~ruther/guix-local

7b6f6e96041d54841bcd6d325539a81f0cd53e8f — Sharlatan Hellseher 1 year, 4 months ago ab3a8e1 
gnu: python-autograd: Update to 1.7.0.

* gnu/packages/machine-learning.scm (python-autograd): Update to 1.7.0.
[source]: Use the latest version tag.
[native-inputs]: Remove python-nose, python-setuptools, and
python-wheel; add python-hatchling.

Change-Id: I42cd6b9ce621c1509f459fb947b09d05635fb79b
patch browse .tar.gz .zip 
1 files changed, 7 insertions(+), 10 deletions(-)

M gnu/packages/machine-learning.scm

M gnu/packages/machine-learning.scm => gnu/packages/machine-learning.scm +7 -10
@@ 2681,27 2681,24 @@ Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for Python.")
    (license license:expat)))

(define-public python-autograd
  (let* ((commit "c6d81ce7eede6db801d4e9a92b27ec5d409d0eab")
         (revision "0")
         (version (git-version "1.5" revision commit)))
    (package
      (name "python-autograd")
      (home-page "https://github.com/HIPS/autograd")
      (version "1.7.0")
      (source (origin
                (method git-fetch)
                (uri (git-reference
                      (url home-page)
                      (commit commit)))
                      (url "https://github.com/HIPS/autograd")
                      (commit (string-append "v" version))))
                (sha256
                 (base32
                  "04kljgydng42xlg044h6nbzxpban1ivd6jzb8ydkngfq88ppipfk"))
                  "1fpnmm3mzw355iq7w751j4mjfcr0yh324cxidba1l22652gg8r8m"))
                (file-name (git-file-name name version))))
      (version version)
      (build-system pyproject-build-system)
      (native-inputs
       (list python-nose python-pytest python-setuptools python-wheel))
       (list python-hatchling python-pytest))
      (propagated-inputs
       (list python-future python-numpy))
      (home-page "https://github.com/HIPS/autograd")
      (synopsis "Efficiently computes derivatives of NumPy code")
      (description "Autograd can automatically differentiate native Python and
NumPy code.  It can handle a large subset of Python's features, including loops,


@@ 2711,7 2708,7 @@ of derivatives.  It supports reverse-mode differentiation
scalar-valued functions with respect to array-valued arguments, as well as
forward-mode differentiation, and the two can be composed arbitrarily.  The
main intended application of Autograd is gradient-based optimization.")
      (license license:expat))))
      (license license:expat)))

(define-public lightgbm
  (package