[scikit-learn] annotated tag debian/0.10.0-1 created (now eab6421)
Andreas Tille
tille at debian.org
Wed Dec 28 13:10:56 UTC 2016
This is an automated email from the git hooks/post-receive script.
tille pushed a change to annotated tag debian/0.10.0-1
in repository scikit-learn.
at eab6421 (tag)
tagging 79749fd2939781e201191ef081143d8a575984e7 (commit)
replaces debian/0.9.0.dfsg-1
tagged by Yaroslav Halchenko
on Thu Jan 12 22:48:29 2012 -0500
- Log -----------------------------------------------------------------
Debian release 0.10.0-1
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Alexandre Gramfort (38):
Merge pull request #376 from fabianp/fast_tests
STY: imports in covariance + pep8
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge pull request #404 from amueller/grid_search_doc
STY: pep8 + naming
DOC: prettify plot_permutation_test_for_classification.py
DOC : adding permutation_test_score to changelog
ENH : adding support for scaling by n_samples of the C parameter in libsvm and liblinear models
FIX : removing param nu from sparse.SVR, C from NuSVR + pep8
$Merge branch 'master' into n_samples_scaling
typo
s/C_scale_n_samples/scale_C
STY: pep8 + pyflakes
Merge pull request #464 from NelleV/FIX_bibtex
Merge branch 'master' into n_samples_scaling
STY: prettify doctest
ENH : adding scale_C in NuSVR
ENH : more contrasted colormap
MISC: typos + subplot adjust
ENH : C scaling of sparse models
Merge remote-tracking branch 'origin/master' into n_samples_scaling
ENH : adding missing scale_C in docstring
Merge pull request #465 from amueller/fastica_wowhiten
STY: PEP 257 in ridge.py
Merge pull request #473 from amueller/dataset_whitespace
Merge pull request #477 from jakevdp/gmm-fix
ENH : avoid global seeding in plot_polynomial_interpolation.py
ENH : clean up plot_feature_selection.py
Merge pull request #482 from DraXus/master
STY : pep8 and add print __doc__ in plot_sparse_coding.py
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
STY : pep8
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
misc
STY: s/grid_points/cv_scores
Merge pull request #495 from vene/sc-mixin
Merge pull request #507 from jakevdp/neighbors-check
Merge pull request #532 from amueller/grid_search_attributes
Andreas Mueller (248):
non-regression test for warm-start intercept shape using a binary dataset
letting intercept_init be of shape (), reshape to (1,) for consistency
added hopefully more intelligible error messages.
pep8
pep8
typo, pep8 and line continuations
test for new error strings
slight beautification (in my opinion)
don't test on error message, just on raise
pep8
DOCS: Image is aligned to the right...
DOC Added documentation for important attributes of GridSearchCV
specify dict type
DOCS: Typo in url
ENH: Adds more verbosity to grid_search. verbose > 2 gives scores while running grid.
Merge pull request #414 from amueller/grid_search_verbosity
DOC: Document "cache size" argument of SVR
COSMIT: remove unused error string.
COSMIT: remove unused error string.
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
ENH: removed kernel cache from fit method of DenseLibSVM, added to __init__ of BaseLibSVM
Added kernel cache argument to init of all SVC and SVR classes. For the moment the conservative 100MB default.
BUG cache_size instead of cache as paramter name
BUG: cache_size also for sparse SVMs
ENH: SVM cache_size default value changed to 200 mb
ENH Sparse SVM: removed cache_size parameter from fit method. Is now part of constructur.
DOC fixed doctests for cache_size parameter
DOC slight reformatting of kernel cache note in module docs.
BUG: minor mistake in earlier commit.
DOC: fogot doctests in python files.
DOC: another doctest.
ENH: in Scaler, warn if fit or transform called with integer data.
Merge pull request #425 from amueller/svm_cache_size
ENH parameter "epsilon" in SVR error messages is given correct name.
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
DOC Made reference to "Getting started" in "Datasets" section a link.
DOC: inline example for precomputed svm kernel
ENH in preprocessing.Scaler, raise warning also if given unsigned int
DOC/Website: Changed link on "support" page to scikit-learn.org, added 0.9 release doc link
DOC fixed whitespace in GridSearchCV doc string so that html doc is generated correctly.
COSMIT removed unused import, pep8
COSMIT pep8 in cluster module, removed unused import
COSMIT pep8 whitespace
COSMIT removed emacs modeline
COSMIT: pep8 whitespaces instead of aligned decimal points
COSMIT indentation
COSMIT ugly line break for pep8
COSMIT reindented for pep8
COSMIT pep8 whitespaces
Merge pull request #447 from amueller/pep8
ENH: in sgd classifier, check that parameter alpha is greater than zero
COSMIT some pep8
some pyflakes
COSMIT more pep8
COSMIT more pyflakes
COSMIT: more pep8. enough for today...
ENH: fastica returns whitening matrix "None" when whitening=False
TEST non-regression test for issue 238, FastICA failing with whiten="False"
COSMIT pep8
COSMIT pyflakes
COSMIT: pep8
COSMIT pep8 in backported sparsetools...
DOC Added Gael's explanations about the memory usage in grid_search / joblib
DOC: Auto example digit classification plot without interpolation and axis.
FIX: typo in with statement
Example for random dataset function.
Random dataset example: make figure look nice on the web
DOC: Added random dataset plot to doc.
COSMIT: random dataset plot prettified
DOC Added comment about equivalence of nu-SVM and C-SVM to the docs
Examples: Replaced NuSVM by rbf SVM in example. RBF-SVMs are really important, NuSVMs not so much imho.
pep8. whoops..
COSMIT: pep8
FIX: Return "None" fist.
Example for finding the hyperparameters in a RBF SVM
Examples: Make SVM parameter estimation look good on the web.
DOC: Fixed legend in iris svm example
DOC Nonlinear SVM example changed to satisfy my sense of aesthetics. Hope you like it.
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
COSMIT pep8
Example illustrating parameters of an RBF SVM
COSMIT removed unused import math in utils/extmath.py
FIX: make kmeans test not raise warning when init is passed.
FIX: make kmeans test not raise warning when init is passed.
DOC Description of the basic dataset API
DOC: Corrections and additions to the dataset docs. Also more detailed docstrings
DOC test fix. Set printoptions to get rid of epison.
FIX: whitespace after ..
DOC test fix finally....
DOC fixed fastica docstring: if whiten=False K=None
ENH linnerud dataset interface adjusted to be consistent with the others
FIX: typo in diabetes docs
DOC RST field lists don't behave as I want them to:(
COSMIT datasets doc using rst tables
FIX This should fix the doctests in the datasets dir. They take quite long, I think it's because of the svmlight loaders. So I didn't include them in the standard make target
COSMIT rst formatting
DOC: Added missing rst label
FIX RST references
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
FIX rst errors in docs
FIX doc rst references
DOC Added link for Satrajit Gosh, removed dead link for Robert Layton since I couldn't find his website.
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
DOC Robert Layton again.
ENH prettify kmeans vs minibatch kmeans example
ENH adjust subplots to look good on the web.
FIX minor typo
FIX errors in doc
FIX minor docfixes
added kernel approximation using monte carlo approximation of fourier transform
ENH pipeline compatible interface to fit, transform and fit_transform
DOC example comparing linear classification, kernel svm and kernel approximation with explicit mapping.
kernel approximation example
DOC beautified kernel approximation example plot
better docs, remove unimplemented kernel approximations
COSMIT pep8
DOC added kernel_approximation module to docs
DOC: placeholder entry in user guide
ENH: renamed D to n_components for consitency
DOC approximate kernel functions narrative docs
DOC: more narrative documentation for kernel_approximation
DOC: references for approximate feature maps
COSMIT: pep8 in kernel approximation test
DOC approximate kernels: added formular for skewed chi squared kernel
COSMIT removed commented out import
ENH: additive chi squared kernel implemented and tested
pep8
DOC: added AdditiveChi2Sampler to doc modules
ENH: Default value for n in AdditiveChi2Sampler
DOC narrative doc for additive chi squared kernel
ENH: sensible defaults for RBFSampler and SkewedChi2Sampler
ENH Added AdditiveChi2Sampler to feature_extraction __init__
BUG: AdditiveChi2Sampler fit method should return self
ENH: in Chi2Samplers, check if input inside inside desired range.
FIX: Renaming of RBFSampler argument
DOC: Move kernel approximation to be a "plot" example.
Don't test as strictly so not to fail randomly..
Example of decision surface of approximate kernel svm
Moving kernel_approximation to the top level
ENH: Restructuring User Guide: kernel_approximation, preprocessing and feature_extraction are under a common chapter, "
DOC: finetuning the narrative docs for kernel_approximation
DOC: kernel_approx make examples show correctly
DOC rst
ENH Addressing some of Gael's comments, mainly naming and docstrings
ENH better testing
ENH fixed location of the legend in kernel_approximation example
DOC more discussion in docstring
ENH timing results in approx kernel example
ENH kernel approximation: More specific references and example referencing the narrative docs.
FIX: use safe_sparse_dot in kernel_approx transform
DOC minor doc improvements, different example
NONSENSE improve the example that i'll remove in a sec
BUG import ...
COSMIT + SPELL
DOC added reference to the user guide in kernel_approximation module
FIX path in plot
FIX typo that cost me half a day of sprinting...
ENH Remove redundant example
FIX fix module links, figure split into two
COSMIT pep8
FIX: Kernel approximation module in references in alphabetical order.
DOC trying to clarify the kernel_approx documentation.
DOC FIX typo
FIX docstring errors...
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
FIX: missing import
FIX: fixed link to Virgile in whats_new
Merge pull request #486 from jakevdp/util-docs
Merge pull request #490 from mblondel/news20_loader
Merge pull request #488 from mblondel/sparse-kmeans
FIX: Added DBSCAN to references
FIX: typo in docs
Merge pull request #417 from larsmans/multilabel
COSMIT minor ticks
FIX getting rid of some more sphinx problems
FIX: SO EINE SCHEISSE!
COSMIT fixing indents in balltree
Merge pull request #510 from amueller/aaarrrgghhh
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
COSMIT "Examples" instead of "Example"
COSMIT Addressing @agramford's comments about whitespace and a minor fix in pipeline.
FIX: Section Returns not Return
COSMIT: Class docs don't have a 'Methods' section. It is autogenerated.
COSMIT Examples not Example
COSMIT make 'References' bold and minor other fixes.
COSMIT underscore fixes
COSMIT "Optional Parameters" Section removed
COSMIT pep8
FIX developers rst malformed
remove unused link
COSMIT remove unused malformed tag
FIX indentation and string literals
FIX backtics for members_, spaces around colon (not cologne)
COSMIT minor docstring stuff
COSMIT remove Methods section
FIX: rename complexity section into notes section
FIX docstring variable names
FIX rename "Details" into "Notes"
FIX remove infinite recursion
COSMIT: Make references link and show up correctly, parameters of __init__ documented in Class, not in function.
COSMIT make formulars show up correctly, use reference formatting for references
COSMIT make references use reference formatting
COSMIT format references and dict stuff...
COSMIT Indentation of formulars
FIX removed duplicate explicit linke for Vlad
FIX: RST indentation and blank lines
FIX RST and references
FIX minor rst
FIX workarounds for docutils bug
FIX whitespace where rst demands it...
FIX workaround for table problem
FIX two more underscores
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into doc_underscores_for_real
COSMIT docs hmm
FIX: don't use latex in rst
FIX + COSMIT rst warnings
COSMIT docs
FIX: fix again errors in NMF after merge
FIX: Document properties in a way that the docstring actually shows
FIX: rst errors in ball_tree
FIX: Notes instead of Note in preprocessing init
FIX remove handles for references as they are not used anywhere and raise warnings if doubled.
Merge pull request #513 from amueller/doc_underscores_for_real
COSMIT docs underscore fixes (again)
COSMIT fixing doc errors and making html docs pretty
COSMIT Minor beautifications and RST error fixes
FIX doctest errors + cosmit
Merge branch 'master' of github.com:scikit-learn/scikit-learn
DOC use SVC in grid search instead of SVR. Iris is a classification dataset as pointed out by @agramfort here:
DOC score returns accuracy, not error
FIX for doctest I just broke :-/
DOC uncommenting doctests in balltree.pyx, addind doctest: +SKIP
COSMIT a little less skips...
ENH Add underscores to estimated attributes in GridSearchCV and deprecation warnings.
ENH renamed best_estimator and best_score in examples and tests.
COSMIT typo
ENH in GaussianNB, let estimated parameters have underscores.
DOC Reworking Bayesian regression documentation
DOC mentioning sparsity of ARD, reblocking text
COSMIT typo, thanks @vmichel for pointing it out.
DOC added reference for sparsity of ARD
COSMIT pep8
DOC fix linking to load_sample_images and load_sample_image in docs
DOC underscores in DeprecationWarnings... shame on me for forgetting that....
DOCs workaround for docutils bug (column alignment problem)
DOC external references go under "references" not "see also". "See also" can only handle internal references
COSMIT typo
whatsnew: gave myself some credit
Bala Subrahmanyam Varanasi (2):
modified 'gid' to 'git'
pep8 compliant
Bogdan Trach (1):
* doc/conf.py: added required latex packages (bm and morefloats)
Brian Holt (183):
Refactored decision trees and forests to support CART algorithm.
Refactored decision trees and forests to support CART algorithm.
Added documentation
make number of classes explicit
Added visualisation and corrected bugs in CART algo
Merge branch 'enh/ensemble' of https://github.com/satra/scikit-learn
Merge https://github.com/scikit-learn/scikit-learn
improved nosetests doctest time
PEP8
Merged decisiontree and tree_model into tree, random_forests to ensemble
20% speed improvement by moving _find_best_split to cython
removed occurances of tree_model
Merge https://github.com/scikit-learn/scikit-learn
Merge branch 'boston' of https://github.com/bdholt1/scikit-learn
FIX: removed the required parameter K
FIX: dataset description
Further optimisation of _find_best_split
Further optimisation of _find_best_split
Refactoring: speedup of decision tree code
Further performance improvements. Now approx 30 - 50% faster than MILK.
Merge branch 'boston'
Updated benchmarking for trees
PEP8
DOC: added documentation for graphviz method
FIX: corrected computation error and typed incoming arrays
FIX: corrected graphviz visualisation.
removed everything except the plain and simple decision tree to make reviewing easier
DOC: Updated the documentation to reflect decision trees.
Corrected newlines, and ensured only tree related changes are in this set
FIX: replaced ad-hoc RNG with suggested scikits.learn implementation. Tidied up dependent examples.
Merge https://github.com/scikit-learn/scikit-learn into enh/tree
Merge https://github.com/scikit-learn/scikit-learn
Removed unused import
PROF: improved speedup thanks to ppret
Merge branch 'enh/tree'
Initialise random state for examples
DOC: Added +ELLIPSIS for examples
ENH: Support binary (-1,1) classification as well as [0,...,K) multiclass classification
Merge git://github.com/scikit-learn/scikit-learn into enh/tree
Removed unnecessary import
Fixed doctest example
Updated documentation for class interface
Minor patches to docs
Optimisation: moved _find_best_split to cython.
DOC: change classification to regression
Merge github.com:scikit-learn/scikit-learn into enh/tree
DOC: corrected doctest
don't allocate a new pm for each call: 3 times faster
Moved to @pprett's faster splitting code (debugged)
Added more debugging info to graphviz
Moved to the version without a sample mask, since correctly implemented it is almost as fast
Fixed error of splitting between identical feature vals
DOC: updated comments
Improved graph visualisation
Move initial entropy computation outside loop.
merged upstream master
merged upstream master
Copied ensemble and random forest classifiers to new branch
Check that labels are in range for multiclass classification
Check that labels are in range for multiclass classification
Fixed regression bug. Thanks @pprett
Merge branch 'upstream-master' into enh/tree2
merged enh/tree2 into enh/tree
Fixed doctest
Enforce 64bit and 32bit types and correct regression bug (divide by zero).
Refactored construct to subsample dimensions.
store all tree parameters in the RF base class so that clone() will work
Revert to _Fixed Doctest_ and added regression bug fix
update to unit test and doc test
enforce type on storage arrays
enforce 64 bit types on parameters
further type enforcement
initialise variables
removed unused import, removed unnecessary backslash
Improved names and documentation for Leaf and Node
Renamed K to n_classes
renamed F to max_features
renamed features to X
renamed labels to y
renamed n_dims to n_features
explained min_split
renamed C to predictions
improve documentation
renamed K to n_classes
COSMIT: improved documentation
renamed pm to label_count
renamed K to n_classes
improved documentation and renamed features and labels
renamed var to variance
fixed comments
Updated docstrings
merged upstream-master into enh/tree
Merge pull request #9 from ogrisel/bdholt1-enh-tree
Merge pull request #10 from ogrisel/bdholt1-enh-tree
merged upstream/master
renamed scikits.learn to sklearn
Push coverage up to 96%, added graphviz test
merging
Merge pull request #11 from pprett/bdholt1-enh/tree
added example usage of graphviz
Merge branch 'enh/tree' of github.com:bdholt1/scikit-learn into enh/tree
fixed unit test of graphviz
added trees (boston and iris datasets)
pep8
moved the min_split test to beginning of recursive_split
group imports by hierarchy
sed s/dimension/feature/g
time is measured in seconds
print left and right child repr in graphviz
Merge branch 'enh/tree' of github.com:bdholt1/scikit-learn into enh/tree
fixed graphviz test failure
added feature_mask to reduce fancy indexing
replaced == with 'not' operator
updated the decision tree docs (not done yet)
use Fortran array layout
corrected feature_mask implementation
allow for different architectures
merged upstream/master moving to sklearn
merged enh/tree
Merge pull request #12 from pprett/bdholt1-enh/tree
Incorporated suggested changes to Graphviz exporter
visit -> export
cosmit: added spaces
cosmit: improved documentation
fixed indentation and added section on memory requirements
Updated documentation to include the iris svg example
improved documentation
np.float64 -> DTYPE. Set DTYPE to np.float32.
make sorting more efficient by transposing and sorting along last axis.
Use a sample mask instead of fancy indexing.
Merge pull request #13 from pprett/bdholt1-enh/tree
COSMIT: corrected comments
made sample_mask a fit parameter
updated documentation to reflect min_density concept
Merge pull request #14 from pprett/bdholt1-enh/tree
there is no more Leaf class
added feature_names to GraphViz export
Tidied up graphviz related code
test for improperly formed feature_names
removed sample_mask parameter
only return values that are used
Merge branch 'master' of github.com:scikit-learn/scikit-learn into enh/tree
Merge branch 'enh/tree' of github.com:bdholt1/scikit-learn into enh/tree
Merge pull request #16 from pprett/bdholt1-enh/tree
use np.isfortran
use None as the default marker
compute node id's on the fly
removed leftover class_counter
Merge pull request #17 from larsmans/enh/tree
added test for pickle-ability
Merge branch 'enh/tree' of github.com:bdholt1/scikit-learn into enh/tree
Merge pull request #19 from pprett/bdholt1-enh/tree
fixed failing docttest
improved tree documentation
included a mathematical formulation for CART
verify that scores from pickled objects are equal to original
pep8
Merge pull request #20 from GaelVaroquaux/tree
COSMIT: +SKIP on classification doctest
rewrote GraphvizExporter into a function export_graphviz
removed duplicate tests (already in fit)
Merge pull request #21 from glouppe/tree
classes can be any integer values
require that the next_sample_larger_than is greater than the previous by at least 1.e-7
regenerate cython
if threshold is indistinguishable from a, choose b
modified threshold comparison from < to <=
Merge branch 'master' of github.com:scikit-learn/scikit-learn into enh/tree
Added tree module to whats_new
release sv_coef memory
tree construction depends on n_features
Merge pull request #22 from ogrisel/bdholt1-enh-tree
Added person webpage
added trailing underscore
Merge branch 'master' of github.com:scikit-learn/scikit-learn into enh/ensemble
Merge pull request #23 from larsmans/enh/ensemble
scikits.learn -> sklearn
update parameter names
Merge branch 'master' of github.com:scikit-learn/scikit-learn into enh/ensemble
remove enforcement of return type
replaced ratio r with sampling with replacement
Re-ran the tests and found that the GaussianNB error was much lower.
Fixed typo
DraXus (2):
peping8 examples
peping8 examples/applications
Fabian Pedregosa (68):
Start of 0.10 development cycle.
Some Python 2.5 fixes.
More python2.5 fixes
FIX: assign NaN to an integer array has no effect on old numpy
Some more changelog stuff.
Update MANIFEST.in: scikit-learn --> sklearn
Add mldata loader and olivetti dataset to changelog.
Faster tests for coordinate_descent.
Add changelog entry.
Merge pull request #375 from VirgileFritsch/mcd
Merge pull request #383 from bdholt1/svm-mem-leak2
Add Brian's name to the Changelog.
FIX: keywords {precompute, Xy} where implemented and documented but unused ...
Cosmetic changes in LARS
FIX: Py3k compatibility.
Delete benchmarks/bench_svm.py
Delette benchmarks/bench_neighbors.py
MISC: More meaningful names for lapack functions in least_angle.
Removed unused parameters in least_angle
Convert to scipy doc convention + add missing options
FIX: array2d was did not return contiguous arrays with order='C' ...
FIX: do not use reshape in libsvm sparse bindings.
Use centralized directory for generated files.
Description for logo: font, color, etc.
DOC: Move practical info into its section and delete duplicates.
Style: webpage tweaks
Style update in documentation.
Doc: minor fixes
Minor update and fixes to linear_model documentation
Minor update and fixes to linear_model documentation
Move implementation details into RST doc.
Docstring conventions.
DOC: rename n -> p
Web page layout tweaks.
Small comment on the dual parameter
Use M.dot instead of np.dot on sparse matrices
FIX: LLE mode='auto' for small matrices and tuples.
FIX: use .toarray() instead of .todense()
COSMETIC: more readable syntax for mult. of sparse matrices.
Merge pull request #466 from amueller/svm_iris_example
Remove useless benchmark.
FIX: broken benchmark
Move uninteresting example to docstring
FIX docstring
Merge pull request #456 from vene/sparse-coder
Remove duplicate definition in RST
Replace unmaintainable test
More robust test for lars_path
Typo in example. Thanks Virgile for the cool example.
Revert code that I erroneously changed
Remove old API change warning
Merge pull request #504 from jakevdp/sphinx-images
FIX: docstring
DOC: exaple for sklearn.test()
FIX: convert lena to float32 (originally it's ints)
FIX: doctest
Still some tweaks for the sklearn.test() example
Remove pylab code from docstring and +SKIP those that requie PIL
FIX: explicit conversion to float64 in ElasticNet
FIX: bug in elasticnet with precompute not being updated correctly.
DOC: complete docstring for regression score function
DOC: restructure docstring of ElasticNet.
Changelog
change version number to 0.10
Mailmap alias
And the winner is ...
DOC: links for people that have webpage.
DOC: some documentation fixes.
Félix-Antoine Fortin (1):
Modified package name in Easy Install section.
Gael Varoquaux (167):
DOC: formatting examples as a topic
ENH: GridSearchCV can has predict_proba
FIX bug introduced in 68e6544
Remove BaseLibLinear.predict_proba not implemented
DOC: Install.rst wrong packaging info
COSMIT
scikits.learn -> scikit-learn in README
`scikits.learn` in the README, to catch google
DOC: fix rst
TEST: skip unreliable doctest
DOC: minor doc ENH for trees
COSMIT: tree code simplification
COSMIT: np.random should never be called
COSMIT: no seeding of the global RNG
ENH: move parameter checking to fit
COSMIT: y is a vector, not a matrix
Cosmit, PEP8
DOC: doc and example cosmetics for trees
DOC: improve spectral clustering docs
API: spectral clustering uses arpack by default
DOC: proper docstring for load_sample_image
API: default in spectral clustering: auto
ENH: add doc target to Makefile
Merge branch 'master' into tree
Minor cosmit
DOC: use random_state in KMeans
DOC: improve silhouette coefficient docs
MISC: better check_build error reporting
PEP08 names in graph_shortest_path
COSMIT
TEST: simplify test case
SPEED tree: 2X in Gini criteria
MISC: mk roc_curve work on lists
MISC: __version__ in scikits.learn
DOC: add IterGrid in reference
COSMIT: no import as
MISC: Warn for integers in scaling/normalize
MISC: better warning message
COSMIT: never use np.linalg, but scipy.linalg
BUG: ProbabilisticPCA.score work with pipeline
MISC: remove links to sourceforge URL
DOC: fix links in mixture
MISC: add citation information
BUG: vectorizer.inverse_transform on arrays
DOC: pdf compilation
ENH: Easier debugging in check_build
ENH check_build: better error msg for local imports
DOC: turn off generation of index pages
ENH: Capture stdout in executed examples
COSMIT: layout in plot_kmeans_digits example
DOC: minor fix to AMI docs
ENH: First sketch of glasso
ENH: example for l1 covariance estimator
ENH: Add cd solver to glasso
COSMIT glasso: docstring and cleanup
ENH: the GLasso estimator
DOC: Better glasso example
TEST: test GLasso
ENH Glasso: don't penalize the diagonal
ENH: Add a GLassoCV
ENH GLassoCV: iteratively-refined Grid search
ENH GLasso: stability on correlated data
ENH GLassoCV: better parameter optimization
TEST GLasso: increase test coverage
DOC: narrative documentation for GLasso
COSMIT: @agramfort's comments
DOC: add sparse inverse covariance in whats_new
PEP8
DOC: rmks on structure recovery
DOC: better stock_market example (WIP)
COSMIT: address most of @ogrisel's comments
ENH: don't echo convergence warning on CV grid
DOC GraphLasso: be explicit about which algorithm
DOC GraphLasso: notes on algorithms and recovery
DOC: docstring in stock market example
DOC/API: integrate make_sparse_spd_matrix
Typo
MISC: address @larsman's comments
API: g_lasso.py -> graph_lasso_.py
DOC: GLasso -> GraphLasso
MISC: @VirgileFritsch and @mblondel's comments
MISC: silence stdout in GraphLassoCV tests
ENH GraphLasso: Silence warning
ENH: graph_lasso works on empirical covariance
BUG: update tests to changes in graph_lasso
BUG: fix layout in examples
MISC: fix rst bug
DOC: put class reference in the banner
COSMIT: prettify plot_oneclass
DOC: rework front page
DOC: Add 'up' relative link
DOC: title for the user guide content file
DOC: don't display empty tocs
MISC: scikits.learn -> sklearn
DOC: proper link structure in examples
DOC: title to relative links
DOC: EPD ships a recent version, but not latest
DOC: state clearly the version number
MISC: plot_stock_market cluster on learned covariance
BUG: fix score() with GraphLasso
Compatibility with numpy 1.1
BUG GraphLassoCV: score() needs a store_precision attribute
DOC: restore 'This page' in sidebar
Merge pull request #463 from npinto/patch-2
MISC: update joblib
BUG: fix joblib doctest
BUG: make the tests pass with numpy 2
COSMIT
COSMIT: prettify datasets docs
Merge pull request #469 from amueller/preprocessing_epsilon_doctest
Merge pull request #471 from amueller/linnerud_renaming
DOC: explicit the __init__ convention
Cosmit on randomized range finder
Merge pull request #475 from amueller/datasets_doctests
BUG: fix RandomizePCA: renaming of fast_svd args
DOC: scikit.learn -> sklearn
BUG: casting with numpy 2.0
BUG: API change in fast_svd
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
Merge branch 'master' into n_samples_scaling
MISC: FutureWarning on C scaling
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
COSMIT: beautify the plot_oneclass example
DOC: outlier detection improve docs and examples
DOC: improve outlier detection docs
API: h -> support_fraction
Cosmit
ENH: use controled random numbers
BUG: follow API change in outlier_detection
MISC: update whats_new
DOC: cosmit in kernel approximation
DOC: removing dangling link
Cosmit in metrics
BUG: fix bug introduced in fd8c
ENH: Store the full cv_scores in grid_search
DOC: add alpha_ to attributes of LassoLarsIC
COSMIT: utils.fixes: document versions
Merge pull request #512 from amueller/doc_consitency
ENH: update joblib
MISC: improve copy_joblib script
ENH: Integrate joblib 0.5.7
Merge pull request #478 from glouppe/tree
DOC: doctest bug
Cosmit: example prettier without colorbar
DOC: add links to examples.
DOC: improve univariate feature selection docs
MISC: move SelectorMixin outside of __init__.py
OPTIM: minor optimization
MISC: better error message
COSMIT
TST: fix doctest
TST: fix touchy doctest
COSMIT: avoid set_cmap and pcolormesh in example
Cosmit in docs
MISC: fix bibtex
ENH: Make LinearRegression work with sparse
DOC: update LinearRegression docstring
FIX: sparse LinearRegression with scipy 0.7.0
ENH: update joblib
MISC: tag explicitely a dependency
ENH: use joblib compression in datasets
MISC: tune test verbosity
ENH: update joblib
DOC: restore index on pages
Merge pull request #526 from amueller/ball_tree_skip_doctests
Merge pull request #537 from amueller/gaussian_nb_underscore
MISC: species distribution example plotted
Gilles Louppe (220):
ENH: Cleaned setup.py
Merge remote-tracking branch 'bdholt1/enh/tree' into tree
DOC: Some docstrings have been rewritten + small cosmetic changes
Merge remote-tracking branch 'bdholt1/enh/tree' into tree
DOC: Improved documentation + cosmit changes
COSMIT: GraphViz exporter cleaned up
ENH: Made apply_tree_sample slightly more efficient + various cosmits
Regenerated _tree.c
Fixed issue #378 on the RFE module
Updated changelog.
Added a numerical stability test to decision trees
Added a numerical stability test to decision trees
Revert "Added a numerical stability test to decision trees"
Merge remote-tracking branch 'upstream/master'
Merge remote-tracking branch 'upstream/master'
Merge remote-tracking branch 'upstream/master'
DOC: Added load_boston in classes.rst
Merge remote-tracking branch 'upstream/master'
Simplified tree module API.
Added some comments
Allow for max_depth to be set to None
Simplified the tree code
Added k_features argument to build randomized trees.
First draft at find_best_random_split (not yet tested)
Renamed k_features to max_features
Added some explanatory comments into the code logic
Re-extended the _build_tree API
Factored is_classification
Added ExtraTreeClassifier and ExtraTreeRegressor
Typo
First draft at forest of random trees (work in progress)
Added some tests
Cosmit
Fixed bugs in forest + first test
Check X is a fortran-array and y is contiguous
Fixed bugs
Added tests of the forest module (work in progress)
Default value of n_trees=10
bootstrap=False for extra-trees
Set random_state=1 in tests
Added documentation in the forest module (work in progress)
Cosmit
Completed documentation
Added some tests
Added predict_log_proba
Added some more tests
Removed old random forest files
Added some more tests
Cosmit
Regenerate _tree.c
Fixed a small bug
Cosmit
Use super()
Use take instead of __get_item__
Rewrote some comments
Cosmit
Revert changes on conf.py (mistake on my part)
Added random_state parameter to _find_split functions
Factored out changes on the ensemble module
Merge remote-tracking branch 'origin/master' into tree
Fixing conflicts
Merge remote-tracking branch 'upstream/master'
Removed extra-trees (for now)
Removed extra-trees from __init__
Removed extra-trees (again!)
Merge pull request #432 from glouppe/tree
Merge remote-tracking branch 'upstream/master'
Merge remote-tracking branch 'upstream/master'
Rebase of @bdholt1's ensemble branch
DOC: Added module descriptions
PEP8: tree.py, forest.py
Merge remote-tracking branch 'upstream/master' into ensemble-rebased
DOC: Added warning and see also
ENH: Modified forest API to make it possible to grid-search the parameters of the underlying trees
Merge remote-tracking branch 'upstream/master' into ensemble-rebased
ENH: Check that base_tree is an estimator
ENH: Make forest derive from BaseEnsemble
Removed Bagging and Boosting modules from this PR
ENH: Make the Forest's API coherent with BaseEnsemble's API
FIX: Don't clone estimators at instantiation
TEST: Added test case for grid-searching over the base tree parameters
ENH: Cosmit
EXAMPLES: Improved plot_tree_regression
Typo
EXAMPLES: Improved plot_iris
EXAMPLES: Added plot_forest_iris
FIX: Trees couldn't be cloned properly
ENH: Added __init__.py into ensemble/tests/
DOC: Improved documentation in the examples
PEP8
TEST: Added tests of BaseEnsemble
TEST: Improved test coverage
EXAMPLES: Fixed a bug in plot_forest_iris
DOC: Cosmitis in the narrative documentation of the tree module
DOC: Improved narrative documentation of the tree module
DOC: Added ensemble methods to TOC
DOC: Added ensemble methods to the class reference
DOC: First draft at the narrative documentation of the ensemble module
DOC: Narrative doc of the ensemble module (work in progress)
DOC: Completed the narrative documentation (work in progress) + What's new
DOC: Fixed What's new
DOC: Last details on the narrative documentation
DOC: Added a last example in the narrative doc
Merge pull request #1 from ogrisel/glouppe-ensemble-rebased
DOC: Address @vene and @satra comments
TEST: Added test_base_estimator
DOC: Cosmit
ENH: Simplified RandomForest and ExtraTrees API
ENH: Use trailing _ for private attributes
DOC: Added warning in make_estimator
DOC: Removed 'default'
FIX: Bug with bootstrapping
FIX: Bug with bootstrapping (2)
FIX: Bug in plot_forest_iris
Merge remote-tracking branch 'upstream/master'
DOC: Use ELLIPSIS in doc-test
Cosmit
ENH: Address @agramfort comments
Benchmark: Added random forests and extra-trees to bench_sgd_covertype.py
Merge remote-tracking branch 'upstream/master'
Merge remote-tracking branch 'upstream/master'
FIX: Use random_state in _find_best_random_split
Merge remote-tracking branch 'upstream/master'
Merge remote-tracking branch 'upstream/master'
First draft at Reference rewrite
DOC: "the scikit-learn" -> "scikit-learn"
DOC: References to user guide sections
DOC: Standardize the module documentation format (work in progress)
DOC: Standardized the module documentation format (2)
DOC: Fixed graph_lasso reference
DOC: "Class Reference" -> "Reference"
DOC: Fixed warning
DOC: Changed sections titles in the reference
Merge pull request #461 from Balu-Varanasi/bug_in_rst_file
Merge pull request #467 from Balu-Varanasi/pep8-compliant
DOC: Fixed broken reference to user guide
Merge remote-tracking branch 'upstream/master'
ENH: Added feature importances to decision trees and to forests
TEST: Added test on feature importances
EXAMPLE: Added examples for feature importances using trees
COSMIT: rfe examples
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
EXAMPLE: Improved plot_forest_importances.py plot
COSMIT: tree examples
DOC: Fixed links to modules in the example gallery
DOC: Fixed broken links
EXAMPLE: Moved to the Olivetti dataset
ENH: Accelerate ensemble of trees by precomputing X_argsorted
FIX: bootstrap=False by default with extra-trees
EXAMPLES: Removed useless import
ENH: Use extra-trees instead of rf
COSMIT: examples
Added links and various cosmits
DOC: Added fetch_olivetti_faces to Reference
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
DOC: Cosmits on the Support page
ENH: Parallel fit/predict/predic_proba/feature_importances in forest
FIX: Ensure random random_states
ENH: use pre_dispatch
DOC: Return->Returns
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
DOC: Cosmit on the reference
ENH: Improved _parallel_predict_proba
DOC: add n_jobs to specs
ENH: Assign chunk of trees to jobs
EXAMPLE: renamed Frankenstein, set cmap in matshow
ENH: Forest -> BaseForest
DOC: Added reference for feature importance
ENH: Revisited importances API
Merge remote-tracking branch 'upstream/master' into tree
EXAMPLE: Fixed API changes
ENH: Missing default value for feature_importances_
ENH: Added SelectorMixin
TEST: Added tests of transform
ENH: Simplified API
DOC: Tree-based feature selection
ENH: don't sum if coef_ is 1-d
ENH: Inherits from TransformerMixin
PEP 257
PEP 257 (bis)
ENH: address @ogrisel comments
FIX: Used np.abs instead of ** 2
Merge remote-tracking branch 'upstream/master' into tree
ENH: Smart thresholds
Cosmit
PEP8
DOC: :mod: link
ENH: no predispatch with chunk strategy
FIX: Address Gael comments
Merge remote-tracking branch 'upstream/master' into parallel-forest
ENH: Simplified parallelization
PEP8
ENH: Simplified code
DOC: Quick docstrings for private functions
FIX: Revert changes
DOC: What's new
Merge pull request #2 from ogrisel/glouppe-parallel-forest
FIX: Address @ogrisel comments (1)
Merge branch 'parallel-forest' of github.com:glouppe/scikit-learn into parallel-forest
TEST: Added tests of parallel computation
DOC: Parallel computations in forest
TEST: Improved coverage of the ensemble package to 100%
DOC: Renamed example (+Parallel)
Merge remote-tracking branch 'upstream/master' into parallel-forest
Merge pull request #491 from glouppe/parallel-forest
DOC: Added missing BSD3 licenses
ENH: Better default values to trees and forests
TEST: Added tests of max_features values
DOC: Review of the narrative doc wrt max_features
DOC: Added warning to default values
DOC: typo
Merge pull request #523 from glouppe/tree-doc
DOC: fix broken doctest
FIX: max_features=None by default on single DT
Merge pull request #527 from otizonaizit/master
FIX: Add reference (stop words)
Merge remote-tracking branch 'upstream/master' into issue349
Merge pull request #528 from glouppe/issue349
DOC: Removed performance and utilities from toctree (they were appearing twice)
DOC: Fixed 'See also' in tree/forest
Jake VanderPlas (52):
add warning flag to balltree + tests
warning_flag doc
add warning messages to KNeighbors
fixes for tests
attempt to address warnings catcher
hack to fix warning test
change warning message
simplify warning test; remove assert_warns from utils
bug: mode='LM' -> mode='LA'
remove unused return_log keyword in GMM
BUG/DOC: address manifold singularity issue
DOC: add utility information for developers
Move graph_shortest_path to utils/graph.py
remove duplicative utils.fixes.arpack_eigsh
Move validation utils to their own submodule
BUG: example plot compatibility with older matplotlib versions
Merge branch 'example-fix'
Merge pull request #4 from glouppe/dev-doc
randomized_range_finder -> randomized_power_iteration
Change logsum to logsumexp for comparability with scipy
BUG: fix scale_C bug in svm
TESTS: remove deprecated NeighborsClassifier calls
species datasets commit
clean up species distribution example
randomized_power_iteration -> randomized_range_finder
typo in fastica doc
Merge commit 'upstream/master' into util-docs
Merge commit 'upstream/master' into util-docs
DOC: add toc for developers resources
DOC: add warning that utils should only be used internally
use joblib for saving species data
Merge commit 'upstream/master' into dataset-fix
fix logsum test
Change depreciated behavior in feature agglomeration example
HACK: sphinx/prevent proliferation of build images in doc
simplify removal of _images dir
remove unneeded import
BallTree -> NearestNeighbors in Isomap
DOC: isomap fixes
convert LLE neighbors to NearestNeighbors object
BallTree -> NearestNeighbors in mean_shift
pep8
Merge pull request #501 from jakevdp/dataset-fix
remove unused import
remove unused imports
pep8
Merge commit 'upstream/master'
COSMIT: pep8
DOC: formatting
DOC: pep8, add quotations, and fix typos
fix for doc math issue
TYPO: generate all images
Jan Hendrik Metzen (3):
Fixed bug in updating structure matrix in ward_tree algorithm.
Added test case that reproduces crashes in old version of ward_tree algorithm.
Performance tweaking in ward_tree.
Juan Manuel Caicedo Carvajal (1):
Check for consistent input in Logistic Regression.
Kenneth C. Arnold (3):
Cosmit
Cosmit
fast_svd: factor out the randomized range finder (more generally useful)
Lars Buitinck (144):
refactor linear models to call as_float_array only from _center_data
unconditionally call as_float_array in LinearModel._center_data
DOC: fix typos
DOC small stuff in base.py and multiclass.py
trees: don't use deprecated cross_val, error messages, use super
typo: threhold -> threshold
DOC minor editing to naive_bayes docs
Merge branch 'tmp'
rename overwrite_Foo params to copy_Foo (and inversed their meaning)
document overwrite_ -> copy_ API change in ChangeLog
BUG LinearSVC.predict would choke on 1-d input (+ regression test)
more helpful error message in SGDClassifier.predict_proba with wrong loss
Merge pull request #357 from larsmans/overwrite-to-copy
fix doctest failures in linear_models docs
refactor and simplify naive_bayes
prevent some copying in sparse SGD
BUG adapt text feature grid search example to new 20news loader
BUG fixed and cosmetics in CountVectorizer
BUG + optimization in GaussianNB
refactor common code of NB estimators into BaseNB class
Refactor/simplify naive Bayes tests
API change: 1-d output from BaseNB.predict_(log_)proba in binary case
ENH SGD error messages better still
FIX embarrassing SyntaxError in linear_model.base
BUG multiclass.predict_binary still relied on old MultinomialNB.predict_proba
DOC prob_predict -> predict_proba in SVM docstrings
Revert "BUG multiclass.predict_binary still relied on old MultinomialNB.predict_proba"
Revert "API change: 1-d output from BaseNB.predict_(log_)proba in binary case"
refactor SVMlight reader and writer
API change in SVMlight reader: handle multiple files with svmlight_load_files
Retry "BUG fixed and cosmetics in CountVectorizer"
CountVectorizer.fit_transformer refactoring, part N
Micro-optimize NMF for memory usage: topic spotting example down by ~17%
Replace two more flatten()s in NMF with ravel()s
FIX broken doctests in NMF + pep8
Allow sparse input to NMF
NMF: cosmit
Refactor ensemble learning code
FIX Issue 379 and use the opportunity to refactor libsvm code
DOC copy-edit naive bayes doc, with an emphasis on the formulas
COSMIT in chi² feature selection
DOC ported latexpdf target from Sphinx 1.0.7-generated Makefile
DOC typos in Ward tree docstring
COSMIT little things in hierarchical.py
BUG NMF topic spotting example would output n_top_words-1 terms
DOC explain multiclass behavior in LogisticRegression
COSMIT pep8 feature_extraction.text
DOC some stuff on input validation
ENH Cython version of SVMlight loader
ENH accept matrix input throughout
COSMIT rename safe_asanyarray to safe_asarray to prevent confusion
DOC correct Google URL
pep8 grid_search.py
FIX replace np.atleast_2d with new utils.array2d
DOC correct and clean up empirical covariance docstrings
ENH test input validation code on memmap arrays
Merge pull request #410 from larsmans/accept-matrix-input
ENH sample_weight argument in discrete NB estimators
BUG handle two-class multilabel case in LabelBinarizer
TEST better test for binary multilabel case in LabelBinarizer
ENH multilabel learning in OneVsRestClassifier
DOC OneVsRestClassifier multilabel stuff
ENH multilabel support in SVMlight loader
DOC multilabel classification in narrative docs
FIX Python 2.5 compat in utils/tests
COSMIT multiclass.predict_ovr
DOC expand Naive Bayes narrative doc (BernoulliNB formula)
COSMIT in naive_bayes
ENH prevent copy in sparse.LogisticRegression
Revert "ENH prevent copy in sparse.LogisticRegression"
DOC typos and style in linear_model docs
COSMIT cleanup sgd Cython code
DOC update cross validation docstrings for default indices=True
BUG handle broken estimators in grid search by cloning them
ENH don't require numeric class labels in SGDClassifier
BUG fix SGD doctests
BUG fix Naive Bayes test + refactor module
DOC typo
ENH support array-like y (lists, tuples) in GridSearchCV
ENH support arbitrary labels in metrics module
COSMIT rm comment in coord descent code about np.dot
COSMIT no need for csr_matrix "cast" in coord descent
ENH prevent copy in PCA if not necessary
FIX use super consistently in SVMs
ENH incrementally build arrays in SVMlight loader to reduce memory usage
Merge pull request #446 from larsmans/svmlight-loader-memory-use
DOC typos in ensemble.forest
drop Python 2.5; no more with statements from the __future__
drop Python 2.5; no more need for utils.fixes.product
drop Python 2.5; document and rm some workarounds for kwargs quirks
COSMIT rm some SciPy pre-0.7 compat code
raise TypeError instead of ValueError in check_arrays
COSMIT docstring fix + US spelling in K-means code
DOC I don't think Ubuntu 10.04 will be the last LTS release
test @deprecated using warnings.catch_warnings
COSMIT use utils.deprecated as a class decorator
don't use assert_in, not supported by nose on buildbot
Revert "FIX: more python2.5 SyntaxError"
Revert "FIX: python2.5 SyntaxError"
COSMIT use urlretrieve and "with" syntax in LFW module
COSMIT use ABCMeta in naive_bayes
COSMIT a few more easy cases of with open syntax
rm Py2.5 compat factorial and combinations from utils.extmath
use cPickle in spectral clustering tests
COSMIT use Python 2.6 except-as syntax
DOC rm Methods section from KMeans docstring
BUG typo in NB error msg
DOC fix datasets.load_digits example
DOC fix datasets.load_digits example, second attempt
COSMIT rename load_vectorized_20newsgroups + DOC + pep8
Merge pull request #2 from mblondel/multilabel
BUG only handle labels specially in SVMlight loader + multilabel
BUG fix off-by-one error in SVMlight format loader
DOC multilabel learning: note that it's experimental + @mueller's remark
DOC document svmlight file loader changes in changelog
COSMIT reorganise utils tests
TST add test for sklearn.utils.extmath.logsum
DOC copyedit kernel approximations docstring
DOC kernel approximations, some last bits
DOC unbreak kernel approx docstrings (UTF-8 + s/References/Notes/g)
Merge branch 'master' into multilabel
ENH add multilabel_ property to OvR and raise NotImplementedError in score
ENH demo sparse KMeans on 20news set (it's slow!)
Merge remote-tracking branch 'vene/lars_multilabel' into multilabel
BUG forget a return keyword in OvR classifier
DOC describe test_ovr_multilabel better
TST extra test for LabelBinarizer's multilabel behavior
COSMIT set union in LabelBinarizer
ENH improve stoplist handling in feature_extraction.text
DOC rm References sections in docstrings
DOC I broke the docs and I liked it
COSMIT make BaseLibSVM an abstract base class
BUG input validation in kernel approximations + pep8
BUG fix Vectorizer to play nicely with Pipeline
Revert "BUG Disallow negative tf-idf weight"
PY3K fix in datasets.samples_generator
DOC cosmetics in SVM docstring
COSMIT reintroduce ABCMeta into BaseSGD*
BUG refactor SGD classes to not store sample_weight
COSMIT rm unused svm.base.dot
BUG use ValueError in BaseLibSVM.coef_
BUG update test for SVMs raising ValueError for coef_
COSMIT remove superfluous imports in svm/sparse/base.py
BUG don't use deprecated attributes in GaussianNB.predict
Mathieu Blondel (55):
Giving due credit (last minute ChangeLog item).
Cosmit.
Merge pull request #354 from amueller/liblinear_parameter_errors
Add dump_svmlight_file.
Export data option in SVG gui.
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge pull request #407 from amueller/sgd_url_typo
BUG: Use threshold in LabelBinarizer in multi-label case.
ENH support decision_function in multi-label classification
Cosmit: used named parameter.
ENH Label indicator matrix support in LabelBinarizer and OVRClassifier
Remove C from NuSVR.
Revert "Remove C from NuSVR."
Revert "FIX : removing param nu from sparse.SVR, C from NuSVR + pep8"
Small comment on the dual parameter in LinearSVC.
Update svmlight loader documentation.
Fix svmlight loader doc.
Implement mean_variance_axis0.
Fix bug with sparse matrices.
Cosmit.
Test edge case.
tmp -> diff
Add score method to KMeans.
Use int for indptr and indices.
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
Sparse matrix support in KMeans.
Vectorized news20 dataset loader.
Merge multilabel branch with master.
Check that LabelBinarizer was fitted.
Multilabel classification dataset generator.
Test multilabel classifier on random dataset.
scale_C will be True in scikit-learn 0.11.
Merge pull request #8 from larsmans/news20_loader
Return bunch object.
Merge pull request #493 from amueller/kernel_approximation_doc
Add to class reference.
Add precompute_distances option back and export it.
Merge branch 'minibatch-kmeans-optim' of https://github.com/ogrisel/scikit-learn into minibatch-kmeans-optim
Address @ogrisel and @amueller's comments.
Better doc for the 20newsgroup dataset loader.
Do not use joblib's memoizer.
Use int16 for more compactness.
Merge branch 'master' into sparse-kmeans
Merge with master.
One more test.
Fix test.
Cosmit in MiniBatchKMeans.
Optimize for high dimensional data.
Use CCA as well in multilabel example.
Add missing reference.
Break down fit_transform into parts.
Cosmit
More tests for nuSVR.
Use rbf_kernel.
Add decision_function to ElasticNet.
Michael Eickenberg (2):
fixed the function definition of cross_val_score
changed cross_val_score doc again
Nelle Varoquaux (1):
FIX - error in the bibtex entry - extra comma that makes bibtex fail
Nicolas Pinto (1):
Add arXiv link to Halko et al. 2009 paper.
Noel Dawe (44):
adding boosting and decision trees
adding bagging and gradboost
minor change
working on interfacing with Cython
minor updates
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
pull from upstream
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
implemented AdaBoost
refactoring
minor fix
minor fix
almost done...
it compiles\!
now it really compiles
minor fix
working on segfault
now it works
trying to fix score bounds
updates
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
sanity check in adaboost
more sanity checks in adaboost
fairly stable now
fixed bug where node cuts were not set but left at 0
working on limiting cases
updates
fixing bug in adaboost
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
updates
minor change
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
bagging now implemented
removing committee for now
updates
adding tests
better demonstration in test module
minor change
bugfix
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn
minor change
pep8
Merge branch 'master' of git://github.com/scikit-learn/scikit-learn into decisiontree
updates
Olivier Grisel (242):
new package name
more renamings
Merge branch 'master' into bdholt1-enh-tree
trailing spaces in pyx file
More style consistency improvements
style: constant in capital letter on top + extract graphviz tree template
cosmit
More style improvements in _tree.pyx
Merge branch 'master' into bdholt1-enh-tree
ENH: doctest simplification by using the cross_val_score func
Merge remote-tracking branch 'bdholt1/enh/tree' into bdholt1-enh-tree
Merge branch 'master' into bdholt1-enh-tree
Merge pull request #353 from amueller/sgd_warm_starts
DOC: cross validation: introduce motivation and basic usage first
Merge branch 'master' of github.com:scikit-learn/scikit-learn
typo: s/accurracy/accuracy/g
Merge pull request #360 from cmd-ntrf/master
ENH: no need for L2 norm on input in doc clustering
ENH: make load_files use a fixed shuffling of the samples
DOC: better svmlight_loader / dumper docstrings
ENH: 30% speed improvements in load_svmlight_file
ENH: remove useless call to strip while staying robust to empty lines
ENH: make MiniBatchKMeans display more info in verbose mode
Merge pull request #373 from larsmans/svmlight
Revert "BUG fixed and cosmetics in CountVectorizer"
ENH: make it possible to skip label assignements in MiniBatchKMeans
thanks to @larsmans, TFIDF is now always positive :)
Merge remote-tracking branch 'bdholt1/enh/tree' into bdholt1-enh-tree
Merge pull request #381 from satra/doc/permutation
FIX: compat with numpy 1.5.1 and earlier in NMF
Merge remote-tracking branch 'bdholt1/enh/tree' into bdholt1-enh-tree
Merge pull request #377 from larsmans/sparse-nmf
pep8
pep8
OPTIM: inplace max in distances computation
OPTIM: avoid unnecessary repeted memory allocations in minibatch k-means
Merge remote-tracking branch 'bdholt1/enh/tree' into bdholt1-enh-tree
cosmit: pep8 and trailing spaces
merge master
DOC: fix broken links + various cosmits
FIX: remove non-ASCII char from silhouette docstrigs
Some clarification of the memory copy issues.
OPTIM: inplace dense minibatch updates and better variable names
cosmit
cosmit: better variable name in MiniBatchKMeans
Merge branch 'master' of github.com:scikit-learn/scikit-learn
ENH: make it possible to control the add variance caused by Randomized SVD
ENH: document clustering example simplification
FIX broken doctests on buildbot + pep257
Merge branch 'master' of github.com:scikit-learn/scikit-learn
first stab at nearest center in cython (+30% perf, need check correctness)
factorized label assignement as a reusable python func for the predict method
use direct blas ddot call and reuse _assign_labels in predict
FIX: broken test cause by the use of todense which return a matrix instance instead of a regular numpy array
WIP on simpler cython impl of the center update (still buggy)
compute inertia + remove code :)
update renamed function call
factorize dot product and bootstrap implementation for the dense case
use cpdef + less array overhead in ddot
started kmeans test suite refactoring
more code factorization
refactored the kmeans tests
test and fix input checks for various dypes
much cheaper yet stable stopping criterion for the minibatch kmeans
FIX: missing relative import marker
Merge pull request #400 from amueller/docs_typo
DOC: LogisticRegression is a wrapper for liblinear.
FIX #401: update tutorial doctests to reflect recent changes and add them to
Merge branch 'master' of github.com:scikit-learn/scikit-learn
DOC: new scikit-learn.org URLs and mention license in README.md
Merge remote-tracking branch 'robertlayton/ami' into robertlayton-ami
measure runtimes for various clustering metrics in adjusted for chance example
FIX warnings by avoiding 0.0 values in the log + cosmit
Merge branch 'master' into minibatch-kmeans-optim
unused import
low memory computation of the square diff
be more consistent with the usual behavior of fitted attributes
base convergence detection on EWA inertia monitoring
various cython cleanups
working in progress to make it possible to use a speedy version based on smoothed inertial only
ENH: more informative error messages when input has invalid shapes
Merge branch 'master' of github.com:scikit-learn/scikit-learn
ENH: more informative error message when shape mismatch in TF IDF transformer
merge master
preparing new stopping criterion impl
ENH: make it possible to pass class_weight='auto' as constructor param for SGDClassifier
Merge branch 'master' into minibatch-kmeans-optim
work in progress (broken tests) on early stopping with both tol and inertia lack of improvement
make min_dist test more explicit
fixed broken test
optimize label assignment for dense minibatch and new test
fix tests
fix tests
start with zero counts in tests
fix bug: x_squared_norms should follow the shuffle...
ensure that the sparse and dense variant of the minibatch update compute the same thing
better default value and parameter handling for max_no_improvement
switch to lazy sampling with explicit index to divide memory usage almost by 2 and decrease code complexity with no measurable impact on the run time
more code simplification
started example to check the convergence stability in various settings
FIX: buggy usage of for / else for k-means n_init loop
DOC: update what's new
tracking changes from master
FIX: broken HMM tests caused by KMeans convergence in one step
merge master
ENH: use integer indexing instead of boolean masks by default for CV
implemented n_init for MiniBatchKMeans
Merge branch 'master' into minibatch-kmeans-optim
refactored the init logic for MiniBatchKMeans
Merge branch 'master' into minibatch-kmeans-optim
fix stability and warning in tests
make k-means++ work on sparse input and use it as default for MB k-means
add version info in deprecation message
factorized out the early stopping logic in a dedicated method
first stab at a reinit strategy that work on low dim data only
new example to emphasize issues with current naive reinit scheme on sparse data
second experiment on reinit that does not work on high dim sparse data either
PEP8 + various cosmits
pep8 in sparse covariance example
PEP8 + PEP257 in samples_generator
PEP257 - docstring style
Merge branch 'master' into minibatch-kmeans-optim
FIX: make the doctests outcome deterministic
DOC: better toplevel docstring
DOC: add simple descriptions in the concrete class docstrings
FIX: workaround what looks like a numerical instability in doctest
Merge pull request #439 from glouppe/ensemble-rebased
Merge pull request #453 from yarikoptic/master
pep8
Merge pull request #452 from glouppe/doc
PEP257 cosmit
cosmit
Update README.txt dependencies info to match the configuration tested on jenkins
cosmit
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
track changes from master
pep8
fix k_means docstring to better match the scikit naming conventions
WIP: n_init refactoring
merge master
Merge pull request #481 from mblondel/mean_var2
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
Merge branch 'master' into minibatch-kmeans-optim
scale tolerance of minibatch kmeans on CSR input variance
delete broken example
example script is not meant to be executed when building the doc as it is slow
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn
typo: accross => across
Merge branch 'master' into minibatch-kmeans-optim
typo: accross => across
Use python int for indices and indptr of scipy sparse matrices to ensure cross platform support
Make init less expensive by default on MinibatchKMeans to avoid dominating computation on large scale datasets
Fix broken duplicated / tests and more practical init
consolidating all cython utils for sparse CSR in the same file under utils
WIP: scaling CSRs
Merge branch 'master' into minibatch-kmeans-optim
FIX compat for errorbar legend for old matplotlib versions
slight optim: remove useless assignment from the inner loop
FIX: numerical instability caused by collapsed allocation of bad clusters to the center of mass
example tweaks
fix text position in example
its
better documentation for the convergence stability example
Merge branch 'master' into minibatch-kmeans-optim
simplify stability evaluation example
enable the kmeans stability as an auto examples as the speed is now fast enough
docstring in cython funcs + better var name: with_sqrt
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into minibatch-kmeans-optim
cosmit
merge master
readd dtype and ccontiguous checks removed by mistake during last conflict resolution
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into minibatch-kmeans-optim
merge master
remove useless dependency on pylab
fixed conflict in import resolution
FIX: validation is a relative package
FIX: py3k - more relative imports
FIX: py3k: string.letters is locale dependent and absent in py3k
Merge branch 'master' into sparse-scaler
WIP: feature scaling for CSR input (lacks some tests)
fix scaling, more tests and docstrings
Merge branch 'master' into sparse-scaler
wording
FIX: py3k integer division in robust covariance estimation
FIX: py3k integer division in samples generator
FIX: in py3k svmlight files must be explicitly opened in binary mode
FIX: py3k bytes split in svmlight format parser
Merge branch 'master' of github.com:scikit-learn/scikit-learn
FIX: py3k need explicit bytes buffers for svmlight format serialization
FIX: py3k need output file in binary mode for svmlight format serialization
FIX: py3k: string formatting is not supported on byte strings
FIX: fix test: integers are valid file descriptors in py3k
Merge branch 'master' into sparse-scaler
FIX: unused cython variable
More checks when transforming sparse matrices with centering scalers + typo
DOC: update narrative documentation
optim: avoid useless memory copy when input is non CSR
DOC: typo / wording
DOC: document sparsefuncs cython routines in developer section.
DOC: wording
DOC: wording
Merge pull request #515 from ogrisel/sparse-scaler
update what's new for sparse scaling
Fix the docstring of the univariate feature selection module to match the scikit conventions
cosmit
typo
cosmit
FIX: None and int comparison not authorized in py3k (in PCA)
FIX: dicts no longer have the has_key method in py3k: test for the method we actually use instead
FIX: make feature extraction work with the new py3k string API too
FIX: py3k's zip is not subscriptable
FIX: handle py3k exception API
FIX: previous fix for py3k str API in feature extraction was a bug in python 2
FIX: pervasive use of unicode in feature extraction for py3k compat
Update random forest face example to use several cores
ENH: make ShuffleSplit able to subsample the data
FIX: ensure fetch_20newsgroups_vectorized outputs CSR matrices to work with cross validators
Merge branch 'master' of github.com:scikit-learn/scikit-learn
Merge pull request #519 from ogrisel/subsampling-shufflesplit
PEP257: docstring cosmits in utils.extmath
ENH: renamed fast_svd to randomized_svd + related improvements
FIX: incomplete test for inverse_transform in text feature extraction
Merge pull request #521 from lucidfrontier45/master
pep8 in forest.py
pep8 in tree.py
pep8 in kmeans tests
more pep8
more pep8
FIX: heisen doctest
DOC: readibility: make colon after 'Parameters' stay on the same line in reference documentation
FIX: Boston is a regression dataset
oops, the last test is about classification, not regression
Merge pull request #529 from eickenberg/doc_fix
ENH: mark coef_ as immutable for linear SVM models trained in the dual
immutable coef for the sparse SVM variant too
mark liblinear coef as immutable too
document the fact that coef_ is readonly for LogisticRegression and LinearSVC
avoid a memory copy in coef_ property
Merge pull request #541 from ogrisel/immutable-readonly-coef
FIX: broken link in SVM doc
Peter Prettenhofer (65):
Merge branch 'enh/tree' of https://github.com/bdholt1/scikit-learn into bdholt1-enh/tree
Merge https://github.com/bdholt1/scikit-learn into bdholt1-enh/tree
Merge branch 'enh/tree' of https://github.com/bdholt1/scikit-learn into bdholt1-enh/tree
introduce reset method for Criterion and implemented linear version of MSE.
fix: weight left and right variance by num samples in each branch
added CART to covertype benchmark -> look at that error rate!
Merge remote branch 'bdholt1/enh/tree' into bdholt1-enh/tree
visitor pattern for export graphviz
cosmit: pep8 + docs
Merge remote branch 'bdholt1/enh/tree' into bdholt1-enh/tree
Merge remote branch 'bdholt1/enh/tree' into bdholt1-enh/tree
use hybrid sample_mask fancy indexing approach.
cosmit: docs + rm comments
added `min_density` parameter to CART
raise ValueError for min_split and max_depth on __init__ rather than fit.
we grow our trees deep
cosmit + n_samples fix
MSE bugfix (MSE.eval used to weight variances by n_left and n_right).
take DTYPE from _tree extension module
fix: inc n_left, n_right before variance computation; hopefully the last bugfix for MSE...
fix doctest + recompile cython code (accident)
make Node an extension type + change class label indexing.
recompile _tree.pyx
make _tree import relative
make node pickleable & tidy up some rebase mistakes
remove obsolete tests
check if y.shape[0] == X.shape[0]; this is especially troublesome for svm.sparse because most people are not aware of the sparse matrix - KFold troubles..
unified predict for sparse and dense SGD.
cosmit
fix: use None as default value for class_weight and sample_weight for sparse OneClassSVM; ample_weight -> sample_weight
cosmit: pep8
added y.shape[0] == X.shape[0] check to DiscreteNB
added X.shape[0] == y.shape[0] check to ElasitcNet
Merge remote branch 'upstream/master'
documented changes in whats_new
Merge remote branch 'upstream/master'
Merge branch 'fix-split-sample-mask' of https://github.com/TimSC/scikit-learn into TimSC-fix-split-sample-mask
compute threshold as t = low + (high - low) / 2.0
cosmit: get rid of gcc warning (q_data_ptr was not initialized)
fix: overflow of `offset` variable if X.shape[0] * X.shape[1] > 250M
fix: broken doctest with precomputed kernel
changed Decision Tree representation to struct of arrays instead of composite structure.
fix: use tree.predict instead of functor
Graphviz visitor now works on array repr.
cosmit: doc strings
use safe_sparse_dot instead of np.dot
changed int64 to int32 in tree repr;
Merge branch 'tree-array-repr'
changed for `for i in 0 <= i < n` to `for i in xrange(n)`.
Merge branch 'tree-array-repr'
changed tree.left and tree.right to tree.children (similar to cluster.hierachical)
fix: sgd module clone issue w/ rho parameter
Merge remote branch 'upstream/master'
fix: learning rate schedule doc.
Merge remote branch 'upstream/master'
fix: rm `nu` argument from sparse.SVR (taken from dense SVR).
don't use dict comprehensions (support python 2.5 and 2.6).
fix: tree doctests + ensemble doctests
fix: xmin -> X.min()
remove obsolete `sparse_coef_` doc string
remove reference to obsolete `sparse_coef_` parameter.
set coef_ to fortran layout after fit - this will enhance the test time performance for predicting singe data points.
added to whats new
cosmit: more detailed doc string for why fortran style arrays
Merge branch 'sgd-fortran-layout'
Robert Layton (60):
Initial Silhouette Coefficient code. no tests yet, and haven't checked it actually works yet as well
Initial test. Not working yet
Merge branch 'silhouette' of https://github.com/robertlayton/scikit-learn into silhouette
Test working, pep8'd and pyflakes'd
Sparse matrix testing
Swapped y, D to distance, labels
silhouette_coefficient -> silhouette_score
Restructured metrics/cluster into a folder with supervised and unsupervised modules
Narrative documentation
Merge remote-tracking branch 'upstream/master' into silhouette
"whats_new" updated
Example updated, which required fixing a backwards compatability bug (adjusted_rand_score not imported in metrics/cluster/__init__.py)
Silhouette added to AP example
Using pairwise_distances in the Silhouette Coefficient. Updates to docs, code, tests and examples
Silhouette calcualted for all forms of k-means in example
Faster version by removing inner loop comprehension
Sampling to improve SC speed
sampling added to silhouette_score, examples updated to match
pep8 and pyflakes
Updated doc with new API
Removed unneeded line from doc
Merge pull request #364 from robertlayton/silhouette
Trying to fix NaN errors, but its not working. Pushing to work on it later.
Mutual information now works (tested!)
AMI now works, and has been tested against the matlab code (test based on this to come!)
Remove phantom double v-measure !?
Added tests. There are two errors, but I'm going to bed. I'll fix them in the morning.
Merge branch 'master' into ami
Merge branch 'ami' of github.com:robertlayton/scikit-learn into ami
- AMI in the cluster examples
Higher level import for ami_score
There is an overflow problem. It can be reproduced with the plot_adjusted_for_chance_measures.py example
Narrative doc, and I think I fixed the overflow issue (more tests to come)
Fixed logs to match the matlab code results.
Test now tests a much larger array
Test actually does what I meant it to do, and works sufficiently
Fixed this example. Tested the others (they worked!)
pep8 and pyflakes
Merge pull request #3 from ogrisel/robertlayton-ami
Optimising the expected mutual information code
Adding old version of EMI, as I'm about to change it
This version doesn't work either. I am uploading for historical sake.
Initial usage of gammaln. Not yet tested
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into ami
Still overflows, but the closest so far. Using gammaln
It works! Still have some optimisation to do, but it works for larger arrays
Moved start and finish outside of loop
comments, pep8 and pyflakes
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into ami
ami_score -> adjusted_mutual_info_score
ami_score -> adjusted_mutual_info_score
"What's new?" AMI!
Merge branch 'ami' of https://github.com/robertlayton/scikit-learn into ami
mutual_information_score -> mutual_info_score
and in plot_adjusted example (mutual_info_score)
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into ami
cosmit
Merge pull request #402 from robertlayton/ami
Fixed values in Adjusted Mutual Information doctests
l1_distances was renamed to manhattan_distances.
Satrajit Ghosh (23):
resolved init
initial import from milk
renamed, additional import
started conversion to scikits
updated information gain and set_entropy functions
modified base classes
updated docstring to reflect use
updated load_iris to return features
enh: updated decision tree classifier and associated example
updated default impurity measure
added new impurity measures
updated random forest classifier to operational status
updated cython script to calculate gini measure
removed classifier.py
resolved conflicts
Merge remote-tracking branch 'noel/decisiontree' into treemerge
fix: trailing-spaces option fixed to be executed
doc: updated docstring for permutation_test_score to reflect nature of p-value given the type of score_func
sty: ran make trailing-spaces
doc: fixed spelling
doc: updated docstring based on feedback
fix: permutation test score averages across folds
fix: added ensemble to setup.
Shiqiao Du (3):
improved computational speed by calling fast scipy build-in function and replaceing double loop
fixed some pep8 warnings
Merge remote branch 'upstream/master'
Tim Sheerman-Chase (3):
Added a fix to prevent tree splits on samples that are
Removed exception from _find_best_split to avoid code bloat.
Removed unnecessary variables
Tiziano Zito (1):
FIX broken links to Rubinstein's K-SVD paper.
Virgile Fritsch (24):
Implements a robust covariance estimator: Rousseeuw's MCD.
Integrate Fabian's comments on Minimum Covariance Determinant.
Implements a robust covariance estimator: Rousseeuw's MCD.
Integrate Fabian's comments on Minimum Covariance Determinant.
Merge branch 'mcd' of github.com:VirgileFritsch/scikit-learn into mcd
BF: index out of bound in GraphLassoCV grid refinement.
Refactor MCD robust covariance estimator: it is easier to regularize.
Merge with Gael's glasso changes.
Make the design even more modular for MinCovDet.
Make the "robustness parameter" accessible through the API.
Integrate Gael's minor comments + Magnify examples + 1D data case.
Remove `correction` and `reweighting` parameters from the API.
Merge pull request #396 from VirgileFritsch/refactor_mcd
OPT: (minor) remove useless determinant computation in FastMCD.
Separate correction and reweighting steps from raw MCD computation.
Add a set of tools and a new object for outliers detection (+ example).
Add tools to perform outlier detection with sklearn + documentation.
Clean working directory
Integrate AlexG's comments on doc and examples + add tests.
Magnify novelty and outlier detection examples again + minor fixes.
DOC: Move Parameters section outside objects __init__ method.
Example on real data (outlier detection on boston housing data set).
Fix bugs + adjust OCSVM parameter in outlier detection example.
Cosmit: address Olivier's comments on examples naming.
Vlad Niculae (74):
MISC: even better check_build error reporting
DOC: added Gaussian Processes to class reference
FIX: keep track of index swapping in OMP
Merge branch 'master' into omp_bug
Merge branch 'omp-bug-test' into omp_bug
Testing for swapped regressors in OMP
Merge branch 'omp-bug-test' into omp_bug
PEP8
Merge branch 'master' into omp_bug
Merge pull request #408 from vene/omp_bug
Skip tests in OMP that fail on old Python versions
Fix one-dimensional y in Gram OMP estimator
Added SparseCoder estimator
Basic testing
DOC: add missing split_sign in docstrings
FIX: 10% of features should be at least 1
PEP257 :)
restore typo
Added SparseCoder to init and class index
initial work on docs
implement noop fit in SparseCoder
clean up test
Fixed doc links
Fixed lena in example
Fixed lena import in denoising example
Merge branch 'master' into sparse-coder
cleaned up imports in test
Merge branch 'master' into sparse-coder
FIX: objective functions in Lasso linear model docs
DOC: correct ordering of returns in dict_learning_online
DOC: clarified dimensions in _update_dict
Fix the API and the scaling inside dict_learning
DOC: specify scaling in linear_model.rst
work on failing tests
Merge branch 'master' into sparse-coder
skip tests that were wrongly passing before
Test for almost equal instead of equal in sparse_encode_error
FIX: slices generation
Hide sparse_encode -- redundant
DOC: add optimization objective to lasso and enet docstrings
DOC: make docstrings as good as I could
Warnings and deprecation
DOC: better cross refs and docstrings
Adapted examples for alpha scaling
Merge branch 'master' into sparse-coder
PEP8
added sparse coding example
s/threhold/threshold
Merge branch 'master' of https://github.com/scikit-learn/scikit-learn into sparse-coder
Add SparseCoder example
Rehauled SparseCoder example
Merge branch 'master' into sc-example
Added @vene's work to the changelog
sparse coding transform is now a mixin
EHN: multilabel samples generator can create different number of labels per instance
pyflakes test_multiclass
Add the samples generator to the references
ENH: Added the synthetic example
ENH: Really added the synthetic example
DOC: add multiclass to class reference
DOC: add example to multiclass.rst
DOC: really add example to multiclass.rst
DOC: add image to narrative doc
Added missing space in PIL warning
DOC update changelog
Add Andy to the author list
Allow unlabeled samples in multilabel ex, collab between @vene and @mblondel on the plane
FIX typo that broke the test
ENH make example more expressive
Change seed to make example behave better
Removed unused imports in species dataset
FIX: issue #540, make omp robust to empty solution
Merge branch 'omp-zerofix'
ENHanced the multilabel example aspect
Xinfan Meng (1):
BUG Disallow negative tf-idf weight
Yaroslav Halchenko (18):
pacify lintian and add ${python:Depends} for python-scikits-learn
DOC: minor typo "precom[p]uted"
DOC: fix name for line_profiler_ext.py extension
DOC: enhancement for Debian installation + fixed various typos
Merge branch '0.10.X' into releases
ENH: we are packaging releases so dfsg rules works on releases branch
fresh changelog for upcoming 0.10.0
Adjusted dfsg and other rules to operate on sklearn directory
Merge releases into DFSG (pruning externals: joblib)
Merge branch 'dfsg' into debian
Dropping cherry-picked fix up_release_sv_coef_memory
refreshed the joblib patch
Python 2.5 compatibility dropped
Operate on 'requested' not all supported Python versions
Merge branch '0.10.X' into releases
Merge releases into DFSG (pruning externals: joblib)
Merge branch 'dfsg' into debian
Added 'set -e' to composite cmdline constructs in debian/rules to prevent swallowing errors. Thanks to Jakub Wilk for citing me the relevant exerpt from Debian policy ;-)
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