Chart consistency + visual cleanup

2026-07-26 23:04:36 +02:00
parent df83623101
commit aad3b85d8f
10 changed files with 326 additions and 293 deletions
@@ -0,0 +1,128 @@
+library(ggplot2)
+library(dplyr)
+library(readr)
+library(stringr)
+library(tidyr)
+
+# Usage: Rscript combined_fault_correlation.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
+# Plots correlation between raw aot and interp fault counts (no C baseline).
+
+args <- commandArgs(trailingOnly = TRUE)
+if (length(args) < 2) {
+  stop("Need at least 2 experiments")
+}
+
+csv_suffix <- if (grepl("\\.csv$", args[length(args)])) {
+  args[length(args)]
+} else {
+  "resultsdata.csv"
+}
+exp_args <- if (grepl("\\.csv$", args[length(args)])) {
+  args[-length(args)]
+} else {
+  args
+}
+
+extract_info <- function(path) {
+  dir_name <- basename(path)
+  match <- str_match(
+    dir_name,
+    "^\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}_(.+?)_(c|aot|interp)_"
+  )
+  if (is.na(match[1, 1])) {
+    warning(paste("Could not parse:", dir_name))
+    return(NULL)
+  }
+  list(base_name = match[1, 2], variant = match[1, 3], path = path)
+}
+
+all_data <- data.frame()
+
+for (arg in exp_args) {
+  info <- extract_info(arg)
+  if (is.null(info)) {
+    next
+  }
+
+  csv_file <- file.path(info$path, csv_suffix)
+  if (!file.exists(csv_file)) {
+    warning(paste("Missing:", csv_file))
+    next
+  }
+
+  df <- read_csv(csv_file, col_types = cols())
+  df$base_name <- info$base_name
+  df$variant <- info$variant
+  all_data <- bind_rows(all_data, df)
+}
+
+if (nrow(all_data) == 0) {
+  stop("No data loaded")
+}
+
+# Ignore OK_MARKER (only plot failures) and sum GROUP1_MARKER with TRAP
+all_data <- all_data |>
+  filter(resulttype != "OK_MARKER") |>
+  mutate(resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype))
+
+all_data <- all_data |>
+  group_by(base_name, variant, benchmark, resulttype) |>
+  summarise(faults = sum(faults), .groups = "drop")
+
+# Only aot/interp matter; C is not used as a baseline here.
+counts <- all_data |> filter(variant %in% c("aot", "interp"))
+
+# Pivot to get aot and interp fault counts side by side
+counts_wide <- counts |>
+  select(base_name, benchmark, resulttype, variant, faults) |>
+  pivot_wider(names_from = variant, values_from = faults) |>
+  filter(!is.na(aot), !is.na(interp))
+
+if (nrow(counts_wide) == 0) {
+  stop("No paired aot/interp fault counts found")
+}
+
+# Compute correlation
+cor_raw <- cor(counts_wide$aot, counts_wide$interp, method = "pearson")
+cor_log <- cor(
+  log10(counts_wide$aot),
+  log10(counts_wide$interp),
+  method = "pearson"
+)
+cat(sprintf("Pearson correlation (raw):   %.4f\n", cor_raw))
+cat(sprintf("Pearson correlation (log10): %.4f\n", cor_log))
+
+# Create plot
+plot <- ggplot(
+  counts_wide,
+  aes(x = aot, y = interp, color = base_name, shape = resulttype)
+) +
+  # geom_abline(
+  #   slope = 1,
+  #   intercept = 0,
+  #   colour = "grey70",
+  #   linetype = "dotted"
+  # ) +
+  geom_point(size = 3, alpha = 0.7) +
+  scale_x_log10(name = "AOT Fault Count") +
+  scale_y_log10(name = "Interpreter Fault Count") +
+  labs(
+    # title = sprintf(
+    #   "Fault Count Correlation (r_raw = %.4f, r_log = %.4f)",
+    #   cor_raw,
+    #   cor_log
+    # ),
+    title = "Fault Count Correlation",
+    color = "Experiment",
+    shape = "Fault Type"
+  ) +
+  theme_minimal() +
+  theme(
+    legend.position = "right",
+    plot.title = element_text(size = 14, face = "bold")
+  )
+
+suffix <- gsub("^resultsdata|\\.csv$", "", csv_suffix)
+outfile <- paste0("injections/fault_count_correlation", suffix, ".svg")
+ggsave(outfile, plot = plot, width = 10, height = 8)
+print(paste("Saved", outfile))