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

URL: Project Homepage

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

Other available revisons

Version	Changelog	Date
0.4.4	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	February 7, 2010, 16:48:00
0.4.3	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/match_distribution.py 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).	September 8, 2009, 20:21:12
0.4.1	Initial Announcement on mloss.org.	May 18, 2008, 17:06:05

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

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