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File python2-scikit-learn.changes of Package python2-scikit-learn
------------------------------------------------------------------- Thu Jul 25 18:22:37 UTC 2019 - Todd R <toddrme2178@gmail.com> - Implement python2-only version since the latest release drops python2 support. ------------------------------------------------------------------- Wed Jan 30 13:45:49 UTC 2019 - Matej Cepl <mcepl@suse.com> - Switch off tests, gh#scikit-learn/scikit-learn#12369 ------------------------------------------------------------------- Tue Jan 29 17:06:46 CET 2019 - mcepl@suse.com - Update to 0.20.2: * This is a bug-fix release with some minor documentation improvements and enhancements to features released in 0.20.0. Note that we also include some API changes in this release, so you might get some extra warnings after updating from 0.20.0. ------------------------------------------------------------------- Wed Oct 24 18:40:50 UTC 2018 - Dirk Mueller <dmueller@suse.com> - update to 0.20.0: * http://scikit-learn.org/stable/whats_new.html#version-0-20-0 Support for Python 3.3 has been officially dropped - drop scikit-learn-skip-test.patch (merged) ------------------------------------------------------------------- Thu May 17 13:39:27 UTC 2018 - tchvatal@suse.com - Skip test sklearn.linear_model.tests.test_logistic.test_max_iter * Upstream plans to fix it in next release * scikit-learn-skip-test.patch ------------------------------------------------------------------- Thu May 17 12:25:53 UTC 2018 - tchvatal@suse.com - Update package to properly state dependencies as in setup.py - Install license file ------------------------------------------------------------------- Mon Oct 30 06:03:24 UTC 2017 - arun@gmx.de - update to version 0.19.1: * 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 + Avoid integer overflows in metrics.matthews_corrcoef. #9693 by Sam Steingold. + Fix ValueError in preprocessing.LabelEncoder when using inverse_transform on unseen labels. #9816 by Charlie Newey. + Fixed a bug in the objective function for manifold.TSNE (both exact and with the Barnes-Hut approximation) when n_components >= 3. #9711 by @goncalo-rodrigues. + Fix regression in model_selection.cross_val_predict where it raised an error with method='predict_proba' for some probabilistic classifiers. #9641 by James Bourbeau. + Fixed a bug where datasets.make_classification modified its input weights. #9865 by Sachin Kelkar. + model_selection.StratifiedShuffleSplit now works with multioutput multiclass or multilabel data with more than 1000 columns. #9922 by Charlie Brummitt. + Fixed a bug with nested and conditional parameter setting, e.g. setting a pipeline step and its parameter at the same time. #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. #9830 by Joel Nothman. + Fix regression in model_selection.cross_val_predict where it no longer accepted X as a list. #9600 by Rasul Kerimov. + Fixed handling of model_selection.cross_val_predict for binary classification with method='decision_function'. #9593 by Reiichiro Nakano and core devs. + Fix regression in pipeline.Pipeline where it no longer accepted steps as a tuple. #9604 by Joris Van den Bossche. + Fix bug where n_iter was not properly deprecated, leaving n_iter unavailable for interim use in linear_model.SGDClassifier, linear_model.SGDRegressor, linear_model.PassiveAggressiveClassifier, linear_model.PassiveAggressiveRegressor and linear_model.Perceptron. #9558 by Andreas Müller. + Dataset fetchers make sure temporary files are closed before removing them, which caused errors on Windows. #9847 by Joan Massich. + Fixed a regression in manifold.TSNE where it no longer supported metrics other than ‘euclidean’ and ‘precomputed’. #9623 by Oli Blum. * Enhancements + Our test suite and utils.estimator_checks.check_estimators can now be run without Nose installed. #9697 by Joan Massich. + To improve usability of version 0.19’s pipeline.Pipeline caching, memory now allows joblib.Memory instances. This make use of the new utils.validation.check_memory helper. #9584 by Kumar Ashutosh + Some fixes to examples: #9750, #9788, #9815 + Made a FutureWarning in SGD-based estimators less verbose. #9802 by Vrishank Bhardwaj. ------------------------------------------------------------------- Sun Sep 24 21:50:13 UTC 2017 - arun@gmx.de - update to version 0.19.0: * 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. + cluster.KMeans with sparse X and initial centroids given (bug fix) + cross_decomposition.PLSRegression with scale=True (bug fix) + ensemble.GradientBoostingClassifier and ensemble.GradientBoostingRegressor where min_impurity_split is used (bug fix) + gradient boosting loss='quantile' (bug fix) + ensemble.IsolationForest (bug fix) + feature_selection.SelectFdr (bug fix) + linear_model.RANSACRegressor (bug fix) + linear_model.LassoLars (bug fix) + linear_model.LassoLarsIC (bug fix) + manifold.TSNE (bug fix) + neighbors.NearestCentroid (bug fix) + semi_supervised.LabelSpreading (bug fix) + semi_supervised.LabelPropagation (bug fix) + tree based models where min_weight_fraction_leaf is used (enhancement) * complete changelog at http://scikit-learn.org/stable/whats_new.html ------------------------------------------------------------------- Sun Jun 11 19:37:48 UTC 2017 - toddrme2178@gmail.com - Implement single-spec version - Update source URL - Update to version 0.18.1 * Large number of changes. See: https://github.com/scikit-learn/scikit-learn/blob/0.18.1/doc/whats_new.rst ------------------------------------------------------------------- Mon Jan 11 10:35:20 UTC 2016 - toddrme2178@gmail.com - Switch to proper package name: python-scikit-learn ------------------------------------------------------------------- Fri Nov 20 15:00:00 UTC 2015 - Angelos Tzotsos <tzotsos@opensuse.org> - Update to version 0.17 ------------------------------------------------------------------- Thu Oct 24 12:40:24 UTC 2013 - toddrme2178@gmail.com - Update to version 14.1 * Minor bugfixes - Update to version 14.0 * 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 `Justin Vincent`_, `Lars Buitinck`_, `Subhodeep Moitra`_ 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 significants). By `Norbert Crombach`_ and `Mathieu Blondel`_ . - Added an interactive version of `Andreas Müller`_'s `Machine Learning Cheat Sheet (for scikit-learn) <http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html>`_ 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 `Martin Luessi`_. - 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:`example_applications_plot_out_of_core_classification.py`. By `Eustache Diemert`_. - 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 `Mikhail Korobov`_ - Significant speed improvements for :class:`sklearn.cluster.DBSCAN`_ by `cleverless <https://github.com/cleverless>`_ * API changes: - The :func:`auc_score` was renamed :func:`roc_auc_score`. - Testing scikit-learn with `sklearn.test()` is deprecated. Use `nosetest 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. - Better input validation, warning on unexpected shapes for y. - Fix building on 13.1+ - Update BuildRequires - Cleanup spec file formatting ------------------------------------------------------------------- Thu Oct 24 11:13:53 UTC 2013 - speilicke@suse.com - Require python-setuptools instead of distribute (upstreams merged) ------------------------------------------------------------------- Fri May 3 22:35:40 UTC 2013 - toddrme2178@gmail.com - Update to version 0.13.1 ------------------------------------------------------------------- Sat Oct 13 13:57:04 UTC 2012 - Angelos Tzotsos <tzotsos@opensuse.org> - Update to version 0.12.1 ------------------------------------------------------------------- Sun Jun 3 16:09:34 UTC 2012 - toddrme2178@gmail.com - Clean up spec file - Update to version 0.11 ------------------------------------------------------------------- Wed Mar 7 21:57:46 UTC 2012 - scorot@free.fr - remove unneeded libatals3-devel dependency ------------------------------------------------------------------- Mon Oct 10 00:00:00 UTC 2011 - scorot@gtt.fr - fix python-Sphinx requirement ------------------------------------------------------------------- Sat Oct 23 00:00:00 UTC 2010 - scorot@gtt.fr - first package - version 0.5
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