109 lines
3.5 KiB
R
109 lines
3.5 KiB
R
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library(ISLR) # for Hitters dataset
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library(MAVE)
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library(CVE)
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# Set global parameters.
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seed <- 21
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max.dim <- 5L
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attempts <- 25L
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max.iter <- 100L
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momentum <- 0.0
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name <- "Hitters"
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set.seed(seed)
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# Prepair data for analysis.
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cols <- c("Salary", "AtBat", "Hits", "HmRun", "Runs", "RBI",
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"Walks", "Years", "CAtBat","CHits", "CHmRun","CRuns",
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"CRBI", "CWalks", "PutOuts", "Assists", "Errors")
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outliers <- c(92, # Gary Pettis
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120, # John Moses
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173, # Milt Thompson
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189, # Rick Burleson
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220, # Scott Fletcher
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230, # Tom Foley
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241) # Terry Puhl
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# Subselect as a matrix without outliers as well as reordered
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# and filtered columns.
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ds <- na.omit(Hitters[, cols])[-outliers, ]
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ds$Salary <- log(ds$Salary)
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ds <- scale(ds, center = TRUE, scale = TRUE)
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# Split into data and responce.
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X <- as.matrix(ds[, colnames(ds) != "Salary"])
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Y <- as.matrix(ds[, "Salary"])
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path <- file.path(getwd(), 'results', 'hitters_logger.pdf')
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pdf(path, width = 8.27, height = 11.7) # width, height unit is inces -> A4
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layout(matrix(c(1, 1,
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2, 3,
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4, 5), nrow = 3, byrow = TRUE))
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# Setup histories.
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loss.history <- matrix(NA, max.iter + 1, attempts)
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error.history <- matrix(NA, max.iter + 1, attempts)
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tau.history <- matrix(NA, max.iter + 1, attempts)
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grad.norm.history <- matrix(NA, max.iter + 1, attempts)
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# Define logger for `cve()` method.
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logger <- function(attempt, iter, data) {
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# Note the `<<-` assignement!
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loss.history[iter + 1, attempt] <<- data$loss
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error.history[iter + 1, attempt] <<- data$err
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tau.history[iter + 1, attempt] <<- data$tau
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grad.norm.history[iter + 1, attempt] <<- norm(data$G, 'F')
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}
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dr <- cve(Y ~ X, k = 2L, max.iter = max.iter, attempts = attempts,
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logger = logger)
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B <- coef(dr, 2L)
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loss <- dr$res[['2']]$loss
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textplot <- function(...) {
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text <- unlist(list(...))
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if (length(text) > 20) {
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text <- c(text[1:17],
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' ...... (skipped, text too long) ......',
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text[c(-1, 0) + length(text)])
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}
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plot(NA, xlim = c(0, 1), ylim = c(0, 1),
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bty = 'n', xaxt = 'n', yaxt = 'n', xlab = '', ylab = '')
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for (i in seq_along(text)) {
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text(0, 1 - (i / 20),
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text[[i]], pos = 4)
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}
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}
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# Write metadata.
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textplot(
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paste0("Seed value: ", seed),
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"",
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paste0("Dataset Name: ", name),
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paste0("dim(X) = (", nrow(X), ", ", ncol(X), ")"),
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paste0("dim(B) = (", nrow(B), ", ", ncol(B), ")"),
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"",
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paste0("CVE method: ", dr$method),
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paste0("Max Iterations: ", max.iter),
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paste0("Attempts: ", attempts),
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paste0("Momentum: ", momentum),
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"CVE call:",
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paste0(" > ", format(dr$call)),
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"",
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paste0("True Error: ", NA),
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paste0("loss: ", round(loss, 3))
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)
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# Plot history's
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matplot(loss.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('loss', name),
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ylab = expression(L(V[i])))
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matplot(grad.norm.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('gradient norm', name),
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ylab = expression(group('|', paste(nabla, L(V)), '|')[F]))
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matplot(error.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('error', name),
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ylab = expression(group('|', V[i-1]*V[i-1]^T - V[i]*V[i]^T, '|')[F]))
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matplot(tau.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('learning rate', name),
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ylab = expression(tau[i]))
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