zqlao/gan-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.

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.

Medium post: GANs for tabular data

Used datasets and expriment design

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 of it.

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 encoderthe ROC AUC scores of each dataset were scaled (min-max scale) and then averaged results among the dataset. To determine the best validation strategy, I compared the top score of each dataset for each type of validation.

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

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]