Hello community, here is the log from the commit of package python-scikit-learn for openSUSE:Factory checked in at 2019-07-29 17:28:32 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Comparing /work/SRC/openSUSE:Factory/python-scikit-learn (Old) and /work/SRC/openSUSE:Factory/.python-scikit-learn.new.4126 (New) ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Package is "python-scikit-learn" Mon Jul 29 17:28:32 2019 rev:5 rq:718971 version:0.21.2 Changes: -------- --- /work/SRC/openSUSE:Factory/python-scikit-learn/python-scikit-learn.changes 2019-02-25 17:48:42.202825051 +0100 +++ /work/SRC/openSUSE:Factory/.python-scikit-learn.new.4126/python-scikit-learn.changes 2019-07-29 17:28:46.638249218 +0200 @@ -1,0 +2,546 @@ +Fri Jul 26 16:08:07 UTC 2019 - Todd R <toddrme2178@gmail.com> + +- Update to Version 0.21.2 + + sklearn.decomposition + * Fix: Fixed a bug in cross_decomposition.CCA improving numerical + stability when Y is close to zero.. + + sklearn.metrics + * Fix: Fixed a bug in metrics.euclidean_distances where a part of the + distance matrix was left un-instanciated for suffiently large float32 + datasets (regression introduced in 0.21).. + + sklearn.preprocessing + * Fix: Fixed a bug in preprocessing.OneHotEncoder where the new + drop parameter was not reflected in get_feature_names.. + + sklearn.utils.sparsefuncs + * Fix: Fixed a bug where min_max_axis would fail on 32-bit systems + for certain large inputs. This affects preprocessing.MaxAbsScaler, + preprocessing.normalize and preprocessing.LabelBinarizer.. +- Update to Version 0.21.1 + + sklearn.metrics + * Fix: Fixed a bug in metrics.pairwise_distances where it would raise + AttributeError for boolean metrics when X had a boolean dtype and + Y == None.. + * Fix: Fixed two bugs in metrics.pairwise_distances when + n_jobs > 1. First it used to return a distance matrix with same dtype as + input, even for integer dtype. Then the diagonal was not zeros for euclidean + metric when Y is X.. + + sklearn.neighbors + * Fix: Fixed a bug in neighbors.KernelDensity which could not be + restored from a pickle if sample_weight had been used.. +- Update to Version 0.21.0 + + 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. + * discriminant_analysis.LinearDiscriminantAnalysis for multiclass + classification. |Fix| + * discriminant_analysis.LinearDiscriminantAnalysis with 'eigen' + solver. |Fix| + * linear_model.BayesianRidge |Fix| + * Decision trees and derived ensembles when both max_depth and + max_leaf_nodes are set. |Fix| + * linear_model.LogisticRegression and + linear_model.LogisticRegressionCV with 'saga' solver. |Fix| + * ensemble.GradientBoostingClassifier |Fix| + * sklearn.feature_extraction.text.HashingVectorizer, + sklearn.feature_extraction.text.TfidfVectorizer, and + sklearn.feature_extraction.text.CountVectorizer |Fix| + * neural_network.MLPClassifier |Fix| + * svm.SVC.decision_function and + multiclass.OneVsOneClassifier.decision_function. |Fix| + * linear_model.SGDClassifier and any derived classifiers. |Fix| + * Any model using the linear_model.sag.sag_solver function with a 0 + seed, including linear_model.LogisticRegression, + linear_model.LogisticRegressionCV, linear_model.Ridge, + and linear_model.RidgeCV with 'sag' solver. |Fix| + * linear_model.RidgeCV when using generalized cross-validation + with sparse inputs. |Fix| + 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.) + + Known Major Bugs + * The default max_iter for linear_model.LogisticRegression is too + small for many solvers given the default tol. In particular, we + accidentally changed the default max_iter for the liblinear solver from + 1000 to 100 iterations in released in version 0.16. + In a future release we hope to choose better default max_iter and tol + heuristically depending on the solver. + + Support for Python 3.4 and below has been officially dropped. + + sklearn.base + * API: The R2 score used when calling score on a regressor will use + multioutput='uniform_average' from version 0.23 to keep consistent with + metrics.r2_score. This will influence the score method of all + the multioutput regressors (except for + multioutput.MultiOutputRegressor).. + + sklearn.calibration + * Enhancement: Added support to bin the data passed into + calibration.calibration_curve by quantiles instead of uniformly + between 0 and 1.. + * Enhancement: Allow n-dimensional arrays as input for + calibration.CalibratedClassifierCV.. + + sklearn.cluster + * MajorFeature: A new clustering algorithm: cluster.OPTICS: an + algoritm related to cluster.DBSCAN, that has hyperparameters easier + to set and that scales better, + * Fix: Fixed a bug where cluster.Birch could occasionally raise an + AttributeError.. + * Fix: Fixed a bug in cluster.KMeans where empty clusters weren't + correctly relocated when using sample weights.. + * API: The n_components_ attribute in cluster.AgglomerativeClustering + and cluster.FeatureAgglomeration has been renamed to + n_connected_components_.. + * Enhancement: cluster.AgglomerativeClustering and + cluster.FeatureAgglomeration now accept a distance_threshold + parameter which can be used to find the clusters instead of n_clusters. + + sklearn.compose + * API: compose.ColumnTransformer is no longer an experimental + feature.. + + sklearn.datasets + * Fix: Added support for 64-bit group IDs and pointers in SVMLight files.. + * Fix: datasets.load_sample_images returns images with a deterministic + order.. + + sklearn.decomposition + * Enhancement: decomposition.KernelPCA now has deterministic output + (resolved sign ambiguity in eigenvalue decomposition of the kernel matrix).. + * Fix: Fixed a bug in decomposition.KernelPCA, fit().transform() + now produces the correct output (the same as fit_transform()) in case + of non-removed zero eigenvalues (remove_zero_eig=False). + fit_inverse_transform was also accelerated by using the same trick as + fit_transform to compute the transform of X. + * Fix: Fixed a bug in decomposition.NMF where init = 'nndsvd', + init = 'nndsvda', and init = 'nndsvdar' are allowed when + n_components < n_features instead of + n_components <= min(n_samples, n_features). + * API: The default value of the init argument in + decomposition.non_negative_factorization will change from + random to None in version 0.23 to make it consistent with + decomposition.NMF. A FutureWarning is raised when + the default value is used.. + + sklearn.discriminant_analysis + * Enhancement: discriminant_analysis.LinearDiscriminantAnalysis now + preserves float32 and float64 dtypes. + * Fix: A ChangedBehaviourWarning is now raised when + discriminant_analysis.LinearDiscriminantAnalysis is given as + parameter n_components > min(n_features, n_classes - 1), and + n_components is changed to min(n_features, n_classes - 1) if so. + Previously the change was made, but silently.. + * Fix: Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis + where the predicted probabilities would be incorrectly computed in the + multiclass case. + * Fix: Fixed a bug in discriminant_analysis.LinearDiscriminantAnalysis + where the predicted probabilities would be incorrectly computed with eigen + solver. + + sklearn.dummy + * Fix: Fixed a bug in dummy.DummyClassifier where the + predict_proba method was returning int32 array instead of + float64 for the stratified strategy.. + * Fix: Fixed a bug in dummy.DummyClassifier where it was throwing a + dimension mismatch error in prediction time if a column vector y with + shape=(n, 1) was given at fit time. + + sklearn.ensemble + * MajorFeature: Add two new implementations of + gradient boosting trees: ensemble.HistGradientBoostingClassifier + and ensemble.HistGradientBoostingRegressor. The implementation of + these estimators is inspired by + LightGBM and can be orders of + magnitude faster than ensemble.GradientBoostingRegressor and + ensemble.GradientBoostingClassifier when the number of samples is + larger than tens of thousands of samples. The API of these new estimators + is slightly different, and some of the features from + ensemble.GradientBoostingClassifier and + ensemble.GradientBoostingRegressor are not yet supported. + These new estimators are experimental, which means that their results or + their API might change without any deprecation cycle. To use them, you + need to explicitly import enable_hist_gradient_boosting:: + >>> # explicitly require this experimental feature + >>> from sklearn.experimental import enable_hist_gradient_boosting # noqa + >>> # now you can import normally from sklearn.ensemble + >>> from sklearn.ensemble import HistGradientBoostingClassifier. + * Feature: Add ensemble.VotingRegressor + which provides an equivalent of ensemble.VotingClassifier + for regression problems. + * Efficiency: Make ensemble.IsolationForest prefer threads over + processes when running with n_jobs > 1 as the underlying decision tree + fit calls do release the GIL. This changes reduces memory usage and + communication overhead. + * Efficiency: Make ensemble.IsolationForest more memory efficient + by avoiding keeping in memory each tree prediction.. + * Efficiency: ensemble.IsolationForest now uses chunks of data at + prediction step, thus capping the memory usage.. + * Efficiency: sklearn.ensemble.GradientBoostingClassifier and + sklearn.ensemble.GradientBoostingRegressor now keep the + input y as float64 to avoid it being copied internally by trees.. + * Enhancement: Minimized the validation of X in + ensemble.AdaBoostClassifier and ensemble.AdaBoostRegressor. + * Enhancement: ensemble.IsolationForest now exposes warm_start + parameter, allowing iterative addition of trees to an isolation + forest.. + * Fix: The values of feature_importances_ in all random forest based + models (i.e. + ensemble.RandomForestClassifier, + ensemble.RandomForestRegressor, + ensemble.ExtraTreesClassifier, + ensemble.ExtraTreesRegressor, + ensemble.RandomTreesEmbedding, + ensemble.GradientBoostingClassifier, and + ensemble.GradientBoostingRegressor) now: + > sum up to 1 + > all the single node trees in feature importance calculation are ignored + > in case all trees have only one single node (i.e. a root node), + feature importances will be an array of all zeros. + * Fix: Fixed a bug in ensemble.GradientBoostingClassifier and + ensemble.GradientBoostingRegressor, which didn't support + scikit-learn estimators as the initial estimator. Also added support of + initial estimator which does not support sample weights. and. + * Fix: Fixed the output of the average path length computed in + ensemble.IsolationForest when the input is either 0, 1 or 2. ++++ 349 more lines (skipped) ++++ between /work/SRC/openSUSE:Factory/python-scikit-learn/python-scikit-learn.changes ++++ and /work/SRC/openSUSE:Factory/.python-scikit-learn.new.4126/python-scikit-learn.changes Old: ---- scikit-learn-0.20.2.tar.gz New: ---- scikit-learn-0.21.2.tar.gz ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-scikit-learn.spec ++++++ --- /var/tmp/diff_new_pack.G7H1ut/_old 2019-07-29 17:28:48.098248677 +0200 +++ /var/tmp/diff_new_pack.G7H1ut/_new 2019-07-29 17:28:48.102248676 +0200 @@ -17,46 +17,37 @@ %{?!python_module:%define python_module() python-%{**} python3-%{**}} -%define oldpython python -# test suite just doesn't work and upstream doesn't look like fixing it -# anytime soon, gh#scikit-learn/scikit-learn#12369 -# %%ifarch %%{ix86} x86_64 -# %%bcond_without test -# %%else -%bcond_with test -# %%endif +%define skip_python2 1 Name: python-scikit-learn -Version: 0.20.2 +Version: 0.21.2 Release: 0 Summary: Python modules for machine learning and data mining License: BSD-3-Clause Group: Development/Libraries/Python URL: http://scikit-learn.org/ Source0: https://files.pythonhosted.org/packages/source/s/scikit-learn/scikit-learn-%{version}.tar.gz +BuildRequires: %{python_module Cython} BuildRequires: %{python_module devel} -BuildRequires: %{python_module matplotlib} BuildRequires: %{python_module numpy-devel >= 1.8.2} -BuildRequires: %{python_module pytest} BuildRequires: %{python_module scipy >= 0.13.3} BuildRequires: %{python_module setuptools} -BuildRequires: %{python_module xml} BuildRequires: fdupes BuildRequires: gcc-c++ BuildRequires: gcc-fortran BuildRequires: openblas-devel BuildRequires: python-rpm-macros +# SECTION test requirements +BuildRequires: %{python_module joblib} +BuildRequires: %{python_module matplotlib} +BuildRequires: %{python_module nose} +BuildRequires: %{python_module pytest} +BuildRequires: %{python_module xml} +# /SECTION +Requires: python-joblib Requires: python-matplotlib Requires: python-numpy >= 1.8.2 Requires: python-scipy >= 0.13.3 Requires: python-xml -%if %{with test} -BuildRequires: %{python_module Cython} -BuildRequires: %{python_module nose} -%endif -%ifpython2 -Provides: %{oldpython}-scikits-learn = %{version} -Obsoletes: %{oldpython}-scikits-learn < %{version} -%endif %python_subpackages %description @@ -65,6 +56,7 @@ %prep %setup -q -n scikit-learn-%{version} +rm -rf sklearn/.pytest_cache %build %python_build @@ -73,16 +65,20 @@ %python_install %python_expand %fdupes %{buildroot}%{$python_sitearch} -%if %{with test} +# Precision-related errors on non-x86 platforms +%ifarch %{ix86} x86_64 %check export SKLEARN_SKIP_NETWORK_TESTS=1 NO_TESTS="test_feature_importance_regression or test_minibatch_with_many_reassignments" NO_TESTS="$NO_TESTS or test_sparse_coder_parallel_mmap or test_explained_variances" export NO_TESTS +mv sklearn sklearn_temp +rm -rf build _build.* %{python_expand export PYTHONPATH=%{buildroot}%{$python_sitearch} -# rm -v ensemble/tests/test_gradient_boosting.py tests/test_init.py -py.test-%{$python_bin_suffix} -v -k "not ($NO_TESTS)" sklearn +rm -rf build _build.* +py.test-%{$python_bin_suffix} -p no:cacheprovider -v -k "not ($NO_TESTS)" %{buildroot}%{$python_sitearch}/sklearn } +mv sklearn_temp sklearn %endif %files %{python_files} ++++++ scikit-learn-0.20.2.tar.gz -> scikit-learn-0.21.2.tar.gz ++++++ /work/SRC/openSUSE:Factory/python-scikit-learn/scikit-learn-0.20.2.tar.gz /work/SRC/openSUSE:Factory/.python-scikit-learn.new.4126/scikit-learn-0.21.2.tar.gz differ: char 5, line 1