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 task(self):
listInstClean = get_clean_insts()
listInstNoisy = get_noisy_insts()
listData = []
listNames = ["Clean", "Noisy"]
for listInst, sName in zip([listInstClean, listInstNoisy], listNames):
dt = dtree.build_tree(listInst)
tf = dtree.TreeFold(listInst, listInst)
rslt = dtree.evaluate_classification(tf)
dblCorrect, dblIncorrect = dtree.weight_correct_incorrect(rslt)
dblAccuracy = dblCorrect / (dblCorrect + dblIncorrect)
listData.append(dblAccuracy)
return {
"chart": {
"defaultSeriesType": "column"
},
"title": {
"text": "Clean vs. Noisy Training Set Accuracy"
},
"xAxis": {
"categories": listNames
},
"yAxis": {
"title": {
"text": "Accuracy"
},
"min": 0.0,
"max": 1.0
},
"series": [{
"name": "Training Set Accuracy",
"data": listData
}]
}
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")