LibJungle

This is a basic implementation of decision jungles for multi class classification as proposed by Shotton et al. in [1]. It implements the LSearch training algorithm with the optimizations discussed in [2].

Compile code

If you want to use the tool, you have to compile it from source. You need to have the following tools installed

• gcc >= 4.8 (C++ 11 compliant)
• CMake >= 2.6
• Boost >= 1.40.0 (Filesystem and Tokenizer)
• openMP (no specific version. Only very basic features are required)

Execute the following commands in your shell in order to download and compile the code:

$ hg clone https://bitbucket.org/geekStack/libjungle
# Change to the code directory
$ cd libjungle
# We don't want to have objects files mixed with source files in one directory
# Hence, we compile the project in a build directory
$ mkdir build
$ cd build
# Create the makefile using CMake
$ cmake ../
# Compile the library
$ make
# Test if everything went as expected
$ ./jungle help

If everything went as expected, the last command should output the help dialog.

Training sets

Data is provided in the form of CSV files. Training sets have the following structure:

[class label],[feature 1],...,[feature n]

Important: The class labels must start at 0 and the features are real numbers.

Usage

You can use the tool for learning decision jungles as well as classifying new data based on an already learned model. Please see ./jungle help [command] for detailed information about the arguments individual commands accept.

Train a jungle

You can use the 'train' command in order to train a new jungle. The following command will train a decision jungle of 8 DAGs from the file training_set.txt and will save the model to the file model.txt:

$ ./jungle train training_set.txt model.txt -M=8

Please see jungle help train for further information.

Test performance

You can use the 'classify' command in order to evaluate the performance of a learned decision jungle on a new training set. The following command will output the test error of the decision jungle stored in model.txt on the test set test_set.txt:

$ ./jungle classify test_set.txt model.txt

License and Copyright

Everything except sse.h and fastlog.h is licensed under the following BSD license:

Copyright (c) 2014, Tobias Pohlen tobias.pohlen@rwth-aachen.de All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
• Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
• The names of its contributors may not be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL TOBIAS POHLEN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Fast log2 approximation (sse.h, fastlog.h) Copyright (C) 2011 Paul Mineiro. Further information under: https://code.google.com/p/fastapprox/

Miscellaneous

Please cite the code as follows, if you use it in a publication:

@misc{TPDJ, 
   author = {Tobias Pohlen}, 
   title = {{L}ib{J}ungle - {D}ecision {J}ungle {L}ibrary}, 
   howpublished = {\url{https://bitbucket.org/geekStack/libjungle}}
} 

References

• [1] Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, and Antonio Criminisi, "Decision jungles: Compact and rich models for classification". In C.J.C. Burges, L. Botou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 234-242. Curran Associates, Inc., 2013.
• [2] Tobias Pohlen, "Decision Jungles". RWTH Aachen University. Aachen, Germany.