guix_mirror_bot pushed a commit to branch python-team
in repository guix.
commit 00738befc914771542570c1890a7ac06aec43c30
Author: Sharlatan Hellseher <[email protected]>
AuthorDate: Fri Jan 2 23:56:36 2026 +0000
gnu: Add python-apricot-select.
* gnu/packages/machine-learning.scm (python-apricot-select): New variable.
Change-Id: Ib4e7b76e4025870e73a52d67277f8bc302740d01
---
gnu/packages/machine-learning.scm | 42 +++++++++++++++++++++++++++++++++++++++
1 file changed, 42 insertions(+)
diff --git a/gnu/packages/machine-learning.scm
b/gnu/packages/machine-learning.scm
index 7ebfe07686..6f391fac3a 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")
