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dev-python/hdbscan: merged in Gentoo ::science

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https://gitweb.gentoo.org/proj/sci.git/commit/?id=207a85724b6526e0f3be017a8a953412fcdcf597
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Horea Christian 2023-01-31 04:55:29 -05:00
parent 049df8f6a5
commit 4ff007afb4
4 changed files with 0 additions and 96 deletions
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2
dev-python/hdbscan/Manifest
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DIST hdbscan-0.8.26.tar.gz 10776639 BLAKE2B 7f1cb7b479746e3ff262cce90d48cca42dccabc131a60300ae5448455260eb27387ab2eeeee19257930ee646d06df80fa2997cf1819da18178c5522a8a27a30c SHA512 7337f4246511d22e8e11308e7776aad6929f26674daecc1e174b01673782837815a40820a6565406315b1ae14ed059c4945810ac997abfde9e24abda352de622
DIST hdbscan-0.8.29.tar.gz 11666106 BLAKE2B de324386d7d2178f74ea958fa75e5eccb9dcdeba3d85cecce1150930e2d687caecbc79469de6ad62806c5bd860962bba57f27323c69f3abc6b0d4694a553656d SHA512 fd349c1c6c09f0288a9a2501eb7794fb03139c11261a835d90f1c9cd80955a4d8d075ff3864ddb9cfcbc204a3d0662011074bdb29b66b72d5469f76bc4f7ecca

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dev-python/hdbscan/hdbscan-0.8.26.ebuild
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# Copyright 2021-2023 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
PYTHON_COMPAT=( python3_{10..11} )
DISTUTILS_USE_PEP517=setuptools
inherit distutils-r1
DESCRIPTION="A high performance implementation of HDBSCAN clustering."
HOMEPAGE="https://github.com/scikit-learn-contrib/hdbscan"
SRC_URI="mirror://pypi/${PN:0:1}/${PN}/${P}.tar.gz"
LICENSE="BSD"
SLOT="0"
KEYWORDS="~amd64 ~x86"
IUSE="test"
RESTRICT="test"
# Tests fail, reported to upstream
# https://github.com/scikit-learn-contrib/hdbscan/issues/501
DEPEND=""
RDEPEND="
dev-python/cython[${PYTHON_USEDEP}]
dev-python/joblib[${PYTHON_USEDEP}]
dev-python/numpy[${PYTHON_USEDEP}]
dev-python/scipy[${PYTHON_USEDEP}]
dev-python/six[${PYTHON_USEDEP}]
sci-libs/scikit-learn[${PYTHON_USEDEP}]
"
BDEPEND=""
distutils_enable_tests pytest

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dev-python/hdbscan/hdbscan-0.8.29.ebuild
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# Copyright 2021-2023 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
PYTHON_COMPAT=( python3_{8..10} )
DISTUTILS_USE_PEP517=setuptools
inherit distutils-r1
DESCRIPTION="A high performance implementation of HDBSCAN clustering."
HOMEPAGE="https://github.com/scikit-learn-contrib/hdbscan"
SRC_URI="mirror://pypi/${PN:0:1}/${PN}/${P}.tar.gz"
LICENSE="BSD"
SLOT="0"
KEYWORDS="~amd64 ~x86"
IUSE="test"
# Reported upstream:
# https://github.com/scikit-learn-contrib/hdbscan/issues/501
RESTRICT="test"
DEPEND=""
RDEPEND="
dev-python/cython[${PYTHON_USEDEP}]
dev-python/joblib[${PYTHON_USEDEP}]
dev-python/numpy[${PYTHON_USEDEP}]
dev-python/scipy[${PYTHON_USEDEP}]
dev-python/six[${PYTHON_USEDEP}]
sci-libs/scikit-learn[${PYTHON_USEDEP}]
"
BDEPEND=""
distutils_enable_tests pytest

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dev-python/hdbscan/metadata.xml
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<?xml version='1.0' encoding='UTF-8'?>
<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
<pkgmetadata>
<maintainer type="person">
<email>gentoo@chymera.eu</email>
<name>Horea Christian</name>
</maintainer>
<maintainer type="project">
<email>sci@gentoo.org</email>
<name>Gentoo Science Project</name>
</maintainer>
<longdescription lang="en">
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with
Noise. Performs DBSCAN over varying epsilon values and integrates the result
to find a clustering that gives the best stability over epsilon. This allows
HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more
robust to parameter selection.
In practice this means that HDBSCAN returns a good clustering straight away
with little or no parameter tuning -- and the primary parameter, minimum
cluster size, is intuitive and easy to select. HDBSCAN is ideal for
exploratory data analysis; it's a fast and robust algorithm that you can
trust to return meaningful clusters (if there are any).
</longdescription>
<upstream>
<remote-id type="github">scikit-learn-contrib/hdbscan</remote-id>
</upstream>
</pkgmetadata>