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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>
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@ -600,6 +600,54 @@ implements several methods for sequential model-based optimization.
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@code{skopt} aims to be accessible and easy to use in many contexts.")
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(license license:bsd-3)))
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(define-public python-scikit-surprise
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(package
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(name "python-scikit-surprise")
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(version "1.1.4")
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(source
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(origin
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(method git-fetch)
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(uri (git-reference
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(url "https://github.com/NicolasHug/Surprise")
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(commit (string-append "v" version))))
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(file-name (git-file-name name version))
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(sha256
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(base32 "15ckx2i41vs21sa3yqyj12zr0h4zrcdf3lrwcy2c1cq2bjq7mnvz"))))
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(build-system pyproject-build-system)
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(arguments
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(list
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#:phases
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#~(modify-phases %standard-phases
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(add-before 'check 'set-home
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(lambda _
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;; Change from /homeless-shelter to /tmp for write
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;; permission.
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(setenv "HOME" "/tmp"))))))
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(native-inputs
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(list python-cython-3
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python-pandas
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python-pytest
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python-setuptools
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python-wheel))
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(propagated-inputs
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(list python-joblib
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python-numpy
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python-scikit-learn))
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(home-page "https://surpriselib.com/")
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(synopsis "Recommender system library for Scikit-learn")
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(description
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"This package provides a Python library for building and analyzing
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recommender systems that deal with explicit rating data. It was designed with
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the following purposes in mind:
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@itemize
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@item Provide tools to handle downloaded or user-provided datasets.
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@item Provide ready-to-use prediction algorithms and similarity measures.
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@item Provide a base for creating custom algorithims.
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@item Provide tools to evaluate, analyse and compare algorithm performance.
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@item Provide documentation with precise details regarding library algorithms.
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@end itemize")
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(license license:bsd-3)))
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(define-public python-scikit-survival
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(let ((revision "1")
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;; We need a later commit for support of a more recent sklearn and
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