def get_bag_label(hypo, s):
prediction = 0.0
for tree, weight in zip(hypo[0], hypo[1]):
label = get_label(s, tree)
label = 1 if label == "yes" else -1
prediction += label * weight
return prediction
def AdaBoost_print(S_train, S_test, Attributes, T):
trees = []
alphas = []
f = open("adaboost_out2.txt", "w")
for _ in range(0, T):
tree = id3_with_weight(S_train, Attributes, 1, 0)
trees.append(tree)
norm = 0.0
epsilon = id3_weighted_err(tree, S_train)
f.write(str(_) + "\t" + str(epsilon) + "\t" + str(id3_weighted_err(tree, S_test)) + "\n")
alpha = .5 * math.log((1 - epsilon)/epsilon)
alphas.append(alpha)
for s in S_train:
label = get_label(s, tree)
if label != s["Label"]:
newWeight = s["Weight"] * math.exp(alpha)
s["Weight"] = newWeight
else:
newWeight = s["Weight"] * math.exp(-alpha)
s["Weight"] = newWeight
norm += newWeight
# normalize values
for s in S_train:
s["Weight"] /= norm
return (trees, alphas)
def AdaBoost(S, Attributes, T):
trees = []
alphas = []
for _ in range(0, T):
tree = id3_with_weight(S, Attributes, 1, 0)
trees.append(tree)
norm = 0.0
epsilon = id3_weighted_err(tree, S)
alpha = .5 * math.log((1 - epsilon)/epsilon)
alphas.append(alpha)
for s in S:
label = get_label(s, tree)
if label != s["Label"]:
newWeight = s["Weight"] * math.exp(alpha)
s["Weight"] = newWeight
else:
newWeight = s["Weight"] * math.exp(-alpha)
s["Weight"] = newWeight
norm += newWeight
# normalize values
for s in S:
s["Weight"] /= norm
return (trees, alphas)
def avg_error(Hypothesis, S):
total = 0.0
for tree in Hypothesis[0]:
temp = 0
for s in S:
label = get_label(s, tree)
if s["Label"] != label:
temp += 1
total += temp/float(len(S))
return total/float(len(Hypothesis[0]))
def Bagging_Test(Hypothesis, S):
wrong = 0
for s in S:
prediction = 0
for tree, weight in zip(Hypothesis[0], Hypothesis[1]):
label = get_label(s, tree)
label = 1 if label == "yes" else -1
prediction += label * weight
if s["Label"] == "yes" and prediction > 0:
pass
elif s["Label"] == "no" and prediction < 0:
pass
else:
wrong += 1
return wrong / float(len(S))