./math/py-scikit-learn, Machine learning algorithms for Python

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Branch: CURRENT, Version: 0.22.1nb1, Package name: py38-scikit-learn-0.22.1nb1, Maintainer: pkgsrc-users

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

Required to run:
[math/lapack] [math/blas] [devel/py-setuptools] [math/py-scipy] [math/py-numpy] [devel/py-cython] [lang/gcc7] [lang/python37] [devel/py-joblib]

Required to build:

Master sites:

SHA1: 84f75ff3e6ed92a71d8f241ba520de57e97475f7
RMD160: cee5fd1ff80ec39e9b229b26cf22a25a3664a36d
Filesize: 6780.254 KB

Version history: (Expand)

CVS history: (Expand)

   2021-05-31 09:03:31 by Dr. Thomas Orgis | Files touched by this commit (1)
Log message:
math/py-scikit-learn: drop ineffective SKLEARN_NO_OPENMP

It is wrong to disable OpenMP and this setting does nothing anyway.
Maybe it did in the past. Now, the build tries to use OpenMP if
possible and this is the right thing to do.

Repeat: This change doesn't affect the build at all since that variable
is not checked (since some time?).
   2021-04-20 23:48:14 by Dr. Thomas Orgis | Files touched by this commit (1)
Log message:
math/py-scikit-learn: drop explicit BLAS dependency

This adds nothing on the normal dependency to numpy.
   2021-04-09 16:41:35 by Tobias Nygren | Files touched by this commit (14)
Log message:
revert wrong fix for py-scipy python 3.6 deprecation, fix properly
   2020-10-12 23:52:05 by Jason Bacon | Files touched by this commit (87) | Package updated
Log message:
math/blas, math/lapack: Install interchangeable BLAS system

Install the new interchangeable BLAS system created by Thomas Orgis,
currently supporting Netlib BLAS/LAPACK, OpenBLAS, cblas, lapacke, and
Apple's Accelerate.framework.  This system allows the user to select any
BLAS implementation without modifying packages or using package options, by
setting PKGSRC_BLAS_TYPES in mk.conf. See mk/blas.buildlink3.mk for details.

This commit should not alter behavior of existing packages as the system
defaults to Netlib BLAS/LAPACK, which until now has been the only supported


Add new mk/blas.buildlink3.mk for inclusion in dependent packages
Install compatible Netlib math/blas and math/lapack packages
Update math/blas and math/lapack MAINTAINER approved by adam@
OpenBLAS, cblas, and lapacke will follow in separate commits
Update direct dependents to use mk/blas.buildlink3.mk
Perform recursive revbump
   2020-05-27 21:37:44 by Thomas Klausner | Files touched by this commit (60)
Log message:
*: reset MAINTAINER for fhajny on his request
   2020-02-11 17:06:45 by Min Sik Kim | Files touched by this commit (4) | Package updated
Log message:
math/py-scikit-learn: Update to 0.22.1


- 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
   2020-01-26 18:32:28 by Roland Illig | Files touched by this commit (981)
Log message:
all: migrate homepages from http to https

pkglint -r --network --only "migrate"

As a side-effect of migrating the homepages, pkglint also fixed a few
indentations in unrelated lines. These and the new homepages have been
checked manually.
   2019-06-17 17:01:45 by Adam Ciarcinski | Files touched by this commit (5) | Package updated
Log message:
py-scikit-learn: updated to 0.21.2

Version 0.21.2
Fix Fixed a bug in cross_decomposition.CCA improving numerical stability when Y \ 
is close to zero.

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).

Fix Fixed a bug in preprocessing.OneHotEncoder where the new drop parameter was \ 
not reflected in get_feature_names.

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.

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.

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