;;; Copyright © 2018 Fis Trivial <ybbs.daans@hotmail.com>
;;; Copyright © 2018 Julien Lepiller <julien@lepiller.eu>
;;; Copyright © 2018 Björn Höfling <bjoern.hoefling@bjoernhoefling.de>
+;;; Copyright © 2019 Nicolas Goaziou <mail@nicolasgoaziou.fr>
+;;; Copyright © 2019 Guillaume Le Vaillant <glv@posteo.net>
+;;; Copyright © 2019 Brett Gilio <brettg@gnu.org>
;;;
;;; This file is part of GNU Guix.
;;;
#:use-module (guix utils)
#:use-module (guix download)
#:use-module (guix svn-download)
+ #:use-module (guix build-system asdf)
#:use-module (guix build-system cmake)
#:use-module (guix build-system gnu)
#:use-module (guix build-system ocaml)
#:use-module (gnu packages dejagnu)
#:use-module (gnu packages gcc)
#:use-module (gnu packages glib)
+ #:use-module (gnu packages graphviz)
#:use-module (gnu packages gstreamer)
#:use-module (gnu packages image)
#:use-module (gnu packages linux)
+ #:use-module (gnu packages lisp-xyz)
#:use-module (gnu packages maths)
#:use-module (gnu packages mpi)
#:use-module (gnu packages ocaml)
#:use-module (gnu packages pkg-config)
#:use-module (gnu packages protobuf)
#:use-module (gnu packages python)
+ #:use-module (gnu packages python-science)
#:use-module (gnu packages python-web)
#:use-module (gnu packages python-xyz)
#:use-module (gnu packages serialization)
+ #:use-module (gnu packages sphinx)
#:use-module (gnu packages statistics)
#:use-module (gnu packages sqlite)
#:use-module (gnu packages swig)
(home-page "http://leenissen.dk/fann/wp/")
(synopsis "Fast Artificial Neural Network")
(description
- "FANN is a free open source neural network library, which implements
-multilayer artificial neural networks in C with support for both fully
-connected and sparsely connected networks.")
+ "FANN is a neural network library, which implements multilayer
+artificial neural networks in C with support for both fully connected and
+sparsely connected networks.")
(license license:lgpl2.1))))
(define-public libsvm
(uri (svn-reference
(url "http://svn.code.sf.net/p/ghmm/code/trunk")
(revision svn-revision)))
- (file-name (string-append name "-" version))
+ (file-name (string-append name "-" version "-checkout"))
(sha256
(base32
"0qbq1rqp94l530f043qzp8aw5lj7dng9wq0miffd7spd1ff638wq"))))
(assoc-ref %standard-phases 'check))
(add-before 'check 'fix-PYTHONPATH
(lambda* (#:key inputs outputs #:allow-other-keys)
- (let ((python-version ((@@ (guix build python-build-system)
- get-python-version)
+ (let ((python-version (python-version
(assoc-ref inputs "python"))))
(setenv "PYTHONPATH"
(string-append (getenv "PYTHONPATH")
(string-append indent
"@unittest.skip(\"Disabled by Guix\")\n"
line)))
- #t))
- (add-after 'disable-broken-tests 'autogen
- (lambda _
- (invoke "bash" "autogen.sh"))))))
+ #t)))))
(inputs
`(("python" ,python-2) ; only Python 2 is supported
("libxml2" ,libxml2)))
`(#:configure-flags
(list ,@(match (%current-system)
((or "x86_64-linux" "i686-linux")
- '("-DCMAKE_CXX_FLAGS=-msse4.1"))
+ '("-DCMAKE_CXX_FLAGS=-msse2"))
(_ '())))
#:phases
(modify-phases %standard-phases
(inputs
`(("giflib" ,giflib)
("lapack" ,lapack)
- ("libjpeg" ,libjpeg)
+ ("libjpeg" ,libjpeg-turbo)
("libpng" ,libpng)
("libx11" ,libx11)
("openblas" ,openblas)
(define-public python-scikit-learn
(package
(name "python-scikit-learn")
- (version "0.20.1")
+ (version "0.20.4")
(source
(origin
(method git-fetch)
(file-name (git-file-name name version))
(sha256
(base32
- "0fkhwg3xn1s7ln9q1szq6kwc4jhwvjh8w4kmv9wcrqy7cq3lbv0d"))))
+ "08zbzi8yx5wdlxfx9jap61vg1malc9ajf576w7a0liv6jvvrxlpj"))))
(build-system python-build-system)
(arguments
`(#:phases
(setenv "HOME" "/tmp")
(invoke "pytest" "sklearn" "-m" "not network")))
- ;; FIXME: This fails with permission denied
- (delete 'reset-gzip-timestamps))))
+ (add-before 'reset-gzip-timestamps 'make-files-writable
+ (lambda* (#:key outputs #:allow-other-keys)
+ ;; Make sure .gz files are writable so that the
+ ;; 'reset-gzip-timestamps' phase can do its work.
+ (let ((out (assoc-ref outputs "out")))
+ (for-each make-file-writable
+ (find-files out "\\.gz$"))
+ #t))))))
(inputs
`(("openblas" ,openblas)))
(native-inputs
(replace 'configure
(lambda* (#:key inputs #:allow-other-keys)
(let ((glib (assoc-ref inputs "glib")))
- (setenv "CXXFLAGS" "-std=c++11 -fPIC")
+ (setenv "CXXFLAGS" "-fPIC")
(setenv "CPLUS_INCLUDE_PATH"
(string-append glib "/include/glib-2.0:"
glib "/lib/glib-2.0/include:"
(assoc-ref inputs "gstreamer")
- "/include/gstreamer-1.0:"
- (getenv "CPLUS_INCLUDE_PATH"))))
+ "/include/gstreamer-1.0")))
(substitute* "Makefile"
(("include \\$\\(KALDI_ROOT\\)/src/kaldi.mk") "")
- (("\\$\\(error Cannot find") "#"))))
+ (("\\$\\(error Cannot find") "#"))
+ #t))
(add-before 'build 'build-depend
(lambda* (#:key make-flags #:allow-other-keys)
(apply invoke "make" "depend" make-flags)))
"-DgRPC_SSL_PROVIDER=package"
"-DgRPC_PROTOBUF_PROVIDER=package")))
(inputs
- `(("c-ares" ,c-ares-next)
+ `(("c-ares" ,c-ares/cmake)
("openssl" ,openssl)
("zlib" ,zlib)))
(native-inputs
- `(("protobuf" ,protobuf-next)
+ `(("protobuf" ,protobuf)
("python" ,python-wrapper)))
(home-page "https://grpc.io")
(synopsis "High performance universal RPC framework")
- (description "gRPC is a modern open source high performance @dfn{Remote
-Procedure Call} (RPC) framework that can run in any environment. It can
-efficiently connect services in and across data centers with pluggable support
-for load balancing, tracing, health checking and authentication. It is also
-applicable in last mile of distributed computing to connect devices, mobile
-applications and browsers to backend services.")
+ (description "gRPC is a modern high performance @dfn{Remote Procedure Call}
+(RPC) framework that can run in any environment. It can efficiently connect
+services in and across data centers with pluggable support for load balancing,
+tracing, health checking and authentication. It is also applicable in last
+mile of distributed computing to connect devices, mobile applications and
+browsers to backend services.")
(license license:asl2.0)))
;; Note that Tensorflow includes a "third_party" directory, which seems to not
#t))))))
(native-inputs
`(("pkg-config" ,pkg-config)
- ("protobuf:native" ,protobuf-next) ; protoc
- ("protobuf:src" ,(package-source protobuf-next))
+ ("protobuf:native" ,protobuf-3.6) ; protoc
+ ("protobuf:src" ,(package-source protobuf-3.6))
("eigen:src" ,(package-source eigen-for-tensorflow))
+ ;; install_pip_packages.sh wants setuptools 39.1.0 specifically.
+ ("python-setuptools" ,python-setuptools-for-tensorflow)
+
;; The commit hashes and URLs for third-party source code are taken
;; from "tensorflow/workspace.bzl".
("boringssl-src"
("python-gast" ,python-gast)
("python-grpcio" ,python-grpcio)
("python-numpy" ,python-numpy)
- ("python-protobuf" ,python-protobuf-next)
+ ("python-protobuf" ,python-protobuf-3.6)
("python-six" ,python-six)
("python-termcolo" ,python-termcolor)
("python-wheel" ,python-wheel)))
(inputs
- `(("c-ares" ,c-ares-next)
+ `(("c-ares" ,c-ares)
("eigen" ,eigen-for-tensorflow)
("gemmlowp" ,gemmlowp-for-tensorflow)
("lmdb" ,lmdb)
- ("libjpeg" ,libjpeg)
+ ("libjpeg" ,libjpeg-turbo)
("libpng" ,libpng)
("giflib" ,giflib)
("grpc" ,grpc)
("jsoncpp" ,jsoncpp-for-tensorflow)
("snappy" ,snappy)
("sqlite" ,sqlite)
- ("protobuf" ,protobuf-next)
+ ("protobuf" ,protobuf-3.6)
("python" ,python-wrapper)
("zlib" ,zlib)))
(home-page "https://tensorflow.org")
together building blocks and a subclassing API with an imperative style for
advanced research.")
(license license:asl2.0)))
+
+(define-public python-iml
+ (package
+ (name "python-iml")
+ (version "0.6.2")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (pypi-uri "iml" version))
+ (sha256
+ (base32
+ "1k8szlpm19rcwcxdny9qdm3gmaqq8akb4xlvrzyz8c2d679aak6l"))))
+ (build-system python-build-system)
+ (propagated-inputs
+ `(("ipython" ,python-ipython)
+ ("nose" ,python-nose)
+ ("numpy" ,python-numpy)
+ ("pandas" ,python-pandas)
+ ("scipy" ,python-scipy)))
+ (home-page "http://github.com/interpretable-ml/iml")
+ (synopsis "Interpretable Machine Learning (iML) package")
+ (description "Interpretable ML (iML) is a set of data type objects,
+visualizations, and interfaces that can be used by any method designed to
+explain the predictions of machine learning models (or really the output of
+any function). It currently contains the interface and IO code from the Shap
+project, and it will potentially also do the same for the Lime project.")
+ (license license:expat)))
+
+(define-public python-keras-applications
+ (package
+ (name "python-keras-applications")
+ (version "1.0.8")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (pypi-uri "Keras_Applications" version))
+ (sha256
+ (base32
+ "1rcz31ca4axa6kzhjx4lwqxbg4wvlljkj8qj9a7p9sfd5fhzjyam"))))
+ (build-system python-build-system)
+ ;; The tests require Keras, but this package is needed to build Keras.
+ (arguments '(#:tests? #f))
+ (propagated-inputs
+ `(("python-h5py" ,python-h5py)
+ ("python-numpy" ,python-numpy)))
+ (native-inputs
+ `(("python-pytest" ,python-pytest)
+ ("python-pytest-cov" ,python-pytest-cov)
+ ("python-pytest-pep8" ,python-pytest-pep8)
+ ("python-pytest-xdist" ,python-pytest-xdist)))
+ (home-page "https://github.com/keras-team/keras-applications")
+ (synopsis "Reference implementations of popular deep learning models")
+ (description
+ "This package provides reference implementations of popular deep learning
+models for use with the Keras deep learning framework.")
+ (license license:expat)))
+
+(define-public python-keras-preprocessing
+ (package
+ (name "python-keras-preprocessing")
+ (version "1.1.0")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (pypi-uri "Keras_Preprocessing" version))
+ (sha256
+ (base32
+ "1r98nm4k1svsqjyaqkfk23i31bl1kcfcyp7094yyj3c43phfp3as"))))
+ (build-system python-build-system)
+ (propagated-inputs
+ `(("python-numpy" ,python-numpy)
+ ("python-six" ,python-six)))
+ (native-inputs
+ `(("python-pandas" ,python-pandas)
+ ("python-pillow" ,python-pillow)
+ ("python-pytest" ,python-pytest)
+ ("python-pytest-cov" ,python-pytest-cov)
+ ("python-pytest-xdist" ,python-pytest-xdist)
+ ("tensorflow" ,tensorflow)))
+ (home-page "https://github.com/keras-team/keras-preprocessing/")
+ (synopsis "Data preprocessing and augmentation for deep learning models")
+ (description
+ "Keras Preprocessing is the data preprocessing and data augmentation
+module of the Keras deep learning library. It provides utilities for working
+with image data, text data, and sequence data.")
+ (license license:expat)))
+
+(define-public python-keras
+ (package
+ (name "python-keras")
+ (version "2.2.4")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (pypi-uri "Keras" version))
+ (patches (search-patches "python-keras-integration-test.patch"))
+ (sha256
+ (base32
+ "1j8bsqzh49vjdxy6l1k4iwax5vpjzniynyd041xjavdzvfii1dlh"))))
+ (build-system python-build-system)
+ (arguments
+ `(#:phases
+ (modify-phases %standard-phases
+ (add-after 'unpack 'remove-tests-for-unavailable-features
+ (lambda _
+ (delete-file "keras/backend/theano_backend.py")
+ (delete-file "keras/backend/cntk_backend.py")
+ (delete-file "tests/keras/backend/backend_test.py")
+
+ ;; FIXME: This doesn't work because Tensorflow is missing the
+ ;; coder ops library.
+ (delete-file "tests/keras/test_callbacks.py")
+ #t))
+ (replace 'check
+ (lambda _
+ ;; These tests attempt to download data files from the internet.
+ (delete-file "tests/integration_tests/test_datasets.py")
+ (delete-file "tests/integration_tests/imagenet_utils_test.py")
+
+ (setenv "PYTHONPATH"
+ (string-append (getcwd) "/build/lib:"
+ (getenv "PYTHONPATH")))
+ (invoke "py.test" "-v"
+ "-p" "no:cacheprovider"
+ "--ignore" "keras/utils"))))))
+ (propagated-inputs
+ `(("python-h5py" ,python-h5py)
+ ("python-keras-applications" ,python-keras-applications)
+ ("python-keras-preprocessing" ,python-keras-preprocessing)
+ ("python-numpy" ,python-numpy)
+ ("python-pydot" ,python-pydot)
+ ("python-pyyaml" ,python-pyyaml)
+ ("python-scipy" ,python-scipy)
+ ("python-six" ,python-six)
+ ("tensorflow" ,tensorflow)
+ ("graphviz" ,graphviz)))
+ (native-inputs
+ `(("python-pandas" ,python-pandas)
+ ("python-pytest" ,python-pytest)
+ ("python-pytest-cov" ,python-pytest-cov)
+ ("python-pytest-pep8" ,python-pytest-pep8)
+ ("python-pytest-timeout" ,python-pytest-timeout)
+ ("python-pytest-xdist" ,python-pytest-xdist)
+ ("python-sphinx" ,python-sphinx)
+ ("python-requests" ,python-requests)))
+ (home-page "https://github.com/keras-team/keras")
+ (synopsis "High-level deep learning framework")
+ (description "Keras is a high-level neural networks API, written in Python
+and capable of running on top of TensorFlow. It was developed with a focus on
+enabling fast experimentation. Use Keras if you need a deep learning library
+that:
+
+@itemize
+@item Allows for easy and fast prototyping (through user friendliness,
+ modularity, and extensibility).
+@item Supports both convolutional networks and recurrent networks, as well as
+ combinations of the two.
+@item Runs seamlessly on CPU and GPU.
+@end itemize\n")
+ (license license:expat)))
+
+(define-public sbcl-cl-libsvm-format
+ (let ((commit "3300f84fd8d9f5beafc114f543f9d83417c742fb")
+ (revision "0"))
+ (package
+ (name "sbcl-cl-libsvm-format")
+ (version (git-version "0.1.0" revision commit))
+ (source
+ (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/masatoi/cl-libsvm-format.git")
+ (commit commit)))
+ (file-name (git-file-name name version))
+ (sha256
+ (base32
+ "0284aj84xszhkhlivaigf9qj855fxad3mzmv3zfr0qzb5k0nzwrg"))))
+ (build-system asdf-build-system/sbcl)
+ (native-inputs
+ `(("prove" ,sbcl-prove)
+ ("prove-asdf" ,sbcl-prove-asdf)))
+ (inputs
+ `(("alexandria" ,sbcl-alexandria)))
+ (synopsis "LibSVM data format reader for Common Lisp")
+ (description
+ "This Common Lisp library provides a fast reader for data in LibSVM
+format.")
+ (home-page "https://github.com/masatoi/cl-libsvm-format")
+ (license license:expat))))
+
+(define-public cl-libsvm-format
+ (sbcl-package->cl-source-package sbcl-cl-libsvm-format))
+
+(define-public ecl-cl-libsvm-format
+ (sbcl-package->ecl-package sbcl-cl-libsvm-format))
+
+(define-public sbcl-cl-online-learning
+ (let ((commit "fc7a34f4f161cd1c7dd747d2ed8f698947781423")
+ (revision "0"))
+ (package
+ (name "sbcl-cl-online-learning")
+ (version (git-version "0.5" revision commit))
+ (source
+ (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/masatoi/cl-online-learning.git")
+ (commit commit)))
+ (file-name (git-file-name name version))
+ (sha256
+ (base32
+ "14x95rlg80ay5hv645ki57pqvy12v28hz4k1w0f6bsfi2rmpxchq"))))
+ (build-system asdf-build-system/sbcl)
+ (native-inputs
+ `(("prove" ,sbcl-prove)
+ ("prove-asdf" ,sbcl-prove-asdf)))
+ (inputs
+ `(("cl-libsvm-format" ,sbcl-cl-libsvm-format)
+ ("cl-store" ,sbcl-cl-store)))
+ (arguments
+ `(;; FIXME: Tests pass but then the check phase crashes
+ #:tests? #f))
+ (synopsis "Online Machine Learning for Common Lisp")
+ (description
+ "This library contains a collection of machine learning algorithms for
+online linear classification written in Common Lisp.")
+ (home-page "https://github.com/masatoi/cl-online-learning")
+ (license license:expat))))
+
+(define-public cl-online-learning
+ (sbcl-package->cl-source-package sbcl-cl-online-learning))
+
+(define-public ecl-cl-online-learning
+ (sbcl-package->ecl-package sbcl-cl-online-learning))
+
+(define-public sbcl-cl-random-forest
+ (let ((commit "85fbdd4596d40e824f70f1b7cf239cf544e49d51")
+ (revision "0"))
+ (package
+ (name "sbcl-cl-random-forest")
+ (version (git-version "0.1" revision commit))
+ (source
+ (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/masatoi/cl-random-forest.git")
+ (commit commit)))
+ (file-name (git-file-name name version))
+ (sha256
+ (base32
+ "097xv60i1ndz68sg9p4pc7c5gvyp9i1xgw966b4wwfq3x6hbz421"))))
+ (build-system asdf-build-system/sbcl)
+ (native-inputs
+ `(("prove" ,sbcl-prove)
+ ("prove-asdf" ,sbcl-prove-asdf)
+ ("trivial-garbage" ,sbcl-trivial-garbage)))
+ (inputs
+ `(("alexandria" ,sbcl-alexandria)
+ ("cl-libsvm-format" ,sbcl-cl-libsvm-format)
+ ("cl-online-learning" ,sbcl-cl-online-learning)
+ ("lparallel" ,sbcl-lparallel)))
+ (arguments
+ `(;; The tests download data from the Internet
+ #:tests? #f
+ #:phases
+ (modify-phases %standard-phases
+ (add-after 'unpack 'add-sb-cltl2-dependency
+ (lambda _
+ ;; sb-cltl2 is required by lparallel when using sbcl, but it is
+ ;; not loaded automatically.
+ (substitute* "cl-random-forest.asd"
+ (("\\(in-package :cl-user\\)")
+ "(in-package :cl-user) #+sbcl (require :sb-cltl2)"))
+ #t)))))
+ (synopsis "Random Forest and Global Refinement for Common Lisp")
+ (description
+ "CL-random-forest is an implementation of Random Forest for multiclass
+classification and univariate regression written in Common Lisp. It also
+includes an implementation of Global Refinement of Random Forest.")
+ (home-page "https://github.com/masatoi/cl-random-forest")
+ (license license:expat))))
+
+(define-public cl-random-forest
+ (sbcl-package->cl-source-package sbcl-cl-random-forest))
+
+(define-public ecl-cl-random-forest
+ (sbcl-package->ecl-package sbcl-cl-random-forest))
+
+(define-public gloo
+ (let ((version "0.0.0") ; no proper version tag
+ (commit "ca528e32fea9ca8f2b16053cff17160290fc84ce")
+ (revision "0"))
+ (package
+ (name "gloo")
+ (version (git-version version revision commit))
+ (source
+ (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/facebookincubator/gloo.git")
+ (commit commit)))
+ (file-name (git-file-name name version))
+ (sha256
+ (base32
+ "1q9f80zy75f6njrzrqkmhc0g3qxs4gskr7ns2jdqanxa2ww7a99w"))))
+ (build-system cmake-build-system)
+ (native-inputs
+ `(("googletest" ,googletest)))
+ (arguments
+ `(#:configure-flags '("-DBUILD_TEST=1")
+ #:phases
+ (modify-phases %standard-phases
+ (replace 'check
+ (lambda _
+ (invoke "make" "gloo_test")
+ #t)))))
+ (synopsis "Collective communications library")
+ (description
+ "Gloo is a collective communications library. It comes with a
+number of collective algorithms useful for machine learning applications.
+These include a barrier, broadcast, and allreduce.")
+ (home-page "https://github.com/facebookincubator/gloo")
+ (license license:bsd-3))))
+
+(define-public python-umap-learn
+ (package
+ (name "python-umap-learn")
+ (version "0.3.10")
+ (source
+ (origin
+ (method url-fetch)
+ (uri (pypi-uri "umap-learn" version))
+ (sha256
+ (base32
+ "02ada2yy6km6zgk2836kg1c97yrcpalvan34p8c57446finnpki1"))))
+ (build-system python-build-system)
+ (native-inputs
+ `(("python-nose" ,python-nose)))
+ (propagated-inputs
+ `(("python-numba" ,python-numba)
+ ("python-numpy" ,python-numpy)
+ ("python-scikit-learn" ,python-scikit-learn)
+ ("python-scipy" ,python-scipy)))
+ (home-page "https://github.com/lmcinnes/umap")
+ (synopsis
+ "Uniform Manifold Approximation and Projection")
+ (description
+ "Uniform Manifold Approximation and Projection is a dimension reduction
+technique that can be used for visualisation similarly to t-SNE, but also for
+general non-linear dimension reduction.")
+ (license license:bsd-3)))
HCoop / jackhill / guix / guix.git / blobdiff