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Code for the Avito competition

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kaggle-avito
============

See [Beating the benchmark](http://www.kaggle.com/c/avito-prohibited-content/forums/t/10120/beating-the-benchmark) thread at the competition forum and [http://fastml.com/kaggle-vs-industry-as-seen-through-lens-of-the-avito-competition/](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
	
How to use
----------

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

kaggle-avito

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