909 lines
36 KiB
Scheme
909 lines
36 KiB
Scheme
;;; GNU Guix --- Functional package management for GNU
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;;; Copyright © 2015, 2016, 2017, 2018 Ricardo Wurmus <rekado@elephly.net>
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;;; Copyright © 2016 Efraim Flashner <efraim@flashner.co.il>
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;;; Copyright © 2016, 2017 Marius Bakke <mbakke@fastmail.com>
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;;; Copyright © 2016 Hartmut Goebel <h.goebel@crazy-compilers.com>
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;;; Copyright © 2018 Tobias Geerinckx-Rice <me@tobias.gr>
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;;; Copyright © 2018 Kei Kebreau <kkebreau@posteo.net>
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;;; Copyright © 2018 Mark Meyer <mark@ofosos.org>
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;;; Copyright © 2018 Ben Woodcroft <donttrustben@gmail.com>
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;;; Copyright © 2018 Fis Trivial <ybbs.daans@hotmail.com>
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;;; Copyright © 2018 Julien Lepiller <julien@lepiller.eu>
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;;; Copyright © 2018 Björn Höfling <bjoern.hoefling@bjoernhoefling.de>
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;;;
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;;; This file is part of GNU Guix.
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;;;
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;;; GNU Guix is free software; you can redistribute it and/or modify it
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;;; under the terms of the GNU General Public License as published by
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;;; the Free Software Foundation; either version 3 of the License, or (at
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;;; your option) any later version.
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;;;
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;;; GNU Guix is distributed in the hope that it will be useful, but
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;;; WITHOUT ANY WARRANTY; without even the implied warranty of
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;;; MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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;;; GNU General Public License for more details.
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;;;
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;;; You should have received a copy of the GNU General Public License
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;;; along with GNU Guix. If not, see <http://www.gnu.org/licenses/>.
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(define-module (gnu packages machine-learning)
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#:use-module ((guix licenses) #:prefix license:)
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#:use-module (guix packages)
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#:use-module (guix utils)
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#:use-module (guix download)
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#:use-module (guix svn-download)
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#:use-module (guix build-system cmake)
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#:use-module (guix build-system gnu)
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#:use-module (guix build-system ocaml)
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#:use-module (guix build-system python)
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#:use-module (guix build-system r)
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#:use-module (guix git-download)
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#:use-module (gnu packages)
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#:use-module (gnu packages algebra)
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#:use-module (gnu packages autotools)
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#:use-module (gnu packages boost)
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#:use-module (gnu packages check)
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#:use-module (gnu packages compression)
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#:use-module (gnu packages cran)
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#:use-module (gnu packages dejagnu)
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#:use-module (gnu packages gcc)
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#:use-module (gnu packages image)
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#:use-module (gnu packages maths)
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#:use-module (gnu packages mpi)
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#:use-module (gnu packages ocaml)
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#:use-module (gnu packages onc-rpc)
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#:use-module (gnu packages perl)
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#:use-module (gnu packages pkg-config)
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#:use-module (gnu packages python)
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#:use-module (gnu packages python-xyz)
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#:use-module (gnu packages statistics)
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#:use-module (gnu packages swig)
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#:use-module (gnu packages xml)
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#:use-module (gnu packages xorg))
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(define-public fann
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;; The last release is >100 commits behind, so we package from git.
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(let ((commit "d71d54788bee56ba4cf7522801270152da5209d7"))
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(package
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(name "fann")
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(version (string-append "2.2.0-1." (string-take commit 8)))
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(source (origin
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(method git-fetch)
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(uri (git-reference
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(url "https://github.com/libfann/fann.git")
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(commit commit)))
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(file-name (string-append name "-" version "-checkout"))
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(sha256
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(base32
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"0ibwpfrjs6q2lijs8slxjgzb2llcl6rk3v2ski4r6215g5jjhg3x"))))
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(build-system cmake-build-system)
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(arguments
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`(#:phases
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(modify-phases %standard-phases
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(replace 'check
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(lambda* (#:key outputs #:allow-other-keys)
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(let* ((out (assoc-ref outputs "out")))
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(with-directory-excursion (string-append (getcwd) "/tests")
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(invoke "./fann_tests"))))))))
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(home-page "http://leenissen.dk/fann/wp/")
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(synopsis "Fast Artificial Neural Network")
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(description
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"FANN is a free open source neural network library, which implements
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multilayer artificial neural networks in C with support for both fully
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connected and sparsely connected networks.")
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(license license:lgpl2.1))))
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(define-public libsvm
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(package
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(name "libsvm")
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(version "3.22")
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(source
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(origin
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(method url-fetch)
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(uri (string-append "https://www.csie.ntu.edu.tw/~cjlin/libsvm/"
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name "-" version ".tar.gz"))
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(sha256
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(base32
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"0zd7s19y5vb7agczl6456bn45cj1y64739sslaskw1qk7dywd0bd"))))
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(build-system gnu-build-system)
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(arguments
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`(#:tests? #f ;no "check" target
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#:phases (modify-phases %standard-phases
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(delete 'configure)
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(replace
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'install ; no ‘install’ target
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(lambda* (#:key outputs #:allow-other-keys)
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(let* ((out (assoc-ref outputs "out"))
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(bin (string-append out "/bin/")))
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(mkdir-p bin)
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(for-each (lambda (file)
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(copy-file file (string-append bin file)))
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'("svm-train"
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"svm-predict"
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"svm-scale")))
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#t)))))
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(home-page "http://www.csie.ntu.edu.tw/~cjlin/libsvm/")
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(synopsis "Library for Support Vector Machines")
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(description
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"LIBSVM is a machine learning library for support vector
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classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and
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distribution estimation (one-class SVM). It supports multi-class
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classification.")
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(license license:bsd-3)))
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(define-public python-libsvm
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(package (inherit libsvm)
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(name "python-libsvm")
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(build-system gnu-build-system)
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(arguments
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`(#:tests? #f ;no "check" target
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#:make-flags '("-C" "python")
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#:phases
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(modify-phases %standard-phases
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(delete 'configure)
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(replace
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'install ; no ‘install’ target
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(lambda* (#:key inputs outputs #:allow-other-keys)
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(let ((site (string-append (assoc-ref outputs "out")
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"/lib/python"
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(string-take
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(string-take-right
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(assoc-ref inputs "python") 5) 3)
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"/site-packages/")))
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(substitute* "python/svm.py"
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(("../libsvm.so.2") "libsvm.so.2"))
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(mkdir-p site)
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(for-each (lambda (file)
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(copy-file file (string-append site (basename file))))
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(find-files "python" "\\.py"))
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(copy-file "libsvm.so.2"
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(string-append site "libsvm.so.2")))
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#t)))))
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(inputs
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`(("python" ,python)))
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(synopsis "Python bindings of libSVM")))
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(define-public ghmm
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;; The latest release candidate is several years and a couple of fixes have
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;; been published since. This is why we download the sources from the SVN
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;; repository.
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(let ((svn-revision 2341))
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(package
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(name "ghmm")
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(version (string-append "0.9-rc3-0." (number->string svn-revision)))
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(source (origin
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(method svn-fetch)
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(uri (svn-reference
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(url "http://svn.code.sf.net/p/ghmm/code/trunk")
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(revision svn-revision)))
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(file-name (string-append name "-" version))
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(sha256
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(base32
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"0qbq1rqp94l530f043qzp8aw5lj7dng9wq0miffd7spd1ff638wq"))))
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(build-system gnu-build-system)
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(arguments
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`(#:imported-modules (,@%gnu-build-system-modules
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(guix build python-build-system))
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#:phases
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(modify-phases %standard-phases
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(add-after 'unpack 'enter-dir
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(lambda _ (chdir "ghmm") #t))
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(delete 'check)
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(add-after 'install 'check
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(assoc-ref %standard-phases 'check))
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(add-before 'check 'fix-PYTHONPATH
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(lambda* (#:key inputs outputs #:allow-other-keys)
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(let ((python-version ((@@ (guix build python-build-system)
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get-python-version)
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(assoc-ref inputs "python"))))
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(setenv "PYTHONPATH"
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(string-append (getenv "PYTHONPATH")
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":" (assoc-ref outputs "out")
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"/lib/python" python-version
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"/site-packages")))
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#t))
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(add-after 'enter-dir 'fix-runpath
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(lambda* (#:key outputs #:allow-other-keys)
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(substitute* "ghmmwrapper/setup.py"
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(("^(.*)extra_compile_args = \\[" line indent)
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(string-append indent
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"extra_link_args = [\"-Wl,-rpath="
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(assoc-ref outputs "out") "/lib\"],\n"
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line
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"\"-Wl,-rpath="
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(assoc-ref outputs "out")
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"/lib\", ")))
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#t))
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(add-after 'enter-dir 'disable-broken-tests
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(lambda _
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(substitute* "tests/Makefile.am"
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;; GHMM_SILENT_TESTS is assumed to be a command.
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(("TESTS_ENVIRONMENT.*") "")
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;; Do not build broken tests.
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(("chmm .*") "")
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(("read_fa .*") "")
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(("mcmc .*") "")
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(("label_higher_order_test.*$")
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"label_higher_order_test\n"))
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;; These Python unittests are broken as there is no gato.
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;; See https://sourceforge.net/p/ghmm/support-requests/3/
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(substitute* "ghmmwrapper/ghmmunittests.py"
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(("^(.*)def (testNewXML|testMultipleTransitionClasses|testNewXML)"
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line indent)
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(string-append indent
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"@unittest.skip(\"Disabled by Guix\")\n"
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line)))
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#t))
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(add-after 'disable-broken-tests 'autogen
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(lambda _
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(invoke "bash" "autogen.sh"))))))
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(inputs
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`(("python" ,python-2) ; only Python 2 is supported
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("libxml2" ,libxml2)))
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(native-inputs
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`(("pkg-config" ,pkg-config)
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("dejagnu" ,dejagnu)
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("swig" ,swig)
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("autoconf" ,autoconf)
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("automake" ,automake)
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("libtool" ,libtool)))
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(home-page "http://ghmm.org")
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(synopsis "Hidden Markov Model library")
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(description
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"The General Hidden Markov Model library (GHMM) is a C library with
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additional Python bindings implementing a wide range of types of @dfn{Hidden
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Markov Models} (HMM) and algorithms: discrete, continuous emissions, basic
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training, HMM clustering, HMM mixtures.")
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(license license:lgpl2.0+))))
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(define-public mcl
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(package
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(name "mcl")
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(version "14.137")
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(source (origin
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(method url-fetch)
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(uri (string-append
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"http://micans.org/mcl/src/mcl-"
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(string-replace-substring version "." "-")
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".tar.gz"))
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(sha256
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(base32
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"15xlax3z31lsn62vlg94hkm75nm40q4679amnfg13jm8m2bnhy5m"))))
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(build-system gnu-build-system)
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(arguments
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`(#:configure-flags (list "--enable-blast")))
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(inputs
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`(("perl" ,perl)))
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(home-page "http://micans.org/mcl/")
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(synopsis "Clustering algorithm for graphs")
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(description
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"The MCL algorithm is short for the @dfn{Markov Cluster Algorithm}, a
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fast and scalable unsupervised cluster algorithm for graphs (also known as
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networks) based on simulation of (stochastic) flow in graphs.")
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;; In the LICENCE file and web page it says "The software is licensed
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;; under the GNU General Public License, version 3.", but in several of
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;; the source code files it suggests GPL3 or later.
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;; http://listserver.ebi.ac.uk/pipermail/mcl-users/2016/000376.html
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(license license:gpl3)))
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(define-public ocaml-mcl
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(package
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(name "ocaml-mcl")
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(version "12-068oasis4")
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(source
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(origin
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(method url-fetch)
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(uri (string-append
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"https://github.com/fhcrc/mcl/archive/"
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version ".tar.gz"))
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(file-name (string-append name "-" version ".tar.gz"))
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(sha256
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(base32
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"1l5jbhwjpsj38x8b9698hfpkv75h8hn3kj0gihjhn8ym2cwwv110"))))
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(build-system ocaml-build-system)
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(arguments
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`(#:ocaml ,ocaml-4.02
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#:findlib ,ocaml4.02-findlib
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#:phases
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(modify-phases %standard-phases
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(add-before 'configure 'patch-paths
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(lambda _
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(substitute* "configure"
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(("SHELL = /bin/sh") (string-append "SHELL = "(which "sh"))))
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(substitute* "setup.ml"
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(("LDFLAGS=-fPIC")
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(string-append "LDFLAGS=-fPIC\"; \"SHELL=" (which "sh"))))
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#t)))))
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(home-page "https://github.com/fhcrc/mcl")
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(synopsis "OCaml wrappers around MCL")
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(description
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"This package provides OCaml bindings for the MCL graph clustering
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algorithm.")
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(license license:gpl3)))
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(define-public randomjungle
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(package
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(name "randomjungle")
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(version "2.1.0")
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(source
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(origin
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(method url-fetch)
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(uri (string-append
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"https://www.imbs.uni-luebeck.de/fileadmin/files/Software"
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"/randomjungle/randomjungle-" version ".tar_.gz"))
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(patches (search-patches "randomjungle-disable-static-build.patch"))
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(sha256
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(base32
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"12c8rf30cla71swx2mf4ww9mfd8jbdw5lnxd7dxhyw1ygrvg6y4w"))))
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(build-system gnu-build-system)
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(arguments
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`(#:configure-flags
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(list "--disable-static"
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(string-append "--with-boost="
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(assoc-ref %build-inputs "boost")))
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#:phases
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||
(modify-phases %standard-phases
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||
(add-before
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'configure 'set-CXXFLAGS
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(lambda _
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(setenv "CXXFLAGS" "-fpermissive ")
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#t)))))
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(inputs
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`(("boost" ,boost)
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("gsl" ,gsl)
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("libxml2" ,libxml2)
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("zlib" ,zlib)))
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(native-inputs
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`(("gfortran" ,gfortran)
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("gfortran:lib" ,gfortran "lib")))
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;; Non-portable assembly instructions are used so building fails on
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;; platforms other than x86_64 or i686.
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(supported-systems '("x86_64-linux" "i686-linux"))
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(home-page "https://www.imbs.uni-luebeck.de/forschung/software/details.html#c224")
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(synopsis "Implementation of the Random Forests machine learning method")
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||
(description
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||
"Random Jungle is an implementation of Random Forests. It is supposed to
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analyse high dimensional data. In genetics, it can be used for analysing big
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||
Genome Wide Association (GWA) data. Random Forests is a powerful machine
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learning method. Most interesting features are variable selection, missing
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||
value imputation, classifier creation, generalization error estimation and
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sample proximities between pairs of cases.")
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(license license:gpl3+)))
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||
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(define-public shogun
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(package
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(name "shogun")
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(version "6.1.3")
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||
(source
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||
(origin
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||
(method url-fetch)
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||
(uri (string-append
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||
"ftp://shogun-toolbox.org/shogun/releases/"
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||
(version-major+minor version)
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||
"/sources/shogun-" version ".tar.bz2"))
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||
(sha256
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||
(base32
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||
"1rn9skm3nw6hr7mr3lgp2gfqhi7ii0lyxck7qmqnf8avq349s5jp"))
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||
(modules '((guix build utils)
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||
(ice-9 rdelim)))
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||
(snippet
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||
'(begin
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||
;; Remove non-free sources and files referencing them
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||
(for-each delete-file
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||
(find-files "src/shogun/classifier/svm/"
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||
"SVMLight\\.(cpp|h)"))
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||
(for-each delete-file
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||
(find-files "examples/undocumented/libshogun/"
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||
(string-append
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||
"(classifier_.*svmlight.*|"
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||
"evaluation_cross_validation_locked_comparison).cpp")))
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||
;; Remove non-free functions.
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||
(define (delete-ifdefs file)
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||
(with-atomic-file-replacement file
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||
(lambda (in out)
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||
(let loop ((line (read-line in 'concat))
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||
(skipping? #f))
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||
(if (eof-object? line)
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||
#t
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||
(let ((skip-next?
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||
(or (and skipping?
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||
(not (string-prefix?
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||
"#endif //USE_SVMLIGHT" line)))
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||
(string-prefix?
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||
"#ifdef USE_SVMLIGHT" line))))
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||
(when (or (not skipping?)
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||
(and skipping? (not skip-next?)))
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||
(display line out))
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||
(loop (read-line in 'concat) skip-next?)))))))
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(for-each delete-ifdefs
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||
(append
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||
(find-files "src/shogun/classifier/mkl"
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"^MKLClassification\\.cpp")
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||
(find-files "src/shogun/classifier/svm"
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"^SVMLightOneClass\\.(cpp|h)")
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||
(find-files "src/shogun/multiclass"
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"^ScatterSVM\\.(cpp|h)")
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||
(find-files "src/shogun/kernel/"
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||
"^(Kernel|CombinedKernel|ProductKernel)\\.(cpp|h)")
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||
(find-files "src/shogun/regression/svr"
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"^(MKLRegression|SVRLight)\\.(cpp|h)")
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||
(find-files "src/shogun/transfer/domain_adaptation"
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"^DomainAdaptationSVM\\.(cpp|h)")))
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||
#t))))
|
||
(build-system cmake-build-system)
|
||
(arguments
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||
'(#:tests? #f ;no check target
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||
#:phases
|
||
(modify-phases %standard-phases
|
||
(add-after 'unpack 'delete-broken-symlinks
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||
(lambda _
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||
(for-each delete-file '("applications/arts/data"
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||
"applications/asp/data"
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||
"applications/easysvm/data"
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||
"applications/msplicer/data"
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||
"applications/ocr/data"
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||
"examples/meta/data"
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||
"examples/undocumented/data"))
|
||
#t))
|
||
(add-after 'unpack 'change-R-target-path
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||
(lambda* (#:key outputs #:allow-other-keys)
|
||
(substitute* '("src/interfaces/r/CMakeLists.txt"
|
||
"examples/meta/r/CMakeLists.txt")
|
||
(("\\$\\{R_COMPONENT_LIB_PATH\\}")
|
||
(string-append (assoc-ref outputs "out")
|
||
"/lib/R/library/")))
|
||
#t))
|
||
(add-after 'unpack 'fix-octave-modules
|
||
(lambda* (#:key outputs #:allow-other-keys)
|
||
(substitute* "src/interfaces/octave/CMakeLists.txt"
|
||
(("^include_directories\\(\\$\\{OCTAVE_INCLUDE_DIRS\\}")
|
||
"include_directories(${OCTAVE_INCLUDE_DIRS} ${OCTAVE_INCLUDE_DIRS}/octave")
|
||
;; change target directory
|
||
(("\\$\\{OCTAVE_OCT_LOCAL_API_FILE_DIR\\}")
|
||
(string-append (assoc-ref outputs "out")
|
||
"/share/octave/packages")))
|
||
(substitute* '("src/interfaces/octave/swig_typemaps.i"
|
||
"src/interfaces/octave/sg_print_functions.cpp")
|
||
;; "octave/config.h" and "octave/oct-obj.h" deprecated in Octave.
|
||
(("octave/config\\.h") "octave/octave-config.h")
|
||
(("octave/oct-obj.h") "octave/ovl.h"))
|
||
#t))
|
||
(add-after 'unpack 'move-rxcpp
|
||
(lambda* (#:key inputs #:allow-other-keys)
|
||
(let ((rxcpp-dir "shogun/third-party/rxcpp"))
|
||
(mkdir-p rxcpp-dir)
|
||
(install-file (assoc-ref inputs "rxcpp") rxcpp-dir)
|
||
#t)))
|
||
(add-before 'build 'set-HOME
|
||
;; $HOME needs to be set at some point during the build phase
|
||
(lambda _ (setenv "HOME" "/tmp") #t)))
|
||
#:configure-flags
|
||
(list "-DCMAKE_BUILD_WITH_INSTALL_RPATH=TRUE"
|
||
"-DUSE_SVMLIGHT=OFF" ;disable proprietary SVMLIGHT
|
||
"-DBUILD_META_EXAMPLES=OFF" ;requires unpackaged ctags
|
||
;;"-DINTERFACE_JAVA=ON" ;requires unpackaged jblas
|
||
;;"-DINTERFACE_RUBY=ON" ;requires unpackaged ruby-narray
|
||
;;"-DINTERFACE_PERL=ON" ;"FindPerlLibs" does not exist
|
||
;;"-DINTERFACE_LUA=ON" ;fails because lua doesn't build pkgconfig file
|
||
"-DINTERFACE_OCTAVE=ON"
|
||
"-DINTERFACE_PYTHON=ON"
|
||
"-DINTERFACE_R=ON")))
|
||
(inputs
|
||
`(("python" ,python)
|
||
("numpy" ,python-numpy)
|
||
("r-minimal" ,r-minimal)
|
||
("octave" ,octave-cli)
|
||
("swig" ,swig)
|
||
("eigen" ,eigen)
|
||
("hdf5" ,hdf5)
|
||
("atlas" ,atlas)
|
||
("arpack" ,arpack-ng)
|
||
("lapack" ,lapack)
|
||
("glpk" ,glpk)
|
||
("libxml2" ,libxml2)
|
||
("lzo" ,lzo)
|
||
("zlib" ,zlib)))
|
||
(native-inputs
|
||
`(("pkg-config" ,pkg-config)
|
||
("rxcpp" ,rxcpp)))
|
||
;; Non-portable SSE instructions are used so building fails on platforms
|
||
;; other than x86_64.
|
||
(supported-systems '("x86_64-linux"))
|
||
(home-page "http://shogun-toolbox.org/")
|
||
(synopsis "Machine learning toolbox")
|
||
(description
|
||
"The Shogun Machine learning toolbox provides a wide range of unified and
|
||
efficient Machine Learning (ML) methods. The toolbox seamlessly allows to
|
||
combine multiple data representations, algorithm classes, and general purpose
|
||
tools. This enables both rapid prototyping of data pipelines and extensibility
|
||
in terms of new algorithms.")
|
||
(license license:gpl3+)))
|
||
|
||
(define-public rxcpp
|
||
(package
|
||
(name "rxcpp")
|
||
(version "4.0.0")
|
||
(source
|
||
(origin
|
||
(method url-fetch)
|
||
(uri (string-append "https://github.com/ReactiveX/RxCpp/archive/v"
|
||
version ".tar.gz"))
|
||
(sha256
|
||
(base32
|
||
"0y2isr8dy2n1yjr9c5570kpc9lvdlch6jv0jvw000amwn5d3krsh"))
|
||
(file-name (string-append name "-" version ".tar.gz"))))
|
||
(build-system cmake-build-system)
|
||
(arguments
|
||
`(#:phases
|
||
(modify-phases %standard-phases
|
||
(add-after 'unpack 'remove-werror
|
||
(lambda _
|
||
(substitute* (find-files ".")
|
||
(("-Werror") ""))
|
||
#t))
|
||
(replace 'check
|
||
(lambda _
|
||
(invoke "ctest"))))))
|
||
(native-inputs
|
||
`(("catch" ,catch-framework)))
|
||
(home-page "http://reactivex.io/")
|
||
(synopsis "Reactive Extensions for C++")
|
||
(description
|
||
"The Reactive Extensions for C++ (RxCpp) is a library of algorithms for
|
||
values-distributed-in-time. ReactiveX is a library for composing asynchronous
|
||
and event-based programs by using observable sequences.
|
||
|
||
It extends the observer pattern to support sequences of data and/or events and
|
||
adds operators that allow you to compose sequences together declaratively while
|
||
abstracting away concerns about things like low-level threading,
|
||
synchronization, thread-safety, concurrent data structures, and non-blocking
|
||
I/O.")
|
||
(license license:asl2.0)))
|
||
|
||
(define-public r-adaptivesparsity
|
||
(package
|
||
(name "r-adaptivesparsity")
|
||
(version "1.6")
|
||
(source (origin
|
||
(method url-fetch)
|
||
(uri (cran-uri "AdaptiveSparsity" version))
|
||
(sha256
|
||
(base32
|
||
"0imr5m8mll9j6n4icsv6z9rl5kbnwsp9wvzrg7n90nnmcxq2cz91"))))
|
||
(properties
|
||
`((upstream-name . "AdaptiveSparsity")))
|
||
(build-system r-build-system)
|
||
(arguments
|
||
`(#:phases
|
||
(modify-phases %standard-phases
|
||
(add-after 'unpack 'link-against-armadillo
|
||
(lambda _
|
||
(substitute* "src/Makevars"
|
||
(("PKG_LIBS=" prefix)
|
||
(string-append prefix "-larmadillo"))))))))
|
||
(propagated-inputs
|
||
`(("r-mass" ,r-mass)
|
||
("r-matrix" ,r-matrix)
|
||
("r-rcpp" ,r-rcpp)
|
||
("r-rcpparmadillo" ,r-rcpparmadillo)))
|
||
(inputs
|
||
`(("armadillo" ,armadillo)))
|
||
(home-page "https://cran.r-project.org/web/packages/AdaptiveSparsity")
|
||
(synopsis "Adaptive sparsity models")
|
||
(description
|
||
"This package implements the Figueiredo machine learning algorithm for
|
||
adaptive sparsity and the Wong algorithm for adaptively sparse gaussian
|
||
geometric models.")
|
||
(license license:lgpl3+)))
|
||
|
||
(define-public r-kernlab
|
||
(package
|
||
(name "r-kernlab")
|
||
(version "0.9-27")
|
||
(source
|
||
(origin
|
||
(method url-fetch)
|
||
(uri (cran-uri "kernlab" version))
|
||
(sha256
|
||
(base32
|
||
"1m0xqf6gyvwayz7w3c83y32ayvnlz0jicj8ijk808zq9sh7dbbgn"))))
|
||
(build-system r-build-system)
|
||
(home-page "https://cran.r-project.org/web/packages/kernlab")
|
||
(synopsis "Kernel-based machine learning tools")
|
||
(description
|
||
"This package provides kernel-based machine learning methods for
|
||
classification, regression, clustering, novelty detection, quantile regression
|
||
and dimensionality reduction. Among other methods @code{kernlab} includes
|
||
Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes
|
||
and a QP solver.")
|
||
(license license:gpl2)))
|
||
|
||
(define-public dlib
|
||
(package
|
||
(name "dlib")
|
||
(version "19.7")
|
||
(source (origin
|
||
(method url-fetch)
|
||
(uri (string-append
|
||
"http://dlib.net/files/dlib-" version ".tar.bz2"))
|
||
(sha256
|
||
(base32
|
||
"1mljz02kwkrbggyncxv5fpnyjdybw2qihaacb3js8yfkw12vwpc2"))
|
||
(modules '((guix build utils)))
|
||
(snippet
|
||
'(begin
|
||
;; Delete ~13MB of bundled dependencies.
|
||
(delete-file-recursively "dlib/external")
|
||
(delete-file-recursively "docs/dlib/external")
|
||
#t))))
|
||
(build-system cmake-build-system)
|
||
(arguments
|
||
`(#:phases
|
||
(modify-phases %standard-phases
|
||
(add-after 'unpack 'disable-asserts
|
||
(lambda _
|
||
;; config.h recommends explicitly enabling or disabling asserts
|
||
;; when building as a shared library. By default neither is set.
|
||
(substitute* "dlib/config.h"
|
||
(("^//#define DLIB_DISABLE_ASSERTS") "#define DLIB_DISABLE_ASSERTS"))
|
||
#t))
|
||
(add-after 'disable-asserts 'disable-failing-tests
|
||
(lambda _
|
||
;; One test times out on MIPS, so we need to disable it.
|
||
;; Others are flaky on some platforms.
|
||
(let* ((system ,(or (%current-target-system)
|
||
(%current-system)))
|
||
(disabled-tests (cond
|
||
((string-prefix? "mips64" system)
|
||
'("object_detector" ; timeout
|
||
"data_io"))
|
||
((string-prefix? "armhf" system)
|
||
'("learning_to_track"))
|
||
((string-prefix? "i686" system)
|
||
'("optimization"))
|
||
(else '()))))
|
||
(for-each
|
||
(lambda (test)
|
||
(substitute* "dlib/test/makefile"
|
||
(((string-append "SRC \\+= " test "\\.cpp")) "")))
|
||
disabled-tests)
|
||
#t)))
|
||
(replace 'check
|
||
(lambda _
|
||
;; No test target, so we build and run the unit tests here.
|
||
(let ((test-dir (string-append "../dlib-" ,version "/dlib/test")))
|
||
(with-directory-excursion test-dir
|
||
(invoke "make" "-j" (number->string (parallel-job-count)))
|
||
(invoke "./dtest" "--runall"))
|
||
#t)))
|
||
(add-after 'install 'delete-static-library
|
||
(lambda* (#:key outputs #:allow-other-keys)
|
||
(delete-file (string-append (assoc-ref outputs "out")
|
||
"/lib/libdlib.a"))
|
||
#t)))))
|
||
(native-inputs
|
||
`(("pkg-config" ,pkg-config)
|
||
;; For tests.
|
||
("libnsl" ,libnsl)))
|
||
(inputs
|
||
`(("giflib" ,giflib)
|
||
("lapack" ,lapack)
|
||
("libjpeg" ,libjpeg)
|
||
("libpng" ,libpng)
|
||
("libx11" ,libx11)
|
||
("openblas" ,openblas)
|
||
("zlib" ,zlib)))
|
||
(synopsis
|
||
"Toolkit for making machine learning and data analysis applications in C++")
|
||
(description
|
||
"Dlib is a modern C++ toolkit containing machine learning algorithms and
|
||
tools. It is used in both industry and academia in a wide range of domains
|
||
including robotics, embedded devices, mobile phones, and large high performance
|
||
computing environments.")
|
||
(home-page "http://dlib.net")
|
||
(license license:boost1.0)))
|
||
|
||
(define-public python-scikit-learn
|
||
(package
|
||
(name "python-scikit-learn")
|
||
(version "0.20.1")
|
||
(source
|
||
(origin
|
||
(method git-fetch)
|
||
(uri (git-reference
|
||
(url "https://github.com/scikit-learn/scikit-learn.git")
|
||
(commit version)))
|
||
(file-name (git-file-name name version))
|
||
(sha256
|
||
(base32
|
||
"0fkhwg3xn1s7ln9q1szq6kwc4jhwvjh8w4kmv9wcrqy7cq3lbv0d"))))
|
||
(build-system python-build-system)
|
||
(arguments
|
||
`(#:phases
|
||
(modify-phases %standard-phases
|
||
(add-after 'build 'build-ext
|
||
(lambda _ (invoke "python" "setup.py" "build_ext" "--inplace") #t))
|
||
(replace 'check
|
||
(lambda _
|
||
;; Restrict OpenBLAS threads to prevent segfaults while testing!
|
||
(setenv "OPENBLAS_NUM_THREADS" "1")
|
||
|
||
;; Some tests require write access to $HOME.
|
||
(setenv "HOME" "/tmp")
|
||
|
||
(invoke "pytest" "sklearn" "-m" "not network")))
|
||
;; FIXME: This fails with permission denied
|
||
(delete 'reset-gzip-timestamps))))
|
||
(inputs
|
||
`(("openblas" ,openblas)))
|
||
(native-inputs
|
||
`(("python-pytest" ,python-pytest)
|
||
("python-pandas" ,python-pandas) ;for tests
|
||
("python-cython" ,python-cython)))
|
||
(propagated-inputs
|
||
`(("python-numpy" ,python-numpy)
|
||
("python-scipy" ,python-scipy)))
|
||
(home-page "http://scikit-learn.org/")
|
||
(synopsis "Machine Learning in Python")
|
||
(description
|
||
"Scikit-learn provides simple and efficient tools for data mining and
|
||
data analysis.")
|
||
(license license:bsd-3)))
|
||
|
||
(define-public python2-scikit-learn
|
||
(package-with-python2 python-scikit-learn))
|
||
|
||
(define-public python-autograd
|
||
(let* ((commit "442205dfefe407beffb33550846434baa90c4de7")
|
||
(revision "0")
|
||
(version (git-version "0.0.0" revision commit)))
|
||
(package
|
||
(name "python-autograd")
|
||
(home-page "https://github.com/HIPS/autograd")
|
||
(source (origin
|
||
(method git-fetch)
|
||
(uri (git-reference
|
||
(url home-page)
|
||
(commit commit)))
|
||
(sha256
|
||
(base32
|
||
"189sv2xb0mwnjawa9z7mrgdglc1miaq93pnck26r28fi1jdwg0z4"))
|
||
(file-name (git-file-name name version))))
|
||
(version version)
|
||
(build-system python-build-system)
|
||
(native-inputs
|
||
`(("python-nose" ,python-nose)
|
||
("python-pytest" ,python-pytest)))
|
||
(propagated-inputs
|
||
`(("python-future" ,python-future)
|
||
("python-numpy" ,python-numpy)))
|
||
(arguments
|
||
`(#:phases (modify-phases %standard-phases
|
||
(replace 'check
|
||
(lambda _
|
||
(invoke "py.test" "-v"))))))
|
||
(synopsis "Efficiently computes derivatives of NumPy code")
|
||
(description "Autograd can automatically differentiate native Python and
|
||
NumPy code. It can handle a large subset of Python's features, including loops,
|
||
ifs, recursion and closures, and it can even take derivatives of derivatives
|
||
of derivatives. It supports reverse-mode differentiation
|
||
(a.k.a. backpropagation), which means it can efficiently take gradients of
|
||
scalar-valued functions with respect to array-valued arguments, as well as
|
||
forward-mode differentiation, and the two can be composed arbitrarily. The
|
||
main intended application of Autograd is gradient-based optimization.")
|
||
(license license:expat))))
|
||
|
||
(define-public python2-autograd
|
||
(package-with-python2 python-autograd))
|
||
|
||
(define-public lightgbm
|
||
(package
|
||
(name "lightgbm")
|
||
(version "2.0.12")
|
||
(source (origin
|
||
(method url-fetch)
|
||
(uri (string-append
|
||
"https://github.com/Microsoft/LightGBM/archive/v"
|
||
version ".tar.gz"))
|
||
(sha256
|
||
(base32
|
||
"132zf0yk0545mg72hyzxm102g3hpb6ixx9hnf8zd2k55gas6cjj1"))
|
||
(file-name (string-append name "-" version ".tar.gz"))))
|
||
(native-inputs
|
||
`(("python-pytest" ,python-pytest)
|
||
("python-nose" ,python-nose)))
|
||
(inputs
|
||
`(("openmpi" ,openmpi)))
|
||
(propagated-inputs
|
||
`(("python-numpy" ,python-numpy)
|
||
("python-scipy" ,python-scipy)))
|
||
(arguments
|
||
`(#:configure-flags
|
||
'("-DUSE_MPI=ON")
|
||
#:phases
|
||
(modify-phases %standard-phases
|
||
(replace 'check
|
||
(lambda* (#:key outputs #:allow-other-keys)
|
||
(with-directory-excursion ,(string-append "../LightGBM-" version)
|
||
(invoke "pytest" "tests/c_api_test/test_.py")))))))
|
||
(build-system cmake-build-system)
|
||
(home-page "https://github.com/Microsoft/LightGBM")
|
||
(synopsis "Gradient boosting framework based on decision tree algorithms")
|
||
(description "LightGBM is a gradient boosting framework that uses tree
|
||
based learning algorithms. It is designed to be distributed and efficient with
|
||
the following advantages:
|
||
|
||
@itemize
|
||
@item Faster training speed and higher efficiency
|
||
@item Lower memory usage
|
||
@item Better accuracy
|
||
@item Parallel and GPU learning supported (not enabled in this package)
|
||
@item Capable of handling large-scale data
|
||
@end itemize\n")
|
||
(license license:expat)))
|
||
|
||
(define-public vowpal-wabbit
|
||
;; Language bindings not included.
|
||
(package
|
||
(name "vowpal-wabbit")
|
||
(version "8.5.0")
|
||
(source (origin
|
||
(method url-fetch)
|
||
(uri (string-append
|
||
"https://github.com/JohnLangford/vowpal_wabbit/archive/"
|
||
version ".tar.gz"))
|
||
(sha256
|
||
(base32
|
||
"0clp2kb7rk5sckhllxjr5a651awf4s8dgzg4659yh4hf5cqnf0gr"))
|
||
(file-name (string-append name "-" version ".tar.gz"))))
|
||
(inputs
|
||
`(("boost" ,boost)
|
||
("zlib" ,zlib)))
|
||
(arguments
|
||
`(#:configure-flags
|
||
(list (string-append "--with-boost="
|
||
(assoc-ref %build-inputs "boost")))))
|
||
(build-system gnu-build-system)
|
||
(home-page "https://github.com/JohnLangford/vowpal_wabbit")
|
||
(synopsis "Fast machine learning library for online learning")
|
||
(description "Vowpal Wabbit is a machine learning system with techniques
|
||
such as online, hashing, allreduce, reductions, learning2search, active, and
|
||
interactive learning.")
|
||
(license license:bsd-3)))
|
||
|
||
(define-public python2-fastlmm
|
||
(package
|
||
(name "python2-fastlmm")
|
||
(version "0.2.21")
|
||
(source
|
||
(origin
|
||
(method url-fetch)
|
||
(uri (pypi-uri "fastlmm" version ".zip"))
|
||
(sha256
|
||
(base32
|
||
"1q8c34rpmwkfy3r4d5172pzdkpfryj561897z9r3x22gq7813x1m"))))
|
||
(build-system python-build-system)
|
||
(arguments
|
||
`(#:tests? #f ; some test files are missing
|
||
#:python ,python-2)) ; only Python 2.7 is supported
|
||
(propagated-inputs
|
||
`(("python2-numpy" ,python2-numpy)
|
||
("python2-scipy" ,python2-scipy)
|
||
("python2-matplotlib" ,python2-matplotlib)
|
||
("python2-pandas" ,python2-pandas)
|
||
("python2-scikit-learn" ,python2-scikit-learn)
|
||
("python2-pysnptools" ,python2-pysnptools)))
|
||
(native-inputs
|
||
`(("unzip" ,unzip)
|
||
("python2-cython" ,python2-cython)
|
||
("python2-mock" ,python2-mock)
|
||
("python2-nose" ,python2-nose)))
|
||
(home-page "http://research.microsoft.com/en-us/um/redmond/projects/mscompbio/fastlmm/")
|
||
(synopsis "Perform genome-wide association studies on large data sets")
|
||
(description
|
||
"FaST-LMM, which stands for Factored Spectrally Transformed Linear Mixed
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Models, is a program for performing both single-SNP and SNP-set genome-wide
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association studies (GWAS) on extremely large data sets.")
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(license license:asl2.0)))
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