fast-fisher 0.0.1

Calculate Fisher's exact test very quickly.

Credit

This is heavily based on the work of painyeph.

I basically added numba compilation and easier installation. Nevertheless, it makes a big difference speed-wise!

Installation

pip install git+https://github.com/MrTomRod/FishersExactTest

Functions

test type	p-value	-log( p-value )	-log10( p-value )
left-tailed	test1l(a, b, c, d) or test2l(a, a+b, a+c, a+b+c+d)	mlnTest1l(a, b, c, d) or mlnTest2l(a, a+b, a+c, a+b+c+d)	mlog10Test1l(a, b, c, d) or mlog10Test2l(a, a+b, a+c, a+b+c+d)
right-tailed	test1r(a, b, c, d) or test2r(a, a+b, a+c, a+b+c+d)	mlnTest1r(a, b, c, d) or mlnTest2r(a, a+b, a+c, a+b+c+d)	mlog10Test1r(a, b, c, d) or mlog10Test2r(a, a+b, a+c, a+b+c+d)
two-tailed	test1t(a, b, c, d) or test2t(a, a+b, a+c, a+b+c+d)	mlnTest1t(a, b, c, d) or mlnTest2t(a, a+b, a+c, a+b+c+d)	mlog10Test1t(a, b, c, d) or mlog10Test2t(a, a+b, a+c, a+b+c+d)
all	test1(a, b, c, d) or test2(a, a+b, a+c, a+b+c+d)	mlnTest1(a, b, c, d) or mlnTest2(a, a+b, a+c, a+b+c+d)	mlog10Test1(a, b, c, d) or mlog10Test2(a, a+b, a+c, a+b+c+d)

Speed

Comparison of scipy.stats.fisher_exact, fast_fisher.fast_fisher_python and fast_fisher.fast_fisher_compiled. See benchmark.py.

a	b	c	d	test type	scipy	f_python	f_compiled
8	2	1	5	left-tailed	130 us	3 us	1 us
8	2	1	5	right-tailed	134 us	3 us	0 us
8	2	1	5	two-tailed	884 us	6 us	1 us
100	1000	10000	100000	left-tailed	153 us	57 us	5 us
100	1000	10000	100000	right-tailed	218 us	73 us	6 us
100	1000	10000	100000	two-tailed	174 us	130 us	10 us
10000	100	1000	100000	left-tailed	955 us	8 us	1 us
10000	100	1000	100000	right-tailed	135 us	7 us	1 us
10000	100	1000	100000	two-tailed	62821 us	753 us	53 us
10000	10000	10000	10000	left-tailed	900 us	323 us	26 us
10000	10000	10000	10000	right-tailed	912 us	329 us	27 us
10000	10000	10000	10000	two-tailed	172 us	679 us	53 us

Precision

from numpy import log10, isinf
from scipy.stats import fisher_exact
from fast_fisher import fast_fisher

scipy_fisher = lambda t: fisher_exact([[t[0], t[1]], [t[2], t[3]]])[1]

print(f"{'contingency table':<30} {'fast -log10(pvalue)':<21} {'scipy -log10(pvalue)'}")
for exponent in range(0, 16):
    table = (100, 1, 10, 10 ** exponent)
    fast_mlog = fast_fisher.mlog10Test1t(*table)
    scipy_mlog = -log10(scipy_fisher(table))

    if isinf(scipy_mlog):
        scipy_mlog = 'failed to compute'

    print(f"{str(table):<30} {fast_mlog:<21} {scipy_mlog}")

contingency table              fast -log10(pvalue)   scipy -log10(pvalue)
(100, 1, 10, 1)                0.7268124553699258    0.7268124553698625
(100, 1, 10, 10)               7.831294376070296     7.831294376070258
(100, 1, 10, 100)              46.49556750272154     46.4955675027216
(100, 1, 10, 1000)             128.93472935802276    128.93472935802373
(100, 1, 10, 10000)            226.62104816785       226.62104816785057
(100, 1, 10, 100000)           326.3812661048001     failed to compute
(100, 1, 10, 1000000)          426.35719912501844    failed to compute
(100, 1, 10, 10000000)         526.3547915160074     failed to compute
(100, 1, 10, 100000000)        626.3545507841483     failed to compute
(100, 1, 10, 1000000000)       726.3545273226812     failed to compute
(100, 1, 10, 10000000000)      826.3545146294285     failed to compute
(100, 1, 10, 100000000000)     926.354158998833      failed to compute
(100, 1, 10, 1000000000000)    1026.3583095975994    failed to compute
(100, 1, 10, 10000000000000)   1126.3427388160835    failed to compute
(100, 1, 10, 100000000000000)  1226.447616894783     failed to compute
Traceback (most recent call last):
  File "/usr/lib64/python3.10/code.py", line 90, in runcode
    exec(code, self.locals)
  File "<input>", line 10, in <module>
  File "/tmp/pip-req-build-usaxopn1/fast_fisher/fast_fisher_numba.py", line 244, in mlnTest2t
OverflowError: the grand total of contingency table is too large