science/py-skrebate: Add py-skrebate 0.62

This package includes a scikit-learn-compatible Python implementation of ReBATE,
a suite of Relief-based feature selection algorithms for Machine Learning. These
Relief-Based algorithms (RBAs) are designed for feature weighting/selection as
part of a machine learning pipeline (supervised learning). Presently this
includes the following core RBAs: ReliefF, SURF, SURF*, MultiSURF*, and
MultiSURF. Additionally, an implementation of the iterative TuRF mechanism and
VLSRelief is included.

WWW: https://github.com/EpistasisLab/scikit-rebate

science/Makefile

@@ -292,6 +292,7 @@
     SUBDIR += py-scoria
     SUBDIR += py-segregation
     SUBDIR += py-segyio
+    SUBDIR += py-skrebate
     SUBDIR += py-spaghetti
     SUBDIR += py-spglib
     SUBDIR += py-tensorflow

science/py-skrebate/Makefile

@@ -0,0 +1,24 @@
+# Created by: Po-Chuan Hsieh <sunpoet@FreeBSD.org>
+
+PORTNAME=	skrebate
+PORTVERSION=	0.62
+CATEGORIES=	science python
+MASTER_SITES=	CHEESESHOP
+PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
+
+MAINTAINER=	sunpoet@FreeBSD.org
+COMMENT=	Relief-based feature selection algorithms
+
+LICENSE=	MIT
+LICENSE_FILE=	${WRKSRC}/LICENSE
+
+RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}numpy>=0,1:math/py-numpy@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}scikit-learn>=0:science/py-scikit-learn@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}scipy>=0:science/py-scipy@${PY_FLAVOR}
+
+USES=		python:3.6+
+USE_PYTHON=	autoplist concurrent distutils
+
+NO_ARCH=	yes
+
+.include <bsd.port.mk>

science/py-skrebate/distinfo

@@ -0,0 +1,3 @@
+TIMESTAMP = 1619198369
+SHA256 (skrebate-0.62.tar.gz) = b20dad4dc52f650e1f7960151314840f34251222cae0a78ac23d9f6d377ca558
+SIZE (skrebate-0.62.tar.gz) = 19835

science/py-skrebate/pkg-descr

@@ -0,0 +1,9 @@
+This package includes a scikit-learn-compatible Python implementation of ReBATE,
+a suite of Relief-based feature selection algorithms for Machine Learning. These
+Relief-Based algorithms (RBAs) are designed for feature weighting/selection as
+part of a machine learning pipeline (supervised learning). Presently this
+includes the following core RBAs: ReliefF, SURF, SURF*, MultiSURF*, and
+MultiSURF. Additionally, an implementation of the iterative TuRF mechanism and
+VLSRelief is included.
+
+WWW: https://github.com/EpistasisLab/scikit-rebate