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When the arpack solver is used, it expects an array with a `dtype` attribute. :pr:`27583` by :user:`Guillaume Lemaitre `. :mod:`sklearn.metrics` ...................... - |Fix| Fixes a bug for metrics using `zero_division=np.nan` (e.g. :func:`~metrics.precision_score`) within a paralell loop (e.g. :func:`~model_selection.cross_val_score`) where the singleton for `np.nan` will be different in the sub-processes. :pr:`27573` by :user:`Guillaume Lemaitre `. :mod:`sklearn.tree` ................... - |Fix| Do not leak data via non-initialized memory in decision tree pickle files and make the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève `. @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.12 2023/09/27 10:57:33 adam Exp $ d6 2 @ 1.12 log @py-scikit-learn: updated to 1.3.1 Version 1.3.1 ============= 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. - |Fix| Ridge models with `solver='sparse_cg'` may have slightly different results with scipy>=1.12, because of an underlying change in the scipy solver Changes impacting all modules ----------------------------- - |Fix| The `set_output` API correctly works with list input. Changelog --------- :mod:`sklearn.calibration` .......................... - |Fix| :class:`calibration.CalibratedClassifierCV` can now handle models that produce large prediction scores. Before it was numerically unstable. :mod:`sklearn.cluster` ...................... - |Fix| :class:`cluster.BisectingKMeans` could crash when predicting on data with a different scale than the data used to fit the model. - |Fix| :class:`cluster.BisectingKMeans` now works with data that has a single feature. :mod:`sklearn.cross_decomposition` .................................. - |Fix| :class:`cross_decomposition.PLSRegression` now automatically ravels the output of `predict` if fitted with one dimensional `y`. :mod:`sklearn.ensemble` ....................... - |Fix| Fix a bug in :class:`ensemble.AdaBoostClassifier` with `algorithm="SAMME"` where the decision function of each weak learner should be symmetric (i.e. the sum of the scores should sum to zero for a sample). :mod:`sklearn.feature_selection` ................................ - |Fix| :func:`feature_selection.mutual_info_regression` now correctly computes the result when `X` is of integer dtype. :mod:`sklearn.impute` ..................... - |Fix| :class:`impute.KNNImputer` now correctly adds a missing indicator column in ``transform`` when ``add_indicator`` is set to ``True`` and missing values are observed during ``fit``. :mod:`sklearn.metrics` ...................... - |Fix| Scorers used with :func:`metrics.get_scorer` handle properly multilabel-indicator matrix. :mod:`sklearn.mixture` ...................... - |Fix| The initialization of :class:`mixture.GaussianMixture` from user-provided `precisions_init` for `covariance_type` of `full` or `tied` was not correct, and has been fixed. :mod:`sklearn.neighbors` ........................ - |Fix| :meth:`neighbors.KNeighborsClassifier.predict` no longer raises an exception for `pandas.DataFrames` input. - |Fix| Reintroduce :attr:`sklearn.neighbors.BallTree.valid_metrics` and :attr:`sklearn.neighbors.KDTree.valid_metrics` as public class attributes. - |Fix| :class:`sklearn.model_selection.HalvingRandomSearchCV` no longer raises when the input to the `param_distributions` parameter is a list of dicts. - |Fix| Neighbors based estimators now correctly work when `metric="minkowski"` and the metric parameter `p` is in the range `0 < p < 1`, regardless of the `dtype` of `X`. :mod:`sklearn.preprocessing` ............................ - |Fix| :class:`preprocessing.LabelEncoder` correctly accepts `y` as a keyword argument. - |Fix| :class:`preprocessing.OneHotEncoder` shows a more informative error message when `sparse_output=True` and the output is configured to be pandas. :mod:`sklearn.tree` ................... - |Fix| :func:`tree.plot_tree` now accepts `class_names=True` as documented. - |Fix| The `feature_names` parameter of :func:`tree.plot_tree` now accepts any kind of array-like instead of just a list. @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.11 2023/07/17 19:51:04 adam Exp $ d3 3 a5 3 BLAKE2s (scikit-learn-1.3.1.tar.gz) = 2164e217f6cccff980dd652cea48eb6a782e262238d32d96bcd4ac15881f445d SHA512 (scikit-learn-1.3.1.tar.gz) = e4e7de217f4da177a94f2f7b30e6f41ed61b33528f931151cfc42fa41cb89f15bc681dcbf89851940e984898e3d503d04f4eadc4a4cded752a7d3dfdbad0be5b Size (scikit-learn-1.3.1.tar.gz) = 7508552 bytes @ 1.11 log @py-scikit-learn: updated to 1.3.0 1.3.0 Metadata Routing HDBSCAN: hierarchical density-based clustering TargetEncoder: a new category encoding strategy Missing values support in decision trees New display model_selection.ValidationCurveDisplay Gamma loss for gradient boosting Grouping infrequent categories in preprocessing.OrdinalEncoder @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.10 2023/05/09 08:07:34 adam Exp $ d3 3 a5 3 BLAKE2s (scikit-learn-1.3.0.tar.gz) = 45627d763603b1811c8b0058e2881b6043b73f54b9518bcd50c48ebe80dd4f60 SHA512 (scikit-learn-1.3.0.tar.gz) = 8fc58812750e68b3b3160fdc46f8d485e9584f3bf33470b840fc69d1dfbe3f5b29849bc010e92a0375f109e8e367f9599a4e19accc9f26aca609f6088c77c741 Size (scikit-learn-1.3.0.tar.gz) = 7483039 bytes @ 1.10 log @py-scikit-learn: updated to 1.2.2 Version 1.2.2 Changelog sklearn.base Fix When set_output(transform="pandas"), base.TransformerMixin maintains the index if the transform output is already a DataFrame. sklearn.calibration Fix A deprecation warning is raised when using the base_estimator__ prefix to set parameters of the estimator used in calibration.CalibratedClassifierCV. sklearn.cluster Fix Fixed a bug in cluster.BisectingKMeans, preventing fit to randomly fail due to a permutation of the labels when running multiple inits. sklearn.compose Fix Fixes a bug in compose.ColumnTransformer which now supports empty selection of columns when set_output(transform="pandas"). sklearn.ensemble Fix A deprecation warning is raised when using the base_estimator__ prefix to set parameters of the estimator used in ensemble.AdaBoostClassifier, ensemble.AdaBoostRegressor, ensemble.BaggingClassifier, and ensemble.BaggingRegressor. sklearn.feature_selection Fix Fixed a regression where a negative tol would not be accepted any more by feature_selection.SequentialFeatureSelector. sklearn.inspection Fix Raise a more informative error message in inspection.partial_dependence when dealing with mixed data type categories that cannot be sorted by numpy.unique. This problem usually happen when categories are str and missing values are present using np.nan. sklearn.isotonic Fix Fixes a bug in isotonic.IsotonicRegression where isotonic.IsotonicRegression.predict would return a pandas DataFrame when the global configuration sets transform_output="pandas". sklearn.preprocessing Fix preprocessing.OneHotEncoder.drop_idx_ now properly references the dropped category in the categories_ attribute when there are infrequent categories. Fix preprocessing.OrdinalEncoder now correctly supports encoded_missing_value or unknown_value set to a categories’ cardinality when there is missing values in the training data. sklearn.tree Fix Fixed a regression in tree.DecisionTreeClassifier, tree.DecisionTreeRegressor, tree.ExtraTreeClassifier and tree.ExtraTreeRegressor where an error was no longer raised in version 1.2 when min_sample_split=1. sklearn.utils Fix Fixes a bug in utils.check_array which now correctly performs non-finite validation with the Array API specification. Fix utils.multiclass.type_of_target can identify pandas nullable data types as classification targets. @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.9 2021/10/26 10:56:04 nia Exp $ d3 3 a5 3 BLAKE2s (scikit-learn-1.2.2.tar.gz) = 4a5c5f003b2d60739eaf67a27c4dd977e0db244ea74a912fad3dba0d56f69269 SHA512 (scikit-learn-1.2.2.tar.gz) = 73ebcd49f49607cefbd4c2200e9379ab1b1277067d0a10c09d80e3969d4924506ef90d52ad3173cf1e05268ad4c7812218b2e955798ac123bb078ff08330309e Size (scikit-learn-1.2.2.tar.gz) = 7269934 bytes @ 1.9 log @math: Replace RMD160 checksums with BLAKE2s checksums All checksums have been double-checked against existing RMD160 and SHA512 hashes @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.8 2021/10/07 14:28:27 nia Exp $ d3 3 a5 4 BLAKE2s (scikit-learn-0.22.1.tar.gz) = 10a3aa8d3c6c25727ff157251b565cb2d518834e70fca8456ec6737382569c82 SHA512 (scikit-learn-0.22.1.tar.gz) = 14191baf3457a3d216d74be34497a3677dd91bb1d3916db6928a4fa1ce93a62c5c9c9879a99fe5317fe088b85b00556087748470a93d3b9d89dbac850a00bc26 Size (scikit-learn-0.22.1.tar.gz) = 6942980 bytes SHA1 (patch-setup.py) = 9a3a0a190f947ec98291534e179587db24f5c5d6 @ 1.8 log @math: Remove SHA1 hashes for distfiles @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.7 2020/02/11 16:06:45 minskim Exp $ d3 1 a3 1 RMD160 (scikit-learn-0.22.1.tar.gz) = cee5fd1ff80ec39e9b229b26cf22a25a3664a36d @ 1.7 log @math/py-scikit-learn: Update to 0.22.1 Highlights: - New plotting API - Stacking Classifier and Regressor - Permutation-based feature importance - Native support for missing values for gradient boosting - Precomputed sparse nearest neighbors graph - KNN Based Imputation - Tree pruning - Retrieve dataframes from OpenML - Checking scikit-learn compatibility of an estimator - ROC AUC now supports multiclass classification @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.6 2019/06/17 15:01:45 adam Exp $ a2 1 SHA1 (scikit-learn-0.22.1.tar.gz) = 84f75ff3e6ed92a71d8f241ba520de57e97475f7 @ 1.6 log @py-scikit-learn: updated to 0.21.2 Version 0.21.2 Changelog 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.pairwise.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. Version 0.21.1 This is a bug-fix release to primarily resolve some packaging issues in version 0.21.0. It also includes minor documentation improvements and some bug fixes. Changelog 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. 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 @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.5 2018/10/02 16:53:46 minskim Exp $ d3 4 a6 4 SHA1 (scikit-learn-0.21.2.tar.gz) = 43b3421f73178e30a6f20417d3d5c33f08bf450a RMD160 (scikit-learn-0.21.2.tar.gz) = 60bfa841e1863bc8f5c3c14338e35e57a9f45e76 SHA512 (scikit-learn-0.21.2.tar.gz) = c1fb512b2dac80765087450d48f38bebdebb127dc53e36a85251d4afb509cd4436a6fb90d89efc224697ff804f45f27b6bffb32ff1619c3e05bd4e440a275a85 Size (scikit-learn-0.21.2.tar.gz) = 12238398 bytes a7 1 SHA1 (patch-sklearn_ensemble____hist__gradient__boosting_splitting.c) = 53b3d222c135e8a5ae60311af076460fe0a27b56 @ 1.5 log @math/py-scikit-learn: Update to 0.20.0 Highlights: Missing values in features, represented by NaNs, are now accepted in column-wise preprocessing such as scalers. Each feature is fitted disregarding NaNs, and data containing NaNs can be transformed. The new impute module provides estimators for learning despite missing data. ColumnTransformer handles the case where different features or columns of a pandas.DataFrame need different preprocessing. String or pandas Categorical columns can now be encoded with OneHotEncoder or OrdinalEncoder. TransformedTargetRegressor helps when the regression target needs to be transformed to be modeled. PowerTransformer and KBinsDiscretizer join QuantileTransformer as non-linear transformations. Added sample_weight support to several estimators (including KMeans, BayesianRidge and KernelDensity) and improved stopping criteria in others (including MLPRegressor, GradientBoostingRegressor and SGDRegressor). This release is also the first to be accompanied by a Glossary of Common Terms and API Elements. @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.4 2018/08/06 16:18:12 minskim Exp $ d3 6 a8 4 SHA1 (scikit-learn-0.20.0.tar.gz) = abc1d6ff7f2a682183a01fba664eb931efaebdfc RMD160 (scikit-learn-0.20.0.tar.gz) = d7fea3a02266d5080e495466b4e2351e46b426ad SHA512 (scikit-learn-0.20.0.tar.gz) = f01fb0846e6e778fe6ab575f420143f04c6d35ac860b1b5e6f2a3a1a3be3e8e7ca7e20ec0995eeea80194d18c104f6cb776aadfd9c75cd3fca55b0926faee9c9 Size (scikit-learn-0.20.0.tar.gz) = 28061776 bytes @ 1.4 log @math/py-scikit-learn: Update to 0.19.2 This release is exclusively in order to support Python 3.7. @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.3 2017/11/21 18:45:28 minskim Exp $ d3 4 a6 4 SHA1 (scikit-learn-0.19.2.tar.gz) = 9c08594d6a0acedc7b2101c5b114ab31c57fcd86 RMD160 (scikit-learn-0.19.2.tar.gz) = c6ded64cadf7ba3d3a40abc28835d5d1b852e5cf SHA512 (scikit-learn-0.19.2.tar.gz) = 80c818923d5cc439b0878de56a11ca2f84c586adb40d51c70b77402dc72853a9e96fdfd1af39a547f871a41d7d04f8caa3861ca81a25b64d1dd1e7f176f232f7 Size (scikit-learn-0.19.2.tar.gz) = 9680746 bytes @ 1.3 log @math/py-scikit-learn: Update to 0.19.1 Notable new features since 0.18.2: - `neighbors.LocalOutlierFactor` for anomaly detection - `preprocessing.QuantileTransformer` for robust feature transformation - `multioutput.ClassifierChain` meta-estimator to simply account for dependencies between classes in multilabel problem - multiplicative update in `decomposition.NMF` - multinomial `linear_model.LogisticRegression` with L1 loss @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.2 2017/11/14 22:56:37 minskim Exp $ d3 4 a6 4 SHA1 (scikit-learn-0.19.1.tar.gz) = 3a3b4d6f27c6bdf12f870a7d78af22e1cdec9eb6 RMD160 (scikit-learn-0.19.1.tar.gz) = dfb92fedadc1fecd9934f85321156f3f5d197a2e SHA512 (scikit-learn-0.19.1.tar.gz) = a783067d8b69c8720d00b443d985c32ab366c14e49184ebc4b73d8a8bfd28311461013b17c221a9b64d3a3faa2e46ce1badf77e340f8ae3b2bf08d95ecce0b2c Size (scikit-learn-0.19.1.tar.gz) = 9526647 bytes @ 1.2 log @math/py-scikit-learn: Update to 0.18.2 Changes: - Fixes for compatibility with NumPy 1.13.0 - Minor compatibility changes in the examples @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.1 2017/07/05 21:31:28 minskim Exp $ d3 4 a6 4 SHA1 (scikit-learn-0.18.2.tar.gz) = 0931b7d7c9db97b02fb4de34c4e24a01e2bf000b RMD160 (scikit-learn-0.18.2.tar.gz) = 037129c5d196a55f2b0359f171616c9922b12d4d SHA512 (scikit-learn-0.18.2.tar.gz) = 7c5c7bdd577ad215790654ce1eff6e802aebe53283ab6c5f12684cf99aeecc8976f9a9803f619f549d4d03be0d7634f249046e4b4f15afa12aecb2697e2e0b05 Size (scikit-learn-0.18.2.tar.gz) = 9224516 bytes @ 1.1 log @Import py-scikit-learn-0.18.1 from pkgsrc as math/py-scikit-learn Packaged by Filip Hajny and updated by Kamel Derouiche and me. scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit scientific Python world (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems, accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering. @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.1 2014/09/24 15:01:41 fhajny Exp $ d3 4 a6 4 SHA1 (scikit-learn-0.18.1.tar.gz) = 1d81e86d23761b3eb60ec7841e6acf9f5e208a7d RMD160 (scikit-learn-0.18.1.tar.gz) = 0c17d3083be1c36a05192bf5e8d76669b1ca3696 SHA512 (scikit-learn-0.18.1.tar.gz) = 7149e683424351a28c19501302ece147cb03d4d12b08822eb2b1898a4978b96803323778fbba628008dd7a7c85daea4e9b550a71ee76851f9a09b2baac18a799 Size (scikit-learn-0.18.1.tar.gz) = 8933930 bytes @