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Synthetic Data Quality Score

An evaluation of the effect of sampling procedures on the quality of synthetic tabular data using Gretel's Synthetic Data Quality Score (SQS).

Introduction

While much recent research in probabilistic generative modeling has focused on visual domains such as images, a large portion of synthetic data use cases revolve around structured data such as CSVs and Databases. At Gretel, we have built generative modeling tools to learn distributions from customer data and generate realistic synthetic data from those distributions.
As with any ML project, a key aspect is measuring a model’s performance. We developed the Synthetic Data Quality Score (SQS) to ensure that the tabular output of our generative models maintains important statistical information found in the training data. Here, we walk through the process of how we calculate and interpret our synthetic data quality score, and how you can use it to evaluate the effect that sampling methods have on the quality of synthetically-generated tabular data.

Calculating Gretel's Synthetic Data Quality Score (SQS)​

Figure 1: Comparing correlations of the ground truth dataset and the synthetic dataset.
Figure 2: Comparing variance in the ground truth dataset and synthetic dataset via a principal component analysis.
Figure 3: Discrete mass distributions of each feature in the dataset.

The SQS Formula

Using the distances between correlation values, PCAs, and feature distributions we construct a single scalar for each of the three features above. These real scalar values are in the range [0,100] and are smooth. These two properties are ensured by fitting polynomials over a large number of real tabular datasets. The SQS is then calculated and normalized. An SQS of 100 means the synthetic data is a perfect match and 0 means the model fit was quite poor.
Figure 4: Gretel.ai's formula for calculating the Synthetic Data Quality Score

Evaluating Different Sampling Methods Using SQS

With this measure in place we can now explore how probabilistic sampling can change the quality of synthetically generated data. We use a decoder only Transformer with 1.5 billion parameters fine tuned on the UCI Adult dataset.
The Transformer is trained to model a categorical distribution over a finite vocabulary. The vocabulary consists of encoding tokens of the dataset. There are many ways to sample from this distribution over the vocabulary. The first is to directly sample from the temperature adjusted distribution which performs very well due to the alignment between training and testing. Alternatively we can restrict the categorical distribution to the top k tokens or the nucleus of top p mass or to the set of tokens with high typical information content. These methods perform slightly worse, which is surprising given their prevalence in many NLP generative modeling tasks. Worse of all is beam search, which likely fails due to the sharpness of the categorical distribution.
Our method of choice is an ensemble of sampling methods which performs as well as sampling directly from the categorical distribution while reducing the variance of SQS performance.
Figure 5: Different sampling and synthetic quality score evaluations.