def test3Pickle2(self):
"""
"""
l = 1L << 21
v1 = ds.IntSparseIntVect(l)
self.failUnlessRaises(IndexError, lambda: v1[l + 1])
v1[0] = 1
v1[2] = 2
v1[1 << 12] = 3
self.failUnless(v1 == v1)
v2 = cPickle.loads(cPickle.dumps(v1))
self.failUnless(v2 == v1)
v3 = ds.IntSparseIntVect(v2.ToBinary())
self.failUnless(v2 == v3)
self.failUnless(v1 == v3)
#cPickle.dump(v1,file('isiv.pkl','wb+'))
v3 = cPickle.load(
file(
os.path.join(RDConfig.RDBaseDir,
'Code/DataStructs/Wrap/testData/isiv.pkl'), 'rb'))
self.failUnless(v3 == v1)
def test3Pickle2(self):
"""
"""
l = 1 << 21
v1 = ds.IntSparseIntVect(l)
self.assertRaises(IndexError, lambda: v1[l + 1])
v1[0] = 1
v1[2] = 2
v1[1 << 12] = 3
self.assertTrue(v1 == v1)
v2 = pickle.loads(pickle.dumps(v1))
self.assertTrue(v2 == v1)
v3 = ds.IntSparseIntVect(v2.ToBinary())
self.assertTrue(v2 == v3)
self.assertTrue(v1 == v3)
#pickle.dump(v1,file('isiv.pkl','wb+'))
with open(os.path.join(RDConfig.RDBaseDir, 'Code/DataStructs/Wrap/testData/isiv.pkl'),
'r') as tf:
buf = tf.read().replace('\r\n', '\n').encode('utf-8')
tf.close()
with io.BytesIO(buf) as f:
v3 = pickle.load(f)
self.assertTrue(v3 == v1)
def test4Update(self):
"""
"""
v1 = ds.IntSparseIntVect(5)
self.assertRaises(IndexError, lambda: v1[6])
v1[0] = 1
v1[2] = 2
v1[3] = 3
self.assertTrue(v1 == v1)
v2 = ds.IntSparseIntVect(5)
v2.UpdateFromSequence((0, 2, 3, 3, 2, 3))
self.assertTrue(v1 == v2)
def test6BulkTversky(self):
"""
"""
sz = 10
nToSet = 5
nVs = 6
import random
vs = []
for i in range(nVs):
v = ds.IntSparseIntVect(sz)
for j in range(nToSet):
v[random.randint(0, sz - 1)] = random.randint(1, 10)
vs.append(v)
baseDs = [ds.TverskySimilarity(vs[0], vs[x], .5, .5) for x in range(1, nVs)]
bulkDs = ds.BulkTverskySimilarity(vs[0], vs[1:], 0.5, 0.5)
diceDs = [ds.DiceSimilarity(vs[0], vs[x]) for x in range(1, nVs)]
for i in range(len(baseDs)):
self.assertTrue(feq(baseDs[i], bulkDs[i]))
self.assertTrue(feq(baseDs[i], diceDs[i]))
bulkDs = ds.BulkTverskySimilarity(vs[0], vs[1:], 1.0, 1.0)
taniDs = [ds.TanimotoSimilarity(vs[0], vs[x]) for x in range(1, nVs)]
for i in range(len(bulkDs)):
self.assertTrue(feq(bulkDs[i], taniDs[i]))
taniDs = ds.BulkTanimotoSimilarity(vs[0], vs[1:])
for i in range(len(bulkDs)):
self.assertTrue(feq(bulkDs[i], taniDs[i]))
def testPairValues(self):
import base64
testD=(('CCCO',b'AQAAAAQAAAAAAIAABgAAACGECAABAAAAIoQIAAEAAABBhAgAAQAAACNEGAABAAAAQUQYAAEAAABC\nRBgAAQAAAA==\n'),
('CNc1ccco1',b'AQAAAAQAAAAAAIAAEAAAACOECgABAAAAJIQKAAIAAABBhQoAAgAAAEKFCgABAAAAIsQKAAEAAABB\nxQoAAQAAAELFCgACAAAAIYQQAAEAAABChRAAAQAAAEOFEAACAAAAYYUQAAEAAAAjhBoAAQAAAEGF\nGgABAAAAQoUaAAIAAABhhRoAAQAAAEKIGgABAAAA\n'),
)
for smi,txt in testD:
pkl = base64.decodestring(txt)
fp = rdMD.GetAtomPairFingerprint(Chem.MolFromSmiles(smi))
fp2 = DataStructs.IntSparseIntVect(pkl)
self.assertEqual(DataStructs.DiceSimilarity(fp,fp2),1.0)
self.assertEqual(fp,fp2)
def test1Int(self):
"""
"""
v1 = ds.IntSparseIntVect(5)
self.assertRaises(IndexError, lambda: v1[5])
v1[0] = 1
v1[2] = 2
v1[3] = 3
self.assertTrue(v1 == v1)
self.assertTrue(v1.GetLength() == 5)
v2 = ds.IntSparseIntVect(5)
self.assertTrue(v1 != v2)
v2 |= v1
self.assertTrue(v2 == v1)
v3 = v2 | v1
self.assertTrue(v3 == v1)
onVs = v1.GetNonzeroElements()
self.assertTrue(onVs == {0: 1, 2: 2, 3: 3})
def test5Dice(self):
"""
"""
v1 = ds.IntSparseIntVect(5)
v1[4] = 4
v1[0] = 2
v1[3] = 1
self.assertTrue(feq(ds.DiceSimilarity(v1, v1), 1.0))
v1 = ds.IntSparseIntVect(5)
v1[0] = 2
v1[2] = 1
v1[3] = 4
v1[4] = 6
v2 = ds.IntSparseIntVect(5)
v2[1] = 2
v2[2] = 3
v2[3] = 4
v2[4] = 4
self.assertTrue(feq(ds.DiceSimilarity(v1, v2), 18.0 / 26.))
self.assertTrue(feq(ds.DiceSimilarity(v2, v1), 18.0 / 26.))
def test7ToList(self):
l = [0]*2048
nbits = 2048
bv = ds.IntSparseIntVect(nbits)
for j in range(nbits):
x = random.randrange(0, nbits)
l[x] = x
bv[x] = x
l2 = list(bv)
l3 = bv.ToList()
self.assertEqual(l, l2)
self.assertEqual(l, l3)
def test6BulkDice(self):
"""
"""
sz = 10
nToSet = 5
nVs = 6
import random
vs = []
for i in range(nVs):
v = ds.IntSparseIntVect(sz)
for j in range(nToSet):
v[random.randint(0, sz - 1)] = random.randint(1, 10)
vs.append(v)
baseDs = [ds.DiceSimilarity(vs[0], vs[x]) for x in range(1, nVs)]
bulkDs = ds.BulkDiceSimilarity(vs[0], vs[1:])
for i in range(len(baseDs)):
self.assertTrue(feq(baseDs[i], bulkDs[i]))
