Generate theoretically sound simulated data.
Switch branches/tags
Nothing to show
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
doc
examples
simulate
tests
.coveragerc
.gitignore
LICENSE
MANIFEST.in
README.md
setup.py

README.md

pylearn-simulate

Simulated data are widely used to assess optimisation methods. This is because of their ability to evaluate certain aspects of the methods under study, that are impossible to look into when using real data sets. In the context of convex optimisation, it is never possible to know the exact solution of the minimisation problem with real data and it is a difficult problem even with simulated data. We propose to generalise an approach originally published by Nesterov (2013), for LASSO regression, to a broader family of penalised regression problems.

We would like to generate simulated data for which we know the exact solution of the optimised function. The inputs are: The minimiser b* (p-by-1), a candidate data set X0 (n-by-p), residual vector e (n-by-1), regularisation parameters (in our case they are two: k and g), the signal-to-noise ratio s, and the expression of the function f(b) to minimise.

The candidate version of the dataset may for instance be X0 ~ N(m, S), and the residual vector may be e ~ N(0, 1).

The proposed procedure outputs X and y such that

    b* = argmin f(b, X0, e, k, g, s),

with f a convex function that depends on the parameters defining the simulated data.

Oftentimes in linear regression, simulated data are generated such that

  (1)    y = X * b* + e.

If we want to evaluate an algorithm to minimise the LASSO problem

  (2)    0.5 * ||X * b - y||² + l * |b|,

where ||.||² is the squared L2-norm and |.| is the L1-norm, then we need to use e.g. cross-validation to find l. But the found l is very likely suboptimal, and in any case, we are forced to compare the solution to (1), which is not sparse.

This package thus provides the solution that minimises (2), instead of (1), namely b* and l. Which means that you will be able to compare both speed, sensitivity to noise, correlation, etc., and the actual solutions of different minimisation algorithms.

With this package, pylearn-simulate, it is straight-forward to generate such data. pylearn-simulate is written for Python 2.7.x.

Dependencies

The reference environment for pylearn-simulate is Ubuntu 12.04 LTS with Python 2.7.3, Numpy 1.6.1 and Scipy 0.9.0. More recent versions likely work, but have not been tested thoroughly.

Unless you already have Numpy and Scipy installed, you need to install them:

$ sudo apt-get install python-numpy python-scipy

In order to show plots and to run the examples, you may need to install Matplotlib:

$ sudo apt-get install python-matplotlib

Unless you have your own minimising software, we recommend that you download and install pylearn-parsimony (https://github.com/neurospin/pylearn-parsimony). While pylearn-parsimony is not a requirement to use pylearn-simulate, the examples are using it. Thus, in order to to run all parts of the examples, you will need pylearn-parsimony.

Installation

Easiest installation

Download pylearn-simulate and put it in your PYTHONPATH. Alternatively, put the directory pylearn-simulate/simulate/ in the directory of your project.

Recommended installation

Download the release of pylearn-simulate from https://github.com/neurospin/pylearn-simulate/releases. Unpack the file, go to the pylearn-simulate directory and type:

$ python setup.py install --user

for a local installation in the user's userbase directory (usually in ~/.local/lib/python2.7/site-packages on Unix-like/-based operating systems, such as Linux and OS X, and in %AppData%\Python\Python27\site-packages on Windows), or

$ sudo python setup.py install

for a global installation accessible to all users. You will need to have administrator rights on your computer in order to install software for all users.

You are now ready to use your fresh installation of pylearn-simulate!

Examples

See the examples in pylearn-simulate/examples/.