About:
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, [...]
Changes:
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
below
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
-
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
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 :mod: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.
-
: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
partitioners).
-
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
splits.
-
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
method.
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
changelogs).
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
workshop/tutorial.
-
0.4.7 (Tue, Mar 07 2011) (Total: 12 commits)
A bugfix release
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)
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- Operating System:
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
|
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