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_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 yield_cv_folds(listInst, cFold):
for cvf in dtree.yield_cv_folds(listInst, cFold):
cvf.cMaxLevel = iDepth
yield cvf
def yield_cv_folds(listInst, cFold):
for cvf in dtree.yield_cv_folds(listInst, cFold):
cvf.cMaxLevel = iDepth
yield cvf
