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author | Vinicius Monego <monego@posteo.net> | 2020-11-23 12:40:32 -0300 |
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committer | Leo Famulari <leo@famulari.name> | 2020-11-25 18:38:00 -0500 |
commit | 6a5da0d0342641c935451152b39df34b4f1ac680 (patch) | |
tree | 87e918b1c2d2760931150e43329f99e444cf401a | |
parent | 8cd10a76b886cc92ef20562eeebba53f401e3b35 (diff) | |
download | guix-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.scm | 45 |
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") |