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Lei Xu; Maria Skoularidou; Alfredo Cuesta-Infante; Kalyan Veeramachaneni
Abstract
Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task. Tabular data usually contains a mix of discrete and continuous columns. Continuous columns may have multiple modes whereas discrete columns are sometimes imbalanced making the modeling difficult. Existing statistical and deep neural network models fail to properly model this type of data. We design TGAN, which uses a conditional generative adversarial network to address these challenges. To aid in a fair and thorough comparison, we design a benchmark with 7 simulated and 8 real datasets and several Bayesian network baselines. TGAN outperforms Bayesian methods on most of the real datasets whereas other deep learning methods could not.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| tabular-data-generation-on-adult-census | CopulaGAN | DT Accuracy: 76.29 LR Accuracy: 80.61 Parameters(M): 0.300 RF Accuracy: 80.46 |
| tabular-data-generation-on-adult-census | TVAE | DT Accuracy: 82.8 LR Accuracy: 80.53 Parameters(M): 0.053 RF Accuracy: 83.48 |
| tabular-data-generation-on-adult-census | CTGAN | DT Accuracy: 81.32 LR Accuracy: 83.2 Parameters(M): 0.302 RF Accuracy: 83.53 |
| tabular-data-generation-on-california-housing | CTGAN | DT Mean Squared Error: 0.82 LR Mean Squared Error: 0.61 Parameters(M): 0.197 RF Mean Squared Error: 0.62 |
| tabular-data-generation-on-california-housing | TVAE | DT Mean Squared Error: 0.45 LR Mean Squared Error: 0.65 Parameters(M): 0.045 RF Mean Squared Error: 0.35 |
| tabular-data-generation-on-california-housing | CopulaGAN | DT Mean Squared Error: 1.19 LR Mean Squared Error: 0.98 Parameters(M): 0.201 RF Mean Squared Error: 0.99 |
| tabular-data-generation-on-diabetes | CTGAN | DT Accuracy: 0.4973 LR Accuracy: 0.5093 Parameters(M): 9.6 RF Accuracy: 0.5223 |
| tabular-data-generation-on-diabetes | TVAE | DT Accuracy: 0.5330 LR Accuracy: 0.5634 Parameters(M): 0.359 RF Accuracy: 0.5517 |
| tabular-data-generation-on-diabetes | CopulaGAN | DT Accuracy: 0.385 LR Accuracy: 0.4027 Parameters(M): 9.4 RF Accuracy: 0.3759 |
| tabular-data-generation-on-heloc | CTGAN | DT Accuracy: 61.34 LR Accuracy: 57.72 Parameters(M): 0.277 RF Accuracy: 62.35 |
| tabular-data-generation-on-heloc | TVAE | DT Accuracy: 76.39 LR Accuracy: 71.04 Parameters(M): 62 RF Accuracy: 77.24 |
| tabular-data-generation-on-heloc | CopulaGAN | DT Accuracy: 42.36 LR Accuracy: 42.03 Parameters(M): 0.276 RF Accuracy: 42.35 |
| tabular-data-generation-on-sick | CopulaGAN | DT Accuracy: 93.77 LR Accuracy: 94.57 Parameters(M): 0.226 RF Accuracy: 94.57 |
| tabular-data-generation-on-sick | TVAE | DT Accuracy: 95.39 LR Accuracy: 94.7 Parameters(M): 0.046 RF Accuracy: 94.91 |
| tabular-data-generation-on-sick | CTGAN | DT Accuracy: 92.05 LR Accuracy: 94.44 Parameters(M): 0.222 RF Accuracy: 94.57 |
| tabular-data-generation-on-travel | CTGAN | DT Accuracy: 73.3 LR Accuracy: 73.3 Parameters(M): 0.155 RF Accuracy: 71.41 |
| tabular-data-generation-on-travel | TVAE | DT Accuracy: 81.68 LR Accuracy: 79.58 Parameters(M): 0.036 RF Accuracy: 81.68 |
| tabular-data-generation-on-travel | CopulaGAN | DT Accuracy: 73.61 LR Accuracy: 73.3 Parameters(M): 0.157 RF Accuracy: 73.3 |
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