whats_new.rst

.. currentmodule:: sklearn

Release history

Version 0.19.2

October, 2018

This release is exclusively in order to support Python 3.7.

Related changes

• n_iter_ may vary from previous releases in :class:`linear_model.LogisticRegression` with solver='lbfgs' and :class:`linear_model.HuberRegressor`. For Scipy <= 1.0.0, the optimizer could perform more than the requested maximum number of iterations. Now both estimators will report at most max_iter iterations even if more were performed. :issue:`10723` by Joel Nothman.

Version 0.19.1

October, 2017

This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.19.0.

Note there may be minor differences in TSNE output in this release (due to :issue:`9623`), in the case where multiple samples have equal distance to some sample.

Changelog

API changes

• Reverted the addition of metrics.ndcg_score and metrics.dcg_score which had been merged into version 0.19.0 by error. The implementations were broken and undocumented.
• return_train_score which was added to :class:`model_selection.GridSearchCV`, :class:`model_selection.RandomizedSearchCV` and :func:`model_selection.cross_validate` in version 0.19.0 will be changing its default value from True to False in version 0.21. We found that calculating training score could have a great effect on cross validation runtime in some cases. Users should explicitly set return_train_score to False if prediction or scoring functions are slow, resulting in a deleterious effect on CV runtime, or to True if they wish to use the calculated scores. :issue:`9677` by :user:`Kumar Ashutosh <thechargedneutron>` and Joel Nothman.
• correlation_models and regression_models from the legacy gaussian processes implementation have been belatedly deprecated. :issue:`9717` by :user:`Kumar Ashutosh <thechargedneutron>`.

Bug fixes

• Avoid integer overflows in :func:`metrics.matthews_corrcoef`. :issue:`9693` by :user:`Sam Steingold <sam-s>`.
• Fix ValueError in :class:`preprocessing.LabelEncoder` when using inverse_transform on unseen labels. :issue:`9816` by :user:`Charlie Newey <newey01c>`.
• Fixed a bug in the objective function for :class:`manifold.TSNE` (both exact and with the Barnes-Hut approximation) when n_components >= 3. :issue:`9711` by :user:`goncalo-rodrigues`.
• Fix regression in :func:`model_selection.cross_val_predict` where it raised an error with method='predict_proba' for some probabilistic classifiers. :issue:`9641` by :user:`James Bourbeau <jrbourbeau>`.
• Fixed a bug where :func:`datasets.make_classification` modified its input weights. :issue:`9865` by :user:`Sachin Kelkar <s4chin>`.
• :class:`model_selection.StratifiedShuffleSplit` now works with multioutput multiclass or multilabel data with more than 1000 columns. :issue:`9922` by :user:`Charlie Brummitt <crbrummitt>`.
• Fixed a bug with nested and conditional parameter setting, e.g. setting a pipeline step and its parameter at the same time. :issue:`9945` by Andreas Müller and Joel Nothman.

Regressions in 0.19.0 fixed in 0.19.1:

• Fixed a bug where parallelised prediction in random forests was not thread-safe and could (rarely) result in arbitrary errors. :issue:`9830` by Joel Nothman.
• Fix regression in :func:`model_selection.cross_val_predict` where it no longer accepted X as a list. :issue:`9600` by :user:`Rasul Kerimov <CoderINusE>`.
• Fixed handling of :func:`model_selection.cross_val_predict` for binary classification with method='decision_function'. :issue:`9593` by :user:`Reiichiro Nakano <reiinakano>` and core devs.
• Fix regression in :class:`pipeline.Pipeline` where it no longer accepted steps as a tuple. :issue:`9604` by :user:`Joris Van den Bossche <jorisvandenbossche>`.
• Fix bug where n_iter was not properly deprecated, leaving n_iter unavailable for interim use in :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDRegressor`, :class:`linear_model.PassiveAggressiveClassifier`, :class:`linear_model.PassiveAggressiveRegressor` and :class:`linear_model.Perceptron`. :issue:`9558` by Andreas Müller.
• Dataset fetchers make sure temporary files are closed before removing them, which caused errors on Windows. :issue:`9847` by :user:`Joan Massich <massich>`.
• Fixed a regression in :class:`manifold.TSNE` where it no longer supported metrics other than 'euclidean' and 'precomputed'. :issue:`9623` by :user:`Oli Blum <oliblum90>`.

Enhancements

• Our test suite and :func:`utils.estimator_checks.check_estimators` can now be run without Nose installed. :issue:`9697` by :user:`Joan Massich <massich>`.
• To improve usability of version 0.19's :class:`pipeline.Pipeline` caching, memory now allows joblib.Memory instances. This make use of the new :func:`utils.validation.check_memory` helper. :issue:`9584` by :user:`Kumar Ashutosh <thechargedneutron>`
• Some fixes to examples: :issue:`9750`, :issue:`9788`, :issue:`9815`
• Made a FutureWarning in SGD-based estimators less verbose. :issue:`9802` by :user:`Vrishank Bhardwaj <vrishank97>`.

Code and Documentation Contributors

With thanks to:

Joel Nothman, Loic Esteve, Andreas Mueller, Kumar Ashutosh, Vrishank Bhardwaj, Hanmin Qin, Rasul Kerimov, James Bourbeau, Nagarjuna Kumar, Nathaniel Saul, Olivier Grisel, Roman Yurchak, Reiichiro Nakano, Sachin Kelkar, Sam Steingold, Yaroslav Halchenko, diegodlh, felix, goncalo-rodrigues, jkleint, oliblum90, pasbi, Anthony Gitter, Ben Lawson, Charlie Brummitt, Didi Bar-Zev, Gael Varoquaux, Joan Massich, Joris Van den Bossche, nielsenmarkus11

Version 0.19

August 12, 2017

Highlights

We are excited to release a number of great new features including :class:`neighbors.LocalOutlierFactor` for anomaly detection, :class:`preprocessing.QuantileTransformer` for robust feature transformation, and the :class:`multioutput.ClassifierChain` meta-estimator to simply account for dependencies between classes in multilabel problems. We have some new algorithms in existing estimators, such as multiplicative update in :class:`decomposition.NMF` and multinomial :class:`linear_model.LogisticRegression` with L1 loss (use solver='saga').

Cross validation is now able to return the results from multiple metric evaluations. The new :func:`model_selection.cross_validate` can return many scores on the test data as well as training set performance and timings, and we have extended the scoring and refit parameters for grid/randomized search :ref:`to handle multiple metrics <multimetric_grid_search>`.

You can also learn faster. For instance, the :ref:`new option to cache transformations <pipeline_cache>` in :class:`pipeline.Pipeline` makes grid search over pipelines including slow transformations much more efficient. And you can predict faster: if you're sure you know what you're doing, you can turn off validating that the input is finite using :func:`config_context`.

We've made some important fixes too. We've fixed a longstanding implementation error in :func:`metrics.average_precision_score`, so please be cautious with prior results reported from that function. A number of errors in the :class:`manifold.TSNE` implementation have been fixed, particularly in the default Barnes-Hut approximation. :class:`semi_supervised.LabelSpreading` and :class:`semi_supervised.LabelPropagation` have had substantial fixes. LabelPropagation was previously broken. LabelSpreading should now correctly respect its alpha parameter.

Changed models

The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.

• :class:`cluster.KMeans` with sparse X and initial centroids given (bug fix)
• :class:`cross_decomposition.PLSRegression` with scale=True (bug fix)
• :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` where min_impurity_split is used (bug fix)
• gradient boosting loss='quantile' (bug fix)
• :class:`ensemble.IsolationForest` (bug fix)
• :class:`feature_selection.SelectFdr` (bug fix)
• :class:`linear_model.RANSACRegressor` (bug fix)
• :class:`linear_model.LassoLars` (bug fix)
• :class:`linear_model.LassoLarsIC` (bug fix)
• :class:`manifold.TSNE` (bug fix)
• :class:`neighbors.NearestCentroid` (bug fix)
• :class:`semi_supervised.LabelSpreading` (bug fix)
• :class:`semi_supervised.LabelPropagation` (bug fix)
• tree based models where min_weight_fraction_leaf is used (enhancement)

Details are listed in the changelog below.

(While we are trying to better inform users by providing this information, we cannot assure that this list is complete.)

Changelog

New features

Classifiers and regressors

• Added :class:`multioutput.ClassifierChain` for multi-label classification. By Adam Kleczewski.
• Added solver 'saga' that implements the improved version of Stochastic Average Gradient, in :class:`linear_model.LogisticRegression` and :class:`linear_model.Ridge`. It allows the use of L1 penalty with multinomial logistic loss, and behaves marginally better than 'sag' during the first epochs of ridge and logistic regression. :issue:`8446` by Arthur Mensch.

Other estimators

• Added the :class:`neighbors.LocalOutlierFactor` class for anomaly detection based on nearest neighbors. :issue:`5279` by Nicolas Goix and Alexandre Gramfort.
• Added :class:`preprocessing.QuantileTransformer` class and :func:`preprocessing.quantile_transform` function for features normalization based on quantiles. :issue:`8363` by :user:`Denis Engemann <dengemann>`, :user:`Guillaume Lemaitre <glemaitre>`, Olivier Grisel, Raghav RV, :user:`Thierry Guillemot <tguillemot>`, and Gael Varoquaux.
• The new solver 'mu' implements a Multiplicate Update in :class:`decomposition.NMF`, allowing the optimization of all beta-divergences, including the Frobenius norm, the generalized Kullback-Leibler divergence and the Itakura-Saito divergence. :issue:`5295` by Tom Dupre la Tour.

Model selection and evaluation

• :class:`model_selection.GridSearchCV` and :class:`model_selection.RandomizedSearchCV` now support simultaneous evaluation of multiple metrics. Refer to the :ref:`multimetric_grid_search` section of the user guide for more information. :issue:`7388` by Raghav RV
• Added the :func:`model_selection.cross_validate` which allows evaluation of multiple metrics. This function returns a dict with more useful information from cross-validation such as the train scores, fit times and score times. Refer to :ref:`multimetric_cross_validation` section of the userguide for more information. :issue:`7388` by Raghav RV
• Added :func:`metrics.mean_squared_log_error`, which computes the mean square error of the logarithmic transformation of targets, particularly useful for targets with an exponential trend. :issue:`7655` by :user:`Karan Desai <karandesai-96>`.
• Added :func:`metrics.dcg_score` and :func:`metrics.ndcg_score`, which compute Discounted cumulative gain (DCG) and Normalized discounted cumulative gain (NDCG). :issue:`7739` by :user:`David Gasquez <davidgasquez>`.
• Added the :class:`model_selection.RepeatedKFold` and :class:`model_selection.RepeatedStratifiedKFold`. :issue:`8120` by Neeraj Gangwar.
• Added a scorer based on :class:`metrics.explained_variance_score`. :issue:`9259` by Hanmin Qin.

Miscellaneous

• Validation that input data contains no NaN or inf can now be suppressed using :func:`config_context`, at your own risk. This will save on runtime, and may be particularly useful for prediction time. :issue:`7548` by Joel Nothman.
• Added a test to ensure parameter listing in docstrings match the function/class signature. :issue:`9206` by Alexandre Gramfort and Raghav RV.

Enhancements

Trees and ensembles

• The min_weight_fraction_leaf constraint in tree construction is now more efficient, taking a fast path to declare a node a leaf if its weight is less than 2 * the minimum. Note that the constructed tree will be different from previous versions where min_weight_fraction_leaf is used. :issue:`7441` by :user:`Nelson Liu <nelson-liu>`.
• :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` now support sparse input for prediction. :issue:`6101` by :user:`Ibraim Ganiev <olologin>`.
• :class:`ensemble.VotingClassifier` now allows changing estimators by using :meth:`ensemble.VotingClassifier.set_params`. An estimator can also be removed by setting it to None. :issue:`7674` by :user:`Yichuan Liu <yl565>`.
• :func:`tree.export_graphviz` now shows configurable number of decimal places. :issue:`8698` by :user:`Guillaume Lemaitre <glemaitre>`.
• Added flatten_transform parameter to :class:`ensemble.VotingClassifier` to change output shape of transform method to 2 dimensional. :issue:`7794` by :user:`Ibraim Ganiev <olologin>` and :user:`Herilalaina Rakotoarison <herilalaina>`.

Linear, kernelized and related models

• :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDRegressor`, :class:`linear_model.PassiveAggressiveClassifier`, :class:`linear_model.PassiveAggressiveRegressor` and :class:`linear_model.Perceptron` now expose max_iter and tol parameters, to handle convergence more precisely. n_iter parameter is deprecated, and the fitted estimator exposes a n_iter_ attribute, with actual number of iterations before convergence. :issue:`5036` by Tom Dupre la Tour.
• Added average parameter to perform weight averaging in :class:`linear_model.PassiveAggressiveClassifier`. :issue:`4939` by :user:`Andrea Esuli <aesuli>`.
• :class:`linear_model.RANSACRegressor` no longer throws an error when calling fit if no inliers are found in its first iteration. Furthermore, causes of skipped iterations are tracked in newly added attributes, n_skips_*. :issue:`7914` by :user:`Michael Horrell <mthorrell>`.
• In :class:`gaussian_process.GaussianProcessRegressor`, method predict is a lot faster with return_std=True. :issue:`8591` by :user:`Hadrien Bertrand <hbertrand>`.
• Added return_std to predict method of :class:`linear_model.ARDRegression` and :class:`linear_model.BayesianRidge`. :issue:`7838` by :user:`Sergey Feldman <sergeyf>`.
• Memory usage enhancements: Prevent cast from float32 to float64 in: :class:`linear_model.MultiTaskElasticNet`; :class:`linear_model.LogisticRegression` when using newton-cg solver; and :class:`linear_model.Ridge` when using svd, sparse_cg, cholesky or lsqr solvers. :issue:`8835`, :issue:`8061` by :user:`Joan Massich <massich>` and :user:`Nicolas Cordier <ncordier>` and :user:`Thierry Guillemot <tguillemot>`.

Other predictors

• Custom metrics for the :mod:`neighbors` binary trees now have fewer constraints: they must take two 1d-arrays and return a float. :issue:`6288` by Jake Vanderplas.
• algorithm='auto in :mod:`neighbors` estimators now chooses the most appropriate algorithm for all input types and metrics. :issue:`9145` by :user:`Herilalaina Rakotoarison <herilalaina>` and :user:`Reddy Chinthala <preddy5Pradyumna>`.

Decomposition, manifold learning and clustering

• :class:`cluster.MiniBatchKMeans` and :class:`cluster.KMeans` now use significantly less memory when assigning data points to their nearest cluster center. :issue:`7721` by :user:`Jon Crall <Erotemic>`.
• :class:`decomposition.PCA`, :class:`decomposition.IncrementalPCA` and :class:`decomposition.TruncatedSVD` now expose the singular values from the underlying SVD. They are stored in the attribute singular_values_, like in :class:`decomposition.IncrementalPCA`. :issue:`7685` by :user:`Tommy Löfstedt <tomlof>`
• Fixed the implementation of noise_variance_ in :class:`decomposition.PCA`. :issue:`9108` by Hanmin Qin.
• :class:`decomposition.NMF` now faster when beta_loss=0. :issue:`9277` by :user:`hongkahjun`.
• Memory improvements for method barnes_hut in :class:`manifold.TSNE` :issue:`7089` by :user:`Thomas Moreau <tomMoral>` and Olivier Grisel.
• Optimization schedule improvements for Barnes-Hut :class:`manifold.TSNE` so the results are closer to the one from the reference implementation lvdmaaten/bhtsne by :user:`Thomas Moreau <tomMoral>` and Olivier Grisel.
• Memory usage enhancements: Prevent cast from float32 to float64 in :class:`decomposition.PCA` and :func:`decomposition.randomized_svd_low_rank`. :issue:`9067` by Raghav RV.

Preprocessing and feature selection

• Added norm_order parameter to :class:`feature_selection.SelectFromModel` to enable selection of the norm order when coef_ is more than 1D. :issue:`6181` by :user:`Antoine Wendlinger <antoinewdg>`.
• Added ability to use sparse matrices in :func:`feature_selection.f_regression` with center=True. :issue:`8065` by :user:`Daniel LeJeune <acadiansith>`.
• Small performance improvement to n-gram creation in :mod:`feature_extraction.text` by binding methods for loops and special-casing unigrams. :issue:`7567` by :user:`Jaye Doepke <jtdoepke>`
• Relax assumption on the data for the :class:`kernel_approximation.SkewedChi2Sampler`. Since the Skewed-Chi2 kernel is defined on the open interval (-skewedness; +\infty)^d, the transform function should not check whether X < 0 but whether X < -self.skewedness. :issue:`7573` by :user:`Romain Brault <RomainBrault>`.
• Made default kernel parameters kernel-dependent in :class:`kernel_approximation.Nystroem`. :issue:`5229` by :user:`Saurabh Bansod <mth4saurabh>` and Andreas Müller.

Model evaluation and meta-estimators

• :class:`pipeline.Pipeline` is now able to cache transformers within a pipeline by using the memory constructor parameter. :issue:`7990` by :user:`Guillaume Lemaitre <glemaitre>`.
• :class:`pipeline.Pipeline` steps can now be accessed as attributes of its named_steps attribute. :issue:`8586` by :user:`Herilalaina Rakotoarison <herilalaina>`.
• Added sample_weight parameter to :meth:`pipeline.Pipeline.score`. :issue:`7723` by :user:`Mikhail Korobov <kmike>`.
• Added ability to set n_jobs parameter to :func:`pipeline.make_union`. A TypeError will be raised for any other kwargs. :issue:`8028` by :user:`Alexander Booth <alexandercbooth>`.
• :class:`model_selection.GridSearchCV`, :class:`model_selection.RandomizedSearchCV` and :func:`model_selection.cross_val_score` now allow estimators with callable kernels which were previously prohibited. :issue:`8005` by Andreas Müller .
• :func:`model_selection.cross_val_predict` now returns output of the correct shape for all values of the argument method. :issue:`7863` by :user:`Aman Dalmia <dalmia>`.
• Added shuffle and random_state parameters to shuffle training data before taking prefixes of it based on training sizes in :func:`model_selection.learning_curve`. :issue:`7506` by :user:`Narine Kokhlikyan <NarineK>`.
• :class:`model_selection.StratifiedShuffleSplit` now works with multioutput multiclass (or multilabel) data. :issue:`9044` by Vlad Niculae.
• Speed improvements to :class:`model_selection.StratifiedShuffleSplit`. :issue:`5991` by :user:`Arthur Mensch <arthurmensch>` and Joel Nothman.
• Add shuffle parameter to :func:`model_selection.train_test_split`. :issue:`8845` by :user:`themrmax <themrmax>`
• :class:`multioutput.MultiOutputRegressor` and :class:`multioutput.MultiOutputClassifier` now support online learning using partial_fit. :issue: 8053 by :user:`Peng Yu <yupbank>`.
• Add max_train_size parameter to :class:`model_selection.TimeSeriesSplit` :issue:`8282` by :user:`Aman Dalmia <dalmia>`.
• More clustering metrics are now available through :func:`metrics.get_scorer` and scoring parameters. :issue:`8117` by Raghav RV.

Metrics

• :func:`metrics.matthews_corrcoef` now support multiclass classification. :issue:`8094` by :user:`Jon Crall <Erotemic>`.
• Add sample_weight parameter to :func:`metrics.cohen_kappa_score`. :issue:`8335` by :user:`Victor Poughon <vpoughon>`.

Miscellaneous

• :func:`utils.check_estimator` now attempts to ensure that methods transform, predict, etc. do not set attributes on the estimator. :issue:`7533` by :user:`Ekaterina Krivich <kiote>`.
• Added type checking to the accept_sparse parameter in :mod:`utils.validation` methods. This parameter now accepts only boolean, string, or list/tuple of strings. accept_sparse=None is deprecated and should be replaced by accept_sparse=False. :issue:`7880` by :user:`Josh Karnofsky <jkarno>`.
• Make it possible to load a chunk of an svmlight formatted file by passing a range of bytes to :func:`datasets.load_svmlight_file`. :issue:`935` by :user:`Olivier Grisel <ogrisel>`.
• :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor` now accept non-finite features. :issue:`8931` by :user:`Attractadore`.

Bug fixes

Trees and ensembles

• Fixed a memory leak in trees when using trees with criterion='mae'. :issue:`8002` by Raghav RV.
• Fixed a bug where :class:`ensemble.IsolationForest` uses an an incorrect formula for the average path length :issue:`8549` by Peter Wang.
• Fixed a bug where :class:`ensemble.AdaBoostClassifier` throws ZeroDivisionError while fitting data with single class labels. :issue:`7501` by :user:`Dominik Krzeminski <dokato>`.
• Fixed a bug in :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` where a float being compared to 0.0 using == caused a divide by zero error. :issue:`7970` by :user:`He Chen <chenhe95>`.
• Fix a bug where :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor` ignored the min_impurity_split parameter. :issue:`8006` by :user:`Sebastian Pölsterl <sebp>`.
• Fixed oob_score in :class:`ensemble.BaggingClassifier`. :issue:`8936` by :user:`Michael Lewis <mlewis1729>`
• Fixed excessive memory usage in prediction for random forests estimators. :issue:`8672` by :user:`Mike Benfield <mikebenfield>`.
• Fixed a bug where sample_weight as a list broke random forests in Python 2 :issue:`8068` by :user:`xor`.
• Fixed a bug where :class:`ensemble.IsolationForest` fails when max_features is less than 1. :issue:`5732` by :user:`Ishank Gulati <IshankGulati>`.
• Fix a bug where gradient boosting with loss='quantile' computed negative errors for negative values of ytrue - ypred leading to wrong values when calling __call__. :issue:`8087` by :user:`Alexis Mignon <AlexisMignon>`
• Fix a bug where :class:`ensemble.VotingClassifier` raises an error when a numpy array is passed in for weights. :issue:`7983` by :user:`Vincent Pham <vincentpham1991>`.
• Fixed a bug where :func:`tree.export_graphviz` raised an error when the length of features_names does not match n_features in the decision tree. :issue:`8512` by :user:`Li Li <aikinogard>`.

Linear, kernelized and related models

• Fixed a bug where :func:`linear_model.RANSACRegressor.fit` may run until max_iter if it finds a large inlier group early. :issue:`8251` by :user:`aivision2020`.
• Fixed a bug where :class:`naive_bayes.MultinomialNB` and :class:`naive_bayes.BernoulliNB` failed when alpha=0. :issue:`5814` by :user:`Yichuan Liu <yl565>` and :user:`Herilalaina Rakotoarison <herilalaina>`.
• Fixed a bug where :class:`linear_model.LassoLars` does not give the same result as the LassoLars implementation available in R (lars library). :issue:`7849` by :user:`Jair Montoya Martinez <jmontoyam>`.
• Fixed a bug in :class:`linear_model.RandomizedLasso`, :class:`linear_model.Lars`, :class:`linear_model.LassoLars`, :class:`linear_model.LarsCV` and :class:`linear_model.LassoLarsCV`, where the parameter precompute was not used consistently across classes, and some values proposed in the docstring could raise errors. :issue:`5359` by Tom Dupre la Tour.
• Fix inconsistent results between :class:`linear_model.RidgeCV` and :class:`linear_model.Ridge` when using normalize=True. :issue:`9302` by Alexandre Gramfort.
• Fix a bug where :func:`linear_model.LassoLars.fit` sometimes left coef_ as a list, rather than an ndarray. :issue:`8160` by :user:`CJ Carey <perimosocordiae>`.
• Fix :func:`linear_model.BayesianRidge.fit` to return ridge parameter alpha_ and lambda_ consistent with calculated coefficients coef_ and intercept_. :issue:`8224` by :user:`Peter Gedeck <gedeck>`.
• Fixed a bug in :class:`svm.OneClassSVM` where it returned floats instead of integer classes. :issue:`8676` by :user:`Vathsala Achar <VathsalaAchar>`.
• Fix AIC/BIC criterion computation in :class:`linear_model.LassoLarsIC`. :issue:`9022` by Alexandre Gramfort and :user:`Mehmet Basbug <mehmetbasbug>`.
• Fixed a memory leak in our LibLinear implementation. :issue:`9024` by :user:`Sergei Lebedev <superbobry>`
• Fix bug where stratified CV splitters did not work with :class:`linear_model.LassoCV`. :issue:`8973` by :user:`Paulo Haddad <paulochf>`.
• Fixed a bug in :class:`gaussian_process.GaussianProcessRegressor` when the standard deviation and covariance predicted without fit would fail with a unmeaningful error by default. :issue:`6573` by :user:`Quazi Marufur Rahman <qmaruf>` and Manoj Kumar.

Other predictors

• Fix :class:`semi_supervised.BaseLabelPropagation` to correctly implement LabelPropagation and LabelSpreading as done in the referenced papers. :issue:`9239` by :user:`Andre Ambrosio Boechat <boechat107>`, :user:`Utkarsh Upadhyay <musically-ut>`, and Joel Nothman.

Decomposition, manifold learning and clustering

• Fixed the implementation of :class:`manifold.TSNE`:

• early_exageration parameter had no effect and is now used for the first 250 optimization iterations.

• Fixed the AssertionError: Tree consistency failed exception reported in :issue:`8992`.

• Improve the learning schedule to match the one from the reference implementation lvdmaaten/bhtsne.

  by :user:`Thomas Moreau <tomMoral>` and Olivier Grisel.

• Fix a bug in :class:`decomposition.LatentDirichletAllocation` where the perplexity method was returning incorrect results because the transform method returns normalized document topic distributions as of version 0.18. :issue:`7954` by :user:`Gary Foreman <garyForeman>`.

• Fix output shape and bugs with n_jobs > 1 in :class:`decomposition.SparseCoder` transform and :func:`decomposition.sparse_encode` for one-dimensional data and one component. This also impacts the output shape of :class:`decomposition.DictionaryLearning`. :issue:`8086` by Andreas Müller.

• Fixed the implementation of explained_variance_ in :class:`decomposition.PCA`, :class:`decomposition.RandomizedPCA` and :class:`decomposition.IncrementalPCA`. :issue:`9105` by Hanmin Qin.

• Fixed the implementation of noise_variance_ in :class:`decomposition.PCA`. :issue:`9108` by Hanmin Qin.

• Fixed a bug where :class:`cluster.DBSCAN` gives incorrect result when input is a precomputed sparse matrix with initial rows all zero. :issue:`8306` by :user:`Akshay Gupta <Akshay0724>`

• Fix a bug regarding fitting :class:`cluster.KMeans` with a sparse array X and initial centroids, where X's means were unnecessarily being subtracted from the centroids. :issue:`7872` by :user:`Josh Karnofsky <jkarno>`.

• Fixes to the input validation in :class:`covariance.EllipticEnvelope`. :issue:`8086` by Andreas Müller.

• Fixed a bug in :class:`covariance.MinCovDet` where inputting data that produced a singular covariance matrix would cause the helper method _c_step to throw an exception. :issue:`3367` by :user:`Jeremy Steward <ThatGeoGuy>`

• Fixed a bug in :class:`manifold.TSNE` affecting convergence of the gradient descent. :issue:`8768` by :user:`David DeTomaso <deto>`.

• Fixed a bug in :class:`manifold.TSNE` where it stored the incorrect kl_divergence_. :issue:`6507` by :user:`Sebastian Saeger <ssaeger>`.

• Fixed improper scaling in :class:`cross_decomposition.PLSRegression` with scale=True. :issue:`7819` by :user:`jayzed82 <jayzed82>`.

• :class:`cluster.bicluster.SpectralCoclustering` and :class:`cluster.bicluster.SpectralBiclustering` fit method conforms with API by accepting y and returning the object. :issue:`6126`, :issue:`7814` by :user:`Laurent Direr <ldirer>` and :user:`Maniteja Nandana <maniteja123>`.

• Fix bug where :mod:`mixture` sample methods did not return as many samples as requested. :issue:`7702` by :user:`Levi John Wolf <ljwolf>`.

• Fixed the shrinkage implementation in :class:`neighbors.NearestCentroid`. :issue:`9219` by Hanmin Qin.

Preprocessing and feature selection

• For sparse matrices, :func:`preprocessing.normalize` with return_norm=True will now raise a NotImplementedError with 'l1' or 'l2' norm and with norm 'max' the norms returned will be the same as for dense matrices. :issue:`7771` by Ang Lu.
• Fix a bug where :class:`feature_selection.SelectFdr` did not exactly implement Benjamini-Hochberg procedure. It formerly may have selected fewer features than it should. :issue:`7490` by :user:`Peng Meng <mpjlu>`.
• Fixed a bug where :class:`linear_model.RandomizedLasso` and :class:`linear_model.RandomizedLogisticRegression` breaks for sparse input. :issue:`8259` by :user:`Aman Dalmia <dalmia>`.
• Fix a bug where :class:`feature_extraction.FeatureHasher` mandatorily applied a sparse random projection to the hashed features, preventing the use of :class:`feature_extraction.text.HashingVectorizer` in a pipeline with :class:`feature_extraction.text.TfidfTransformer`. :issue:`7565` by :user:`Roman Yurchak <rth>`.
• Fix a bug where :class:`feature_selection.mutual_info_regression` did not correctly use n_neighbors. :issue:`8181` by :user:`Guillaume Lemaitre <glemaitre>`.

Model evaluation and meta-estimators

• Fixed a bug where :func:`model_selection.BaseSearchCV.inverse_transform` returns self.best_estimator_.transform() instead of self.best_estimator_.inverse_transform(). :issue:`8344` by :user:`Akshay Gupta <Akshay0724>` and :user:`Rasmus Eriksson <MrMjauh>`.
• Added classes_ attribute to :class:`model_selection.GridSearchCV`, :class:`model_selection.RandomizedSearchCV`, :class:`grid_search.GridSearchCV`, and :class:`grid_search.RandomizedSearchCV` that matches the classes_ attribute of best_estimator_. :issue:`7661` and :issue:`8295` by :user:`Alyssa Batula <abatula>`, :user:`Dylan Werner-Meier <unautre>`, and :user:`Stephen Hoover <stephen-hoover>`.
• Fixed a bug where :func:`model_selection.validation_curve` reused the same estimator for each parameter value. :issue:`7365` by :user:`Aleksandr Sandrovskii <Sundrique>`.
• :func:`model_selection.permutation_test_score` now works with Pandas types. :issue:`5697` by :user:`Stijn Tonk <equialgo>`.
• Several fixes to input validation in :class:`multiclass.OutputCodeClassifier` :issue:`8086` by Andreas Müller.
• :class:`multiclass.OneVsOneClassifier`'s partial_fit now ensures all classes are provided up-front. :issue:`6250` by :user:`Asish Panda <kaichogami>`.
• Fix :func:`multioutput.MultiOutputClassifier.predict_proba` to return a list of 2d arrays, rather than a 3d array. In the case where different target columns had different numbers of classes, a ValueError would be raised on trying to stack matrices with different dimensions. :issue:`8093` by :user:`Peter Bull <pjbull>`.
• Cross validation now works with Pandas datatypes that that have a read-only index. :issue:`9507` by Loic Esteve.

Metrics

• :func:`metrics.average_precision_score` no longer linearly interpolates between operating points, and instead weighs precisions by the change in recall since the last operating point, as per the Wikipedia entry. (#7356). By :user:`Nick Dingwall <ndingwall>` and Gael Varoquaux.
• Fix a bug in :func:`metrics.classification._check_targets` which would return 'binary' if y_true and y_pred were both 'binary' but the union of y_true and y_pred was 'multiclass'. :issue:`8377` by Loic Esteve.
• Fixed an integer overflow bug in :func:`metrics.confusion_matrix` and hence :func:`metrics.cohen_kappa_score`. :issue:`8354`, :issue:`7929` by Joel Nothman and :user:`Jon Crall <Erotemic>`.
• Fixed passing of gamma parameter to the chi2 kernel in :func:`metrics.pairwise.pairwise_kernels` :issue:`5211` by :user:`Nick Rhinehart <nrhine1>`, :user:`Saurabh Bansod <mth4saurabh>` and Andreas Müller.

Miscellaneous

• Fixed a bug when :func:`datasets.make_classification` fails when generating more than 30 features. :issue:`8159` by :user:`Herilalaina Rakotoarison <herilalaina>`.
• Fixed a bug where :func:`datasets.make_moons` gives an incorrect result when n_samples is odd. :issue:`8198` by :user:`Josh Levy <levy5674>`.
• Some fetch_ functions in :mod:`datasets` were ignoring the download_if_missing keyword. :issue:`7944` by :user:`Ralf Gommers <rgommers>`.
• Fix estimators to accept a sample_weight parameter of type pandas.Series in their fit function. :issue:`7825` by Kathleen Chen.
• Fix a bug in cases where numpy.cumsum may be numerically unstable, raising an exception if instability is identified. :issue:`7376` and :issue:`7331` by Joel Nothman and :user:`yangarbiter`.
• Fix a bug where :meth:`base.BaseEstimator.__getstate__` obstructed pickling customizations of child-classes, when used in a multiple inheritance context. :issue:`8316` by :user:`Holger Peters <HolgerPeters>`.
• Update Sphinx-Gallery from 0.1.4 to 0.1.7 for resolving links in documentation build with Sphinx>1.5 :issue:`8010`, :issue:`7986` by :user:`Oscar Najera <Titan-C>`
• Add data_home parameter to :func:`sklearn.datasets.fetch_kddcup99`. :issue:`9289` by Loic Esteve.
• Fix dataset loaders using Python 3 version of makedirs to also work in Python 2. :issue:`9284` by :user:`Sebastin Santy <SebastinSanty>`.
• Several minor issues were fixed with thanks to the alerts of [lgtm.com](http://lgtm.com). :issue:`9278` by :user:`Jean Helie <jhelie>`, among others.

API changes summary

Trees and ensembles

• Gradient boosting base models are no longer estimators. By Andreas Müller.
• All tree based estimators now accept a min_impurity_decrease parameter in lieu of the min_impurity_split, which is now deprecated. The min_impurity_decrease helps stop splitting the nodes in which the weighted impurity decrease from splitting is no longer alteast min_impurity_decrease. :issue:`8449` by Raghav RV.

Linear, kernelized and related models

• n_iter parameter is deprecated in :class:`linear_model.SGDClassifier`, :class:`linear_model.SGDRegressor`, :class:`linear_model.PassiveAggressiveClassifier`, :class:`linear_model.PassiveAggressiveRegressor` and :class:`linear_model.Perceptron`. By Tom Dupre la Tour.

Other predictors

• :class:`neighbors.LSHForest` has been deprecated and will be removed in 0.21 due to poor performance. :issue:`9078` by :user:`Laurent Direr <ldirer>`.
• :class:`neighbors.NearestCentroid` no longer purports to support metric='precomputed' which now raises an error. :issue:`8515` by :user:`Sergul Aydore <sergulaydore>`.
• The alpha parameter of :class:`semi_supervised.LabelPropagation` now has no effect and is deprecated to be removed in 0.21. :issue:`9239` by :user:`Andre Ambrosio Boechat <boechat107>`, :user:`Utkarsh Upadhyay <musically-ut>`, and Joel Nothman.

Decomposition, manifold learning and clustering

• Deprecate the doc_topic_distr argument of the perplexity method in :class:`decomposition.LatentDirichletAllocation` because the user no longer has access to the unnormalized document topic distribution needed for the perplexity calculation. :issue:`7954` by :user:`Gary Foreman <garyForeman>`.
• The n_topics parameter of :class:`decomposition.LatentDirichletAllocation` has been renamed to n_components and will be removed in version 0.21. :issue:`8922` by :user:`Attractadore`.
• :meth:`decomposition.SparsePCA.transform`'s ridge_alpha parameter is deprecated in preference for class parameter. :issue:`8137` by :user:`Naoya Kanai <naoyak>`.
• :class:`cluster.DBSCAN` now has a metric_params parameter. :issue:`8139` by :user:`Naoya Kanai <naoyak>`.

Preprocessing and feature selection

• :class:`feature_selection.SelectFromModel` now has a partial_fit method only if the underlying estimator does. By Andreas Müller.
• :class:`feature_selection.SelectFromModel` now validates the threshold parameter and sets the threshold_ attribute during the call to fit, and no longer during the call to transform`. By Andreas Müller.
• The non_negative parameter in :class:`feature_extraction.FeatureHasher` has been deprecated, and replaced with a more principled alternative, alternate_sign. :issue:`7565` by :user:`Roman Yurchak <rth>`.
• :class:`linear_model.RandomizedLogisticRegression`, and :class:`linear_model.RandomizedLasso` have been deprecated and will be removed in version 0.21. :issue:`8995` by :user:`Ramana.S <sentient07>`.

Model evaluation and meta-estimators

• Deprecate the fit_params constructor input to the :class:`model_selection.GridSearchCV` and :class:`model_selection.RandomizedSearchCV` in favor of passing keyword parameters to the fit methods of those classes. Data-dependent parameters needed for model training should be passed as keyword arguments to fit, and conforming to this convention will allow the hyperparameter selection classes to be used with tools such as :func:`model_selection.cross_val_predict`. :issue:`2879` by :user:`Stephen Hoover <stephen-hoover>`.
• In version 0.21, the default behavior of splitters that use the test_size and train_size parameter will change, such that specifying train_size alone will cause test_size to be the remainder. :issue:`7459` by :user:`Nelson Liu <nelson-liu>`.
• :class:`multiclass.OneVsRestClassifier` now has partial_fit, decision_function and predict_proba methods only when the underlying estimator does. :issue:`7812` by Andreas Müller and :user:`Mikhail Korobov <kmike>`.
• :class:`multiclass.OneVsRestClassifier` now has a partial_fit method only if the underlying estimator does. By Andreas Müller.
• The decision_function output shape for binary classification in :class:`multiclass.OneVsRestClassifier` and :class:`multiclass.OneVsOneClassifier` is now (n_samples,) to conform to scikit-learn conventions. :issue:`9100` by Andreas Müller.
• The :func:`multioutput.MultiOutputClassifier.predict_proba` function used to return a 3d array (n_samples, n_classes, n_outputs). In the case where different target columns had different numbers of classes, a ValueError would be raised on trying to stack matrices with different dimensions. This function now returns a list of arrays where the length of the list is n_outputs, and each array is (n_samples, n_classes) for that particular output. :issue:`8093` by :user:`Peter Bull <pjbull>`.
• Replace attribute named_steps dict to :class:`utils.Bunch` in :class:`pipeline.Pipeline` to enable tab completion in interactive environment. In the case conflict value on named_steps and dict attribute, dict behavior will be prioritized. :issue:`8481` by :user:`Herilalaina Rakotoarison <herilalaina>`.

Miscellaneous

• Deprecate the y parameter in transform and inverse_transform. The method should not accept y parameter, as it's used at the prediction time. :issue:`8174` by :user:`Tahar Zanouda <tzano>`, Alexandre Gramfort and Raghav RV.

• SciPy >= 0.13.3 and NumPy >= 1.8.2 are now the minimum supported versions for scikit-learn. The following backported functions in :mod:`utils` have been removed or deprecated accordingly. :issue:`8854` and :issue:`8874` by :user:`Naoya Kanai <naoyak>`

• The store_covariances and covariances_ parameters of :class:`discriminant_analysis.QuadraticDiscriminantAnalysis` has been renamed to store_covariance and covariance_ to be consistent with the corresponding parameter names of the :class:`discriminant_analysis.LinearDiscriminantAnalysis`. They will be removed in version 0.21. :issue:`7998` by :user:`Jiacheng <mrbeann>`

  Removed in 0.19:

  • utils.fixes.argpartition
  • utils.fixes.array_equal
  • utils.fixes.astype
  • utils.fixes.bincount
  • utils.fixes.expit
  • utils.fixes.frombuffer_empty
  • utils.fixes.in1d
  • utils.fixes.norm
  • utils.fixes.rankdata
  • utils.fixes.safe_copy

  Deprecated in 0.19, to be removed in 0.21:

  • utils.arpack.eigs
  • utils.arpack.eigsh
  • utils.arpack.svds
  • utils.extmath.fast_dot
  • utils.extmath.logsumexp
  • utils.extmath.norm
  • utils.extmath.pinvh
  • utils.graph.graph_laplacian
  • utils.random.choice
  • utils.sparsetools.connected_components
  • utils.stats.rankdata

• Estimators with both methods decision_function and predict_proba are now required to have a monotonic relation between them. The method check_decision_proba_consistency has been added in utils.estimator_checks to check their consistency. :issue:`7578` by :user:`Shubham Bhardwaj <shubham0704>`

• All checks in utils.estimator_checks, in particular :func:`utils.estimator_checks.check_estimator` now accept estimator instances. Most other checks do not accept estimator classes any more. :issue:`9019` by Andreas Müller.

• Ensure that estimators' attributes ending with _ are not set in the constructor but only in the fit method. Most notably, ensemble estimators (deriving from :class:`ensemble.BaseEnsemble`) now only have self.estimators_ available after fit. :issue:`7464` by Lars Buitinck and Loic Esteve.

Code and Documentation Contributors

Thanks to everyone who has contributed to the maintenance and improvement of the project since version 0.18, including:

Joel Nothman, Loic Esteve, Andreas Mueller, Guillaume Lemaitre, Olivier Grisel, Hanmin Qin, Raghav RV, Alexandre Gramfort, themrmax, Aman Dalmia, Gael Varoquaux, Naoya Kanai, Tom Dupré la Tour, Rishikesh, Nelson Liu, Taehoon Lee, Nelle Varoquaux, Aashil, Mikhail Korobov, Sebastin Santy, Joan Massich, Roman Yurchak, RAKOTOARISON Herilalaina, Thierry Guillemot, Alexandre Abadie, Carol Willing, Balakumaran Manoharan, Josh Karnofsky, Vlad Niculae, Utkarsh Upadhyay, Dmitry Petrov, Minghui Liu, Srivatsan, Vincent Pham, Albert Thomas, Jake VanderPlas, Attractadore, JC Liu, alexandercbooth, chkoar, Óscar Nájera, Aarshay Jain, Kyle Gilliam, Ramana Subramanyam, CJ Carey, Clement Joudet, David Robles, He Chen, Joris Van den Bossche, Karan Desai, Katie Luangkote, Leland McInnes, Maniteja Nandana, Michele Lacchia, Sergei Lebedev, Shubham Bhardwaj, akshay0724, omtcyfz, rickiepark, waterponey, Vathsala Achar, jbDelafosse, Ralf Gommers, Ekaterina Krivich, Vivek Kumar, Ishank Gulati, Dave Elliott, ldirer, Reiichiro Nakano, Levi John Wolf, Mathieu Blondel, Sid Kapur, Dougal J. Sutherland, midinas, mikebenfield, Sourav Singh, Aseem Bansal, Ibraim Ganiev, Stephen Hoover, AishwaryaRK, Steven C. Howell, Gary Foreman, Neeraj Gangwar, Tahar, Jon Crall, dokato, Kathy Chen, ferria, Thomas Moreau, Charlie Brummitt, Nicolas Goix, Adam Kleczewski, Sam Shleifer, Nikita Singh, Basil Beirouti, Giorgio Patrini, Manoj Kumar, Rafael Possas, James Bourbeau, James A. Bednar, Janine Harper, Jaye, Jean Helie, Jeremy Steward, Artsiom, John Wei, Jonathan LIgo, Jonathan Rahn, seanpwilliams, Arthur Mensch, Josh Levy, Julian Kuhlmann, Julien Aubert, Jörn Hees, Kai, shivamgargsya, Kat Hempstalk, Kaushik Lakshmikanth, Kennedy, Kenneth Lyons, Kenneth Myers, Kevin Yap, Kirill Bobyrev, Konstantin Podshumok, Arthur Imbert, Lee Murray, toastedcornflakes, Lera, Li Li, Arthur Douillard, Mainak Jas, tobycheese, Manraj Singh, Manvendra Singh, Marc Meketon, MarcoFalke, Matthew Brett, Matthias Gilch, Mehul Ahuja, Melanie Goetz, Meng, Peng, Michael Dezube, Michal Baumgartner, vibrantabhi19, Artem Golubin, Milen Paskov, Antonin Carette, Morikko, MrMjauh, NALEPA Emmanuel, Namiya, Antoine Wendlinger, Narine Kokhlikyan, NarineK, Nate Guerin, Angus Williams, Ang Lu, Nicole Vavrova, Nitish Pandey, Okhlopkov Daniil Olegovich, Andy Craze, Om Prakash, Parminder Singh, Patrick Carlson, Patrick Pei, Paul Ganssle, Paulo Haddad, Paweł Lorek, Peng Yu, Pete Bachant, Peter Bull, Peter Csizsek, Peter Wang, Pieter Arthur de Jong, Ping-Yao, Chang, Preston Parry, Puneet Mathur, Quentin Hibon, Andrew Smith, Andrew Jackson, 1kastner, Rameshwar Bhaskaran, Rebecca Bilbro, Remi Rampin, Andrea Esuli, Rob Hall, Robert Bradshaw, Romain Brault, Aman Pratik, Ruifeng Zheng, Russell Smith, Sachin Agarwal, Sailesh Choyal, Samson Tan, Samuël Weber, Sarah Brown, Sebastian Pölsterl, Sebastian Raschka, Sebastian Saeger, Alyssa Batula, Abhyuday Pratap Singh, Sergey Feldman, Sergul Aydore, Sharan Yalburgi, willduan, Siddharth Gupta, Sri Krishna, Almer, Stijn Tonk, Allen Riddell, Theofilos Papapanagiotou, Alison, Alexis Mignon, Tommy Boucher, Tommy Löfstedt, Toshihiro Kamishima, Tyler Folkman, Tyler Lanigan, Alexander Junge, Varun Shenoy, Victor Poughon, Vilhelm von Ehrenheim, Aleksandr Sandrovskii, Alan Yee, Vlasios Vasileiou, Warut Vijitbenjaronk, Yang Zhang, Yaroslav Halchenko, Yichuan Liu, Yuichi Fujikawa, affanv14, aivision2020, xor, andreh7, brady salz, campustrampus, Agamemnon Krasoulis, ditenberg, elena-sharova, filipj8, fukatani, gedeck, guiniol, guoci, hakaa1, hongkahjun, i-am-xhy, jakirkham, jaroslaw-weber, jayzed82, jeroko, jmontoyam, jonathan.striebel, josephsalmon, jschendel, leereeves, martin-hahn, mathurinm, mehak-sachdeva, mlewis1729, mlliou112, mthorrell, ndingwall, nuffe, yangarbiter, plagree, pldtc325, Breno Freitas, Brett Olsen, Brian A. Alfano, Brian Burns, polmauri, Brandon Carter, Charlton Austin, Chayant T15h, Chinmaya Pancholi, Christian Danielsen, Chung Yen, Chyi-Kwei Yau, pravarmahajan, DOHMATOB Elvis, Daniel LeJeune, Daniel Hnyk, Darius Morawiec, David DeTomaso, David Gasquez, David Haberthür, David Heryanto, David Kirkby, David Nicholson, rashchedrin, Deborah Gertrude Digges, Denis Engemann, Devansh D, Dickson, Bob Baxley, Don86, E. Lynch-Klarup, Ed Rogers, Elizabeth Ferriss, Ellen-Co2, Fabian Egli, Fang-Chieh Chou, Bing Tian Dai, Greg Stupp, Grzegorz Szpak, Bertrand Thirion, Hadrien Bertrand, Harizo Rajaona, zxcvbnius, Henry Lin, Holger Peters, Icyblade Dai, Igor Andriushchenko, Ilya, Isaac Laughlin, Iván Vallés, Aurélien Bellet, JPFrancoia, Jacob Schreiber, Asish Mahapatra

Version 0.18.2

June 20, 2017

Last release with Python 2.6 support

Scikit-learn 0.18 is the last major release of scikit-learn to support Python 2.6. Later versions of scikit-learn will require Python 2.7 or above.

Changelog

• Fixes for compatibility with NumPy 1.13.0: :issue:`7946` :issue:`8355` by Loic Esteve.
• Minor compatibility changes in the examples :issue:`9010` :issue:`8040` :issue:`9149`.

Code Contributors

Aman Dalmia, Loic Esteve, Nate Guerin, Sergei Lebedev

Version 0.18.1

November 11, 2016

Changelog

Enhancements

• Improved sample_without_replacement speed by utilizing numpy.random.permutation for most cases. As a result, samples may differ in this release for a fixed random state. Affected estimators:

  • :class:`ensemble.BaggingClassifier`
  • :class:`ensemble.BaggingRegressor`
  • :class:`linear_model.RANSACRegressor`
  • :class:`model_selection.RandomizedSearchCV`
  • :class:`random_projection.SparseRandomProjection`

  This also affects the :meth:`datasets.make_classification` method.

Bug fixes

• Fix issue where min_grad_norm and n_iter_without_progress parameters were not being utilised by :class:`manifold.TSNE`. :issue:`6497` by :user:`Sebastian Säger <ssaeger>`
• Fix bug for svm's decision values when decision_function_shape is ovr in :class:`svm.SVC`. :class:`svm.SVC`'s decision_function was incorrect from versions 0.17.0 through 0.18.0. :issue:`7724` by Bing Tian Dai
• Attribute explained_variance_ratio of :class:`discriminant_analysis.LinearDiscriminantAnalysis` calculated with SVD and Eigen solver are now of the same length. :issue:`7632` by :user:`JPFrancoia <JPFrancoia>`
• Fixes issue in :ref:`univariate_feature_selection` where score functions were not accepting multi-label targets. :issue:`7676` by :user:`Mohammed Affan <affanv14>`
• Fixed setting parameters when calling fit multiple times on :class:`feature_selection.SelectFromModel`. :issue:`7756` by Andreas Müller
• Fixes issue in partial_fit method of :class:`multiclass.OneVsRestClassifier` when number of classes used in partial_fit was less than the total number of classes in the data. :issue:`7786` by Srivatsan Ramesh
• Fixes issue in :class:`calibration.CalibratedClassifierCV` where the sum of probabilities of each class for a data was not 1, and CalibratedClassifierCV now handles the case where the training set has less number of classes than the total data. :issue:`7799` by Srivatsan Ramesh
• Fix a bug where :class:`sklearn.feature_selection.SelectFdr` did not exactly implement Benjamini-Hochberg procedure. It formerly may have selected fewer features than it should. :issue:`7490` by :user:`Peng Meng <mpjlu>`.
• :class:`sklearn.manifold.LocallyLinearEmbedding` now correctly handles integer inputs. :issue:`6282` by Jake Vanderplas.
• The min_weight_fraction_leaf parameter of tree-based classifiers and regressors now assumes uniform sample weights by default if the sample_weight argument is not passed to the fit function. Previously, the parameter was silently ignored. :issue:`7301` by :user:`Nelson Liu <nelson-liu>`.
• Numerical issue with :class:`linear_model.RidgeCV` on centered data when n_features > n_samples. :issue:`6178` by Bertrand Thirion
• Tree splitting criterion classes' cloning/pickling is now memory safe :issue:`7680` by :user:`Ibraim Ganiev <olologin>`.
• Fixed a bug where :class:`decomposition.NMF` sets its n_iters_ attribute in transform(). :issue:`7553` by :user:`Ekaterina Krivich <kiote>`.
• :class:`sklearn.linear_model.LogisticRegressionCV` now correctly handles string labels. :issue:`5874` by Raghav RV.
• Fixed a bug where :func:`sklearn.model_selection.train_test_split` raised an error when stratify is a list of string labels. :issue:`7593` by Raghav RV.
• Fixed a bug where :class:`sklearn.model_selection.GridSearchCV` and :class:`sklearn.model_selection.RandomizedSearchCV` were not pickleable because of a pickling bug in np.ma.MaskedArray. :issue:`7594` by Raghav RV.
• All cross-validation utilities in :mod:`sklearn.model_selection` now permit one time cross-validation splitters for the cv parameter. Also non-deterministic cross-validation splitters (where multiple calls to split produce dissimilar splits) can be used as cv parameter. The :class:`sklearn.model_selection.GridSearchCV` will cross-validate each parameter setting on the split produced by the first split call to the cross-validation splitter. :issue:`7660` by Raghav RV.
• Fix bug where :meth:`preprocessing.MultiLabelBinarizer.fit_transform` returned an invalid CSR matrix. :issue:`7750` by :user:`CJ Carey <perimosocordiae>`.
• Fixed a bug where :func:`metrics.pairwise.cosine_distances` could return a small negative distance. :issue:`7732` by :user:`Artsion <asanakoy>`.

API changes summary

Trees and forests

• The min_weight_fraction_leaf parameter of tree-based classifiers and regressors now assumes uniform sample weights by default if the sample_weight argument is not passed to the fit function. Previously, the parameter was silently ignored. :issue:`7301` by :user:`Nelson Liu <nelson-liu>`.
• Tree splitting criterion classes' cloning/pickling is now memory safe. :issue:`7680` by :user:`Ibraim Ganiev <olologin>`.

Linear, kernelized and related models

• Length of explained_variance_ratio of :class:`discriminant_analysis.LinearDiscriminantAnalysis` changed for both Eigen and SVD solvers. The attribute has now a length of min(n_components, n_classes - 1). :issue:`7632` by :user:`JPFrancoia <JPFrancoia>`
• Numerical issue with :class:`linear_model.RidgeCV` on centered data when n_features > n_samples. :issue:`6178` by Bertrand Thirion

Version 0.18

September 28, 2016

Last release with Python 2.6 support

Scikit-learn 0.18 will be the last version of scikit-learn to support Python 2.6. Later versions of scikit-learn will require Python 2.7 or above.

Model Selection Enhancements and API Changes

• The model_selection module

  The new module :mod:`sklearn.model_selection`, which groups together the functionalities of formerly :mod:`sklearn.cross_validation`, :mod:`sklearn.grid_search` and :mod:`sklearn.learning_curve`, introduces new possibilities such as nested cross-validation and better manipulation of parameter searches with Pandas.

  Many things will stay the same but there are some key differences. Read below to know more about the changes.

• Data-independent CV splitters enabling nested cross-validation

  The new cross-validation splitters, defined in the :mod:`sklearn.model_selection`, are no longer initialized with any data-dependent parameters such as y. Instead they expose a :func:`split` method that takes in the data and yields a generator for the different splits.

  This change makes it possible to use the cross-validation splitters to perform nested cross-validation, facilitated by :class:`model_selection.GridSearchCV` and :class:`model_selection.RandomizedSearchCV` utilities.

• The enhanced cv_results_ attribute

  The new cv_results_ attribute (of :class:`model_selection.GridSearchCV` and :class:`model_selection.RandomizedSearchCV`) introduced in lieu of the grid_scores_ attribute is a dict of 1D arrays with elements in each array corresponding to the parameter settings (i.e. search candidates).

  The cv_results_ dict can be easily imported into pandas as a DataFrame for exploring the search results.

  The cv_results_ arrays include scores for each cross-validation split (with keys such as 'split0_test_score'), as well as their mean ('mean_test_score') and standard deviation ('std_test_score').

  The ranks for the search candidates (based on their mean cross-validation score) is available at cv_results_['rank_test_score'].

  The parameter values for each parameter is stored separately as numpy masked object arrays. The value, for that search candidate, is masked if the corresponding parameter is not applicable. Additionally a list of all the parameter dicts are stored at cv_results_['params'].

• Parameters n_folds and n_iter renamed to n_splits

  Some parameter names have changed: The n_folds parameter in new :class:`model_selection.KFold`, :class:`model_selection.GroupKFold` (see below for the name change), and :class:`model_selection.StratifiedKFold` is now renamed to n_splits. The n_iter parameter in :class:`model_selection.ShuffleSplit`, the new class :class:`model_selection.GroupShuffleSplit` and :class:`model_selection.StratifiedShuffleSplit` is now renamed to n_splits.

• Rename of splitter classes which accepts group labels along with data

  The cross-validation splitters LabelKFold, LabelShuffleSplit, LeaveOneLabelOut and LeavePLabelOut have been renamed to :class:`model_selection.GroupKFold`, :class:`model_selection.GroupShuffleSplit`, :class:`model_selection.LeaveOneGroupOut` and :class:`model_selection.LeavePGroupsOut` respectively.

  Note the change from singular to plural form in :class:`model_selection.LeavePGroupsOut`.

• Fit parameter labels renamed to groups

  The labels parameter in the :func:`split` method of the newly renamed splitters :class:`model_selection.GroupKFold`, :class:`model_selection.LeaveOneGroupOut`, :class:`model_selection.LeavePGroupsOut`, :class:`model_selection.GroupShuffleSplit` is renamed to groups following the new nomenclature of their class names.

• Parameter n_labels renamed to n_groups

  The parameter n_labels in the newly renamed :class:`model_selection.LeavePGroupsOut` is changed to n_groups.

• Training scores and Timing information

  cv_results_ also includes the training scores for each cross-validation split (with keys such as 'split0_train_score'), as well as their mean ('mean_train_score') and standard deviation ('std_train_score'). To avoid the cost of evaluating training score, set return_train_score=False.

  Additionally the mean and standard deviation of the times taken to split, train and score the model across all the cross-validation splits is available at the key 'mean_time' and 'std_time' respectively.

Changelog

New features

Classifiers and Regressors

• The Gaussian Process module has been reimplemented and now offers classification and regression estimators through :class:`gaussian_process.GaussianProcessClassifier` and :class:`gaussian_process.GaussianProcessRegressor`. Among other things, the new implementation supports kernel engineering, gradient-based hyperparameter optimization or sampling of functions from GP prior and GP posterior. Extensive documentation and examples are provided. By Jan Hendrik Metzen.
• Added new supervised learning algorithm: :ref:`Multi-layer Perceptron <multilayer_perceptron>` :issue:`3204` by :user:`Issam H. Laradji <IssamLaradji>`
• Added :class:`linear_model.HuberRegressor`, a linear model robust to outliers. :issue:`5291` by Manoj Kumar.
• Added the :class:`multioutput.MultiOutputRegressor` meta-estimator. It converts single output regressors to multi-output regressors by fitting one regressor per output. By :user:`Tim Head <betatim>`.

Other estimators

• New :class:`mixture.GaussianMixture` and :class:`mixture.BayesianGaussianMixture` replace former mixture models, employing faster inference for sounder results. :issue:`7295` by :user:`Wei Xue <xuewei4d>` and :user:`Thierry Guillemot <tguillemot>`.
• Class :class:`decomposition.RandomizedPCA` is now factored into :class:`decomposition.PCA` and it is available calling with parameter svd_solver='randomized'. The default number of n_iter for 'randomized' has changed to 4. The old behavior of PCA is recovered by svd_solver='full'. An additional solver calls arpack and performs truncated (non-randomized) SVD. By default, the best solver is selected depending on the size of the input and the number of components requested. :issue:`5299` by :user:`Giorgio Patrini <giorgiop>`.
• Added two functions for mutual information estimation: :func:`feature_selection.mutual_info_classif` and :func:`feature_selection.mutual_info_regression`. These functions can be used in :class:`feature_selection.SelectKBest` and :class:`feature_selection.SelectPercentile` as score functions. By :user:`Andrea Bravi <AndreaBravi>` and :user:`Nikolay Mayorov <nmayorov>`.
• Added the :class:`ensemble.IsolationForest` class for anomaly detection based on random forests. By Nicolas Goix.
• Added algorithm="elkan" to :class:`cluster.KMeans` implementing Elkan's fast K-Means algorithm. By Andreas Müller.

Model selection and evaluation

• Added :func:`metrics.cluster.fowlkes_mallows_score`, the Fowlkes Mallows Index which measures the similarity of two clusterings of a set of points By :user:`Arnaud Fouchet <afouchet>` and :user:`Thierry Guillemot <tguillemot>`.
• Added :func:`metrics.calinski_harabaz_score`, which computes the Calinski and Harabaz score to evaluate the resulting clustering of a set of points. By :user:`Arnaud Fouchet <afouchet>` and :user:`Thierry Guillemot <tguillemot>`.
• Added new cross-validation splitter :class:`model_selection.TimeSeriesSplit` to handle time series data. :issue:`6586` by :user:`YenChen Lin <yenchenlin>`
• The cross-validation iterators are replaced by cross-validation splitters available from :mod:`sklearn.model_selection`, allowing for nested cross-validation. See :ref:`model_selection_changes` for more information. :issue:`4294` by Raghav RV.

Enhancements

Trees and ensembles

• Added a new splitting criterion for :class:`tree.DecisionTreeRegressor`, the mean absolute error. This criterion can also be used in :class:`ensemble.ExtraTreesRegressor`, :class:`ensemble.RandomForestRegressor`, and the gradient boosting estimators. :issue:`6667` by :user:`Nelson Liu <nelson-liu>`.
• Added weighted impurity-based early stopping criterion for decision tree growth. :issue:`6954` by :user:`Nelson Liu <nelson-liu>`
• The random forest, extra tree and decision tree estimators now has a method decision_path which returns the decision path of samples in the tree. By Arnaud Joly.
• A new example has been added unveiling the decision tree structure. By Arnaud Joly.
• Random forest, extra trees, decision trees and gradient boosting estimator accept the parameter min_samples_split and min_samples_leaf provided as a percentage of the training samples. By :user:`yelite <yelite>` and Arnaud Joly.
• Gradient boosting estimators accept the parameter criterion to specify to splitting criterion used in built decision trees. :issue:`6667` by :user:`Nelson Liu <nelson-liu>`.
• The memory footprint is reduced (sometimes greatly) for :class:`ensemble.bagging.BaseBagging` and classes that inherit from it, i.e, :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor`, and :class:`ensemble.IsolationForest`, by dynamically generating attribute estimators_samples_ only when it is needed. By :user:`David Staub <staubda>`.
• Added n_jobs and sample_weight parameters for :class:`ensemble.VotingClassifier` to fit underlying estimators in parallel. :issue:`5805` by :user:`Ibraim Ganiev <olologin>`.

Linear, kernelized and related models

• In :class:`linear_model.LogisticRegression`, the SAG solver is now available in the multinomial case. :issue:`5251` by Tom Dupre la Tour.
• :class:`linear_model.RANSACRegressor`, :class:`svm.LinearSVC` and :class:`svm.LinearSVR` now support sample_weight. By :user:`Imaculate <Imaculate>`.
• Add parameter loss to :class:`linear_model.RANSACRegressor` to measure the error on the samples for every trial. By Manoj Kumar.
• Prediction of out-of-sample events with Isotonic Regression (:class:`isotonic.IsotonicRegression`) is now much faster (over 1000x in tests with synthetic data). By :user:`Jonathan Arfa <jarfa>`.
• Isotonic regression (:class:`isotonic.IsotonicRegression`) now uses a better algorithm to avoid O(n^2) behavior in pathological cases, and is also generally faster (:issue:`#6691`). By Antony Lee.
• :class:`naive_bayes.GaussianNB` now accepts data-independent class-priors through the parameter priors. By :user:`Guillaume Lemaitre <glemaitre>`.
• :class:`linear_model.ElasticNet` and :class:`linear_model.Lasso` now works with np.float32 input data without converting it into np.float64. This allows to reduce the memory consumption. :issue:`6913` by :user:`YenChen Lin <yenchenlin>`.
• :class:`semi_supervised.LabelPropagation` and :class:`semi_supervised.LabelSpreading` now accept arbitrary kernel functions in addition to strings knn and rbf. :issue:`5762` by :user:`Utkarsh Upadhyay <musically-ut>`.

Decomposition, manifold learning and clustering

• Added inverse_transform function to :class:`decomposition.NMF` to compute data matrix of original shape. By :user:`Anish Shah <AnishShah>`.
• :class:`cluster.KMeans` and :class:`cluster.MiniBatchKMeans` now works with np.float32 and np.float64 input data without converting it. This allows to reduce the memory consumption by using np.float32. :issue:`6846` by :user:`Sebastian Säger <ssaeger>` and :user:`YenChen Lin <yenchenlin>`.

Preprocessing and feature selection

• :class:`preprocessing.RobustScaler` now accepts quantile_range parameter. :issue:`5929` by :user:`Konstantin Podshumok <podshumok>`.
• :class:`feature_extraction.FeatureHasher` now accepts string values. :issue:`6173` by :user:`Ryad Zenine <ryadzenine>` and :user:`Devashish Deshpande <dsquareindia>`.
• Keyword arguments can now be supplied to func in :class:`preprocessing.FunctionTransformer` by means of the kw_args parameter. By Brian McFee.
• :class:`feature_selection.SelectKBest` and :class:`feature_selection.SelectPercentile` now accept score functions that take X, y as input and return only the scores. By :user:`Nikolay Mayorov <nmayorov>`.

Model evaluation and meta-estimators

• :class:`multiclass.OneVsOneClassifier` and :class:`multiclass.OneVsRestClassifier` now support partial_fit. By :user:`Asish Panda <kaichogami>` and :user:`Philipp Dowling <phdowling>`.
• Added support for substituting or disabling :class:`pipeline.Pipeline` and :class:`pipeline.FeatureUnion` components using the set_params interface that powers :mod:`sklearn.grid_search`. See :ref:`sphx_glr_auto_examples_plot_compare_reduction.py` By Joel Nothman and :user:`Robert McGibbon <rmcgibbo>`.
• The new cv_results_ attribute of :class:`model_selection.GridSearchCV` (and :class:`model_selection.RandomizedSearchCV`) can be easily imported into pandas as a DataFrame. Ref :ref:`model_selection_changes` for more information. :issue:`6697` by Raghav RV.
• Generalization of :func:`model_selection.cross_val_predict`. One can pass method names such as predict_proba to be used in the cross validation framework instead of the default predict. By :user:`Ori Ziv <zivori>` and :user:`Sears Merritt <merritts>`.
• The training scores and time taken for training followed by scoring for each search candidate are now available at the cv_results_ dict. See :ref:`model_selection_changes` for more information. :issue:`7325` by :user:`Eugene Chen <eyc88>` and Raghav RV.

Metrics

• Added labels flag to :class:`metrics.log_loss` to explicitly provide the labels when the number of classes in y_true and y_pred differ. :issue:`7239` by :user:`Hong Guangguo <hongguangguo>` with help from :user:`Mads Jensen <indianajensen>` and :user:`Nelson Liu <nelson-liu>`.
• Support sparse contingency matrices in cluster evaluation (:mod:`metrics.cluster.supervised`) to scale to a large number of clusters. :issue:`7419` by :user:`Gregory Stupp <stuppie>` and Joel Nothman.
• Add sample_weight parameter to :func:`metrics.matthews_corrcoef`. By :user:`Jatin Shah <jatinshah>` and Raghav RV.
• Speed up :func:`metrics.silhouette_score` by using vectorized operations. By Manoj Kumar.
• Add sample_weight parameter to :func:`metrics.confusion_matrix`. By :user:`Bernardo Stein <DanielSidhion>`.

Miscellaneous

• Added n_jobs parameter to :class:`feature_selection.RFECV` to compute the score on the test folds in parallel. By Manoj Kumar
• Codebase does not contain C/C++ cython generated files: they are generated during build. Distribution packages will still contain generated C/C++ files. By :user:`Arthur Mensch <arthurmensch>`.
• Reduce the memory usage for 32-bit float input arrays of :func:`utils.sparse_func.mean_variance_axis` and :func:`utils.sparse_func.incr_mean_variance_axis` by supporting cython fused types. By :user:`YenChen Lin <yenchenlin>`.
• The :func:`ignore_warnings` now accept a category argument to ignore only the warnings of a specified type. By :user:`Thierry Guillemot <tguillemot>`.
• Added parameter return_X_y and return type (data, target) : tuple option to :func:`load_iris` dataset :issue:`7049`, :func:`load_breast_cancer` dataset :issue:`7152`, :func:`load_digits` dataset, :func:`load_diabetes` dataset, :func:`load_linnerud` dataset, :func:`load_boston` dataset :issue:`7154` by :user:`Manvendra Singh<manu-chroma>`.
• Simplification of the clone function, deprecate support for estimators that modify parameters in __init__. :issue:`5540` by Andreas Müller.
• When unpickling a scikit-learn estimator in a different version than the one the estimator was trained with, a UserWarning is raised, see :ref:`the documentation on model persistence <persistence_limitations>` for more details. (:issue:`7248`) By Andreas Müller.

Bug fixes

Trees and ensembles

• Random forest, extra trees, decision trees and gradient boosting won't accept anymore min_samples_split=1 as at least 2 samples are required to split a decision tree node. By Arnaud Joly
• :class:`ensemble.VotingClassifier` now raises NotFittedError if predict, transform or predict_proba are called on the non-fitted estimator. by Sebastian Raschka.
• Fix bug where :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` would perform poorly if the random_state was fixed (:issue:`7411`). By Joel Nothman.
• Fix bug in ensembles with randomization where the ensemble would not set random_state on base estimators in a pipeline or similar nesting. (:issue:`7411`). Note, results for :class:`ensemble.BaggingClassifier` :class:`ensemble.BaggingRegressor`, :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` will now differ from previous versions. By Joel Nothman.

Linear, kernelized and related models

• Fixed incorrect gradient computation for loss='squared_epsilon_insensitive' in :class:`linear_model.SGDClassifier` and :class:`linear_model.SGDRegressor` (:issue:`6764`). By :user:`Wenhua Yang <geekoala>`.
• Fix bug in :class:`linear_model.LogisticRegressionCV` where solver='liblinear' did not accept class_weights='balanced. (:issue:`6817`). By Tom Dupre la Tour.
• Fix bug in :class:`neighbors.RadiusNeighborsClassifier` where an error occurred when there were outliers being labelled and a weight function specified (:issue:`6902`). By LeonieBorne.
• Fix :class:`linear_model.ElasticNet` sparse decision function to match output with dense in the multioutput case.

Decomposition, manifold learning and clustering

• :class:`decomposition.RandomizedPCA` default number of iterated_power is 4 instead of 3. :issue:`5141` by :user:`Giorgio Patrini <giorgiop>`.
• :func:`utils.extmath.randomized_svd` performs 4 power iterations by default, instead or 0. In practice this is enough for obtaining a good approximation of the true eigenvalues/vectors in the presence of noise. When n_components is small (< .1 * min(X.shape)) n_iter is set to 7, unless the user specifies a higher number. This improves precision with few components. :issue:`5299` by :user:`Giorgio Patrini<giorgiop>`.
• Whiten/non-whiten inconsistency between components of :class:`decomposition.PCA` and :class:`decomposition.RandomizedPCA` (now factored into PCA, see the New features) is fixed. components_ are stored with no whitening. :issue:`5299` by :user:`Giorgio Patrini <giorgiop>`.
• Fixed bug in :func:`manifold.spectral_embedding` where diagonal of unnormalized Laplacian matrix was incorrectly set to 1. :issue:`4995` by :user:`Peter Fischer <yanlend>`.
• Fixed incorrect initialization of :func:`utils.arpack.eigsh` on all occurrences. Affects :class:`cluster.bicluster.SpectralBiclustering`, :class:`decomposition.KernelPCA`, :class:`manifold.LocallyLinearEmbedding`, and :class:`manifold.SpectralEmbedding` (:issue:`5012`). By :user:`Peter Fischer <yanlend>`.
• Attribute explained_variance_ratio_ calculated with the SVD solver of :class:`discriminant_analysis.LinearDiscriminantAnalysis` now returns correct results. By :user:`JPFrancoia <JPFrancoia>`

Preprocessing and feature selection

• :func:`preprocessing.data._transform_selected` now always passes a copy of X to transform function when copy=True (:issue:`7194`). By Caio Oliveira.

Model evaluation and meta-estimators

• :class:`model_selection.StratifiedKFold` now raises error if all n_labels for individual classes is less than n_folds. :issue:`6182` by :user:`Devashish Deshpande <dsquareindia>`.
• Fixed bug in :class:`model_selection.StratifiedShuffleSplit` where train and test sample could overlap in some edge cases, see :issue:`6121` for more details. By Loic Esteve.
• Fix in :class:`sklearn.model_selection.StratifiedShuffleSplit` to return splits of size train_size and test_size in all cases (:issue:`6472`). By Andreas Müller.
• Cross-validation of :class:`OneVsOneClassifier` and :class:`OneVsRestClassifier` now works with precomputed kernels. :issue:`7350` by :user:`Russell Smith <rsmith54>`.
• Fix incomplete predict_proba method delegation from :class:`model_selection.GridSearchCV` to :class:`linear_model.SGDClassifier` (:issue:`7159`) by Yichuan Liu.

Metrics

• Fix bug in :func:`metrics.silhouette_score` in which clusters of size 1 were incorrectly scored. They should get a score of 0. By Joel Nothman.
• Fix bug in :func:`metrics.silhouette_samples` so that it now works with arbitrary labels, not just those ranging from 0 to n_clusters - 1.
• Fix bug where expected and adjusted mutual information were incorrect if cluster contingency cells exceeded 2**16. By Joel Nothman.
• :func:`metrics.pairwise.pairwise_distances` now converts arrays to boolean arrays when required in scipy.spatial.distance. :issue:`5460` by Tom Dupre la Tour.
• Fix sparse input support in :func:`metrics.silhouette_score` as well as example examples/text/document_clustering.py. By :user:`YenChen Lin <yenchenlin>`.
• :func:`metrics.roc_curve` and :func:`metrics.precision_recall_curve` no longer round y_score values when creating ROC curves; this was causing problems for users with very small differences in scores (:issue:`7353`).

Miscellaneous

• :func:`model_selection.tests._search._check_param_grid` now works correctly with all types that extends/implements Sequence (except string), including range (Python 3.x) and xrange (Python 2.x). :issue:`7323` by Viacheslav Kovalevskyi.
• :func:`utils.extmath.randomized_range_finder` is more numerically stable when many power iterations are requested, since it applies LU normalization by default. If n_iter<2 numerical issues are unlikely, thus no normalization is applied. Other normalization options are available: 'none', 'LU' and 'QR'. :issue:`5141` by :user:`Giorgio Patrini <giorgiop>`.
• Fix a bug where some formats of scipy.sparse matrix, and estimators with them as parameters, could not be passed to :func:`base.clone`. By Loic Esteve.
• :func:`datasets.load_svmlight_file` now is able to read long int QID values. :issue:`7101` by :user:`Ibraim Ganiev <olologin>`.

API changes summary

Linear, kernelized and related models

• residual_metric has been deprecated in :class:`linear_model.RANSACRegressor`. Use loss instead. By Manoj Kumar.
• Access to public attributes .X_ and .y_ has been deprecated in :class:`isotonic.IsotonicRegression`. By :user:`Jonathan Arfa <jarfa>`.

Decomposition, manifold learning and clustering

• The old :class:`mixture.DPGMM` is deprecated in favor of the new :class:`mixture.BayesianGaussianMixture` (with the parameter weight_concentration_prior_type='dirichlet_process'). The new class solves the computational problems of the old class and computes the Gaussian mixture with a Dirichlet process prior faster than before. :issue:`7295` by :user:`Wei Xue <xuewei4d>` and :user:`Thierry Guillemot <tguillemot>`.
• The old :class:`mixture.VBGMM` is deprecated in favor of the new :class:`mixture.BayesianGaussianMixture` (with the parameter weight_concentration_prior_type='dirichlet_distribution'). The new class solves the computational problems of the old class and computes the Variational Bayesian Gaussian mixture faster than before. :issue:`6651` by :user:`Wei Xue <xuewei4d>` and :user:`Thierry Guillemot <tguillemot>`.
• The old :class:`mixture.GMM` is deprecated in favor of the new :class:`mixture.GaussianMixture`. The new class computes the Gaussian mixture faster than before and some of computational problems have been solved. :issue:`6666` by :user:`Wei Xue <xuewei4d>` and :user:`Thierry Guillemot <tguillemot>`.

Model evaluation and meta-estimators

• The :mod:`sklearn.cross_validation`, :mod:`sklearn.grid_search` and :mod:`sklearn.learning_curve` have been deprecated and the classes and functions have been reorganized into the :mod:`sklearn.model_selection` module. Ref :ref:`model_selection_changes` for more information. :issue:`4294` by Raghav RV.
• The grid_scores_ attribute of :class:`model_selection.GridSearchCV` and :class:`model_selection.RandomizedSearchCV` is deprecated in favor of the attribute cv_results_. Ref :ref:`model_selection_changes` for more information. :issue:`6697` by Raghav RV.
• The parameters n_iter or n_folds in old CV splitters are replaced by the new parameter n_splits since it can provide a consistent and unambiguous interface to represent the number of train-test splits. :issue:`7187` by :user:`YenChen Lin <yenchenlin>`.
• classes parameter was renamed to labels in :func:`metrics.hamming_loss`. :issue:`7260` by :user:`Sebastián Vanrell <srvanrell>`.
• The splitter classes LabelKFold, LabelShuffleSplit, LeaveOneLabelOut and LeavePLabelsOut are renamed to :class:`model_selection.GroupKFold`, :class:`model_selection.GroupShuffleSplit`, :class:`model_selection.LeaveOneGroupOut` and :class:`model_selection.LeavePGroupsOut` respectively. Also the parameter labels in the :func:`split` method of the newly renamed splitters :class:`model_selection.LeaveOneGroupOut` and :class:`model_selection.LeavePGroupsOut` is renamed to groups. Additionally in :class:`model_selection.LeavePGroupsOut`, the parameter n_labels is renamed to n_groups. :issue:`6660` by Raghav RV.
• Error and loss names for scoring parameters are now prefixed by 'neg_', such as neg_mean_squared_error. The unprefixed versions are deprecated and will be removed in version 0.20. :issue:`7261` by :user:`Tim Head <betatim>`.

Code Contributors

Aditya Joshi, Alejandro, Alexander Fabisch, Alexander Loginov, Alexander Minyushkin, Alexander Rudy, Alexandre Abadie, Alexandre Abraham, Alexandre Gramfort, Alexandre Saint, alexfields, Alvaro Ulloa, alyssaq, Amlan Kar, Andreas Mueller, andrew giessel, Andrew Jackson, Andrew McCulloh, Andrew Murray, Anish Shah, Arafat, Archit Sharma, Ariel Rokem, Arnaud Joly, Arnaud Rachez, Arthur Mensch, Ash Hoover, asnt, b0noI, Behzad Tabibian, Bernardo, Bernhard Kratzwald, Bhargav Mangipudi, blakeflei, Boyuan Deng, Brandon Carter, Brett Naul, Brian McFee, Caio Oliveira, Camilo Lamus, Carol Willing, Cass, CeShine Lee, Charles Truong, Chyi-Kwei Yau, CJ Carey, codevig, Colin Ni, Dan Shiebler, Daniel, Daniel Hnyk, David Ellis, David Nicholson, David Staub, David Thaler, David Warshaw, Davide Lasagna, Deborah, definitelyuncertain, Didi Bar-Zev, djipey, dsquareindia, edwinENSAE, Elias Kuthe, Elvis DOHMATOB, Ethan White, Fabian Pedregosa, Fabio Ticconi, fisache, Florian Wilhelm, Francis, Francis O'Donovan, Gael Varoquaux, Ganiev Ibraim, ghg, Gilles Louppe, Giorgio Patrini, Giovanni Cherubin, Giovanni Lanzani, Glenn Qian, Gordon Mohr, govin-vatsan, Graham Clenaghan, Greg Reda, Greg Stupp, Guillaume Lemaitre, Gustav Mörtberg, halwai, Harizo Rajaona, Harry Mavroforakis, hashcode55, hdmetor, Henry Lin, Hobson Lane, Hugo Bowne-Anderson, Igor Andriushchenko, Imaculate, Inki Hwang, Isaac Sijaranamual, Ishank Gulati, Issam Laradji, Iver Jordal, jackmartin, Jacob Schreiber, Jake Vanderplas, James Fiedler, James Routley, Jan Zikes, Janna Brettingen, jarfa, Jason Laska, jblackburne, jeff levesque, Jeffrey Blackburne, Jeffrey04, Jeremy Hintz, jeremynixon, Jeroen, Jessica Yung, Jill-Jênn Vie, Jimmy Jia, Jiyuan Qian, Joel Nothman, johannah, John, John Boersma, John Kirkham, John Moeller, jonathan.striebel, joncrall, Jordi, Joseph Munoz, Joshua Cook, JPFrancoia, jrfiedler, JulianKahnert, juliathebrave, kaichogami, KamalakerDadi, Kenneth Lyons, Kevin Wang, kingjr, kjell, Konstantin Podshumok, Kornel Kielczewski, Krishna Kalyan, krishnakalyan3, Kvle Putnam, Kyle Jackson, Lars Buitinck, ldavid, LeiG, LeightonZhang, Leland McInnes, Liang-Chi Hsieh, Lilian Besson, lizsz, Loic Esteve, Louis Tiao, Léonie Borne, Mads Jensen, Maniteja Nandana, Manoj Kumar, Manvendra Singh, Marco, Mario Krell, Mark Bao, Mark Szepieniec, Martin Madsen, MartinBpr, MaryanMorel, Massil, Matheus, Mathieu Blondel, Mathieu Dubois, Matteo, Matthias Ekman, Max Moroz, Michael Scherer, michiaki ariga, Mikhail Korobov, Moussa Taifi, mrandrewandrade, Mridul Seth, nadya-p, Naoya Kanai, Nate George, Nelle Varoquaux, Nelson Liu, Nick James, NickleDave, Nico, Nicolas Goix, Nikolay Mayorov, ningchi, nlathia, okbalefthanded, Okhlopkov, Olivier Grisel, Panos Louridas, Paul Strickland, Perrine Letellier, pestrickland, Peter Fischer, Pieter, Ping-Yao, Chang, practicalswift, Preston Parry, Qimu Zheng, Rachit Kansal, Raghav RV, Ralf Gommers, Ramana.S, Rammig, Randy Olson, Rob Alexander, Robert Lutz, Robin Schucker, Rohan Jain, Ruifeng Zheng, Ryan Yu, Rémy Léone, saihttam, Saiwing Yeung, Sam Shleifer, Samuel St-Jean, Sartaj Singh, Sasank Chilamkurthy, saurabh.bansod, Scott Andrews, Scott Lowe, seales, Sebastian Raschka, Sebastian Saeger, Sebastián Vanrell, Sergei Lebedev, shagun Sodhani, shanmuga cv, Shashank Shekhar, shawpan, shengxiduan, Shota, shuckle16, Skipper Seabold, sklearn-ci, SmedbergM, srvanrell, Sébastien Lerique, Taranjeet, themrmax, Thierry, Thierry Guillemot, Thomas, Thomas Hallock, Thomas Moreau, Tim Head, tKammy, toastedcornflakes, Tom, TomDLT, Toshihiro Kamishima, tracer0tong, Trent Hauck, trevorstephens, Tue Vo, Varun, Varun Jewalikar, Viacheslav, Vighnesh Birodkar, Vikram, Villu Ruusmann, Vinayak Mehta, walter, waterponey, Wenhua Yang, Wenjian Huang, Will Welch, wyseguy7, xyguo, yanlend, Yaroslav Halchenko, yelite, Yen, YenChenLin, Yichuan Liu, Yoav Ram, Yoshiki, Zheng RuiFeng, zivori, Óscar Nájera

.. currentmodule:: sklearn

Version 0.17.1

February 18, 2016

Changelog

Bug fixes

• Upgrade vendored joblib to version 0.9.4 that fixes an important bug in joblib.Parallel that can silently yield to wrong results when working on datasets larger than 1MB: https://github.com/joblib/joblib/blob/0.9.4/CHANGES.rst
• Fixed reading of Bunch pickles generated with scikit-learn version <= 0.16. This can affect users who have already downloaded a dataset with scikit-learn 0.16 and are loading it with scikit-learn 0.17. See :issue:`6196` for how this affected :func:`datasets.fetch_20newsgroups`. By Loic Esteve.
• Fixed a bug that prevented using ROC AUC score to perform grid search on several CPU / cores on large arrays. See :issue:`6147` By Olivier Grisel.
• Fixed a bug that prevented to properly set the presort parameter in :class:`ensemble.GradientBoostingRegressor`. See :issue:`5857` By Andrew McCulloh.
• Fixed a joblib error when evaluating the perplexity of a :class:`decomposition.LatentDirichletAllocation` model. See :issue:`6258` By Chyi-Kwei Yau.

Version 0.17

November 5, 2015

Changelog

New features

• All the Scaler classes but :class:`preprocessing.RobustScaler` can be fitted online by calling partial_fit. By :user:`Giorgio Patrini <giorgiop>`.
• The new class :class:`ensemble.VotingClassifier` implements a "majority rule" / "soft voting" ensemble classifier to combine estimators for classification. By Sebastian Raschka.
• The new class :class:`preprocessing.RobustScaler` provides an alternative to :class:`preprocessing.StandardScaler` for feature-wise centering and range normalization that is robust to outliers. By :user:`Thomas Unterthiner <untom>`.
• The new class :class:`preprocessing.MaxAbsScaler` provides an alternative to :class:`preprocessing.MinMaxScaler` for feature-wise range normalization when the data is already centered or sparse. By :user:`Thomas Unterthiner <untom>`.
• The new class :class:`preprocessing.FunctionTransformer` turns a Python function into a Pipeline-compatible transformer object. By Joe Jevnik.
• The new classes :class:`cross_validation.LabelKFold` and :class:`cross_validation.LabelShuffleSplit` generate train-test folds, respectively similar to :class:`cross_validation.KFold` and :class:`cross_validation.ShuffleSplit`, except that the folds are conditioned on a label array. By Brian McFee, :user:`Jean Kossaifi <JeanKossaifi>` and Gilles Louppe.
• :class:`decomposition.LatentDirichletAllocation` implements the Latent Dirichlet Allocation topic model with online variational inference. By :user:`Chyi-Kwei Yau <chyikwei>`, with code based on an implementation by Matt Hoffman. (:issue:`3659`)
• The new solver sag implements a Stochastic Average Gradient descent and is available in both :class:`linear_model.LogisticRegression` and :class:`linear_model.Ridge`. This solver is very efficient for large datasets. By :user:`Danny Sullivan <dsullivan7>` and Tom Dupre la Tour. (:issue:`4738`)
• The new solver cd implements a Coordinate Descent in :class:`decomposition.NMF`. Previous solver based on Projected Gradient is still available setting new parameter solver to pg, but is deprecated and will be removed in 0.19, along with :class:`decomposition.ProjectedGradientNMF` and parameters sparseness, eta, beta and nls_max_iter. New parameters alpha and l1_ratio control L1 and L2 regularization, and shuffle adds a shuffling step in the cd solver. By Tom Dupre la Tour and Mathieu Blondel.

Enhancements

• :class:`manifold.TSNE` now supports approximate optimization via the Barnes-Hut method, leading to much faster fitting. By Christopher Erick Moody. (:issue:`4025`)
• :class:`cluster.mean_shift_.MeanShift` now supports parallel execution, as implemented in the mean_shift function. By :user:`Martino Sorbaro <martinosorb>`.
• :class:`naive_bayes.GaussianNB` now supports fitting with sample_weight. By Jan Hendrik Metzen.
• :class:`dummy.DummyClassifier` now supports a prior fitting strategy. By Arnaud Joly.
• Added a fit_predict method for :class:`mixture.GMM` and subclasses. By :user:`Cory Lorenz <clorenz7>`.
• Added the :func:`metrics.label_ranking_loss` metric. By Arnaud Joly.
• Added the :func:`metrics.cohen_kappa_score` metric.
• Added a warm_start constructor parameter to the bagging ensemble models to increase the size of the ensemble. By :user:`Tim Head <betatim>`.
• Added option to use multi-output regression metrics without averaging. By Konstantin Shmelkov and :user:`Michael Eickenberg<eickenberg>`.
• Added stratify option to :func:`cross_validation.train_test_split` for stratified splitting. By Miroslav Batchkarov.
• The :func:`tree.export_graphviz` function now supports aesthetic improvements for :class:`tree.DecisionTreeClassifier` and :class:`tree.DecisionTreeRegressor`, including options for coloring nodes by their majority class or impurity, showing variable names, and using node proportions instead of raw sample counts. By Trevor Stephens.
• Improved speed of newton-cg solver in :class:`linear_model.LogisticRegression`, by avoiding loss computation. By Mathieu Blondel and Tom Dupre la Tour.
• The class_weight="auto" heuristic in classifiers supporting class_weight was deprecated and replaced by the class_weight="balanced" option, which has a simpler formula and interpretation. By Hanna Wallach and Andreas Müller.
• Add class_weight parameter to automatically weight samples by class frequency for :class:`linear_model.PassiveAgressiveClassifier`. By Trevor Stephens.
• Added backlinks from the API reference pages to the user guide. By Andreas Müller.
• The labels parameter to :func:`sklearn.metrics.f1_score`, :func:`sklearn.metrics.fbeta_score`, :func:`sklearn.metrics.recall_score` and :func:`sklearn.metrics.precision_score` has been extended. It is now possible to ignore one or more labels, such as where a multiclass problem has a majority class to ignore. By Joel Nothman.
• Add sample_weight support to :class:`linear_model.RidgeClassifier`. By Trevor Stephens.
• Provide an option for sparse output from :func:`sklearn.metrics.pairwise.cosine_similarity`. By :user:`Jaidev Deshpande <jaidevd>`.
• Add :func:`minmax_scale` to provide a function interface for :class:`MinMaxScaler`. By :user:`Thomas Unterthiner <untom>`.
• dump_svmlight_file now handles multi-label datasets. By Chih-Wei Chang.
• RCV1 dataset loader (:func:`sklearn.datasets.fetch_rcv1`). By Tom Dupre la Tour.
• The "Wisconsin Breast Cancer" classical two-class classification dataset is now included in scikit-learn, available with :func:`sklearn.dataset.load_breast_cancer`.
• Upgraded to joblib 0.9.3 to benefit from the new automatic batching of short tasks. This makes it possible for scikit-learn to benefit from parallelism when many very short tasks are executed in parallel, for instance by the :class:`grid_search.GridSearchCV` meta-estimator with n_jobs > 1 used with a large grid of parameters on a small dataset. By Vlad Niculae, Olivier Grisel and Loic Esteve.
• For more details about changes in joblib 0.9.3 see the release notes: https://github.com/joblib/joblib/blob/master/CHANGES.rst#release-093
• Improved speed (3 times per iteration) of :class:`decomposition.DictLearning` with coordinate descent method from :class:`linear_model.Lasso`. By :user:`Arthur Mensch <arthurmensch>`.
• Parallel processing (threaded) for queries of nearest neighbors (using the ball-tree) by Nikolay Mayorov.
• Allow :func:`datasets.make_multilabel_classification` to output a sparse y. By Kashif Rasul.
• :class:`cluster.DBSCAN` now accepts a sparse matrix of precomputed distances, allowing memory-efficient distance precomputation. By Joel Nothman.
• :class:`tree.DecisionTreeClassifier` now exposes an apply method for retrieving the leaf indices samples are predicted as. By :user:`Daniel Galvez <galv>` and Gilles Louppe.
• Speed up decision tree regressors, random forest regressors, extra trees regressors and gradient boosting estimators by computing a proxy of the impurity improvement during the tree growth. The proxy quantity is such that the split that maximizes this value also maximizes the impurity improvement. By Arnaud Joly, :user:`Jacob Schreiber <jmschrei>` and Gilles Louppe.
• Speed up tree based methods by reducing the number of computations needed when computing the impurity measure taking into account linear relationship of the computed statistics. The effect is particularly visible with extra trees and on datasets with categorical or sparse features. By Arnaud Joly.
• :class:`ensemble.GradientBoostingRegressor` and :class:`ensemble.GradientBoostingClassifier` now expose an apply method for retrieving the leaf indices each sample ends up in under each try. By :user:`Jacob Schreiber <jmschrei>`.
• Add sample_weight support to :class:`linear_model.LinearRegression`. By Sonny Hu. (:issue:`#4881`)
• Add n_iter_without_progress to :class:`manifold.TSNE` to control the stopping criterion. By Santi Villalba. (:issue:`5186`)
• Added optional parameter random_state in :class:`linear_model.Ridge` , to set the seed of the pseudo random generator used in sag solver. By Tom Dupre la Tour.
• Added optional parameter warm_start in :class:`linear_model.LogisticRegression`. If set to True, the solvers lbfgs, newton-cg and sag will be initialized with the coefficients computed in the previous fit. By Tom Dupre la Tour.
• Added sample_weight support to :class:`linear_model.LogisticRegression` for the lbfgs, newton-cg, and sag solvers. By Valentin Stolbunov. Support added to the liblinear solver. By Manoj Kumar.
• Added optional parameter presort to :class:`ensemble.GradientBoostingRegressor` and :class:`ensemble.GradientBoostingClassifier`, keeping default behavior the same. This allows gradient boosters to turn off presorting when building deep trees or using sparse data. By :user:`Jacob Schreiber <jmschrei>`.
• Altered :func:`metrics.roc_curve` to drop unnecessary thresholds by default. By :user:`Graham Clenaghan <gclenaghan>`.
• Added :class:`feature_selection.SelectFromModel` meta-transformer which can be used along with estimators that have coef_ or feature_importances_ attribute to select important features of the input data. By :user:`Maheshakya Wijewardena <maheshakya>`, Joel Nothman and Manoj Kumar.
• Added :func:`metrics.pairwise.laplacian_kernel`. By Clyde Fare.
• :class:`covariance.GraphLasso` allows separate control of the convergence criterion for the Elastic-Net subproblem via the enet_tol parameter.
• Improved verbosity in :class:`decomposition.DictionaryLearning`.
• :class:`ensemble.RandomForestClassifier` and :class:`ensemble.RandomForestRegressor` no longer explicitly store the samples used in bagging, resulting in a much reduced memory footprint for storing random forest models.
• Added positive option to :class:`linear_model.Lars` and :func:`linear_model.lars_path` to force coefficients to be positive. (:issue:`5131`)
• Added the X_norm_squared parameter to :func:`metrics.pairwise.euclidean_distances` to provide precomputed squared norms for X.
• Added the fit_predict method to :class:`pipeline.Pipeline`.
• Added the :func:`preprocessing.min_max_scale` function.

Bug fixes

• Fixed non-determinism in :class:`dummy.DummyClassifier` with sparse multi-label output. By Andreas Müller.
• Fixed the output shape of :class:`linear_model.RANSACRegressor` to (n_samples, ). By Andreas Müller.
• Fixed bug in :class:`decomposition.DictLearning` when n_jobs < 0. By Andreas Müller.
• Fixed bug where :class:`grid_search.RandomizedSearchCV` could consume a lot of memory for large discrete grids. By Joel Nothman.
• Fixed bug in :class:`linear_model.LogisticRegressionCV` where penalty was ignored in the final fit. By Manoj Kumar.
• Fixed bug in :class:`ensemble.forest.ForestClassifier` while computing oob_score and X is a sparse.csc_matrix. By :user:`Ankur Ankan <ankurankan>`.
• All regressors now consistently handle and warn when given y that is of shape (n_samples, 1). By Andreas Müller and Henry Lin. (:issue:`5431`)
• Fix in :class:`cluster.KMeans` cluster reassignment for sparse input by Lars Buitinck.
• Fixed a bug in :class:`lda.LDA` that could cause asymmetric covariance matrices when using shrinkage. By Martin Billinger.
• Fixed :func:`cross_validation.cross_val_predict` for estimators with sparse predictions. By Buddha Prakash.
• Fixed the predict_proba method of :class:`linear_model.LogisticRegression` to use soft-max instead of one-vs-rest normalization. By Manoj Kumar. (:issue:`5182`)
• Fixed the :func:`partial_fit` method of :class:`linear_model.SGDClassifier` when called with average=True. By :user:`Andrew Lamb <andylamb>`. (:issue:`5282`)
• Dataset fetchers use different filenames under Python 2 and Python 3 to avoid pickling compatibility issues. By Olivier Grisel. (:issue:`5355`)
• Fixed a bug in :class:`naive_bayes.GaussianNB` which caused classification results to depend on scale. By Jake Vanderplas.
• Fixed temporarily :class:`linear_model.Ridge`, which was incorrect when fitting the intercept in the case of sparse data. The fix automatically changes the solver to 'sag' in this case. :issue:`5360` by Tom Dupre la Tour.
• Fixed a performance bug in :class:`decomposition.RandomizedPCA` on data with a large number of features and fewer samples. (:issue:`4478`) By Andreas Müller, Loic Esteve and :user:`Giorgio Patrini <giorgiop>`.
• Fixed bug in :class:`cross_decomposition.PLS` that yielded unstable and platform dependent output, and failed on fit_transform. By :user:`Arthur Mensch <arthurmensch>`.
• Fixes to the Bunch class used to store datasets.
• Fixed :func:`ensemble.plot_partial_dependence` ignoring the percentiles parameter.
• Providing a set as vocabulary in CountVectorizer no longer leads to inconsistent results when pickling.
• Fixed the conditions on when a precomputed Gram matrix needs to be recomputed in :class:`linear_model.LinearRegression`, :class:`linear_model.OrthogonalMatchingPursuit`, :class:`linear_model.Lasso` and :class:`linear_model.ElasticNet`.
• Fixed inconsistent memory layout in the coordinate descent solver that affected :class:`linear_model.DictionaryLearning` and :class:`covariance.GraphLasso`. (:issue:`5337`) By Olivier Grisel.
• :class:`manifold.LocallyLinearEmbedding` no longer ignores the reg parameter.
• Nearest Neighbor estimators with custom distance metrics can now be pickled. (:issue:`4362`)
• Fixed a bug in :class:`pipeline.FeatureUnion` where transformer_weights were not properly handled when performing grid-searches.
• Fixed a bug in :class:`linear_model.LogisticRegression` and :class:`linear_model.LogisticRegressionCV` when using class_weight='balanced'```or ``class_weight='auto'. By Tom Dupre la Tour.
• Fixed bug :issue:`5495` when doing OVR(SVC(decision_function_shape="ovr")). Fixed by :user:`Elvis Dohmatob <dohmatob>`.

API changes summary

• Attribute data_min, data_max and data_range in :class:`preprocessing.MinMaxScaler` are deprecated and won't be available from 0.19. Instead, the class now exposes data_min_, data_max_ and data_range_. By :user:`Giorgio Patrini <giorgiop>`.
• All Scaler classes now have an scale_ attribute, the feature-wise rescaling applied by their transform methods. The old attribute std_ in :class:`preprocessing.StandardScaler` is deprecated and superseded by scale_; it won't be available in 0.19. By :user:`Giorgio Patrini <giorgiop>`.
• :class:`svm.SVC`` and :class:`svm.NuSVC` now have an decision_function_shape parameter to make their decision function of shape (n_samples, n_classes) by setting decision_function_shape='ovr'. This will be the default behavior starting in 0.19. By Andreas Müller.
• Passing 1D data arrays as input to estimators is now deprecated as it caused confusion in how the array elements should be interpreted as features or as samples. All data arrays are now expected to be explicitly shaped (n_samples, n_features). By :user:`Vighnesh Birodkar <vighneshbirodkar>`.
• :class:`lda.LDA` and :class:`qda.QDA` have been moved to :class:`discriminant_analysis.LinearDiscriminantAnalysis` and :class:`discriminant_analysis.QuadraticDiscriminantAnalysis`.
• The store_covariance and tol parameters have been moved from the fit method to the constructor in :class:`discriminant_analysis.LinearDiscriminantAnalysis` and the store_covariances and tol parameters have been moved from the fit method to the constructor in :class:`discriminant_analysis.QuadraticDiscriminantAnalysis`.
• Models inheriting from _LearntSelectorMixin will no longer support the transform methods. (i.e, RandomForests, GradientBoosting, LogisticRegression, DecisionTrees, SVMs and SGD related models). Wrap these models around the metatransfomer :class:`feature_selection.SelectFromModel` to remove features (according to coefs_ or feature_importances_) which are below a certain threshold value instead.
• :class:`cluster.KMeans` re-runs cluster-assignments in case of non-convergence, to ensure consistency of predict(X) and labels_. By :user:`Vighnesh Birodkar <vighneshbirodkar>`.
• Classifier and Regressor models are now tagged as such using the _estimator_type attribute.
• Cross-validation iterators always provide indices into training and test set, not boolean masks.
• The decision_function on all regressors was deprecated and will be removed in 0.19. Use predict instead.
• :func:`datasets.load_lfw_pairs` is deprecated and will be removed in 0.19. Use :func:`datasets.fetch_lfw_pairs` instead.
• The deprecated hmm module was removed.
• The deprecated Bootstrap cross-validation iterator was removed.
• The deprecated Ward and WardAgglomerative classes have been removed. Use :class:`clustering.AgglomerativeClustering` instead.
• :func:`cross_validation.check_cv` is now a public function.
• The property residues_ of :class:`linear_model.LinearRegression` is deprecated and will be removed in 0.19.
• The deprecated n_jobs parameter of :class:`linear_model.LinearRegression` has been moved to the constructor.
• Removed deprecated class_weight parameter from :class:`linear_model.SGDClassifier`'s fit method. Use the construction parameter instead.
• The deprecated support for the sequence of sequences (or list of lists) multilabel format was removed. To convert to and from the supported binary indicator matrix format, use :class:`MultiLabelBinarizer <preprocessing.MultiLabelBinarizer>`.
• The behavior of calling the inverse_transform method of Pipeline.pipeline will change in 0.19. It will no longer reshape one-dimensional input to two-dimensional input.
• The deprecated attributes indicator_matrix_, multilabel_ and classes_ of :class:`preprocessing.LabelBinarizer` were removed.
• Using gamma=0 in :class:`svm.SVC` and :class:`svm.SVR` to automatically set the gamma to 1. / n_features is deprecated and will be removed in 0.19. Use gamma="auto" instead.

Code Contributors

Aaron Schumacher, Adithya Ganesh, akitty, Alexandre Gramfort, Alexey Grigorev, Ali Baharev, Allen Riddell, Ando Saabas, Andreas Mueller, Andrew Lamb, Anish Shah, Ankur Ankan, Anthony Erlinger, Ari Rouvinen, Arnaud Joly, Arnaud Rachez, Arthur Mensch, banilo, Barmaley.exe, benjaminirving, Boyuan Deng, Brett Naul, Brian McFee, Buddha Prakash, Chi Zhang, Chih-Wei Chang, Christof Angermueller, Christoph Gohlke, Christophe Bourguignat, Christopher Erick Moody, Chyi-Kwei Yau, Cindy Sridharan, CJ Carey, Clyde-fare, Cory Lorenz, Dan Blanchard, Daniel Galvez, Daniel Kronovet, Danny Sullivan, Data1010, David, David D Lowe, David Dotson, djipey, Dmitry Spikhalskiy, Donne Martin, Dougal J. Sutherland, Dougal Sutherland, edson duarte, Eduardo Caro, Eric Larson, Eric Martin, Erich Schubert, Fernando Carrillo, Frank C. Eckert, Frank Zalkow, Gael Varoquaux, Ganiev Ibraim, Gilles Louppe, Giorgio Patrini, giorgiop, Graham Clenaghan, Gryllos Prokopis, gwulfs, Henry Lin, Hsuan-Tien Lin, Immanuel Bayer, Ishank Gulati, Jack Martin, Jacob Schreiber, Jaidev Deshpande, Jake Vanderplas, Jan Hendrik Metzen, Jean Kossaifi, Jeffrey04, Jeremy, jfraj, Jiali Mei, Joe Jevnik, Joel Nothman, John Kirkham, John Wittenauer, Joseph, Joshua Loyal, Jungkook Park, KamalakerDadi, Kashif Rasul, Keith Goodman, Kian Ho, Konstantin Shmelkov, Kyler Brown, Lars Buitinck, Lilian Besson, Loic Esteve, Louis Tiao, maheshakya, Maheshakya Wijewardena, Manoj Kumar, MarkTab marktab.net, Martin Ku, Martin Spacek, MartinBpr, martinosorb, MaryanMorel, Masafumi Oyamada, Mathieu Blondel, Matt Krump, Matti Lyra, Maxim Kolganov, mbillinger, mhg, Michael Heilman, Michael Patterson, Miroslav Batchkarov, Nelle Varoquaux, Nicolas, Nikolay Mayorov, Olivier Grisel, Omer Katz, Óscar Nájera, Pauli Virtanen, Peter Fischer, Peter Prettenhofer, Phil Roth, pianomania, Preston Parry, Raghav RV, Rob Zinkov, Robert Layton, Rohan Ramanath, Saket Choudhary, Sam Zhang, santi, saurabh.bansod, scls19fr, Sebastian Raschka, Sebastian Saeger, Shivan Sornarajah, SimonPL, sinhrks, Skipper Seabold, Sonny Hu, sseg, Stephen Hoover, Steven De Gryze, Steven Seguin, Theodore Vasiloudis, Thomas Unterthiner, Tiago Freitas Pereira, Tian Wang, Tim Head, Timothy Hopper, tokoroten, Tom Dupré la Tour, Trevor Stephens, Valentin Stolbunov, Vighnesh Birodkar, Vinayak Mehta, Vincent, Vincent Michel, vstolbunov, wangz10, Wei Xue, Yucheng Low, Yury Zhauniarovich, Zac Stewart, zhai_pro, Zichen Wang

Version 0.16.1

April 14, 2015

Changelog

Bug fixes

• Allow input data larger than block_size in :class:`covariance.LedoitWolf` by Andreas Müller.
• Fix a bug in :class:`isotonic.IsotonicRegression` deduplication that caused unstable result in :class:`calibration.CalibratedClassifierCV` by Jan Hendrik Metzen.
• Fix sorting of labels in func:preprocessing.label_binarize by Michael Heilman.
• Fix several stability and convergence issues in :class:`cross_decomposition.CCA` and :class:`cross_decomposition.PLSCanonical` by Andreas Müller
• Fix a bug in :class:`cluster.KMeans` when precompute_distances=False on fortran-ordered data.
• Fix a speed regression in :class:`ensemble.RandomForestClassifier`'s predict and predict_proba by Andreas Müller.
• Fix a regression where utils.shuffle converted lists and dataframes to arrays, by Olivier Grisel

Version 0.16

March 26, 2015

Highlights

• Speed improvements (notably in :class:`cluster.DBSCAN`), reduced memory requirements, bug-fixes and better default settings.
• Multinomial Logistic regression and a path algorithm in :class:`linear_model.LogisticRegressionCV`.
• Out-of core learning of PCA via :class:`decomposition.IncrementalPCA`.
• Probability callibration of classifiers using :class:`calibration.CalibratedClassifierCV`.
• :class:`cluster.Birch` clustering method for large-scale datasets.
• Scalable approximate nearest neighbors search with Locality-sensitive hashing forests in :class:`neighbors.LSHForest`.
• Improved error messages and better validation when using malformed input data.
• More robust integration with pandas dataframes.

Changelog

New features

• The new :class:`neighbors.LSHForest` implements locality-sensitive hashing for approximate nearest neighbors search. By :user:`Maheshakya Wijewardena<maheshakya>`.
• Added :class:`svm.LinearSVR`. This class uses the liblinear implementation of Support Vector Regression which is much faster for large sample sizes than :class:`svm.SVR` with linear kernel. By Fabian Pedregosa and Qiang Luo.
• Incremental fit for :class:`GaussianNB <naive_bayes.GaussianNB>`.
• Added sample_weight support to :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor`. By Arnaud Joly.
• Added the :func:`metrics.label_ranking_average_precision_score` metrics. By Arnaud Joly.
• Add the :func:`metrics.coverage_error` metrics. By Arnaud Joly.
• Added :class:`linear_model.LogisticRegressionCV`. By Manoj Kumar, Fabian Pedregosa, Gael Varoquaux and Alexandre Gramfort.
• Added warm_start constructor parameter to make it possible for any trained forest model to grow additional trees incrementally. By :user:`Laurent Direr<ldirer>`.
• Added sample_weight support to :class:`ensemble.GradientBoostingClassifier` and :class:`ensemble.GradientBoostingRegressor`. By Peter Prettenhofer.
• Added :class:`decomposition.IncrementalPCA`, an implementation of the PCA algorithm that supports out-of-core learning with a partial_fit method. By Kyle Kastner.
• Averaged SGD for :class:`SGDClassifier <linear_model.SGDClassifier>` and :class:`SGDRegressor <linear_model.SGDRegressor>` By :user:`Danny Sullivan <dsullivan7>`.
• Added :func:`cross_val_predict <cross_validation.cross_val_predict>` function which computes cross-validated estimates. By Luis Pedro Coelho
• Added :class:`linear_model.TheilSenRegressor`, a robust generalized-median-based estimator. By :user:`Florian Wilhelm <FlorianWilhelm>`.
• Added :func:`metrics.median_absolute_error`, a robust metric. By Gael Varoquaux and :user:`Florian Wilhelm <FlorianWilhelm>`.
• Add :class:`cluster.Birch`, an online clustering algorithm. By Manoj Kumar, Alexandre Gramfort and Joel Nothman.
• Added shrinkage support to :class:`discriminant_analysis.LinearDiscriminantAnalysis` using two new solvers. By :user:`Clemens Brunner <cle1109>` and Martin Billinger.
• Added :class:`kernel_ridge.KernelRidge`, an implementation of kernelized ridge regression. By Mathieu Blondel and Jan Hendrik Metzen.
• All solvers in :class:`linear_model.Ridge` now support sample_weight. By Mathieu Blondel.
• Added :class:`cross_validation.PredefinedSplit` cross-validation for fixed user-provided cross-validation folds. By :user:`Thomas Unterthiner <untom>`.
• Added :class:`calibration.CalibratedClassifierCV`, an approach for calibrating the predicted probabilities of a classifier. By Alexandre Gramfort, Jan Hendrik Metzen, Mathieu Blondel and :user:`Balazs Kegl <kegl>`.

Enhancements

• Add option return_distance in :func:`hierarchical.ward_tree` to return distances between nodes for both structured and unstructured versions of the algorithm. By Matteo Visconti di Oleggio Castello. The same option was added in :func:`hierarchical.linkage_tree`. By Manoj Kumar
• Add support for sample weights in scorer objects. Metrics with sample weight support will automatically benefit from it. By Noel Dawe and Vlad Niculae.
• Added newton-cg and lbfgs solver support in :class:`linear_model.LogisticRegression`. By Manoj Kumar.
• Add selection="random" parameter to implement stochastic coordinate descent for :class:`linear_model.Lasso`, :class:`linear_model.ElasticNet` and related. By Manoj Kumar.
• Add sample_weight parameter to :func:`metrics.jaccard_similarity_score` and :func:`metrics.log_loss`. By :user:`Jatin Shah <jatinshah>`.
• Support sparse multilabel indicator representation in :class:`preprocessing.LabelBinarizer` and :class:`multiclass.OneVsRestClassifier` (by :user:`Hamzeh Alsalhi <hamsal>` with thanks to Rohit Sivaprasad), as well as evaluation metrics (by Joel Nothman).
• Add sample_weight parameter to metrics.jaccard_similarity_score. By Jatin Shah.
• Add support for multiclass in metrics.hinge_loss. Added labels=None as optional parameter. By Saurabh Jha.
• Add sample_weight parameter to metrics.hinge_loss. By Saurabh Jha.
• Add multi_class="multinomial" option in :class:`linear_model.LogisticRegression` to implement a Logistic Regression solver that minimizes the cross-entropy or multinomial loss instead of the default One-vs-Rest setting. Supports lbfgs and newton-cg solvers. By Lars Buitinck and Manoj Kumar. Solver option newton-cg by Simon Wu.
• DictVectorizer can now perform fit_transform on an iterable in a single pass, when giving the option sort=False. By :user:`Dan Blanchard <dan-blanchard>`.
• :class:`GridSearchCV` and :class:`RandomizedSearchCV` can now be configured to work with estimators that may fail and raise errors on individual folds. This option is controlled by the error_score parameter. This does not affect errors raised on re-fit. By :user:`Michal Romaniuk <romaniukm>`.
• Add digits parameter to metrics.classification_report to allow report to show different precision of floating point numbers. By :user:`Ian Gilmore <agileminor>`.
• Add a quantile prediction strategy to the :class:`dummy.DummyRegressor`. By :user:`Aaron Staple <staple>`.
• Add handle_unknown option to :class:`preprocessing.OneHotEncoder` to handle unknown categorical features more gracefully during transform. By Manoj Kumar.
• Added support for sparse input data to decision trees and their ensembles. By Fares Hedyati and Arnaud Joly.
• Optimized :class:`cluster.AffinityPropagation` by reducing the number of memory allocations of large temporary data-structures. By Antony Lee.
• Parellization of the computation of feature importances in random forest. By Olivier Grisel and Arnaud Joly.
• Add n_iter_ attribute to estimators that accept a max_iter attribute in their constructor. By Manoj Kumar.
• Added decision function for :class:`multiclass.OneVsOneClassifier` By Raghav RV and :user:`Kyle Beauchamp <kyleabeauchamp>`.
• :func:`neighbors.kneighbors_graph` and :func:`radius_neighbors_graph` support non-Euclidean metrics. By Manoj Kumar
• Parameter connectivity in :class:`cluster.AgglomerativeClustering` and family now accept callables that return a connectivity matrix. By Manoj Kumar.
• Sparse support for :func:`paired_distances`. By Joel Nothman.
• :class:`cluster.DBSCAN` now supports sparse input and sample weights and has been optimized: the inner loop has been rewritten in Cython and radius neighbors queries are now computed in batch. By Joel Nothman and Lars Buitinck.
• Add class_weight parameter to automatically weight samples by class frequency for :class:`ensemble.RandomForestClassifier`, :class:`tree.DecisionTreeClassifier`, :class:`ensemble.ExtraTreesClassifier` and :class:`tree.ExtraTreeClassifier`. By Trevor Stephens.
• :class:`grid_search.RandomizedSearchCV` now does sampling without replacement if all parameters are given as lists. By Andreas Müller.
• Parallelized calculation of :func:`pairwise_distances` is now supported for scipy metrics and custom callables. By Joel Nothman.
• Allow the fitting and scoring of all clustering algorithms in :class:`pipeline.Pipeline`. By Andreas Müller.
• More robust seeding and improved error messages in :class:`cluster.MeanShift` by Andreas Müller.
• Make the stopping criterion for :class:`mixture.GMM`, :class:`mixture.DPGMM` and :class:`mixture.VBGMM` less dependent on the number of samples by thresholding the average log-likelihood change instead of its sum over all samples. By Hervé Bredin.
• The outcome of :func:`manifold.spectral_embedding` was made deterministic by flipping the sign of eigenvectors. By :user:`Hasil Sharma <Hasil-Sharma>`.
• Significant performance and memory usage improvements in :class:`preprocessing.PolynomialFeatures`. By Eric Martin.
• Numerical stability improvements for :class:`preprocessing.StandardScaler` and :func:`preprocessing.scale`. By Nicolas Goix
• :class:`svm.SVC` fitted on sparse input now implements decision_function. By Rob Zinkov and Andreas Müller.
• :func:`cross_validation.train_test_split` now preserves the input type, instead of converting to numpy arrays.

Documentation improvements

• Added example of using :class:`FeatureUnion` for heterogeneous input. By :user:`Matt Terry <mrterry>`
• Documentation on scorers was improved, to highlight the handling of loss functions. By :user:`Matt Pico <MattpSoftware>`.
• A discrepancy between liblinear output and scikit-learn's wrappers is now noted. By Manoj Kumar.
• Improved documentation generation: examples referring to a class or function are now shown in a gallery on the class/function's API reference page. By Joel Nothman.
• More explicit documentation of sample generators and of data transformation. By Joel Nothman.
• :class:`sklearn.neighbors.BallTree` and :class:`sklearn.neighbors.KDTree` used to point to empty pages stating that they are aliases of BinaryTree. This has been fixed to show the correct class docs. By Manoj Kumar.
• Added silhouette plots for analysis of KMeans clustering using :func:`metrics.silhouette_samples` and :func:`metrics.silhouette_score`. See :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`

Bug fixes

• Metaestimators now support ducktyping for the presence of decision_function, predict_proba and other methods. This fixes behavior of :class:`grid_search.GridSearchCV`, :class:`grid_search.RandomizedSearchCV`, :class:`pipeline.Pipeline`, :class:`feature_selection.RFE`, :class:`feature_selection.RFECV` when nested. By Joel Nothman
• The scoring attribute of grid-search and cross-validation methods is no longer ignored when a :class:`grid_search.GridSearchCV` is given as a base estimator or the base estimator doesn't have predict.
• The function :func:`hierarchical.ward_tree` now returns the children in the same order for both the structured and unstructured versions. By Matteo Visconti di Oleggio Castello.
• :class:`feature_selection.RFECV` now correctly handles cases when step is not equal to 1. By :user:`Nikolay Mayorov <nmayorov>`
• The :class:`decomposition.PCA` now undoes whitening in its inverse_transform. Also, its components_ now always have unit length. By :user:`Michael Eickenberg <eickenberg>`.
• Fix incomplete download of the dataset when :func:`datasets.download_20newsgroups` is called. By Manoj Kumar.
• Various fixes to the Gaussian processes subpackage by Vincent Dubourg and Jan Hendrik Metzen.
• Calling partial_fit with class_weight=='auto' throws an appropriate error message and suggests a work around. By :user:`Danny Sullivan <dsullivan7>`.
• :class:`RBFSampler <kernel_approximation.RBFSampler>` with gamma=g formerly approximated :func:`rbf_kernel <metrics.pairwise.rbf_kernel>` with gamma=g/2.; the definition of gamma is now consistent, which may substantially change your results if you use a fixed value. (If you cross-validated over gamma, it probably doesn't matter too much.) By :user:`Dougal Sutherland <dougalsutherland>`.
• Pipeline object delegate the classes_ attribute to the underlying estimator. It allows, for instance, to make bagging of a pipeline object. By Arnaud Joly
• :class:`neighbors.NearestCentroid` now uses the median as the centroid when metric is set to manhattan. It was using the mean before. By Manoj Kumar
• Fix numerical stability issues in :class:`linear_model.SGDClassifier` and :class:`linear_model.SGDRegressor` by clipping large gradients and ensuring that weight decay rescaling is always positive (for large l2 regularization and large learning rate values). By Olivier Grisel
• When compute_full_tree is set to "auto", the full tree is built when n_clusters is high and is early stopped when n_clusters is low, while the behavior should be vice-versa in :class:`cluster.AgglomerativeClustering` (and friends). This has been fixed By Manoj Kumar
• Fix lazy centering of data in :func:`linear_model.enet_path` and :func:`linear_model.lasso_path`. It was centered around one. It has been changed to be centered around the origin. By Manoj Kumar
• Fix handling of precomputed affinity matrices in :class:`cluster.AgglomerativeClustering` when using connectivity constraints. By :user:`Cathy Deng <cathydeng>`
• Correct partial_fit handling of class_prior for :class:`sklearn.naive_bayes.MultinomialNB` and :class:`sklearn.naive_bayes.BernoulliNB`. By Trevor Stephens.
• Fixed a crash in :func:`metrics.precision_recall_fscore_support` when using unsorted labels in the multi-label setting. By Andreas Müller.
• Avoid skipping the first nearest neighbor in the methods radius_neighbors, kneighbors, kneighbors_graph and radius_neighbors_graph in :class:`sklearn.neighbors.NearestNeighbors` and family, when the query data is not the same as fit data. By Manoj Kumar.
• Fix log-density calculation in the :class:`mixture.GMM` with tied covariance. By Will Dawson
• Fixed a scaling error in :class:`feature_selection.SelectFdr` where a factor n_features was missing. By Andrew Tulloch
• Fix zero division in :class:`neighbors.KNeighborsRegressor` and related classes when using distance weighting and having identical data points. By Garret-R.
• Fixed round off errors with non positive-definite covariance matrices in GMM. By :user:`Alexis Mignon <AlexisMignon>`.
• Fixed a error in the computation of conditional probabilities in :class:`naive_bayes.BernoulliNB`. By Hanna Wallach.
• Make the method radius_neighbors of :class:`neighbors.NearestNeighbors` return the samples lying on the boundary for algorithm='brute'. By Yan Yi.
• Flip sign of dual_coef_ of :class:`svm.SVC` to make it consistent with the documentation and decision_function. By Artem Sobolev.
• Fixed handling of ties in :class:`isotonic.IsotonicRegression`. We now use the weighted average of targets (secondary method). By Andreas Müller and Michael Bommarito.

API changes summary

• :class:`GridSearchCV <grid_search.GridSearchCV>` and :func:`cross_val_score <cross_validation.cross_val_score>` and other meta-estimators don't convert pandas DataFrames into arrays any more, allowing DataFrame specific operations in custom estimators.
• :func:`multiclass.fit_ovr`, :func:`multiclass.predict_ovr`, :func:`predict_proba_ovr`, :func:`multiclass.fit_ovo`, :func:`multiclass.predict_ovo`, :func:`multiclass.fit_ecoc` and :func:`multiclass.predict_ecoc` are deprecated. Use the underlying estimators instead.
• Nearest neighbors estimators used to take arbitrary keyword arguments and pass these to their distance metric. This will no longer be supported in scikit-learn 0.18; use the metric_params argument instead.

• n_jobs parameter of the fit method shifted to the constructor of the

  LinearRegression class.

• The predict_proba method of :class:`multiclass.OneVsRestClassifier` now returns two probabilities per sample in the multiclass case; this is consistent with other estimators and with the method's documentation, but previous versions accidentally returned only the positive probability. Fixed by Will Lamond and Lars Buitinck.
• Change default value of precompute in :class:`ElasticNet` and :class:`Lasso` to False. Setting precompute to "auto" was found to be slower when n_samples > n_features since the computation of the Gram matrix is computationally expensive and outweighs the benefit of fitting the Gram for just one alpha. precompute="auto" is now deprecated and will be removed in 0.18 By Manoj Kumar.
• Expose positive option in :func:`linear_model.enet_path` and :func:`linear_model.enet_path` which constrains coefficients to be positive. By Manoj Kumar.
• Users should now supply an explicit average parameter to :func:`sklearn.metrics.f1_score`, :func:`sklearn.metrics.fbeta_score`, :func:`sklearn.metrics.recall_score` and :func:`sklearn.metrics.precision_score` when performing multiclass or multilabel (i.e. not binary) classification. By Joel Nothman.
• scoring parameter for cross validation now accepts 'f1_micro', 'f1_macro' or 'f1_weighted'. 'f1' is now for binary classification only. Similar changes apply to 'precision' and 'recall'. By Joel Nothman.
• The fit_intercept, normalize and return_models parameters in :func:`linear_model.enet_path` and :func:`linear_model.lasso_path` have been removed. They were deprecated since 0.14
• From now onwards, all estimators will uniformly raise NotFittedError (:class:`utils.validation.NotFittedError`), when any of the predict like methods are called before the model is fit. By Raghav RV.
• Input data validation was refactored for more consistent input validation. The check_arrays function was replaced by check_array and check_X_y. By Andreas Müller.
• Allow X=None in the methods radius_neighbors, kneighbors, kneighbors_graph and radius_neighbors_graph in :class:`sklearn.neighbors.NearestNeighbors` and family. If set to None, then for every sample this avoids setting the sample itself as the first nearest neighbor. By Manoj Kumar.
• Add parameter include_self in :func:`neighbors.kneighbors_graph` and :func:`neighbors.radius_neighbors_graph` which has to be explicitly set by the user. If set to True, then the sample itself is considered as the first nearest neighbor.
• thresh parameter is deprecated in favor of new tol parameter in :class:`GMM`, :class:`DPGMM` and :class:`VBGMM`. See Enhancements section for details. By Hervé Bredin.
• Estimators will treat input with dtype object as numeric when possible. By Andreas Müller
• Estimators now raise ValueError consistently when fitted on empty data (less than 1 sample or less than 1 feature for 2D input). By Olivier Grisel.
• The shuffle option of :class:`.linear_model.SGDClassifier`, :class:`linear_model.SGDRegressor`, :class:`linear_model.Perceptron`, :class:`linear_model.PassiveAgressiveClassifier` and :class:`linear_model.PassiveAgressiveRegressor` now defaults to True.
• :class:`cluster.DBSCAN` now uses a deterministic initialization. The random_state parameter is deprecated. By :user:`Erich Schubert <kno10>`.

Code Contributors

A. Flaxman, Aaron Schumacher, Aaron Staple, abhishek thakur, Akshay, akshayah3, Aldrian Obaja, Alexander Fabisch, Alexandre Gramfort, Alexis Mignon, Anders Aagaard, Andreas Mueller, Andreas van Cranenburgh, Andrew Tulloch, Andrew Walker, Antony Lee, Arnaud Joly, banilo, Barmaley.exe, Ben Davies, Benedikt Koehler, bhsu, Boris Feld, Borja Ayerdi, Boyuan Deng, Brent Pedersen, Brian Wignall, Brooke Osborn, Calvin Giles, Cathy Deng, Celeo, cgohlke, chebee7i, Christian Stade-Schuldt, Christof Angermueller, Chyi-Kwei Yau, CJ Carey, Clemens Brunner, Daiki Aminaka, Dan Blanchard, danfrankj, Danny Sullivan, David Fletcher, Dmitrijs Milajevs, Dougal J. Sutherland, Erich Schubert, Fabian Pedregosa, Florian Wilhelm, floydsoft, Félix-Antoine Fortin, Gael Varoquaux, Garrett-R, Gilles Louppe, gpassino, gwulfs, Hampus Bengtsson, Hamzeh Alsalhi, Hanna Wallach, Harry Mavroforakis, Hasil Sharma, Helder, Herve Bredin, Hsiang-Fu Yu, Hugues SALAMIN, Ian Gilmore, Ilambharathi Kanniah, Imran Haque, isms, Jake VanderPlas, Jan Dlabal, Jan Hendrik Metzen, Jatin Shah, Javier López Peña, jdcaballero, Jean Kossaifi, Jeff Hammerbacher, Joel Nothman, Jonathan Helmus, Joseph, Kaicheng Zhang, Kevin Markham, Kyle Beauchamp, Kyle Kastner, Lagacherie Matthieu, Lars Buitinck, Laurent Direr, leepei, Loic Esteve, Luis Pedro Coelho, Lukas Michelbacher, maheshakya, Manoj Kumar, Manuel, Mario Michael Krell, Martin, Martin Billinger, Martin Ku, Mateusz Susik, Mathieu Blondel, Matt Pico, Matt Terry, Matteo Visconti dOC, Matti Lyra, Max Linke, Mehdi Cherti, Michael Bommarito, Michael Eickenberg, Michal Romaniuk, MLG, mr.Shu, Nelle Varoquaux, Nicola Montecchio, Nicolas, Nikolay Mayorov, Noel Dawe, Okal Billy, Olivier Grisel, Óscar Nájera, Paolo Puggioni, Peter Prettenhofer, Pratap Vardhan, pvnguyen, queqichao, Rafael Carrascosa, Raghav R V, Rahiel Kasim, Randall Mason, Rob Zinkov, Robert Bradshaw, Saket Choudhary, Sam Nicholls, Samuel Charron, Saurabh Jha, sethdandridge, sinhrks, snuderl, Stefan Otte, Stefan van der Walt, Steve Tjoa, swu, Sylvain Zimmer, tejesh95, terrycojones, Thomas Delteil, Thomas Unterthiner, Tomas Kazmar, trevorstephens, tttthomasssss, Tzu-Ming Kuo, ugurcaliskan, ugurthemaster, Vinayak Mehta, Vincent Dubourg, Vjacheslav Murashkin, Vlad Niculae, wadawson, Wei Xue, Will Lamond, Wu Jiang, x0l, Xinfan Meng, Yan Yi, Yu-Chin

Version 0.15.2

September 4, 2014

Bug fixes

• Fixed handling of the p parameter of the Minkowski distance that was previously ignored in nearest neighbors models. By :user:`Nikolay Mayorov <nmayorov>`.
• Fixed duplicated alphas in :class:`linear_model.LassoLars` with early stopping on 32 bit Python. By Olivier Grisel and Fabian Pedregosa.
• Fixed the build under Windows when scikit-learn is built with MSVC while NumPy is built with MinGW. By Olivier Grisel and :user:`Federico Vaggi <FedericoV>`.
• Fixed an array index overflow bug in the coordinate descent solver. By Gael Varoquaux.
• Better handling of numpy 1.9 deprecation warnings. By Gael Varoquaux.
• Removed unnecessary data copy in :class:`cluster.KMeans`. By Gael Varoquaux.
• Explicitly close open files to avoid ResourceWarnings under Python 3. By Calvin Giles.
• The transform of :class:`discriminant_analysis.LinearDiscriminantAnalysis` now projects the input on the most discriminant directions. By Martin Billinger.
• Fixed potential overflow in _tree.safe_realloc by Lars Buitinck.
• Performance optimization in :class:`isotonic.IsotonicRegression`. By Robert Bradshaw.
• nose is non-longer a runtime dependency to import sklearn, only for running the tests. By Joel Nothman.
• Many documentation and website fixes by Joel Nothman, Lars Buitinck :user:`Matt Pico <MattpSoftware>`, and others.

Version 0.15.1

August 1, 2014

Bug fixes

• Made :func:`cross_validation.cross_val_score` use :class:`cross_validation.KFold` instead of :class:`cross_validation.StratifiedKFold` on multi-output classification problems. By :user:`Nikolay Mayorov <nmayorov>`.
• Support unseen labels :class:`preprocessing.LabelBinarizer` to restore the default behavior of 0.14.1 for backward compatibility. By :user:`Hamzeh Alsalhi <hamsal>`.
• Fixed the :class:`cluster.KMeans` stopping criterion that prevented early convergence detection. By Edward Raff and Gael Varoquaux.
• Fixed the behavior of :class:`multiclass.OneVsOneClassifier`. in case of ties at the per-class vote level by computing the correct per-class sum of prediction scores. By Andreas Müller.
• Made :func:`cross_validation.cross_val_score` and :class:`grid_search.GridSearchCV` accept Python lists as input data. This is especially useful for cross-validation and model selection of text processing pipelines. By Andreas Müller.
• Fixed data input checks of most estimators to accept input data that implements the NumPy __array__ protocol. This is the case for for pandas.Series and pandas.DataFrame in recent versions of pandas. By Gael Varoquaux.
• Fixed a regression for :class:`linear_model.SGDClassifier` with class_weight="auto" on data with non-contiguous labels. By Olivier Grisel.

Version 0.15

July 15, 2014

Highlights

• Many speed and memory improvements all across the code
• Huge speed and memory improvements to random forests (and extra trees) that also benefit better from parallel computing.
• Incremental fit to :class:`BernoulliRBM <neural_network.BernoulliRBM>`
• Added :class:`cluster.AgglomerativeClustering` for hierarchical agglomerative clustering with average linkage, complete linkage and ward strategies.
• Added :class:`linear_model.RANSACRegressor` for robust regression models.
• Added dimensionality reduction with :class:`manifold.TSNE` which can be used to visualize high-dimensional data.

Changelog

New features

• Added :class:`ensemble.BaggingClassifier` and :class:`ensemble.BaggingRegressor` meta-estimators for ensembling any kind of base estimator. See the :ref:`Bagging <bagging>` section of the user guide for details and examples. By Gilles Louppe.
• New unsupervised feature selection algorithm :class:`feature_selection.VarianceThreshold`, by Lars Buitinck.
• Added :class:`linear_model.RANSACRegressor` meta-estimator for the robust fitting of regression models. By :user:`Johannes Schönberger <ahojnnes>`.
• Added :class:`cluster.AgglomerativeClustering` for hierarchical agglomerative clustering with average linkage, complete linkage and ward strategies, by Nelle Varoquaux and Gael Varoquaux.
• Shorthand constructors :func:`pipeline.make_pipeline` and :func:`pipeline.make_union` were added by Lars Buitinck.
• Shuffle option for :class:`cross_validation.StratifiedKFold`. By :user:`Jeffrey Blackburne <jblackburne>`.
• Incremental learning (partial_fit) for Gaussian Naive Bayes by Imran Haque.
• Added partial_fit to :class:`BernoulliRBM <neural_network.BernoulliRBM>` By :user:`Danny Sullivan <dsullivan7>`.
• Added :func:`learning_curve <learning_curve.learning_curve>` utility to chart performance with respect to training size. See :ref:`sphx_glr_auto_examples_model_selection_plot_learning_curve.py`. By Alexander Fabisch.
• Add positive option in :class:`LassoCV <linear_model.LassoCV>` and :class:`ElasticNetCV <linear_model.ElasticNetCV>`. By Brian Wignall and Alexandre Gramfort.
• Added :class:`linear_model.MultiTaskElasticNetCV` and :class:`linear_model.MultiTaskLassoCV`. By Manoj Kumar.
• Added :class:`manifold.TSNE`. By Alexander Fabisch.

Enhancements

• Add sparse input support to :class:`ensemble.AdaBoostClassifier` and :class:`ensemble.AdaBoostRegressor` meta-estimators. By :user:`Hamzeh Alsalhi <hamsal>`.
• Memory improvements of decision trees, by Arnaud Joly.
• Decision trees can now be built in best-first manner by using max_leaf_nodes as the stopping criteria. Refactored the tree code to use either a stack or a priority queue for tree building. By Peter Prettenhofer and Gilles Louppe.
• Decision trees can now be fitted on fortran- and c-style arrays, and non-continuous arrays without the need to make a copy. If the input array has a different dtype than np.float32, a fortran- style copy will be made since fortran-style memory layout has speed advantages. By Peter Prettenhofer and Gilles Louppe.
• Speed improvement of regression trees by optimizing the the computation of the mean square error criterion. This lead to speed improvement of the tree, forest and gradient boosting tree modules. By Arnaud Joly
• The img_to_graph and grid_tograph functions in :mod:`sklearn.feature_extraction.image` now return np.ndarray instead of np.matrix when return_as=np.ndarray. See the Notes section for more information on compatibility.
• Changed the internal storage of decision trees to use a struct array. This fixed some small bugs, while improving code and providing a small speed gain. By Joel Nothman.
• Reduce memory usage and overhead when fitting and predicting with forests of randomized trees in parallel with n_jobs != 1 by leveraging new threading backend of joblib 0.8 and releasing the GIL in the tree fitting Cython code. By Olivier Grisel and Gilles Louppe.
• Speed improvement of the :mod:`sklearn.ensemble.gradient_boosting` module. By Gilles Louppe and Peter Prettenhofer.
• Various enhancements to the :mod:`sklearn.ensemble.gradient_boosting` module: a warm_start argument to fit additional trees, a max_leaf_nodes argument to fit GBM style trees, a monitor fit argument to inspect the estimator during training, and refactoring of the verbose code. By Peter Prettenhofer.
• Faster :class:`sklearn.ensemble.ExtraTrees` by caching feature values. By Arnaud Joly.
• Faster depth-based tree building algorithm such as decision tree, random forest, extra trees or gradient tree boosting (with depth based growing strategy) by avoiding trying to split on found constant features in the sample subset. By Arnaud Joly.
• Add min_weight_fraction_leaf pre-pruning parameter to tree-based methods: the minimum weighted fraction of the input samples required to be at a leaf node. By Noel Dawe.
• Added :func:`metrics.pairwise_distances_argmin_min`, by Philippe Gervais.
• Added predict method to :class:`cluster.AffinityPropagation` and :class:`cluster.MeanShift`, by Mathieu Blondel.
• Vector and matrix multiplications have been optimised throughout the library by Denis Engemann, and Alexandre Gramfort. In particular, they should take less memory with older NumPy versions (prior to 1.7.2).
• Precision-recall and ROC examples now use train_test_split, and have more explanation of why these metrics are useful. By Kyle Kastner
• The training algorithm for :class:`decomposition.NMF` is faster for sparse matrices and has much lower memory complexity, meaning it will scale up gracefully to large datasets. By Lars Buitinck.
• Added svd_method option with default value to "randomized" to :class:`decomposition.FactorAnalysis` to save memory and significantly speedup computation by Denis Engemann, and Alexandre Gramfort.
• Changed :class:`cross_validation.StratifiedKFold` to try and preserve as much of the original ordering of samples as possible so as not to hide overfitting on datasets with a non-negligible level of samples dependency. By Daniel Nouri and Olivier Grisel.
• Add multi-output support to :class:`gaussian_process.GaussianProcess` by John Novak.
• Support for precomputed distance matrices in nearest neighbor estimators by Robert Layton and Joel Nothman.
• Norm computations optimized for NumPy 1.6 and later versions by Lars Buitinck. In particular, the k-means algorithm no longer needs a temporary data structure the size of its input.
• :class:`dummy.DummyClassifier` can now be used to predict a constant output value. By Manoj Kumar.
• :class:`dummy.DummyRegressor` has now a strategy parameter which allows to predict the mean, the median of the training set or a constant output value. By :user:`Maheshakya Wijewardena <maheshakya>`.
• Multi-label classification output in multilabel indicator format is now supported by :func:`metrics.roc_auc_score` and :func:`metrics.average_precision_score` by Arnaud Joly.
• Significant performance improvements (more than 100x speedup for large problems) in :class:`isotonic.IsotonicRegression` by Andrew Tulloch.
• Speed and memory usage improvements to the SGD algorithm for linear models: it now uses threads, not separate processes, when n_jobs>1. By Lars Buitinck.
• Grid search and cross validation allow NaNs in the input arrays so that preprocessors such as :class:`preprocessing.Imputer <preprocessing.Imputer>` can be trained within the cross validation loop, avoiding potentially skewed results.
• Ridge regression can now deal with sample weights in feature space (only sample space until then). By :user:`Michael Eickenberg <eickenberg>`. Both solutions are provided by the Cholesky solver.
• Several classification and regression metrics now support weighted samples with the new sample_weight argument: :func:`metrics.accuracy_score`, :func:`metrics.zero_one_loss`, :func:`metrics.precision_score`, :func:`metrics.average_precision_score`, :func:`metrics.f1_score`, :func:`metrics.fbeta_score`, :func:`metrics.recall_score`, :func:`metrics.roc_auc_score`, :func:`metrics.explained_variance_score`, :func:`metrics.mean_squared_error`, :func:`metrics.mean_absolute_error`, :func:`metrics.r2_score`. By Noel Dawe.
• Speed up of the sample generator :func:`datasets.make_multilabel_classification`. By Joel Nothman.

Documentation improvements

• The :ref:`Working With Text Data <text_data_tutorial>` tutorial has now been worked in to the main documentation's tutorial section. Includes exercises and skeletons for tutorial presentation. Original tutorial created by several authors including Olivier Grisel, Lars Buitinck and many others. Tutorial integration into the scikit-learn documentation by Jaques Grobler
• Added :ref:`Computational Performance <computational_performance>` documentation. Discussion and examples of prediction latency / throughput and different factors that have influence over speed. Additional tips for building faster models and choosing a relevant compromise between speed and predictive power. By :user:`Eustache Diemert <oddskool>`.

Bug fixes

• Fixed bug in :class:`decomposition.MiniBatchDictionaryLearning` : partial_fit was not working properly.
• Fixed bug in :class:`linear_model.stochastic_gradient` : l1_ratio was used as (1.0 - l1_ratio) .
• Fixed bug in :class:`multiclass.OneVsOneClassifier` with string labels
• Fixed a bug in :class:`LassoCV <linear_model.LassoCV>` and :class:`ElasticNetCV <linear_model.ElasticNetCV>`: they would not pre-compute the Gram matrix with precompute=True or precompute="auto" and n_samples > n_features. By Manoj Kumar.
• Fixed incorrect estimation of the degrees of freedom in :func:`feature_selection.f_regression` when variates are not centered. By :user:`Virgile Fritsch <VirgileFritsch>`.
• Fixed a race condition in parallel processing with pre_dispatch != "all" (for instance, in cross_val_score). By Olivier Grisel.
• Raise error in :class:`cluster.FeatureAgglomeration` and :class:`cluster.WardAgglomeration` when no samples are given, rather than returning meaningless clustering.
• Fixed bug in :class:`gradient_boosting.GradientBoostingRegressor` with loss='huber': gamma might have not been initialized.
• Fixed feature importances as computed with a forest of randomized trees when fit with sample_weight != None and/or with bootstrap=True. By Gilles Louppe.

API changes summary

• :mod:`sklearn.hmm` is deprecated. Its removal is planned for the 0.17 release.
• Use of :class:`covariance.EllipticEnvelop` has now been removed after deprecation. Please use :class:`covariance.EllipticEnvelope` instead.
• :class:`cluster.Ward` is deprecated. Use :class:`cluster.AgglomerativeClustering` instead.
• :class:`cluster.WardClustering` is deprecated. Use
• :class:`cluster.AgglomerativeClustering` instead.
• :class:`cross_validation.Bootstrap` is deprecated. :class:`cross_validation.KFold` or :class:`cross_validation.ShuffleSplit` are recommended instead.
• Direct support for the sequence of sequences (or list of lists) multilabel format is deprecated. To convert to and from the supported binary indicator matrix format, use :class:`MultiLabelBinarizer <preprocessing.MultiLabelBinarizer>`. By Joel Nothman.
• Add score method to :class:`PCA <decomposition.PCA>` following the model of probabilistic PCA and deprecate :class:`ProbabilisticPCA <decomposition.ProbabilisticPCA>` model whose score implementation is not correct. The computation now also exploits the matrix inversion lemma for faster computation. By Alexandre Gramfort.
• The score method of :class:`FactorAnalysis <decomposition.FactorAnalysis>` now returns the average log-likelihood of the samples. Use score_samples to get log-likelihood of each sample. By Alexandre Gramfort.
• Generating boolean masks (the setting indices=False) from cross-validation generators is deprecated. Support for masks will be removed in 0.17. The generators have produced arrays of indices by default since 0.10. By Joel Nothman.
• 1-d arrays containing strings with dtype=object (as used in Pandas) are now considered valid classification targets. This fixes a regression from version 0.13 in some classifiers. By Joel Nothman.
• Fix wrong explained_variance_ratio_ attribute in :class:`RandomizedPCA <decomposition.RandomizedPCA>`. By Alexandre Gramfort.
• Fit alphas for each l1_ratio instead of mean_l1_ratio in :class:`linear_model.ElasticNetCV` and :class:`linear_model.LassoCV`. This changes the shape of alphas_ from (n_alphas,) to (n_l1_ratio, n_alphas) if the l1_ratio provided is a 1-D array like object of length greater than one. By Manoj Kumar.
• Fix :class:`linear_model.ElasticNetCV` and :class:`linear_model.LassoCV` when fitting intercept and input data is sparse. The automatic grid of alphas was not computed correctly and the scaling with normalize was wrong. By Manoj Kumar.
• Fix wrong maximal number of features drawn (max_features) at each split for decision trees, random forests and gradient tree boosting. Previously, the count for the number of drawn features started only after one non constant features in the split. This bug fix will affect computational and generalization performance of those algorithms in the presence of constant features. To get back previous generalization performance, you should modify the value of max_features. By Arnaud Joly.
• Fix wrong maximal number of features drawn (max_features) at each split for :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor`. Previously, only non constant features in the split was counted as drawn. Now constant features are counted as drawn. Furthermore at least one feature must be non constant in order to make a valid split. This bug fix will affect computational and generalization performance of extra trees in the presence of constant features. To get back previous generalization performance, you should modify the value of max_features. By Arnaud Joly.
• Fix :func:`utils.compute_class_weight` when class_weight=="auto". Previously it was broken for input of non-integer dtype and the weighted array that was returned was wrong. By Manoj Kumar.
• Fix :class:`cross_validation.Bootstrap` to return ValueError when n_train + n_test > n. By :user:`Ronald Phlypo <rphlypo>`.

People

List of contributors for release 0.15 by number of commits.

• 312 Olivier Grisel
• 275 Lars Buitinck
• 221 Gael Varoquaux
• 148 Arnaud Joly
• 134 Johannes Schönberger
• 119 Gilles Louppe
• 113 Joel Nothman
• 111 Alexandre Gramfort
• 95 Jaques Grobler
• 89 Denis Engemann
• 83 Peter Prettenhofer
• 83 Alexander Fabisch
• 62 Mathieu Blondel
• 60 Eustache Diemert
• 60 Nelle Varoquaux
• 49 Michael Bommarito
• 45 Manoj-Kumar-S
• 28 Kyle Kastner
• 26 Andreas Mueller
• 22 Noel Dawe
• 21 Maheshakya Wijewardena
• 21 Brooke Osborn
• 21 Hamzeh Alsalhi
• 21 Jake VanderPlas
• 21 Philippe Gervais
• 19 Bala Subrahmanyam Varanasi
• 12 Ronald Phlypo
• 10 Mikhail Korobov
• 8 Thomas Unterthiner
• 8 Jeffrey Blackburne
• 8 eltermann
• 8 bwignall
• 7 Ankit Agrawal
• 7 CJ Carey
• 6 Daniel Nouri
• 6 Chen Liu
• 6 Michael Eickenberg
• 6 ugurthemaster
• 5 Aaron Schumacher
• 5 Baptiste Lagarde
• 5 Rajat Khanduja
• 5 Robert McGibbon
• 5 Sergio Pascual
• 4 Alexis Metaireau
• 4 Ignacio Rossi
• 4 Virgile Fritsch
• 4 Sebastian Säger
• 4 Ilambharathi Kanniah
• 4 sdenton4
• 4 Robert Layton
• 4 Alyssa
• 4 Amos Waterland
• 3 Andrew Tulloch
• 3 murad
• 3 Steven Maude
• 3 Karol Pysniak
• 3 Jacques Kvam
• 3 cgohlke
• 3 cjlin
• 3 Michael Becker
• 3 hamzeh
• 3 Eric Jacobsen
• 3 john collins
• 3 kaushik94
• 3 Erwin Marsi
• 2 csytracy
• 2 LK
• 2 Vlad Niculae
• 2 Laurent Direr
• 2 Erik Shilts
• 2 Raul Garreta
• 2 Yoshiki Vázquez Baeza
• 2 Yung Siang Liau
• 2 abhishek thakur
• 2 James Yu
• 2 Rohit Sivaprasad
• 2 Roland Szabo
• 2 amormachine
• 2 Alexis Mignon
• 2 Oscar Carlsson
• 2 Nantas Nardelli
• 2 jess010
• 2 kowalski87
• 2 Andrew Clegg
• 2 Federico Vaggi
• 2 Simon Frid
• 2 Félix-Antoine Fortin
• 1 Ralf Gommers
• 1 t-aft
• 1 Ronan Amicel
• 1 Rupesh Kumar Srivastava
• 1 Ryan Wang
• 1 Samuel Charron
• 1 Samuel St-Jean
• 1 Fabian Pedregosa
• 1 Skipper Seabold
• 1 Stefan Walk
• 1 Stefan van der Walt
• 1 Stephan Hoyer
• 1 Allen Riddell
• 1 Valentin Haenel
• 1 Vijay Ramesh
• 1 Will Myers
• 1 Yaroslav Halchenko
• 1 Yoni Ben-Meshulam
• 1 Yury V. Zaytsev
• 1 adrinjalali
• 1 ai8rahim
• 1 alemagnani
• 1 alex
• 1 benjamin wilson
• 1 chalmerlowe
• 1 dzikie drożdże
• 1 jamestwebber
• 1 matrixorz
• 1 popo
• 1 samuela
• 1 François Boulogne
• 1 Alexander Measure
• 1 Ethan White
• 1 Guilherme Trein
• 1 Hendrik Heuer
• 1 IvicaJovic
• 1 Jan Hendrik Metzen
• 1 Jean Michel Rouly
• 1 Eduardo Ariño de la Rubia
• 1 Jelle Zijlstra
• 1 Eddy L O Jansson
• 1 Denis
• 1 John
• 1 John Schmidt
• 1 Jorge Cañardo Alastuey
• 1 Joseph Perla
• 1 Joshua Vredevoogd
• 1 José Ricardo
• 1 Julien Miotte
• 1 Kemal Eren
• 1 Kenta Sato
• 1 David Cournapeau
• 1 Kyle Kelley
• 1 Daniele Medri
• 1 Laurent Luce
• 1 Laurent Pierron
• 1 Luis Pedro Coelho
• 1 DanielWeitzenfeld
• 1 Craig Thompson
• 1 Chyi-Kwei Yau
• 1 Matthew Brett
• 1 Matthias Feurer
• 1 Max Linke
• 1 Chris Filo Gorgolewski
• 1 Charles Earl
• 1 Michael Hanke
• 1 Michele Orrù
• 1 Bryan Lunt
• 1 Brian Kearns
• 1 Paul Butler
• 1 Paweł Mandera
• 1 Peter
• 1 Andrew Ash
• 1 Pietro Zambelli
• 1 staubda

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

Version 0.13.1

February 23, 2013

The 0.13.1 release only fixes some bugs and does not add any new functionality.

Changelog

• Fixed a testing error caused by the function :func:`cross_validation.train_test_split` being interpreted as a test by Yaroslav Halchenko.
• Fixed a bug in the reassignment of small clusters in the :class:`cluster.MiniBatchKMeans` by Gael Varoquaux.
• Fixed default value of gamma in :class:`decomposition.KernelPCA` by Lars Buitinck.
• Updated joblib to 0.7.0d by Gael Varoquaux.
• Fixed scaling of the deviance in :class:`ensemble.GradientBoostingClassifier` by Peter Prettenhofer.
• Better tie-breaking in :class:`multiclass.OneVsOneClassifier` by Andreas Müller.
• Other small improvements to tests and documentation.

People

List of contributors for release 0.13.1 by number of commits.

• 16 Lars Buitinck
• 12 Andreas Müller
• 8 Gael Varoquaux
• 5 Robert Marchman
• 3 Peter Prettenhofer
• 2 Hrishikesh Huilgolkar
• 1 Bastiaan van den Berg
• 1 Diego Molla
• 1 Gilles Louppe
• 1 Mathieu Blondel
• 1 Nelle Varoquaux
• 1 Rafael Cunha de Almeida
• 1 Rolando Espinoza La fuente
• 1 Vlad Niculae
• 1 Yaroslav Halchenko

Version 0.13

January 21, 2013

New Estimator Classes

• :class:`dummy.DummyClassifier` and :class:`dummy.DummyRegressor`, two data-independent predictors by Mathieu Blondel. Useful to sanity-check your estimators. See :ref:`dummy_estimators` in the user guide. Multioutput support added by Arnaud Joly.
• :class:`decomposition.FactorAnalysis`, a transformer implementing the classical factor analysis, by Christian Osendorfer and Alexandre Gramfort. See :ref:`FA` in the user guide.
• :class:`feature_extraction.FeatureHasher`, a transformer implementing the "hashing trick" for fast, low-memory feature extraction from string fields by Lars Buitinck and :class:`feature_extraction.text.HashingVectorizer` for text documents by Olivier Grisel See :ref:`feature_hashing` and :ref:`hashing_vectorizer` for the documentation and sample usage.
• :class:`pipeline.FeatureUnion`, a transformer that concatenates results of several other transformers by Andreas Müller. See :ref:`feature_union` in the user guide.
• :class:`random_projection.GaussianRandomProjection`, :class:`random_projection.SparseRandomProjection` and the function :func:`random_projection.johnson_lindenstrauss_min_dim`. The first two are transformers implementing Gaussian and sparse random projection matrix by Olivier Grisel and Arnaud Joly. See :ref:`random_projection` in the user guide.
• :class:`kernel_approximation.Nystroem`, a transformer for approximating arbitrary kernels by Andreas Müller. See :ref:`nystroem_kernel_approx` in the user guide.
• :class:`preprocessing.OneHotEncoder`, a transformer that computes binary encodings of categorical features by Andreas Müller. See :ref:`preprocessing_categorical_features` in the user guide.
• :class:`linear_model.PassiveAggressiveClassifier` and :class:`linear_model.PassiveAggressiveRegressor`, predictors implementing an efficient stochastic optimization for linear models by Rob Zinkov and Mathieu Blondel. See :ref:`passive_aggressive` in the user guide.
• :class:`ensemble.RandomTreesEmbedding`, a transformer for creating high-dimensional sparse representations using ensembles of totally random trees by Andreas Müller. See :ref:`random_trees_embedding` in the user guide.
• :class:`manifold.SpectralEmbedding` and function :func:`manifold.spectral_embedding`, implementing the "laplacian eigenmaps" transformation for non-linear dimensionality reduction by Wei Li. See :ref:`spectral_embedding` in the user guide.
• :class:`isotonic.IsotonicRegression` by Fabian Pedregosa, Alexandre Gramfort and Nelle Varoquaux,

Changelog

• :func:`metrics.zero_one_loss` (formerly metrics.zero_one) now has option for normalized output that reports the fraction of misclassifications, rather than the raw number of misclassifications. By Kyle Beauchamp.
• :class:`tree.DecisionTreeClassifier` and all derived ensemble models now support sample weighting, by Noel Dawe and Gilles Louppe.
• Speedup improvement when using bootstrap samples in forests of randomized trees, by Peter Prettenhofer and Gilles Louppe.
• Partial dependence plots for :ref:`gradient_boosting` in :func:`ensemble.partial_dependence.partial_dependence` by Peter Prettenhofer. See :ref:`sphx_glr_auto_examples_ensemble_plot_partial_dependence.py` for an example.
• The table of contents on the website has now been made expandable by Jaques Grobler.
• :class:`feature_selection.SelectPercentile` now breaks ties deterministically instead of returning all equally ranked features.
• :class:`feature_selection.SelectKBest` and :class:`feature_selection.SelectPercentile` are more numerically stable since they use scores, rather than p-values, to rank results. This means that they might sometimes select different features than they did previously.
• Ridge regression and ridge classification fitting with sparse_cg solver no longer has quadratic memory complexity, by Lars Buitinck and Fabian Pedregosa.
• Ridge regression and ridge classification now support a new fast solver called lsqr, by Mathieu Blondel.
• Speed up of :func:`metrics.precision_recall_curve` by Conrad Lee.
• Added support for reading/writing svmlight files with pairwise preference attribute (qid in svmlight file format) in :func:`datasets.dump_svmlight_file` and :func:`datasets.load_svmlight_file` by Fabian Pedregosa.
• Faster and more robust :func:`metrics.confusion_matrix` and :ref:`clustering_evaluation` by Wei Li.
• :func:`cross_validation.cross_val_score` now works with precomputed kernels and affinity matrices, by Andreas Müller.
• LARS algorithm made more numerically stable with heuristics to drop regressors too correlated as well as to stop the path when numerical noise becomes predominant, by Gael Varoquaux.
• Faster implementation of :func:`metrics.precision_recall_curve` by Conrad Lee.
• New kernel :class:`metrics.chi2_kernel` by Andreas Müller, often used in computer vision applications.
• Fix of longstanding bug in :class:`naive_bayes.BernoulliNB` fixed by Shaun Jackman.
• Implemented predict_proba in :class:`multiclass.OneVsRestClassifier`, by Andrew Winterman.
• Improve consistency in gradient boosting: estimators :class:`ensemble.GradientBoostingRegressor` and :class:`ensemble.GradientBoostingClassifier` use the estimator :class:`tree.DecisionTreeRegressor` instead of the :class:`tree._tree.Tree` data structure by Arnaud Joly.
• Fixed a floating point exception in the :ref:`decision trees <tree>` module, by Seberg.
• Fix :func:`metrics.roc_curve` fails when y_true has only one class by Wei Li.
• Add the :func:`metrics.mean_absolute_error` function which computes the mean absolute error. The :func:`metrics.mean_squared_error`, :func:`metrics.mean_absolute_error` and :func:`metrics.r2_score` metrics support multioutput by Arnaud Joly.
• Fixed class_weight support in :class:`svm.LinearSVC` and :class:`linear_model.LogisticRegression` by Andreas Müller. The meaning of class_weight was reversed as erroneously higher weight meant less positives of a given class in earlier releases.
• Improve narrative documentation and consistency in :mod:`sklearn.metrics` for regression and classification metrics by Arnaud Joly.
• Fixed a bug in :class:`sklearn.svm.SVC` when using csr-matrices with unsorted indices by Xinfan Meng and Andreas Müller.
• :class:`MiniBatchKMeans`: Add random reassignment of cluster centers with little observations attached to them, by Gael Varoquaux.

API changes summary

• Renamed all occurrences of n_atoms to n_components for consistency. This applies to :class:`decomposition.DictionaryLearning`, :class:`decomposition.MiniBatchDictionaryLearning`, :func:`decomposition.dict_learning`, :func:`decomposition.dict_learning_online`.
• Renamed all occurrences of max_iters to max_iter for consistency. This applies to :class:`semi_supervised.LabelPropagation` and :class:`semi_supervised.label_propagation.LabelSpreading`.
• Renamed all occurrences of learn_rate to learning_rate for consistency in :class:`ensemble.BaseGradientBoosting` and :class:`ensemble.GradientBoostingRegressor`.
• The module sklearn.linear_model.sparse is gone. Sparse matrix support was already integrated into the "regular" linear models.
• :func:`sklearn.metrics.mean_square_error`, which incorrectly returned the accumulated error, was removed. Use mean_squared_error instead.
• Passing class_weight parameters to fit methods is no longer supported. Pass them to estimator constructors instead.
• GMMs no longer have decode and rvs methods. Use the score, predict or sample methods instead.
• The solver fit option in Ridge regression and classification is now deprecated and will be removed in v0.14. Use the constructor option instead.
• :class:`feature_extraction.text.DictVectorizer` now returns sparse matrices in the CSR format, instead of COO.
• Renamed k in :class:`cross_validation.KFold` and :class:`cross_validation.StratifiedKFold` to n_folds, renamed n_bootstraps to n_iter in cross_validation.Bootstrap.
• Renamed all occurrences of n_iterations to n_iter for consistency. This applies to :class:`cross_validation.ShuffleSplit`, :class:`cross_validation.StratifiedShuffleSplit`, :func:`utils.randomized_range_finder` and :func:`utils.randomized_svd`.
• Replaced rho in :class:`linear_model.ElasticNet` and :class:`linear_model.SGDClassifier` by l1_ratio. The rho parameter had different meanings; l1_ratio was introduced to avoid confusion. It has the same meaning as previously rho in :class:`linear_model.ElasticNet` and (1-rho) in :class:`linear_model.SGDClassifier`.
• :class:`linear_model.LassoLars` and :class:`linear_model.Lars` now store a list of paths in the case of multiple targets, rather than an array of paths.
• The attribute gmm of :class:`hmm.GMMHMM` was renamed to gmm_ to adhere more strictly with the API.
• :func:`cluster.spectral_embedding` was moved to :func:`manifold.spectral_embedding`.
• Renamed eig_tol in :func:`manifold.spectral_embedding`, :class:`cluster.SpectralClustering` to eigen_tol, renamed mode to eigen_solver.
• Renamed mode in :func:`manifold.spectral_embedding` and :class:`cluster.SpectralClustering` to eigen_solver.
• classes_ and n_classes_ attributes of :class:`tree.DecisionTreeClassifier` and all derived ensemble models are now flat in case of single output problems and nested in case of multi-output problems.
• The estimators_ attribute of :class:`ensemble.gradient_boosting.GradientBoostingRegressor` and :class:`ensemble.gradient_boosting.GradientBoostingClassifier` is now an array of :class:'tree.DecisionTreeRegressor'.
• Renamed chunk_size to batch_size in :class:`decomposition.MiniBatchDictionaryLearning` and :class:`decomposition.MiniBatchSparsePCA` for consistency.
• :class:`svm.SVC` and :class:`svm.NuSVC` now provide a classes_ attribute and support arbitrary dtypes for labels y. Also, the dtype returned by predict now reflects the dtype of y during fit (used to be np.float).
• Changed default test_size in :func:`cross_validation.train_test_split` to None, added possibility to infer test_size from train_size in :class:`cross_validation.ShuffleSplit` and :class:`cross_validation.StratifiedShuffleSplit`.
• Renamed function :func:`sklearn.metrics.zero_one` to :func:`sklearn.metrics.zero_one_loss`. Be aware that the default behavior in :func:`sklearn.metrics.zero_one_loss` is different from :func:`sklearn.metrics.zero_one`: normalize=False is changed to normalize=True.
• Renamed function :func:`metrics.zero_one_score` to :func:`metrics.accuracy_score`.
• :func:`datasets.make_circles` now has the same number of inner and outer points.
• In the Naive Bayes classifiers, the class_prior parameter was moved from fit to __init__.

People

List of contributors for release 0.13 by number of commits.

• 364 Andreas Müller
• 143 Arnaud Joly
• 137 Peter Prettenhofer
• 131 Gael Varoquaux
• 117 Mathieu Blondel
• 108 Lars Buitinck
• 106 Wei Li
• 101 Olivier Grisel
• 65 Vlad Niculae
• 54 Gilles Louppe
• 40 Jaques Grobler
• 38 Alexandre Gramfort
• 30 Rob Zinkov
• 19 Aymeric Masurelle
• 18 Andrew Winterman
• 17 Fabian Pedregosa
• 17 Nelle Varoquaux
• 16 Christian Osendorfer
• 14 Daniel Nouri
• 13 :user:`Virgile Fritsch <VirgileFritsch>`
• 13 syhw
• 12 Satrajit Ghosh
• 10 Corey Lynch
• 10 Kyle Beauchamp
• 9 Brian Cheung
• 9 Immanuel Bayer
• 9 mr.Shu
• 8 Conrad Lee
• 8 James Bergstra
• 7 Tadej Janež
• 6 Brian Cajes
• 6 Jake Vanderplas
• 6 Michael
• 6 Noel Dawe
• 6 Tiago Nunes
• 6 cow
• 5 Anze
• 5 Shiqiao Du
• 4 Christian Jauvin
• 4 Jacques Kvam
• 4 Richard T. Guy
• 4 Robert Layton
• 3 Alexandre Abraham
• 3 Doug Coleman
• 3 Scott Dickerson
• 2 ApproximateIdentity
• 2 John Benediktsson
• 2 Mark Veronda
• 2 Matti Lyra
• 2 Mikhail Korobov
• 2 Xinfan Meng
• 1 Alejandro Weinstein
• 1 Alexandre Passos
• 1 Christoph Deil
• 1 Eugene Nizhibitsky
• 1 Kenneth C. Arnold
• 1 Luis Pedro Coelho
• 1 Miroslav Batchkarov
• 1 Pavel
• 1 Sebastian Berg
• 1 Shaun Jackman
• 1 Subhodeep Moitra
• 1 bob
• 1 dengemann
• 1 emanuele
• 1 x006

Version 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

Changelog

• 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>`

People

• 14 Peter Prettenhofer
• 12 Gael Varoquaux
• 10 Andreas Müller
• 5 Lars Buitinck
• 3 :user:`Virgile Fritsch <VirgileFritsch>`
• 1 Alexandre Gramfort
• 1 Gilles Louppe
• 1 Mathieu Blondel

Version 0.12

September 4, 2012

Changelog

• 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 :func:`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 :class:`decomposition.ProbabilisticPCA` score function by Wei Li.
• Fixed feature importance computation in :ref:`gradient_boosting`.

API changes summary

• 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 it's order reversed, in order to keep it consistent with the order of the returned fpr and tpr.
• In :class:`hmm` objects, like :class:`hmm.GaussianHMM`, :class:`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`.
• :class:`svm.sparse.SVC` and other sparse SVM classes are now deprecated. The all classes in the :ref:`svm` module now automatically select the sparse or dense representation base 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.

People

• 267 Andreas Müller
• 94 Gilles Louppe
• 89 Gael Varoquaux
• 79 Peter Prettenhofer
• 60 Mathieu Blondel
• 57 Alexandre Gramfort
• 52 Vlad Niculae
• 45 Lars Buitinck
• 44 Nelle Varoquaux
• 37 Jaques Grobler
• 30 Alexis Mignon
• 30 Immanuel Bayer
• 27 Olivier Grisel
• 16 Subhodeep Moitra
• 13 Yannick Schwartz
• 12 :user:`@kernc <kernc>`
• 11 :user:`Virgile Fritsch <VirgileFritsch>`
• 9 Daniel Duckworth
• 9 Fabian Pedregosa
• 9 Robert Layton
• 8 John Benediktsson
• 7 Marko Burjek
• 5 Nicolas Pinto
• 4 Alexandre Abraham
• 4 Jake Vanderplas
• 3 Brian Holt
• 3 Edouard Duchesnay
• 3 Florian Hoenig
• 3 flyingimmidev
• 2 Francois Savard
• 2 Hannes Schulz
• 2 Peter Welinder
• 2 Yaroslav Halchenko
• 2 Wei Li
• 1 Alex Companioni
• 1 Brandyn A. White
• 1 Bussonnier Matthias
• 1 Charles-Pierre Astolfi
• 1 Dan O'Huiginn
• 1 David Cournapeau
• 1 Keith Goodman
• 1 Ludwig Schwardt
• 1 Olivier Hervieu
• 1 Sergio Medina
• 1 Shiqiao Du
• 1 Tim Sheerman-Chase
• 1 buguen

Version 0.11

May 7, 2012

Changelog

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 :class:`sklearn.cross_validation.StratifiedShuffleSplit`, which is a :class:`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.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:`KMeans`, by Robert Layton.
• Improved :ref:`cross_validation` and :ref:`grid_search` documentation and introduced the new :func:`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 :class:`_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.

API changes summary

• :class:`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:`KNeighborsClassifier`, :class:`RadiusNeighborsClassifier`, :class:`KNeighborsRegressor` and/or :class:`RadiusNeighborsRegressor` instead.
• Sparse classes in the :ref:`sgd` module are now deprecated.
• In :class:`mixture.GMM`, :class:`mixture.DPGMM` and :class:`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 :class:`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:`LogisticRegression`, :class:`LinearSVC`, :class:`SVC` and :class:`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 :class:`feature_selection.text.Vectorizer` is deprecated and replaced by :class:`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 :class:`feature_selection.text.TfidfVectorizer` and :class:`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 :class:`feature_selection.text.TfidfVectorizer` now derives directly from :class:`feature_selection.text.CountVectorizer` to make grid search trivial.
• methods rvs in :class:`_BaseHMM` module are now deprecated. sample should be used instead.
• Beam pruning option in :class:`_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:`ShuffleSplit` are now consistent with :class:`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 :class:`Bootstrap` are now consistent with :class:`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.

People

• 282 Andreas Müller
• 239 Peter Prettenhofer
• 198 Gael Varoquaux
• 129 Olivier Grisel
• 114 Mathieu Blondel
• 103 Clay Woolam
• 96 Lars Buitinck
• 88 Jaques Grobler
• 82 Alexandre Gramfort
• 50 Bertrand Thirion
• 42 Robert Layton
• 28 flyingimmidev
• 26 Jake Vanderplas
• 26 Shiqiao Du
• 21 Satrajit Ghosh
• 17 David Marek
• 17 Gilles Louppe
• 14 Vlad Niculae
• 11 Yannick Schwartz
• 10 Fabian Pedregosa
• 9 fcostin
• 7 Nick Wilson
• 5 Adrien Gaidon
• 5 Nicolas Pinto
• 4 David Warde-Farley
• 5 Nelle Varoquaux
• 5 Emmanuelle Gouillart
• 3 Joonas Sillanpää
• 3 Paolo Losi
• 2 Charles McCarthy
• 2 Roy Hyunjin Han
• 2 Scott White
• 2 ibayer
• 1 Brandyn White
• 1 Carlos Scheidegger
• 1 Claire Revillet
• 1 Conrad Lee
• 1 Edouard Duchesnay
• 1 Jan Hendrik Metzen
• 1 Meng Xinfan
• 1 Rob Zinkov
• 1 Shiqiao
• 1 Udi Weinsberg
• 1 Virgile Fritsch
• 1 Xinfan Meng
• 1 Yaroslav Halchenko
• 1 jansoe
• 1 Leon Palafox

Version 0.10

January 11, 2012

Changelog

• 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 :class:`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.
• :class:`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.

API changes summary

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:`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:`metrics.cluster` have been refactored but the changes are backwards compatible. They have been moved to the :mod:`metrics.cluster.supervised`, along with :mod:`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 make 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.

• :func:`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.

People

The following people contributed to scikit-learn since last release:

• 246 Andreas Müller
• 242 Olivier Grisel
• 220 Gilles Louppe
• 183 Brian Holt
• 166 Gael Varoquaux
• 144 Lars Buitinck
• 73 Vlad Niculae
• 65 Peter Prettenhofer
• 64 Fabian Pedregosa
• 60 Robert Layton
• 55 Mathieu Blondel
• 52 Jake Vanderplas
• 44 Noel Dawe
• 38 Alexandre Gramfort
• 24 :user:`Virgile Fritsch <VirgileFritsch>`
• 23 Satrajit Ghosh
• 3 Jan Hendrik Metzen
• 3 Kenneth C. Arnold
• 3 Shiqiao Du
• 3 Tim Sheerman-Chase
• 3 Yaroslav Halchenko
• 2 Bala Subrahmanyam Varanasi
• 2 DraXus
• 2 Michael Eickenberg
• 1 Bogdan Trach
• 1 Félix-Antoine Fortin
• 1 Juan Manuel Caicedo Carvajal
• 1 Nelle Varoquaux
• 1 Nicolas Pinto
• 1 Tiziano Zito
• 1 Xinfan Meng

Version 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 program.

Changelog

• 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.univariate_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.pairwise_distances` and :func:`metrics.pairwise.pairwise_kernels` by Robert Layton
• :class:`Mini-Batch K-Means <cluster.MiniBatchKMeans>` by Nelle Varoquaux and Peter Prettenhofer.
• :ref:`mldata` utilities by Pietro Berkes.
• :ref:`olivetti_faces` by David Warde-Farley.

API changes summary

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.

People

38 people contributed to this release.

• 387 Vlad Niculae
• 320 Olivier Grisel
• 192 Lars Buitinck
• 179 Gael Varoquaux
• 168 Fabian Pedregosa (INRIA, Parietal Team)
• 127 Jake Vanderplas
• 120 Mathieu Blondel
• 85 Alexandre Passos
• 67 Alexandre Gramfort
• 57 Peter Prettenhofer
• 56 Gilles Louppe
• 42 Robert Layton
• 38 Nelle Varoquaux
• 32 :user:`Jean Kossaifi <JeanKossaifi>`
• 30 Conrad Lee
• 22 Pietro Berkes
• 18 andy
• 17 David Warde-Farley
• 12 Brian Holt
• 11 Robert
• 8 Amit Aides
• 8 :user:`Virgile Fritsch <VirgileFritsch>`
• 7 Yaroslav Halchenko
• 6 Salvatore Masecchia
• 5 Paolo Losi
• 4 Vincent Schut
• 3 Alexis Metaireau
• 3 Bryan Silverthorn
• 3 Andreas Müller
• 2 Minwoo Jake Lee
• 1 Emmanuelle Gouillart
• 1 Keith Goodman
• 1 Lucas Wiman
• 1 Nicolas Pinto
• 1 Thouis (Ray) Jones
• 1 Tim Sheerman-Chase

Version 0.8

May 11, 2011

scikit-learn 0.8 was released on May 2011, one month after the first "international" scikit-learn coding sprint 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.

Changelog

Several new modules where 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` 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

People that made this release possible preceded by number of commits:

• 159 Olivier Grisel
• 96 Gael Varoquaux
• 96 Vlad Niculae
• 94 Fabian Pedregosa
• 36 Alexandre Gramfort
• 32 Paolo Losi
• 31 Edouard Duchesnay
• 30 Mathieu Blondel
• 25 Peter Prettenhofer
• 22 Nicolas Pinto

• 11 :user:`Virgile Fritsch <VirgileFritsch>`

  • 7 Lars Buitinck
  • 6 Vincent Michel
  • 5 Bertrand Thirion
  • 4 Thouis (Ray) Jones
  • 4 Vincent Schut
  • 3 Jan Schlüter
  • 2 Julien Miotte
  • 2 Matthieu Perrot
  • 2 Yann Malet
  • 2 Yaroslav Halchenko
  • 1 Amit Aides
  • 1 Andreas Müller
  • 1 Feth Arezki
  • 1 Meng Xinfan

Version 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 where added to this release.

Changelog

• 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 :class:`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 for more information [Fabian Pedregosa].
• Documentation improvements: Added :class:`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.euclidean_distances` and to :class:`pca.RandomizedPCA` [James Bergstra].
• Fix compilation issues under NetBSD [Kamel Ibn Hassen Derouiche]
• Allow input sequences of different lengths in :class:`hmm.GaussianHMM` [Ron Weiss].
• Fix bug in affinity propagation caused by incorrect indexing [Xinfan Meng]

People

People that made this release possible preceded by number of commits:

• 85 Fabian Pedregosa
• 67 Mathieu Blondel
• 20 Alexandre Gramfort
• 19 James Bergstra
• 14 Dan Yamins
• 13 Olivier Grisel
• 12 Gael Varoquaux
• 4 Edouard Duchesnay
• 4 Ron Weiss
• 2 Satrajit Ghosh
• 2 Vincent Dubourg
• 1 Emmanuelle Gouillart
• 1 Kamel Ibn Hassen Derouiche
• 1 Paolo Losi
• 1 VirgileFritsch
• 1 Yaroslav Halchenko
• 1 Xinfan Meng

Version 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.

Changelog

• New stochastic gradient 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:`grid_search.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_svm_gui.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

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

Version 0.5

October 11, 2010

Changelog

New classes

• Support for sparse matrices in some classifiers of modules svm and linear_model (see :class:`svm.sparse.SVC`, :class:`svm.sparse.SVR`, :class:`svm.sparse.LinearSVC`, :class:`linear_model.sparse.Lasso`, :class:`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 :class:`hmm.GaussianHMM`, :class:`hmm.MultinomialHMM`, :class:`hmm.GMMHMM`)
• New module feature_extraction (see :ref:`class reference <feature_extraction_ref>`)
• New FastICA algorithm in module sklearn.fastica

Documentation

• Improved documentation for many modules, now separating narrative documentation from the class reference. As an example, see documentation for the SVM module and the complete class reference.

Fixes

• 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.

Examples

• 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_document_classification_20newsgroups.py`
• Many more examples. See here the full list of examples.

External dependencies

• Joblib is now a dependency of this package, although it is shipped with (sklearn.externals.joblib).

Removed modules

• Module ann (Artificial Neural Networks) has been removed from the distribution. Users wanting this sort of algorithms should take a look into pybrain.

Misc

• New sphinx theme for the web page.

Authors

The following is a list of authors for this release, preceded by number of commits:

• 262 Fabian Pedregosa
• 240 Gael Varoquaux
• 149 Alexandre Gramfort
• 116 Olivier Grisel
• 40 Vincent Michel
• 38 Ron Weiss
• 23 Matthieu Perrot
• 10 Bertrand Thirion
• 7 Yaroslav Halchenko
• 9 VirgileFritsch
• 6 Edouard Duchesnay
• 4 Mathieu Blondel
• 1 Ariel Rokem
• 1 Matthieu Brucher

Version 0.4

August 26, 2010

Changelog

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 ...

Authors

The committer list for this release is the following (preceded by number of commits):

• 143 Fabian Pedregosa
• 35 Alexandre Gramfort
• 34 Olivier Grisel
• 11 Gael Varoquaux
• 5 Yaroslav Halchenko
• 2 Vincent Michel
• 1 Chris Filo Gorgolewski

Earlier versions

Earlier versions included contributions by Fred Mailhot, David Cooke, David Huard, Dave Morrill, Ed Schofield, Travis Oliphant, Pearu Peterson.