In jranke/mkin: Kinetic Evaluation of Chemical Degradation Data

library(knitr)
opts_chunk$set(tidy = FALSE, cache = FALSE)
library("mkin")

Each system is characterized by the operating system type, the CPU type, the mkin version, and, as in June 2022 the current R version lead to worse performance, the R version. A compiler was available, so if no analytical solution was available, compiled ODE models are used.

Every fit is only performed once, so the accuracy of the benchmarks is limited.

The following wrapper function for mmkin is used because the way the error model is specified was changed in mkin version 0.9.49.1.

if (packageVersion("mkin") > "0.9.48.1") {
  mmkin_bench <- function(models, datasets, error_model = "const") {
    mmkin(models, datasets, error_model = error_model, cores = 1, quiet = TRUE)
  }
} else {
  mmkin_bench <- function(models, datasets, error_model = NULL) {
    mmkin(models, datasets, reweight.method = error_model, cores = 1, quiet = TRUE)
  }
}

cpu_model <- benchmarkme::get_cpu()$model_name
# Abbreviate CPU identifiers
cpu_model <- gsub("AMD ", "", cpu_model)
cpu_model <- gsub("Intel\\(R\\) Core\\(TM\\) ", "", cpu_model)
cpu_model <- gsub(" Eight-Core Processor", "", cpu_model)
cpu_model <- gsub(" 16-Core Processor", "", cpu_model)
cpu_model <- gsub(" CPU @ 2.50GHz", "", cpu_model)

operating_system <- Sys.info()[["sysname"]]
mkin_version <- as.character(packageVersion("mkin"))
R_version <- paste0(R.version$major, ".", R.version$minor)
system_string <- paste0(operating_system, ", ", cpu_model, ", mkin ", mkin_version, ", R ", R_version)

if (dir.exists("~/git/mkin")) {
  benchmark_path = normalizePath("~/git/mkin/vignettes/web_only/mkin_benchmarks.rda")
} else {
  benchmark_path = normalizePath("~/projects/mkin/vignettes/web_only/mkin_benchmarks.rda")
}

load(benchmark_path)

# Used for reformatting the data on 2022-06-30
# mkin_benchmarks[, "R"] <- NA
# mkin_benchmarks <- mkin_benchmarks[c(2, 1, 15, 3, 4:14)]
# mkin_benchmarks[, "CPU"] <- gsub("AMD.*", "Ryzen 7 1700", mkin_benchmarks[, "CPU"])
# mkin_benchmarks[, "CPU"] <- gsub("Intel.*", "i7-4710MQ", mkin_benchmarks[, "CPU"])
# rownames(mkin_benchmarks) <- gsub("AMD Ryzen 7 1700 Eight-Core Processor", "Ryzen 7 1700", rownames(mkin_benchmarks))
# rownames(mkin_benchmarks) <- gsub("Intel\\(R\\) Core\\(TM\\) i7-4710MQ CPU @ 2.50GHz", "i7-4710MQ", rownames(mkin_benchmarks))
# rownames(mkin_benchmarks) <- gsub(" version", "", rownames(mkin_benchmarks))

mkin_benchmarks[system_string, c("CPU", "OS", "mkin", "R")] <-
  c(cpu_model, operating_system, mkin_version, R_version)

Test cases

Parent only:

FOCUS_C <- FOCUS_2006_C
FOCUS_D <- subset(FOCUS_2006_D, value != 0)
parent_datasets <- list(FOCUS_C, FOCUS_D)


t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), parent_datasets))[["elapsed"]]
t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), parent_datasets,
    error_model = "tc"))[["elapsed"]]

One metabolite:

SFO_SFO <- mkinmod(
  parent = mkinsub("SFO", "m1"),
  m1 = mkinsub("SFO"))
FOMC_SFO <- mkinmod(
  parent = mkinsub("FOMC", "m1"),
  m1 = mkinsub("SFO"))
DFOP_SFO <- mkinmod(
  parent = mkinsub("FOMC", "m1"), # erroneously used FOMC twice, not fixed for consistency
  m1 = mkinsub("SFO"))
t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D)))[["elapsed"]]
t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),
    error_model = "tc"))[["elapsed"]]
t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D),
    error_model = "obs"))[["elapsed"]]

Two metabolites, synthetic data:

m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
                           M1 = mkinsub("SFO", "M2"),
                           M2 = mkinsub("SFO"),
                           use_of_ff = "max", quiet = TRUE)

m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
                           M1 = mkinsub("SFO"),
                           M2 = mkinsub("SFO"),
                           use_of_ff = "max", quiet = TRUE)

SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data

DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data

t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))[["elapsed"]]
t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))[["elapsed"]]

t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a),
    error_model = "tc"))[["elapsed"]]
t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c),
    error_model = "tc"))[["elapsed"]]

t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a),
    error_model = "obs"))[["elapsed"]]
t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c),
    error_model = "obs"))[["elapsed"]]

mkin_benchmarks[system_string, paste0("t", 1:11)] <-
  c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
save(mkin_benchmarks, file = benchmark_path, version = 2)
# Hide rownames from kable for results section
rownames(mkin_benchmarks) <- NULL

Results

Benchmarks for all available error models are shown. They are intended for improving mkin, not for comparing CPUs or operating systems. All trademarks belong to their respective owners.

Parent only

Constant variance (t1) and two-component error model (t2) for four models fitted to two datasets, i.e. eight fits for each test.

kable(mkin_benchmarks[, c(1:4, 5:6)])

One metabolite

Constant variance (t3), two-component error model (t4), and variance by variable (t5) for three models fitted to one dataset, i.e. three fits for each test.

kable(mkin_benchmarks[, c(1:4, 7:9)])

Two metabolites

Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.