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]))