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BUG: Fix np.quantile([Fraction(2,1)], 0.5) #24711

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Jan 30, 2024
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BUG: Fix np.quantile([Fraction(2,1)], 0.5) #24711

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5b9730c
BUG: Fix np.quantile([Fraction(2,1)], 0.5)
eendebakpt Sep 14, 2023
4332695
address review comments
eendebakpt Sep 19, 2023
577d8d4
pass type instead of dtype instance
eendebakpt Sep 19, 2023
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BUG: Fix np.quantile([Fraction(2,1)], 0.5)
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@eendebakpt
eendebakpt committed Nov 8, 2023
commit 5b9730c0a7bca6b1647c836b9cc709b778a72369
3 changes: 2 additions & 1 deletion numpy/lib/_function_base_impl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4585,7 +4585,8 @@ def _lerp(a, b, t, out=None):
diff_b_a = subtract(b, a)
# asanyarray is a stop-gap until gh-13105
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.

if lerp_interpolation.ndim == 0 and out is None:
lerp_interpolation = lerp_interpolation[()] # unpack 0d arrays
return lerp_interpolation
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4 changes: 4 additions & 0 deletions numpy/lib/tests/test_function_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3604,6 +3604,10 @@ def test_fraction(self):
assert_equal(q, Fraction(7, 2))
assert_equal(type(q), Fraction)

q = np.quantile(x, .5)
assert_equal(q, 1.75)
assert_equal(type(q), np.float64)

q = np.quantile(x, Fraction(1, 2))
assert_equal(q, Fraction(7, 4))
assert_equal(type(q), Fraction)
Expand Down