# cartLiteBoostTV
# ::rtemis::
# 2018 E.D. Gennatas www.lambdamd.org
# made learning.rate into vector
# TODO: add error vector (1 * n.iter)
# TODO: make learning.rate input into max.iter vector
#' Boost an \pkg{rtemis} learner for regression
#'
#' Perform regression by boosting a base learner
#'
#' If `learning.rate` is set to 0, a nullmod will be created
#'
#' @inheritParams boost
#' @param mod.params Named list of arguments for `cartLite`
#' @param weights.p Float (0, 1]: Percent of weights to set to 1, the rest will be set to `weights.0`. Default = 1
#' @param weights.0 Float (0, 1): Set weights of excluded cases to this number. Default = 0, which is equivalent to
#' excluding them, in which case, these cases can act as a validation set
#' @param learning.rate Float (0, 1] Learning rate for the additive steps
#' @param init Float: Initial value for prediction. Default = mean(y)
#' @param seed Integer: Set seed to allow reproducibility when `weights.p` is not 1
#' @param max.iter Integer: Maximum number of iterations (additive steps) to perform. Default = 10
#' @param trace Integer: If > 0, print diagnostic info to console
#' @param base.verbose Logical: `verbose` argument passed to learner
#' @param print.error.plot String or Integer: "final" plots a training and validation (if available) error curve at the
#' end of training. If integer, plot training and validation error curve every this many iterations
#' during training. "none" for no plot. Default = "final"
#' @param print.base.plot Logical: Passed to `print.plot` argument of base learner, i.e. if TRUE, print error plot
#' for each base learner. Default = FALSE
#' @param prefix Internal
#' @param ... Additional parameters to be passed to `cartLite`
#'
#' @author E.D. Gennatas
#' @keywords internal
#' @export
cartLiteBoostTV <- function(x, y = NULL,
x.valid = NULL, y.valid = NULL,
x.test = NULL, y.test = NULL,
resid = NULL,
boost.obj = NULL,
mod.params = list(),
weights.p = 1,
weights.0 = 0,
weights = NULL,
learning.rate = .1,
max.iter = 10,
init = NULL,
seed = NULL,
x.name = NULL,
y.name = NULL,
question = NULL,
base.verbose = FALSE,
verbose = TRUE,
trace = 0,
print.progress.every = 5,
print.error.plot = "final",
prefix = NULL,
plot.theme = rtTheme,
plot.fitted = NULL,
plot.predicted = NULL,
print.plot = FALSE,
print.base.plot = FALSE,
plot.type = "l",
outdir = NULL, ...) {
# Intro ----
if (missing(x)) {
print(args(boost))
return(invisible(9))
}
if (!is.null(outdir)) outdir <- normalizePath(outdir, mustWork = FALSE)
logFile <- if (!is.null(outdir)) {
paste0(outdir, "/", sys.calls()[[1]][[1]], ".", format(Sys.time(), "%Y%m%d.%H%M%S"), ".log")
} else {
NULL
}
start.time <- intro(verbose = verbose, logFile = logFile)
mod.name <- "CARTLITEBOOSTTV"
# Arguments ----
if (is.null(x.name)) x.name <- getName(x, "x")
if (is.null(y.name)) y.name <- getName(y, "y")
if (!verbose) print.plot <- FALSE
verbose <- verbose | !is.null(logFile)
# if (save.mod & is.null(outdir)) outdir <- paste0("./s.", mod.name)
if (!is.null(outdir)) outdir <- paste0(normalizePath(outdir, mustWork = FALSE), "/")
extra.args <- list(...)
mod.params <- c(mod.params, extra.args)
# Data ----
dt <- prepare_data(x, y, x.test, y.test,
x.valid = x.valid, y.valid = y.valid,
verbose = verbose
)
x <- dt$x
y <- dt$y
x.test <- dt$x.test
y.test <- dt$y.test
x.valid <- dt$x.valid
y.valid <- dt$y.valid
xnames <- dt$xnames
type <- dt$type
# .weights <- if (is.null(weights) & ifw) dt$weights else weights
# x0 <- if (upsample|downsample) dt$x0 else x
# y0 <- if (upsample|downsample) dt$y0 else y
if (verbose) dataSummary(x, y, x.test, y.test, type)
if (print.plot) {
if (is.null(plot.fitted)) plot.fitted <- if (is.null(y.test)) TRUE else FALSE
if (is.null(plot.predicted)) plot.predicted <- if (!is.null(y.test)) TRUE else FALSE
} else {
plot.fitted <- plot.predicted <- FALSE
}
if (is.null(init)) init <- mean(y)
train.ncases <- length(y)
if (is.null(weights)) weights <- rep(1, train.ncases)
if (!is.null(x.valid)) {
valid.ncases <- length(y.valid)
trainval.ncases <- train.ncases + valid.ncases
valid.index <- (train.ncases + 1):trainval.ncases
weights1 <- c(weights, rep(0, valid.ncases))
x1 <- rbind(x, x.valid)
y1 <- c(y, y.valid)
} else {
x1 <- x
y1 <- y
weights1 <- weights
}
# Boost ----
learner <- "cartLite"
learner.name <- "Classification and Regression Tree"
learner.short <- "CARTlite"
if (verbose) {
parameterSummary(
mod.params,
init,
max.iter,
learning.rate,
weights.p,
weights.0
)
}
if (trace > 0) msg2("Initial MSE =", mse(y, init))
# '- New series ----
# init learning.rate vector
if (is.null(boost.obj)) {
mods <- list()
Fval <- penult.fitted <- init
.learning.rate <- numeric()
error <- vector("numeric")
error[[1]] <- mse(y, Fval) # will be overwritten, needed for while statement
if (!is.null(x.valid)) {
error.valid <- vector("numeric")
Fvalid <- init
} else {
error.valid <- predicted.valid <- Fvalid <- NULL
}
i <- 1
if (verbose) msg2("[ Boosting ", learner.name, "... ]", sep = "")
} else {
.learning.rate <- boost.obj$mod$learning.rate
# '- Expand series ----
mods <- boost.obj$mod$mods
Fval <- penult.fitted <- boost.obj$mod$fitted_tv # CHECK
error <- boost.obj$mod$error
if (!is.null(x.valid)) {
error.valid <- boost.obj$mod$error.valid
Fvalid <- boost.obj$mod$predicted.valid
} else {
error.valid <- predicted.valid <- Fvalid <- NULL
}
max.iter <- max.iter + length(mods)
i <- length(mods) + 1
if (trace > 0) msg2("i =", i)
if (verbose) msg2("[ Expanding boosted ", learner.name, "... ]", sep = "")
} # / Expand
if (is.null(resid)) resid <- y - Fval
resid1 <- if (!is.null(x.valid)) c(resid, rep(0, valid.ncases)) else resid
# Print error during training
if (max.iter == 1 && is.null(boost.obj)) {
print.progress.index <- FALSE
print.error.plot <- "none"
} else if (print.progress.every < max.iter) {
print.progress.index <- seq(print.progress.every, max.iter, print.progress.every)
} else {
print.progress.index <- max.iter
}
# Print error plot
if (max.iter > 1 && is.numeric(print.error.plot)) {
if (print.error.plot < max.iter) {
print.error.plot.index <- seq(print.error.plot, max.iter, print.error.plot)
} else {
print.error.plot.index <- max.iter
}
print.error.plot <- "iter"
}
# '- Iterate learner ----
if (!is.null(seed)) set.seed(seed)
while (i <= max.iter) {
.learning.rate[i] <- learning.rate
if (trace > 0) msg2("learning.rate is", .learning.rate[i])
if (trace > 0) msg2("i =", i)
if (weights.p < 1) {
holdout.index <- sample(train.ncases, (1 - weights.p) * train.ncases)
weights1[holdout.index] <- weights.0
}
mod.args <- c(
list(
x = x1, y = resid1,
weights = weights1,
save.fitted = TRUE
# x.test = x.valid, y.test = y.valid,
# verbose = base.verbose,
),
mod.params
)
# '- Train base learner ----
if (.learning.rate[i] != 0) {
mods[[i]] <- do.call(learner, args = mod.args)
} else {
mods[[i]] <- list(fitted = Fval)
class(mods[[i]]) <- c("nullmod", "list")
}
names(mods)[i] <- paste0(learner.short, ".", i)
fitted <- mods[[i]]$fitted
Fval <- Fval + .learning.rate[i] * fitted
if (i == max.iter - 1) penult.fitted <- Fval # CHECK: limit to train.ncases?
# resid <- y - Fval[seq(train.ncases)]
resid1 <- y1 - Fval
error[[i]] <- mse(y, Fval[seq(train.ncases)])
if (!is.null(x.valid)) {
predicted.valid <- fitted[valid.index]
Fvalid <- Fval[valid.index]
error.valid[[i]] <- mse(y.valid, Fvalid)
if (verbose && i %in% print.progress.index) {
if (verbose) {
msg2("Iteration #", i, ": Training MSE = ",
ddSci(error[[i]]),
"; Validation MSE = ", ddSci(error.valid[[i]]),
sep = ""
)
}
}
} else {
if (verbose && i %in% print.progress.index) {
msg2("Iteration #", i, ": Training MSE = ", ddSci(error[[i]]), sep = "")
}
}
if (print.error.plot == "iter" && i %in% print.error.plot.index) {
if (is.null(x.valid)) {
mplot3_xy(seq(error), error,
type = plot.type,
xlab = "Iteration", ylab = "MSE",
x.axis.at = seq(error),
main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
theme = plot.theme
)
} else {
mplot3_xy(seq(error), list(training = error, validation = error.valid),
type = plot.type,
xlab = "Iteration", ylab = "MSE", group.adj = .95,
x.axis.at = seq(error),
main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
theme = plot.theme
)
}
}
i <- i + 1
}
if (verbose && i > max.iter) msg2("Reached max iterations")
if (print.error.plot == "final") {
if (is.null(x.valid)) {
mplot3_xy(seq(error), error,
type = plot.type,
xlab = "Iteration", ylab = "MSE",
x.axis.at = seq(error),
main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
theme = plot.theme
)
} else {
mplot3_xy(seq(error), list(
Training = error,
Validation = error.valid
),
type = plot.type,
xlab = "Iteration", ylab = "MSE", group.adj = .95,
x.axis.at = seq(error),
main = paste0(prefix, learner.short, " Boosting"), zerolines = FALSE,
theme = plot.theme
)
}
}
# '- boost object ----
obj <- list(
mod.name = mod.name,
learning.rate = .learning.rate,
init = init,
penult.fitted = penult.fitted,
train.ncases = train.ncases,
fitted_tv = Fval,
last.fitted_tv = fitted,
predicted.valid = Fvalid,
error = error,
error.valid = error.valid,
mods = mods
)
class(obj) <- c("cartLiteBoostTV", "list")
# Fitted ----
error.train <- mod_error(y, obj$fitted_tv[seq(train.ncases)])
if (verbose) errorSummary(error.train)
# Predicted ----
predicted <- error.test <- NULL
if (!is.null(x.test)) {
predicted <- predict(obj, x.test)
if (!is.null(y.test)) {
error.test <- mod_error(y.test, predicted)
if (verbose) errorSummary(error.test)
}
}
# Outro ----
parameters <- list(
mod = learner.short,
mod.params = mod.params,
init = init,
n.its = length(error),
learning.rate = learning.rate,
max.iter = max.iter,
# case.p = case.p,
weights.p = weights.p,
weights.0 = weights.0,
weights = weights
)
extra <- list(error.valid = error.valid)
rt <- rtModSet(
mod = obj,
mod.name = mod.name,
type = type,
parameters = parameters,
call = NULL,
y.train = y,
y.test = y.test,
x.name = x.name,
y.name = y.name,
xnames = xnames,
fitted = obj$fitted_tv[seq(train.ncases)],
se.fit = NULL,
error.train = error.train,
predicted = predicted,
se.prediction = NULL,
error.test = error.test,
varimp = NULL,
question = question,
extra = extra
)
rtMod.out(rt,
print.plot,
plot.fitted,
plot.predicted,
y.test,
mod.name,
outdir,
save.mod = FALSE,
verbose,
plot.theme
)
outro(start.time, verbose = verbose, sinkOff = ifelse(is.null(logFile), FALSE, TRUE))
rt
} # rtemis::cartLiteBoostTV
#' Print method for [cartLiteBoostTV] object
#'
#' @method print cartLiteBoostTV
#'
#' @param x `cartLiteBoostTV` object
#' @param ... Additional arguments
#'
#' @author E.D. Gennatas
#' @export
print.cartLiteBoostTV <- function(x, ...) {
mod.name <- "CART lite"
n.iter <- length(x$mods)
cat("\n A boosted", mod.name, "model with", n.iter, "iterations\n")
cat(" and a learning rate of", x$learning.rate[1], "\n\n")
invisible(x)
} # rtemis::print.cartLiteBoostTV
#' Predict method for `cartLiteBoostTV` object
#'
#' @param object `cartLiteBoostTV` object
#' @param newdata Set of predictors
#' @param n.feat Integer: N of features to use. Default = NCOL(newdata)
#' @param n.iter Integer: N of iterations to predict from. Default = (all available)
#' @param as.matrix Logical: If TRUE, return predictions from each iterations. Default = FALSE
#' @param verbose Logical: If TRUE, print messages to console. Default = FALSE
#' @param n.cores Integer: Number of cores to use. Default = `rtCores`
#' @param ... Unused
#'
#' @method predict cartLiteBoostTV
#' @author E.D. Gennatas
#' @export
predict.cartLiteBoostTV <- function(object,
newdata = NULL,
n.feat = NCOL(newdata),
n.iter = NULL,
as.matrix = FALSE,
verbose = FALSE,
n.cores = rtCores, ...) {
if (inherits(object, "rtMod") && inherits(object$mod, "cartLiteBoostTV")) {
object <- object$mod
if (verbose) msg2("Found rtemis cartLiteBoostTV object")
} else if (inherits(object, "cartLiteBoostTV")) {
if (verbose) msg2("Found cartLiteBoostTV object")
} else {
stop("Please provide cartLiteBoostTV object")
}
if (is.null(newdata)) {
return(object$fitted_tv[seq(object$train.ncases)])
}
if (!is.null(newdata)) {
if (!is.data.frame(newdata)) {
.colnames <- if (!is.null(colnames(newdata))) colnames(newdata) else paste0("V", seq_len(NCOL(newdata)))
newdata <- as.data.frame(newdata)
colnames(newdata) <- .colnames
newdata <- newdata[, seq(n.feat), drop = FALSE]
}
}
if (is.null(n.iter)) n.iter <- length(object$mods)
if (!as.matrix) {
predicted <- rowSums(cbind(
rep(object$init, NROW(newdata)),
pbapply::pbsapply(seq(n.iter), \(i)
predict.cartLite(object$mods[[i]], newdata) * object$learning.rate[i],
cl = n.cores
)
))
} else {
predicted.n <- pbapply::pbsapply(seq(n.iter), \(i)
predict.cartLite(object$mods[[i]], newdata) * object$learning.rate[i],
cl = n.cores
)
predicted <- matrix(nrow = NROW(newdata), ncol = n.iter)
predicted[, 1] <- object$init + predicted.n[, 1]
for (i in seq(n.iter)[-1]) {
predicted[, i] <- predicted[, i - 1] + predicted.n[, i]
}
}
predicted
} # rtemis::predict.cartLiteBoostTV
#' Expand boosting series
#'
#' Expand a [cartLiteBoostTV] object by adding more iterations
#'
#' @inheritParams boost
#' @param object [cartLiteBoostTV] object
#'
#' @author E.D. Gennatas
#' @keywords internal
#' @noRd
expand.cartLiteBoostTV <- function(object,
x, y = NULL,
x.valid = NULL, y.valid = NULL,
x.test = NULL, y.test = NULL,
resid = NULL,
mod.params = NULL,
max.iter = 10,
learning.rate = NULL,
# case.p = 1,
weights.p = 1,
weights.0 = 0,
seed = NULL,
prefix = NULL,
verbose = TRUE,
trace = 0,
print.error.plot = "final",
print.plot = FALSE) {
if (is.null(y)) y <- object$y.train
if (is.null(mod.params)) mod.params <- object$parameters$mod.params
if (is.null(learning.rate)) learning.rate <- object$parameters$learning.rate
cartLiteBoostTV(
x = x, y = y,
x.valid = x.valid, y.valid = y.valid,
x.test = x.test, y.test = y.test,
resid = resid,
boost.obj = object,
mod.params = mod.params,
# case.p = case.p,
weights.p = weights.p,
weights.0 = weights.0,
learning.rate = learning.rate,
max.iter = max.iter,
seed = seed,
prefix = prefix,
verbose = verbose,
trace = trace,
print.error.plot = print.error.plot,
print.plot = print.plot
)
} # rtemis::expand.cartLiteBoostTV
#' Place model in [cartLiteBoostTV] structure
#'
#' @inheritParams as.boost
#' @param object rtMod model
#' @param learning.rate Float: Learning rate for new boost object. Default = 1
#' @param init Float: Initial value for new boost object. Default = 0
#' @param apply.lr Logical: Only considered is `x = NULL`. If TRUE, new boost object's fitted values will
#' be object$fitted * learning.rate, otherwise object$fitted
#'
#' @author E.D. Gennatas
#' @keywords internal
#' @noRd
# TODO: add x = NULL, if not NULL calculate fitted values
as.cartLiteBoostTV <- function(object,
x,
y = NULL,
x.valid = NULL,
y.valid = NULL,
learning.rate = 1,
init = 0,
apply.lr = TRUE) {
if (!inherits(object, "cartLite")) {
stop("Please provide cartLite object")
}
mods <- list(CARTlite.1 = object)
fitted <- init + predict(object, x)
if (apply.lr) fitted <- fitted * learning.rate
error <- if (!is.null(y)) mse(y, fitted) else NULL
if (!is.null(x.valid)) {
predicted.valid <- init + predict(object, x.valid)
if (apply.lr) predicted.valid <- predicted.valid * learning.rate
if (!is.null(y.valid)) {
error.valid <- mse(y.valid, predicted.valid)
} else {
error.valid <- NULL
}
} else {
predicted.valid <- error.valid <- NULL
}
obj <- list(
mod.name = "CARTLITEBOOSTTV",
learning.rate = learning.rate,
init = init,
penult.fitted = NULL,
train.ncases = NROW(x),
fitted_tv = c(fitted, predicted.valid),
last.fitted_tv = c(fitted, predicted.valid),
predicted.valid = predicted.valid,
error = error,
error.valid = error.valid,
mods = mods
)
class(obj) <- c("cartLiteBoostTV", "list")
# Outro ----
parameters <- list(
mod = object$mod.name,
mod.params = object$parameters,
init = init,
n.its = 1,
learning.rate = learning.rate,
max.iter = 1
)
extra <- list(error.valid = NULL)
rt <- rtModSet(
rtclass = "rtMod",
mod = obj,
mod.name = "CARTLITEBOOSTTV",
type = "Regression",
parameters = parameters,
call = NULL,
y.train = object$y,
y.test = object$y.test,
x.name = object$x.name,
y.name = object$y.name,
xnames = object$xnames,
fitted = fitted,
se.fit = NULL,
error.train = object$error.train,
predicted = object$predicted,
se.prediction = NULL,
error.test = object$error.test,
varimp = NULL,
question = object$question,
extra = extra
)
rt
} # rtemis::as.cartLiteBoostTV
#' \pkg{rtemis} internals: Update [cartLiteBoostTV] object's fitted values
#'
#' Calculate new fitted values for a [cartLiteBoostTV] object.
#' Advanced use only: run with new `x` or after updating learning.rate in object
#'
#' @method update cartLiteBoostTV
#' @param object [cartLiteBoostTV] object
#' @param x Data frame: Features
#' @param last.step.only Logical: If TRUE, `x` must be provided and only the last meta model will be updated
#' using this `x`
#'
#' @return [cartLiteBoostTV] object
#' @author E.D. Gennatas
#' @return Nothing; updates `object` in-place
#' @keywords internal
#' @noRd
update.cartLiteBoostTV <- function(object,
x = NULL,
x.valid = NULL,
trace = 0,
last.step.only = FALSE,
n.cores = rtCores, ...) {
if (trace > 0) fitted.orig <- object$fitted_tv
# fitted <- plyr::laply(object$mod$mods, function(i) i$fitted)
# Create n.iter x n.cases fitted values; one row per iteration
if (is.null(x)) {
# fitted <- t(vapply(object$mod$mods, function(i) i$fitted, vector("numeric", length(object$fitted_tv))))
fitted <- t(sapply(object$mod$mods, \(i) i$fitted))
} else {
if (!last.step.only) {
fitted <- t(as.data.frame(pbapply::pblapply(object$mod$mods, \(i) predict(i, x), cl = n.cores)))
} else {
u <- length(object$mod$mods)
object$mod$mods[[u]]$fitted_tv <- predict(object$mod$mods[[u]], x)
fitted <- t(vapply(
object$mod$mods,
\(i) i$fitted, vector("numeric", length(object$fitted))
))
}
}
# TODO: finish x.valid
# if (is.null(x.valid)) {
# predicted.valid
# }
# Multiply each row by its corresponding learning.rate, and sum all n.case-length vectors to get fitted value
object$mod$fitted_tv <- object$mod$init + colSums(fitted * object$mod$learning.rate)
object$fitted <- object$mod$fitted_tv[seq(object$mod$train.ncases)]
object$error.train <- mod_error(object$y.train, object$mod$fitted_tv[seq(object$mod$train.ncases)])
if (trace > 0) {
mse.orig <- mse(object$y.train, fitted.orig)
mse.new <- mse(object$y.train, fitted)
msg20("old MSE = ", mse.orig, "; new MSE = ", mse.new)
# if (mse.new > mse.orig) warning("Whatever you did, it didn't really help:\nnew MSE is higher than original")
}
} # rtemis::update.cartLiteBoostTV
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