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gnu: python-pgmpy: Skip more tests, run in parallel.

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* gnu/packages/statistics.scm (python-pgmpy) [arguments] <test-flags>:
Skip 4 more tests, run in up to 8 threads to improve performance,
supported upstream.
[native-inputs]: Add python-pytest-xdist.

Change-Id: I7071114299dc2dc4e52e79a36fcda0d12892653f
This commit is contained in:
Sharlatan Hellseher 2025-04-24 11:25:07 +01:00
parent 897d8a0d20
commit e4cd1b9d8f
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1 changed files with 23 additions and 9 deletions
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gnu/packages/statistics.scm
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@ -3328,11 +3328,24 @@ statistical summary in arrays and enumerables.")
"1hg6wrg3jcac71zn4gknni1wrn38wa86ka3sgp2bndz59mx6sr2s"))))
(build-system pyproject-build-system)
(arguments
(list #:test-flags
#~(list "-k" (string-append
"not test_pillai"
" and not test_estimate_with_cache_no_llm_calls"
" and not test_estimate_with_orientations"))))
(list
#:test-flags
#~(list "--numprocesses" (number->string (min 8 (parallel-job-count)))
"-k" (string-join
;; AssertionError: False is not true
(list "not test_query_evidence"
;; ValueError: Experimental support for categorical
;; data is not implemented for current tree method
;; yet.
"test_pillai"
"test_estimate_with_cache_no_llm_calls"
"test_estimate_with_orientations"
;; _flapack.error: (liwork>=max(1,10*n)||liwork==-1)
;; failed for 10th keyword liwork: dsyevr:liwork=1
"test_estimate"
"test_score_bnlearn"
"test_score_manual")
" and not "))))
(propagated-inputs (list python-daft
python-joblib
python-networkx
@ -3348,13 +3361,14 @@ statistical summary in arrays and enumerables.")
(native-inputs (list python-mock
python-pyro-ppl
python-pytest
python-pytest-xdist
python-setuptools
python-wheel
python-xgboost))
(home-page "https://github.com/pgmpy/pgmpy")
(synopsis "Probabilistic Graphical Models library")
(description "This package provides a library for Probabilistic
Graphical Models. It can be used for learning (Structure and Parameter),
inference (Probabilistic and Causal), and simulations in Bayesian
Networks.")
(description
"This package provides a library for Probabilistic Graphical Models. It
can be used for learning (Structure and Parameter), inference (Probabilistic
and Causal), and simulations in Bayesian Networks.")
(license license:expat)))