Introduction to synthesizer

Package version 0.2.0.

Use citation('synthesizer') to cite the package.

Introduction

synthetiser is an R package for quickly and easily synthesizing data. It also provides a few basic functions based on pMSE to measure some utility of the synthesized data.

The package supports numerical, categorical/ordinal, and mixed data.

At the moment the method used seems promising but we are working on investigating where the method shines and where it fails. So we have no guarantees yet on utility, privacy, and so on. Having said that, our preliminary results are promesing, and using the package is very easy.

Installation

The latest CRAN release can be installed as follows.

install.packages("synthesizer")

Next, the package can be loaded. You can use packageVersion (from base R) to check which version you have installed.

> library(synthesizer)
> # check the package version
> packageVersion("synthesizer")
[1] ‘0.2.0’

A first example

We will use the iris dataset, that is built into R.

> data(iris)
> head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Creating a synthetic version of this dataset is easy.

> set.seed(1)
> synth_iris <- synthesize(iris)

To compare the datasets we can make some side-by-side scatterplots.

Although the synthesized dataset shows more variance, it does mimic the clusture structure of the original dataset.

By default, synthesize will return a dataset of the same size as the input dataset. However it is possible to ask for any number of records.

> more_synth <- synthesize(iris, n=250)
> dim(more_synth)
[1] 250   5

Checking quality

The pMSE method is a popular way of measuring the quality of a dataset. The idea is to train a model to predict whether a record is synthetic or not. The mean squared error of prediction is the measure, so smaller is better.

> pmse(synth=synth_iris, real=iris)
[1] 0.005406737

How it works

Synthetic data is prepared as follows.

Given an original dataset with n records:

1. For each numeric variable in the dataset, determine the emperical inverse cumulative density function (ECDF), and use linear interpolation to interpolate between the data points. The observed minimum and maximum are also the minimum and maximum of the synthetic univariate distribution. Sample n values using inverse transform sampling with the linear interpolated inverse ECDF
2. For each categorical variable, sample n values with replacement.
3. Reorder the synthetic dataset such that the rank order combinations of the synthetic data match those of the original dataset.

If less than m < n records are needed, sample m records uniformly from the dataset just created. If m > n records are needed, create ⌈m/n⌉ synthetic datasets of size m and sample uniformly m records from the combined data sets.