MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
The Modular toolkit for Data Processing (MDP) package is a library of widely used data processing algorithms, and the possibility to combine them together to form pipelines for building more complex data processing software.
MDP has been designed to be used as-is and as a framework for scientific data processing development.
From the user’s perspective, MDP consists of a collection of units, which process data. For example, these include algorithms for supervised and unsupervised learning, principal and independent components analysis and classification.
These units can be chained into data processing flows, to create pipelines as well as more complex feed-forward network architectures. Given a set of input data, MDP takes care of training and executing all nodes in the network in the correct order and passing intermediate data between the nodes. This allows the user to specify complex algorithms as a series of simpler data processing steps.
The number of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
Particular care has been taken to make computations efficient in terms of speed and memory. To reduce the memory footprint, it is possible to perform learning using batches of data. For large data-sets, it is also possible to specify that MDP should use single precision floating point numbers rather than double precision ones. Finally, calculations can be parallelised using the parallel subpackage, which offers a parallel implementation of the basic nodes and flows.
From the developer’s perspective, MDP is a framework that makes the implementation of new supervised and unsupervised learning algorithms easy and straightforward. The basic class, Node, takes care of tedious tasks like numerical type and dimensionality checking, leaving the developer free to concentrate on the implementation of the learning and execution phases. Because of the common interface, the node then automatically integrates with the rest of the library and can be used in a network together with other nodes.
A node can have multiple training phases and even an undetermined number of phases. Multiple training phases mean that the training data is presented multiple times to the same node. This allows the implementation of algorithms that need to collect some statistics on the whole input before proceeding with the actual training, and others that need to iterate over a training phase until a convergence criterion is satisfied. It is possible to train each phase using chunks of input data if the chunks are given as an iterable. Moreover, crash recovery can be optionally enabled, which will save the state of the flow in case of a failure for later inspection.
MDP is distributed under the open source BSD license. It 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’s side, the variety of readily available algorithms, and the reusability of the implemented nodes also make it a useful educational tool.