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Tue, 18 Dec 2018 06:36:18 +0000 (UTC) (envelope-from yuri@FreeBSD.org) Received: from repo.freebsd.org ([127.0.1.37]) by repo.freebsd.org (8.15.2/8.15.2) with ESMTP id wBI6aHH0004761; Tue, 18 Dec 2018 06:36:17 GMT (envelope-from yuri@FreeBSD.org) Received: (from yuri@localhost) by repo.freebsd.org (8.15.2/8.15.2/Submit) id wBI6aH41004757; Tue, 18 Dec 2018 06:36:17 GMT (envelope-from yuri@FreeBSD.org) Message-Id: <201812180636.wBI6aH41004757@repo.freebsd.org> X-Authentication-Warning: repo.freebsd.org: yuri set sender to yuri@FreeBSD.org using -f From: Yuri Victorovich Date: Tue, 18 Dec 2018 06:36:17 +0000 (UTC) To: ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org Subject: svn commit: r487730 - in head/science: . py-GPyOpt X-SVN-Group: ports-head X-SVN-Commit-Author: yuri X-SVN-Commit-Paths: in head/science: . py-GPyOpt X-SVN-Commit-Revision: 487730 X-SVN-Commit-Repository: ports MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit X-Rspamd-Queue-Id: 3A9FB82904 X-Spamd-Bar: -- Authentication-Results: mx1.freebsd.org X-Spamd-Result: default: False [-2.93 / 15.00]; local_wl_from(0.00)[FreeBSD.org]; NEURAL_HAM_MEDIUM(-1.00)[-0.997,0]; NEURAL_HAM_SHORT(-0.93)[-0.932,0]; ASN(0.00)[asn:11403, ipnet:2610:1c1:1::/48, country:US]; NEURAL_HAM_LONG(-1.00)[-0.997,0] 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: Tue, 18 Dec 2018 06:36:19 -0000 Author: yuri Date: Tue Dec 18 06:36:16 2018 New Revision: 487730 URL: https://svnweb.freebsd.org/changeset/ports/487730 Log: New port: science/py-GPyOpt: Bayesian optimization toolbox based on GPy Added: head/science/py-GPyOpt/ head/science/py-GPyOpt/Makefile (contents, props changed) head/science/py-GPyOpt/distinfo (contents, props changed) head/science/py-GPyOpt/pkg-descr (contents, props changed) Modified: head/science/Makefile Modified: head/science/Makefile ============================================================================== --- head/science/Makefile Tue Dec 18 06:24:14 2018 (r487729) +++ head/science/Makefile Tue Dec 18 06:36:16 2018 (r487730) @@ -206,6 +206,7 @@ SUBDIR += pulseview SUBDIR += py-DendroPy SUBDIR += py-GPy + SUBDIR += py-GPyOpt SUBDIR += py-PyFR SUBDIR += py-lifelines SUBDIR += py-MDAnalysis Added: head/science/py-GPyOpt/Makefile ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/science/py-GPyOpt/Makefile Tue Dec 18 06:36:16 2018 (r487730) @@ -0,0 +1,24 @@ +# $FreeBSD$ + +PORTNAME= GPyOpt +DISTVERSION= 1.2.5 +CATEGORIES= science python +MASTER_SITES= CHEESESHOP +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= yuri@FreeBSD.org +COMMENT= Bayesian optimization toolbox based on GPy + +LICENSE= BSD3CLAUSE +LICENSE_FILE= ${WRKSRC}/LICENSE.txt + +RUN_DEPENDS= ${PYNUMPY} \ + ${PYTHON_PKGNAMEPREFIX}scipy>=0.16:science/py-scipy@${PY_FLAVOR} \ + ${PYTHON_PKGNAMEPREFIX}GPy>=1.8>0:science/py-GPy@${PY_FLAVOR} + +USES= python +USE_PYTHON= distutils concurrent autoplist + +NO_ARCH= yes + +.include Added: head/science/py-GPyOpt/distinfo ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/science/py-GPyOpt/distinfo Tue Dec 18 06:36:16 2018 (r487730) @@ -0,0 +1,3 @@ +TIMESTAMP = 1545114436 +SHA256 (GPyOpt-1.2.5.tar.gz) = f92276dd47dc5129ca3e83f6604e4f902a845bbd3226d9c2c6a2f6b942973a18 +SIZE (GPyOpt-1.2.5.tar.gz) = 55158 Added: head/science/py-GPyOpt/pkg-descr ============================================================================== --- /dev/null 00:00:00 1970 (empty, because file is newly added) +++ head/science/py-GPyOpt/pkg-descr Tue Dec 18 06:36:16 2018 (r487730) @@ -0,0 +1,13 @@ +GPyOpt is a Python open-source library for Bayesian Optimization developed by +the Machine Learning group of the University of Sheffield. It is based on GPy, +a Python framework for Gaussian process modelling. + +With GPyOpt you can: +* Automatically configure your models and Machine Learning algorithms. +* Design your wet-lab experiments saving time and money. + +Among other functionalities, with GPyOpt you can design experiments in parallel, +use cost models and mix different types of variables in your designs. Many users +already use GpyOpt for research purposes. + +WWW: https://sheffieldml.github.io/GPyOpt/