Synthetic data with SDV and CTGAN

import pandas as pd
import warnings

warnings.filterwarnings('ignore')

data = pd.read_csv("data/svm-hyperparameters-train-features.csv")

data.head()

	Pclass	Sex	Age	SibSp	Fare
0	3	1	22.0	1	7.2500
1	1	0	38.0	1	71.2833
2	3	0	26.0	0	7.9250
3	1	0	35.0	1	53.1000
4	3	1	35.0	0	8.0500

data.describe(include='all')

	Pclass	Sex	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	891.000000	891.000000	891.000000
mean	2.308642	0.647587	29.758889	0.523008	0.381594	32.204208
std	0.836071	0.477990	13.002570	1.102743	0.806057	49.693429
min	1.000000	0.000000	0.420000	0.000000	0.000000	0.000000
25%	2.000000	0.000000	22.000000	0.000000	0.000000	7.910400
50%	3.000000	1.000000	30.000000	0.000000	0.000000	14.454200
75%	3.000000	1.000000	35.000000	1.000000	0.000000	31.000000
max	3.000000	1.000000	80.000000	8.000000	6.000000	512.329200

from sdv.tabular import CTGAN

model = CTGAN()

model.fit(data)

new_data = model.sample(200)

new_data.head()

	Pclass	Age	SibSp	Parch	Fare
0	2	2.842574	6	2	28.927918
1	2	47.380061	1	1	122.939126
2	2	42.536188	0	0	36.907182
3	1	28.853204	2	2	37.291651
4	0	35.857498	3	1	77.988031

new_data.describe(include='all')

	Pclass	Sex	Age	SibSp	Parch	Fare
count	200.000000	200.000000	200.000000	200.000000	200.000000	200.000000
mean	1.310000	0.255000	27.552343	1.170000	1.250000	104.137308
std	0.858829	0.436955	14.735829	1.730919	1.399031	137.597823
min	0.000000	0.000000	-6.115006	0.000000	0.000000	-85.821346
25%	1.000000	0.000000	19.907786	0.000000	0.000000	27.131476
50%	1.000000	0.000000	29.180020	0.000000	1.000000	56.792348
75%	2.000000	1.000000	36.793752	2.000000	2.000000	102.704251
max	3.000000	1.000000	64.004638	8.000000	6.000000	747.924987

from sdv.evaluation import evaluate

evaluate(new_data, data)

0.19950789269381727

model = CTGAN(
    epochs=500,
    batch_size=100,
    generator_dim=(256, 256, 256),
    discriminator_dim=(256, 256, 256)
)

model.fit(data)

new_data = model.sample(200)

new_data.head()

	Pclass	Sex	Age	SibSp	Parch	Fare
0	1	0	29.551254	0	0	257.363295
1	1	1	49.643384	1	-1	53.014717
2	0	0	-14.547607	0	2	0.694069
3	3	0	7.065562	5	0	19.361019
4	3	0	28.684305	0	2	-256.489558

new_data.describe(include='all')

	Pclass	Sex	Age	SibSp	Parch	Fare
count	200.000000	200.000000	200.000000	200.000000	200.000000	200.000000
mean	1.330000	0.215000	28.184757	1.200000	1.015000	153.406430
std	0.880327	0.411853	24.932518	2.682532	1.737779	278.082600
min	0.000000	0.000000	-17.001736	-4.000000	-1.000000	-433.103355
25%	1.000000	0.000000	12.779717	0.000000	0.000000	13.491768
50%	1.000000	0.000000	28.262440	0.000000	0.000000	45.898936
75%	2.000000	0.000000	34.990247	1.000000	1.000000	215.423687
max	3.000000	1.000000	93.451393	13.000000	8.000000	1104.455566

evaluate(new_data, data)

0.20902599406872358