.. -*- mode: rst -*-

.. _chap_changelog:

.. index:: changelog

****************************
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:

  https://github.com/PyMVPA/PyMVPA/commits/maint%2F0.4

In addition there is also a somewhat unconventional visual changelog:

  http://www.pymvpa.org/history.html

'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.

  * Many, many, many


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
    - :class:`GPRLinearWeights` -- correct access to weights
    - :class:`FslEV3` -- gzip import and getNEVs
    - :func:`read_fsl_design` -- correct splitting of the fields
    - :func:`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.
    - :file:`doc/examples/searchlight_minimal.py` - 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/nested_cv.py` 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
      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 :mod:`~mvpa.clfs.sg` interface
    - Deducing number of SVs for SVR (LIBSVM)
    - Correction of significance in the tails of non-parametric
      tests

  * Miscellaneous:

    - Development repository moved to http://github.com/PyMVPA/PyMVPA

* 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
      Classifier.
    - :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,
      autocorrelation).
    - 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 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).
    - :mod:`~mvpa.clfs.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: :class:`~mvpa.clfs.knn.kNN` and
      :class:`~mvpa.featsel.ifs.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):

    - Further optimized LIBSVM bindings.
    - Copy-if-sorted in
      :class:`~mvpa.datasets.base.Dataset.selectFeatures`.

  * 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/match_distribution.py` for example.
    - :class:`~mvpa.clfs.meta.TreeClassifier` -- construct simple
      hierarchies of classifiers.
    - :func:`~mvpa.base.info.wtf` 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
      :class:`~mvpa.datasets.nifti.ERNiftiDataset`.
    - :meth:`~mvpa.misc.io.base.SampleAttributes.toEvents` to create
      lists of :class:`~mvpa.misc.support.Event`.
    - :file:`mvpa-prep-fmri` was extended with plotting of motion
      correction parameters.
    - :class:`~mvpa.misc.io.base.ColumnData` 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
      :class:`~mvpa.clfs.meta.TreeClassifier`).

  * Documentation (25 DOC commits): citations, spelling corrections,
    etc.


* 0.4.2 (Mon, 25 May 2009)

  * New correlation stability measure
    (:class:`~mvpa.measures.corrstability.CorrStability`).
  * 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
    :class:`~mvpa.measures.splitmeasure.TScoredFeaturewiseMeasure`.
  * 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
    (:class:`~mvpa.misc.fsl.base.FslGLMDesign`).
  * 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
    :envvar:`MVPA_MATPLOTLIB_BACKEND`.
  * 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
    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
    :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:`~mvpa.misc.io.hamster.Hamster` 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
      :mod:`~mvpa.clfs.meta`.
    - Splitters definitions moved from :mod:`~mvpa.datasets.splitter` into
      :mod:`~mvpa.datasets.splitters`


* 0.4.0 (Sat, 15 Nov 2008)

  * Add :class:`~mvpa.misc.io.hamster.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 :envvar:`MVPA_SVM_BACKEND` (libsvm or
    shogun).
  * Non-deterministic tests in the unittest battery are now configurable via
    :envvar:`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 :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:`~mvpa.misc.support.Event` 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:`~mvpa.misc.support.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 :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
    reference.
  * New manual section about meta-classifiers (contributed by James M.
    Hughes).
  * 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
    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 :class:`~mvpa.datasets.channel.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 (: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. 'clfs_examples.py'
    is adjusted accordingly to demonstrate the new feature.
  * New :meth:`mvpa.clfs.base.Classifier.summary` method to dump classifier
    settings.
  * 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:`~mvpa.misc.io.meg.TuebingenMEG` reader failed in some cases.
  * Several improvements (docs and implementation) for building PyMVPA on
    MacOS X.
  * New convenience accessor methods (:meth:`~mvpa.datasets.base.Dataset.select`,
    :meth:`~mvpa.datasets.base.Dataset.where` and
    :meth:`~mvpa.datasets.base.Dataset.__getitem__`) for
    :class`~mvpa.datasets.base.Dataset`.
  * New :func:`mvpa.seed()` function to configure the random number generators
    from user code.
  * Added reader for a MEG sensor locations format
    (:class:`~mvpa.misc.io.meg.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)

  * First public release.
