for report : now generating standalone figures

code/for_report/inference_analyze.R

@@ -9,6 +9,7 @@ library("kableExtra")
 library("stringr")
 library("here")
 library("tikzDevice")
+options(tikzDocumentDeclaration = "\\documentclass[10pt]{standalone}")
 
 
 meanse <- function(x, ...) {
@@ -145,7 +146,7 @@ for (model in c("sep", "iid", "pi", "rho", "pirho")) {
         caption = paste0(
             "\\label{subtab:ari_per_model_", model,
             "}Quality metrics for ",
-            ifelse(model != "sep", paste0(model_name, "$\\text{-}colBiSBM$"), "$sep\\text{-}BiSBM$")
+            ifelse(model != "sep", paste0(model_name, "$\\text{-colBiSBM}$"), "$sep\\text{-BiSBM}$")
         ),
         col.names = kable_ari_colnames,
         format = "latex"
@@ -159,7 +160,7 @@ for (model in c("sep", "iid", "pi", "rho", "pirho")) {
             caption = paste0(
                 "\\label{subtab:blocrecov_per_model_", model,
                 "}Bloc recovery for ",
-                ifelse(model != "sep", paste0(model_name, "$\\text{-}colBiSBM$"), "$sep\\text{-}BiSBM$")
+                ifelse(model != "sep", paste0(model_name, "$\\text{-colBiSBM}$"), "$sep\\text{-BiSBM}$")
             ),
             col.names = kable_blocrecov_colnames,
             format = "latex"
@@ -173,7 +174,7 @@ for (model in c("sep", "iid", "pi", "rho", "pirho")) {
         gsub("\\begin{table}", "\\begin{subtable}{\\textwidth}", x = _, fixed = TRUE) |>
         gsub("\\end{table}", "\\end{subtable}", x = _, fixed = TRUE)
     cat("",
-        "\\begin{table}[!htb]",
+        "\\begin{table}[H]",
         "\\centering",
         paste0("\\caption{\\label{tab:inference_results_", model, "}Inference results for ", model_name, "}"),
         both_kables,
@@ -239,10 +240,11 @@ if (!dir.exists(output_tikz_folder)) {
 
 tikz(
     file = file.path(output_tikz_folder, "model-proportions.tex"), width = 4L,
-    height = 3L
+    height = 3L,
+    standAlone = TRUE
 )
 levels(proportion_preferred_data$preferred_model) <- c(
-    "sep", "iid", "$\\pi$", "$\\rho$",
+    "sep", "$iid$", "$\\pi$", "$\\rho$",
     "$\\pi\\rho$"
 )
 plot <- proportion_preferred_data |>
@@ -261,7 +263,11 @@ plot <- proportion_preferred_data |>
     xlab("$\\epsilon_{\\alpha}$") +
     ylab("Preferred model proportions") +
     theme_minimal() +
-    theme(aspect.ratio = 1L, axis.text.x = element_text(angle = -45, vjust = .5, hjust = 0)) +
+    theme(
+        aspect.ratio = 1L,
+        axis.text.x = element_text(angle = -45, vjust = .5, hjust = 0),
+        axis.text.y = element_text(size = 6)
+    ) +
     geom_col(position = "stack")
 print(plot)
 dev.off()
@@ -289,7 +295,7 @@ ARI_type.labs <- c("$\\overline{\\mbox{ARI}}_d$", "$\\mbox{ARI}_d$")
 names(ARI_type.labs) <- c("mean", "double")
 
 levels(averaged_data$model) <- c(
-    "sep", "iid", "$\\pi$", "$\\rho$",
+    "sep", "$iid$", "$\\pi$", "$\\rho$",
     "$\\pi\\rho$"
 )
 
@@ -320,7 +326,8 @@ ARI_plots <- ggplot(averaged_data) +
 
 tikz(
     file = file.path(output_tikz_folder, "ari-plots.tex"), width = 6L,
-    height = 5L
+    height = 5L,
+    standAlone = TRUE
 )
 print(ARI_plots)
 dev.off()

code/for_report/model_selection_analyze.R

@@ -9,6 +9,7 @@ require("dplyr")
 require("here")
 require("ggh4x")
 require("tikzDevice")
+options(tikzDocumentDeclaration = "\\documentclass[10pt]{standalone}")
 
 
 ## ----setup, echo = FALSE, include= FALSE----------------------------------------------------------------------------------------------------------------------------
@@ -101,15 +102,15 @@ table_df <- full_join(x = bloc_recovery_df, y = model_proportion_df)
 #| fig.height = 4,
 #| dpi=300
 levels(model_comparison_eps_pi$preferred_model) <- c(
-    "sep", "iid", "$\\pi$", "$\\rho$",
+    "sep", "$iid$", "$\\pi$", "$\\rho$",
     "$\\pi\\rho$"
 )
 levels(model_comparison_eps_rho$preferred_model) <- c(
-    "sep", "iid", "$\\pi$", "$\\rho$",
+    "sep", "$iid$", "$\\pi$", "$\\rho$",
     "$\\pi\\rho$"
 )
 levels(model_comparison_eps_pi_rho$preferred_model) <- c(
-    "sep", "iid", "$\\pi$", "$\\rho$",
+    "$iid$", "$\\pi$", "$\\rho$",
     "$\\pi\\rho$"
 )
 
@@ -142,7 +143,8 @@ if (!dir.exists(output_tikz_folder)) {
 
 tikz(
     file = file.path(output_tikz_folder, "eps-pi-rho-preferred.tex"), width = 6,
-    height = 5
+    height = 5,
+    standAlone = TRUE
 )
 print(plot_pi_rho)
 dev.off()

code/for_report/na_robustness_analyze.R

@@ -6,9 +6,11 @@ library("dplyr")
 library("stringr")
 library("knitr")
 library("kableExtra")
+library("vctrs")
 library("stringr")
 library("here")
 library("tikzDevice")
+options(tikzDocumentDeclaration = "\\documentclass[10pt]{standalone}")
 
 
 meanse <- function(x, ...) {
@@ -45,41 +47,39 @@ results_df <- do.call(rbind, lapply(matching_filenames, readRDS)) |>
 auc_df <-
     results_df |>
     select(-contains(c("ari", "elapsed_secs"))) |>
-    mutate(auc_diff = auc_colBiSBM - auc_LBM) |>
+    pivot_longer(
+        cols = c(auc_LBM, auc_colBiSBM),
+        values_to = "auc",
+        names_prefix = "auc_",
+        names_to = "method"
+    ) |>
+    mutate(model = ifelse(method == "LBM", paste0("sep-", model), model)) |>
     mutate_at(vars(model, struct), as.factor) |>
     mutate(model = forcats::fct_relevel(
         model,
-        "iid", "pi", "rho", "pirho"
-    ))
-# pivot_longer(
-#     cols = c(auc_LBM, auc_colBiSBM),
-#     values_to = "auc",
-#     names_prefix = "auc_",
-#     names_to = "method"
-# ) |>
+        "iid", "sep-iid", "pi", "sep-pi", "rho", "sep-rho", "pirho", "sep-pirho"
+    )) |>
+    filter(prop_NAs <= 0.7)
 
-auc_df_summarised <- auc_df |>
-    group_by(prop_NAs, model, struct) |>
-    summarise_at(vars(auc_diff), list("auc_mean" = mean, "auc_sd" = sd))
-
-levels(auc_df$model) <- c(
-    "$iid$", "$\\pi$", "$\\rho$",
-    "$\\pi\\rho$"
-)
+levels(auc_df[["model"]]) <- levels(auc_df[["model"]]) |>
+    str_replace(fixed("iid"), "$iid$") |>
+    str_replace(fixed("pirho"), "$\\pi\\rho$") |>
+    str_replace("pi$", "$\\\\pi$") |>
+    str_replace("rho$", "$\\\\rho$")
 
 auc_plot <- ggplot(auc_df) +
-    aes(x = factor(prop_NAs), y = auc_diff, fill = model) +
+    aes(x = factor(prop_NAs), y = auc, fill = model, color = model) +
     geom_boxplot(notch = TRUE) +
-    geom_hline(yintercept = 0, color = "red", linetype = "dashed") +
-    labs(x = "$p_{\\texttt{NA}}$", y = "$\\Delta\\mbox{AUC}$") +
-    scale_fill_okabe_ito(order = 2:9) +
+    labs(x = "$p_{\\texttt{NA}}$", y = "AUC") +
+    scale_fill_okabe_ito(order = vec_interleave(seq(2, 5), seq(6, 9)), alpha = 0.5) +
+    scale_color_okabe_ito(order = vec_interleave(seq(2, 5), seq(6, 9))) +
     guides(
         fill = guide_legend(title = "Model"),
         color = guide_legend(title = "Model")
     ) +
     facet_grid(vars(struct)) +
     theme_minimal()
-
+auc_plot
 output_tikz_folder <- here(
     "mia-rapport-2024", "tikz", "simulations",
     "na_robustness"
@@ -89,56 +89,57 @@ if (!dir.exists(output_tikz_folder)) {
 }
 
 tikz(
-    file = file.path(output_tikz_folder, "auc-model.tex"), width = 5L,
-    height = 2.5
+    file = file.path(output_tikz_folder, "auc-model.tex"), width = 6.5,
+    height = 3.5,
+    standAlone = TRUE
 )
 print(auc_plot)
 dev.off()
 ## ARI plots
 ari_df <- results_df |>
     select(-contains(c("auc", "elapsed_secs"))) |>
+    group_by(prop_NAs, model) |>
+    pivot_longer(
+        cols = c(arirow_LBM:aricol_colBiSBM),
+        values_to = "ari", names_to = c("dim", "method"),
+        names_pattern = "(row|col)_(LBM|colBiSBM)"
+    ) |>
+    mutate(model = ifelse(method == "LBM", paste0("sep-", model), model)) |>
     mutate_at(vars(model, struct), as.factor) |>
     mutate(model = forcats::fct_relevel(
         model,
-        "iid", "pi", "rho", "pirho"
+        "iid", "sep-iid", "pi", "sep-pi", "rho", "sep-rho", "pirho", "sep-pirho"
     )) |>
-    group_by(prop_NAs, model) |>
-    mutate(
-        arirow_diff = arirow_colBiSBM - arirow_LBM,
-        aricol_diff = aricol_colBiSBM - aricol_LBM
-    ) |>
-    select(-c(arirow_LBM:aricol_colBiSBM)) |>
-    pivot_longer(
-        cols = c(arirow_diff:aricol_diff),
-        values_to = "ari_diff", names_to = c("dim"),
-        names_pattern = "(row|col)"
-    )
+    filter(prop_NAs <= 0.7)
 
-dim.labs <- c("$d = 1$", "$d = 2$")
+levels(ari_df[["model"]]) <- levels(ari_df[["model"]]) |>
+    str_replace(fixed("iid"), "$iid$") |>
+    str_replace(fixed("pirho"), "$\\pi\\rho$") |>
+    str_replace("pi$", "$\\\\pi$") |>
+    str_replace("rho$", "$\\\\rho$")
+
+
+dim.labs <- c("Rows : $d = 1$", "Columns : $d = 2$")
 names(dim.labs) <- c("row", "col")
 
-levels(ari_df$model) <- c(
-    "$iid$", "$\\pi$", "$\\rho$",
-    "$\\pi\\rho$"
-)
-
 ari_plot <- ggplot(ari_df) +
-    aes(x = factor(prop_NAs), y = ari_diff, fill = model) +
+    aes(x = factor(prop_NAs), y = ari, fill = model, color = model) +
     geom_boxplot(notch = TRUE) +
-    geom_hline(yintercept = 0, color = "red", linetype = "dashed") +
-    labs(x = "$p_{\\texttt{NA}}$", y = "$\\Delta\\mbox{ARI}$") +
+    labs(x = "$p_{\\texttt{NA}}$", y = "ARI") +
     guides(
         fill = guide_legend(title = "Model"),
         color = guide_legend(title = "Model")
     ) +
-    scale_fill_okabe_ito(order = 2:9) +
+    scale_fill_okabe_ito(order = vec_interleave(seq(2, 5), seq(6, 9)), alpha = 0.5) +
+    scale_color_okabe_ito(order = vec_interleave(seq(2, 5), seq(6, 9))) +
     facet_grid(struct ~ dim, labeller = labeller(dim = dim.labs)) +
     theme_minimal() +
     theme(axis.text.x = element_text(size = 5))
 
 tikz(
-    file = file.path(output_tikz_folder, "ari-dim-model.tex"), width = 5.5,
-    height = 2.5
+    file = file.path(output_tikz_folder, "ari-dim-model.tex"), width = 6.5,
+    height = 4,
+    standAlone = TRUE
 )
 print(ari_plot)
 dev.off()

code/for_report/network_clustering_analyze.R

@@ -9,6 +9,7 @@ library("kableExtra")
 library("stringr")
 library("here")
 library("tikzDevice")
+options(tikzDocumentDeclaration = "\\documentclass[10pt]{standalone}")
 
 
 
@@ -46,11 +47,14 @@ df_netclust$model <- df_netclust$model |>
 ari_plot <- ggplot(df_netclust) +
     aes(x = as.factor(epsilon), y = ARI) +
     scale_color_okabe_ito(order = 2L:9L) +
-    scale_fill_okabe_ito(order = 2L:9L) +
+    scale_fill_okabe_ito(order = 2L:9L, alpha = 0.5) +
     xlab("$\\epsilon$") +
-    guides(fill = guide_legend(title = "Model")) +
+    guides(
+        fill = guide_legend(title = "Model"),
+        color = guide_legend(title = "Model")
+    ) +
     ylab("ARI of the clustering") +
-    geom_boxplot(aes(fill = model), notch = TRUE) +
+    geom_boxplot(aes(fill = model, color = model), notch = TRUE) +
     theme_minimal()
 
 output_tikz_folder <- here(
@@ -64,7 +68,8 @@ if (!dir.exists(output_tikz_folder)) {
 
 tikz(
     file = file.path(output_tikz_folder, "ari-clustering.tex"), width = 5L,
-    height = 3L
+    height = 3L,
+    standAlone = TRUE
 )
 print(ari_plot)
 dev.off()

mia-rapport-2024

@@ -1 +1 @@
-Subproject commit cc77bcc7fc6d75293ecf5791aad75d7d11be0287
+Subproject commit fae7c807e64a1ffb73b0f3b37ed44f5c4379aa84