3 Idiots' Approach for Display Advertising Challenge
====================================================
This README introduces how to run our code up. For the introduction to our
approach, please see
http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf
System Requirement
==================
- 64-bit Unix-like operating system (We tested our code on Ubuntu 13.10)
- Python3
- g++ (with C++11 and OpenMP support)
- at least 20GB memory and 50GB disk space
- The datasets used in the competition:
train.csv (md5sum: ebf87fe3daa7d729e5c9302947050f41) and
test.csv (md5sum: 8016f59e45abb37ae7f6e7956f30e052)
Step-by-step
============
This section is divided into two parts. The first part introduces how to run
our code with a toy example. After you finish this part, please follow the
second part to run with the whole dataset.
Tiny Example
------------
1. Create two symbolic links.
$ ln -s train.tiny.csv train.csv
$ ln -s test.tiny.csv test.csv
2. Compile excutables, prepare files, and scan necessary information from the
training data.
$ make
3. Generate the prediction file "submission.csv".
$ ./run.py
4. Generate the checksum for submission.csv.
$ md5sum submission.csv
d29c2f9e846a0ea77a6f56e983316360 submission.csv
If you see the above checksum, congratulations!! Please run the following
command and move to the next sub-section.
$ make clean
$ rm -f train.csv test.csv
The Whole Dataset
-----------------
1. Copy or link the training and test data to this directory. Their name should
be "train.csv" and "test.csv", respectively.
2. Check if your dataset is correct.
$ ./check.py
2. Compile excutables, prepare files, and scan necessary information from the
training data. These step takes around an hour.
$ make
3. Generate the prediction file "submission.csv". (You may want to use more
threads. Please see "Miscellaneous 1".)
$ run.py
Miscellaneous
=============
1. By default we use only one thread, so it may take a long time to train the
model. If you have multi-core CPUs, you may want to set NR_THREAD in run.py
to use all cores. On our machine with two six-core CPUs (intel E5-2620), it
takes around 3.5 hours when all cores are used.
2. Our algorithms is non-deterministic when multiple threads are used. (That
is, the results can be slightly different when you run the script two or
more times.) In our experience, the variances generally do not exceed
0.0001 (LogLoss).
3. This script generates a prediction with around 0.44490/0.44480 on
public/private leaderboards. If you want the prediction with around
0.44460/0.44450, please change the following setting in run.py.
./gbdt -t 30 ---> ./gbdt -t 50
./fm -k 4 -t 11 ---> ./fm -k 8 -t 15
Training with this setting takes around 8 hours on our machine.
4. The dataset used for competiion was officially removed after the end of the
competiion. Though Criteo released another dataset, the format is different
from the dataset we used in the competition. So please notice that you need
the dataset used in the competition to run our code.
5. If you have any question, please send your email to:
guestwalk@gmail.com (Yu-Chin's email)