doc/whats_new/older_versions.rst
.. include:: _contributors.rst
.. currentmodule:: sklearn
.. _changes_0_12.1:
October 8, 2012
The 0.12.1 release is a bug-fix release with no additional features, but is instead a set of bug fixes
Improved numerical stability in spectral embedding by Gael Varoquaux_
Doctest under windows 64bit by Gael Varoquaux_
Documentation fixes for elastic net by Andreas Müller_ and
Alexandre Gramfort_
Proper behavior with fortran-ordered NumPy arrays by Gael Varoquaux_
Make GridSearchCV work with non-CSR sparse matrix by Lars Buitinck_
Fix parallel computing in MDS by Gael Varoquaux_
Fix Unicode support in count vectorizer by Andreas Müller_
Fix MinCovDet breaking with X.shape = (3, 1) by :user:Virgile Fritsch <VirgileFritsch>
Fix clone of SGD objects by Peter Prettenhofer_
Stabilize GMM by :user:Virgile Fritsch <VirgileFritsch>
Peter Prettenhofer_Gael Varoquaux_Andreas Müller_Lars Buitinck_Virgile Fritsch <VirgileFritsch>Alexandre Gramfort_Gilles Louppe_Mathieu Blondel_.. _changes_0_12:
September 4, 2012
Various speed improvements of the :ref:decision trees <tree> module, by
Gilles Louppe_.
:class:~ensemble.GradientBoostingRegressor and
:class:~ensemble.GradientBoostingClassifier now support feature subsampling
via the max_features argument, by Peter Prettenhofer_.
Added Huber and Quantile loss functions to
:class:~ensemble.GradientBoostingRegressor, by Peter Prettenhofer_.
:ref:Decision trees <tree> and :ref:forests of randomized trees <forest>
now support multi-output classification and regression problems, by
Gilles Louppe_.
Added :class:~preprocessing.LabelEncoder, a simple utility class to
normalize labels or transform non-numerical labels, by Mathieu Blondel_.
Added the epsilon-insensitive loss and the ability to make probabilistic
predictions with the modified huber loss in :ref:sgd, by
Mathieu Blondel_.
Added :ref:multidimensional_scaling, by Nelle Varoquaux.
SVMlight file format loader now detects compressed (gzip/bzip2) files and
decompresses them on the fly, by Lars Buitinck_.
SVMlight file format serializer now preserves double precision floating
point values, by Olivier Grisel_.
A common testing framework for all estimators was added, by Andreas Müller_.
Understandable error messages for estimators that do not accept
sparse input by Gael Varoquaux_
Speedups in hierarchical clustering by Gael Varoquaux_. In
particular building the tree now supports early stopping. This is
useful when the number of clusters is not small compared to the
number of samples.
Add MultiTaskLasso and MultiTaskElasticNet for joint feature selection,
by Alexandre Gramfort_.
Added metrics.auc_score and
:func:metrics.average_precision_score convenience functions by Andreas Müller_.
Improved sparse matrix support in the :ref:feature_selection
module by Andreas Müller_.
New word boundaries-aware character n-gram analyzer for the
:ref:text_feature_extraction module by :user:@kernc <kernc>.
Fixed bug in spectral clustering that led to single point clusters
by Andreas Müller_.
In :class:~feature_extraction.text.CountVectorizer, added an option to
ignore infrequent words, min_df by Andreas Müller_.
Add support for multiple targets in some linear models (ElasticNet, Lasso
and OrthogonalMatchingPursuit) by Vlad Niculae_ and
Alexandre Gramfort_.
Fixes in decomposition.ProbabilisticPCA score function by Wei Li.
Fixed feature importance computation in
:ref:gradient_boosting.
The old scikits.learn package has disappeared; all code should import
from sklearn instead, which was introduced in 0.9.
In :func:metrics.roc_curve, the thresholds array is now returned
with its order reversed, in order to keep it consistent with the order
of the returned fpr and tpr.
In hmm objects, like hmm.GaussianHMM,
hmm.MultinomialHMM, etc., all parameters must be passed to the
object when initialising it and not through fit. Now fit will
only accept the data as an input parameter.
For all SVM classes, a faulty behavior of gamma was fixed. Previously,
the default gamma value was only computed the first time fit was called
and then stored. It is now recalculated on every call to fit.
All Base classes are now abstract meta classes so that they can not be
instantiated.
:func:cluster.ward_tree now also returns the parent array. This is
necessary for early-stopping in which case the tree is not
completely built.
In :class:~feature_extraction.text.CountVectorizer the parameters
min_n and max_n were joined to the parameter n_gram_range to
enable grid-searching both at once.
In :class:~feature_extraction.text.CountVectorizer, words that appear
only in one document are now ignored by default. To reproduce
the previous behavior, set min_df=1.
Fixed API inconsistency: :meth:linear_model.SGDClassifier.predict_proba now
returns 2d array when fit on two classes.
Fixed API inconsistency: :meth:discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function
and :meth:discriminant_analysis.LinearDiscriminantAnalysis.decision_function now return 1d arrays
when fit on two classes.
Grid of alphas used for fitting :class:~linear_model.LassoCV and
:class:~linear_model.ElasticNetCV is now stored
in the attribute alphas_ rather than overriding the init parameter
alphas.
Linear models when alpha is estimated by cross-validation store
the estimated value in the alpha_ attribute rather than just
alpha or best_alpha.
:class:~ensemble.GradientBoostingClassifier now supports
:meth:~ensemble.GradientBoostingClassifier.staged_predict_proba, and
:meth:~ensemble.GradientBoostingClassifier.staged_predict.
svm.sparse.SVC and other sparse SVM classes are now deprecated.
All classes in the :ref:svm module now automatically select the
sparse or dense representation based on the input.
All clustering algorithms now interpret the array X given to fit as
input data, in particular :class:~cluster.SpectralClustering and
:class:~cluster.AffinityPropagation which previously expected affinity matrices.
For clustering algorithms that take the desired number of clusters as a parameter,
this parameter is now called n_clusters.
Andreas Müller_Gilles Louppe_Gael Varoquaux_Peter Prettenhofer_Mathieu Blondel_Alexandre Gramfort_Vlad Niculae_Lars Buitinck_Jaques Grobler_Olivier Grisel_@kernc <kernc>Virgile Fritsch <VirgileFritsch>Fabian Pedregosa_Robert Layton_Nicolas Pinto_Jake Vanderplas_Brian Holt_Edouard Duchesnay_Yaroslav Halchenko_.. _changes_0_11:
May 7, 2012
Highlights .............
Gradient boosted regression trees (:ref:gradient_boosting)
for classification and regression by Peter Prettenhofer_
and Scott White_ .
Simple dict-based feature loader with support for categorical variables
(:class:~feature_extraction.DictVectorizer) by Lars Buitinck_.
Added Matthews correlation coefficient (:func:metrics.matthews_corrcoef)
and added macro and micro average options to
:func:~metrics.precision_score, :func:metrics.recall_score and
:func:~metrics.f1_score by Satrajit Ghosh_.
:ref:out_of_bag of generalization error for :ref:ensemble
by Andreas Müller_.
Randomized sparse linear models for feature
selection, by Alexandre Gramfort_ and Gael Varoquaux_
:ref:label_propagation for semi-supervised learning, by Clay
Woolam. Note the semi-supervised API is still work in progress,
and may change.
Added BIC/AIC model selection to classical :ref:gmm and unified
the API with the remainder of scikit-learn, by Bertrand Thirion_
Added sklearn.cross_validation.StratifiedShuffleSplit, which is
a sklearn.cross_validation.ShuffleSplit with balanced splits,
by Yannick Schwartz.
:class:~sklearn.neighbors.NearestCentroid classifier added, along with a
shrink_threshold parameter, which implements shrunken centroid
classification, by Robert Layton_.
Other changes ..............
Merged dense and sparse implementations of :ref:sgd module and
exposed utility extension types for sequential
datasets seq_dataset and weight vectors weight_vector
by Peter Prettenhofer_.
Added partial_fit (support for online/minibatch learning) and
warm_start to the :ref:sgd module by Mathieu Blondel_.
Dense and sparse implementations of :ref:svm classes and
:class:~linear_model.LogisticRegression merged by Lars Buitinck_.
Regressors can now be used as base estimator in the :ref:multiclass
module by Mathieu Blondel_.
Added n_jobs option to :func:metrics.pairwise_distances
and :func:metrics.pairwise.pairwise_kernels for parallel computation,
by Mathieu Blondel_.
:ref:k_means can now be run in parallel, using the n_jobs argument
to either :ref:k_means or :class:cluster.KMeans, by Robert Layton_.
Improved :ref:cross_validation and :ref:grid_search documentation
and introduced the new cross_validation.train_test_split
helper function by Olivier Grisel_
:class:~svm.SVC members coef_ and intercept_ changed sign for
consistency with decision_function; for kernel==linear,
coef_ was fixed in the one-vs-one case, by Andreas Müller_.
Performance improvements to efficient leave-one-out cross-validated
Ridge regression, esp. for the n_samples > n_features case, in
:class:~linear_model.RidgeCV, by Reuben Fletcher-Costin.
Refactoring and simplification of the :ref:text_feature_extraction
API and fixed a bug that caused possible negative IDF,
by Olivier Grisel_.
Beam pruning option in _BaseHMM module has been removed since it
is difficult to Cythonize. If you are interested in contributing a Cython
version, you can use the python version in the git history as a reference.
Classes in :ref:neighbors now support arbitrary Minkowski metric for
nearest neighbors searches. The metric can be specified by argument p.
covariance.EllipticEnvelop is now deprecated.
Please use :class:~covariance.EllipticEnvelope instead.
NeighborsClassifier and NeighborsRegressor are gone in the module
:ref:neighbors. Use the classes :class:~neighbors.KNeighborsClassifier,
:class:~neighbors.RadiusNeighborsClassifier, :class:~neighbors.KNeighborsRegressor
and/or :class:~neighbors.RadiusNeighborsRegressor instead.
Sparse classes in the :ref:sgd module are now deprecated.
In mixture.GMM, mixture.DPGMM and mixture.VBGMM,
parameters must be passed to an object when initialising it and not through
fit. Now fit will only accept the data as an input parameter.
methods rvs and decode in GMM module are now deprecated.
sample and score or predict should be used instead.
attribute _scores and _pvalues in univariate feature selection
objects are now deprecated.
scores_ or pvalues_ should be used instead.
In :class:~linear_model.LogisticRegression, :class:~svm.LinearSVC,
:class:~svm.SVC and :class:~svm.NuSVC, the class_weight parameter is
now an initialization parameter, not a parameter to fit. This makes grid
searches over this parameter possible.
LFW data is now always shape (n_samples, n_features) to be
consistent with the Olivetti faces dataset. Use images and
pairs attribute to access the natural images shapes instead.
In :class:~svm.LinearSVC, the meaning of the multi_class parameter
changed. Options now are 'ovr' and 'crammer_singer', with
'ovr' being the default. This does not change the default behavior
but hopefully is less confusing.
Class feature_selection.text.Vectorizer is deprecated and
replaced by feature_selection.text.TfidfVectorizer.
The preprocessor / analyzer nested structure for text feature
extraction has been removed. All those features are
now directly passed as flat constructor arguments
to feature_selection.text.TfidfVectorizer and
feature_selection.text.CountVectorizer, in particular the
following parameters are now used:
analyzer can be 'word' or 'char' to switch the default
analysis scheme, or use a specific python callable (as previously).
tokenizer and preprocessor have been introduced to make it
still possible to customize those steps with the new API.
input explicitly control how to interpret the sequence passed to
fit and predict: filenames, file objects or direct (byte or
Unicode) strings.
charset decoding is explicit and strict by default.
the vocabulary, fitted or not is now stored in the
vocabulary_ attribute to be consistent with the project
conventions.
Class feature_selection.text.TfidfVectorizer now derives directly
from feature_selection.text.CountVectorizer to make grid
search trivial.
methods rvs in _BaseHMM module are now deprecated.
sample should be used instead.
Beam pruning option in _BaseHMM module is removed since it is
difficult to be Cythonized. If you are interested, you can look in the
history codes by git.
The SVMlight format loader now supports files with both zero-based and one-based column indices, since both occur "in the wild".
Arguments in class :class:~model_selection.ShuffleSplit are now consistent with
:class:~model_selection.StratifiedShuffleSplit. Arguments test_fraction and
train_fraction are deprecated and renamed to test_size and
train_size and can accept both float and int.
Arguments in class Bootstrap are now consistent with
:class:~model_selection.StratifiedShuffleSplit. Arguments n_test and
n_train are deprecated and renamed to test_size and
train_size and can accept both float and int.
Argument p added to classes in :ref:neighbors to specify an
arbitrary Minkowski metric for nearest neighbors searches.
Andreas Müller_Peter Prettenhofer_Gael Varoquaux_Olivier Grisel_Mathieu Blondel_Lars Buitinck_Jaques Grobler_Alexandre Gramfort_Bertrand Thirion_Robert Layton_Jake Vanderplas_Satrajit Ghosh_David Marek_Gilles Louppe_Vlad Niculae_Fabian Pedregosa_Nicolas Pinto_David Warde-Farley_Edouard Duchesnay_Rob Zinkov_.. _changes_0_10:
January 11, 2012
Python 2.5 compatibility was dropped; the minimum Python version needed to use scikit-learn is now 2.6.
:ref:sparse_inverse_covariance estimation using the graph Lasso, with
associated cross-validated estimator, by Gael Varoquaux_
New :ref:Tree <tree> module by Brian Holt, Peter Prettenhofer,
Satrajit Ghosh_ and Gilles Louppe_. The module comes with complete
documentation and examples.
Fixed a bug in the RFE module by Gilles Louppe_ (issue #378).
Fixed a memory leak in :ref:svm module by Brian Holt_ (issue #367).
Faster tests by Fabian Pedregosa_ and others.
Silhouette Coefficient cluster analysis evaluation metric added as
:func:~sklearn.metrics.silhouette_score by Robert Layton.
Fixed a bug in :ref:k_means in the handling of the n_init parameter:
the clustering algorithm used to be run n_init times but the last
solution was retained instead of the best solution by Olivier Grisel_.
Minor refactoring in :ref:sgd module; consolidated dense and sparse
predict methods; Enhanced test time performance by converting model
parameters to fortran-style arrays after fitting (only multi-class).
Adjusted Mutual Information metric added as
:func:~sklearn.metrics.adjusted_mutual_info_score by Robert Layton.
Models like SVC/SVR/LinearSVC/LogisticRegression from libsvm/liblinear
now support scaling of C regularization parameter by the number of
samples by Alexandre Gramfort_.
New :ref:Ensemble Methods <ensemble> module by Gilles Louppe_ and
Brian Holt_. The module comes with the random forest algorithm and the
extra-trees method, along with documentation and examples.
:ref:outlier_detection: outlier and novelty detection, by
:user:Virgile Fritsch <VirgileFritsch>.
:ref:kernel_approximation: a transform implementing kernel
approximation for fast SGD on non-linear kernels by
Andreas Müller_.
Fixed a bug due to atom swapping in :ref:OMP by Vlad Niculae_.
:ref:SparseCoder by Vlad Niculae_.
:ref:mini_batch_kmeans performance improvements by Olivier Grisel_.
:ref:k_means support for sparse matrices by Mathieu Blondel_.
Improved documentation for developers and for the :mod:sklearn.utils
module, by Jake Vanderplas_.
Vectorized 20newsgroups dataset loader
(:func:~sklearn.datasets.fetch_20newsgroups_vectorized) by
Mathieu Blondel_.
:ref:multiclass by Lars Buitinck_.
Utilities for fast computation of mean and variance for sparse matrices
by Mathieu Blondel_.
Make :func:~sklearn.preprocessing.scale and
sklearn.preprocessing.Scaler work on sparse matrices by
Olivier Grisel_
Feature importances using decision trees and/or forest of trees,
by Gilles Louppe_.
Parallel implementation of forests of randomized trees by
Gilles Louppe_.
sklearn.cross_validation.ShuffleSplit can subsample the train
sets as well as the test sets by Olivier Grisel_.
Errors in the build of the documentation fixed by Andreas Müller_.
Here are the code migration instructions when upgrading from scikit-learn version 0.9:
Some estimators that may overwrite their inputs to save memory previously
had overwrite_ parameters; these have been replaced with copy_
parameters with exactly the opposite meaning.
This particularly affects some of the estimators in :mod:~sklearn.linear_model.
The default behavior is still to copy everything passed in.
The SVMlight dataset loader :func:~sklearn.datasets.load_svmlight_file no
longer supports loading two files at once; use load_svmlight_files
instead. Also, the (unused) buffer_mb parameter is gone.
Sparse estimators in the :ref:sgd module use dense parameter vector
coef_ instead of sparse_coef_. This significantly improves
test time performance.
The :ref:covariance module now has a robust estimator of
covariance, the Minimum Covariance Determinant estimator.
Cluster evaluation metrics in :mod:~sklearn.metrics.cluster have been refactored
but the changes are backwards compatible. They have been moved to the
metrics.cluster.supervised, along with
metrics.cluster.unsupervised which contains the Silhouette
Coefficient.
The permutation_test_score function now behaves the same way as
cross_val_score (i.e. uses the mean score across the folds.)
Cross Validation generators now use integer indices (indices=True)
by default instead of boolean masks. This makes it more intuitive to
use with sparse matrix data.
The functions used for sparse coding, sparse_encode and
sparse_encode_parallel have been combined into
:func:~sklearn.decomposition.sparse_encode, and the shapes of the arrays
have been transposed for consistency with the matrix factorization setting,
as opposed to the regression setting.
Fixed an off-by-one error in the SVMlight/LibSVM file format handling;
files generated using :func:~sklearn.datasets.dump_svmlight_file should be
re-generated. (They should continue to work, but accidentally had one
extra column of zeros prepended.)
BaseDictionaryLearning class replaced by SparseCodingMixin.
sklearn.utils.extmath.fast_svd has been renamed
:func:~sklearn.utils.extmath.randomized_svd and the default
oversampling is now fixed to 10 additional random vectors instead
of doubling the number of components to extract. The new behavior
follows the reference paper.
The following people contributed to scikit-learn since last release:
Andreas Müller_Olivier Grisel_Gilles Louppe_Brian Holt_Gael Varoquaux_Lars Buitinck_Vlad Niculae_Peter Prettenhofer_Fabian Pedregosa_Mathieu Blondel_Jake Vanderplas_Alexandre Gramfort_Virgile Fritsch <VirgileFritsch>Satrajit Ghosh_Yaroslav Halchenko_Nicolas Pinto_.. _changes_0_9:
September 21, 2011
scikit-learn 0.9 was released on September 2011, three months after the 0.8
release and includes the new modules :ref:manifold, :ref:dirichlet_process
as well as several new algorithms and documentation improvements.
This release also includes the dictionary-learning work developed by
Vlad Niculae_ as part of the Google Summer of Code <https://developers.google.com/open-source/gsoc>_ program.
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.. |banner3| image:: ../auto_examples/decomposition/images/thumb/sphx_glr_plot_kernel_pca_thumb.png :target: ../auto_examples/decomposition/plot_kernel_pca.html
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New :ref:manifold module by Jake Vanderplas_ and
Fabian Pedregosa_.
New :ref:Dirichlet Process <dirichlet_process> Gaussian Mixture
Model by Alexandre Passos_
:ref:neighbors module refactoring by Jake Vanderplas_ :
general refactoring, support for sparse matrices in input, speed and
documentation improvements. See the next section for a full list of API
changes.
Improvements on the :ref:feature_selection module by
Gilles Louppe_ : refactoring of the RFE classes, documentation
rewrite, increased efficiency and minor API changes.
:ref:SparsePCA by Vlad Niculae, Gael Varoquaux and
Alexandre Gramfort_
Printing an estimator now behaves independently of architectures
and Python version thanks to :user:Jean Kossaifi <JeanKossaifi>.
:ref:Loader for libsvm/svmlight format <libsvm_loader> by
Mathieu Blondel_ and Lars Buitinck_
Documentation improvements: thumbnails in
example gallery by Fabian Pedregosa_.
Important bugfixes in :ref:svm module (segfaults, bad
performance) by Fabian Pedregosa_.
Added :ref:multinomial_naive_bayes and :ref:bernoulli_naive_bayes
by Lars Buitinck_
Text feature extraction optimizations by Lars Buitinck
Chi-Square feature selection
(:func:feature_selection.chi2) by Lars Buitinck_.
:ref:sample_generators module refactoring by Gilles Louppe_
:ref:multiclass by Mathieu Blondel_
Ball tree rewrite by Jake Vanderplas_
Implementation of :ref:dbscan algorithm by Robert Layton
Kmeans predict and transform by Robert Layton
Preprocessing module refactoring by Olivier Grisel_
Faster mean shift by Conrad Lee
New Bootstrap, :ref:ShuffleSplit and various other
improvements in cross validation schemes by Olivier Grisel_ and
Gael Varoquaux_
Adjusted Rand index and V-Measure clustering evaluation metrics by Olivier Grisel_
Added :class:Orthogonal Matching Pursuit <linear_model.OrthogonalMatchingPursuit> by Vlad Niculae_
Added 2D-patch extractor utilities in the :ref:feature_extraction module by Vlad Niculae_
Implementation of :class:~linear_model.LassoLarsCV
(cross-validated Lasso solver using the Lars algorithm) and
:class:~linear_model.LassoLarsIC (BIC/AIC model
selection in Lars) by Gael Varoquaux_
and Alexandre Gramfort_
Scalability improvements to :func:metrics.roc_curve by Olivier Hervieu
Distance helper functions :func:metrics.pairwise_distances
and :func:metrics.pairwise.pairwise_kernels by Robert Layton
:class:Mini-Batch K-Means <cluster.MiniBatchKMeans> by Nelle Varoquaux and Peter Prettenhofer.
mldata utilities by Pietro Berkes.
:ref:olivetti_faces_dataset by David Warde-Farley_.
Here are the code migration instructions when upgrading from scikit-learn version 0.8:
The scikits.learn package was renamed sklearn. There is
still a scikits.learn package alias for backward compatibility.
Third-party projects with a dependency on scikit-learn 0.9+ should upgrade their codebase. For instance, under Linux / MacOSX just run (make a backup first!)::
find -name "*.py" | xargs sed -i 's/\bscikits.learn\b/sklearn/g'
Estimators no longer accept model parameters as fit arguments:
instead all parameters must be only be passed as constructor
arguments or using the now public set_params method inherited
from :class:~base.BaseEstimator.
Some estimators can still accept keyword arguments on the fit
but this is restricted to data-dependent values (e.g. a Gram matrix
or an affinity matrix that are precomputed from the X data matrix.
The cross_val package has been renamed to cross_validation
although there is also a cross_val package alias in place for
backward compatibility.
Third-party projects with a dependency on scikit-learn 0.9+ should upgrade their codebase. For instance, under Linux / MacOSX just run (make a backup first!)::
find -name "*.py" | xargs sed -i 's/\bcross_val\b/cross_validation/g'
The score_func argument of the
sklearn.cross_validation.cross_val_score function is now expected
to accept y_test and y_predicted as only arguments for
classification and regression tasks or X_test for unsupervised
estimators.
gamma parameter for support vector machine algorithms is set
to 1 / n_features by default, instead of 1 / n_samples.
The sklearn.hmm has been marked as orphaned: it will be removed
from scikit-learn in version 0.11 unless someone steps up to
contribute documentation, examples and fix lurking numerical
stability issues.
sklearn.neighbors has been made into a submodule. The two previously
available estimators, NeighborsClassifier and NeighborsRegressor
have been marked as deprecated. Their functionality has been divided
among five new classes: NearestNeighbors for unsupervised neighbors
searches, KNeighborsClassifier & RadiusNeighborsClassifier
for supervised classification problems, and KNeighborsRegressor
& RadiusNeighborsRegressor for supervised regression problems.
sklearn.ball_tree.BallTree has been moved to
sklearn.neighbors.BallTree. Using the former will generate a warning.
sklearn.linear_model.LARS() and related classes (LassoLARS,
LassoLARSCV, etc.) have been renamed to
sklearn.linear_model.Lars().
All distance metrics and kernels in sklearn.metrics.pairwise now have a Y
parameter, which by default is None. If not given, the result is the distance
(or kernel similarity) between each sample in Y. If given, the result is the
pairwise distance (or kernel similarity) between samples in X to Y.
sklearn.metrics.pairwise.l1_distance is now called manhattan_distance,
and by default returns the pairwise distance. For the component wise distance,
set the parameter sum_over_features to False.
Backward compatibility package aliases and other deprecated classes and functions will be removed in version 0.11.
38 people contributed to this release.
Vlad Niculae_Olivier Grisel_Lars Buitinck_Gael Varoquaux_Fabian Pedregosa_ (INRIA, Parietal Team)Jake Vanderplas_Mathieu Blondel_Alexandre Passos_Alexandre Gramfort_Peter Prettenhofer_Gilles Louppe_Jean Kossaifi <JeanKossaifi>Virgile Fritsch <VirgileFritsch>Yaroslav Halchenko_Andreas Müller_Nicolas Pinto_.. _changes_0_8:
May 11, 2011
scikit-learn 0.8 was released on May 2011, one month after the first
"international" scikit-learn coding sprint <https://github.com/scikit-learn/scikit-learn/wiki/Upcoming-events>_ and is
marked by the inclusion of important modules: :ref:hierarchical_clustering,
:ref:cross_decomposition, :ref:NMF, initial support for Python 3 and by important
enhancements and bug fixes.
Several new modules were introduced during this release:
New :ref:hierarchical_clustering module by Vincent Michel,
Bertrand Thirion, Alexandre Gramfort and Gael Varoquaux_.
:ref:kernel_pca implementation by Mathieu Blondel_
:ref:labeled_faces_in_the_wild_dataset by Olivier Grisel_.
New :ref:cross_decomposition module by Edouard Duchesnay_.
:ref:NMF module Vlad Niculae_
Implementation of the :ref:oracle_approximating_shrinkage algorithm by
:user:Virgile Fritsch <VirgileFritsch> in the :ref:covariance module.
Some other modules benefited from significant improvements or cleanups.
Initial support for Python 3: builds and imports cleanly,
some modules are usable while others have failing tests by Fabian Pedregosa_.
:class:~decomposition.PCA is now usable from the Pipeline object by Olivier Grisel_.
Guide :ref:performance-howto by Olivier Grisel_.
Fixes for memory leaks in libsvm bindings, 64-bit safer BallTree by Lars Buitinck.
bug and style fixing in :ref:k_means algorithm by Jan Schlüter.
Add attribute converged to Gaussian Mixture Models by Vincent Schut.
Implemented transform, predict_log_proba in
:class:~discriminant_analysis.LinearDiscriminantAnalysis By Mathieu Blondel_.
Refactoring in the :ref:svm module and bug fixes by Fabian Pedregosa,
Gael Varoquaux and Amit Aides.
Refactored SGD module (removed code duplication, better variable naming),
added interface for sample weight by Peter Prettenhofer_.
Wrapped BallTree with Cython by Thouis (Ray) Jones.
Added function :func:svm.l1_min_c by Paolo Losi.
Typos, doc style, etc. by Yaroslav Halchenko, Gael Varoquaux,
Olivier Grisel, Yann Malet, Nicolas Pinto, Lars Buitinck and
Fabian Pedregosa_.
People that made this release possible preceded by number of commits:
Olivier Grisel_Gael Varoquaux_Vlad Niculae_Fabian Pedregosa_Alexandre Gramfort_Edouard Duchesnay_Mathieu Blondel_Peter Prettenhofer_Nicolas Pinto_Virgile Fritsch <VirgileFritsch>Bertrand Thirion_Matthieu Perrot_Yaroslav Halchenko_Andreas Müller_.. _changes_0_7:
March 2, 2011
scikit-learn 0.7 was released in March 2011, roughly three months after the 0.6 release. This release is marked by the speed improvements in existing algorithms like k-Nearest Neighbors and K-Means algorithm and by the inclusion of an efficient algorithm for computing the Ridge Generalized Cross Validation solution. Unlike the preceding release, no new modules were added to this release.
Performance improvements for Gaussian Mixture Model sampling [Jan Schlüter].
Implementation of efficient leave-one-out cross-validated Ridge in
:class:~linear_model.RidgeCV [Mathieu Blondel_]
Better handling of collinearity and early stopping in
:func:linear_model.lars_path [Alexandre Gramfort_ and Fabian Pedregosa_].
Fixes for liblinear ordering of labels and sign of coefficients
[Dan Yamins, Paolo Losi, Mathieu Blondel_ and Fabian Pedregosa_].
Performance improvements for Nearest Neighbors algorithm in
high-dimensional spaces [Fabian Pedregosa_].
Performance improvements for :class:~cluster.KMeans [Gael Varoquaux_ and James Bergstra_].
Sanity checks for SVM-based classes [Mathieu Blondel_].
Refactoring of neighbors.NeighborsClassifier and
:func:neighbors.kneighbors_graph: added different algorithms for
the k-Nearest Neighbor Search and implemented a more stable
algorithm for finding barycenter weights. Also added some
developer documentation for this module, see
notes_neighbors <https://github.com/scikit-learn/scikit-learn/wiki/Neighbors-working-notes>_ for more information [Fabian Pedregosa_].
Documentation improvements: Added pca.RandomizedPCA and
:class:~linear_model.LogisticRegression to the class
reference. Also added references of matrices used for clustering
and other fixes [Gael Varoquaux, Fabian Pedregosa, Mathieu Blondel, Olivier Grisel, Virgile Fritsch , Emmanuelle
Gouillart]
Binded decision_function in classes that make use of liblinear_,
dense and sparse variants, like :class:~svm.LinearSVC or
:class:~linear_model.LogisticRegression [Fabian Pedregosa_].
Performance and API improvements to
:func:metrics.pairwise.euclidean_distances and to
pca.RandomizedPCA [James Bergstra_].
Fix compilation issues under NetBSD [Kamel Ibn Hassen Derouiche]
Allow input sequences of different lengths in hmm.GaussianHMM
[Ron Weiss_].
Fix bug in affinity propagation caused by incorrect indexing [Xinfan Meng]
People that made this release possible preceded by number of commits:
Fabian Pedregosa_Mathieu Blondel_Alexandre Gramfort_James Bergstra_Olivier Grisel_Gael Varoquaux_Edouard Duchesnay_Ron Weiss_Yaroslav Halchenko_.. _changes_0_6:
December 21, 2010
scikit-learn 0.6 was released on December 2010. It is marked by the inclusion of several new modules and a general renaming of old ones. It is also marked by the inclusion of new example, including applications to real-world datasets.
New stochastic gradient <https://scikit-learn.org/stable/modules/sgd.html>_ descent
module by Peter Prettenhofer. The module comes with complete
documentation and examples.
Improved svm module: memory consumption has been reduced by 50%,
heuristic to automatically set class weights, possibility to
assign weights to samples (see
:ref:sphx_glr_auto_examples_svm_plot_weighted_samples.py for an example).
New :ref:gaussian_process module by Vincent Dubourg. This module
also has great documentation and some very neat examples. See
example_gaussian_process_plot_gp_regression.py or
example_gaussian_process_plot_gp_probabilistic_classification_after_regression.py
for a taste of what can be done.
It is now possible to use liblinear's Multi-class SVC (option
multi_class in :class:~svm.LinearSVC)
New features and performance improvements of text feature extraction.
Improved sparse matrix support, both in main classes
(:class:~model_selection.GridSearchCV) as in modules
sklearn.svm.sparse and sklearn.linear_model.sparse.
Lots of cool new examples and a new section that uses real-world
datasets was created. These include:
:ref:sphx_glr_auto_examples_applications_plot_face_recognition.py,
:ref:sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py,
:ref:sphx_glr_auto_examples_applications_wikipedia_principal_eigenvector.py and
others.
Faster :ref:least_angle_regression algorithm. It is now 2x
faster than the R version on worst case and up to 10x times faster
on some cases.
Faster coordinate descent algorithm. In particular, the full path
version of lasso (:func:linear_model.lasso_path) is more than
200x times faster than before.
It is now possible to get probability estimates from a
:class:~linear_model.LogisticRegression model.
module renaming: the glm module has been renamed to linear_model, the gmm module has been included into the more general mixture model and the sgd module has been included in linear_model.
Lots of bug fixes and documentation improvements.
People that made this release possible preceded by number of commits:
207 Olivier Grisel_
167 Fabian Pedregosa_
97 Peter Prettenhofer_
68 Alexandre Gramfort_
59 Mathieu Blondel_
55 Gael Varoquaux_
33 Vincent Dubourg
21 Ron Weiss_
9 Bertrand Thirion
3 Alexandre Passos_
3 Anne-Laure Fouque
2 Ronan Amicel
1 Christian Osendorfer_
.. _changes_0_5:
October 11, 2010
Support for sparse matrices in some classifiers of modules
svm and linear_model (see svm.sparse.SVC,
svm.sparse.SVR, svm.sparse.LinearSVC,
linear_model.sparse.Lasso, linear_model.sparse.ElasticNet)
New :class:~pipeline.Pipeline object to compose different estimators.
Recursive Feature Elimination routines in module
:ref:feature_selection.
Addition of various classes capable of cross validation in the
linear_model module (:class:~linear_model.LassoCV, :class:~linear_model.ElasticNetCV,
etc.).
New, more efficient LARS algorithm implementation. The Lasso
variant of the algorithm is also implemented. See
:class:~linear_model.lars_path, :class:~linear_model.Lars and
:class:~linear_model.LassoLars.
New Hidden Markov Models module (see classes
hmm.GaussianHMM, hmm.MultinomialHMM, hmm.GMMHMM)
New module feature_extraction (see :ref:class reference <feature_extraction_ref>)
New FastICA algorithm in module sklearn.fastica
documentation for the SVM module <https://scikit-learn.org/stable/modules/svm.html>_ and the
complete class reference <https://scikit-learn.org/stable/modules/classes.html>_.API changes: adhere variable names to PEP-8, give more meaningful names.
Fixes for svm module to run on a shared memory context (multiprocessing).
It is again possible to generate latex (and thus PDF) from the sphinx docs.
new examples using some of the mlcomp datasets:
sphx_glr_auto_examples_mlcomp_sparse_document_classification.py (since removed) and
:ref:sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py
Many more examples. See here <https://scikit-learn.org/stable/auto_examples/index.html>_
the full list of examples.
The following is a list of authors for this release, preceded by number of commits:
August 26, 2010
Major changes in this release include:
Coordinate Descent algorithm (Lasso, ElasticNet) refactoring & speed improvements (roughly 100x times faster).
Coordinate Descent Refactoring (and bug fixing) for consistency with R's package GLMNET.
New metrics module.
New GMM module contributed by Ron Weiss.
Implementation of the LARS algorithm (without Lasso variant for now).
feature_selection module redesign.
Migration to GIT as version control system.
Removal of obsolete attrselect module.
Rename of private compiled extensions (added underscore).
Removal of legacy unmaintained code.
Documentation improvements (both docstring and rst).
Improvement of the build system to (optionally) link with MKL. Also, provide a lite BLAS implementation in case no system-wide BLAS is found.
Lots of new examples.
Many, many bug fixes ...
The committer list for this release is the following (preceded by number of commits):
Earlier versions included contributions by Fred Mailhot, David Cooke, David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.