def test1HierarchPick(self):
fname = os.path.join(RDConfig.RDBaseDir, 'Code', 'SimDivPickers',
'Wrap', 'test_data', 'points.csv')
with open(fname) as infil:
lines = infil.readlines()
self.dataPts = numpy.zeros((len(lines), 2), 'd')
labels = []
i = 0
for line in lines:
tlst = line.strip().split(',')
self.dataPts[i, 0] = float(tlst[1])
self.dataPts[i, 1] = float(tlst[2])
labels.append(int(tlst[3]))
i += 1
self.dMat = rdmmc.GetEuclideanDistMat(self.dataPts)
pkr = rdSimDivPickers.HierarchicalClusterPicker(
rdSimDivPickers.ClusterMethod.WARD)
clusters = pkr.Cluster(self.dMat, i, 2)
# check that each of the clusters have the same label
for cl in clusters:
clbl = labels[cl[0]]
for id in cl:
assert clbl == labels[id]
hierarch = pkr.Pick(self.dMat, i, 2)
self.assertEqual(tuple(hierarch), (1, 30))
def ClusterBits(self, corrMat):
# clustering code actually needs distances so, take 1/val for each element in corMat
distMat = 1 / corrMat
pkr = rdsimdiv.HierarchicalClusterPicker(self._type)
cls = pkr.Cluster(distMat, len(self._bidList), self._nClusters)
# map the clusters to the actual bit ids
self._clusters = []
for cl in cls:
self._clusters.append([self._bidList[i] for i in cl])
def testNonUniqueCrash(self):
from rdkit import DataStructs
sz = 10
nbits = 20
nBitsToSet = int(nbits * .3)
N = 12
vs = []
for i in range(sz):
bv = DataStructs.ExplicitBitVect(nbits)
for j in range(nBitsToSet):
val = int(nbits * random.random())
bv.SetBit(val)
vs.append(bv)
vs.append(bv)
def taniFunc(i, j, bvs=vs):
d = 1 - DataStructs.FingerprintSimilarity(bvs[i], bvs[j])
return d
picker = rdSimDivPickers.MaxMinPicker()
try:
mm1 = picker.LazyPick(taniFunc, len(vs), N)
except:
ok = False
else:
ok = True
self.assertTrue(ok)
self.assertEqual(len(mm1), N)
picker = None
picker = rdSimDivPickers.MaxMinPicker()
try:
mm2 = picker.LazyBitVectorPick(vs, len(vs), N)
except:
ok = False
else:
ok = True
self.assertTrue(ok)
self.assertEqual(len(mm2), N)
self.assertEqual(tuple(mm2), tuple(mm1))
picker = None
ds = []
nvs = len(vs)
for i in range(nvs):
for j in range(i + 1, nvs):
d = taniFunc(i, j)
ds.append(d)
m = numpy.array(ds)
picker = rdSimDivPickers.HierarchicalClusterPicker(
rdSimDivPickers.ClusterMethod.WARD)
p1 = list(picker.Pick(m, nvs, N))
def testIssue208(self):
sz = 10
N = 3
m = []
for i in range(sz):
for j in range(i + 1, sz):
m.append(random.random())
m = numpy.array(m)
picker = rdSimDivPickers.HierarchicalClusterPicker(rdSimDivPickers.ClusterMethod.WARD)
p1 = list(picker.Pick(m, sz, N))
p1.sort()
p2 = list(picker.Pick(m, sz, N))
p2.sort()
self.assertEqual(p1, p2)
def testInts(self):
""" make sure we can handle ints too """
sz = 10
N = 3
m = []
for i in range(sz):
for j in range(i + 1, sz):
m.append(int(100 * random.random()))
m = numpy.array(m)
picker = rdSimDivPickers.HierarchicalClusterPicker(rdSimDivPickers.ClusterMethod.WARD)
p1 = list(picker.Pick(m, sz, N))
p1.sort()
p2 = list(picker.Pick(m, sz, N))
p2.sort()
self.assertEqual(p1, p2)
def testNonUniqueCrash(self):
from rdkit import DataStructs
sz = 300
nbits = 40
nBitsToSet = int(nbits * .3)
N = 8
vs = []
for i in range(sz):
bv = DataStructs.ExplicitBitVect(nbits)
for j in range(nBitsToSet):
val = int(nbits * random.random())
bv.SetBit(val)
vs.append(bv)
vs.append(bv)
def taniFunc(i, j, bvs=vs):
d = 1 - DataStructs.FingerprintSimilarity(bvs[i], bvs[j])
return d
picker = rdSimDivPickers.MaxMinPicker()
mm1 = picker.LazyPick(taniFunc, len(vs), N)
self.assertEqual(len(mm1), N)
picker = None
picker = rdSimDivPickers.MaxMinPicker()
mm2 = picker.LazyBitVectorPick(vs, len(vs), N)
self.assertEqual(len(mm2), N)
picker = rdSimDivPickers.MaxMinPicker()
mm3 = picker.LazyBitVectorPick(vs, len(vs), N)
self.assertEqual(len(mm3), N)
# we get the occasional dupe randomly,
# make sure we don't get three dupes in a row
self.assertTrue(tuple(mm2) != tuple(mm1)) or (tuple(mm3) != tuple(mm1))
picker = None
ds = []
nvs = len(vs)
for i in range(nvs):
for j in range(i + 1, nvs):
d = taniFunc(i, j)
ds.append(d)
m = numpy.array(ds)
picker = rdSimDivPickers.HierarchicalClusterPicker(
rdSimDivPickers.ClusterMethod.WARD)
p1 = list(picker.Pick(m, nvs, N))
def test1HierarchPick(self):
infil = open("test_data/points.csv", 'r')
lines = infil.readlines()
infil.close()
self.dataPts = numpy.zeros((len(lines), 2), 'd')
labels = []
i = 0
for line in lines:
tlst = line.strip().split(',')
self.dataPts[i, 0] = float(tlst[1])
self.dataPts[i, 1] = float(tlst[2])
labels.append(int(tlst[3]))
i += 1
self.dMat = rdmmc.GetEuclideanDistMat(self.dataPts)
pkr = rdSimDivPickers.HierarchicalClusterPicker(
rdSimDivPickers.ClusterMethod.WARD)
clusters = pkr.Cluster(self.dMat, i, 2)
# check that each of the clusters have the same label
for cl in clusters:
clbl = labels[cl[0]]
for id in cl:
assert clbl == labels[id]
hierarch = pkr.Pick(self.dMat, i, 2)
assert tuple(hierarch) == (1, 30)
