def DtModuleData1(data_set_csv, m_file_name):
title, data_set = csvOperation.readCsv(data_set_csv) #读取可达集的表头和数据集
sign = search_sign(m_file_name) #读取对称符号
print sign
true_title = titleChange(title, sign) #去表头中的除对称符号，得到原始表头。将表头小写化统一名称
true_data_set = dataSetChange(data_set, sign) #除去数据集中的对称符号
return true_title, true_data_set
def priority_test(csvfile):
result = {}
title, dataSet = csvOperation.readCsv(csvfile)
for member in title:
if member == 'Chan2[NODE_1].Cmd' or member == 'ExGntd' or member == 'ShrSet[NODE_1]' or member == 'CurCmd':
result[member] = 0
print('1')
else:
result[member] = 0.1
return result
def createTree(atom_csv, undefined_percentage): #输入为处理过的可达集、优先级字典
title, dataSet = csvOperation.readCsv(atom_csv)
#add 0822 for only one class 可能返回字符串，而不是字典
classList = [example[-1] for example in dataSet]
if len(set(classList)) == 1:
return '(' + title[-1] + ' = ' + classList[0] + ')'
else:
myTree = id3.createTree(
dataSet, title[:-1], undefined_percentage
) #0726,修改函数id3.createTree，加入优先级判定依据，即undefined_percentage
return myTree
def percentage(csvfile):
title, dataSet = csvOperation.readCsv(csvfile)
undefined_percentage_dict = {}
number_of_data = len(dataSet)
for i in range(len(title)):
undefined_counter = 0 #初始化计数器
for j in range(number_of_data):
if dataSet[j][i].lower() == 'undefined': #j代表行数，i代表列数
undefined_counter = undefined_counter + 1
undefined_percentage_dict[
title[i]] = undefined_counter / number_of_data
return undefined_percentage_dict
def convert(origin_csv, atom_txt, atom_csv):
atom_list = csvOperation.txtToList(atom_txt)
origin_title, origin_dataSet = csvOperation.readCsv(origin_csv)
left_list = csvOperation.getLeft(atom_list[:])
convert_list = csvOperation.getConvertList(origin_title, left_list)
right_list = csvOperation.getRight(atom_list[:])
atom_dataset = csvOperation.dataSetToAtomDataSet(origin_dataSet,
convert_list, right_list,
origin_title)
newtitle, newdataset = csvOperation.creatAtomCsv(atom_list[:],
atom_dataset[:],
origin_dataSet)
csvOperation.creatCsv(newtitle, newdataset, atom_csv)
def chooseClassifyAttribute(origin_csv, attribute_list):
title1, dataSet = csvOperation.readCsv(origin_csv)
title = read_txt('title.txt')
result = attribute_list[0] #默认返回第一属性，防止所有属性包含的状态种类数均为1
for i in range(len(title)):
title[i] = title[i].replace('NODE_', '').lower()
if len(attribute_list) == 1: #只有一种属性
return attribute_list[0]
else: #有多种属性
for member in attribute_list:
if member not in title:
print 'error treemain 153 member not in title!!!!!!!!'
print 'memeber :' + member
print 'title :' + title
else:
position = title.index(member)
temp_list = [example[position]
for example in dataSet] #找到数据集中该位置的所有取值
temp_set = set(temp_list)
temp_set_list = list(temp_set)
if len(temp_set_list) != 1: #该属性包含的状态不只一个，选择该属性作为分类属性
result = member
break
return result