Description:

Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, Ridge Regressions, Sparse Multinomial Logistic Regression, GPR. RFE, I-RELIEF), and bindings to external ML libraries (libsvm, shogun, R). While it is not limited to neuroimaging data (e.g. FMRI) it is eminently suited for such datasets.

It is actively developed project, thus you might better off trying it from the version control system which contains 0.5 development going forward. See http://dev.pymvpa.org for the upcoming website/documentation. Please see documentation on how to obtain and "build" from sources.

Changes to previous version:

0.4.4 (Mon, Feb 2 2010) (Total: 144 commits)

Primarily a bugfix release, probably the last in 0.4 series since development for 0.5 release is leaping forward.

• New functionality (19 NF commits):

o GNB implements Gaussian Naïve Bayes Classifier.

o read_fsl_design() to read FSL FEAT design.fsf files (Contributed by Russell A. Poldrack).

o SequenceStats to provide basic statistics on labels sequence (counter-balancing, autocorrelation).

o New exceptions DegenerateInputError and FailedToTrainError to be thrown by classifiers primarily during training/testing.

o Debug target STATMC to report on progress of Monte-Carlo sampling (during permutation testing).

• Refactored (15 RF commits):

o To get users prepared to 0.5 release, internally and in some examples/documentation, access to states and parameters is done via corresponding collections, not from the top level object (e.g. clf.states.predictions instead of soon-to-be-deprecated clf.predictions). That should lead also to improved performance.

o Adopted copy.py from python2.6 (support Ellipsis as well). ed (38 BF commits):

o GLM output does not depend on the enabled states any more.

o Variety of docstrings fixed and/or improved.

o Do not derive NaN scaling for SVM’s C whenever data is degenerate (lead to never finishing SVM training).

o sg : + KRR is optional now – avoids crashing if KRR is not available.

• tolerance to absent set_precompute_matrix in svmlight in recent shogun versions.

• support for recent (present in 0.9.1) API change in exposing debug levels.

o Python 2.4 compatibility issues: kNN and IFS

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: None

Tags: Shogun, Python, Eeg, Classification, Regression, Support Vector Machines, K Nearest Neighbor Classification, Pca, Rfe, Neuroscience, Fmri, Framework, Gpr, Lars, Smlr, Meg

Archive: download here

Comments

Yaroslav Halchenko (on May 18, 2008, 17:07:37)

It is actively developed project at the moment, thus it is preferable to don't rely on releases but rather use master branch of git repository mentioned on the project homepage

Yaroslav Halchenko (on September 8, 2009, 20:21:46)

0.4.3 release update

Yaroslav Halchenko (on September 8, 2009, 20:29:35)

updated entry to don't be treated as PRE

Leave a comment

You must be logged in to post comments.