Update Info:
*Base Release*

Available in Package Hub : 15 SP2

Available in Package Hub : 15 SP2

AArch64 | |

ppc64le | |

s390x | |

x86-64 |

python3-scikit-learn

- added provide sklearn and python-sklearn as this is name most users will search for

- Require at least pytest 4.x for testing

- version update to 0.22.1 * Clear definition of the public API * Deprecations: using FutureWarning from now on * Certain estimators and functions may produce different models * See https://scikit-learn.org/stable/whats_new/v0.22.html#version-0-22-0

- Update to Version 0.21.3 + sklearn.cluster * Fix: Fixed a bug in :class:`cluster.KMeans` where computation with `init='random'` was single threaded for `n_jobs > 1` or `n_jobs = -1`. * Fix: Fixed a bug in :class:`cluster.OPTICS` where users were unable to pass float `min_samples` and `min_cluster_size`. * Fix: Fixed a bug in :class:`cluster.KMeans` where KMeans++ initialisation could rarely result in an IndexError.. + sklearn.compose * Fix: Fixed an issue in :class:`compose.ColumnTransformer` where using DataFrames whose column order differs between :func:``fit`` and :func:``transform`` could lead to silently passing incorrect columns to the ``remainder`` transformer. + sklearn.datasets * Fix: :func:`datasets.fetch_california_housing`, :func:`datasets.fetch_covtype`, :func:`datasets.fetch_kddcup99`, :func:`datasets.fetch_olivetti_faces`, :func:`datasets.fetch_rcv1`, and :func:`datasets.fetch_species_distributions` try to persist the previously cache using the new ``joblib`` if the cached data was persisted using the deprecated ``sklearn.externals.joblib``. This behavior is set to be deprecated and removed in v0.23. . + sklearn.ensemble * Fix: Fix zero division error in :func:`HistGradientBoostingClassifier` and :func:`HistGradientBoostingRegressor`. + sklearn.impute * Fix: Fixed a bug in :class:`impute.SimpleImputer` and :class:`impute.IterativeImputer` so that no errors are thrown when there are missing values in training data. + sklearn.inspection * Fix: Fixed a bug in :func:`inspection.plot_partial_dependence` where ``target`` parameter was not being taken into account for multiclass problems. :pr:`14393` by :user:`Guillem G. Subies <guillemgsubies>`. + sklearn.linear_model * Fix: Fixed a bug in :class:`linear_model.LogisticRegressionCV` where ``refit=False`` would fail depending on the ``'multiclass'`` and ``'penalty'`` parameters (regression introduced in 0.21). * Fix: Compatibility fix for :class:`linear_model.ARDRegression` and Scipy>=1.3.0. Adapts to upstream changes to the default `pinvh` cutoff threshold which otherwise results in poor accuracy in some cases. + sklearn.neighbors * Fix: Fixed a bug in :class:`neighbors.NeighborhoodComponentsAnalysis` where the validation of initial parameters ``n_components``, ``max_iter`` and ``tol`` required too strict types. + sklearn.tree * Fix: Fixed bug in :func:`tree.export_text` when the tree has one feature and a single feature name is passed in. * Fix: Fixed an issue with :func:`plot_tree` where it displayed entropy calculations even for `gini` criterion in DecisionTreeClassifiers.

- 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. * Fix: Fixed a bug in ensemble.GradientBoostingClassifier where the gradients would be incorrectly computed in multiclass classification problems.. * Fix: Fixed a bug in ensemble.GradientBoostingClassifier where validation sets for early stopping were not sampled with stratification.. * Fix: Fixed a bug in ensemble.GradientBoostingClassifier where the default initial prediction of a multiclass classifier would predict the classes priors instead of the log of the priors.. * Fix: Fixed a bug in ensemble.RandomForestClassifier where the predict method would error for multiclass multioutput forests models if any targets were strings.. * Fix: Fixed a bug in ensemble.gradient_boosting.LossFunction and ensemble.gradient_boosting.LeastSquaresError where the default value of learning_rate in update_terminal_regions is not consistent with the document and the caller functions. Note however that directly using these loss functions is deprecated.. * Fix: ensemble.partial_dependence (and consequently the new version sklearn.inspection.partial_dependence) now takes sample weights into account for the partial dependence computation when the gradient boosting model has been trained with sample weights.. * API: ensemble.partial_dependence and ensemble.plot_partial_dependence are now deprecated in favor of inspection.partial_dependence and inspection.plot_partial_dependence. and * Fix: ensemble.VotingClassifier and ensemble.VotingRegressor were failing during fit in one of the estimators was set to None and sample_weight was not None.. * API: ensemble.VotingClassifier and ensemble.VotingRegressor accept 'drop' to disable an estimator in addition to None to be consistent with other estimators (i.e., pipeline.FeatureUnion and compose.ColumnTransformer).. + sklearn.externals * API: Deprecated externals.six since we have dropped support for Python 2.7.. + sklearn.feature_extraction * Fix: If input='file' or input='filename', and a callable is given as the analyzer, sklearn.feature_extraction.text.HashingVectorizer, sklearn.feature_extraction.text.TfidfVectorizer, and sklearn.feature_extraction.text.CountVectorizer now read the data from the file(s) and then pass it to the given analyzer, instead of passing the file name(s) or the file object(s) to the analyzer.. + sklearn.impute * MajorFeature: Added impute.IterativeImputer, which is a strategy for imputing missing values by modeling each feature with missing values as a function of other features in a round-robin fashion. The API of IterativeImputer is experimental and subject to change without any deprecation cycle. To use them, you need to explicitly import enable_iterative_imputer:: >>> from sklearn.experimental import enable_iterative_imputer # noqa >>> # now you can import normally from sklearn.impute >>> from sklearn.impute import IterativeImputer * Feature: The impute.SimpleImputer and impute.IterativeImputer have a new parameter 'add_indicator', which simply stacks a impute.MissingIndicator transform into the output of the imputer's transform. That allows a predictive estimator to account for missingness. * Fix: In impute.MissingIndicator avoid implicit densification by raising an exception if input is sparse add missing_values property is set to 0.. * Fix: Fixed two bugs in impute.MissingIndicator. First, when X is sparse, all the non-zero non missing values used to become explicit False in the transformed data. Then, when features='missing-only', all features used to be kept if there were no missing values at all.. + sklearn.inspection (new subpackage) * Feature: Partial dependence plots (inspection.plot_partial_dependence) are now supported for any regressor or classifier (provided that they have a predict_proba method). + sklearn.isotonic * Feature: Allow different dtypes (such as float32) in isotonic.IsotonicRegression. + sklearn.linear_model * Enhancement: linear_model.Ridge now preserves float32 and float64 dtypes. * Feature: linear_model.LogisticRegression and linear_model.LogisticRegressionCV now support Elastic-Net penalty, with the 'saga' solver.. * Feature: Added linear_model.lars_path_gram, which is linear_model.lars_path in the sufficient stats mode, allowing users to compute linear_model.lars_path without providing X and y.. * Efficiency: linear_model.make_dataset now preserves float32 and float64 dtypes, reducing memory consumption in stochastic gradient, SAG and SAGA solvers. * Enhancement: linear_model.LogisticRegression now supports an unregularized objective when penalty='none' is passed. This is equivalent to setting C=np.inf with l2 regularization. Not supported by the liblinear solver.. * Enhancement: sparse_cg solver in linear_model.Ridge now supports fitting the intercept (i.e. fit_intercept=True) when inputs are sparse.. * Enhancement: The coordinate descent solver used in Lasso, ElasticNet, etc. now issues a ConvergenceWarning when it completes without meeting the desired toleranbce. * Fix: Fixed a bug in linear_model.LogisticRegression and linear_model.LogisticRegressionCV with 'saga' solver, where the weights would not be correctly updated in some cases.. * Fix: Fixed the posterior mean, posterior covariance and returned regularization parameters in linear_model.BayesianRidge. The posterior mean and the posterior covariance were not the ones computed with the last update of the regularization parameters and the returned regularization parameters were not the final ones. Also fixed the formula of the log marginal likelihood used to compute the score when compute_score=True.. * Fix: Fixed a bug in linear_model.LassoLarsIC, where user input copy_X=False at instance creation would be overridden by default parameter value copy_X=True in fit. * Fix: Fixed a bug in linear_model.LinearRegression that was not returning the same coeffecients and intercepts with fit_intercept=True in sparse and dense case. * Fix: Fixed a bug in linear_model.HuberRegressor that was broken when X was of dtype bool.. * Fix: Fixed a performance issue of saga and sag solvers when called in a joblib.Parallel setting with n_jobs > 1 and backend="threading", causing them to perform worse than in the sequential case.. * Fix: Fixed a bug in linear_model.stochastic_gradient.BaseSGDClassifier that was not deterministic when trained in a multi-class setting on several threads.. * Fix: Fixed bug in linear_model.ridge_regression, linear_model.Ridge and linear_model.RidgeClassifier that caused unhandled exception for arguments return_intercept=True and solver=auto (default) or any other solver different from sag. * Fix: linear_model.ridge_regression will now raise an exception if return_intercept=True and solver is different from sag. Previously, only warning was issued. * Fix: linear_model.ridge_regression will choose sparse_cg solver for sparse inputs when solver=auto and sample_weight is provided (previously cholesky solver was selected). * API: The use of linear_model.lars_path with X=None while passing Gram is deprecated in version 0.21 and will be removed in version 0.23. Use linear_model.lars_path_gram instead.. * API: linear_model.logistic_regression_path is deprecated in version 0.21 and will be removed in version 0.23.. * Fix: linear_model.RidgeCV with generalized cross-validation now correctly fits an intercept when fit_intercept=True and the design matrix is sparse. + sklearn.manifold * Efficiency: Make manifold.tsne.trustworthiness use an inverted index instead of an np.where lookup to find the rank of neighbors in the input space. This improves efficiency in particular when computed with lots of neighbors and/or small datasets.. + sklearn.metrics * Feature: Added the metrics.max_error metric and a corresponding 'max_error' scorer for single output regression.. * Feature: Add metrics.multilabel_confusion_matrix, which calculates a confusion matrix with true positive, false positive, false negative and true negative counts for each class. This facilitates the calculation of set-wise metrics such as recall, specificity, fall out and miss rate. * Feature: metrics.jaccard_score has been added to calculate the Jaccard coefficient as an evaluation metric for binary, multilabel and multiclass tasks, with an interface analogous to metrics.f1_score. * Feature: Added metrics.pairwise.haversine_distances which can be accessed with metric='pairwise' through metrics.pairwise_distances and estimators. (Haversine distance was previously available for nearest neighbors calculation.) * Efficiency: Faster metrics.pairwise_distances with n_jobs > 1 by using a thread-based backend, instead of process-based backends. * Efficiency: The pairwise manhattan distances with sparse input now uses the BLAS shipped with scipy instead of the bundled BLAS. * Enhancement: Use label accuracy instead of micro-average on metrics.classification_report to avoid confusion. micro-average is only shown for multi-label or multi-class with a subset of classes because it is otherwise identical to accuracy. * Enhancement: Added beta parameter to metrics.homogeneity_completeness_v_measure and metrics.v_measure_score to configure the tradeoff between homogeneity and completeness. * Fix: The metric metrics.r2_score is degenerate with a single sample and now it returns NaN and raises exceptions.UndefinedMetricWarning.. * Fix: Fixed a bug where metrics.brier_score_loss will sometimes return incorrect result when there's only one class in y_true.. * Fix: Fixed a bug in metrics.label_ranking_average_precision_score where sample_weight wasn't taken into account for samples with degenerate labels.. * API: The parameter labels in metrics.hamming_loss is deprecated in version 0.21 and will be removed in version 0.23. * Fix: The function metrics.pairwise.euclidean_distances, and therefore several estimators with metric='euclidean', suffered from numerical precision issues with float32 features. Precision has been increased at the cost of a small drop of performance. * API: metrics.jaccard_similarity_score is deprecated in favour of the more consistent metrics.jaccard_score. The former behavior for binary and multiclass targets is broken.. + sklearn.mixture * Fix: Fixed a bug in mixture.BaseMixture and therefore on estimators based on it, i.e. mixture.GaussianMixture and mixture.BayesianGaussianMixture, where fit_predict and fit.predict were not equivalent.. + sklearn.model_selection * Feature: Classes ~model_selection.GridSearchCV and ~model_selection.RandomizedSearchCV now allow for refit=callable to add flexibility in identifying the best estimator. See sphx_glr_auto_examples_model_selection_plot_grid_search_refit_callable.py. * Enhancement: Classes ~model_selection.GridSearchCV, ~model_selection.RandomizedSearchCV, and methods ~model_selection.cross_val_score, ~model_selection.cross_val_predict, ~model_selection.cross_validate, now print train scores when return_train_scores is True and verbose > 2. For ~model_selection.learning_curve, and ~model_selection.validation_curve only the latter is required. * Enhancement: Some CV splitter classes and model_selection.train_test_split now raise ValueError when the resulting training set is empty.. * Fix: Fixed a bug where model_selection.StratifiedKFold shuffles each class's samples with the same random_state, making shuffle=True ineffective.. * Fix: Added ability for model_selection.cross_val_predict to handle multi-label (and multioutput-multiclass) targets with predict_proba-type methods.. * Fix: Fixed an issue in ~model_selection.cross_val_predict where method="predict_proba" returned always 0.0 when one of the classes was excluded in a cross-validation fold. + sklearn.multiclass * Fix: Fixed an issue in multiclass.OneVsOneClassifier.decision_function where the decision_function value of a given sample was different depending on whether the decision_function was evaluated on the sample alone or on a batch containing this same sample due to the scaling used in decision_function.. + sklearn.multioutput * Fix: Fixed a bug in multioutput.MultiOutputClassifier where the predict_proba method incorrectly checked for predict_proba attribute in the estimator object. + sklearn.neighbors * MajorFeature: Added neighbors.NeighborhoodComponentsAnalysis for metric learning, which implements the Neighborhood Components Analysis algorithm. * API: Methods in neighbors.NearestNeighbors : ~neighbors.NearestNeighbors.kneighbors, ~neighbors.NearestNeighbors.radius_neighbors, ~neighbors.NearestNeighbors.kneighbors_graph, ~neighbors.NearestNeighbors.radius_neighbors_graph now raise NotFittedError, rather than AttributeError, when called before fit. + sklearn.neural_network * Fix: Fixed a bug in neural_network.MLPClassifier and neural_network.MLPRegressor where the option shuffle=False was being ignored.. * Fix: Fixed a bug in neural_network.MLPClassifier where validation sets for early stopping were not sampled with stratification. In the multilabel case however, splits are still not stratified.. + sklearn.pipeline * Feature: pipeline.Pipeline can now use indexing notation (e.g. my_pipeline[0:-1]) to extract a subsequence of steps as another Pipeline instance. A Pipeline can also be indexed directly to extract a particular step (e.g. my_pipeline['svc']), rather than accessing named_steps.. * Feature: Added optional parameter verbose in pipeline.Pipeline, compose.ColumnTransformer and pipeline.FeatureUnion and corresponding make_ helpers for showing progress and timing of each step. * Enhancement: pipeline.Pipeline now supports using 'passthrough' as a transformer, with the same effect as None.. * Enhancement: pipeline.Pipeline implements __len__ and therefore len(pipeline) returns the number of steps in the pipeline.. + sklearn.preprocessing * Feature: preprocessing.OneHotEncoder now supports dropping one feature per category with a new drop parameter.. * Efficiency: preprocessing.OneHotEncoder and preprocessing.OrdinalEncoder now handle pandas DataFrames more efficiently.. * Efficiency: Make preprocessing.MultiLabelBinarizer cache class mappings instead of calculating it every time on the fly. * Efficiency: preprocessing.PolynomialFeatures now supports compressed sparse row (CSR) matrices as input for degrees 2 and 3. This is typically much faster than the dense case as it scales with matrix density and expansion degree (on the order of density^degree), and is much, much faster than the compressed sparse column (CSC) case.. * Efficiency: Speed improvement in preprocessing.PolynomialFeatures, in the dense case. Also added a new parameter order which controls output order for further speed performances.. * Fix: Fixed the calculation overflow when using a float16 dtype with preprocessing.StandardScaler. * Fix: Fixed a bug in preprocessing.QuantileTransformer and preprocessing.quantile_transform to force n_quantiles to be at most equal to n_samples. Values of n_quantiles larger than n_samples were either useless or resulting in a wrong approximation of the cumulative distribution function estimator.. * API: The default value of copy in preprocessing.quantile_transform will change from False to True in 0.23 in order to make it more consistent with the default copy values of other functions in preprocessing and prevent unexpected side effects by modifying the value of X inplace.. + sklearn.svm * Fix: Fixed an issue in svm.SVC.decision_function when decision_function_shape='ovr'. The decision_function value of a given sample was different depending on whether the decision_function was evaluated on the sample alone or on a batch containing this same sample due to the scaling used in decision_function.. + sklearn.tree * Feature: Decision Trees can now be plotted with matplotlib using tree.plot_tree without relying on the dot library, removing a hard-to-install dependency.. * Feature: Decision Trees can now be exported in a human readable textual format using tree.export_text. * Feature: get_n_leaves() and get_depth() have been added to tree.BaseDecisionTree and consequently all estimators based on it, including tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier, and tree.ExtraTreeRegressor.. * Fix: Trees and forests did not previously predict multi-output classification targets with string labels, despite accepting them in fit.. * Fix: Fixed an issue with tree.BaseDecisionTree and consequently all estimators based on it, including tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier, and tree.ExtraTreeRegressor, where they used to exceed the given max_depth by 1 while expanding the tree if max_leaf_nodes and max_depth were both specified by the user. Please note that this also affects all ensemble methods using decision trees.. + sklearn.utils * Feature: utils.resample now accepts a stratify parameter for sampling according to class distributions.. * API: Deprecated warn_on_dtype parameter from utils.check_array and utils.check_X_y. Added explicit warning for dtype conversion in check_pairwise_arrays if the metric being passed is a pairwise boolean metric.. + Multiple modules * MajorFeature: The __repr__() method of all estimators (used when calling print(estimator)) has been entirely re-written, building on Python's pretty printing standard library. All parameters are printed by default, but this can be altered with the print_changed_only option in sklearn.set_config.. * MajorFeature: Add estimators tags: these are annotations of estimators that allow programmatic inspection of their capabilities, such as sparse matrix support, supported output types and supported methods. Estimator tags also determine the tests that are run on an estimator when check_estimator is called. * Efficiency: Memory copies are avoided when casting arrays to a different dtype in multiple estimators.. * Fix: Fixed a bug in the implementation of the our_rand_r helper function that was not behaving consistently across platforms. + Miscellaneous * Enhancement: Joblib is no longer vendored in scikit-learn, and becomes a dependency. Minimal supported version is joblib 0.11, however using version >= 0.13 is strongly recommended.. + Changes to estimator checks These changes mostly affect library developers. * Add check_fit_idempotent to ~utils.estimator_checks.check_estimator, which checks that when fit is called twice with the same data, the ouput of predict, predict_proba, transform, and decision_function does not change. * Many checks can now be disabled or configured with estimator_tags..

Version: 0.19.1-bp150.1.4
*** Mon Oct 30 2017 arun@gmx.de **
*** Sun Sep 24 2017 arun@gmx.de **
*** Sun Jun 11 2017 toddrme2178@gmail.com **
*** Mon Jan 11 2016 toddrme2178@gmail.com **
*** Fri Nov 20 2015 Angelos Tzotsos <tzotsos@opensuse.org> **
*** Thu Oct 24 2013 toddrme2178@gmail.com **
*** Thu Oct 24 2013 speilicke@suse.com **
*** Fri May 03 2013 toddrme2178@gmail.com **
*** Sat Oct 13 2012 Angelos Tzotsos <tzotsos@opensuse.org> **
*** Sun Jun 03 2012 toddrme2178@gmail.com **
*** Wed Mar 07 2012 scorot@free.fr **
*** Mon Oct 10 2011 scorot@gtt.fr **
*** Sat Oct 23 2010 scorot@gtt.fr **

- 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.

- 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

- 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

- Switch to proper package name: python-scikit-learn

- Update to version 0.17

- 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 Jane?. - :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

- Require python-setuptools instead of distribute (upstreams merged)

- Update to version 0.13.1

- Update to version 0.12.1

- Clean up spec file - Update to version 0.11

- remove unneeded libatals3-devel dependency

- fix python-Sphinx requirement

- first package - version 0.5

Version: 0.20.2-bp151.2.2
*** Wed Jan 30 2019 Matej Cepl <mcepl@suse.com> **
*** Tue Jan 29 2019 mcepl@suse.com **
*** Wed Oct 24 2018 Dirk Mueller <dmueller@suse.com> **
*** Thu May 17 2018 tchvatal@suse.com **
*** Thu May 17 2018 tchvatal@suse.com **

- Switch off tests, gh#scikit-learn/scikit-learn#12369

- 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.

- 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)

- Skip test sklearn.linear_model.tests.test_logistic.test_max_iter * Upstream plans to fix it in next release * scikit-learn-skip-test.patch

- Update package to properly state dependencies as in setup.py - Install license file