forked from ycjuan/kaggle-2014-criteo
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME
152 lines (89 loc) · 4.06 KB
/
README
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
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
LIBFFM
======
This most important model used in this solution is called ``field-aware
factorization machines.'' If you want to use this model, please download LIBFFM
from:
http://www.csie.ntu.edu.tw/~r01922136/libffm
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 40GB memory and 100GB disk space
Get The Dataset
===============
Criteo released the data set after the end of the competition. However, the
format of the released data set is different from the one used in the
competition; the format used in the comptition is in csv format, while the
released one is in text format.
Our scripts require csv format. If you already have the dataset used in the
competition, please skip to the next section. Otherwise, we provide a script to
convert the files in text format to csv format. Please follow these steps:
1. Download the data set from Criteo.
http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/
2. Decompress and make sure the files are correct.
$ md5sum dac.tar.gz
df9b1b3766d9ff91d5ca3eb3d23bed27 dac.tar.gz
$ tar -xzf dac.tar.gz
$ md5sum train.txt test.txt
4dcfe6c4b7783585d4ae3c714994f26a train.txt
94ccf2787a67fd3d6e78a62129af0ed9 test.txt
3. Use ``txt2csv.py'' to convert training and test data to csv format.
$ converters/txt2csv.py tr train.txt train.csv
$ converters/txt2csv.py te test.txt test_without_label.csv
4. Add dummy labels to test.csv to make the format be consistent with train.csv.
$ utils/add_dummy_label.py test_without_label.csv test.csv
5. Calculate the checksum of produced file.
$ md5sum train.csv test.csv
ebf87fe3daa7d729e5c9302947050f41 train.csv
cd6ac893856ab5aa904ec1ad5ef9218b test.csv
Now you can follow the steps in ``Step-by-step'' section to run our experiments.
Step-by-step
============
First, compile executables:
$ make
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 tr.csv
$ ln -s test.tiny.csv te.csv
2. Generate the prediction file "submission.csv".
$ run.py
3. Generate the checksum for submission.csv.
$ md5sum submission.csv
19a913d1577c3d419f62e38c34305341 submission.csv
The Whole Dataset
-----------------
1. Copy or link the dataset generated in the section ``Get The Dataset'' to
this directory. Their names should be "tr.csv" and "te.csv", respectively.
2. 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 more cores.
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 of logloss generally do not
exceed 0.0001.
3. This script generates a prediction with around 0.44510 / 0.44500 on
public / private leaderboards, which are slightly worse than what we had
during the competition. The difference is due to some minor changes.
If you want to reproduce our best results, please do:
$ git checkout v1.0
For detailed instructions, please follow README in that version.
Contributors
============
YuChin Juan, Wei-Sheng Chin, and Yong Zhuang
If you have any question, please send your email to:
[email protected] (YuChin)