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Ganblr Toolbox

Project description


GANBLR is a tabular data generation model...

Usage Example

In this example we load the Adult Dataset* which is a built-in demo dataset. We use GANBLR to learn from the real data and then generate some synthetic data.

from ganblr.utils import get_demo_data
from ganblr import GANBLR

# this is a discrete version of adult since GANBLR requires discrete data.
df = get_demo_data('adult')
x, y = df.values[:,:-1], df.values[:,-1]

model = GANBLR(), y, epochs = 10)

#generate synthetic data
synthetic_data = model.sample(1000)

The steps to generate synthetic data using GANBLR++ are similar to GANBLR, but require an additional parameter numerical_columns to tell the model the index of the numerical columns.

from ganblr.utils import get_demo_data
from ganblr import GANBLRPP
import numpy as np

# raw adult
df = get_demo_data('adult-raw')
x, y = df.values[:,:-1], df.values[:,-1]

def is_numerical(dtype):
    return dtype.kind in 'iuf'

column_is_numerical = df.dtypes.apply(is_numerical).values
numerical_columns = np.argwhere(column_is_numerical).ravel()

model = GANBLRPP(numerical_columns), y, epochs = 10)

#generate synthetic data
synthetic_data = model.sample(1000)


We recommend you to install ganblr through pip:

pip install ganblr

Alternatively, you can also clone the repository and install it from sources.

git clone
cd ganblr
python install


If you use GANBLR, please cite the following work:

Y. Zhang, N. A. Zaidi, J. Zhou and G. Li, "GANBLR: A Tabular Data Generation Model," 2021 IEEE International Conference on Data Mining (ICDM), 2021, pp. 181-190, doi: 10.1109/ICDM51629.2021.00103.

    author={Zhang, Yishuo and Zaidi, Nayyar A. and Zhou, Jiahui and Li, Gang},  
    booktitle={2021 IEEE International Conference on Data Mining (ICDM)},   
    title={GANBLR: A Tabular Data Generation Model},   
    author = {Yishuo Zhang and Nayyar Zaidi and Jiahui Zhou and Gang Li},
    title = {<bold>GANBLR++</bold>: Incorporating Capacity to Generate Numeric Attributes and Leveraging Unrestricted Bayesian Networks},
    booktitle = {Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
    pages = {298-306},
    doi = {10.1137/1.9781611977172.34},

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