def calEntropy(confidition, feature):
h_feature = {}
h_a_feature = {}
data = select_table(confidition)
cases = len(data)
num_good = float(data.count(('是', )))
num_bad = float(len(data) - num_good)
hd = - (math.log(num_good/cases,2)*\
num_good/cases +\
math.log(num_bad/cases,2)*num_bad/cases)
for idx in feature:
labels = []
if idx == "编号" or idx == "好瓜":
continue
for label in select_table(confidition, label=idx, distict=True):
if len(label) == 1:
labels.append(label[0])
for label in labels:
now_confidition = confidition
if not now_confidition:
now_confidition = " where "
else:
now_confidition = now_confidition + " and "
now_confidition = now_confidition + " " + idx + " == '" + label + "'"
feature_data = select_table(condition=now_confidition)
#print("condition",now_confidition)
k_ = float(len(feature_data)) / cases
label_good = float(feature_data.count(('是', )))
#print("feature_data",feature_data)
m_ = float(label_good) / len(feature_data) # 好瓜率
# 只有好瓜，坏瓜两种情况，直接加
hk = 0
hf = 0
if label_good != 0: # good
hk = m_ * math.log(m_, 2)
if len(feature_data) - label_good != 0: # bad
hk = hk + (1 - m_) * math.log((1 - m_), 2)
h_feature[idx] = h_feature.get(idx, 0) + k_ * hk
if k_:
h_a_feature[idx] = h_a_feature.get(idx,
0) + k_ * math.log(k_, 2)
h_feature[idx] = h_feature.get(idx, 0) * -1
h_a_feature[idx] = h_a_feature.get(idx, 0) * -1
h_feature[idx] = (hd - h_feature[idx]) / h_a_feature[idx]
return h_feature
def calGini(confidition, feature):
gini_name = {}
gini_num = {}
data = select_table(confidition)
cases = len(data)
num_good = float(data.count(('是', )))
for idx in feature:
labels = []
if idx == "编号" or idx == "好瓜":
continue
for label in select_table(confidition, label=idx, distict=True):
if len(label) == 1:
labels.append(label[0])
minGini = 999
minName = ""
for label in labels:
temp = 999
now_confidition = confidition
not_confidition = confidition
if not now_confidition:
now_confidition = " where "
not_confidition = " where "
else:
now_confidition = now_confidition + " and "
not_confidition = not_confidition + " and "
now_confidition = now_confidition + " " + idx + " = '" + label + "'"
not_confidition = not_confidition + " " + idx + " != '" + label + "'"
feature_data = select_table(condition=now_confidition)
other_feature_data = select_table(condition=not_confidition)
#print("condition",now_confidition)
k_ = float(len(feature_data)) / cases
label_good = float(feature_data.count(('是', )))
other_label_good = float(other_feature_data.count(('是', )))
other_ = float(other_label_good) / len(other_feature_data)
m_ = float(label_good) / len(feature_data) # 好瓜率
temp = k_ * (2 * m_ * (1 - m_)) + (1 - k_) * (2 * other_ *
(1 - other_))
if temp < minGini:
minGini = temp
minName = label
gini_name[idx] = minName
gini_num[idx] = minGini
return gini_num, gini_name
def checkSameLable(confidition):
data = select_table(condition=confidition)
cases = len(data)
num_good = float(data.count(('是', )))
if num_good == cases:
return True, '好瓜'
elif num_good == 0:
return True, '坏瓜'
else:
return False, None
def buildId3Tree(confidition, root, feature):
# 移除 root from 特征集
global cnt
feature.remove(root)
# get labels
labels = []
for label in select_table(confidition, label=root, distict=True):
if len(label) == 1:
labels.append(label[0])
for label in labels:
now_confidition = confidition
if not now_confidition:
now_confidition = " where "
else:
now_confidition = now_confidition + " and "
now_confidition = now_confidition + " " + root + " = '" + label + "'"
# 如果属于同一类返回
flag, nflag = checkSameLable(now_confidition)
if flag:
nflag = "LeafNode:" + str(cnt) + " " + nflag
G.add_node(nflag, fontname="SimHei")
G.add_edge(root,
nflag,
label=label,
fontname="SimHei",
color="black",
style="dashed",
penwidth=1.5)
cnt = cnt + 1
continue
# 计算增益比
h_feature = calEntropy(now_confidition, feature)
# 选增益比最大的特征作为节点
maxidx = max(h_feature, key=h_feature.get)
print("maxidx=", maxidx)
# 以该节点为根构建子树
G.add_node((maxidx), label=label)
G.add_edge(root, maxidx, label=label)
buildId3Tree(now_confidition, root, feature)