pip install sdgx
# 导入相关模块
from sdgx.models.single_table.ctgan import CTGAN
from sdgx.data_process.sampling.sampler import DataSamplerCTGAN
from sdgx.data_processors.transformers.transform import DataTransformer
from sdgx.utils.io.csv_utils import *
# 读取数据
demo_data, discrete_cols = get_demo_single_table()真实数据如下：
age workclass fnlwgt ... hours-per-week native-country class
0 27 Private 177119 ... 44 United-States <=50K
1 27 Private 216481 ... 40 United-States <=50K
2 25 Private 256263 ... 40 United-States <=50K
3 46 Private 147640 ... 40 United-States <=50K
4 45 Private 172822 ... 76 United-States >50K
... ... ... ... ... ... ... ...
32556 43 Local-gov 33331 ... 40 United-States >50K
32557 44 Private 98466 ... 35 United-States <=50K
32558 23 Private 45317 ... 40 United-States <=50K
32559 45 Local-gov 215862 ... 45 United-States >50K
32560 25 Private 186925 ... 48 United-States <=50K
[32561 rows x 15 columns]
#定义模型
model = GeneratorCTGAN(epochs=10,\
transformer= DataTransformer,\
sampler=DataSamplerCTGAN)
#训练模型
model.fit(demo_data, discrete_cols)
# 生成合成数据
sampled_data = model.sample(1000)合成数据如下：
age workclass fnlwgt ... hours-per-week native-country class
0 33 Private 276389 ... 41 United-States >50K
1 33 Self-emp-not-inc 296948 ... 54 United-States <=50K
2 67 Without-pay 266913 ... 51 Columbia <=50K
3 49 Private 423018 ... 41 United-States >50K
4 22 Private 295325 ... 39 United-States >50K
5 63 Private 234140 ... 65 United-States <=50K
6 42 Private 243623 ... 52 United-States <=50K
7 75 Private 247679 ... 41 United-States <=50K
8 79 Private 332237 ... 41 United-States >50K
9 28 State-gov 837932 ... 99 United-States <=50K
# 导入相关模块
from sdgx.models.single_table.cwamt import CWAMT
from sdgx.utils.io.csv_utils import *
# 读取数据
data = get_multi_table()真实数据如下：
{'tables': {'table1': {'table_name': 'train', 'table_value': Store DayOfWeek Date ... Promo StateHoliday SchoolHoliday
0 1 5 2015-07-31 ... 1 0 1
1 2 5 2015-07-31 ... 1 0 1
2 3 5 2015-07-31 ... 1 0 1
3 4 5 2015-07-31 ... 1 0 1
4 5 5 2015-07-31 ... 1 0 1
... ... ... ... ... ... ... ...
1017204 1111 2 2013-01-01 ... 0 a 1
1017205 1112 2 2013-01-01 ... 0 a 1
1017206 1113 2 2013-01-01 ... 0 a 1
1017207 1114 2 2013-01-01 ... 0 a 1
1017208 1115 2 2013-01-01 ... 0 a 1
[1017209 rows x 9 columns]}, 'table2': {'table_name': 'store', 'table_value': Store StoreType ... Promo2SinceYear PromoInterval
0 1 c ... NaN NaN
1 2 a ... 2010.0 Jan,Apr,Jul,Oct
2 3 a ... 2011.0 Jan,Apr,Jul,Oct
3 4 c ... NaN NaN
4 5 a ... NaN NaN
... ... ... ... ... ...
1110 1111 a ... 2013.0 Jan,Apr,Jul,Oct
1111 1112 c ... NaN NaN
1112 1113 a ... NaN NaN
1113 1114 a ... NaN NaN
1114 1115 d ... 2012.0 Mar,Jun,Sept,Dec
[1115 rows x 10 columns]}}, 'relations': {'table1-table2': 'store'}}
#定义模型
model = CWAMT()
#训练模型
model.fit(data)
# 生成合成数据
sampled = model.generate(num_rows=10)合成数据如下：
{'table1': {'table_name': 'train', 'table_value': Store DayOfWeek Date ... Promo StateHoliday SchoolHoliday
0 3 2 2013-01-01 ... 0 a 1
1 5 2 2013-01-01 ... 0 a 1
2 5 2 2013-01-01 ... 0 a 1
3 6 2 2013-01-01 ... 0 a 1
4 2 2 2013-01-01 ... 0 a 1
5 1 2 2013-01-01 ... 0 a 1
6 7 2 2013-01-01 ... 0 a 1
7 2 2 2013-01-01 ... 0 a 1
8 8 2 2013-01-01 ... 0 a 1
9 5 2 2013-01-01 ... 0 a 1
10 9 2 2013-01-01 ... 0 a 1
11 3 2 2013-01-01 ... 0 a 1
12 2 2 2013-01-01 ... 0 a 1
13 4 2 2013-01-01 ... 0 a 1
14 4 2 2013-01-01 ... 0 a 1
15 7 2 2013-01-01 ... 0 a 1
16 8 2 2013-01-01 ... 0 a 1
17 10 2 2013-01-01 ... 0 a 1
18 3 2 2013-01-01 ... 0 a 1
19 7 2 2013-01-01 ... 0 a 1
[20 rows x 9 columns]}, 'table2': {'table_name': 'store', 'table_value': Store StoreType ... Promo2SinceYear PromoInterval
0 1 a ... 2013.0 Jan,Apr,Jul,Oct
1 2 a ... 2010.0 Jan,Apr,Jul,Oct
2 3 a ... NaN NaN
3 4 c ... 2012.0 Jan,Apr,Jul,Oct
4 5 c ... NaN NaN
5 6 a ... 2013.0 Jan,Apr,Jul,Oct
6 7 c ... NaN NaN
7 8 a ... NaN NaN
8 9 a ... NaN NaN
9 10 d ... 2012.0 Mar,Jun,Sept,Dec
[10 rows x 10 columns]}}