Further fixes for Scipy 1.15 update for PR and nightly CI #6213
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rapids-bot[bot] merged 9 commits intorapidsai:branch-25.02from Jan 13, 2025
Merged
Further fixes for Scipy 1.15 update for PR and nightly CI #6213rapids-bot[bot] merged 9 commits intorapidsai:branch-25.02from
rapids-bot[bot] merged 9 commits intorapidsai:branch-25.02from
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dantegd
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bug
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/ok to test |
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/ok to test |
viclafargue
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Jan 13, 2025
betatim
reviewed
Jan 13, 2025
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| @given( | ||
| dtype=floating_dtypes(sizes=(32, 64)), | ||
| dtype=st.sampled_from((np.float32, np.float64)), |
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For my education: can you explain a bit why this is needed? A quick look at floating_dtypes makes me think it also uses sampled_from on the inside. But maybe returns strings instead of dtype objects?
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The floating_dtypes will generate all possible dtypes for the given sizes, including those with different endianness:
>>> from hypothesis.extra.numpy import floating_dtypes
>>> f = floating_dtypes(sizes=(16,32))
>>> f.example()
dtype('float16')
>>> f.example()
dtype('>f2')
>>> f.example()
dtype('float32')
>>> f.example()
dtype('>f4')
>>> f.example()
dtype('float16')The change here represents an stronger assumption on the expected types.
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/merge |
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Nightly CI revealed a bug between hypothesis
floating_dtypes(sizes=(32, 64)and building sparse matrices, this PR usesst.sampled_from((np.float32, np.float64)to solve the issue.Additionally, cudf.pandas active made one dataset in ARIMA pytests fail, so disabling that one while we look further into it.