Log message:
revert wrong fix for py-scipy python 3.6 deprecation, fix properly

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
implementation.

Details:

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

Log message:
*: reset MAINTAINER for fhajny on his request

Log message:
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

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.

Log message:
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

Log message:
*: update email for fhajny

Log message:
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.

Log message:
math/py-scikit-learn: Update to 0.19.2

This release is exclusively in order to support Python 3.7.

Log message:
Remove dependencies unused if the Accelerate framework exists

Bump PKGREVISION.