This content refers to an unreleased development version of PyMVPA
To provide the most recent news and documentation www.pymvpa.org reflects the
development 0.6 series of PyMVPA. If you are interested in the
documentation of the previous stable 0.4 series of PyMVPA, please
visit v04.pymvpa.org.
Finished transition to nibabel conventions in plot_lightbox
Addressed matplotlib.hist API change
Various adjustments in the tests batteries (nibabel 1.1.0
compatibility, etc)
New functionality
Explicit new argument flatten to from_wizard – default
behavior changed if mapper was provided as well
Enhancements
Elaborated __str__ and __repr__ for some Classifiers and
Measures
0.6.0~rc3 (Thu, Apr 12 2011)
Fixes
Bugfixes regarding the interaction of FlattenMapper and
BoxcarMapper that affected event-related analyses.
Splitter now handles attribute value None for splitting
properly.
GNBSearchlight handling of roi_ids.
More robust detection of mod:scikits.learn and nipy
externals.
New functionality
Added a Repeater node to yield a dataset multiple times and
Sifter node to exclude some datasets. Consequently, the
“nosplitting” mode of Splitter got removed at the same time.
tools/niils – little tool to list details
(dimensionality, scaling, etc) of the files in nibabel-supported formats.
Enhancements
Numerous documentation fixes.
Various improvements and increased flexibility of null distribution
estimation of Measures.
All attribute are now reported in sorted order when printing a dataset.
fmri_dataset now also stores the input image type.
Crossvalidation can now take a custom Splitter instance. Moreover, the
default splitter of CrossValidation is more robust in terms of number and
type of created splits for common usage patterns (i.e. together with
partitioners).
CrossValidation takes any custom Node as errorfx argument.
ConfusionMatrix can now be used as an errorfx in Crossvalidation.
LOE(ACC):LinearOrderEffectinACC was added to
ConfusionMatrix to detect trends in performances across
splits.
A Nodes postproc is now accessible as a property.
RepeatedMeasure has a new ‘concat_as’ argument that allows results to be
concatenated along the feature axis. The default behavior, stacking as
multiple samples, is unchanged.
Searchlight now has the ability to mark the center/seed of an ROI in
with a feature attribute in the generated datasets.
debug takes args parameter for delayed string
comprehensions. It should reduce run-time impact of debug()
calls in regular, non -O mode of Python operation.
String summaries and representations (provided by __str__
and __repr__) were made more exhaustive and more coherent.
Additional properties to access initial constructor arguments
were added to variety of classes.
Internal changes
New debug target STDOUT to allow attaching metrics
(e.g. traceback, timestamps) to regular output printed to stdout
New set of decorators to help with unittests
@nodebug to disable specific debug targets for the duration
of the test.
@reseed_rng to guarantee consistent random data given
initial seeding.
@with_tempfile to provide a tempfile name which would get
removed upon completion (test success or failure)
Dropping daily testing of maint/0.5 branch – RIP.
Collections were provided with adequate (deep|)copy.
And Dataset was refactored to use Collections copy
method.
update-* Makefile rules automatically should fast-forward
corresponding website-updates branch
MVPA_TESTS_VERBOSITY controls also numpy warnings now.
Dataset.__array__ provides original array instead of copy
(unless dtype is provided)
Also adapts changes from 0.4.6 and 0.4.7 (see corresponding
changelogs).
This is a special release, because it has never seen the general public.
A summary of fundamental changes introduced in this development version
can be seen in the release notes.
Most notably, this version was to first to come with a comprehensive two-day
workshop/tutorial.
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)
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)
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).
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.
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.
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.
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: double_gamma_hrf() 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.
