library(colSBM)
library(here)
library(tidyverse)
library(future.apply)

read.csv(here("data", "projects_supinfo.csv.gz")) -> projects_supinfo

projects_supinfo %>%
    group_by(country, project) %>%
    distinct() %>%
    count()


list_matrices <- readRDS(here("data", "list_matrices.rds"))

names(list_matrices)

portuguese_projects <- projects_supinfo %>%
    filter(country == "Portugal") %>%
    select(project) %>%
    distinct() %>%
    pull() %>%
    as.vector()

portuguese_matrices <- list_matrices[portuguese_projects]

rm(list_matrices)

binary_portuguese_matrices <- lapply(portuguese_matrices, function(x) {
    x[x > 0] <- 1
    x
})
options(future.globals.maxSize = Inf)
plan(multisession, workers = 3L)
set.seed(123)
fit_portuguese <- estimate_colBiSBM(
    netlist = binary_portuguese_matrices,
    colsbm_model = "iid",
    net_id = names(binary_portuguese_matrices),
    global_opts = list(backend = "no_mc")
)
save_path <- here("results", "colSBM")
if (!dir.exists(save_path)) {
    dir.create(save_path, recursive = TRUE)
}
saveRDS(fit_portuguese, file = here(save_path, "colSBM_portuguese.rds"))