Project details for PyMVPA Multivariate Pattern Analysis in Python

Screenshot PyMVPA Multivariate Pattern Analysis in Python 0.4.3

by yarikoptic - September 8, 2009, 20:21:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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. Please see documentation on how to obtain and "build" from sources.

Changes to previous version:

Online documentation editor is no longer available due to low demand – please submit changes via email.

Performance (Contributed by Valentin Haenel) (3 OPT commits):

  • Further optimized LIBSVM bindings.
  • Copy-if-sorted in selectFeatures.

New functionality (25 NF commits):

  • ProcrusteanMapper with orthogonal and oblique transformations.
  • Ability to generate simple reports using reportlab. See/run examples/ for example.
  • TreeClassifier – construct simple hierarchies of classifiers.
  • wtf() to report information about the system/PyMVPA to be included in the bug reports.
  • Parameter ‘reverse’ to swap training/testing splits in Splitter .
  • Example code for the analysis of event-related dataset using ERNiftiDataset.
  • toEvents() to create lists of Event.
  • mvpa-prep-fmri was extended with plotting of motion correction parameters.
  • ColumnData can be explicitly told either file contains a header.
  • In XMLBasedAtlas (e.g. fsl atlases) it is now possible to provide custom ‘image_file’ to get maps or indexes for the areas given an atlas’s volume registered into subject space.
  • Updated included LIBSVM version to 2.89 and provided support for its “silencing”.

Refactored (27 RF commits):

  • Dataset’s copy() with deep=False allows for shallow copying the dataset.
  • FeatureSelectionClassifier s in warehouse not to reuse the same classifiers, but to use clones.

Fixed (70 BF commits):

  • OneWayAnova: previously degrees of freedom were not considered while computing F-scores.
  • Majority voting strategy in kNN: it was not working.
  • Various fixes to ensure cross-platform building (numpy header locations, etc).
  • Stability fixes in ConfusionMatrix.
  • idsonboundaries(): samples at the end of the sequence were not handled properly.
  • Proper “untraining” of FeatureSelectionClassifier s classifiers which use sensitivities: it could lead to various unpleasant side-effects if the same slave classifier was used simultaneously by multiple MetaClassifiers (like TreeClassifier).
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


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

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