123 lines
3.2 KiB
R
123 lines
3.2 KiB
R
library(ggplot2)
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library(dplyr)
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library(readr)
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library(stringr)
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library(tidyr)
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# Usage: Rscript ratio_correlation.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
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# Plots correlation between aot/c and interp/c ratios
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args <- commandArgs(trailingOnly = TRUE)
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if (length(args) < 2) {
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stop("Need at least 2 experiments")
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}
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csv_suffix <- if (grepl("\\.csv$", args[length(args)])) {
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args[length(args)]
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} else {
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"resultsdata.csv"
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}
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exp_args <- if (grepl("\\.csv$", args[length(args)])) {
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args[-length(args)]
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} else {
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args
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}
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extract_info <- function(path) {
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dir_name <- basename(path)
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match <- str_match(
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dir_name,
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"^\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}_(.+?)_(c|aot|interp)_"
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)
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if (is.na(match[1, 1])) {
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warning(paste("Could not parse:", dir_name))
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return(NULL)
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}
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list(base_name = match[1, 2], variant = match[1, 3], path = path)
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}
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all_data <- data.frame()
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for (arg in exp_args) {
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info <- extract_info(arg)
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if (is.null(info)) {
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next
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}
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csv_file <- file.path(info$path, csv_suffix)
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if (!file.exists(csv_file)) {
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warning(paste("Missing:", csv_file))
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next
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}
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df <- read_csv(csv_file, col_types = cols())
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df$base_name <- info$base_name
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df$variant <- info$variant
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all_data <- bind_rows(all_data, df)
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}
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if (nrow(all_data) == 0) {
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stop("No data loaded")
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}
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# Ignore OK_MARKER (only plot failures) and sum GROUP1_MARKER with TRAP
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all_data <- all_data |>
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filter(resulttype != "OK_MARKER") |>
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mutate(resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype))
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all_data <- all_data |>
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group_by(base_name, variant, benchmark, resulttype) |>
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summarise(faults = sum(faults), .groups = "drop")
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baseline <- all_data |> filter(variant == "c")
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comparisons <- all_data |> filter(variant != "c")
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ratios <- comparisons |>
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left_join(
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baseline |> select(base_name, benchmark, resulttype, faults),
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by = c("base_name", "benchmark", "resulttype"),
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suffix = c("", "_baseline")
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) |>
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filter(!is.na(faults_baseline), faults_baseline > 0) |>
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mutate(ratio = faults / faults_baseline)
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if (nrow(ratios) == 0) {
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stop("No ratios computed (missing baseline or zero values)")
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}
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# Pivot to get aot and interp ratios side by side
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ratio_wide <- ratios |>
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select(base_name, benchmark, resulttype, variant, ratio) |>
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pivot_wider(names_from = variant, values_from = ratio) |>
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filter(!is.na(aot), !is.na(interp))
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if (nrow(ratio_wide) == 0) {
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stop("No paired aot/interp ratios found")
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}
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# Compute correlation
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cor_result <- cor(ratio_wide$aot, ratio_wide$interp, method = "pearson")
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cat(sprintf("Pearson correlation: %.4f\n", cor_result))
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# Create plot
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plot <- ggplot(
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ratio_wide,
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aes(x = aot, y = interp, color = base_name, shape = resulttype)
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) +
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geom_point(size = 3, alpha = 0.7) +
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scale_x_log10(name = "AOT / C Ratio") +
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scale_y_log10(name = "Interpreter / C Ratio") +
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labs(
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title = sprintf("Ratio Correlation (r = %.4f)", cor_result),
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color = "Target",
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shape = "Marker"
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) +
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theme_minimal() +
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theme(
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legend.position = "right",
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plot.title = element_text(size = 14, face = "bold")
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)
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ggsave("injections/ratio_correlation.svg", plot = plot, width = 10, height = 8)
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print("Saved ratio_correlation.svg")
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