Synthetic data with SDV and CopulaGAN

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 CopulaGAN

model = CopulaGAN()

model.fit(data)

new_data = model.sample(200)

new_data.head()

	Pclass	Sex	Age	SibSp	Parch	Fare
0	1	0	23.113076	1	0	29.937752
1	3	1	29.918377	1	0	5.958775
2	1	1	76.710166	0	0	202.889201
3	3	0	46.581959	1	1	89.833194
4	1	0	33.135297	1	2	118.551008

new_data.describe(include='all')

	Pclass	Sex	Age	SibSp	Parch	Fare
count	200.000000	200.000000	200.000000	200.00000	200.000000	200.000000
mean	1.960000	0.540000	37.131076	1.10500	0.975000	69.046022
std	0.831781	0.499648	17.131095	1.03408	1.196467	70.354667
min	1.000000	0.000000	3.409883	0.00000	0.000000	3.135404
25%	1.000000	0.000000	27.213419	0.00000	0.000000	10.772323
50%	2.000000	1.000000	31.788050	1.00000	0.000000	42.555842
75%	3.000000	1.000000	49.398770	2.00000	2.000000	105.964913
max	3.000000	1.000000	78.841709	5.00000	5.000000	294.906651

from sdv.evaluation import evaluate

evaluate(new_data, data)

0.2793053940561327

model = CopulaGAN(
    field_transformers={
        'Pclass': 'categorical',
        'Sex': 'categorical',
        'Age': 'float',
        'SibSp': 'boolean',
        'Parch': 'integer',
        'Fare': 'float'
    },
    field_distributions={
        'Fare': 'truncated_gaussian'
    }
)

model.fit(data)

new_data = model.sample(200)

new_data.head()

	Pclass	Sex	Age	SibSp	Parch	Fare
0	2	1	55.113425	1	0	-9.755683e-08
1	2	1	42.257149	1	2	7.252636e+00
2	3	1	30.128989	1	0	7.708430e+00
3	2	1	30.838961	1	0	-1.184872e-07
4	3	1	30.370534	1	0	3.530599e+00

new_data.describe(include='all')

	Pclass	Sex	Age	SibSp	Parch	Fare
count	200.000000	200.000000	200.000000	200.000000	200.000000	2.000000e+02
mean	2.265000	0.630000	33.075478	0.715000	0.915000	2.587770e+01
std	0.726231	0.484016	13.472204	0.452547	1.189474	3.599977e+01
min	1.000000	0.000000	-1.122344	0.000000	0.000000	-1.191555e-07
25%	2.000000	0.000000	26.064943	0.000000	0.000000	6.589631e+00
50%	2.000000	1.000000	30.706679	1.000000	0.000000	1.182216e+01
75%	3.000000	1.000000	41.977015	1.000000	2.000000	2.797455e+01
max	3.000000	1.000000	74.046067	1.000000	5.000000	2.014112e+02

evaluate(new_data, data)

0.3533601962024023

Synthetic data with SDV and CopulaGAN

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