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authorVinicius Monego <monego@posteo.net>2020-11-23 12:40:32 -0300
committerLeo Famulari <leo@famulari.name>2020-11-25 18:38:00 -0500
commit6a5da0d0342641c935451152b39df34b4f1ac680 (patch)
tree87e918b1c2d2760931150e43329f99e444cf401a
parent8cd10a76b886cc92ef20562eeebba53f401e3b35 (diff)
downloadguix-6a5da0d0342641c935451152b39df34b4f1ac680.tar
guix-6a5da0d0342641c935451152b39df34b4f1ac680.tar.gz
gnu: Add python-opentsne.
* gnu/packages/machine-learning.scm (python-opentsne): New variable. Signed-off-by: Leo Famulari <leo@famulari.name>
-rw-r--r--gnu/packages/machine-learning.scm45
1 files changed, 45 insertions, 0 deletions
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm
index 2ad148b48e..f1d0922ef2 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -896,6 +896,51 @@ data analysis.")
for k-neighbor-graph construction and approximate nearest neighbor search.")
(license license:bsd-2)))
+(define-public python-opentsne
+ (package
+ (name "python-opentsne")
+ (version "0.4.4")
+ (source
+ (origin
+ ;; No tests in the PyPI tarball.
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/pavlin-policar/openTSNE")
+ (commit (string-append "v" version))))
+ (file-name (string-append name "-" version "-checkout"))
+ (sha256
+ (base32 "08wamsssmyf6511cbmglm67dp48i6xazs89m1cskdk219v90bc76"))))
+ (build-system python-build-system)
+ (arguments
+ `(#:phases
+ (modify-phases %standard-phases
+ ;; Benchmarks require the 'macosko2015' data files.
+ (add-after 'unpack 'delete-benchmark
+ (lambda _
+ (delete-file-recursively "benchmarks")
+ #t))
+ ;; Numba needs a writable dir to cache functions.
+ (add-before 'check 'set-numba-cache-dir
+ (lambda _
+ (setenv "NUMBA_CACHE_DIR" "/tmp")
+ #t)))))
+ (native-inputs
+ `(("python-cython" ,python-cython)))
+ (inputs
+ `(("fftw" ,fftw)))
+ (propagated-inputs
+ `(("python-numpy" ,python-numpy)
+ ("python-pynndescent" ,python-pynndescent)
+ ("python-scikit-learn" ,python-scikit-learn)
+ ("python-scipy" ,python-scipy)))
+ (home-page "https://github.com/pavlin-policar/openTSNE")
+ (synopsis "Extensible, parallel implementations of t-SNE")
+ (description
+ "This is a modular Python implementation of t-Distributed Stochastic
+Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for
+visualizing high-dimensional data sets.")
+ (license license:bsd-3)))
+
(define-public python-scikit-rebate
(package
(name "python-scikit-rebate")