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[bug#31432] [PATCH 1/2] gnu: Add lightgbm.
From: |
Fis Trivial |
Subject: |
[bug#31432] [PATCH 1/2] gnu: Add lightgbm. |
Date: |
Tue, 12 Jun 2018 10:01:16 +0000 |
* gnu/packages/machine-learning.scm (lightgbm): New variable.
---
gnu/packages/machine-learning.scm | 47 +++++++++++++++++++++++++++++++++++++++
1 file changed, 47 insertions(+)
diff --git a/gnu/packages/machine-learning.scm
b/gnu/packages/machine-learning.scm
index 15e4d4574..499248965 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -47,6 +47,7 @@
#:use-module (gnu packages gcc)
#:use-module (gnu packages image)
#:use-module (gnu packages maths)
+ #:use-module (gnu packages mpi)
#:use-module (gnu packages ocaml)
#:use-module (gnu packages perl)
#:use-module (gnu packages pkg-config)
@@ -786,3 +787,49 @@ main intended application of Autograd is gradient-based
optimization.")
(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:
+
address@hidden
address@hidden Faster training speed and higher efficiency
address@hidden Lower memory usage
address@hidden Better accuracy
address@hidden Parallel and GPU learning supported
address@hidden Capable of handling large-scale data
address@hidden itemize\n")
+ (license license:expat)))
--
2.14.4