## ----setup_ops, include = FALSE-----------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "figures/benchmarking-pls2-",
  fig.width = 7,
  fig.height = 5,
  dpi = 150,
  message = FALSE,
  warning = FALSE
)

LOCAL <- identical(Sys.getenv("LOCAL"), "TRUE")
set.seed(2025)

## ----setup, message=FALSE-----------------------------------------------------
library(bigPLSR)
library(bigmemory)
library(bench)
set.seed(456)

## ----eval=FALSE---------------------------------------------------------------
# bench::mark(
#   dense = pls_fit(X[], Y_mat, ncomp = ncomp, algorithm = "rkhs_xy"),
#   streaming = pls_fit(X, Y, ncomp = ncomp, backend = "bigmem",
#                       algorithm = "kf_pls", chunk_size = 1024L)
# )

## ----eval=FALSE---------------------------------------------------------------
# future::plan(future::multisession, workers = 2)
# pls_cross_validate(X[], Y_mat, ncomp = 4, folds = 3,
#                    parallel = TRUE)
# future::plan(future::sequential)

## ----data-generation, eval=LOCAL, cache=TRUE----------------------------------
n <- 1200
p <- 60
q <- 3
ncomp <- 4

X <- bigmemory::big.matrix(nrow = n, ncol = p, type = "double")
X[,] <- matrix(rnorm(n * p), nrow = n)

loading_matrix <- matrix(rnorm(p * q), nrow = p)
latent_scores <- matrix(rnorm(n * q), nrow = n)
Y_mat <- scale(latent_scores %*% t(loading_matrix[1:q, , drop = FALSE]) +
                 matrix(rnorm(n * q, sd = 0.5), nrow = n))

Y <- bigmemory::big.matrix(nrow = n, ncol = q, type = "double")
Y[,] <- Y_mat

X[1:6, 1:6]
Y[1:6, 1:min(6, q)]

## ----internal-benchmark, eval=LOCAL, cache=TRUE-------------------------------
internal_bench <- bench::mark(
  dense_simpls = pls_fit(as.matrix(X[]), Y_mat, ncomp = ncomp,
                         backend = "arma", algorithm = "simpls"),
  streaming_simpls = pls_fit(X, Y, ncomp = ncomp, backend = "bigmem",
                             algorithm = "simpls", chunk_size = 512L),
  dense_nipals = pls_fit(as.matrix(X[]), Y_mat, ncomp = ncomp,
                         backend = "arma", algorithm = "nipals"),
  streaming_nipals = pls_fit(X, Y, ncomp = ncomp, backend = "bigmem",
                             algorithm = "nipals", chunk_size = 512L),
  iterations = 15,
  check = FALSE
)
internal_bench

## ----external-benchmark-------------------------------------------------------
data("external_pls_benchmarks", package = "bigPLSR")
subset(external_pls_benchmarks, task == "pls2")