def test_cv_score(self):
def label_weight(listInst, fLabel):
dblWeight = 0.0
for inst in listInst:
if inst.fLabel == fLabel:
dblWeight += inst.dblWeight
return dblWeight
cValues = 4
fxnGen = build_consistent_generator(cValues=cValues,
fxnGenWeight=random.random)
cInst = random.randint(30, 60)
listLeft = fxnGen(cInst)
listRight = [
dtree.Instance([cAttr + cValues + 1
for cAttr in inst.listAttrs], inst.fLabel)
for inst in fxnGen(cInst)
]
fMajL = dtree.majority_label(listLeft)
fMajR = dtree.majority_label(listRight)
iterableFolds = [
dtree.TreeFold(listLeft, listRight),
dtree.TreeFold(listRight, listLeft)
]
dblScore = dtree.cv_score(iterableFolds)
dblL = label_weight(listRight, fMajL)
dblR = label_weight(listLeft, fMajR)
dblTotalWeight = sum([inst.dblWeight for inst in listRight + listLeft])
self.assertAlmostEqual((dblL + dblR) / dblTotalWeight, dblScore)
def test_evaluate_classification(self):
def increase_values(inst):
listIncreased = [c + cValues + 1 for c in inst.listAttrs]
return dtree.Instance(listIncreased, not fMajorityLabel)
def filter_unclassifiable(listInst):
dt = dtree.build_tree(listInst)
return [inst for inst in listInst
if dtree.classify(dt, inst) == inst.fLabel]
cValues = 2
fxnGen = build_instance_generator(cValues=cValues)
listInst = fxnGen(15)
force_instance_consistency(listInst)
listInst = filter_unclassifiable(listInst)
fMajorityLabel = dtree.majority_label(listInst)
listInstImpossible = map(increase_values, listInst)
listInstTest = listInst + listInstImpossible
cvf = dtree.TreeFold(listInst, listInstTest)
rslt = dtree.evaluate_classification(cvf)
self.assertEqual(len(listInst), len(rslt.listInstCorrect))
self.assertEqual(len(listInstImpossible), len(rslt.listInstIncorrect))
self.assertTrue(check_instance_membership(
listInst, rslt.listInstCorrect), "Missing correct instances")
self.assertTrue(check_instance_membership(
listInstImpossible, rslt.listInstIncorrect),
"Missing incorrect instances")
def test_evaluate_classification(self):
def increase_values(inst):
listIncreased = [c+cValues+1 for c in inst.listAttrs]
return dtree.Instance(listIncreased, not fMajorityLabel)
def filter_unclassifiable(listInst):
dt = dtree.build_tree(listInst)
return [inst for inst in listInst
if dtree.classify(dt,inst) == inst.fLabel]
cValues = 2
fxnGen = build_instance_generator(cValues=cValues)
listInst = fxnGen(15)
force_instance_consistency(listInst)
listInst = filter_unclassifiable(listInst)
fMajorityLabel = dtree.majority_label(listInst)
listInstImpossible = map(increase_values,listInst)
listInstTest = listInst + listInstImpossible
cvf = dtree.TreeFold(listInst, listInstTest)
rslt = dtree.evaluate_classification(cvf)
self.assertEqual(len(listInst), len(rslt.listInstCorrect))
self.assertEqual(len(listInstImpossible), len(rslt.listInstIncorrect))
self.assertTrue(check_instance_membership(
listInst, rslt.listInstCorrect), "Missing correct instances")
self.assertTrue(check_instance_membership(
listInstImpossible, rslt.listInstIncorrect),
"Missing incorrect instances")
def test_build_tree_no_gain(self):
listAttr = randlist(0,5,10)
listInst = [dtree.Instance(listAttr, randbool())]*random.randint(25,30)
dt = dtree.build_tree(listInst)
fMajorityLabel = dtree.majority_label(listInst)
self.assertTrue(dt.is_leaf())
self.assertEquals(dt.fLabel, fMajorityLabel)
def test_majority_label(self):
fxnGenTrue = build_instance_generator(1.0)
fxnGenFalse = build_instance_generator(0.0)
cLenTrue = random.randint(5, 10)
cLenFalse = random.randint(5, 10)
if cLenTrue == cLenFalse:
cLenTrue += 1
listInst = fxnGenTrue(cLenTrue) + fxnGenFalse(cLenFalse)
fMajorityLabel = dtree.majority_label(listInst)
self.assertEqual(fMajorityLabel, cLenTrue > cLenFalse)
def test_majority_label(self):
fxnGenTrue = build_instance_generator(1.0)
fxnGenFalse = build_instance_generator(0.0)
cLenTrue = random.randint(5,10)
cLenFalse = random.randint(5,10)
if cLenTrue == cLenFalse:
cLenTrue += 1
listInst = fxnGenTrue(cLenTrue) + fxnGenFalse(cLenFalse)
fMajorityLabel = dtree.majority_label(listInst)
self.assertEqual(fMajorityLabel, cLenTrue > cLenFalse)
def test_cv_score(self):
def label_weight(listInst, fLabel):
dblWeight = 0.0
for inst in listInst:
if inst.fLabel == fLabel:
dblWeight += inst.dblWeight
return dblWeight
cValues = 4
fxnGen = build_consistent_generator(cValues=cValues)
cInst = random.randint(30,60)
listLeft = fxnGen(cInst)
listRight = [dtree.Instance([cAttr+cValues+1
for cAttr in inst.listAttrs],
inst.fLabel) for inst in fxnGen(cInst)]
fMajL = dtree.majority_label(listLeft)
fMajR = dtree.majority_label(listRight)
iterableFolds = dtree.yield_cv_folds(listLeft + listRight, 2)
dblScore = dtree.cv_score(iterableFolds)
dblL = label_weight(listRight, fMajL)
dblR = label_weight(listLeft, fMajR)
self.assertAlmostEqual((dblL + dblR)/(2.0*cInst), dblScore)
def test_majority_label_weighted(self):
dblScale = 25.0
def gen_insts_for_label(fLabel):
dblW = random.random() * dblScale
listInst = []
dblInstWeight = 0.0
while dblInstWeight < dblW:
dblNextWeight = random.random()
listInst.append(dtree.Instance([],fLabel,dblNextWeight))
dblInstWeight += dblNextWeight
return listInst,dblInstWeight
listInstT,dblT = gen_insts_for_label(True)
listInstF,dblF = gen_insts_for_label(False)
listInstAll = listInstT + listInstF
random.shuffle(listInstAll)
fMajorityLabel = dtree.majority_label(listInstAll)
self.assertEqual(dblT > dblF, fMajorityLabel)