1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
|
from pytest import mark
from joblib import Memory
from lap import lapjv, lapmod
from lap.lapmod import get_cost
try:
from lap_old import lapjv as lapjv_old
except ImportError:
print(
'''If you get here, you do not have the old lapjv to compare to.
git clone git@github.com:gatagat/lapjv.git lapjv-old
cd lapjv-old
git checkout old
python setup.py build_ext -i
mv lapjv lapjv_old
And run the benchmark:
LAPJV_OLD=lapjv-old bench.sh
''')
lapjv_old = None
from pymatgen.optimization.linear_assignment import LinearAssignment
from centrosome.lapjv import lapjv as lapjv_centrosome
from lap.tests.test_utils import (
sparse_from_dense,
sparse_from_dense_CS, get_cost_CS,
get_dense_int
)
cachedir = '/tmp/lapjv-cache'
memory = Memory(cachedir=cachedir, verbose=1)
@memory.cache
def get_data(seed):
cost = get_dense_int(100, 1000, hard=True, seed=seed)
opt = lapjv(cost)[0]
return cost, opt
seeds = [1299821, 15485867, 32452867, 49979693]
if lapjv_old is not None:
@mark.parametrize('seed', seeds)
def test_JV_old(benchmark, seed):
cost, opt = get_data(seed)
ret = benchmark(lapjv_old, cost)
assert ret[0] == opt
@mark.parametrize('seed', seeds)
def test_JV(benchmark, seed):
cost, opt = get_data(seed)
ret = benchmark(lapjv, cost)
assert ret[0] == opt
@mark.parametrize('seed', seeds)
def test_MODPY(benchmark, seed):
cost, opt = get_data(seed)
n, cc, ii, kk = sparse_from_dense(cost)
ret = benchmark(lapmod, n, cc, ii, kk, fast=False, return_cost=False)
assert get_cost(n, cc, ii, kk, ret[0]) == opt
@mark.parametrize('seed', seeds)
def test_MOD_c(benchmark, seed):
cost, opt = get_data(seed)
n, cc, ii, kk = sparse_from_dense(cost)
ret = benchmark(lapmod, n, cc, ii, kk, fast=True, return_cost=False)
assert get_cost(n, cc, ii, kk, ret[0]) == opt
@mark.parametrize('seed', seeds)
def test_PMG(benchmark, seed):
cost, opt = get_data(seed)
ret = benchmark(LinearAssignment, cost)
assert ret.min_cost == opt
@mark.parametrize('seed', seeds)
def test_CSCY(benchmark, seed):
cost, opt = get_data(seed)
i, j, cc = sparse_from_dense_CS(cost)
ret = benchmark(lapjv_centrosome, i, j, cc)
assert get_cost_CS(cost, ret[0]) == opt
|
