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xbob.paper.example 0.2.0

Example of an article using Bob for reproducible experiments

This package demonstrates how to provide scripts and source code relying on Bob for reproducible machine learning experiments. In particular, this document explains how to produce the Receiver Operating Characteristic (ROC) of our paper.

If you use this package and/or its results, please cite the following publications:

  1. The original paper attached with this satellite package:

    @article{Bob_2013,
      author = {L. El~Shafey AND A. Anjos AND M. G\"unther AND E. Khoury AND I. Chingovska AND F. Moulin AND S. Marcel},
      title = {Bob: A Free Library for Reproducible Machine Learning},
      year = {2013},
    }
    
  2. To use the MNIST database, you should also mention the following paper, where it is introduced:

    @article{LeCun_1998,
      author = {Y. LeCun AND L. Bottou AND Y. Bengio AND P. Haffner},
      title = {Gradient-Based Learning Applied to Document Recognition},
      journal = {Proceedings of the IEEE},
      month = {November},
      volume = {86},
      number = {11},
      pages = {2278-2324},
      year = {1998}
    }
    

This package is built upon two other satellite packages:

  1. xbob.db.mnist which contains easy accessors to the MNIST database
  2. xbob.mlp.lbfgs which shows how to extend Bob with an additional learning algorithm.

This decomposition in three pieces is performed for clarity and reusability. Data access, learning algorithm implementation and scripts for generating the plots and evaluating the performances are, hence, well separated. In addition, anyone could reuse any of these satellite packages easily for his own work.

Raw data

The data used in the paper is publicly available and can be downloaded and installed prior to using the scripts provided in this package. Visit THE MNIST DATABASE of handwritten digits website, download the four provided .gz files and save them in a directory of your choice, without extracting the archives. If the path to the downloaded data is not supplied when running the any of the scripts, this database will be downloaded on the fly in a temporary directory at each script call.

Installation

First, you have to install Bob following the instructions there. Afterward, you need a copy of this package.

Note

If you are reading this page through our GitHub portal and not through PyPI, note the development tip of the package may not be stable or become unstable in a matter of moments.

Go to http://pypi.python.org/pypi/xbob.paper.example to download the latest stable version of this package.

There are 2 options you can follow to get this package installed and operational on your computer: you can use automatic installers like pip (or easy_install) or manually download, unpack and use zc.buildout to create a virtual work environment just for this package. In both cases, the two dependences listed above will be automatically downloaded and installed.

Using an automatic installer

Using pip is the easiest (shell commands are marked with a $ signal):

$ pip install xbob.paper.example

You can also do the same with easy_install:

$ easy_install xbob.paper.example

This will download and install this package plus any other required dependencies. It will also verify if the version of Bob you have installed is compatible.

This scheme works well with virtual environments by virtualenv or if you have root access to your machine. Otherwise, we recommend you use the next option.

Using zc.buildout

Download the latest version of this package from PyPI and unpack it in your working area. The installation of the toolkit itself uses buildout. You don't need to understand its inner workings to use this package. Here is a recipe to get you started:

$ python bootstrap.py
$ ./bin/buildout

These 2 commands should download and install all non-installed dependencies and get you a fully operational test and development environment.

Note

The python shell used in the first line of the previous command set determines the python interpreter that will be used for all scripts developed inside this package. Because this package makes use of Bob, you must make sure that the bootstrap.py script is called with the same interpreter used to build Bob, or unexpected problems might occur.

If Bob is installed by the administrator of your system, it is safe to consider it uses the default python interpreter. In this case, the above 2 command lines should work as expected. If you have Bob installed somewhere else on a private directory, edit the file buildout.cfg before running ./bin/buildout. Find the section named buildout and edit or add the line prefixes to point to the directory where Bob is installed or built. For example:

[buildout]
...
prefixes=/Users/crazyfox/work/bob/build

User Guide

It is assumed you have followed the installation instructions for the package and got this package installed and optionally the MNIST database downloaded and uncompressed in a directory. You should have all required utilities sitting inside a binary directory depending on your installation strategy (utilities will be inside the bin directory if you used the buildout option).

Paper Layout: How to Reproduce our Results

The paper demonstrates how to use and extend the Bob toolkit by considering two learning algorithms applied to multilayer perceptrons (MLP). The first one is the R-prop algorithm, which is already integrated into Bob. The second one relies on the L-BFGS optimization procedure, and is integrated in a separate satellite package to demonstrate how the Bob library might be extended. Furthermore, this satellite package aims at only providing that plug these bricks together to evaluate algorithms on a specific dataset (MNIST), and to generate plots. The two learning algorithms mentionned above will be applied to two different multilayer perceptron architectures. The first one has no hidden layer (linear classifier), whereas the second one has a hidden layer of 50 nodes.

References:

Algorithm 1 - R-prop:

@inproceedings{Riedmiller_1993,
  author = {M. Riedmiller AND H. Braun},
  title = {{Direct Adaptive Method for Faster Backpropagation Learning: The {RPROP} Algorithm}},
  pages = {586--591},
  volume = {Proceedings of the IEEE International Conference on Neural Networks},
  year = {1993}
}

Algorithm 2 - L-BFGS:

@article{Byrd_1994,
  author = {R. H. Byrd AND J. Nocedal AND R. B. Schnabel},
  title = {Representations of quasi-{N}ewton matrices and their use in limited memory methods},
  issn={0025-5610},
  journal = {Mathematical Programming},
  volume = {63},
  number = {1-3},
  doi = {10.1007/BF01582063},
  publisher = {Springer-Verlag},
  pages = {129-156},
  year = {1994},
}

Training the four multilayer perceptrons

Two scripts are provided to learn multilayer perceptrons. One makes use of the R-prop algorithm, whereas the other one relies on the L-BFGS optimization technique.

In the paper, the ROC was generated by applying these two learning algorithms to two different multilayer perceptron architectures as described above.

To run the training process, use the following command:

$ ./bin/rprop_training.py -d /root/of/database -H 0 -m rprop_H00.hdf5

This will train a multilayer perceptron with 0 hidden nodes (linear classifier), using 50 iterations of R-Prop, save the resulting multilayer perceptron into an HDF5 file and finally compute the classification error rate on the test set of MNIST.

Since the MNIST database used for this example is pretty small, you could also run this script without specifying the path to the database, which will in this case be automatically downloaded in a temporary directory before calling the learning procedure.

Next, you could learn the other three multilayer perceptrons in a completely similar way, as follows:

$ ./bin/rprop_training.py -d /root/of/database -H 50 -m rprop_H50.hdf5
$ ./bin/lbfgs_training.py -d /root/of/database -H  0 -m lbfgs_H00.hdf5
$ ./bin/lbfgs_training.py -d /root/of/database -H 50 -m lbfgs_H50.hdf5

On a recent workstation (Intel core i7), each (single-thread) script should complete in less than an hour. If you have a multi-core CPU, you could of course run the scripts in parallel.

Plotting the ROC for the four systems

Once you have learned the multilayer perceptrons, you can easily plot the ROC using the following command:

$ ./bin/plot.py -d /root/of/database -m rprop_H00.hdf5 rprop_H50.hdf5 lbfgs_H00.hdf5 lbfgs_H50.hdf5 \
  -l '1-layer MLP (R-prop)' '2-layer MLP (R-prop)' '1-layer MLP (L-BFGS)' '2-layer MLP (L-BFGS)' -o roc.pdf

This will compute the scores on the test set of the MNIST database for each multilayer perceptron, and plot the performances on a ROC, which is saved as a pdf file.

Evaluating to get the classification error rate

If you have a trained multilayer perceptron, you can easily compute the classification error rate (CER) using the following command:

$ ./bin/evaluate.py -d /root/of/database -m MLP_TO_EVALUATE.hdf5

This will return the CER on the test set of the MNIST database.

Getting further

If you are interested into packaging your own work into your own satellite package, you could reuse the layout of this package. You can also find more detailed information in this tutorial.

Learning by looking at other examples is also a good practice.

In case of problem or question, just ask for help to the Bob's community through the mailing list or the issue tracker.

 
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