Script 'mail_helper' called by obssrc Hello community, here is the log from the commit of package python-scikit-learn for openSUSE:Factory checked in at 2022-02-03 23:16:13 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Comparing /work/SRC/openSUSE:Factory/python-scikit-learn (Old) and /work/SRC/openSUSE:Factory/.python-scikit-learn.new.1898 (New) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-scikit-learn" Thu Feb 3 23:16:13 2022 rev:16 rq:950579 version:1.0.2 Changes: -------- --- /work/SRC/openSUSE:Factory/python-scikit-learn/python-scikit-learn.changes 2021-06-11 22:30:37.806125639 +0200 +++ /work/SRC/openSUSE:Factory/.python-scikit-learn.new.1898/python-scikit-learn.changes 2022-02-03 23:16:46.340495692 +0100 @@ -1,0 +2,31 @@ +Wed Feb 2 02:07:05 UTC 2022 - Steve Kowalik <steven.kowa...@suse.com> + +- Update to 1.0.2: + * Fixed an infinite loop in cluster.SpectralClustering by moving an iteration counter from try to except. #21271 by Tyler Martin. + * datasets.fetch_openml is now thread safe. Data is first downloaded to a temporary subfolder and then renamed. #21833 by Siavash Rezazadeh. + * Fixed the constraint on the objective function of decomposition.DictionaryLearning, decomposition.MiniBatchDictionaryLearning, decomposition.SparsePCA and decomposition.MiniBatchSparsePCA to be convex and match the referenced article. #19210 by J??r??mie du Boisberranger. + * ensemble.RandomForestClassifier, ensemble.RandomForestRegressor, ensemble.ExtraTreesClassifier, ensemble.ExtraTreesRegressor, and ensemble.RandomTreesEmbedding now raise a ValueError when bootstrap=False and max_samples is not None. #21295 Haoyin Xu. + * Solve a bug in ensemble.GradientBoostingClassifier where the exponential loss was computing the positive gradient instead of the negative one. #22050 by Guillaume Lemaitre. + * Fixed feature_selection.SelectFromModel by improving support for base estimators that do not set feature_names_in_. #21991 by Thomas Fan. + * Fix a bug in linear_model.RidgeClassifierCV where the method predict was performing an argmax on the scores obtained from decision_function instead of returning the multilabel indicator matrix. #19869 by Guillaume Lemaitre. + * linear_model.LassoLarsIC now correctly computes AIC and BIC. An error is now raised when n_features > n_samples and when the noise variance is not provided. #21481 by Guillaume Lemaitre and Andr??s Babino. + * Fixed an unnecessary error when fitting manifold.Isomap with a precomputed dense distance matrix where the neighbors graph has multiple disconnected components. #21915 by Tom Dupre la Tour. + * All sklearn.metrics.DistanceMetric subclasses now correctly support read-only buffer attributes. This fixes a regression introduced in 1.0.0 with respect to 0.24.2. #21694 by Julien Jerphanion. + * neighbors.KDTree and neighbors.BallTree correctly supports read-only buffer attributes. #21845 by Thomas Fan. + * Fixes compatibility bug with NumPy 1.22 in preprocessing.OneHotEncoder. #21517 by Thomas Fan. + * Prevents tree.plot_tree from drawing out of the boundary of the figure. #21917 by Thomas Fan. + * Support loading pickles of decision tree models when the pickle has been generated on a platform with a different bitness. A typical example is to train and pickle the model on 64 bit machine and load the model on a 32 bit machine for prediction. #21552 by Lo??c Est??ve. + * Non-fit methods in the following classes do not raise a UserWarning when fitted on DataFrames with valid feature names: covariance.EllipticEnvelope, ensemble.IsolationForest, ensemble.AdaBoostClassifier, neighbors.KNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsClassifier, neighbors.RadiusNeighborsRegressor. #21199 by Thomas Fan. + * Fixed calibration.CalibratedClassifierCV to take into account sample_weight when computing the base estimator prediction when ensemble=False. #20638 by Julien Bohn??. + * Fixed a bug in calibration.CalibratedClassifierCV with method="sigmoid" that was ignoring the sample_weight when computing the the Bayesian priors. #21179 by Guillaume Lemaitre. + * Compute y_std properly with multi-target in sklearn.gaussian_process.GaussianProcessRegressor allowing proper normalization in multi-target scene. #20761 by Patrick de C. T. R. Ferreira. + * Fixed a bug in feature_extraction.CountVectorizer and feature_extraction.TfidfVectorizer by raising an error when ???min_idf??? or ???max_idf??? are floating-point numbers greater than 1. #20752 by Alek Lefebvre. + * linear_model.LogisticRegression now raises a better error message when the solver does not support sparse matrices with int64 indices. #21093 by Tom Dupre la Tour. + * neighbors.KNeighborsClassifier, neighbors.KNeighborsRegressor, neighbors.RadiusNeighborsClassifier, neighbors.RadiusNeighborsRegressor with metric="precomputed" raises an error for bsr and dok sparse matrices in methods: fit, kneighbors and radius_neighbors, due to handling of explicit zeros in bsr and dok sparse graph formats. #21199 by Thomas Fan. + * pipeline.Pipeline.get_feature_names_out correctly passes feature names out from one step of a pipeline to the next. #21351 by Thomas Fan. + * svm.SVC and svm.SVR check for an inconsistency in its internal representation and raise an error instead of segfaulting. This fix also resolves CVE-2020-28975. #21336 by Thomas Fan. + * manifold.TSNE now avoids numerical underflow issues during affinity matrix computation. + * manifold.Isomap now connects disconnected components of the neighbors graph along some minimum distance pairs, instead of changing every infinite distances to zero. + * Many others, see full changelog at https://scikit-learn.org/dev/whats_new/v1.0.html + +------------------------------------------------------------------- Old: ---- scikit-learn-0.24.2.tar.gz New: ---- scikit-learn-1.0.2.tar.gz ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-scikit-learn.spec ++++++ --- /var/tmp/diff_new_pack.vA01Ah/_old 2022-02-03 23:16:47.140490231 +0100 +++ /var/tmp/diff_new_pack.vA01Ah/_new 2022-02-03 23:16:47.144490203 +0100 @@ -1,7 +1,7 @@ # # spec file for package python-scikit-learn # -# Copyright (c) 2021 SUSE LLC +# Copyright (c) 2022 SUSE LLC # # All modifications and additions to the file contributed by third parties # remain the property of their copyright owners, unless otherwise agreed @@ -16,13 +16,11 @@ # -%{?!python_module:%define python_module() python-%{**} python3-%{**}} +%{?!python_module:%define python_module() python3-%{**}} %define skip_python2 1 -# SciPy 1.6.0 and NumPy 1.20 dropped Python 3.6 support. -%define skip_python36 1 %bcond_with extratest Name: python-scikit-learn -Version: 0.24.2 +Version: 1.0.2 Release: 0 Summary: Python modules for machine learning and data mining License: BSD-3-Clause @@ -31,8 +29,8 @@ BuildRequires: %{python_module Cython >= 0.28.5} BuildRequires: %{python_module devel} BuildRequires: %{python_module joblib >= 0.11} -BuildRequires: %{python_module numpy-devel >= 1.13.3} -BuildRequires: %{python_module scipy >= 0.19.1} +BuildRequires: %{python_module numpy-devel >= 1.14.6} +BuildRequires: %{python_module scipy >= 1.1.0} BuildRequires: %{python_module setuptools} BuildRequires: %{python_module threadpoolctl >= 2.0.0} BuildRequires: %{python_module xml} @@ -42,8 +40,8 @@ BuildRequires: openblas-devel BuildRequires: python-rpm-macros Requires: python-joblib >= 0.11 -Requires: python-numpy >= 1.13.3 -Requires: python-scipy >= 0.19.1 +Requires: python-numpy >= 1.14.6 +Requires: python-scipy >= 1.0.0 Requires: python-threadpoolctl >= 2.0.0 Requires: python-xml Provides: python-sklearn ++++++ scikit-learn-0.24.2.tar.gz -> scikit-learn-1.0.2.tar.gz ++++++ /work/SRC/openSUSE:Factory/python-scikit-learn/scikit-learn-0.24.2.tar.gz /work/SRC/openSUSE:Factory/.python-scikit-learn.new.1898/scikit-learn-1.0.2.tar.gz differ: char 5, line 1