-
Notifications
You must be signed in to change notification settings - Fork 1
/
Transaction_Data_Maker.py
174 lines (152 loc) · 7.13 KB
/
Transaction_Data_Maker.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
'''
简单模拟识别信用卡诈骗
生成3000个银行账户的10000条交易数据
从中筛选出10分钟内的连续两次同账户交易
然后再从中筛选出一次交易小于100而下一次交易大于1000的交易
每一步的结果都生成csv文件
依赖faker和pandas
需要先 pip install faker pandas
'''
import csv
# 导入faker模块
from faker import Faker
# 导入random模块
import random
# 导入datetime模块
import datetime
# 导入pandas模块
import pandas as pd
# 实例化faker对象
fake = Faker()
# 生成账号和姓名
def generate_account_and_name():
# 生成一个visa类型的信用卡号
credit_card_number = fake.credit_card_number(card_type= 'visa')[:16]
# 生成一个名字
name = fake.name()
# 生成一个18到80岁的生日
birthdate = fake.date_of_birth(minimum_age=18, maximum_age=80)
# 生成一个SSN号
id_number = birthdate.strftime("%y%m%d") + str(fake.random_number(digits=3))[:9]
# 返回信用卡号，名字，SSN号
return (credit_card_number, name, id_number)
# 生成交易金额
def generate_transaction_amount(low = -10000, high = 10000):
# 生成一个[low, high]之间的随机数
amount = random.randint(low, high)
# 判断amount的正负，生成in或者out
direction = 'in' if amount >= 0 else 'out'
# 返回绝对值和方向
return (abs(amount), direction)
# 生成交易时间
def generate_transaction_time():
# 生成一个2022年12月30日
start_date = datetime.date(2022, 12, 30)
# 生成一个2022年12月31日
end_date = datetime.date(2022, 12, 31)
# 计算两个日期的差值
time_between_dates = end_date - start_date
# 计算两个日期的天数差
days_between_dates = time_between_dates.days
# 随机生成一个[0, days_between_dates]之间的数
random_number_of_days = random.randrange(days_between_dates)
# 生成一个[start_date, end_date]之间的随机日期
random_date = start_date + datetime.timedelta(days=random_number_of_days)
# 生成一个[0, 23], [0, 59], [0, 59], [0, 999999]之间的随机时间
# random_time = datetime.time(random.randint(0, 23), random.randint(0, 59), random.randint(0, 59), random.randint(0, 999999))
# 生成一个[0, 23], [0, 59], [0, 59]之间的随机时间
random_time = datetime.time(random.randint(0, 23), random.randint(0, 59), random.randint(0, 59))
# 生成一个指定日期的指定时间
return datetime.datetime.combine(random_date, random_time)
# 将数据写入csv文件
def write_to_csv(data,file_name = 'data'):
# 生成文件名
file_name = file_name+'.csv'
# 以写的方式打开文件
with open(file_name, "w", newline="") as f:
# 创建csv写入器
writer = csv.writer(f, delimiter=",")
# 写入表头
writer.writerow(["Credit Card Number", "Name", "ID Number", "Amount", "Direction", "Transaction Time"])
# 遍历数据，写入csv文件
for item in data:
writer.writerow([item[0], item[1], item[2], item[3], item[4], item[5]])
# 生成数据和账户
# 参数：num_of_accounts：账户数量；number_of_transactions：交易次数
# 返回：transaction_list：交易列表
def generate_data_and_account(num_of_accounts, number_of_transactions):
# 创建字典和列表
data_dict = {}
data_list = []
# 循环生成账户和名称
for i in range(num_of_accounts):
credit_card_number, name, id_number = generate_account_and_name()
# 如果id_number在字典中，则从字典中取出credit_card_number，否则将credit_card_number添加到字典中
if id_number in data_dict:
credit_card_number = data_dict[id_number]
else:
data_dict[id_number] = credit_card_number
# 将账户信息添加到列表中
data_list.append((credit_card_number, name, id_number))
# 创建交易列表
transaction_list = []
# 循环生成交易信息
for i in range(number_of_transactions):
# 从列表中随机选择账户信息
account = random.choice(data_list)
# 将账户信息、交易金额、交易时间添加到交易列表中
transaction_list.append((account[0], account[1], account[2], *generate_transaction_amount(), generate_transaction_time()))
# 对交易列表按照交易时间排序
transaction_list.sort(key = lambda x: x[5])
# 返回交易列表
return transaction_list
num_of_accounts = 3000 # 修改这里设置你想要生成的账户数目
number_of_transactions = 100000 # 修改这里设置你想要生成的交易条目
data_list = generate_data_and_account(num_of_accounts, number_of_transactions) # 调用函数来生成账户和交易数据
# write_to_csv(data_list,'generated_data')
# 生成交易数据
df = pd.DataFrame(data_list, columns=['Credit Card Number', 'Name', 'ID Number', 'Amount', 'Direction', 'Transaction Time'])
df.to_csv('transaction_data_generated.csv', index=False)
print("Data generated successfully!")
# 对每个账号的每笔交易按照时间排序
df = df.sort_values(['Credit Card Number', 'Transaction Time'])
# 计算每个账号每笔交易的时间差
df['Time Delta'] = df.groupby('Credit Card Number')['Transaction Time'].diff()
# 创建一个空的DataFrame
new_df = pd.DataFrame(columns=df.columns)
# 遍历每个账号
for account in df['Credit Card Number'].unique():
# 获取每个账号的每笔交易
account_df = df[df['Credit Card Number'] == account]
# 获取每笔交易时间差小于10分钟的交易
account_df = account_df[(account_df['Time Delta'] <= pd.Timedelta(minutes=10))]
# 如果交易笔数大于1，则将交易添加到new_df中
if len(account_df) > 1:
new_df = pd.concat([new_df, account_df])
# new_df = pd.concat([new_df.dropna(how='all'), account_df.dropna(how='all')], axis=0)
# 删除时间差列
new_df = new_df.drop(columns=['Time Delta'])
# 将new_df保存到csv文件中
new_df.to_csv('transaction_data_suspecious.csv', index=False)
print("Data of suspecious accounts generated successfully!")
# 对每个账号的每笔交易按照时间排序
new_df = new_df.sort_values(['Credit Card Number', 'Transaction Time'])
# 创建一个空的DataFrame
fraud_df = pd.DataFrame(columns=new_df.columns)
# 警戒值，签一次交易低于低值，后一次交易高于高值，就判定为潜在危险账户
low_value =100
high_value = 1000
# 遍历每个账号
for account in new_df['Credit Card Number'].unique():
# 获取每个账号的每笔交易
account_df = new_df[new_df['Credit Card Number'] == account]
# 对每笔交易按照时间排序
account_df = account_df.sort_values('Transaction Time')
# 遍历每笔交易
for i in range(1, len(account_df)):
# 如果前一笔交易金额小于低值，后一笔交易金额大于高值，则将这两笔交易添加到fraud_df中
if account_df.iloc[i-1]['Amount'] < low_value and account_df.iloc[i]['Amount'] > high_value:
fraud_df = pd.concat([fraud_df, account_df.iloc[i-1:i+1]])
# 将fraud_df保存到csv文件中
fraud_df.to_csv('transaction_data_fraud.csv', index=False)
print("Data of fraud accounts generated successfully!")