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.. -*- mode: rst -*-
.. _chap_changelog:
.. index:: changelog
Development Changelog
This changelog only lists rather macroscopic changes to PyMVPA. The full VCS
changelog for 2.x series of PyMVPA is available here:
.. note::
You could find relevant information on 0.4.x series at .
In addition there is also a somewhat unconventional `visual changelog`_.
.. _visual changelog:
'Closes' and 'Fixes' statement IDs refer to the Debian and Github bug
tracking systems accordingly and can be queried by visiting the URLs::<closed bug id><fixed bug id>
* 2.2.0 (Sun, Sep 16 2012)
* New functionality (14 commits)
- New HDF5-based storage backend for
:class:`~mvpa2.measures.searchlight.Searchlight` the can significantly
speed up serialization of large result dataset in parallelized
- New fast searchlight
:class:`~mvpa2.measures.nnsearchlight.M1NNSearchlight` (and
helper :func:`~mvpa2.measures.nnsearchlight.sphere_m1nnsearchlight`) to
run mean-1-nearest-neighbor searchlights.
- New mappers for adding an axis to a dataset
(:class:`~mvpa2.mappers.shape.AddAxisMapper`), and for transposing a
dataset (:class:`~mvpa2.mappers.shape.TransposeMapper`).
- Improved implementation of SciPy's :func:`~mvpa2.misc.stats.ttest_1samp`
with support for masked arrays and alternative hypotheses.
- Individual tutorial chapters are now available for download as IPython
notebooks. A ``rst2ipynb`` converter is available in ``tools/``.
- New ``pymvpa2-tutorial`` command line utility to start a PyMVPA tutorial
session, either in a console IPython session, or using the IPython
notebook server.
- New wrapper functions for data generators/loaders in ``sklearn.datasets``,
available in :mod:`mvpa2.datasets.sources.sklearn_data`.
* Enhancements (89 commits)
- Initial Python 3 compatibility (spear-headed by Tiziano Zito).
- Bayesian hypothesis testing with
:class:`~mvpa2.clfs.transerror.BayesConfusionHypothesis` now supports
literal hypotheses specification, custom hypotheses subsets, and
computing of posterior probabilities.
- Allow for accessing fitted distributions in
- Extensions and improvements to the tutorial chapter on statistical
- Expose distance function as a property `dfx` of
- Extended :class:`~mvpa2.generators.base.Sifter` with ability to discard
unbalanced partitions.
- :func:`~mvpa2.base.hdf5.h5save` now creates missing directories
automatically by default.
- Dedicated training for
:class:`~mvpa2.algorithms.hyperalignment.Hyperalignment`, and new
auto-train capability.
- :class:`~mvpa2.clfs.transerror.BayesConfusionHypothesis` now computes
optional posterior probabilities, and supports hypothesis definitions
using literal labels.
* API changes
- All command line tools have been renamed to have a consistent 'pymvpa2-'
* Fixes (77 commits)
- HDF5 now properly stores object-type ndarray, where it the array shape
was unintentionally modified on-load before (Fixes #84).
- HDF5 can now reconstruct 'builtin' objects (Fixes #86).
- Check value data type and convert to float when collecting performance
statistics to avoid numerical problems.
- Do not fail in :class:`~mvpa2.clfs.transerror.BayesConfusionHypothesis`
when a dataset does not provide class labels.
* 2.1.0 (Fri, June 29 2012)
* Fixes
- :func:`` failed to convert an array of
``False`` values into ``slice(None, 0, None)`` (Fixes #56).
- A number of fixes to the HDF5 IO code that ignored parts of an object's
state when custom ``__reduce__()`` implementations were used (Fixes #42),
and had problems storing metaclass types (Fixes #78).
- Proper single quotes in documentation code snippets within PDFs.
- Memory leak (model pointer) in LIBSVM bindings.
* Enhancements
- All searchlight implementations can now optionally store the IDs of all
features for each generated ROI (conditional attr. ``roi_feature_ids``)
- Add :func:`~mvpa2.misc.neighborhood.scatter_neighborhoods` to aid
sparse sampling of spaces.
- Add :class:`~mvpa2.clfs.transerror.ConfusionMatrixError` to compute
confusion matrices with an error function interface (e.g. for
``CrossValidation(errorfx=...)``). This class existed for a long time, but
was hidden in the unit tests.
- Add :class:`~mvpa2.clfs.transerror.Confusion` to compute
confusion matrices with a Node interface (e.g. for
``CrossValidation(postproc=...)``). This is useful if confusion matrices
are necessary as an intermediate result and further processing with
other nodes is desired.
* New functionality
- Add :class:`~mvpa2.clfs.transerror.BayesConfusionHypothesis` to perform
Bayesian hypothesis testing of multi-class confusion statistics. This is
useful to assess the likelihood of a particular (or all possible)
grouping of classes being distinguishable.
- Add :class:`~mvpa2.mappers.fxy.FxyMapper` to perform arbitrary
computations involving two datasets.
- Add :class:`~mvpa2.mappers.base.CombinedMapper` to run a dataset through
a set of mappers and combine their outputs.
- Add :class:`~mvpa2.measures.statsmodels_adaptor.UnivariateStatsModels` a wrapper
for using models from the statsmodels_ package as a
- Add :class:`~mvpa2.misc.dcov.dCOV` and
:func:`~mvpa2.misc.dcov.dcorcoef` to quantify independence of
(multivariate) signals.
* API changes
- Deprecating ``GLM`` that is now implemented with UnivariateStatsModels.
This deprecated GLM class no longer supports the ``zstat`` calculation,
and none of its previous conditional attributes are available anymore.
* 2.0.1 (Tue, Mar 27 2012)
Primarily a bugfix release
* Fixes (21 BF commits)
- HDF5 storage -- handle loading of objects with bound
- Use system-wide ``autosummary/`` for sphinx >= 1.1.2
(Closes: #658593).
- :class:`~mvpa2.base.state.ConditionalAttribute` should not loose
value in deepcopy when default off (Fixes #63).
- Correct handling of scalar mean/std values for
- MRI data import via _img2data now works with unicode
filenames (Fixes #60).
- Should work with IPython >= 0.11 now (Fixes #59).
- Various small fixes to improve tests and functionality.
- Fix :class:`SMLR` segfaults on Windows (Thanks cgohlke for the patch).
* Enhancements (29 ENH, OPT, and NF commits)
- :class:`~mvpa2.mappers.fx.FxMapper` calls functions natively
(instead of a slow row/column at a time) if they carry ``axis``
as the 2nd argument. Provides tremendous speed up for
:func:`mean_sample` etc.
- :func:``
generator for random unique combinations.
- :func:`~mvpa2.misc.fx.dual_gaussian` made more robust by not
handling negative values for the standard deviations.
- :func:`~mvpa2.misc.fx.dual_positive_gaussian`.
- Expose "sensitivities" for :class:`PLR`.
- :class:`~mvpa2.generators.partition.NFoldPartitioner` float
option for ``cvtype`` and intelligent behavior on 'random'
limited by 'count' given a large number of folds.
- Add few additional learners from :mod:`sklearn` to the warehouses:
ExtraTrees, RandomForest, LassoLarsIC.
- ``__repr__`` for :class:`Partitioner`s.
- Add new performance metric -- F1 score -- in the confusion matrix
summary stats.
- :class:`CachedQueryEngine` does not rely now on id but on the
exact value of the query parameters (converted to hashable
types). Before it could behave incorrectly on rare occasions.
* API changes
- Deprecating ``.splitattr`` if favor of ``.attr`` in
* 2.0.0 (Mon, Dec 19 2011)
This release aggregates all the changes occurred between official
releases in 0.4 series and various snapshot releases (in 0.5 and 0.6
series). To get better overview of high level changes see
:ref:`release notes for 0.5 <chap_release_notes_0.5>` and :ref:`0.6
<chap_release_notes_0.6>` as well as summaries of release candidates
* Fixes (23 BF commits)
- Significance level in the right tail was fixed to include the
value tested -- otherwise resulted in optimistic bias (or
absurdly high significance in improbable case if all estimates
having the same value).
- Compatible with the upcoming IPython 0.12 and renamed sklearn
(Fixes #57).
- Do not double-train ``slave`` classifiers while assessing
sensitivities (Fixes #53).
* Enhancements (30 ENH + 3 NF commits)
- Resolving voting ties in kNN based on mean distance, and
randomly in SMLR.
- :class:`kNN`\'s ``ca.estimates`` now contains dictionaries with
votes for each class.
- Consistent zscoring in :class:`Hyperalignment`.
* 2.0.0~rc5 (Wed, Oct 19 2011)
* **Major**: to allow easy co-existence of stable PyMVPA 0.4.x, 0.6
development ``mvpa`` module was renamed into mod:`mvpa2`.
* Fixes
- Compatible with the new Shogun 1.x series.
- Compatible with the new h5py 2.x series.
- :file:`mvpa-prep-fmri` -- various compatibility fixes and smoke testing.
- Deepcopying :class:`SummaryStatistics` during __add__.
* Enhancements
- Tutorial uses :mod:`mvpa2.tutorial_suite` now.
- Better suppression of R warnings when needed.
- Internal attributes of many classes were exposed as properties.
- More unification of ``__repr__`` for many classes.
* 0.6.0~rc4 (Wed, Jun 14 2011)
* Fixes
- Finished transition to :mod:`nibabel` conventions in `plot_lightbox`.
- Addressed :mod:`matplotlib.hist` API change.
- Various adjustments in the tests batteries (:mod:`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
* 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
- `GNBSearchlight` handling of ``roi_ids``.
- More robust detection of mod:`scikits.learn` and :mod:`nipy`
* 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.
- :file:`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
- `CrossValidation` takes any custom Node as ``errorfx`` argument.
- `ConfusionMatrix` can now be used as an ``errorfx`` in Crossvalidation.
- ``LOE(ACC): Linear Order Effect in ACC`` was added to
`ConfusionMatrix` to detect trends in performances across
- A `Node`\ s 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.
- `Collection`\ s were provided with adequate ``(deep|)copy``.
And `Dataset` was refactored to use `Collection`\ s ``copy``
- ``update-*`` Makefile rules automatically should fast-forward
corresponding ``website-updates`` branch
- `MVPA_TESTS_VERBOSITY` controls also :mod:`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
* 0.6.0~rc2 (Thu, Mar 3 2011)
* Various fixes in the mvpa.atlas module.
* 0.6.0~rc1 (Thu, Feb 24 2011)
* Many, many, many
* For an overview of the most drastic changes :ref:`see constantly
evolving release notes for 0.6 <chap_release_notes_0.6>`
* 0.5.0 (sometime in March 2010)
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 :ref:`release notes <chap_release_notes_0.5>`.
Most notably, this version was to first to come with a comprehensive two-day
* 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.
- :file:`doc/examples/` - best error is the
minimal one
* Enhancements
- :class:`~mvpa.clfs.gnb.GNB` can now tolerate training datasets
with a single label
- :class:`~mvpa.clfs.meta.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
- :class:`~mvpa.clfs.plr.PLR` robustification
* Enhancements
- Enforce suppression of numpy warnings while running unittests.
Also setting verbosity >= 3 enables all warnings (Python, NumPy,
and PyMVPA)
- :file:`doc/examples/` example (adopted from 0.5)
- Introduced base class :class:`~mvpa.clfs.base.LearnerError` for
classifiers' exceptions (adopted from 0.5)
- Adjusted example data to live upto nibabel's warranty of NIfTI
- 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 :mod:`` interface
- Deducing number of SVs for SVR (LIBSVM)
- Correction of significance in the tails of non-parametric
* Miscellaneous:
- Development repository moved to
* 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):
- :class:`~mvpa.clfs.gnb.GNB` implements Gaussian Naïve Bayes
- :func:`~mvpa.misc.fsl.base.read_fsl_design` to read FSL FEAT design.fsf
files (Contributed by Russell A. Poldrack).
- :class:`~mvpa.datasets.miscfx.SequenceStats` to provide basic
statistics on labels sequence (counter-balancing,
- New exceptions :class:`~mvpa.clfs.base.DegenerateInputError` and
:class:`~mvpa.clfs.base.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 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).
- :mod:`` :
+ 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: :class:`~mvpa.clfs.knn.kNN` and
* 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):
- Further optimized LIBSVM bindings.
- Copy-if-sorted in
* New functionality (25 NF commits):
- :class:`~mvpa.mappers.procrustean.ProcrusteanMapper` with
orthogonal and oblique transformations.
- Ability to generate simple reports using :mod:`reportlab`.
See/run :file:`examples/` for example.
- :class:`~mvpa.clfs.meta.TreeClassifier` -- construct simple
hierarchies of classifiers.
- :func:`` to report information about the
system/PyMVPA to be included in the bug reports.
- Parameter 'reverse' to swap training/testing splits in
:class:`~mvpa.datasets.splitters.Splitter` .
- Example code for the analysis of event-related dataset using
- :meth:`` to create
lists of :class:``.
- :file:`mvpa-prep-fmri` was extended with plotting of motion
correction parameters.
- :class:`` can be explicitly told
either file contains a header.
- In :class:`~mvpa.atlases.base.XMLBasedAtlas`
(e.g. :mod:`~mvpa.atlases.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 :meth:`~mvpa.datasets.base.Dataset.copy` with
deep=False allows for shallow copying the dataset.
- :class:`~mvpa.clfs.meta.FeatureSelectionClassifier` s in
:mod:`~mvpa.clfs.warehouse` not to reuse the same classifiers,
but to use clones.
* Fixed (70 BF commits):
- :class:`~mvpa.measures.anova.OneWayAnova`: previously degrees of
freedom were not considered while computing F-scores.
- Majority voting strategy in :class:`~mvpa.clfs.knn.kNN`: it was
not working.
- Various fixes to ensure cross-platform building (:mod:`numpy` header
locations, etc).
- Stability fixes in :class:`~mvpa.clfs.transerror.ConfusionMatrix`.
- :meth:`~mvpa.datasets.base.Dataset.idsonboundaries`: samples
at the end of the sequence were not handled properly.
- Proper "untraining" of
:class:`~mvpa.clfs.meta.FeatureSelectionClassifier` s
classifiers which use sensitivities: it could lead to various
unpleasant side-effects if the same slave classifier was used
simultaneously by multiple :class:`MetaClassifiers` (like
* Documentation (25 DOC commits): citations, spelling corrections,
* 0.4.2 (Mon, 25 May 2009)
* New correlation stability measure
* New elastic net classifier (:class:`~mvpa.clfs.enet.ENET`).
* New GLM-Net regression/classifier (:class:`~mvpa.clfs.glmnet.GLMNET`).
* New measure :class:`~mvpa.measures.anova.CompoundOneWayAnova`.
* New measure :class:`~mvpa.measures.ds.DSMDatasetMeasure`.
* New meta-measure
* New basic :class:`~mvpa.measures.glm.GLM` implementation.
* New examples for Gaussian process regression.
* New example showing a searchlight analysis employing a dissimilarity
matrix based measure.
* New :class:`~mvpa.mappers.zscore.ZScoreMapper`.
* New import helper for FSL design matrices
* New implementation of a mapper using a self-organizing map
(:class:`~mvpa.mappers.som.SimpleSOMMapper`) and a corresponding example.
* Matplotlib backend is now configurable via
* PyMVPA version is now avialable from :data:`mvpa.__version__`.
* Renamed `mvpa.misc.plot.errLinePLot` to
:func:`~mvpa.misc.plot.plotErrLine` for consistency.
* Fixed :class:`~mvpa.datasets.splitters.NFoldSplitter` to support N-3 and
larger splits.
* Improved speed of LIBSVM backend. Thanks to Valentin Haenel and Tiziano
* 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
:func:`mvpa.test`, this allow to easily run unittests from within Python.
* :class:`~mvpa.datasets.nifti.NiftiDataset` capable to handle files
with less than 4 dimensions, which can, optionally, be provided as
a list of filenames or :class:`~nifti.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 :mod:`~mvpa.suite`:
- Pre-populated warehouses of classifiers and regressions are
renamed from clfs and regrs into
:data:`~mvpa.clfs.warehouse.clfswh` and
:data:`~mvpa.clfs.warehouse.regrswh` respectively.
- :class:`` is not derived from
:class:`dict` any longer -- just from a basic :class:`object` class.
API includes methods 'dump', 'asdict' and a property 'registered'.
* Changes (code refactorings) which *should not impact* any user who
imports from :mod:`~mvpa.suite`:
- Meta classifiers definitions moved from :mod:`~mvpa.clfs.base` into
- Splitters definitions moved from :mod:`~mvpa.datasets.splitter` into
* 0.4.0 (Sat, 15 Nov 2008)
* Add :class:``, 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 :envvar:`MVPA_SVM_BACKEND` (libsvm or
* Non-deterministic tests in the unittest battery are now configurable via
* 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 :class:`~mvpa.mappers.base.CombinedMapper` to map between multiple,
independent dataspace and a common feature space.
* New :class:`~mvpa.mappers.base.ChainMapper` to create chains of mappers
of abitrary lenght (e.g. to build preprocessing pipelines).
* New :class:`~mvpa.datasets.event.EventDataset` to rapidly extract
boxcar-shaped samples from data array using a simple list of
:class:`` definitions.
* Removed obsolete MetricMapper class. :class:`~mvpa.mappers.base.Mapper`
itself provides the facilities for dealing with metrics.
* :class:`~mvpa.mappers.boxcar.BoxcarMapper` can now handle data with more
than four dimensions/axis and also performs reverse mapping of single
boxcar samples.
* :class:`~mvpa.misc.fsl.base.FslEV3` can now convert EV3 files into
a list of :class:`` 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 :class:`~mvpa.clfs.lars.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
* New manual section about meta-classifiers (contributed by James M.
* New minimal example for a 'getting started' section in the manual.
* Former :envvar:`MVPA_QUICKTEST` was renamed to :envvar:`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
* 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 :class:`` 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 (:func:`~mvpa.misc.plot.base.plotBars`).
* Dataset supports mapping of string labels onto numerical labels, removing
the need to perform this mapping manually in user code. ''
is adjusted accordingly to demonstrate the new feature.
* New :meth:`mvpa.clfs.base.Classifier.summary` method to dump classifier
* Improved and more flexible :func:`~mvpa.misc.plot.erp.plotERPs`.
* New :class:`~mvpa.measures.irelief.IterativeRelief` sensitivity analyzer.
* Added visualization of confusion matrices via
:meth:`mvpa.clfs.transerror.ConfusionMatrix.plot` inspired by Ingo Fründ.
* The PyMVPA version is now globally available in :data:`mvpa.pymvpa_version`.
* BugFix: :class:`` reader failed in some cases.
* Several improvements (docs and implementation) for building PyMVPA on
MacOS X.
* New convenience accessor methods (:meth:``,
:meth:`~mvpa.datasets.base.Dataset.where` and
:meth:`~mvpa.datasets.base.Dataset.__getitem__`) for
* New :func:`mvpa.seed()` function to configure the random number generators
from user code.
* Added reader for a MEG sensor locations format
* 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
* 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
* 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/ to
quickly fix your custom code.
* Change all references to PyMVPA website to
* 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
Additionally, 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 '').
* 0.2.1 (Sun, 15 Jun 2008)
* Several improvements to make building PyMVPA on Windows systems easy
(e.g. added dedicated 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
* Added ability to compute stats/probabilities for all measures and transfer
* 0.1.0 (Wed, 20 Feb 2008)
* First public release.
.. _statsmodels:
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