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TST: stats.ttest_1samp: fix xslow test #20642

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May 5, 2024
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TST: stats.ttest_1samp: fix xslow test #20642

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TST: stats.ttest_1samp: fix xslow test
mdhaber May 4, 2024
43db12f
TST: stats.ttest_1samp: adjust float generation strategy
mdhaber May 5, 2024
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6 changes: 3 additions & 3 deletions scipy/stats/tests/test_stats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3711,8 +3711,8 @@ def test_1samp_ci_iv(self, xp):
def test_pvalue_ci(self, alpha, data_axis, alternative, xp):
# test relationship between one-sided p-values and confidence intervals
data, axis = data_axis
data = data.astype(copy=True) # ensure byte order
data = xp.asarray(data)
data = data.astype(np.float64, copy=True) # ensure byte order
data = xp.asarray(data, dtype=xp.float64)
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Suggested change
data = data.astype(np.float64, copy=True) # ensure byte order
data = xp.asarray(data, dtype=xp.float64)
data = data.astype(np.float64, copy=True) # ensure byte order
# `float64` is needed here because of the range of `alpha`
data = xp.asarray(data, dtype=xp.float64)

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I did check that all these incantations pass locally on x86_64 Linux, while they fail on main:

SCIPY_XSLOW=1 SCIPY_DEVICE=cuda python dev.py test -j 32 -b all -- -k "test_pvalue_ci"
SCIPY_XSLOW=1 python dev.py test -j 32 -b all -- -k "test_pvalue_ci"
SCIPY_XSLOW=1 python dev.py test -j 32 -- -k "test_pvalue_ci"

I tried to play picky reviewer and force float32 instead of float64 locally, and saw overflow/invalid value warnings, so that seems like a plausible justification for non-stats expert at least.

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(obivously the first one has limited value, since it is cpu only)

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Am I missing something with the "ensure byte order" comment? Can we not just include the copy in asarray?

Suggested change
data = data.astype(np.float64, copy=True) # ensure byte order
data = xp.asarray(data, dtype=xp.float64)
# `float64` is needed here because of the range of `alpha`
data = xp.asarray(data, dtype=xp.float64, copy=True)

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@mdhaber mdhaber May 5, 2024 •
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hypothesis generates them in some nonstandard format that torch.asarray won't accept. Might be possible to make it generate more standard float64s.

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okay, if that isn't possible then I think this looks okay. Maybe worth adding a comment as to why.

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I adjusted the test strategy to generate normal float64s. I think that the old strategy must have been left over from before the test took its final form.

res = stats.ttest_1samp(data, 0.,
alternative=alternative, axis=axis)
l, u = res.confidence_interval(confidence_level=alpha)
Expand All @@ -3722,7 +3722,7 @@ def test_pvalue_ci(self, alpha, data_axis, alternative, xp):
res = stats.ttest_1samp(data, popmean, alternative=alternative, axis=axis)
shape = list(data.shape)
shape.pop(axis)
ref = xp.broadcast_to(xp.asarray(1-alpha), shape)
ref = xp.broadcast_to(xp.asarray(1-alpha, dtype=xp.float64), shape)
xp_assert_close(res.pvalue, ref)


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