Synthetic

code/simulations/clustering/02_synthetic_clustering_analysis.qmd

@@ -0,0 +1,80 @@
+---
+format:
+  html:
+    embed-resources: true
+title: Clustering avec `colSBM` des données synthétiques
+execute: 
+  echo: false
+  warning: false
+---
+
+# Analyse
+
+```{r}
+library(colSBM)
+library(here)
+library(stringr)
+library(tidyr)
+library(dplyr)
+library(aricode)
+library(reshape2)
+
+library(ggplot2)
+library(profvis)
+```
+
+```{r}
+root_app_folder <- file.path(here(), "code", "applications")
+source(file.path(root_app_folder, "utils.R"))
+data_folder <- file.path(here(), "code", "results", "simulations", "clustering", "synthetic")
+files_vec <- get_recent_files(data_folder, n = 16L, pattern = ".Rds")
+files_vec <- identify_models(files_vec, pattern = "(iid|pirho|pi|rho)")
+names(files_vec) <- names(files_vec) %>% str_replace_all(pattern = "_", "~")
+list_clustering <- files_vec
+# list_collection <- readRDS(file.path(data_folder, "dore_collection_iid_24-05-24_18-07-50.Rds"))
+```
+
+## Analyse par modèle
+
+Les clustering donne le critère suivant :
+```{r}
+vec_bicl <- sapply(list_clustering, function(clustering) {
+    list_collection <- readRDS(clustering)
+    unlisted_best_partition <- unlist(
+        extract_best_bipartite_partition(list_collection)
+    )
+    BICL <- sum(sapply(unlisted_best_partition, function(col) col$BICL))
+    BICL
+})
+
+names(vec_bicl) <- names(files_vec)
+knitr::kable(vec_bicl,
+    caption = "BICL par modèle", col.names = "$BICL$",
+    row.names = TRUE
+)
+```
+
+:::{.panel-tabset}
+
+```{r write_tabs}
+#| warning: false
+#| output: asis
+
+# Generate content for each model using knit_expand
+for (clustering_idx in seq_len(length(list_clustering))) {
+    clustering <- list_clustering[clustering_idx]
+    model <- names(clustering)
+    expanded_content <- knitr::knit_expand(file = file.path(root_app_folder, "base_analysis.qmd"), clustering = clustering, model = model)
+    res <- knitr::knit_child(text = expanded_content, quiet = TRUE)
+    cat(res, sep = "\n")
+    cat("\n")
+}
+```
+
+:::
+
+## Profiling
+
+```{r}
+profvis(prof_input = file.path(data_folder, "profiling_17-06-24_11-59-35.out"))
+```

code/simulations/clustering/synthetic_clustering.R

@@ -96,7 +96,8 @@ profvis(
 save_file <- file.path(
     save_folder, paste0(
         sprintf("%s_collection_", application_name),
-        model, "_eps_", eps, "_", start_time, "_maxsteps_500.Rds"
+        model, "_seed_", seed, "_eps_",
+        eps, "_", start_time, "_maxsteps_500.Rds"
     )
 )