From 4ff007afb40e32f67967b3872822c7e03e9cc2d8 Mon Sep 17 00:00:00 2001 From: Horea Christian Date: Tue, 31 Jan 2023 04:55:29 -0500 Subject: [PATCH] dev-python/hdbscan: merged in Gentoo ::science https://gitweb.gentoo.org/proj/sci.git/commit/?id=207a85724b6526e0f3be017a8a953412fcdcf597 --- dev-python/hdbscan/Manifest | 2 -- dev-python/hdbscan/hdbscan-0.8.26.ebuild | 33 ------------------------ dev-python/hdbscan/hdbscan-0.8.29.ebuild | 33 ------------------------ dev-python/hdbscan/metadata.xml | 28 -------------------- 4 files changed, 96 deletions(-) delete mode 100644 dev-python/hdbscan/Manifest delete mode 100644 dev-python/hdbscan/hdbscan-0.8.26.ebuild delete mode 100644 dev-python/hdbscan/hdbscan-0.8.29.ebuild delete mode 100644 dev-python/hdbscan/metadata.xml diff --git a/dev-python/hdbscan/Manifest b/dev-python/hdbscan/Manifest deleted file mode 100644 index b162353..0000000 --- a/dev-python/hdbscan/Manifest +++ /dev/null @@ -1,2 +0,0 @@ -DIST hdbscan-0.8.26.tar.gz 10776639 BLAKE2B 7f1cb7b479746e3ff262cce90d48cca42dccabc131a60300ae5448455260eb27387ab2eeeee19257930ee646d06df80fa2997cf1819da18178c5522a8a27a30c SHA512 7337f4246511d22e8e11308e7776aad6929f26674daecc1e174b01673782837815a40820a6565406315b1ae14ed059c4945810ac997abfde9e24abda352de622 -DIST hdbscan-0.8.29.tar.gz 11666106 BLAKE2B de324386d7d2178f74ea958fa75e5eccb9dcdeba3d85cecce1150930e2d687caecbc79469de6ad62806c5bd860962bba57f27323c69f3abc6b0d4694a553656d SHA512 fd349c1c6c09f0288a9a2501eb7794fb03139c11261a835d90f1c9cd80955a4d8d075ff3864ddb9cfcbc204a3d0662011074bdb29b66b72d5469f76bc4f7ecca diff --git a/dev-python/hdbscan/hdbscan-0.8.26.ebuild b/dev-python/hdbscan/hdbscan-0.8.26.ebuild deleted file mode 100644 index 91e83eb..0000000 --- a/dev-python/hdbscan/hdbscan-0.8.26.ebuild +++ /dev/null @@ -1,33 +0,0 @@ -# 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[${PYTHON_USEDEP}] -" -BDEPEND="" - -distutils_enable_tests pytest diff --git a/dev-python/hdbscan/hdbscan-0.8.29.ebuild b/dev-python/hdbscan/hdbscan-0.8.29.ebuild deleted file mode 100644 index 85d049d..0000000 --- a/dev-python/hdbscan/hdbscan-0.8.29.ebuild +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright 2021-2023 Gentoo Authors -# Distributed under the terms of the GNU General Public License v2 - -EAPI=8 - -PYTHON_COMPAT=( python3_{8..10} ) -DISTUTILS_USE_PEP517=setuptools -inherit distutils-r1 - -DESCRIPTION="A high performance implementation of HDBSCAN clustering." -HOMEPAGE="https://github.com/scikit-learn-contrib/hdbscan" -SRC_URI="mirror://pypi/${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 diff --git a/dev-python/hdbscan/metadata.xml b/dev-python/hdbscan/metadata.xml deleted file mode 100644 index b43e627..0000000 --- a/dev-python/hdbscan/metadata.xml +++ /dev/null @@ -1,28 +0,0 @@ - - - - - gentoo@chymera.eu - Horea Christian - - - sci@gentoo.org - Gentoo Science Project - - - 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). - - - scikit-learn-contrib/hdbscan - -