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R/plot.train.R
In caret: Classification and Regression Training
#' Plot Method for the train Class
#'
#' This function takes the output of a \code{\link{train}} object and creates a
#' line or level plot using the \pkg{lattice} or \pkg{ggplot2} libraries.
#'
#' If there are no tuning parameters, or none were varied, an error is
#' produced.
#'
#' If the model has one tuning parameter with multiple candidate values, a plot
#' is produced showing the profile of the results over the parameter. Also, a
#' plot can be produced if there are multiple tuning parameters but only one is
#' varied.
#'
#' If there are two tuning parameters with different values, a plot can be
#' produced where a different line is shown for each value of of the other
#' parameter. For three parameters, the same line plot is created within
#' conditioning panels/facets of the other parameter.
#'
#' Also, with two tuning parameters (with different values), a levelplot (i.e.
#' un-clustered heatmap) can be created. For more than two parameters, this
#' plot is created inside conditioning panels/facets.
#'
#' @aliases plot.train ggplot.train
#' @param x an object of class \code{\link{train}}.
#' @param metric What measure of performance to plot. Examples of possible
#' values are "RMSE", "Rsquared", "Accuracy" or "Kappa". Other values can be
#' used depending on what metrics have been calculated.
#' @param plotType a string describing the type of plot (\code{"scatter"},
#' \code{"level"} or \code{"line"} (\code{plot} only))
#' @param digits an integer specifying the number of significant digits used to
#' label the parameter value.
#' @param xTrans a function that will be used to scale the x-axis in scatter
#' plots.
#' @param data an object of class \code{\link{train}}.
#' @param output either "data", "ggplot" or "layered". The first returns a data
#' frame while the second returns a simple \code{ggplot} object with no layers.
#' The third value returns a plot with a set of layers.
#' @param nameInStrip a logical: if there are more than 2 tuning parameters,
#' should the name and value be included in the panel title?
#' @param highlight a logical: if \code{TRUE}, a diamond is placed around the
#' optimal parameter setting for models using grid search.
#' @param mapping,environment unused arguments to make consistent with
#' \pkg{ggplot2} generic method
#' @param \dots \code{plot} only: specifications to be passed to
#' \code{\link[lattice]{levelplot}}, \code{\link[lattice]{xyplot}},
#' \code{\link[lattice:xyplot]{stripplot}} (for line plots). The function
#' automatically sets some arguments (e.g. axis labels) but passing in values
#' here will over-ride the defaults
#' @author Max Kuhn
#' @seealso \code{\link{train}}, \code{\link[lattice]{levelplot}},
#' \code{\link[lattice]{xyplot}}, \code{\link[lattice:xyplot]{stripplot}},
#' \code{\link[ggplot2]{ggplot}}
#' @references Kuhn (2008), ``Building Predictive Models in R Using the caret''
#' (\doi{10.18637/jss.v028.i05})
#' @keywords hplot
#' @method plot train
#' @export
#' @examples
#'
#'
#' \dontrun{
#' library(klaR)
#' rdaFit <- train(Species ~ .,
#' data = iris,
#' method = "rda",
#' control = trainControl(method = "cv"))
#' plot(rdaFit)
#' plot(rdaFit, plotType = "level")
#'
#' ggplot(rdaFit) + theme_bw()
#'
#' }
#'
#' @export plot.train
"plot.train" <- function(x,
plotType = "scatter",
metric = x$metric[1],
digits = getOption("digits") - 3,
xTrans = NULL,
nameInStrip = FALSE,
...)
{
## Error trap
if(!(plotType %in% c("level", "scatter", "line"))) stop("plotType must be either level, scatter or line")
cutpoints <- c(0, 1.9, 2.9, 3.9, Inf)
## define some functions
prettyVal <- function(u, dig, Name = NULL)
{
if(is.numeric(u))
{
if(!is.null(Name)) u <- paste(gsub(".", " ", Name, fixed = TRUE),
": ",
format(u, digits = dig), sep = "")
return(factor(u))
} else return(if(!is.factor(u)) as.factor(u) else u)
}
## Get tuning parameter info
params <- as.character(x$modelInfo$parameters$parameter)
if(grepl("adapt", x$control$method))
warning("When using adaptive resampling, this plot may not accurately capture the relationship between the tuning parameters and model performance.")
plotIt <- "yes"
if(all(params == "parameter"))
{
plotIt <- "There are no tuning parameters for this model."
} else {
dat <- x$results
## Some exceptions for pretty printing
if(x$method == "nb") dat$usekernel <- factor(ifelse(dat$usekernel, "Nonparametric", "Gaussian"))
if(x$method == "gam") dat$select <- factor(ifelse(dat$select, "Feature Selection", "No Feature Selection"))
if(x$method == "qrnn") dat$bag <- factor(ifelse(dat$bag, "Bagging", "No Bagging"))
if(x$method == "C5.0") dat$winnow <- factor(ifelse(dat$winnow, "Winnowing", "No Winnowing"))
## if(x$method %in% c("M5Rules", "M5", "PART")) dat$pruned <- factor(ifelse(dat$pruned == "Yes", "Pruned", "Unpruned"))
## if(x$method %in% c("M5Rules", "M5")) dat$smoothed <- factor(ifelse(dat$smoothed == "Yes", "Smoothed", "Unsmoothed"))
if(x$method == "M5") dat$rules <- factor(ifelse(dat$rules == "Yes", "Rules", "Trees"))
## if(x$method == "vbmpRadial") dat$estimateTheta <- factor(ifelse(dat$estimateTheta == "Yes", "Estimate Theta", "Do Not Estimate Theta"))
## Check to see which tuning parameters were varied
paramValues <- apply(dat[,params,drop = FALSE],
2,
function(x) length(unique(x)))
##paramValues <- paramValues[order(paramValues)]
if(any(paramValues > 1))
{
params <- names(paramValues)[paramValues > 1]
} else plotIt <- "There are no tuning parameters with more than 1 value."
}
if(plotIt == "yes")
{
p <- length(params)
dat <- dat[, c(metric, params)]
if(p > 1) {
numUnique <- unlist(lapply(dat[, -1], function(x) length(unique(x))))
numUnique <- sort(numUnique, decreasing = TRUE)
dat <- dat[, c(metric, names(numUnique))]
params <- names(numUnique)
}
## The conveintion is that the first parameter (in
## position #2 of dat) is plotted on the x-axis,
## the second parameter is the grouping variable
## and the rest are conditioning variables
if(!is.null(xTrans) & plotType == "scatter") dat[,2] <- xTrans(dat[,2])
## We need to pretty-up some of the values of grouping
## or conditioning variables
resampText <- resampName(x, FALSE)
if(plotType %in% c("line", "scatter"))
{
if(plotType == "scatter")
{
if(p >= 2) for(i in 3:ncol(dat))
dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL)
} else {
for(i in 2:ncol(dat))
dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL)
}
for(i in 2:ncol(dat)) if(is.logical(dat[,i])) dat[,i] <- factor(dat[,i])
if(p > 2 & nameInStrip) {
strip_vars <- params[-(1:2)]
strip_lab <- subset(x$modelInfo$parameters, parameter %in% strip_vars)$label
for(i in seq_along(strip_vars))
dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": "))
}
## make formula
form <- if(p <= 2)
{
as.formula(
paste(metric, "~", params[1], sep = ""))
} else as.formula(paste(metric, "~", params[1], "|",
paste(params[-(1:2)], collapse = "*"),
sep = ""))
defaultArgs <- list(x = form,
data = dat,
groups = if(p > 1) dat[,params[2]] else NULL)
if(length(list(...)) > 0) defaultArgs <- c(defaultArgs, list(...))
lNames <- names(defaultArgs)
if(!("ylab" %in% lNames)) defaultArgs$ylab <- paste(metric, resampText)
if(!("type" %in% lNames) & plotType == "scatter") defaultArgs$type <- c("g", "o")
if(!("type" %in% lNames) & plotType == "line") defaultArgs$type <- c("g", "o")
if(p > 1)
{
## I apologize in advance for the following 3 line kludge.
groupCols <- 4
if(length(unique(dat[,3])) < 4) groupCols <- length(unique(dat[,3]))
if(length(unique(dat[,3])) %in% 5:6) groupCols <- 3
groupCols <- as.numeric(
cut(length(unique(dat[,3])),
cutpoints,
include.lowest = TRUE))
if(!(any(c("key", "auto.key") %in% lNames)))
defaultArgs$auto.key <- list(columns = groupCols,
lines = TRUE,
title = as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[2]],
cex.title = 1)
}
if(!("xlab" %in% lNames)) defaultArgs$xlab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[1]]
if(plotType == "scatter")
{
out <- do.call("xyplot", defaultArgs)
} else {
## line plot #########################
out <- do.call("stripplot", defaultArgs)
}
}
if(plotType == "level")
{
if(p == 1) stop("There must be at least 2 tuning parameters with multiple values")
for(i in 2:ncol(dat))
dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL)
if(p > 2 & nameInStrip) {
strip_vars <- params[-(1:2)]
strip_lab <- subset(x$modelInfo$parameters, parameter %in% strip_vars)$label
for(i in seq_along(strip_vars))
dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": "))
}
## make formula
form <- if(p <= 2)
{
as.formula(paste(metric, "~", params[1], "*", params[2], sep = ""))
} else as.formula(paste(metric, "~", params[1], "*", params[2], "|",
paste(params[-(1:2)], collapse = "*"),
sep = ""))
defaultArgs <- list(x = form, data = dat)
if(length(list(...)) > 0) defaultArgs <- c(defaultArgs, list(...))
lNames <- names(defaultArgs)
if(!("sub" %in% lNames)) defaultArgs$sub <- paste(metric, resampText)
if(!("xlab" %in% lNames)) defaultArgs$xlab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[1]]
if(!("ylab" %in% lNames)) defaultArgs$ylab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[2]]
out <- do.call("levelplot", defaultArgs)
}
} else stop(plotIt)
out
}
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#' Plot Method for the train Class
#'
#' This function takes the output of a \code{\link{train}} object and creates a
#' line or level plot using the \pkg{lattice} or \pkg{ggplot2} libraries.
#'
#' If there are no tuning parameters, or none were varied, an error is
#' produced.
#'
#' If the model has one tuning parameter with multiple candidate values, a plot
#' is produced showing the profile of the results over the parameter. Also, a
#' plot can be produced if there are multiple tuning parameters but only one is
#' varied.
#'
#' If there are two tuning parameters with different values, a plot can be
#' produced where a different line is shown for each value of of the other
#' parameter. For three parameters, the same line plot is created within
#' conditioning panels/facets of the other parameter.
#'
#' Also, with two tuning parameters (with different values), a levelplot (i.e.
#' un-clustered heatmap) can be created. For more than two parameters, this
#' plot is created inside conditioning panels/facets.
#'
#' @aliases plot.train ggplot.train
#' @param x an object of class \code{\link{train}}.
#' @param metric What measure of performance to plot. Examples of possible
#' values are "RMSE", "Rsquared", "Accuracy" or "Kappa". Other values can be
#' used depending on what metrics have been calculated.
#' @param plotType a string describing the type of plot (\code{"scatter"},
#' \code{"level"} or \code{"line"} (\code{plot} only))
#' @param digits an integer specifying the number of significant digits used to
#' label the parameter value.
#' @param xTrans a function that will be used to scale the x-axis in scatter
#' plots.
#' @param data an object of class \code{\link{train}}.
#' @param output either "data", "ggplot" or "layered". The first returns a data
#' frame while the second returns a simple \code{ggplot} object with no layers.
#' The third value returns a plot with a set of layers.
#' @param nameInStrip a logical: if there are more than 2 tuning parameters,
#' should the name and value be included in the panel title?
#' @param highlight a logical: if \code{TRUE}, a diamond is placed around the
#' optimal parameter setting for models using grid search.
#' @param mapping,environment unused arguments to make consistent with
#' \pkg{ggplot2} generic method
#' @param \dots \code{plot} only: specifications to be passed to
#' \code{\link[lattice]{levelplot}}, \code{\link[lattice]{xyplot}},
#' \code{\link[lattice:xyplot]{stripplot}} (for line plots). The function
#' automatically sets some arguments (e.g. axis labels) but passing in values
#' here will over-ride the defaults
#' @author Max Kuhn
#' @seealso \code{\link{train}}, \code{\link[lattice]{levelplot}},
#' \code{\link[lattice]{xyplot}}, \code{\link[lattice:xyplot]{stripplot}},
#' \code{\link[ggplot2]{ggplot}}
#' @references Kuhn (2008), ``Building Predictive Models in R Using the caret''
#' (\doi{10.18637/jss.v028.i05})
#' @keywords hplot
#' @method plot train
#' @export
#' @examples
#'
#'
#' \dontrun{
#' library(klaR)
#' rdaFit <- train(Species ~ .,
#' data = iris,
#' method = "rda",
#' control = trainControl(method = "cv"))
#' plot(rdaFit)
#' plot(rdaFit, plotType = "level")
#'
#' ggplot(rdaFit) + theme_bw()
#'
#' }
#'
#' @export plot.train
"plot.train" <- function(x,
plotType = "scatter",
metric = x$metric[1],
digits = getOption("digits") - 3,
xTrans = NULL,
nameInStrip = FALSE,
...)
{
## Error trap
if(!(plotType %in% c("level", "scatter", "line"))) stop("plotType must be either level, scatter or line")
cutpoints <- c(0, 1.9, 2.9, 3.9, Inf)
## define some functions
prettyVal <- function(u, dig, Name = NULL)
{
if(is.numeric(u))
{
if(!is.null(Name)) u <- paste(gsub(".", " ", Name, fixed = TRUE),
": ",
format(u, digits = dig), sep = "")
return(factor(u))
} else return(if(!is.factor(u)) as.factor(u) else u)
}
## Get tuning parameter info
params <- as.character(x$modelInfo$parameters$parameter)
if(grepl("adapt", x$control$method))
warning("When using adaptive resampling, this plot may not accurately capture the relationship between the tuning parameters and model performance.")
plotIt <- "yes"
if(all(params == "parameter"))
{
plotIt <- "There are no tuning parameters for this model."
} else {
dat <- x$results
## Some exceptions for pretty printing
if(x$method == "nb") dat$usekernel <- factor(ifelse(dat$usekernel, "Nonparametric", "Gaussian"))
if(x$method == "gam") dat$select <- factor(ifelse(dat$select, "Feature Selection", "No Feature Selection"))
if(x$method == "qrnn") dat$bag <- factor(ifelse(dat$bag, "Bagging", "No Bagging"))
if(x$method == "C5.0") dat$winnow <- factor(ifelse(dat$winnow, "Winnowing", "No Winnowing"))
## if(x$method %in% c("M5Rules", "M5", "PART")) dat$pruned <- factor(ifelse(dat$pruned == "Yes", "Pruned", "Unpruned"))
## if(x$method %in% c("M5Rules", "M5")) dat$smoothed <- factor(ifelse(dat$smoothed == "Yes", "Smoothed", "Unsmoothed"))
if(x$method == "M5") dat$rules <- factor(ifelse(dat$rules == "Yes", "Rules", "Trees"))
## if(x$method == "vbmpRadial") dat$estimateTheta <- factor(ifelse(dat$estimateTheta == "Yes", "Estimate Theta", "Do Not Estimate Theta"))
## Check to see which tuning parameters were varied
paramValues <- apply(dat[,params,drop = FALSE],
2,
function(x) length(unique(x)))
##paramValues <- paramValues[order(paramValues)]
if(any(paramValues > 1))
{
params <- names(paramValues)[paramValues > 1]
} else plotIt <- "There are no tuning parameters with more than 1 value."
}
if(plotIt == "yes")
{
p <- length(params)
dat <- dat[, c(metric, params)]
if(p > 1) {
numUnique <- unlist(lapply(dat[, -1], function(x) length(unique(x))))
numUnique <- sort(numUnique, decreasing = TRUE)
dat <- dat[, c(metric, names(numUnique))]
params <- names(numUnique)
}
## The conveintion is that the first parameter (in
## position #2 of dat) is plotted on the x-axis,
## the second parameter is the grouping variable
## and the rest are conditioning variables
if(!is.null(xTrans) & plotType == "scatter") dat[,2] <- xTrans(dat[,2])
## We need to pretty-up some of the values of grouping
## or conditioning variables
resampText <- resampName(x, FALSE)
if(plotType %in% c("line", "scatter"))
{
if(plotType == "scatter")
{
if(p >= 2) for(i in 3:ncol(dat))
dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL)
} else {
for(i in 2:ncol(dat))
dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL)
}
for(i in 2:ncol(dat)) if(is.logical(dat[,i])) dat[,i] <- factor(dat[,i])
if(p > 2 & nameInStrip) {
strip_vars <- params[-(1:2)]
strip_lab <- subset(x$modelInfo$parameters, parameter %in% strip_vars)$label
for(i in seq_along(strip_vars))
dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": "))
}
## make formula
form <- if(p <= 2)
{
as.formula(
paste(metric, "~", params[1], sep = ""))
} else as.formula(paste(metric, "~", params[1], "|",
paste(params[-(1:2)], collapse = "*"),
sep = ""))
defaultArgs <- list(x = form,
data = dat,
groups = if(p > 1) dat[,params[2]] else NULL)
if(length(list(...)) > 0) defaultArgs <- c(defaultArgs, list(...))
lNames <- names(defaultArgs)
if(!("ylab" %in% lNames)) defaultArgs$ylab <- paste(metric, resampText)
if(!("type" %in% lNames) & plotType == "scatter") defaultArgs$type <- c("g", "o")
if(!("type" %in% lNames) & plotType == "line") defaultArgs$type <- c("g", "o")
if(p > 1)
{
## I apologize in advance for the following 3 line kludge.
groupCols <- 4
if(length(unique(dat[,3])) < 4) groupCols <- length(unique(dat[,3]))
if(length(unique(dat[,3])) %in% 5:6) groupCols <- 3
groupCols <- as.numeric(
cut(length(unique(dat[,3])),
cutpoints,
include.lowest = TRUE))
if(!(any(c("key", "auto.key") %in% lNames)))
defaultArgs$auto.key <- list(columns = groupCols,
lines = TRUE,
title = as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[2]],
cex.title = 1)
}
if(!("xlab" %in% lNames)) defaultArgs$xlab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[1]]
if(plotType == "scatter")
{
out <- do.call("xyplot", defaultArgs)
} else {
## line plot #########################
out <- do.call("stripplot", defaultArgs)
}
}
if(plotType == "level")
{
if(p == 1) stop("There must be at least 2 tuning parameters with multiple values")
for(i in 2:ncol(dat))
dat[,i] <- prettyVal(dat[,i], dig = digits, Name = if(i > 3) params[i-1] else NULL)
if(p > 2 & nameInStrip) {
strip_vars <- params[-(1:2)]
strip_lab <- subset(x$modelInfo$parameters, parameter %in% strip_vars)$label
for(i in seq_along(strip_vars))
dat[, strip_vars[i]] <- factor(paste(strip_lab[i], dat[, strip_vars[i]], sep = ": "))
}
## make formula
form <- if(p <= 2)
{
as.formula(paste(metric, "~", params[1], "*", params[2], sep = ""))
} else as.formula(paste(metric, "~", params[1], "*", params[2], "|",
paste(params[-(1:2)], collapse = "*"),
sep = ""))
defaultArgs <- list(x = form, data = dat)
if(length(list(...)) > 0) defaultArgs <- c(defaultArgs, list(...))
lNames <- names(defaultArgs)
if(!("sub" %in% lNames)) defaultArgs$sub <- paste(metric, resampText)
if(!("xlab" %in% lNames)) defaultArgs$xlab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[1]]
if(!("ylab" %in% lNames)) defaultArgs$ylab <- as.character(x$modelInfo$parameter$label)[x$modelInfo$parameter$parameter == params[2]]
out <- do.call("levelplot", defaultArgs)
}
} else stop(plotIt)
out
}
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