def up(self):
info.read_file()
tb.buildTree(info.alist)
ft4 = tkFont.Font(size=12)
str0 = update(i1.get(), i2.get(), i3.get(), i4.get(), i5.get(),
i6.get())
Label(self, text=str0, font=ft4).pack()
def delete(e1):#e1.姓名
print("->已选择 删除成员记录")
# print("请选择操作:")
name = e1
result = bs.circle("姓名", name)
if result == -1:
str1 = "您所查询的成员不存在。"
return str1
else:
if len(result) > 1:
print("查询到多位成员，请输入其对应的编号以选择成员：")
for i in result:
print(i)
print(bs.alist[i])
idx = input("显示完毕，请选择：")
idx = int(idx)
else:
idx = result[0]
if tb.family[idx].kids == []:
if tb.family[idx].spouse == -1: # 情况1：删除对象无后代，无配偶，直接删除
del bs.alist[idx]
save_file(bs.alist)
tb.buildTree(bs.alist)
str1 = "删除成功！"
return str1
else: # 情况2：删除对象无后代，有配偶，一并删除
del bs.alist[idx]
spouse = tb.family[idx].spouse
del bs.alist[spouse - 1]
save_file(bs.alist)
tb.buildTree(bs.alist)
str1 = "删除成功！"
return str1
else:
clist = []
count = 0
if tb.family[idx].spouse == -1:
if len(tb.family[idx].kids) == 1: # 情况3：删除对象有后代，无配偶，只有一个孩子，孩子做根节点
search_kids_and_spouse(idx,clist,count)
del bs.alist[idx - count]
str1 = save_new_file(clist, bs.alist)
return str1
else: # 情况4：删除对象有后代，无配偶，有多个孩子，该对象做根节点
clist.append(bs.alist[idx])
search_kids_and_spouse(idx,clist,count)
str1 = save_new_file(clist, bs.alist)
return str1
else: # 情况5：删除对象有后代，有配偶，配偶做根节点
spouse = tb.family[idx].spouse
search_kids_and_spouse(idx, clist,count)
del bs.alist[idx - count]
str1 = save_new_file(clist, bs.alist)
return str1
def update(i1, i2, i3, i4, i5, i6): # 江:更改操作 #桂：i1姓名 i2序号 i3新信息 i4修改亲属的名字 i5是否要修改亲属关系 i6描述新关系
print("->已选择 更新成员记录")
name = i1
result = bs.circle("姓名", i1)
if result == -1:
str1 = "您所查询的成员不存在。"
return str1
else:
if len(result) > 1:
print("查询到多位成员，请输入其对应的编号以选择成员：")
for i in result:
print(i)
print(bs.alist[i])
idx = input("显示完毕，请选择：")
idx = int(idx)
else:
idx = result[0]
# print("请输入需更改的信息编号")
func = i2
func = int(func)
if func == 0:
return
elif func == 9:
# tmp = input("请输入要更改的亲属：")
tmp = i4
# j = input('是否更改与该亲属的关系（1.是 2.否）')
j = i5
j = int(j)
if j == 2:
rela_idx = bs.alist[idx]['关系']
str1 = search_tree_idx(rela_idx,idx, name, tmp)
return str1
elif j == 1:
# rela_idx = input("请输入上述亲属为该成员的（0.配偶 1.父亲/母亲 2.儿子/女儿）：")
rela_idx = i6
str1 = search_tree_idx(rela_idx, idx, name, tmp)
return str1
# elif func == 10:
# tmp = bs.alist[idx]['亲属']
# E = format(tmp)
# # rela_idx = input("请输入{}为该成员的（0.配偶 1.父亲/母亲 2.儿子/女儿）：".format(tmp))
# rela_idx = i7
# search_tree_idx(rela_idx, idx, name, tmp)
else:
# str = "请输入要更改的{}：".format(bs.title[func - 1])
# tmp = input(str)
tmp = i3
bs.alist[idx][bs.title[func - 1]] = tmp
tb.buildTree(bs.alist)
save_file(bs.alist)
str1 = "修改成功！"
return str1
def evaluate_kfold(dataset, process_dataset, max_depth, min_size, n_folds,
*args):
folds = kcs.cross_validation_split(dataset, n_folds)
scores = list()
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
X_train, y_train = process_dataset(train_set)
i = 0
for row in X_train:
row.append(y_train[i])
i += 1
node = tb.build_tree(X_train, max_depth, min_size)
predicted = list()
X_test, y_test = process_dataset(fold)
for row in X_test:
predicted.append(predict(node, row))
accuracy = accuarcy_metric(actual=y_test, predicted=predicted)
f1 = f1_score(y_test, predicted)
scores.append({'accuracy': accuracy, 'F1_score': f1})
return scores
def search_tree_idx(rela_idx,idx,name,tmp):
rela = find_rela(name, tmp)
if rela != None:
if rela_idx == 0:
if tb.family[rela].spouse == -1: # 不存在配偶方可修改
bs.alist[idx]["亲属"] = tmp
bs.alist[idx]["关系"] = rela_idx
tb.buildTree(bs.alist)
save_file(bs.alist)
str1 = "修改成功！"
return str1
else:
str1 = "该亲属已存在配偶"
return str1
else: # 其他情况可直接修改
bs.alist[idx]["亲属"] = tmp
bs.alist[idx]["关系"] = rela_idx
tb.buildTree(bs.alist)
save_file(bs.alist)
str1 = "修改成功！"
return str1
def evaluate_kfold(dataset, process_dataset, max_depth, min_size, n_folds,
*args):
folds = kcs.cross_validation_split(dataset, n_folds)
scores = list()
best_node = []
training_s = []
for fold in folds:
train_set = list(folds)
train_set.remove(fold)
train_set = sum(train_set, [])
X_train, y_train = process_dataset(train_set)
i = 0
for row in X_train:
row.append(y_train[i])
i += 1
node = tb.build_tree(X_train, max_depth, min_size)
best_node.append(node)
predicted = list()
training = list()
X_test, y_test = process_dataset(fold)
for row in X_test:
predicted.append(predict(node, row))
for test in X_train:
training.append(predict(node, test))
accuracy = accuarcy_metric(actual=y_test, predicted=predicted)
train_score = accuarcy_metric(actual=y_train, predicted=training)
f1 = f1_score(y_test, predicted)
scores.append({
'training_score': train_score,
'accuracy': accuracy,
'F1_score': f1
})
training_s.append(train_score)
#get position for max Accuracy
position = training_s.index(max(training_s))
best_tree = best_node[position]
return scores, best_tree
'cdNorm_st': nscore_st
}
except EOFError:
break
except:
try:
print("Quote error on: %d" % unit['id'])
q1_stanford = _getNLPToks_(unit['q1']['raw'].replace(
'"', '').replace("'", ''))
q2_stanford = _getNLPToks_(unit['q2']['raw'].replace(
'"', '').replace("'", ''))
tree_1 = tb.tree()
tree_2 = tb.tree()
# Generate a tree structure
tb._generateTree_(q1_stanford['parse'], tree_1)
tb._generateTree_(q2_stanford['parse'], tree_2)
# Flip the trees
tb._flipTree_(tree_1)
tb._flipTree_(tree_2)
(rscore_st,
nscore_st) = tk._CollinsDuffy_(tree_1, tree_2,
sys.argv[2], 1,
sys.argv[3])
def averageP(self):
info.read_file()
tb.buildTree(info.alist)
ft4 = tkFont.Font(size=12)
str6 = avg_people()
Label(self, text=str6, font=ft4).pack()
def searchA(self):
info.read_file()
tb.buildTree(info.alist)
ft4 = tkFont.Font(size=12)
str2 = tb.search_rela_info(p1.get(), p2.get(), p3.get(), p4.get())
Label(self, text=str2, font=ft4).pack()
def dell(self):
info.read_file()
tb.buildTree(info.alist)
ft4 = tkFont.Font(size=12)
strn = delete(e1.get())
Label(self, text=strn, font=ft4).pack()
def TS(self):
info.read_file() #三行测试功能函数，可删除
ft3 = tkFont.Font(size=14)
tb.buildTree(bs.alist)
tree_search(family[0], 0)
Label(self, text='树形图已经显示在代码下方运行结果区域', font=ft3).pack(side=TOP)