This package contains all the source code to reproduce the numerical experiments described in the paper. It contains a parallelized implementation of the Binary Sparse Coding (BSC) [1], Gaussian Sparse Coding (GSC) [2], Maximum Causes Analysis (MCA) [3], Maximum Magnitude Causes Analysis (MMCA) [4], Ternary Sparse Coding (TSC) [5], and Discrete Sparse Coding [7] models. All these probabilistic generative models are trained using a truncated Expectation Maximization (EM) algorithm [6].
Python related dependencies can be installed using:
$ pip install -r requirements.txt
MPI4PY also requires a system level installation of MPI. You can do that on MacOS using Homebrew:
$ brew install mpich
for Ubuntu systems:
$ sudo apt install mpich
for any other system you might wish to review the relevent section of the MPI4PY installation guidelines
prosper/ - Python library/framework for MPI parallelized EM-based algorithms. The models' implementations can be found in prosper/em/camodels/.
examples/ - Small examples for initializing and running the models
To install the library run:
$ git clone https://github.com/ml-uol/prosper.git
$ cd prosper
$ python setup.py install
Optionally you can replace the final line with:
$ python setup.py develop
This option installs the library using links and it allows the user to edit the library without reinstalling it (useful for Prosper developers).
To run some toy examples:
$ cd examples/barstest
$ python bars-learning-and-inference.py param-bars-<...>.py
where <...> should be appropriately replaced to correspond to one of the parameter files available in the directory. The bars-run-all.py script should then initialize and run the algorithm which corresponds to the chosen parameter file.
The results produced by the code are stored in a 'results.h5' file under "./output/.../". The file stores the model parameters (e.g., W, pi etc.) for each EM iteration performed. To read the results file, you can use openFile function of the standard tables package in python. Moreover, the results files can also be easily read by other packages such as Matlab etc.
The code uses MPI based parallelization. If you have parallel resources (i.e., a multi-core system or a compute cluster), the provided code can make a use of parallel compute resources by evenly distributing the training data among multiple cores.
To run the same script as above, e.g.,
a) On a multi-core machine with 32 cores:
$ mpirun -np 32 bars-learning-and-inference.py param-bars-<...>.py
b) On a cluster:
$ mpirun --hostfile machines python bars-learning-and-inference.py param-bars-<...>.py
where 'machines' contains a list of suitable machines.
See your MPI documentation for the details on how to start MPI parallelized programs.
[1] M. Henniges, G. Puertas, J. Bornschein, J. Eggert, and J. Lücke (2010). Binary Sparse Coding. Proc. LVA/ICA 2010, LNCS 6365, 450-457.
[2] A.-S. Sheikh, J. A. Shelton, J. Lücke (2014). A Truncated EM Approach for Spike-and-Slab Sparse Coding. Journal of Machine Learning Research, 15:2653-2687.
[3] G. Puertas, J. Bornschein, and J. Lücke (2010). The Maximal Causes of Natural Scenes are Edge Filters. Advances in Neural Information Processing Systems 23, 1939-1947.
[4] J. Bornschein, M. Henniges, J. Lücke (2013). Are V1 simple cells optimized for visual occlusions? A comparative study. PLOS Computational Biology 9(6): e1003062.
[5] G. Exarchakis, M. Henniges, J. Eggert, and J. Lücke (2012). Ternary Sparse Coding. International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), 204-212.
[6] J. Lücke and J. Eggert (2010). Expectation Truncation and the Benefits of Preselection in Training Generative Models. Journal of Machine Learning Research 11:2855-2900.
[7] G. Exarchakis, and J. Lücke (2017). Discrete Sparse Coding. Neural Computation, 29(11), 2979-3013.