[Numpy-discussion] np.any and np.all short-circuiting

Citi, Luca lciti@essex.ac...
Thu Sep 24 16:43:41 CDT 2009

I noticed that python's "any" can be faster than numpy's "any" (and the similarly for "all").
Then I wondered why.
I realized that numpy implements "any" as logical_or.reduce (and "all" as logical_and.reduce).
This means that numpy cannot take advantage of short-circuiting.
Looking at the timings confirmed my suspects.
I think python fetches one element at the time from the array and as soon as any of them is true it returns true.
Instead, numpy goes on until the end of the array even if the very first element is already true.
Looking at the code I think I found a way to fix it.
I don't see a reason why it should not work. It seems to work. But you never know.
I wanted to run the test suite.
I am unable to run the test on the svn version, neither from .../build/lib... nor form a different folder using sys.path.insert(0, '.../build/lib...').
In the first case I get "NameError: name 'numeric' is not defined"
while in the second case zero tests are successfully performed :-)
What is the correct way of running the tests (without installing the development version in the system)?
Is there some expert of the inner numpy core able to tell whether the approach is correct and won't break something?
I opened a ticket for this:

In the following table any(x) is python's version, np.any(x) is numpy's, while *np.any(x)* is mine.


x = np.zeros(100000, dtype=bool)
x[i] = True
%timeit any(x)
%timeit np.any(x)

x = np.ones(100000, dtype=bool)
x[i] = False
%timeit all(x)
%timeit np.all(x)


i          any(x)        np.any(x)    *np.any(x)*
//         6.84 ms       831 µs        189  µs
50000      3.41 ms       832 µs        98   µs
10000      683  µs       831 µs        24.7 µs
1000       68.9 µs       859 µs        8.41 µs
100        7.92 µs       888 µs        6.9  µs
10         1.42 µs       832 µs        6.68 µs
0          712  ns       831 µs        6.65 µs


i          all(x)        np.all(x)    *np.all(x)*
//         6.65 ms       676 µs        300 µs
50000      3.32 ms       677 µs        154 µs
10000      666  µs       676 µs        36.4 µs
1000       67.9 µs       686 µs        9.86 µs
100        7.53 µs       677 µs        7.26 µs
10         1.39 µs       676 µs        7.06 µs
0          716  ns       678 µs        6.96 µs

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