To visualize the search space, t-SNE and UMAP were used. t-SNE was performed with scikitlearn. Principal component analysis (PCA) was first performed to reduce the data to 50 dimensions. This was then fit for t-SNE to reduce it further to 2 dimensions. UMAP was used as well for comparison. The data were reduced to two dimensions, with the number of neighbors as 25 (size of the neighborhood to look at), the minimum distance for clustering as 0.001, and the distance between points is calculated with the Jaccard metric.