Environment and data setup
The main objective of this chapter is to introduce the different mechanisms and thought processes associated with neuro-symbolic programming. This chapter is not designed as a programming crash course for symbolic or deep learning. For this purpose, we will work with the Red and White Wine Dataset (https://www.kaggle.com/datasets/numberswithkartik/red-white-wine-dataset) – publicly available in Kaggle. This dataset consists of 12 features describing different wine characteristics (such as the density and residual sugar, to name a couple) and a binary label representing whether said wine is a red or white wine. Some characteristics that made this dataset ideal for our use case were the following:
- It has around 6,000 samples, making it ideal for showing the power of NSAI by varying the size of the training data
- It does not require much data pre-processing or engineering
- It is a standard binary classification task, making it more straightforward...
