Speaker
Tiziano Zito
(Institute for Theoretical Biology, Berlin)
Description
We present release 2.0 of the Modular toolkit for Data Processing
(MDP), a data processing framework written in Python and based on
numpy (the most popular numerical extensions to Python).
From the user's perspective, MDP consists of a collection of trainable
algorithms or other data processing units (nodes) that can be combined
into data processing flows. Given a sequence of input data, MDP takes
care of successively training or executing all nodes in the flow. This
structure allows to specify complex algorithms as a sequence of
simpler data processing steps in a natural way. Training can be
performed using small chunks of input data, so that the use of
very large data sets becomes possible while reducing the memory
requirements. Memory usage can also be minimized by defining the
internals of the nodes to be single precision.
From the developer's perspective, MDP is a framework to make the
implementation of algorithms easier. The basic class 'Node' takes care
of tedious tasks like type and dimension checking, leaving the
developer free to concentrate on the implementation of the training
and execution phases. The node then automatically integrates with the
rest of the library and can be used in a flow together with other
nodes.
MDP 2.0 introduces some important structural changes. It is now
possible to implement nodes with multiple training phases and even
nodes with an undetermined number of phases. This allows for example
the implementation of algorithms that need to collect some statistics
on the whole input before proceeding with the actual training, or
others that need to iterate over a training phase until a convergence
criterion is satisfied. The ability to train each phase using chunks
of input data is maintained if the chunks are generated with
iterators. Moreover, it is now possible to define nodes that require
supervised training in a very straightforward way by passing additional
arguments (e.g., labels or a target output) to the 'train' method.
Moreover, new algorithms have been added, expanding the base of
readily available basic data processing elements. Currently
implemented algorithms include Principal Component Analysis, two
flavors of Independent Component Analysis, Slow Feature Analysis,
Gaussian Classifiers, Growing Neural Gas, Fisher Discriminant
Analysis, and Factor Analysis.
MDP has been written in the context of theoretical research in
neuroscience, but it has been designed to be helpful in any context
where trainable data processing algorithms are used. Its simplicity on
the user side together with the reusability of the implemented nodes
make it also a valid educational tool.
As its user base is steadily increasing, MDP appears as a good
candidate for becoming a common repository of user-supplied,
freely available, Python implemented data processing algorithms.
http://mdp-toolkit.sourceforge.net
Primary authors
Pietro Berkes
(Gatsby Computational Neuroscience Unit, London)
Tiziano Zito
(Institute for Theoretical Biology, Berlin)
