From owner-svn-ports-all@freebsd.org Sun Dec 29 12:46:13 2019 Return-Path: Delivered-To: svn-ports-all@mailman.nyi.freebsd.org Received: from mx1.freebsd.org (mx1.freebsd.org [IPv6:2610:1c1:1:606c::19:1]) by mailman.nyi.freebsd.org (Postfix) with ESMTP id 085B61DE5AF; Sun, 29 Dec 2019 12:46:13 +0000 (UTC) (envelope-from sunpoet@FreeBSD.org) Received: from mxrelay.nyi.freebsd.org (mxrelay.nyi.freebsd.org [IPv6:2610:1c1:1:606c::19:3]) (using TLSv1.3 with cipher TLS_AES_256_GCM_SHA384 (256/256 bits) server-signature RSA-PSS (4096 bits) client-signature RSA-PSS (4096 bits) client-digest SHA256) (Client CN "mxrelay.nyi.freebsd.org", Issuer "Let's Encrypt Authority X3" (verified OK)) by mx1.freebsd.org (Postfix) with ESMTPS id 47m0fr6QP5z4T4s; Sun, 29 Dec 2019 12:46:12 +0000 (UTC) (envelope-from sunpoet@FreeBSD.org) Received: from repo.freebsd.org (repo.freebsd.org [IPv6:2610:1c1:1:6068::e6a:0]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (Client did not present a certificate) by mxrelay.nyi.freebsd.org (Postfix) with ESMTPS id D7C5219A96; Sun, 29 Dec 2019 12:46:12 +0000 (UTC) (envelope-from sunpoet@FreeBSD.org) Received: from repo.freebsd.org ([127.0.1.37]) by repo.freebsd.org (8.15.2/8.15.2) with ESMTP id xBTCkCTa094633; Sun, 29 Dec 2019 12:46:12 GMT (envelope-from sunpoet@FreeBSD.org) Received: (from sunpoet@localhost) by repo.freebsd.org (8.15.2/8.15.2/Submit) id xBTCkCXs094630; Sun, 29 Dec 2019 12:46:12 GMT (envelope-from sunpoet@FreeBSD.org) Message-Id: <201912291246.xBTCkCXs094630@repo.freebsd.org> X-Authentication-Warning: repo.freebsd.org: sunpoet set sender to sunpoet@FreeBSD.org using -f From: Sunpoet Po-Chuan Hsieh Date: Sun, 29 Dec 2019 12:46:12 +0000 (UTC) To: ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org Subject: svn commit: r521283 - in head/math: . py-hdbscan X-SVN-Group: ports-head X-SVN-Commit-Author: sunpoet X-SVN-Commit-Paths: in head/math: . py-hdbscan X-SVN-Commit-Revision: 521283 X-SVN-Commit-Repository: ports MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit X-BeenThere: svn-ports-all@freebsd.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: SVN commit messages for the ports tree List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Sun, 29 Dec 2019 12:46:13 -0000 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 +# $FreeBSD$ + +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 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