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recent version of PyMVPA and its documentation.
PyMVPA Development Changelog
This changelog only lists rather macroscopic changes to PyMVPA. The full VCS
changelog for 0.4.x series of PyMVPA is available here:
In addition there is also a somewhat unconventional visual changelog:
‘Closes’ statement IDs refer to the Debian bug tracking system and can be
queried by visiting the URL:
http://bugs.debian.org/<bug id>
- Unreleased changes
Changes described here are not yet released, but available from VCS
repository.
Releases
0.4.8 (Tue, Apr 23 2012) (Total: 14 commits)
A bugfix release
- Fixed
- Compatibility with libsvm 3.10, shogun >= 1.0 (Closes: #655643)
- import ma directly from numpy
- GPRLinearWeights – correct access to weights
- FslEV3 – gzip import and getNEVs
- read_fsl_design() – correct splitting of the fields
- score() – assure std to be an array during application
- RF
- extensions are built inplace
0.4.7 (Tue, Mar 07 2011) (Total: 12 commits)
A bugfix release
- Fixed
- Addressed the issue with input NIfTI files having scl_* fields
set: it could result in incorrect analyses and
map2nifti-produced NIfTI files. Now input files account for
scaling/offset if scl_ fields direct to do so. Moreover upon
map2nifti, those fields get reset.
- doc/examples/searchlight_minimal.py - best error is the
minimal one
- Enhancements
- GNB can now tolerate training datasets
with a single label
- TreeClassifier can have trailing nodes
with no classifier assigned
0.4.6 (Tue, Feb 01 2011) (Total: 20 commits)
A bugfix release
- Fixed (few BF commits):
- Compatibility with numpy 1.5.1 (histogram) and scipy 0.8.0
(workaround for a regression in legendre)
- Compatibility with libsvm 3.0
- PLR robustification
- Enhancements
- Enforce suppression of numpy warnings while running unittests.
Also setting verbosity >= 3 enables all warnings (Python, NumPy,
and PyMVPA)
- doc/examples/nested_cv.py example (adopted from 0.5)
- Introduced base class LearnerError for
classifiers’ exceptions (adopted from 0.5)
- Adjusted example data to live upto nibabel’s warranty of NIfTI
standard-compliance
- More robust operation of MC iterations – skip iterations where
classifier experienced difficulties and raise an exception
(e.g. due to degenerate data)
0.4.5 (Fri, Oct 01 2010) (Total: 27 commits)
A bugfix release
- Fixed (13 BF commits):
- Compatible with LIBSVM >= 2.91 (Closes: #583018)
- No string exceptions raised (Python 2.6 compatibility)
- Setting of shrinking parameter in sg interface
- Deducing number of SVs for SVR (LIBSVM)
- Correction of significance in the tails of non-parametric
tests
- Miscellaneous:
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):
- GNB implements Gaussian Naïve Bayes
Classifier.
- read_fsl_design() to read FSL FEAT design.fsf
files (Contributed by Russell A. Poldrack).
- SequenceStats to provide basic
statistics on labels sequence (counter-balancing,
autocorrelation).
- New exceptions DegenerateInputError and
FailedToTrainError to be thrown by
classifiers primarily during training/testing.
- Debug target STATMC to report on progress of Monte-Carlo
sampling (during permutation testing).
- Refactored (15 RF commits):
- 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.
- Adopted copy.py from python2.6 (support Ellipsis as well).
- Fixed (38 BF commits):
- GLM output does not depend on the enabled states any more.
- Variety of docstrings fixed and/or improved.
- Do not derive NaN scaling for SVM’s C whenever data is
degenerate (lead to never finishing SVM training).
- 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.
- Python 2.4 compatibility issues: kNN and
IFS
0.4.3 (Sat, 5 Sep 2009) (Total: 165 commits)
- Online documentation editor is no longer available due to low
demand – please submit changes via email.
- Performance (Contributed by Valentin Haenel) (3 OPT commits):
- 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):
- 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).
- Documentation (25 DOC commits): citations, spelling corrections,
etc.
0.4.2 (Mon, 25 May 2009)
- New correlation stability measure
(CorrStability).
- New elastic net classifier (ENET).
- New GLM-Net regression/classifier (GLMNET).
- New measure CompoundOneWayAnova.
- New measure DSMDatasetMeasure.
- New meta-measure
TScoredFeaturewiseMeasure.
- New basic GLM implementation.
- New examples for Gaussian process regression.
- New example showing a searchlight analysis employing a dissimilarity
matrix based measure.
- New ZScoreMapper.
- New import helper for FSL design matrices
(FslGLMDesign).
- New implementation of a mapper using a self-organizing map
(SimpleSOMMapper) and a corresponding example.
- Matplotlib backend is now configurable via
MVPA_MATPLOTLIB_BACKEND.
- PyMVPA version is now avialable from mvpa.__version__.
- Renamed mvpa.misc.plot.errLinePLot to
plotErrLine() for consistency.
- Fixed NFoldSplitter to support N-3 and
larger splits.
- Improved speed of LIBSVM backend. Thanks to Valentin Haenel and Tiziano
Zito.
- Updated included LIBSVM version to 2.89.
- Adjust LIBSVM Python interface for recent NumPy API and latest LIBSVM
release 2.89.
- Refactored examples parser into a standalone tool to turn PyMVPA examples
into restructured text sources.
0.4.1 (Sat, 24 Jan 2009)
- Unit tests and example data are now also installed. In conjunction with
mvpa.test(), this allow to easily run unittests from within Python.
- NiftiDataset capable to handle files
with less than 4 dimensions, which can, optionally, be provided as
a list of filenames or NiftiImage objects. That
makes it easy to load data from a sequence of files.
- Changes (code refactorings) which might impact any user who
imports from suite:
- Pre-populated warehouses of classifiers and regressions are
renamed from clfs and regrs into
clfswh and
regrswh respectively.
- Hamster is not derived from
dict any longer – just from a basic object class.
API includes methods ‘dump’, ‘asdict’ and a property ‘registered’.
- Changes (code refactorings) which should not impact any user who
imports from suite:
- Meta classifiers definitions moved from base into
meta.
- Splitters definitions moved from splitter into
splitters
0.4.0 (Sat, 15 Nov 2008)
- Add Hamster, as a simple facility to easily
store any serializable objects in a compressed file and later on resurrect
all of them with a single line of code.
- SVM backend is now configurable via MVPA_SVM_BACKEND (libsvm or
shogun).
- Non-deterministic tests in the unittest battery are now configurable via
MVPA_TESTS_LABILE.
- New helper to determine and plot the best matching distribution(s) for
the data (matchDistribution, plotDistributionMatches). It is WiP
thus API can change in the upcoming release.
- Simplifies API of mappers.
- Splitters can now limit the number of splits automatically.
- New CombinedMapper to map between multiple,
independent dataspace and a common feature space.
- New ChainMapper to create chains of mappers
of abitrary lenght (e.g. to build preprocessing pipelines).
- New EventDataset to rapidly extract
boxcar-shaped samples from data array using a simple list of
Event definitions.
- Removed obsolete MetricMapper class. Mapper
itself provides the facilities for dealing with metrics.
- BoxcarMapper can now handle data with more
than four dimensions/axis and also performs reverse mapping of single
boxcar samples.
- FslEV3 can now convert EV3 files into
a list of Event instances.
- Results of tests for external dependencies are now stored in PyMVPA’s
config manager (mvpa.cfg) and can be stored to a file (not done
automatically at the moment). This will significantly decrease the time
needed to import the mvpa module, as it prevents the repeated and lengthy
tests for working externals.
- Initial support for ROC computing and AUC as an accuracy measure.
- Weights of LARS are now available via LARSWeights.
- Added an initial list of MVPA-related references to the manual, tagged with
keywords and comments as well is DOI or similar URL reference to the
original document.
- Added initial glossary to the manual.
- New ‘Module reference’, as a middle-ground between manual and API
reference.
- New manual section about meta-classifiers (contributed by James M.
Hughes).
- New minimal example for a ‘getting started’ section in the manual.
- Former MVPA_QUICKTEST was renamed to MVPA_TESTS_QUICK.
- Update installation instructions for RPM-based distributions to make use
of the OpenSUSE Build Service.
- Updated install instructions for several RPM-based GNU/Linux
distributions.
- Switch from distutils to numpy.distutils (no change in dependencies).
- Depend on PyNIfTI >= 0.20081017.1 and gain a smaller memory footprint when
accessing NIfTI files via all datasets with NIfTI support.
- Added workaround to make PyMVPA work with older Shogun releases and those
from 0.6.4 on, which introduced backward-incompatible API changes.
0.3.1 (Sun, 14 Sep 2008)
- New manual section about feature selection with a focus on RFE.
Contributed by James M. Hughes.
- New dataset type ChannelDataset for data
structured in channels. Might be useful for data modalities like EEG and
MEG. This dataset includes support for common preprocessing steps like
resampling and baseline signal substraction.
- Plotting of topographies on heads. Thanks to Ingo Fründ for contributing
this code. Additionally, a new example shows how to do such plots.
- New general purpose function for generating barplots and candlestick plots
with error bars (plotBars()).
- Dataset supports mapping of string labels onto numerical labels, removing
the need to perform this mapping manually in user code. ‘clfs_examples.py’
is adjusted accordingly to demonstrate the new feature.
- New mvpa.clfs.base.Classifier.summary() method to dump classifier
settings.
- Improved and more flexible plotERPs().
- New IterativeRelief sensitivity analyzer.
- Added visualization of confusion matrices via
mvpa.clfs.transerror.ConfusionMatrix.plot() inspired by Ingo Fründ.
- The PyMVPA version is now globally available in mvpa.pymvpa_version.
- BugFix: TuebingenMEG reader failed in some cases.
- Several improvements (docs and implementation) for building PyMVPA on
MacOS X.
- New convenience accessor methods (select(),
where() and
__getitem__()) for
:class`~mvpa.datasets.base.Dataset`.
- New mvpa.seed() function to configure the random number generators
from user code.
- Added reader for a MEG sensor locations format
(TuebingenMEGSensorLocations).
- Initial model selection support for GRP (using openopt).
- And tons of minor bugfixes, additional tests and improved documentation.
0.3.0 (Mon, 18 Aug 2008)
- Import of binary EEP files (used by EEProbe) and EEPDataset class.
- Initial version of a meta dataset class (MetaDataset). This is a container
for multiple datasets, which behaves like a dataset itself.
- Regression performance is summarized now within RegressionStatistics.
- Error functions: CorrErrorPFx, RelativeRMSErrorFx.
- Measures: CorrCoef.
- Data generators: chirp, wr1996
- Few more examples: curvefitting, kerneldemo, smellit, projections
- Updated kNN classifier. kNN is now able to use custom distance function
to determine that nearest neighbors. It also (re)gained the ability to do
simple majority or weighted voting.
- Some initial convenience functions for plotting typical results and data
exploration.
- Unified configuration handling with support for user-specific and
analysis-specific config files, as well as the ability to override all
config settings via environment variables. The configuration handling is
used for PyMVPA internal settings, but can also be easily used for
custom (user-)settings.
- Improved modularity, e.g. SciPy is not required anymore, but still very
useful.
- Initial implementations of ICA and PCA mapper using functionality provided
by MDP. These mappers are more or less untested and should be used with
great care.
- Further improved docstrings of some classes, but still a long way to go.
- New ‘boxcar’ mapper, which is the similar to the already present
transformWithBoxCar() function, but implemented as a mapper.
- New SampleGroupMapper that can be used for e.g. block averaging of
samples. See new FAQ item.
- Stripped redundant suffixes from module names, e.g.
mvpa.datasets.niftidataset -> mvpa.datasets.nifti
- mvpa.misc.cmdline variables opt* and opts* were groupped within
opt and optss class instances. Also names of the options were
changed to match ‘dest’ of the options. Use tools/refactor.py to
quickly fix your custom code.
- Change all references to PyMVPA website to www.pymvpa.org.
- Make website stylesheet compatible with sphinx 0.4.
- Several minor improvements of the compatibility with MacOS.
- Extended FAQ section of the manual.
- Bugfix: doubleGammaHRF() ignoring K2 argument.
0.2.2 (Tue, 17 Jun 2008)
- Extended build instructions: Added section on OpenSUSE.
- Replaced ugly PYMVPA_LIBSVM environment variable to trigger compiling the
LIBSVM wrapper with a proper ‘–with-libsvm’ switch in setup.py.
Additionally, setup.py now detects if included LIBSVM has been built and
enables LIBSVM wrapper automatically in this case.
- Added proper Makefiles for LIBSVM copy, with configurable compiler flags.
- Added ‘setup.cfg’ to remove the need to manually specify swig-opts
(Windows specific configuration is in ‘setup.cfg.win’).
0.2.1 (Sun, 15 Jun 2008)
- Several improvements to make building PyMVPA on Windows systems easy
(e.g. added dedicated Makefile.win to build a binary installer).
- Improved and extended documentation for building and installing PyMVPA.
- Include a minimal copy of the required (patched) LIBSVM library (currently
version 2.85.0) for convenience. This copy is automatically compiled and
used for the LIBSVM wrapper when PyMVPA built using the Make approach.
0.2.0 (Wed, 29 May 2008)
- New Splitter class (HalfSplitter) to split into first and second half.
- New Splitter class (CustomSplitter) to allow for splits with an arbitrary
number of datasets per split and the ability to specify the association
of samples with any of those datasets (not just the validation set).
- New sparse multinomial logistic regression (SMLR) classifier and
associated sensitivity analyzer.
- New least angle regression classifier (LARS).
- New Gaussian process regression classifier (GPR).
- Initial documentation on extending PyMVPA.
- Switch to Sphinx for documentation handling.
- New example comparing the performance of all classifiers on some
artificial datasets.
- New data mapper performing singular value decomposition (SVDMapper) and an
example showing its usage.
- More sophisticated data preprocessing: removal of non-linear trends and
other arbitrary confounding regressors.
- New Harvester class to feed data from arbitrary generators into multiple
objects and store results of returned values and arbitrary properties.
- Added documentation about how to build patched libsvm version with sane
debug output.
- libsvm bindings are not build by default anymore. Instructions on how to
reenable them are available in the manual.
- New wrapper from SVM implementation of the Shogun toolbox.
- Important bugfix in RFE, which reported incorrect feature ids in some
cases.
- Added ability to compute stats/probabilities for all measures and transfer
errors.
0.1.0 (Wed, 20 Feb 2008)
