~ruther/guix-local

16c71b7fe328a159f6e6d13b42ee23b448465905 — Antero Mejr 1 year, 11 months ago 1c4a008
gnu: Add python-scikit-surprise.

* gnu/packages/python-science.scm (python-scikit-surprise): New variable.

Change-Id: I9b5e13f0c985f34bb0bd759e76ebd27221a340a4
Signed-off-by: Sharlatan Hellseher <sharlatanus@gmail.com>
1 files changed, 48 insertions(+), 0 deletions(-)

M gnu/packages/python-science.scm
M gnu/packages/python-science.scm => gnu/packages/python-science.scm +48 -0
@@ 600,6 600,54 @@ implements several methods for sequential model-based optimization.
@code{skopt} aims to be accessible and easy to use in many contexts.")
    (license license:bsd-3)))

(define-public python-scikit-surprise
  (package
    (name "python-scikit-surprise")
    (version "1.1.4")
    (source
     (origin
       (method git-fetch)
       (uri (git-reference
             (url "https://github.com/NicolasHug/Surprise")
             (commit (string-append "v" version))))
       (file-name (git-file-name name version))
       (sha256
        (base32 "15ckx2i41vs21sa3yqyj12zr0h4zrcdf3lrwcy2c1cq2bjq7mnvz"))))
    (build-system pyproject-build-system)
    (arguments
     (list
      #:phases
      #~(modify-phases %standard-phases
          (add-before 'check 'set-home
            (lambda _
              ;; Change from /homeless-shelter to /tmp for write
              ;; permission.
              (setenv "HOME" "/tmp"))))))
    (native-inputs
     (list python-cython-3
           python-pandas
           python-pytest
           python-setuptools
           python-wheel))
    (propagated-inputs
     (list python-joblib
           python-numpy
           python-scikit-learn))
    (home-page "https://surpriselib.com/")
    (synopsis "Recommender system library for Scikit-learn")
    (description
     "This package provides a Python library for building and analyzing
recommender systems that deal with explicit rating data.  It was designed with
the following purposes in mind:
@itemize
@item Provide tools to handle downloaded or user-provided datasets.
@item Provide ready-to-use prediction algorithms and similarity measures.
@item Provide a base for creating custom algorithims.
@item Provide tools to evaluate, analyse and compare algorithm performance.
@item Provide documentation with precise details regarding library algorithms.
@end itemize")
    (license license:bsd-3)))

(define-public python-scikit-survival
  (let ((revision "1")
        ;; We need a later commit for support of a more recent sklearn and