pkgsrc/math/py-scikit-learn/distinfo

$NetBSD: distinfo,v 1.9 2021/10/26 10:56:04 nia Exp $

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
math: Replace RMD160 checksums with BLAKE2s checksums All checksums have been double-checked against existing RMD160 and SHA512 hashes 2021-10-26 12:55:21 +02:00			$NetBSD: distinfo,v 1.9 2021/10/26 10:56:04 nia Exp $
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. 2017-07-05 23:31:28 +02:00
math: Replace RMD160 checksums with BLAKE2s checksums All checksums have been double-checked against existing RMD160 and SHA512 hashes 2021-10-26 12:55:21 +02:00			`BLAKE2s (scikit-learn-0.22.1.tar.gz) = 10a3aa8d3c6c25727ff157251b565cb2d518834e70fca8456ec6737382569c82`
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 2020-02-11 17:06:45 +01:00			`SHA512 (scikit-learn-0.22.1.tar.gz) = 14191baf3457a3d216d74be34497a3677dd91bb1d3916db6928a4fa1ce93a62c5c9c9879a99fe5317fe088b85b00556087748470a93d3b9d89dbac850a00bc26`
			`Size (scikit-learn-0.22.1.tar.gz) = 6942980 bytes`
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 2019-06-17 17:01:45 +02:00			`SHA1 (patch-setup.py) = 9a3a0a190f947ec98291534e179587db24f5c5d6`