def _Abcd(predicted, actual):
predicted_txt = []
abcd = Abcd(db='Traing', rx='Testing')
def isDef(x):
return "Defective" if x > 0 else "Non-Defective"
for data in predicted:
predicted_txt +=[isDef(data)]
for act, pre in zip(actual, predicted_txt):
abcd.tell(act, pre)
abcd.header()
score = abcd.ask()
# pdb.set_trace()
return score
def _Abcd(testleaf, testdata, train):
# train=[]
test = []
abcd = Abcd(db='Traing', rx='Testing')
def isDef(x):
return "Defective" if x > The.option.threshold else "Non-Defective"
for leaf, data in zip(testleaf, testdata):
try:
test += [isDef(leaf.score)]
# test +=[isDef(majorityscore(data,leaf))]
except Exception: # go to middle points
# give the median of all rows in this point
# pdb.set_trace()
test += [isDef(leafscore(leaf))]
continue
for actual, predicted in zip(train, test):
abcd.tell(actual, predicted)
abcd.header()
score = abcd.ask()
return score
def _Abcd(testleaf, testdata, train):
# train=[]
test = []
abcd = Abcd(db='Traing', rx='Testing')
def isDef(x):
return "Defective" if x > The.option.threshold else "Non-Defective"
for leaf, data in zip(testleaf, testdata):
try:
test += [isDef(leaf.score)]
# test +=[isDef(majorityscore(data,leaf))]
except Exception: # go to middle points
# give the median of all rows in this point
# pdb.set_trace()
test += [isDef(leafscore(leaf))]
continue
for actual, predicted in zip(train, test):
abcd.tell(actual, predicted)
abcd.header()
score = abcd.ask()
return score
