BUG: Fix np.quantile([Fraction(2,1)], 0.5)#24711
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seberg merged 3 commits intonumpy:mainfrom Jan 30, 2024
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eendebakpt
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The debug test failure seems to be an Ubuntu problem, the requested file is missing from the upstream directory. I suspect it is due to a micro upgrade in the Ubuntu version. |
seberg
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Sep 19, 2023
numpy/lib/_function_base_impl.py
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| lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out)) | ||
| subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5) | ||
| subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5, | ||
| casting='unsafe') |
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This may sound weird, but we should probably also add dtype=type(lerp_interpolation.dtype).
If we have unsafe casting, this means that at least to some degree we ignore the precision of the weights of the quantiles for the output precision (promotion). Can you give me a hint in how things are currently? Does the same happen if we use np.longdouble for the quantile level (q)?
(Sorry, but I am a bit surprised how many subtleties there still seem to be along promotion in quantiles :(, and hoping we can clarify things a bit for me and everyone else.)
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@seberg I tested with
import numpy as np
print(np,np.__version__)
x = np.linspace(.1, .7, 5, dtype=np.longdouble)
for q in [0, 1, 0., 1., .5]:
r = np.quantile(x, q)
print(f'{q} ({type(q)}) -> {r} ({type(r)})')
r = np.quantile(x, np.longdouble(q))
print(f'{q} ({type(q)}) -> {r} ({type(r)})')
print('----')
x = np.array([0, 1])
for q in [0, 1, 0., 1., .5]:
r = np.quantile(x, q)
print(f'{q} ({type(q)}) -> {r} ({type(r)})')
r = np.quantile(x, np.longdouble(q))
print(f'{q} ({type(q)}) -> {r} ({type(r)})')
Output on 1.26:
<module 'numpy' from '/home/eendebakpt/py310/lib/python3.10/site-packages/numpy/__init__.py'> 1.26.0
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
----
0 (<class 'int'>) -> 0 (<class 'numpy.int64'>)
0 (<class 'int'>) -> 0.0 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 1 (<class 'numpy.int64'>)
1 (<class 'int'>) -> 1.0 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.float64'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.float64'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.float64'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.longdouble'>)
Output with this PR:
<module 'numpy' from '/mnt/data/numpy/numpy/__init__.py'> 2.0.0.dev0+git20231107.a555e7d
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
0 (<class 'int'>) -> 0.1 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 0.7 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.1 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 0.7 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.4 (<class 'numpy.longdouble'>)
----
0 (<class 'int'>) -> 0 (<class 'numpy.int64'>)
0 (<class 'int'>) -> 0.0 (<class 'numpy.longdouble'>)
1 (<class 'int'>) -> 1 (<class 'numpy.int64'>)
1 (<class 'int'>) -> 1.0 (<class 'numpy.longdouble'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.float64'>)
0.0 (<class 'float'>) -> 0.0 (<class 'numpy.longdouble'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.float64'>)
1.0 (<class 'float'>) -> 1.0 (<class 'numpy.longdouble'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.float64'>)
0.5 (<class 'float'>) -> 0.5 (<class 'numpy.longdouble'>)
If you want I can add tests for this case as well.
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@seberg ping. |
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Thanks @eendebakpt, sorry for forgetting to look over this again. I think it should be fine to do this cast and it seems like you gave it a spin with that test. So let's give it a shot. |
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* BUG: Fix np.quantile([Fraction(2,1)], 0.5) * address review comments * pass type instead of dtype instance
charris
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BUG: Fix np.quantile([Fraction(2,1)], 0.5) (#24711)
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On main:
In this PR we update the
np.quantileto handle the casenp.quantile(arr, 0.5)as well.Also see #24592