UMAP in R with rPython

To use the Python version of UMAP in R, you first need to install it from github. The following code defines a function, which internally calls the UMAP Python function¹.

#install.packages(rPython)
umap <- function(x,n_neighbors=10,min_dist=0.1,metric="euclidean"){
  x <- as.matrix(x)
  colnames(x) <- NULL
  rPython::python.exec( c( "def umap(data,n,mdist,metric):",
                  "\timport umap" ,
                  "\timport numpy",
                  "\tembedding = umap.UMAP(n_neighbors=n,min_dist=mdist,metric=metric).fit_transform(data)",
                  "\tres = embedding.tolist()",
                  "\treturn res"))

  res <- rPython::python.call( "umap", x,n_neighbors,min_dist,metric)
  do.call("rbind",res)
}

The parameters are set to what is recommended by the authors. There are many different distance metrics implemented in the Python version that can also be used in this R function. Check out the Python code for options.

Below is a quick example using the infamous iris data.

data(iris)
res <- umap(iris[,1:4])
tibble(x = res[,1],y = res[,2],species = iris$Species) %>% 
  ggplot(aes(x = x,y = y,col = species))+
  geom_point()+
  theme(legend.position = "bottom")

In my last post on dimensionality reduction methods, I used FIFA 18 player data to illustrate different methods. Of course we can also use this data with UMAP.

fifa_umap <- umap(fifa_data)

Here is what the result looks like.

tibble(x = fifa_umap[,1], y = fifa_umap[,2], Position = fifa_tbl$position2) %>% 
  ggplot(aes(x = x,y = y, col = Position))+
  geom_point()+
  theme(legend.position = "bottom")

One of the authors said in a tweeet, that inter-cluster distances are captured well by UMAP. For the FIFA player data this seems to be the case. The sausage-like point cloud transitions from defensive players to offensive players on the x axis. Midfielders are nicely embedded in between. I am pretty sure, however, that tweaking the parameters may yield even better results.