v0.14.rst.txt

.. currentmodule:: sklearn

Version 0.14

August 7, 2013

Changelog

• Missing values with sparse and dense matrices can be imputed with the transformer :class:`preprocessing.Imputer` by `Nicolas Trésegnie`_.
• The core implementation of decisions trees has been rewritten from scratch, allowing for faster tree induction and lower memory consumption in all tree-based estimators. By `Gilles Louppe`_.
• Added :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor`, by `Noel Dawe`_ and `Gilles Louppe`_. See the :ref:`AdaBoost <adaboost>` section of the user guide for details and examples.
• Added :class:`grid_search.RandomizedSearchCV` and :class:`grid_search.ParameterSampler` for randomized hyperparameter optimization. By `Andreas Müller`_.
• Added :ref:`biclustering <biclustering>` algorithms (:class:`sklearn.cluster.bicluster.SpectralCoclustering` and :class:`sklearn.cluster.bicluster.SpectralBiclustering`), data generation methods (:func:`sklearn.datasets.make_biclusters` and :func:`sklearn.datasets.make_checkerboard`), and scoring metrics (:func:`sklearn.metrics.consensus_score`). By `Kemal Eren`_.
• Added :ref:`Restricted Boltzmann Machines<rbm>` (:class:`neural_network.BernoulliRBM`). By `Yann Dauphin`_.
• Python 3 support by :user:`Justin Vincent <justinvf>`, `Lars Buitinck`_, :user:`Subhodeep Moitra <smoitra87>` and `Olivier Grisel`_. All tests now pass under Python 3.3.
• Ability to pass one penalty (alpha value) per target in :class:`linear_model.Ridge`, by @eickenberg and `Mathieu Blondel`_.
• Fixed :mod:`sklearn.linear_model.stochastic_gradient.py` L2 regularization issue (minor practical significance). By :user:`Norbert Crombach <norbert>` and `Mathieu Blondel`_ .
• Added an interactive version of `Andreas Müller`_'s Machine Learning Cheat Sheet (for scikit-learn) to the documentation. See :ref:`Choosing the right estimator <ml_map>`. By `Jaques Grobler`_.
• :class:`grid_search.GridSearchCV` and :func:`cross_validation.cross_val_score` now support the use of advanced scoring function such as area under the ROC curve and f-beta scores. See :ref:`scoring_parameter` for details. By `Andreas Müller`_ and `Lars Buitinck`_. Passing a function from :mod:`sklearn.metrics` as score_func is deprecated.
• Multi-label classification output is now supported by :func:`metrics.accuracy_score`, :func:`metrics.zero_one_loss`, :func:`metrics.f1_score`, :func:`metrics.fbeta_score`, :func:`metrics.classification_report`, :func:`metrics.precision_score` and :func:`metrics.recall_score` by `Arnaud Joly`_.
• Two new metrics :func:`metrics.hamming_loss` and :func:`metrics.jaccard_similarity_score` are added with multi-label support by `Arnaud Joly`_.
• Speed and memory usage improvements in :class:`feature_extraction.text.CountVectorizer` and :class:`feature_extraction.text.TfidfVectorizer`, by Jochen Wersdörfer and Roman Sinayev.
• The min_df parameter in :class:`feature_extraction.text.CountVectorizer` and :class:`feature_extraction.text.TfidfVectorizer`, which used to be 2, has been reset to 1 to avoid unpleasant surprises (empty vocabularies) for novice users who try it out on tiny document collections. A value of at least 2 is still recommended for practical use.
• :class:`svm.LinearSVC`, :class:`linear_model.SGDClassifier` and :class:`linear_model.SGDRegressor` now have a sparsify method that converts their coef_ into a sparse matrix, meaning stored models trained using these estimators can be made much more compact.
• :class:`linear_model.SGDClassifier` now produces multiclass probability estimates when trained under log loss or modified Huber loss.
• Hyperlinks to documentation in example code on the website by :user:`Martin Luessi <mluessi>`.
• Fixed bug in :class:`preprocessing.MinMaxScaler` causing incorrect scaling of the features for non-default feature_range settings. By `Andreas Müller`_.
• max_features in :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor` and all derived ensemble estimators now supports percentage values. By `Gilles Louppe`_.
• Performance improvements in :class:`isotonic.IsotonicRegression` by `Nelle Varoquaux`_.
• :func:`metrics.accuracy_score` has an option normalize to return the fraction or the number of correctly classified sample by `Arnaud Joly`_.
• Added :func:`metrics.log_loss` that computes log loss, aka cross-entropy loss. By Jochen Wersdörfer and `Lars Buitinck`_.
• A bug that caused :class:`ensemble.AdaBoostClassifier`'s to output incorrect probabilities has been fixed.
• Feature selectors now share a mixin providing consistent transform, inverse_transform and get_support methods. By `Joel Nothman`_.
• A fitted :class:`grid_search.GridSearchCV` or :class:`grid_search.RandomizedSearchCV` can now generally be pickled. By `Joel Nothman`_.
• Refactored and vectorized implementation of :func:`metrics.roc_curve` and :func:`metrics.precision_recall_curve`. By `Joel Nothman`_.
• The new estimator :class:`sklearn.decomposition.TruncatedSVD` performs dimensionality reduction using SVD on sparse matrices, and can be used for latent semantic analysis (LSA). By `Lars Buitinck`_.
• Added self-contained example of out-of-core learning on text data :ref:`sphx_glr_auto_examples_applications_plot_out_of_core_classification.py`. By :user:`Eustache Diemert <oddskool>`.
• The default number of components for :class:`sklearn.decomposition.RandomizedPCA` is now correctly documented to be n_features. This was the default behavior, so programs using it will continue to work as they did.
• :class:`sklearn.cluster.KMeans` now fits several orders of magnitude faster on sparse data (the speedup depends on the sparsity). By `Lars Buitinck`_.
• Reduce memory footprint of FastICA by `Denis Engemann`_ and `Alexandre Gramfort`_.
• Verbose output in :mod:`sklearn.ensemble.gradient_boosting` now uses a column format and prints progress in decreasing frequency. It also shows the remaining time. By `Peter Prettenhofer`_.
• :mod:`sklearn.ensemble.gradient_boosting` provides out-of-bag improvement :attr:`~sklearn.ensemble.GradientBoostingRegressor.oob_improvement_` rather than the OOB score for model selection. An example that shows how to use OOB estimates to select the number of trees was added. By `Peter Prettenhofer`_.
• Most metrics now support string labels for multiclass classification by `Arnaud Joly`_ and `Lars Buitinck`_.
• New OrthogonalMatchingPursuitCV class by `Alexandre Gramfort`_ and `Vlad Niculae`_.
• Fixed a bug in :class:`sklearn.covariance.GraphLassoCV`: the 'alphas' parameter now works as expected when given a list of values. By Philippe Gervais.
• Fixed an important bug in :class:`sklearn.covariance.GraphLassoCV` that prevented all folds provided by a CV object to be used (only the first 3 were used). When providing a CV object, execution time may thus increase significantly compared to the previous version (bug results are correct now). By Philippe Gervais.
• :class:`cross_validation.cross_val_score` and the :mod:`grid_search` module is now tested with multi-output data by `Arnaud Joly`_.
• :func:`datasets.make_multilabel_classification` can now return the output in label indicator multilabel format by `Arnaud Joly`_.
• K-nearest neighbors, :class:`neighbors.KNeighborsRegressor` and :class:`neighbors.RadiusNeighborsRegressor`, and radius neighbors, :class:`neighbors.RadiusNeighborsRegressor` and :class:`neighbors.RadiusNeighborsClassifier` support multioutput data by `Arnaud Joly`_.
• Random state in LibSVM-based estimators (:class:`svm.SVC`, :class:`NuSVC`, :class:`OneClassSVM`, :class:`svm.SVR`, :class:`svm.NuSVR`) can now be controlled. This is useful to ensure consistency in the probability estimates for the classifiers trained with probability=True. By `Vlad Niculae`_.
• Out-of-core learning support for discrete naive Bayes classifiers :class:`sklearn.naive_bayes.MultinomialNB` and :class:`sklearn.naive_bayes.BernoulliNB` by adding the partial_fit method by `Olivier Grisel`_.
• New website design and navigation by `Gilles Louppe`_, `Nelle Varoquaux`_, Vincent Michel and `Andreas Müller`_.
• Improved documentation on :ref:`multi-class, multi-label and multi-output classification <multiclass>` by `Yannick Schwartz`_ and `Arnaud Joly`_.
• Better input and error handling in the :mod:`metrics` module by `Arnaud Joly`_ and `Joel Nothman`_.
• Speed optimization of the :mod:`hmm` module by :user:`Mikhail Korobov <kmike>`
• Significant speed improvements for :class:`sklearn.cluster.DBSCAN` by cleverless

API changes summary

• The :func:`auc_score` was renamed :func:`roc_auc_score`.
• Testing scikit-learn with sklearn.test() is deprecated. Use nosetests sklearn from the command line.
• Feature importances in :class:`tree.DecisionTreeClassifier`, :class:`tree.DecisionTreeRegressor` and all derived ensemble estimators are now computed on the fly when accessing the feature_importances_ attribute. Setting compute_importances=True is no longer required. By `Gilles Louppe`_.
• :class:`linear_model.lasso_path` and :class:`linear_model.enet_path` can return its results in the same format as that of :class:`linear_model.lars_path`. This is done by setting the return_models parameter to False. By `Jaques Grobler`_ and `Alexandre Gramfort`_
• :class:`grid_search.IterGrid` was renamed to :class:`grid_search.ParameterGrid`.
• Fixed bug in :class:`KFold` causing imperfect class balance in some cases. By `Alexandre Gramfort`_ and Tadej Janež.
• :class:`sklearn.neighbors.BallTree` has been refactored, and a :class:`sklearn.neighbors.KDTree` has been added which shares the same interface. The Ball Tree now works with a wide variety of distance metrics. Both classes have many new methods, including single-tree and dual-tree queries, breadth-first and depth-first searching, and more advanced queries such as kernel density estimation and 2-point correlation functions. By `Jake Vanderplas`_
• Support for scipy.spatial.cKDTree within neighbors queries has been removed, and the functionality replaced with the new :class:`KDTree` class.
• :class:`sklearn.neighbors.KernelDensity` has been added, which performs efficient kernel density estimation with a variety of kernels.
• :class:`sklearn.decomposition.KernelPCA` now always returns output with n_components components, unless the new parameter remove_zero_eig is set to True. This new behavior is consistent with the way kernel PCA was always documented; previously, the removal of components with zero eigenvalues was tacitly performed on all data.
• gcv_mode="auto" no longer tries to perform SVD on a densified sparse matrix in :class:`sklearn.linear_model.RidgeCV`.
• Sparse matrix support in :class:`sklearn.decomposition.RandomizedPCA` is now deprecated in favor of the new TruncatedSVD.
• :class:`cross_validation.KFold` and :class:`cross_validation.StratifiedKFold` now enforce n_folds >= 2 otherwise a ValueError is raised. By `Olivier Grisel`_.
• :func:`datasets.load_files`'s charset and charset_errors parameters were renamed encoding and decode_errors.
• Attribute oob_score_ in :class:`sklearn.ensemble.GradientBoostingRegressor` and :class:`sklearn.ensemble.GradientBoostingClassifier` is deprecated and has been replaced by oob_improvement_ .
• Attributes in OrthogonalMatchingPursuit have been deprecated (copy_X, Gram, ...) and precompute_gram renamed precompute for consistency. See #2224.
• :class:`sklearn.preprocessing.StandardScaler` now converts integer input to float, and raises a warning. Previously it rounded for dense integer input.
• :class:`sklearn.multiclass.OneVsRestClassifier` now has a decision_function method. This will return the distance of each sample from the decision boundary for each class, as long as the underlying estimators implement the decision_function method. By `Kyle Kastner`_.
• Better input validation, warning on unexpected shapes for y.

People

List of contributors for release 0.14 by number of commits.

• 277 Gilles Louppe
• 245 Lars Buitinck
• 187 Andreas Mueller
• 124 Arnaud Joly
• 112 Jaques Grobler
• 109 Gael Varoquaux
• 107 Olivier Grisel
• 102 Noel Dawe
• 99 Kemal Eren
• 79 Joel Nothman
• 75 Jake VanderPlas
• 73 Nelle Varoquaux
• 71 Vlad Niculae
• 65 Peter Prettenhofer
• 64 Alexandre Gramfort
• 54 Mathieu Blondel
• 38 Nicolas Trésegnie
• 35 eustache
• 27 Denis Engemann
• 25 Yann N. Dauphin
• 19 Justin Vincent
• 17 Robert Layton
• 15 Doug Coleman
• 14 Michael Eickenberg
• 13 Robert Marchman
• 11 Fabian Pedregosa
• 11 Philippe Gervais
• 10 Jim Holmström
• 10 Tadej Janež
• 10 syhw
• 9 Mikhail Korobov
• 9 Steven De Gryze
• 8 sergeyf
• 7 Ben Root
• 7 Hrishikesh Huilgolkar
• 6 Kyle Kastner
• 6 Martin Luessi
• 6 Rob Speer
• 5 Federico Vaggi
• 5 Raul Garreta
• 5 Rob Zinkov
• 4 Ken Geis
• 3 A. Flaxman
• 3 Denton Cockburn
• 3 Dougal Sutherland
• 3 Ian Ozsvald
• 3 Johannes Schönberger
• 3 Robert McGibbon
• 3 Roman Sinayev
• 3 Szabo Roland
• 2 Diego Molla
• 2 Imran Haque
• 2 Jochen Wersdörfer
• 2 Sergey Karayev
• 2 Yannick Schwartz
• 2 jamestwebber
• 1 Abhijeet Kolhe
• 1 Alexander Fabisch
• 1 Bastiaan van den Berg
• 1 Benjamin Peterson
• 1 Daniel Velkov
• 1 Fazlul Shahriar
• 1 Felix Brockherde
• 1 Félix-Antoine Fortin
• 1 Harikrishnan S
• 1 Jack Hale
• 1 JakeMick
• 1 James McDermott
• 1 John Benediktsson
• 1 John Zwinck
• 1 Joshua Vredevoogd
• 1 Justin Pati
• 1 Kevin Hughes
• 1 Kyle Kelley
• 1 Matthias Ekman
• 1 Miroslav Shubernetskiy
• 1 Naoki Orii
• 1 Norbert Crombach
• 1 Rafael Cunha de Almeida
• 1 Rolando Espinoza La fuente
• 1 Seamus Abshere
• 1 Sergey Feldman
• 1 Sergio Medina
• 1 Stefano Lattarini
• 1 Steve Koch
• 1 Sturla Molden
• 1 Thomas Jarosch
• 1 Yaroslav Halchenko