summaryrefslogtreecommitdiff
path: root/gnu/packages/machine-learning.scm
diff options
context:
space:
mode:
Diffstat (limited to 'gnu/packages/machine-learning.scm')
-rw-r--r--gnu/packages/machine-learning.scm168
1 files changed, 165 insertions, 3 deletions
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm
index 67ea736284..ba7772f66b 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -10,6 +10,7 @@
;;; 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>
;;;
;;; This file is part of GNU Guix.
;;;
@@ -53,6 +54,7 @@
#: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)
@@ -67,6 +69,7 @@
#: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)
@@ -668,7 +671,7 @@ geometric models.")
`(#: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
@@ -792,7 +795,7 @@ computing environments.")
(define-public python-scikit-learn
(package
(name "python-scikit-learn")
- (version "0.20.1")
+ (version "0.20.3")
(source
(origin
(method git-fetch)
@@ -802,7 +805,7 @@ computing environments.")
(file-name (git-file-name name version))
(sha256
(base32
- "0fkhwg3xn1s7ln9q1szq6kwc4jhwvjh8w4kmv9wcrqy7cq3lbv0d"))))
+ "08aaby5zphfxy83mggg35bwyka7wk91l2qijh8kk0bl08dikq8dl"))))
(build-system python-build-system)
(arguments
`(#:phases
@@ -1753,3 +1756,162 @@ API for beginners that allows users to build models quickly by plugging
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))
+ (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)))