See Beating the benchmark thread at the competition forum and http://fastml.com/kaggle-vs-industry-as-seen-through-lens-of-the-avito-competition/ for more info.
The by_category folder contains code for training a separate model for each category.
construct_line.py - a helper module used by tsv2vw.py
load_attribs.py - a demo of how to load attribs
predict.py - produce a solution file for Kaggle from VW predictions
score.py - get validation scores
tsv2vw.py - convert data to VW format
python tsv2vw.py train.tsv train.vw
python tsv2vw.py test.tsv test.vw
vw -b 29 --loss_function logistic -c --passes 20 train.vw -P 1e5
If you don't have enough memory for -b 29, try smaller values. Output:
number of examples per pass = 3596240
passes used = 14
weighted example sum = 5.03474e+07
weighted label sum = -4.34148e+07
average loss = 0.0395241 h
best constant = -0.862306
total feature number = 2567343892
Now disable validation mode with --holdout_off and run 14 passes:
vw -b 29 --loss_function logistic -c --passes 14 -d train.vw -P 1e5 --holdout_off -f model
vw -t -i model -d test.vw -p predicshuns.txt
python predict.py predicshuns.txt predicshuns_for_kaggle.txt
That's it. If you're doing validation, here's how to get scores:
python score.py test_v.vw p_v.txt