input is too small comparing the number of features).
EDIT2:
Tried to print the average loss for every pass of examples, and see that it mostly remains constant.
$ cat dist/sh600000.feat | vw --l1 1e-8 --l2 1e-8 -f dist/model -P 3112 --passes 10 -b 24 --cache_file dist/cache
using l1 regularization = 1e-08
using l2 regularization = 1e-08
Num weight bits = 24
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
final_regressor = dist/model
using cache_file = dist/cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.498822 0.498822 3112 3112.0 0.0800 0.0015 79 h
0.476677 0.454595 6224 6224.0 -0.2200 -0.0085 79 h
0.466413 0.445856 9336 9336.0 0.0200 -0.0022 79 h
0.490221 0.561506 12448 12448.0 0.0700 -0.1113 79 h
finished run
number of examples per pass = 2847
passes used = 5
weighted example sum = 14236
weighted label sum = -155.98
average loss = 0.490685 h
best constant = -0.0109567
total feature number = 1121506
Also another try without the --l1, --l2 and -b parameters:
$ cat dist/sh600000.feat | vw -f dist/model -P 3112 --passes 10 --cache_file dist/cacheNum weight bits = 18
learning rate = 0.5
initial_t = 0
power_t = 0.5
decay_learning_rate = 1
final_regressor = dist/model
using cache_file = dist/cache
ignoring text input in favor of cache input
num sources = 1
average since example example current current current
loss last counter weight label predict features
0.520286 0.520286 3112 3112.0 0.0800 -0.0021 79 h
0.488581 0.456967 6224 6224.0 -0.2200 -0.0137 79 h
0.474247 0.445538 9336 9336.0 0.0200 -0.0299 79 h
0.496580 0.563450 12448 12448.0 0.0700 -0.1727 79 h
0.533413 0.680958 15560 15560.0 -0.1700 0.0322 79 h
0.524531 0.480201 18672 18672.0 -0.9800 -0.0573 79 h
finished run
number of examples per pass = 2801
passes used = 7
weighted example sum = 19608
weighted label sum = -212.58
average loss = 0.491739 h
best constant = -0.0108415
total feature number = 1544713
Does that mean it's normal for average loss to go up during one pass, but as long as multiple pass gets the same loss then it's fine?

Answer 1

Model file stores only non-zero weights. So most likely others got nulled especially if you are using --l1
It may be caused by many reasons. Perhaps your dataset isn't shuffled well enough. If you sort your dataset so examples labeled -1 will be in first half and examples labeled 1 will be in second then your model will show very good convergence on first half, but you'll see avg loss bump as it reaches 2nd half. So it may be disbalance in dataset. As for last two losses - these are holdout losses (marked with 'h' at end of line) and may point that model is overfitted. Pls refer to my other answer.
Well, in master branch usage of -b 32 is even currently blocked. You shall use up to -b 31. On practice -b 24-28 is usually enough even for dozens of thousands of features.
I would recommend you to get up-to-date VW version from github