147 lines
3.9 KiB
R
147 lines
3.9 KiB
R
library(ggplot2)
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library(dplyr)
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library(readr)
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# Usage: Rscript combined_instr_fault_correlation.r exp1 exp2 ... queries_dir charts_dir [faults_file]
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args <- commandArgs(trailingOnly = TRUE)
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# TODO: I should probably stop duplicating this each time
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csv_suffix <- if (grepl("\\.csv$", args[length(args)])) {
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args[length(args)]
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} else {
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"faults.csv"
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}
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tail_args <- if (grepl("\\.csv$", 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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if (length(tail_args) < 3) {
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stop(paste(
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"Usage: combined_instr_fault_correlation.r",
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"<exp1> <exp2> ... <queries_dir> <charts_dir> [faults_file]"
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))
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}
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charts_dir <- tail_args[length(tail_args)]
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queries_dir <- tail_args[length(tail_args) - 1]
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exp_args <- tail_args[-c(length(tail_args) - 1, length(tail_args))]
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# Don't use counts from faults.csv, as that would correlate completely
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# because only mnemonics with faults are listed there
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# NOTE: Doesn't match selected filters for faults.csv
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mnemonics_file <- "mnemonics.csv"
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freq_data <- data.frame()
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faults_data <- data.frame()
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# TODO: I should probably stop duplicating this each time
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for (arg in exp_args) {
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mnem_path <- file.path(queries_dir, paste0(arg, "_", mnemonics_file))
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faults_path <- file.path(queries_dir, paste0(arg, "_", csv_suffix))
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if (!file.exists(mnem_path)) {
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warning(paste("Missing:", mnem_path))
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next
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}
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if (!file.exists(faults_path)) {
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warning(paste("Missing:", faults_path))
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next
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}
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mdf <- read_csv(mnem_path, col_types = cols())
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mdf$experiment <- basename(arg)
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freq_data <- bind_rows(freq_data, mdf)
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fdf <- read_csv(faults_path, col_types = cols())
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fdf$experiment <- basename(arg)
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faults_data <- bind_rows(faults_data, fdf)
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}
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if (nrow(freq_data) == 0 || nrow(faults_data) == 0) {
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stop("No data loaded")
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}
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# x-axis: mnemonics.csv counts
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instr_freq <- freq_data |>
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filter(!is.na(mnemonic), mnemonic != "NULL") |>
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group_by(mnemonic) |>
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summarise(instr_count = sum(count, na.rm = TRUE), .groups = "drop")
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# y-axis: no OK_MARKERs, sum GROUP1_MARKER + TRAP.
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marker_count <- faults_data |>
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filter(!is.na(mnemonic), mnemonic != "NULL") |>
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filter(resulttype != "OK_MARKER") |>
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mutate(
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resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype)
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) |>
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group_by(mnemonic) |>
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summarise(marker_count = sum(faults, na.rm = TRUE), .groups = "drop")
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# Join instr_freq and marker_count, only keep entries both dataframes have
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correlation <- instr_freq |>
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inner_join(marker_count, by = "mnemonic") |>
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filter(instr_count > 0, marker_count > 0)
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if (nrow(correlation) < 2) {
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stop("Not enough mnemonics to compute a correlation")
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}
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cor_raw <- cor(
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correlation$instr_count,
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correlation$marker_count,
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method = "pearson"
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)
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cor_log <- cor(
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log10(correlation$instr_count),
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log10(correlation$marker_count),
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method = "pearson"
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)
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cat(sprintf("Pearson correlation (raw): %.4f\n", cor_raw))
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cat(sprintf("Pearson correlation (log10): %.4f\n", cor_log))
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plot <- ggplot(
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correlation,
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aes(x = instr_count, y = marker_count)
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) +
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geom_smooth(
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method = "lm",
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se = FALSE,
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colour = "grey50",
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linetype = "dashed"
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) +
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geom_point(aes(colour = mnemonic), size = 3, alpha = 0.8) +
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geom_text(
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aes(label = mnemonic),
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size = 3,
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vjust = -0.8,
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check_overlap = TRUE
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) +
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scale_x_log10(name = "Instruction Executions") +
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scale_y_log10(name = "Fault Count") +
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labs(
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# title = sprintf(
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# "Instruction / Fault Correlation (r_raw = %.4f, r_log = %.4f)",
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# cor_raw,
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# cor_log
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# ),
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title = "Instruction / Fault Correlation",
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colour = "Mnemonic"
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) +
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theme_minimal() +
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theme(
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legend.position = "none",
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plot.title = element_text(size = 14, face = "bold")
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)
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suffix <- gsub("^faults|\\.csv$", "", csv_suffix)
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dir.create(charts_dir, showWarnings = FALSE, recursive = TRUE)
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outfile <- file.path(
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charts_dir,
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paste0("instr_fault_correlation", suffix, ".svg")
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)
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ggsave(outfile, plot = plot, width = 10, height = 8)
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print(paste("Saved", outfile))
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