Date: Sun, 29 Dec 2019 12:46:12 +0000 (UTC) From: Sunpoet Po-Chuan Hsieh <sunpoet@FreeBSD.org> To: ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org Subject: svn commit: r521283 - in head/math: . py-hdbscan Message-ID: <201912291246.xBTCkCXs094630@repo.freebsd.org>
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Author: sunpoet Date: Sun Dec 29 12:46:11 2019 New Revision: 521283 URL: https://svnweb.freebsd.org/changeset/ports/521283 Log: Add py-hdbscan 0.8.24 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). WWW: https://github.com/scikit-learn-contrib/hdbscan Added: head/math/py-hdbscan/ head/math/py-hdbscan/Makefile (contents, props changed) head/math/py-hdbscan/distinfo (contents, props changed) head/math/py-hdbscan/pkg-descr (contents, props changed) Modified: head/math/Makefile Modified: head/math/Makefile ============================================================================== --- head/math/Makefile Sun Dec 29 12:46:06 2019 (r521282) +++ head/math/Makefile Sun Dec 29 12:46:11 2019 (r521283) @@ -719,6 +719,7 @@ SUBDIR += py-grandalf SUBDIR += py-graphillion SUBDIR += py-gym + SUBDIR += py-hdbscan SUBDIR += py-igakit SUBDIR += py-igraph SUBDIR += py-intspan Added: head/math/py-hdbscan/Makefile ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/math/py-hdbscan/Makefile Sun Dec 29 12:46:11 2019 (r521283) @@ -0,0 +1,26 @@ +# Created by: Po-Chuan Hsieh <sunpoet@FreeBSD.org> +# $FreeBSD$ + +PORTNAME= hdbscan +PORTVERSION= 0.8.24 +CATEGORIES= math python +MASTER_SITES= CHEESESHOP +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= sunpoet@FreeBSD.org +COMMENT= Clustering based on density with variable density clusters + +LICENSE= BSD3CLAUSE +LICENSE_FILE= ${WRKSRC}/LICENSE + +BUILD_DEPENDS= ${PYNUMPY} +RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}joblib>=0:devel/py-joblib@${PY_FLAVOR} \ + ${PYNUMPY} \ + ${PYTHON_PKGNAMEPREFIX}scikit-learn>=0:science/py-scikit-learn@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}scipy>=0:science/py-scipy@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}six>=0:devel/py-six@${PY_FLAVOR} + +USES= python +USE_PYTHON= autoplist concurrent cython distutils + +.include <bsd.port.mk> Added: head/math/py-hdbscan/distinfo ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/math/py-hdbscan/distinfo Sun Dec 29 12:46:11 2019 (r521283) @@ -0,0 +1,3 @@ +TIMESTAMP = 1577523844 +SHA256 (hdbscan-0.8.24.tar.gz) = fe31a7ea0ce2c9babd190a195e491834ff9f64c74daa4ca94fa65a88f701269a +SIZE (hdbscan-0.8.24.tar.gz) = 4356868 Added: head/math/py-hdbscan/pkg-descr ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/math/py-hdbscan/pkg-descr Sun Dec 29 12:46:11 2019 (r521283) @@ -0,0 +1,14 @@ +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). + +WWW: https://github.com/scikit-learn-contrib/hdbscan
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