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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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