Release history

Version 0.19.2

October, 2018

This release is exclusively in order to support Python 3.7.

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 #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 model_selection.GridSearchCV, model_selection.RandomizedSearchCV and 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. #9677 by Kumar Ashutosh and Joel Nothman.
  • correlation_models and regression_models from the legacy gaussian processes implementation have been belatedly deprecated. #9717 by Kumar Ashutosh.

Bug fixes

Regressions in 0.19.0 fixed in 0.19.1:

Enhancements

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 neighbors.LocalOutlierFactor for anomaly detection, preprocessing.QuantileTransformer for robust feature transformation, and the 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 decomposition.NMF and multinomial linear_model.LogisticRegression with L1 loss (use solver='saga').

Cross validation is now able to return the results from multiple metric evaluations. The new 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 to handle multiple metrics.

You can also learn faster. For instance, the new option to cache transformations in 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 config_context.

We’ve made some important fixes too. We’ve fixed a longstanding implementation error in metrics.average_precision_score, so please be cautious with prior results reported from that function. A number of errors in the manifold.TSNE implementation have been fixed, particularly in the default Barnes-Hut approximation. semi_supervised.LabelSpreading and 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.

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

Other estimators

Model selection and evaluation

Miscellaneous

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

Enhancements

Trees and ensembles

Linear, kernelized and related models

Other predictors

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

Decomposition, manifold learning and clustering

Preprocessing and feature selection

Model evaluation and meta-estimators

Metrics

Miscellaneous

Bug fixes

Trees and ensembles

Linear, kernelized and related models

Other predictors

Decomposition, manifold learning and clustering

Preprocessing and feature selection

Model evaluation and meta-estimators

Metrics

Miscellaneous

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. #8449 by Raghav RV.

Linear, kernelized and related models

Other predictors

Decomposition, manifold learning and clustering

Preprocessing and feature selection

Model evaluation and meta-estimators

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. #8174 by Tahar Zanouda, 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 utils have been removed or deprecated accordingly. #8854 and #8874 by Naoya Kanai

  • The store_covariances and covariances_ parameters of discriminant_analysis.QuadraticDiscriminantAnalysis has been renamed to store_covariance and covariance_ to be consistent with the corresponding parameter names of the discriminant_analysis.LinearDiscriminantAnalysis. They will be removed in version 0.21. #7998 by Jiacheng

    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. #7578 by Shubham Bhardwaj

  • All checks in utils.estimator_checks, in particular utils.estimator_checks.check_estimator now accept estimator instances. Most other checks do not accept estimator classes any more. #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 ensemble.BaseEnsemble) now only have self.estimators_ available after fit. #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

Code Contributors

Aman Dalmia, Loic Esteve, Nate Guerin, Sergei Lebedev

Version 0.18.1

November 11, 2016

Changelog

Enhancements

Bug fixes

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. #7301 by Nelson Liu.
  • Tree splitting criterion classes’ cloning/pickling is now memory safe. #7680 by Ibraim Ganiev.

Linear, kernelized and related models

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 sklearn.model_selection, which groups together the functionalities of formerly sklearn.cross_validation, sklearn.grid_search and 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 sklearn.model_selection, are no longer initialized with any data-dependent parameters such as y. Instead they expose a 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 model_selection.GridSearchCV and model_selection.RandomizedSearchCV utilities.

  • The enhanced cv_results_ attribute

    The new cv_results_ attribute (of model_selection.GridSearchCV and 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 model_selection.KFold, model_selection.GroupKFold (see below for the name change), and model_selection.StratifiedKFold is now renamed to n_splits. The n_iter parameter in model_selection.ShuffleSplit, the new class model_selection.GroupShuffleSplit and 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 model_selection.GroupKFold, model_selection.GroupShuffleSplit, model_selection.LeaveOneGroupOut and model_selection.LeavePGroupsOut respectively.

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

  • Fit parameter labels renamed to groups

    The labels parameter in the split method of the newly renamed splitters model_selection.GroupKFold, model_selection.LeaveOneGroupOut, model_selection.LeavePGroupsOut, 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 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

Other estimators

Model selection and evaluation

Enhancements

Trees and ensembles

Linear, kernelized and related models

Decomposition, manifold learning and clustering

Preprocessing and feature selection

Model evaluation and meta-estimators

Metrics

Miscellaneous

  • Added n_jobs parameter to 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 Arthur Mensch.
  • Reduce the memory usage for 32-bit float input arrays of utils.sparse_func.mean_variance_axis and utils.sparse_func.incr_mean_variance_axis by supporting cython fused types. By YenChen Lin.
  • The ignore_warnings now accept a category argument to ignore only the warnings of a specified type. By Thierry Guillemot.
  • Added parameter return_X_y and return type (data, target) : tuple option to load_iris dataset #7049, load_breast_cancer dataset #7152, load_digits dataset, load_diabetes dataset, load_linnerud dataset, load_boston dataset #7154 by Manvendra Singh.
  • Simplification of the clone function, deprecate support for estimators that modify parameters in __init__. #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 the documentation on model persistence for more details. (#7248) By Andreas Müller.

Bug fixes

Trees and ensembles

Linear, kernelized and related models

Decomposition, manifold learning and clustering

Preprocessing and feature selection

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

Model evaluation and meta-estimators

Metrics

Miscellaneous

  • 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). #7323 by Viacheslav Kovalevskyi.
  • 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'. #5141 by Giorgio Patrini.
  • Fix a bug where some formats of scipy.sparse matrix, and estimators with them as parameters, could not be passed to base.clone. By Loic Esteve.
  • datasets.load_svmlight_file now is able to read long int QID values. #7101 by Ibraim Ganiev.

API changes summary

Linear, kernelized and related models

Decomposition, manifold learning and clustering

Model evaluation and meta-estimators

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

Version 0.17.1

February 18, 2016

Changelog

Bug fixes

Version 0.17

November 5, 2015

Changelog

New features

Enhancements

Bug fixes

API changes summary

  • Attribute data_min, data_max and data_range in 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 Giorgio Patrini.
  • All Scaler classes now have an scale_ attribute, the feature-wise rescaling applied by their transform methods. The old attribute std_ in preprocessing.StandardScaler is deprecated and superseded by scale_; it won’t be available in 0.19. By Giorgio Patrini.
  • svm.SVC` and 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 Vighnesh Birodkar.
  • lda.LDA and qda.QDA have been moved to discriminant_analysis.LinearDiscriminantAnalysis and discriminant_analysis.QuadraticDiscriminantAnalysis.
  • The store_covariance and tol parameters have been moved from the fit method to the constructor in discriminant_analysis.LinearDiscriminantAnalysis and the store_covariances and tol parameters have been moved from the fit method to the constructor in 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 feature_selection.SelectFromModel to remove features (according to coefs_ or feature_importances_) which are below a certain threshold value instead.
  • cluster.KMeans re-runs cluster-assignments in case of non-convergence, to ensure consistency of predict(X) and labels_. By Vighnesh Birodkar.
  • 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.
  • datasets.load_lfw_pairs is deprecated and will be removed in 0.19. Use 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 clustering.AgglomerativeClustering instead.
  • cross_validation.check_cv is now a public function.
  • The property residues_ of linear_model.LinearRegression is deprecated and will be removed in 0.19.
  • The deprecated n_jobs parameter of linear_model.LinearRegression has been moved to the constructor.
  • Removed deprecated class_weight parameter from 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 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 preprocessing.LabelBinarizer were removed.
  • Using gamma=0 in svm.SVC and 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

Version 0.16

March 26, 2015

Highlights

Changelog

New features

Enhancements

Documentation improvements

Bug fixes

API changes summary

  • GridSearchCV and cross_val_score and other meta-estimators don’t convert pandas DataFrames into arrays any more, allowing DataFrame specific operations in custom estimators.
  • multiclass.fit_ovr, multiclass.predict_ovr, predict_proba_ovr, multiclass.fit_ovo, multiclass.predict_ovo, multiclass.fit_ecoc and 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 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 ElasticNet and 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 linear_model.enet_path and linear_model.enet_path which constrains coefficients to be positive. By Manoj Kumar.
  • Users should now supply an explicit average parameter to sklearn.metrics.f1_score, sklearn.metrics.fbeta_score, sklearn.metrics.recall_score and 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 linear_model.enet_path and linear_model.lasso_path have been removed. They were deprecated since 0.14
  • From now onwards, all estimators will uniformly raise NotFittedError (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 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 neighbors.kneighbors_graph and 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 GMM, DPGMM and 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 linear_model.SGDClassifier, linear_model.SGDRegressor, linear_model.Perceptron, linear_model.PassiveAgressiveClassifier and linear_model.PassiveAgressiveRegressor now defaults to True.
  • cluster.DBSCAN now uses a deterministic initialization. The random_state parameter is deprecated. By Erich Schubert.

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

Version 0.15.1

August 1, 2014

Bug fixes

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 BernoulliRBM
  • Added cluster.AgglomerativeClustering for hierarchical agglomerative clustering with average linkage, complete linkage and ward strategies.
  • Added linear_model.RANSACRegressor for robust regression models.
  • Added dimensionality reduction with manifold.TSNE which can be used to visualize high-dimensional data.

Changelog

New features

Enhancements

Documentation improvements

  • The Working With Text Data 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 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 Eustache Diemert.

Bug fixes

  • Fixed bug in decomposition.MiniBatchDictionaryLearning : partial_fit was not working properly.
  • Fixed bug in linear_model.stochastic_gradient : l1_ratio was used as (1.0 - l1_ratio) .
  • Fixed bug in multiclass.OneVsOneClassifier with string labels
  • Fixed a bug in LassoCV and 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 feature_selection.f_regression when variates are not centered. By Virgile Fritsch.
  • Fixed a race condition in parallel processing with pre_dispatch != "all" (for instance, in cross_val_score). By Olivier Grisel.
  • Raise error in cluster.FeatureAgglomeration and cluster.WardAgglomeration when no samples are given, rather than returning meaningless clustering.
  • Fixed bug in 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

  • sklearn.hmm is deprecated. Its removal is planned for the 0.17 release.
  • Use of covariance.EllipticEnvelop has now been removed after deprecation. Please use covariance.EllipticEnvelope instead.
  • cluster.Ward is deprecated. Use cluster.AgglomerativeClustering instead.
  • cluster.WardClustering is deprecated. Use
  • cluster.AgglomerativeClustering instead.
  • cross_validation.Bootstrap is deprecated. cross_validation.KFold or 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 MultiLabelBinarizer. By Joel Nothman.
  • Add score method to PCA following the model of probabilistic PCA and deprecate 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 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 RandomizedPCA. By Alexandre Gramfort.
  • Fit alphas for each l1_ratio instead of mean_l1_ratio in linear_model.ElasticNetCV and 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 linear_model.ElasticNetCV and 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 ensemble.ExtraTreesClassifier and 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 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 cross_validation.Bootstrap to return ValueError when n_train + n_test > n. By Ronald Phlypo.

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
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  • 1 jamestwebber
  • 1 matrixorz
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  • 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
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  • 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
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  • 1 Chris Filo Gorgolewski
  • 1 Charles Earl
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  • 1 Andrew Ash
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  • 1 staubda

Version 0.14

August 7, 2013

Changelog

API changes summary

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
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  • 1 Naoki Orii
  • 1 Norbert Crombach
  • 1 Rafael Cunha de Almeida
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  • 1 Seamus Abshere
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  • 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

People

List of contributors for release 0.13.1 by number of commits.

Version 0.13

January 21, 2013

New Estimator Classes

Changelog

API changes summary

People

List of contributors for release 0.13 by number of commits.

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

Version 0.12

September 4, 2012

Changelog

API changes summary

People

Version 0.11

May 7, 2012

Changelog

Highlights

Other changes

API changes summary

  • covariance.EllipticEnvelop is now deprecated - Please use covariance.EllipticEnvelope instead.
  • NeighborsClassifier and NeighborsRegressor are gone in the module Nearest Neighbors. Use the classes KNeighborsClassifier, RadiusNeighborsClassifier, KNeighborsRegressor and/or RadiusNeighborsRegressor instead.
  • Sparse classes in the Stochastic Gradient Descent module are now deprecated.
  • In mixture.GMM, mixture.DPGMM and mixture.VBGMM, parameters must be passed to an object when initialising it and not through fit. Now fit will only accept the data as an input parameter.
  • methods rvs and decode in GMM module are now deprecated. sample and score or predict should be used instead.
  • attribute _scores and _pvalues in univariate feature selection objects are now deprecated. scores_ or pvalues_ should be used instead.
  • In LogisticRegression, LinearSVC, SVC and 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 svm.LinearSVC, the meaning of the multi_class parameter changed. Options now are 'ovr' and 'crammer_singer', with 'ovr' being the default. This does not change the default behavior but hopefully is less confusing.
  • Class feature_selection.text.Vectorizer is deprecated and replaced by feature_selection.text.TfidfVectorizer.
  • The preprocessor / analyzer nested structure for text feature extraction has been removed. All those features are now directly passed as flat constructor arguments to feature_selection.text.TfidfVectorizer and feature_selection.text.CountVectorizer, in particular the following parameters are now used:
  • analyzer can be 'word' or 'char' to switch the default analysis scheme, or use a specific python callable (as previously).
  • tokenizer and preprocessor have been introduced to make it still possible to customize those steps with the new API.
  • input explicitly control how to interpret the sequence passed to fit and predict: filenames, file objects or direct (byte or Unicode) strings.
  • charset decoding is explicit and strict by default.
  • the vocabulary, fitted or not is now stored in the vocabulary_ attribute to be consistent with the project conventions.
  • Class feature_selection.text.TfidfVectorizer now derives directly from feature_selection.text.CountVectorizer to make grid search trivial.
  • methods rvs in _BaseHMM module are now deprecated. sample should be used instead.
  • Beam pruning option in _BaseHMM module is removed since it is difficult to be Cythonized. If you are interested, you can look in the history codes by git.
  • The SVMlight format loader now supports files with both zero-based and one-based column indices, since both occur “in the wild”.
  • Arguments in class ShuffleSplit are now consistent with StratifiedShuffleSplit. Arguments test_fraction and train_fraction are deprecated and renamed to test_size and train_size and can accept both float and int.
  • Arguments in class Bootstrap are now consistent with 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 Nearest Neighbors to specify an arbitrary Minkowski metric for nearest neighbors searches.

People

Version 0.10

January 11, 2012

Changelog

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 linear_model. The default behavior is still to copy everything passed in.

  • The SVMlight dataset loader 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 Stochastic Gradient Descent module use dense parameter vector coef_ instead of sparse_coef_. This significantly improves test time performance.

  • The Covariance estimation module now has a robust estimator of covariance, the Minimum Covariance Determinant estimator.

  • Cluster evaluation metrics in metrics.cluster have been refactored but the changes are backwards compatible. They have been moved to the metrics.cluster.supervised, along with metrics.cluster.unsupervised which contains the Silhouette Coefficient.

  • The permutation_test_score function now behaves the same way as cross_val_score (i.e. uses the mean score across the folds.)

  • Cross Validation generators now use integer indices (indices=True) by default instead of boolean masks. This 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 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 sklearn.datasets.dump_svmlight_file should be re-generated. (They should continue to work, but accidentally had one extra column of zeros prepended.)

  • BaseDictionaryLearning class replaced by SparseCodingMixin.

  • sklearn.utils.extmath.fast_svd has been renamed 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:

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 Manifold learning, The 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.

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Changelog

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 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.

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: Hierarchical clustering, Cross decomposition, Non-negative matrix factorization (NMF or NNMF), initial support for Python 3 and by important enhancements and bug fixes.

Changelog

Several new modules where introduced during this release:

Some other modules benefited from significant improvements or cleanups.

People

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

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

People

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

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 SVM: Weighted samples for an example).
  • New Gaussian Processes 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 svm.LinearSVC)
  • New features and performance improvements of text feature extraction.
  • Improved sparse matrix support, both in main classes (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: Faces recognition example using eigenfaces and SVMs, Species distribution modeling, Libsvm GUI, Wikipedia principal eigenvector and others.
  • Faster 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 (linear_model.lasso_path) is more than 200x times faster than before.
  • It is now possible to get probability estimates from a 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:

Version 0.5

October 11, 2010

Changelog

New classes

  • Support for sparse matrices in some classifiers of modules svm and linear_model (see svm.sparse.SVC, svm.sparse.SVR, svm.sparse.LinearSVC, linear_model.sparse.Lasso, linear_model.sparse.ElasticNet)
  • New pipeline.Pipeline object to compose different estimators.
  • Recursive Feature Elimination routines in module Feature selection.
  • Addition of various classes capable of cross validation in the linear_model module (linear_model.LassoCV, linear_model.ElasticNetCV, etc.).
  • New, more efficient LARS algorithm implementation. The Lasso variant of the algorithm is also implemented. See linear_model.lars_path, linear_model.Lars and linear_model.LassoLars.
  • New Hidden Markov Models module (see classes hmm.GaussianHMM, hmm.MultinomialHMM, hmm.GMMHMM)
  • New module feature_extraction (see class reference)
  • New FastICA algorithm in module sklearn.fastica

Documentation

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

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.