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 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152
|
from __future__ import division
from rdkit import RDConfig
import unittest
from rdkit.DataManip.Metric import rdMetricMatrixCalc as rdmmc
import numpy
import random
from rdkit import DataStructs
def feq(v1, v2, tol2=1e-4):
return abs(v1 - v2) <= tol2
class TestCase(unittest.TestCase):
def setUp(self):
pass
def test0DistsArray(self):
exp = numpy.array([1., 1.414213, 1.0], 'd')
# initialize a double array and check if get back the expected distances
desc = numpy.zeros((3, 2), 'd')
desc[1, 0] = 1.0
desc[2, 0] = 1.0
desc[2, 1] = 1.0
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# repeat with an flaot array
desc = numpy.zeros((3, 2), 'f')
desc[1, 0] = 1.0
desc[2, 0] = 1.0
desc[2, 1] = 1.0
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# finally with an interger array
desc = numpy.zeros((3, 2), 'i')
desc[1, 0] = 1
desc[2, 0] = 1
desc[2, 1] = 1
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
def ctest1DistsListArray(self):
exp = numpy.array([1., 1.414213, 1.0], 'd')
desc = [numpy.array([0.0, 0.0], 'd'), numpy.array([1.0, 0.0], 'd'),
numpy.array([1.0, 1.0], 'd')]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# repeat the test with a list of numpy.arrays of floats
desc = [numpy.array([0.0, 0.0], 'f'), numpy.array([1.0, 0.0], 'f'),
numpy.array([1.0, 1.0], 'f')]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
# repeat the test with a list of numpy.arrays of ints
desc = [numpy.array([0, 0], 'i'), numpy.array([1, 0], 'i'), numpy.array([1, 1], 'i')]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
def test2DistListList(self):
exp = numpy.array([1., 1.414213, 1.0], 'd')
desc = [[0.0, 0.0], [1.0, 0.0], [1.0, 1.0]]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
#test with ints
desc = [[0, 0], [1, 0], [1, 1]]
dmat = rdmmc.GetEuclideanDistMat(desc)
for i in range(numpy.shape(dmat)[0]):
assert feq(dmat[i], exp[i])
def test3Compare(self):
n = 30
m = 5
dscArr = numpy.zeros((n, m), 'd')
for i in range(n):
for j in range(m):
dscArr[i, j] = random.random()
dmatArr = rdmmc.GetEuclideanDistMat(dscArr)
dscLL = []
for i in range(n):
row = []
for j in range(m):
row.append(dscArr[i, j])
dscLL.append(row)
dmatLL = rdmmc.GetEuclideanDistMat(dscLL)
assert numpy.shape(dmatArr) == numpy.shape(dmatLL)
for i in range(n * (n - 1) // 2):
assert feq(dmatArr[i], dmatLL[i])
def test4ebv(self):
n = 30
m = 2048
dm = 800
lst = []
for i in range(n):
v = DataStructs.ExplicitBitVect(m)
for j in range(dm):
v.SetBit(random.randrange(0, m))
lst.append(v)
dMat = rdmmc.GetTanimotoDistMat(lst)
sMat = rdmmc.GetTanimotoSimMat(lst)
for i in range(n * (n - 1) // 2):
assert feq(sMat[i] + dMat[i], 1.0)
def test5sbv(self):
n = 30
m = 2048
dm = 800
lst = []
for i in range(n):
v = DataStructs.SparseBitVect(m)
for j in range(dm):
v.SetBit(random.randrange(0, m))
lst.append(v)
dMat = rdmmc.GetTanimotoDistMat(lst)
sMat = rdmmc.GetTanimotoSimMat(lst)
for i in range(n * (n - 1) // 2):
assert feq(sMat[i] + dMat[i], 1.0)
if __name__ == '__main__':
unittest.main()
|