130 lines
3.4 KiB
R
130 lines
3.4 KiB
R
library(ggplot2)
|
|
library(dplyr)
|
|
library(readr)
|
|
library(stringr)
|
|
library(tidyr)
|
|
|
|
# Usage: Rscript ratio_correlation_no_mem.r exp_abspath1 exp_abspath2 ... [resultsdata_file]
|
|
# Plots correlation between aot/c and interp/c ratios, ignoring mem benchmark
|
|
# NOTE: Just a copy of the ratio_correlation.r script where I've changed the filter in line 67
|
|
|
|
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$variant
|
|
all_data <- bind_rows(all_data, df)
|
|
}
|
|
|
|
if (nrow(all_data) == 0) {
|
|
stop("No data loaded")
|
|
}
|
|
|
|
# Ignore OK_MARKER (only plot failures) and sum GROUP1_MARKER with TRAP
|
|
# Also ignore mem benchmark
|
|
all_data <- all_data |>
|
|
filter(resulttype %in% c("TRAP", "GROUP1_MARKER"), benchmark == "ip") |>
|
|
mutate(resulttype = ifelse(resulttype == "GROUP1_MARKER", "TRAP", resulttype))
|
|
|
|
all_data <- all_data |>
|
|
group_by(base_name, variant, benchmark, resulttype) |>
|
|
summarise(faults = sum(faults), .groups = "drop")
|
|
|
|
baseline <- all_data |> filter(variant == "c")
|
|
comparisons <- all_data |> filter(variant != "c")
|
|
|
|
ratios <- comparisons |>
|
|
left_join(
|
|
baseline |> select(base_name, benchmark, resulttype, faults),
|
|
by = c("base_name", "benchmark", "resulttype"),
|
|
suffix = c("", "_baseline")
|
|
) |>
|
|
filter(!is.na(faults_baseline), faults_baseline > 0) |>
|
|
mutate(ratio = faults / faults_baseline)
|
|
|
|
if (nrow(ratios) == 0) {
|
|
stop("No ratios computed (missing baseline or zero values)")
|
|
}
|
|
|
|
# Pivot to get aot and interp ratios side by side
|
|
ratio_wide <- ratios |>
|
|
select(base_name, benchmark, resulttype, variant, ratio) |>
|
|
pivot_wider(names_from = variant, values_from = ratio) |>
|
|
filter(!is.na(aot), !is.na(interp))
|
|
|
|
if (nrow(ratio_wide) == 0) {
|
|
stop("No paired aot/interp ratios found")
|
|
}
|
|
|
|
# Compute correlation
|
|
cor_result <- cor(ratio_wide$aot, ratio_wide$interp, method = "pearson")
|
|
cat(sprintf("Pearson correlation: %.4f\n", cor_result))
|
|
|
|
# Create plot
|
|
plot <- ggplot(
|
|
ratio_wide,
|
|
aes(x = aot, y = interp, color = base_name, shape = resulttype)
|
|
) +
|
|
geom_point(size = 3, alpha = 0.7) +
|
|
scale_x_log10(name = "AOT / C Ratio") +
|
|
scale_y_log10(name = "Interpreter / C Ratio") +
|
|
labs(
|
|
title = sprintf("Ratio Correlation (r = %.4f)", cor_result),
|
|
color = "Experiment",
|
|
shape = "Marker"
|
|
) +
|
|
theme_minimal() +
|
|
theme(
|
|
legend.position = "right",
|
|
plot.title = element_text(size = 14, face = "bold")
|
|
)
|
|
|
|
ggsave(
|
|
"injections/ratio_correlation_customized.svg",
|
|
plot = plot,
|
|
width = 10,
|
|
height = 8
|
|
)
|
|
print("Saved ratio_correlation_customized.svg")
|