123 lines
3.8 KiB
R
123 lines
3.8 KiB
R
# Usage:
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# ~$ Rscript runtime_test.R
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#' Writes log information to console. (to not get bored^^)
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tell.user <- function(name, start.time, i, length) {
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cat("\rRunning Test (", name, "):",
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i, "/", length,
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" - elapsed:", format(Sys.time() - start.time), "\033[K")
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}
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subspace.dist <- function(B1, B2){
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P1 <- B1 %*% solve(t(B1) %*% B1) %*% t(B1)
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P2 <- B2 %*% solve(t(B2) %*% B2) %*% t(B2)
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return(norm(P1 - P2, type = 'F'))
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}
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# Number of simulations
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SIM.NR <- 20
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# maximal number of iterations in curvilinear search algorithm
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MAXIT <- 50
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# number of arbitrary starting values for curvilinear optimization
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ATTEMPTS <- 10
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# set names of datasets
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dataset.names <- c("M1", "M2", "M3", "M4", "M5")
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# Set used CVE method
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methods <- c("simple") # c("legacy", "simple", "sgd", "linesearch")
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library(CVE) # load CVE
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if ("legacy" %in% methods) {
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# Source legacy code (but only if needed)
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source("CVE_legacy/function_script.R")
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}
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# Setup error and time tracking variables
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error <- matrix(NA, SIM.NR, length(methods) * length(dataset.names))
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time <- matrix(NA, SIM.NR, ncol(error))
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colnames(error) <- kronecker(paste0(dataset.names, '-'), methods, paste0)
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colnames(time) <- colnames(error)
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# Create new log file and write CSV (actualy TSV) header.
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# (do not overwrite existing logs)
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log.nr <- length(list.files('tmp/', pattern = '.*\\.log'))
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file <- file.path('tmp', paste0('test', log.nr, '.log'))
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cat('dataset\tmethod\terror\ttime\n', sep = '', file = file)
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# Open a new pdf device for plotting into (do not overwrite existing ones)
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pdf(file.path('tmp', paste0('test', log.nr, '.pdf')))
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# only for telling user (to stdout)
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count <- 0
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start.time <- Sys.time()
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# Start simulation loop.
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for (sim in 1:SIM.NR) {
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# Repeat for each dataset.
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for (name in dataset.names) {
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count <- count + 1
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tell.user(name, start.time, count, SIM.NR * length(dataset.names))
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# Create a new dataset
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ds <- dataset(name)
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# Prepare X, Y and combine to data matrix
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Y <- ds$Y
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X <- ds$X
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data <- cbind(Y, X)
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# get dimensions
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dim <- ncol(X)
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truedim <- ncol(ds$B)
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for (method in methods) {
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if (tolower(method) == "legacy") {
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dr.time <- system.time(
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dr <- stiefl_opt(data,
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k = dim - truedim,
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k0 = ATTEMPTS,
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h = estimate.bandwidth(X, k = truedim, nObs = sqrt(nrow(X))),
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maxit = MAXIT
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)
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)
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dr$B <- fill_base(dr$est_base)[, 1:truedim]
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} else {
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dr.time <- system.time(
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dr <- cve.call(X, Y,
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method = method,
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k = truedim,
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attempts = ATTEMPTS
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)
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)
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dr <- dr[[truedim]]
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}
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key <- paste0(name, '-', method)
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error[sim, key] <- subspace.dist(dr$B, ds$B) / sqrt(2 * truedim)
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time[sim, key] <- dr.time["elapsed"]
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# Log results to file (mostly for long running simulations)
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cat(paste0(
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c(name, method, error[sim, key], time[sim, key]),
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collapse = '\t'
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), '\n',
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sep = '', file = file, append = TRUE
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)
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}
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}
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}
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cat("\n\n## Time [sec] Means:\n")
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print(colMeans(time))
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cat("\n## Error Means:\n")
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print(colMeans(error))
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at <- seq(ncol(error)) + rep(seq(ncol(error) / length(methods)) - 1, each = length(methods))
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boxplot(error,
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main = paste0("Error (Nr of simulations ", SIM.NR, ")"),
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ylab = "Error",
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las = 2,
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at = at
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)
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boxplot(time,
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main = paste0("Time (Nr of simulations ", SIM.NR, ")"),
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ylab = "Time [sec]",
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las = 2,
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at = at
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)
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