## -----------------------------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.path = "figures/benchmark-long-",
  fig.width  = 6.5,
  fig.height = 4.5,
  dpi = 150,
  message = FALSE,
  warning = FALSE
)

LOCAL <- identical(Sys.getenv("LOCAL"), "TRUE")

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# library(bigPLSR)
# library(ggplot2)
# library(dplyr)
# library(tidyr)
# library(forcats)
# 
# data("external_pls_benchmarks", package = "bigPLSR")
# 
# head(external_pls_benchmarks)

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# bench <- external_pls_benchmarks %>%
#   mutate(
#     mem_mib      = mem_alloc_bytes / 1024^2,
#     log_time     = log10(median_time_s),
#     log_mem_mib  = log10(pmax(mem_mib, 1e-6)),
#     impl         = paste(package, algorithm, sep = "::")
#   )

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# pls1_sizes <- bench %>%
#   filter(task == "pls1") %>%
#   count(n, p, q, sort = TRUE)
# 
# pls1_sizes

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# size_pls1 <- pls1_sizes %>% slice(1L) %>% select(n, p, q)
# 
# pls1_subset <- bench %>%
#   semi_join(size_pls1, by = c("n", "p", "q")) %>%
#   filter(task == "pls1")

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# ggplot(pls1_subset,
#        aes(x = ncomp, y = median_time_s,
#            colour = package, linetype = algorithm)) +
#   geom_line() +
#   geom_point() +
#   scale_y_log10() +
#   labs(
#     x = "Number of components",
#     y = "Median runtime (seconds, log scale)",
#     title = "PLS1: fixed data size, varying components"
#   ) +
#   theme_minimal()

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# ggplot(pls1_subset,
#        aes(x = ncomp, y = mem_mib,
#            colour = package, linetype = algorithm)) +
#   geom_line() +
#   geom_point() +
#   labs(
#     x = "Number of components",
#     y = "Memory allocated (MiB)",
#     title = "PLS1: fixed data size, memory behaviour"
#   ) +
#   theme_minimal()

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# reference_impl <- "pls::simpls"
# 
# ## 1) Reference rows for pls1
# refs_pls1 <- bench %>%
#   filter(task == "pls1", impl == reference_impl) %>%
#   select(
#     n, p, q, ncomp,
#     time_ref = median_time_s,
#     mem_ref  = mem_mib
#   )
# 
# ## 2) Join and compute ratios (only where a reference exists)
# ratios_pls1 <- bench %>%
#   filter(task == "pls1") %>%
#   left_join(refs_pls1, by = c("n", "p", "q", "ncomp")) %>%
#   filter(!is.na(time_ref), !is.na(mem_ref)) %>%
#   mutate(
#     rel_time = median_time_s / time_ref,
#     rel_mem  = mem_mib      / mem_ref
#   )
# 
# ggplot(ratios_pls1,
#        aes(x = impl, y = rel_time)) +
#   geom_hline(yintercept = 1, linetype = "dashed", colour = "grey50") +
#   geom_boxplot() +
#   coord_flip() +
#   scale_y_log10() +
#   labs(
#     x = "Implementation",
#     y = "Runtime ratio vs reference (log scale)",
#     title = "PLS1: runtime ratios relative to pls::simpls"
#   ) +
#   theme_minimal()

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# ggplot(ratios_pls1,
#        aes(x = impl, y = rel_mem)) +
#   geom_hline(yintercept = 1, linetype = "dashed", colour = "grey50") +
#   geom_boxplot() +
#   coord_flip() +
#   scale_y_log10() +
#   labs(
#     x = "Implementation",
#     y = "Memory ratio vs reference (log scale)",
#     title = "PLS1: memory ratios relative to pls::simpls"
#   ) +
#   theme_minimal()

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# pls2_sizes <- bench %>%
#   filter(task == "pls2") %>%
#   count(n, p, q, sort = TRUE)
# 
# pls2_sizes

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# size_pls2 <- pls2_sizes %>% slice(1L) %>% select(n, p, q)
# 
# pls2_subset <- bench %>%
#   semi_join(size_pls2, by = c("n", "p", "q")) %>%
#   filter(task == "pls2")

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# ggplot(pls2_subset,
#        aes(x = ncomp, y = median_time_s,
#            colour = package, linetype = algorithm)) +
#   geom_line() +
#   geom_point() +
#   scale_y_log10() +
#   labs(
#     x = "Number of components",
#     y = "Median runtime (seconds, log scale)",
#     title = "PLS2: fixed data size, varying components"
#   ) +
#   theme_minimal()

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# ggplot(pls2_subset,
#        aes(x = ncomp, y = mem_mib,
#            colour = package, linetype = algorithm)) +
#   geom_line() +
#   geom_point() +
#   labs(
#     x = "Number of components",
#     y = "Memory allocated (MiB)",
#     title = "PLS2: fixed data size, memory behaviour"
#   ) +
#   theme_minimal()

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# pls2_q_grid <- bench %>%
#   filter(task == "pls2") %>%
#   count(n, p, q, sort = TRUE)
# 
# head(pls2_q_grid)

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# grid_example <- pls2_q_grid %>%
#   slice(1L) %>%
#   select(n, p)
# 
# pls2_q_subset <- bench %>%
#   semi_join(grid_example, by = c("n", "p")) %>%
#   filter(task == "pls2", ncomp == max(ncomp))
# 
# ggplot(pls2_q_subset,
#        aes(x = q, y = median_time_s,
#            colour = package, linetype = algorithm)) +
#   geom_line() +
#   geom_point() +
#   scale_y_log10() +
#   labs(
#     x = "Number of responses q",
#     y = "Median runtime (seconds, log scale)",
#     title = "PLS2: influence of q at fixed n and p"
#   ) +
#   theme_minimal()

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# bench_kernel <- bench %>%
#   filter(algorithm %in% c("kernelpls", "widekernelpls"))

## ----eval=LOCAL, cache=TRUE---------------------------------------------------
# kern_pls1 <- bench_kernel %>% filter(task == "pls1")
# 
# ggplot(kern_pls1,
#        aes(x = n, y = median_time_s,
#            colour = package, linetype = algorithm)) +
#   geom_line() +
#   geom_point() +
#   scale_y_log10() +
#   labs(
#     x = "Number of observations n",
#     y = "Median runtime (seconds, log scale)",
#     title = "Kernel PLS1: scaling with n"
#   ) +
#   theme_minimal()