# s_CART.R
# ::rtemis::
# E.D. Gennatas rtemis.org
#' Classification and Regression Trees \[C, R, S\]
#'
#' Train a CART for regression or classification using `rpart`
#'
#' \[gS\] indicates grid search will be performed automatically if more than one
#' value is passed
#'
#' @inheritParams s_GLM
#' @param method Character: "auto", "anova", "poisson", "class" or "exp".
#' @param cp \[gS\] Float: Complexity threshold for allowing a split.
#' @param maxdepth \[gS\] Integer: Maximum depth of tree.
#' @param maxcompete Integer: The number of competitor splits saved in the
#' output
#' @param usesurrogate See `rpart::rpart.control`
#' @param xval Integer: Number of cross-validations
#' @param surrogatestyle See `rpart::rpart.control`
#' @param maxsurrogate Integer: The number of surrogate splits retained in the
#' output (See `rpart::rpart.control`).
#' @param minsplit \[gS\] Integer: Minimum number of cases that must belong in a
#' node before considering a split.
#' @param minbucket \[gS\] Integer: Minimum number of cases allowed in a child
#' node.
#' @param prune.cp \[gS\] Numeric: Complexity for cost-complexity pruning after
#' tree is built
#' @param use.prune.rpart.rt (Testing only, do not change)
#' @param return.unpruned Logical: If TRUE and `prune.cp` is set, return
#' unpruned tree under `extra` in `rtMod`.
#' @param grid.resample.params List: Output of [setup.resample] defining
#' grid search parameters.
#' @param gridsearch.type Character: Type of grid search to perform:
#' "exhaustive" or "randomized".
#' @param gridsearch.randomized.p Float (0, 1): If
#' `gridsearch.type = "randomized"`, randomly test this proportion of
#' combinations.
#' @param save.gridrun Logical: If TRUE, save grid search models.
#' @param metric Character: Metric to minimize, or maximize if
#' `maximize = TRUE` during grid search. Default = NULL, which results in
#' "Balanced Accuracy" for Classification,
#' "MSE" for Regression, and "Coherence" for Survival Analysis.
#' @param maximize Logical: If TRUE, `metric` will be maximized if grid
#' search is run.
#' @param parms List of additional parameters for the splitting function.
#' See `rpart::rpart("parms")`
#' @param cost Vector, Float (> 0): One for each variable in the model.
#' See `rpart::rpart("cost")`
#' @param model Logical: If TRUE, keep a copy of the model.
#' @param grid.verbose Logical: Passed to `gridSearchLearn`
#' @param n.cores Integer: Number of cores to use.
#'
#' @return Object of class `rtMod`
#' @author E.D. Gennatas
#' @seealso [train_cv] for external cross-validation
#' @family Supervised Learning
#' @family Tree-based methods
#' @family Interpretable models
#' @export
s_CART <- function(x, y = NULL,
x.test = NULL, y.test = NULL,
x.name = NULL, y.name = NULL,
weights = NULL,
ifw = TRUE,
ifw.type = 2,
upsample = FALSE,
downsample = FALSE,
resample.seed = NULL,
method = "auto",
parms = NULL,
minsplit = 2,
minbucket = round(minsplit / 3),
cp = 0.01,
maxdepth = 20,
maxcompete = 0,
maxsurrogate = 0,
usesurrogate = 2,
surrogatestyle = 0,
xval = 0,
cost = NULL,
model = TRUE,
prune.cp = NULL,
use.prune.rpart.rt = TRUE,
return.unpruned = FALSE,
grid.resample.params = setup.resample("kfold", 5),
gridsearch.type = c("exhaustive", "randomized"),
gridsearch.randomized.p = .1,
save.gridrun = FALSE,
metric = NULL,
maximize = NULL,
n.cores = rtCores,
print.plot = FALSE,
plot.fitted = NULL,
plot.predicted = NULL,
plot.theme = rtTheme,
question = NULL,
verbose = TRUE,
grid.verbose = verbose,
outdir = NULL,
save.mod = ifelse(!is.null(outdir), TRUE, FALSE)) {
# .call <- match.call()
tree.depth <- getFromNamespace("tree.depth", "rpart")
# Intro ----
if (missing(x)) {
print(args(s_CART))
return(invisible(9))
}
if (!is.null(outdir)) outdir <- paste0(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 <- "CART"
# Dependencies ----
dependency_check("rpart")
# Arguments ----
if (is.null(y) && NCOL(x) < 2) {
print(args(s_CART))
stop("y is missing")
}
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), "/")
gridsearch.type <- match.arg(gridsearch.type)
# Data ----
dt <- prepare_data(x, y, x.test, y.test,
ifw = ifw,
ifw.type = ifw.type,
upsample = upsample,
downsample = downsample,
resample.seed = resample.seed,
verbose = verbose
)
x <- dt$x
y <- dt$y
x.test <- dt$x.test
y.test <- dt$y.test
xnames <- dt$xnames
type <- dt$type
.weights <- if (is.null(weights) && ifw) dt$weights else weights
# x0, y0 are passed to gridSearchLearn
x0 <- if (upsample || downsample) dt$x0 else x
y0 <- if (upsample || downsample) dt$y0 else y
if (verbose) dataSummary(x, y, x.test, y.test, type)
if (type != "Survival") df.train <- data.frame(y = y, x)
if (method == "auto") {
if (type == "Regression") {
method <- "anova"
if (is.null(metric)) metric <- "MSE"
if (is.null(maximize)) maximize <- FALSE
} else if (type == "Classification") {
method <- "class"
if (is.null(metric)) metric <- "Balanced Accuracy"
if (is.null(maximize)) maximize <- TRUE
} else if (type == "Survival") {
method <- "exp"
if (is.null(metric)) metric <- "Concordance"
if (is.null(maximize)) maximize <- TRUE
}
}
if (is.null(cost)) cost <- rep(1, NCOL(x))
if (print.plot) {
if (is.null(plot.fitted)) plot.fitted <- if (is.null(y.test)) TRUE else FALSE
if (is.null(plot.predicted)) plot.predicted <- if (!is.null(y.test)) TRUE else FALSE
} else {
plot.fitted <- plot.predicted <- FALSE
}
if (is.null(metric)) {
if (type == "Classification") {
metric <- "Balanced Accuracy"
} else if (type == "Regression") {
metric <- "MSE"
} else {
metric <- "Concordance"
}
}
if (type == "Classification") n.classes <- length(levels(y))
# Formula ----
features <- paste(xnames, collapse = " + ")
.formula <- as.formula(paste0(y.name, " ~ ", features))
# Grid Search ----
if (gridCheck(maxdepth, minsplit, minbucket, cp, prune.cp)) {
gs <- gridSearchLearn(
x0, y0,
mod = mod.name,
resample.params = grid.resample.params,
grid.params = list(
maxdepth = maxdepth,
minsplit = minsplit,
minbucket = minbucket,
cp = cp,
prune.cp = prune.cp
),
fixed.params = list(
method = method,
model = model,
maxcompete = maxcompete,
maxsurrogate = maxsurrogate,
usesurrogate = usesurrogate,
surrogatestyle = surrogatestyle,
xval = xval,
cost = cost,
ifw = ifw,
ifw.type = ifw.type,
upsample = upsample,
downsample = downsample,
resample.seed = resample.seed
),
search.type = gridsearch.type,
randomized.p = gridsearch.randomized.p,
weights = weights,
metric = metric,
maximize = maximize,
save.mod = save.gridrun,
verbose = grid.verbose,
n.cores = n.cores
)
maxdepth <- gs$best.tune$maxdepth
minsplit <- gs$best.tune$minsplit
minbucket <- gs$best.tune$minbucket
cp <- gs$best.tune$cp
prune.cp <- gs$best.tune$prune.cp
} else {
gs <- NULL
}
parameters <- list(
prune.cp = prune.cp,
method = method,
model = model,
minsplit = minsplit,
minbucket = minbucket,
cp = cp,
maxcompete = maxcompete,
maxsurrogate = maxsurrogate,
usesurrogate = usesurrogate,
surrogatestyle = surrogatestyle,
maxdepth = maxdepth,
xval = xval,
cost = cost
)
control <- list(
minsplit = minsplit,
minbucket = minbucket,
cp = cp,
maxcompete = maxcompete,
maxsurrogate = maxsurrogate,
usesurrogate = usesurrogate,
surrogatestyle = surrogatestyle,
maxdepth = maxdepth,
xval = xval
)
# rpart ----
if (verbose) msg2("Training CART...", newline.pre = TRUE)
mod <- rpart::rpart(
formula = .formula,
data = df.train,
weights = .weights,
method = method,
model = model,
control = control,
cost = cost,
parms = parms
)
# Cost-Complexity Pruning ----
if (!is.null(prune.cp)) {
if (return.unpruned) mod.unpruned <- mod
if (use.prune.rpart.rt) {
mod <- prune.rpart.rt(mod, cp = prune.cp)
} else {
mod <- rpart::prune(mod, cp = prune.cp)
}
}
# Fitted ----
fitted.prob <- NULL
if (type == "Regression" || type == "Survival") {
fitted <- predict(mod, x, type = "vector")
} else if (type == "Classification") {
if (n.classes == 2) {
fitted.prob <- predict(mod, x, type = "prob")[, rtenv$binclasspos]
} else {
fitted.prob <- predict(mod, x, type = "prob")
}
fitted <- predict(mod, x, type = "class")
}
attr(fitted, "names") <- NULL
error.train <- mod_error(y, fitted, fitted.prob)
if (verbose) errorSummary(error.train, mod.name)
# Predicted ----
predicted.prob <- predicted <- error.test <- NULL
if (!is.null(x.test)) {
if (type == "Regression" || type == "Survival") {
predicted <- predict(mod, x.test, type = "vector")
predicted.prob <- NULL
} else if (type == "Classification") {
predicted.prob <- predict(mod, x.test, type = "prob")[, rtenv$binclasspos]
predicted <- predict(mod, x.test, type = "class")
}
attr(predicted, "names") <- NULL
if (!is.null(y.test)) {
error.test <- mod_error(y.test, predicted, predicted.prob)
if (verbose) errorSummary(error.test, mod.name)
} else {
error.test <- NULL
}
} else {
predicted <- predicted.prob <- error.test <- NULL
}
# Outro ----
varimp <- rep(NA, NCOL(x))
varimp.cart <- if (!is.null(mod$variable.importance)) as.matrix(mod$variable.importance) else NULL
varimp.index <- match(rownames(varimp.cart), colnames(x))
varimp[varimp.index] <- varimp.cart
varimp[is.na(varimp)] <- 0
names(varimp) <- colnames(x)
extra <- list(
imetrics = list(
n.nodes = NROW(mod$frame),
depth = max(tree.depth(as.numeric(rownames(mod$frame))))
)
)
if (!is.null(prune.cp) && return.unpruned) extra$mod.unpruned <- mod.unpruned
rt <- rtModSet(
rtclass = "rtMod",
mod = mod,
mod.name = mod.name,
type = type,
gridsearch = gs,
parameters = parameters,
# call = .call,
y.train = y,
y.test = y.test,
x.name = x.name,
y.name = y.name,
xnames = xnames,
fitted = fitted,
fitted.prob = fitted.prob,
se.fit = NULL,
error.train = error.train,
predicted = predicted,
predicted.prob = predicted.prob,
se.prediction = NULL,
error.test = error.test,
varimp = varimp,
question = question,
extra = extra
)
rtMod.out(
rt,
print.plot,
plot.fitted,
plot.predicted,
y.test,
mod.name,
outdir,
save.mod,
verbose,
plot.theme
)
outro(start.time,
verbose = verbose,
sinkOff = ifelse(is.null(logFile), FALSE, TRUE)
)
rt
} # rtemis::s_CART
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