def base64_to_bfp(b64):
packed = [ord(c) for c in b64.decode("base64")]
unpacked = ''.join('{:08b}'.format(i) for i in packed)
n = len(unpacked)
on_bits = [idx for idx, val in enumerate(unpacked) if val == '1']
bfp = ExplicitBitVect(n)
bfp.SetBitsFromList(on_bits)
return bfp
def test3(self):
examples = [
['a', 1, 0, 1, 0, 1],
['b', 1, 0, 0, 0, 1],
['c', 1, 0, 1, 0, 0],
['d', 0, 1, 1, 0, 0],
['e', 0, 1, 1, 1, 0],
]
nvars = len(examples[0]) - 2
attrs = list(range(1, nvars + 1))
npvals = [0] + [2] * nvars + [2]
qBounds = [0] + [0] * nvars + [0]
mdl = CrossValidate.makeNBClassificationModel(examples, attrs, npvals,
qBounds)
nWrong = 0
for eg in examples:
p = mdl.ClassifyExample(eg)
if p != eg[-1]:
nWrong += 1
self.assertEqual(nWrong, 1)
bitEx = []
for eg in examples:
newEg = [eg[0], None, eg[-1]]
bv = ExplicitBitVect(nvars)
for i in range(nvars):
if eg[i + 1]:
bv.SetBit(i)
newEg[1] = bv
bitEx.append(newEg)
attrs = list(range(nvars))
mdl2 = CrossValidate.makeNBClassificationModel(bitEx,
attrs,
npvals,
qBounds,
useSigs=True)
nWrong = 0
for eg in bitEx:
p = mdl2.ClassifyExample(eg)
if p != eg[-1]:
nWrong += 1
self.assertEqual(nWrong, 1)
# now compare:
for i in range(len(bitEx)):
eg = examples[i]
p1 = mdl.ClassifyExample(eg)
bitEg = bitEx[i]
p2 = mdl2.ClassifyExample(bitEg)
self.assertEqual(p1, p2)
v1 = mdl.GetClassificationDetails()
v2 = mdl.GetClassificationDetails()
self.assertAlmostEqual(v1, v2, 4)
def test1(self):
t1 = self.tree
bv = ExplicitBitVect(5)
ex = ['nm',bv]
self.assertFalse(t1.ClassifyExample(ex))
bv.SetBit(1)
self.assertTrue(t1.ClassifyExample(ex))
bv.SetBit(0)
self.assertTrue(t1.ClassifyExample(ex))
bv.SetBit(2)
self.assertFalse(t1.ClassifyExample(ex))
def test1(self):
t1 = self.tree
bv = ExplicitBitVect(5)
ex = ['nm', bv]
self.failIf(t1.ClassifyExample(ex))
bv.SetBit(1)
self.failUnless(t1.ClassifyExample(ex))
bv.SetBit(0)
self.failUnless(t1.ClassifyExample(ex))
bv.SetBit(2)
self.failIf(t1.ClassifyExample(ex))
def test4(self):
examples = [
['a', 1, 0, 1, 0, 1],
['b', 1, 0, 0, 0, 1],
['c', 1, 0, 1, 0, 0],
['d', 0, 1, 1, 0, 0],
['e', 0, 1, 1, 1, 0],
]
nvars = len(examples[0]) - 2
attrs = range(1, nvars + 1)
origNVars = nvars
nvars = 10
npvals = [0] + [2] * nvars + [2]
qBounds = [0] + [0] * nvars + [0]
bitEx = []
for eg in examples:
newEg = [eg[0], None, eg[-1]]
bv = ExplicitBitVect(nvars)
for i in range(origNVars):
if eg[i + 1]:
bv.SetBit(i)
# this bit will yield perfect accuracy if
# the attrs argument isn't being used properly:
if eg[-1]:
bv.SetBit(origNVars)
newEg[1] = bv
bitEx.append(newEg)
attrs = range(origNVars)
mdl2 = CrossValidate.makeNBClassificationModel(bitEx,
attrs,
npvals,
qBounds,
useSigs=True)
nWrong = 0
for eg in bitEx:
p = mdl2.ClassifyExample(eg)
if p != eg[-1]:
nWrong += 1
self.assertEqual(nWrong, 1)
def _test5(self): # disabled because CMIM was removed # pragma: nocover
examples = [
['a', 1, 0, 1, 0, 1, 1, 0, 1],
['b', 1, 0, 0, 0, 1, 0, 0, 1],
['c', 1, 0, 1, 0, 1, 1, 0, 0],
['d', 0, 1, 1, 0, 1, 0, 0, 0],
['e', 0, 1, 1, 1, 0, 1, 0, 0],
]
nvars = len(examples[0]) - 2
npvals = [0] + [2] * nvars + [2]
qBounds = [0] + [0] * nvars + [0]
bitEx = []
for eg in examples:
newEg = [eg[0], None, eg[-1]]
bv = ExplicitBitVect(nvars)
for i in range(nvars):
if eg[i + 1]:
bv.SetBit(i)
# this bit will yield perfect accuracy if
# the attrs argument isn't being used properly:
newEg[1] = bv
bitEx.append(newEg)
attrs = list(range(nvars))
mdl2 = CrossValidate.makeNBClassificationModel(bitEx,
attrs,
npvals,
qBounds,
useSigs=True,
useCMIM=2)
nWrong = 0
for eg in bitEx:
p = mdl2.ClassifyExample(eg)
if p != eg[-1]:
nWrong += 1
self.assertEqual(nWrong, 1)
def test2(self):
t1 = self.tree
vc = VectCollection()
bv = ExplicitBitVect(5)
bv.SetBitsFromList([0])
vc.AddVect(1,bv)
bv = ExplicitBitVect(5)
bv.SetBitsFromList([1,2])
vc.AddVect(2,bv)
ex = ['nm',bv,1]
self.assertTrue(t1.ClassifyExample(ex))
bv = ExplicitBitVect(5)
bv.SetBitsFromList([0,2])
vc.AddVect(1,bv)
ex = ['nm',bv,1]
self.assertFalse(t1.ClassifyExample(ex))
def build_from_mongo(biochem_modelseed):
rxn_fingerprints = {}
for document in biochem_modelseed.find():
#print(rxn_id)
rxn_id = document['_id']
#stoich = document['neutral_smiles_stoich']
if 'diff' in document and 'stru' in document:
#print(stoich)
diff_bytes = document['diff']
stru_bytes = document['stru']
#print(len(diff_bytes), len(stru_bytes))
#smarts = make_rxn_smarts(stoich)
#print(smarts)
#stru_bytes2, diff_bytes2 = make_fingerprints(smarts)
#print(len(diff_bytes2.ToBinary()), len(stru_bytes2.ToBinary()))
diff_fingerprints = UIntSparseIntVect(diff_bytes)
stru_fingerprints = ExplicitBitVect(stru_bytes)
rxn_fingerprints[rxn_id] = (stru_fingerprints, diff_fingerprints)
matcher = FingerprintMatcher()
matcher.fingerprints = rxn_fingerprints
return matcher
def pandas_series_to_bv(s):
bv = ExplicitBitVect(len(s))
on_bits = np.where(s >= True)[0].tolist()
bv.SetBitsFromList(on_bits)
return bv
def test3(self):
from BuildSigTree import BuildSigTree
examples = []
bv = ExplicitBitVect(2)
vc = VectCollection()
vc.AddVect(1,bv)
examples.append(['a',vc,1])
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1,bv)
examples.append(['c',vc,0])
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1,bv)
examples.append(['c2',vc,0])
bv = ExplicitBitVect(2)
bv.SetBit(0)
vc = VectCollection()
vc.AddVect(1,bv)
examples.append(['d',vc,0])
bv = ExplicitBitVect(2)
bv.SetBit(0)
vc = VectCollection()
vc.AddVect(1,bv)
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc.AddVect(2,bv)
examples.append(['d2',vc,0])
bv = ExplicitBitVect(2)
bv.SetBit(0)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1,bv)
examples.append(['d',vc,1])
bv = ExplicitBitVect(2)
bv.SetBit(0)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1,bv)
examples.append(['e',vc,1])
t = BuildSigTree(examples,2,metric=InfoTheory.InfoType.ENTROPY,
maxDepth=2,verbose=0)
self.assertEqual(t.GetName(),'Bit-0')
self.assertEqual(t.GetLabel(),0)
c0 = t.GetChildren()[0]
self.assertEqual(c0.GetName(),'Bit-1')
self.assertEqual(c0.GetLabel(),1)
c1 = t.GetChildren()[1]
self.assertEqual(c1.GetName(),'Bit-1')
self.assertEqual(c1.GetLabel(),1)
bv = ExplicitBitVect(2)
bv.SetBit(0)
vc = VectCollection()
vc.AddVect(1,bv)
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc.AddVect(2,bv)
r = t.ClassifyExample(['t',vc,0])
self.assertEqual(r,0)
def hex_to_stru_fingerprints(hex_str):
bts = binascii.unhexlify(hex_str)
fp = ExplicitBitVect(bts)
return fp
def test3(self):
examples = []
bv = ExplicitBitVect(2)
vc = VectCollection()
vc.AddVect(1, bv)
examples.append(['a', vc, 1])
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1, bv)
examples.append(['c', vc, 0])
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1, bv)
examples.append(['c2', vc, 0])
bv = ExplicitBitVect(2)
bv.SetBit(0)
vc = VectCollection()
vc.AddVect(1, bv)
examples.append(['d', vc, 0])
bv = ExplicitBitVect(2)
bv.SetBit(0)
vc = VectCollection()
vc.AddVect(1, bv)
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc.AddVect(2, bv)
examples.append(['d2', vc, 0])
bv = ExplicitBitVect(2)
bv.SetBit(0)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1, bv)
examples.append(['d', vc, 1])
bv = ExplicitBitVect(2)
bv.SetBit(0)
bv.SetBit(1)
vc = VectCollection()
vc.AddVect(1, bv)
examples.append(['e', vc, 1])
f = StringIO()
with redirect_stdout(f):
t = BuildSigTree(examples,
2,
metric=InfoTheory.InfoType.ENTROPY,
maxDepth=2,
verbose=True)
self.assertIn('Build', f.getvalue())
self.assertEqual(t.GetName(), 'Bit-0')
self.assertEqual(t.GetLabel(), 0)
c0 = t.GetChildren()[0]
self.assertEqual(c0.GetName(), 'Bit-1')
self.assertEqual(c0.GetLabel(), 1)
c1 = t.GetChildren()[1]
self.assertEqual(c1.GetName(), 'Bit-1')
self.assertEqual(c1.GetLabel(), 1)
bv = ExplicitBitVect(2)
bv.SetBit(0)
vc = VectCollection()
vc.AddVect(1, bv)
bv = ExplicitBitVect(2)
bv.SetBit(1)
vc.AddVect(2, bv)
r = t.ClassifyExample(['t', vc, 0])
self.assertEqual(r, 0)