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science/py-skrebate: Add py-skrebate 0.62

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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
This commit is contained in:
Po-Chuan Hsieh 2021-04-26 04:11:26 +08:00
parent f3ca692f8b
commit a15ff33a68
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GPG Key ID: 9A4BD10F002DD04B
4 changed files with 37 additions and 0 deletions
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1
science/Makefile
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@ -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

24
science/py-skrebate/Makefile Normal file
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@ -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>

3
science/py-skrebate/distinfo Normal file
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@ -0,0 +1,3 @@
TIMESTAMP = 1619198369
SHA256 (skrebate-0.62.tar.gz) = b20dad4dc52f650e1f7960151314840f34251222cae0a78ac23d9f6d377ca558
SIZE (skrebate-0.62.tar.gz) = 19835

9
science/py-skrebate/pkg-descr Normal file
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@ -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