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| author | Sharlatan Hellseher <sharlatanus@gmail.com> | 2026-01-02 23:56:36 +0000 |
|---|---|---|
| committer | Rutherther <rutherther@ditigal.xyz> | 2026-01-25 21:21:00 +0100 |
| commit | e0ee924579153af4bdb13e5ba63c8a932d754a68 (patch) | |
| tree | 7ac9f6d931af0cc253c703d04aacea5effae240c /gnu/packages/machine-learning.scm | |
| parent | 80ea353362328b8891b339c6b7fce6c76946d52e (diff) | |
gnu: Add python-apricot-select.
* gnu/packages/machine-learning.scm (python-apricot-select): New variable.
Change-Id: Ib4e7b76e4025870e73a52d67277f8bc302740d01
Signed-off-by: Rutherther <rutherther@ditigal.xyz>
Diffstat (limited to 'gnu/packages/machine-learning.scm')
| -rw-r--r-- | gnu/packages/machine-learning.scm | 42 |
1 files changed, 42 insertions, 0 deletions
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm index 1b3d0a8aa67..63b546e7547 100644 --- a/gnu/packages/machine-learning.scm +++ b/gnu/packages/machine-learning.scm @@ -204,6 +204,48 @@ frameworks.") representations and sentence classification.") (license license:expat))) +(define-public python-apricot-select + (package + (name "python-apricot-select") + ;; 0.6.1 was released in 2021 + (properties '((commit . "962f597a57fcb880a3b19befa7a3eebccc6b5228") + (revision . "0"))) + (version (git-version "0.6.1" + (assoc-ref properties 'revision) + (assoc-ref properties 'commit))) + (source + (origin + (method git-fetch) + (uri (git-reference + (url "https://github.com/jmschrei/apricot") + (commit (assoc-ref properties 'commit)))) + (file-name (git-file-name name version)) + (sha256 + (base32 "16hj76nzdr4pbx7wy5f3237f9c1d0yizmz1skix0rwlvjpj3rc9x")))) + (build-system pyproject-build-system) + (arguments + ;; See: <https://github.com/jmschrei/apricot/issues/19>. + (list #:tests? #f)) ;tests are very compue havy + (native-inputs + (list python-pytest + python-pytest-xdist + python-setuptools + python-scikit-learn)) + (propagated-inputs + (list python-numba + python-numpy + python-scipy + python-tqdm)) + (home-page "https://github.com/jmschrei/apricot") + (synopsis "Submodular selection of representative sets for ML models") + (description + "@code{apricot} implements submodular optimization for the purpose of +summarizing massive data sets into minimally redundant subsets that are still +representative of the original data. These subsets are useful for both +visualizing the modalities in the data and for training accurate machine +learning models with just a fraction of the examples and compute.") + (license license:expat))) + (define-public python-autograd-gamma (package (name "python-autograd-gamma") |