r other_clustering.r

Posted 2021-05-24

g_adjmatrix <- get.adjacency(graph=g_mock, type="both", sparse = F, attr = "weight")

ap_bin <- "python3 ~/Downloads/apcluster.py"
ap_pyth <- run_ap(X = "g_mock", adj_mat = as.matrix(g_adjmatrix), ap_bin = ap_bin, damping = 0.8, maxit = 500, covit = 25)

clust_ap_pyth <- ap_pyth$com[match(get.vertex.attribute(g_mock, "name"), ap_pyth$vertex) ] %>% as.numeric

com_app <- make_clusters(g_mock, clust_ap_pyth + 1, algorithm = 'APp', modularity = TRUE)
V(g_mock)$com_app <- as.character(membership(com_app))


g_adjmatrix <- as_adjacency_matrix(graph=g_mock, type="both", sparse = F, attr = "weight")
hd_bin <- "~/Downloads/hdb.py"
hd_pyth <- run_hdbscan(X = "g_mock", adj_mat = as.matrix(1 - g_adjmatrix), hdbscan_bin = hd_bin, alpha = 1, min_cluster_size = 2, min_samples = 1)
## Parsing AP output. 21 communities detected.
clust_hd_pyth <- hd_pyth$com[match(get.vertex.attribute(g_mock, "name"), hd_pyth$vertex) ] %>% as.numeric

clust_hdb <- make_clusters(g_mock, clust_hd_pyth + 2, algorithm = 'hdbscan', modularity = TRUE)
V(g_mock)$com_hdb <- as.character(membership(clust_hdb))




########
run_ap <- function(X, adj_mat = adj_mat, damping = 0.8, maxit = 500, covit = 25, ap_bin = ap_bin, preference = 'None'){
  
  out_file <- paste("~/Downloads/", X,"-ap.out", sep = "") 
  g_name <- paste("~/Downloads/", X,".csv", sep = "")
  
  ap_command <- paste(ap_bin, g_name, out_file, damping, maxit, covit, preference, sep = " ")
  
  write.table(adj_mat, file = g_name, sep = ",", quote = F )  
  system(ap_command)
  
  #map_file <- paste(out_folder, "/", X, ".map", sep = "")
  ap_results <- parse_ap(out_file) 
}

run_hdbscan <- function(X, adj_mat = adj_mat , hdbscan_bin = hdbscan_bin, alpha = 1.0, min_samples = "None", min_cluster_size = 5){
  
  out_file <- paste("~/Downloads/", X,"-hdbscan.out", sep = "") 
  g_name <- paste("~/Downloads/", X,".csv", sep = "")
  
  ap_command <- paste(hdbscan_bin, g_name, out_file, alpha, min_cluster_size, min_samples, sep = " ")
  
  write.table(adj_mat, file = g_name, sep = ",", quote = F )  
  system(ap_command)
  
  #map_file <- paste(out_folder, "/", X, ".map", sep = "")
  ap_results <- parse_ap(out_file) 
}

# Parse AP results -------------------------------------------------------

parse_ap <- function(X){
  modules <- read.table(file = X, stringsAsFactors = F, sep = "\t") %>%
    set_colnames(c("vertex", "com"))
  cat(paste("Parsing AP output.", length(unique(modules$com)), "communities detected.\n"))
  com <- modules
}