def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = list(range(1, nvars + 1))
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5000, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.5000, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.0360, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.7022, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.16635, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -0.3659, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.2121, 4)
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True,
calcTotalError=True)
self.assertAlmostEqual(mod._classProbs[0], 0.515151, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.484848, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.40315, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.62185, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.19965, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], 0.4305, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.80305, 4)
self.assertAlmostEqual(err, 0.14563, 4)
mod, err = CrossValidate.CrossValidationDriver(
examples,
attrs,
npvals,
qBounds,
silent=True,
replacementSelection=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5131578, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.4868421, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.036, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.93465, 4)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.6696, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.19965, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -1.06785, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.3, 4)
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 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 = 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 = 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(p1, p2, 4)
def _test5(self) : # disabled because CMIM was removed
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
attrs = range(1,nvars+1)
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 = 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 test2NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = list(range(1, nvars + 1))
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
mEstimateVal=20.0,
silent=True)
self.assertTrue(isinstance(mod, NaiveBayesClassifier))
self.assertAlmostEqual(err, 0.1818, 4)
self.assertEqual(mod.GetName(), '')
mod.SetName('modelName')
self.assertEqual(mod.GetName(), 'modelName')
mod.NameModel(None)
self.assertEqual(mod.GetName(), 'NaiveBayesClassifier')
self.assertGreater(len(mod.GetExamples()), 0)
self.assertGreater(len(mod.GetTrainingExamples()), 0)
self.assertEqual(sorted(mod.GetTrainingExamples() + mod.GetExamples()),
sorted(examples))
def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
silent=True)
self.assertAlmostEqual(mod._classProbs[0], 0.5000, 4)
self.assertAlmostEqual(mod._classProbs[1], 0.5000, 4)
self.assertAlmostEqual(mod._QBoundVals[1][0], -0.0360, 4)
self.assertAlmostEqual(mod._QBoundVals[1][1], 0.114)
self.assertAlmostEqual(mod._QBoundVals[2][0], -0.7022, 4)
self.assertAlmostEqual(mod._QBoundVals[2][1], -0.16635, 4)
self.assertAlmostEqual(mod._QBoundVals[3][0], -0.3659, 4)
self.assertAlmostEqual(mod._QBoundVals[3][1], 0.4305, 4)
self.assertAlmostEqual(err, 0.2121, 4)
def test2NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples,
attrs,
npvals,
qBounds,
mEstimateVal=20.0)
assert feq(err, 0.19354)
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 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.failUnless(nWrong==1)
def test1NaiveBayes(self):
fName = os.path.join(RDConfig.RDCodeDir, 'ML', 'NaiveBayes',
'test_data', 'stddata.csv')
data = DataUtils.TextFileToData(fName)
examples = data.GetNamedData()
nvars = data.GetNVars()
attrs = range(1, nvars + 1)
npvals = [0] + [3] * nvars + [2]
qBounds = [0] + [2] * nvars + [0]
mod, err = CrossValidate.CrossValidationDriver(examples, attrs, npvals,
qBounds)
assert feq(mod._classProbs[0], 0.54167)
assert feq(mod._classProbs[1], 0.45833)
assert feq(mod._QBoundVals[1][0], -0.56995)
assert feq(mod._QBoundVals[1][1], 0.114)
assert feq(mod._QBoundVals[2][0], -0.7022)
assert feq(mod._QBoundVals[2][1], -0.2347)
assert feq(mod._QBoundVals[3][0], -0.3659)
assert feq(mod._QBoundVals[3][1], 1.17275)
assert feq(err, 0.16129)
