GANs for tabular data

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

• Arxiv article: "Tabular GANs for uneven distribution"
• Medium post: GANs for tabular data

How to use library

• Installation: pip install tabgan
• To generate new data to train by sampling and then filtering by adversarial training call GANGenerator().generate_data_pipe:

from tabgan.sampler import OriginalGenerator, GANGenerator
import pandas as pd
import numpy as np

if __name__ == "__main__":
    # random input data
    train = pd.DataFrame(np.random.randint(-10, 150, size=(50, 4)), columns=list("ABCD"))
    target = pd.DataFrame(np.random.randint(0, 2, size=(50, 1)), columns=list("Y"))
    test = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list("ABCD"))

    # generate data
    new_train1, new_target1 = OriginalGenerator().generate_data_pipe(train, target, test, )
    new_train1, new_target1 = GANGenerator().generate_data_pipe(train, target, test, )

    # example with all params defined
    new_train3, new_target3 = GANGenerator(gen_x_times=1.1, cat_cols=None, bot_filter_quantile=0.001,
                                           top_filter_quantile=0.999,
                                           is_post_process=True,
                                           adversaial_model_params={
                                               "metrics": "AUC", "max_depth": 2,
                                               "max_bin": 100, "n_estimators": 500,
                                               "learning_rate": 0.02, "random_state": 42,
                                           }, pregeneration_frac=2,
                                           epochs=500).generate_data_pipe(train, target,
                                                                          test, deep_copy=True,
                                                                          only_adversarial=False,
                                                                          use_adversarial=True)

Both samplers OriginalGenerator and GANGenerator have same input parameters:

• gen_x_times: float = 1.1 - how much data to generate, output might be less because of postprocessing and adversarial filtering
• cat_cols: list = None - categorical columns
• bot_filter_quantile: float = 0.001 - bottom quantile for postprocess filtering
• top_filter_quantile: float = 0.999 - bottom quantile for postprocess filtering
• is_post_process: bool = True - perform or not postfiltering, if false bot_filter_quantile and top_filter_quantile ignored
• adversaial_model_params: dict params for adversarial filtering model, default values for binary task
• pregeneration_frac: float = 2 - for generataion step gen_x_times * pregeneration_frac amount of data will generated. However in postprocessing (1 + gen_x_times) % of original data will be returned
• epochs: int = 500 - for how many epochs train GAN samplers, ignored for OriginalGenerator

For generate_data_pipe methods params:

• train_df: pd.DataFrame Train dataframe which has separate target
• target: pd.DataFrame Input target for the train dataset
• test_df: pd.DataFrame Test dataframe - newly generated train dataframe should be close to it
• deep_copy: bool = True - make copy of input files or not. If not input dataframes will be overridden
• only_adversarial: bool = False - only adversarial fitering to train dataframe will be performed
• use_adversarial: bool = True - perform or not adversarial filtering
• @return: -> Tuple[pd.DataFrame, pd.DataFrame] - Newly generated train dataframe and test data

Thus, you may use this library to improve your dataset quality:

def fit_predict(clf, X_train, y_train, X_test, y_test):
    clf.fit(X_train, y_train)
    return sklearn.metrics.roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1])


if __name__ == "__main__":
    dataset = sklearn.datasets.load_breast_cancer()
    clf = sklearn.ensemble.RandomForestClassifier(n_estimators=25, max_depth=6)
    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
        pd.DataFrame(dataset.data), pd.DataFrame(dataset.target, columns=["target"]), test_size=0.33, random_state=42)
    print("initial metric", fit_predict(clf, X_train, y_train, X_test, y_test))

    new_train1, new_target1 = OriginalGenerator().generate_data_pipe(X_train, y_train, X_test, )
    print("OriginalGenerator metric", fit_predict(clf, new_train1, new_target1, X_test, y_test))

    new_train1, new_target1 = GANGenerator().generate_data_pipe(X_train, y_train, X_test, )
    print("GANGenerator metric", fit_predict(clf, new_train1, new_target1, X_test, y_test))

Datasets and experiment design

Running experiment

To run experiment follow these steps:

1. Clone the repository. All required dataset are stored in ./Research/data folder
2. Install requirements pip install -r requirements.txt
3. Run all experiments python ./Research/run_experiment.py. Run all experiments python run_experiment.py. You may add more datasets, adjust validation type and categorical encoders.
4. Observe metrics across all experiment in console or in ./Research/results/fit_predict_scores.txt

Task formalization

Let say we have T_train and T_test (train and test set respectively). We need to train the model on T_train and make predictions on T_test. However, we will increase the train by generating new data by GAN, somehow similar to T_test, without using ground truth labels.

Experiment design

Let say we have T_train and T_test (train and test set respectively). The size of T_train is smaller and might have different data distribution. First of all, we train CTGAN on T_train with ground truth labels (step 1), then generate additional data T_synth (step 2). Secondly, we train boosting in an adversarial way on concatenated ** T_train** and T_synth (target set to 0) with T_test (target set to 1) (steps 3 & 4). The goal is to apply newly trained adversarial boosting to obtain rows more like T_test. Note - initial ground truth labels aren"t used for adversarial training. As a result, we take top rows from T_train and T_synth sorted by correspondence to ** T_test** (steps 5 & 6), and train new boosting on them and check results on T_test.

Picture 1.1 Experiment design and workflow

Of course for the benchmark purposes we will test ordinal training without these tricks and another original pipeline but without CTGAN (in step 3 we won"t use T_sync).

Datasets

All datasets came from different domains. They have a different number of observations, number of categorical and numerical features. The objective for all datasets - binary classification. Preprocessing of datasets were simple: removed all time-based columns from datasets. Remaining columns were either categorical or numerical.

Table 1.1 Used datasets

Name	Total points	Train points	Test points	Number of features	Number of categorical features	Short description
Telecom	7.0k	4.2k	2.8k	20	16	Churn prediction for telecom data
Adult	48.8k	29.3k	19.5k	15	8	Predict if persons" income is bigger 50k
Employee	32.7k	19.6k	13.1k	10	9	Predict an employee"s access needs, given his/her job role
Credit	307.5k	184.5k	123k	121	18	Loan repayment
Mortgages	45.6k	27.4k	18.2k	20	9	Predict if house mortgage is founded
Taxi	892.5k	535.5k	357k	8	5	Predict the probability of an offer being accepted by a certain driver
Poverty_A	37.6k	22.5k	15.0k	41	38	Predict whether or not a given household for a given country is poor or not

Results

To determine the best sampling strategy, ROC AUC scores of each dataset were scaled (min-max scale) and then averaged among the dataset.

Table 1.2 Different sampling results across the dataset, higher is better (100% - maximum per dataset ROC AUC)

dataset_name	None	gan	sample_original
credit	0.997	0.998	0.997
employee	0.986	0.966	0.972
mortgages	0.984	0.964	0.988
poverty_A	0.937	0.950	0.933
taxi	0.966	0.938	0.987
adult	0.995	0.967	0.998
telecom	0.995	0.868	0.992

Table 1.3 Different sampling results, higher is better for a mean (ROC AUC), lower is better for std (100% - maximum per dataset ROC AUC)

sample_type	mean	std
None	0.980	0.036
gan	0.969	0.06
sample_original	0.981	0.032

Table 1.4 same_target_prop is equal 1 then the target rate for train and test are different no more than 5%. Higher is better.

sample_type	same_target_prop	prop_test_score
None	0	0.964
None	1	0.985
gan	0	0.966
gan	1	0.945
sample_original	0	0.973
sample_original	1	0.984

Acknowledgments

The author would like to thank Open Data Science community [7] for many valuable discussions and educational help in the growing field of machine and deep learning. Also, special big thanks to Sber [8] for allowing solving such tasks and providing computational resources.

References

[1] Jonathan Hui. GAN — What is Generative Adversarial Networks GAN? (2018), medium article

[2]Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. Generative Adversarial Networks (2014). arXiv:1406.2661

[3] Lei Xu LIDS, Kalyan Veeramachaneni. Synthesizing Tabular Data using Generative Adversarial Networks (2018). arXiv: 1811.11264v1 [cs.LG]

[4] Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, Kalyan Veeramachaneni. Modeling Tabular Data using Conditional GAN (2019). arXiv:1907.00503v2 [cs.LG]

[5] Denis Vorotyntsev. Benchmarking Categorical Encoders (2019). Medium post

[6] Insaf Ashrapov. GAN-for-tabular-data (2020). Github repository.

[7] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila. Analyzing and Improving the Image Quality of StyleGAN (2019) arXiv:1912.04958v2 [cs.CV]

[8] ODS.ai: Open data science (2020), https://ods.ai/

[9] Sber (2020), https://www.sberbank.ru/