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")
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 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}],
}