R/generatePartialDependence.R
In berndbischl/mlr: Machine Learning in R
Defines functions
plotPartialDependence
print.PartialDependenceData
numDerivWrapper
doDerivativeMarginalPrediction
generatePartialDependenceData
Documented in
generatePartialDependenceData
plotPartialDependence
#' @title Generate partial dependence.
#' @importFrom data.table data.table melt
#'
#' @description
#' Estimate how the learned prediction function is affected by one or more features.
#' For a learned function f(x) where x is partitioned into x_s and x_c, the partial dependence of
#' f on x_s can be summarized by averaging over x_c and setting x_s to a range of values of interest,
#' estimating E_(x_c)(f(x_s, x_c)). The conditional expectation of f at observation i is estimated similarly.
#' Additionally, partial derivatives of the marginalized function w.r.t. the features can be computed.
#'
#' @family partial_dependence
#' @family generate_plot_data
#' @aliases PartialDependenceData
#'
#' @param obj ([WrappedModel])\cr
#' Result of [train].
#' @param input ([data.frame] | [Task])\cr
#' Input data.
#' @param features [character]\cr
#' A vector of feature names contained in the training data.
#' If not specified all features in the `input` will be used.
#' @param interaction (`logical(1)`)\cr
#' Whether the `features` should be interacted or not. If `TRUE` then the Cartesian product of the
#' prediction grid for each feature is taken, and the partial dependence at each unique combination of
#' values of the features is estimated. Note that if the length of `features` is greater than two,
#' [plotPartialDependence] cannot be used.
#' If `FALSE` each feature is considered separately. In this case `features` can be much longer
#' than two.
#' Default is `FALSE`.
#' @param derivative (`logical(1)`)\cr
#' Whether or not the partial derivative of the learned function with respect to the features should be
#' estimated. If `TRUE` `interaction` must be `FALSE`. The partial derivative of individual
#' observations may be estimated. Note that computation time increases as the learned prediction function
#' is evaluated at `gridsize` points * the number of points required to estimate the partial derivative.
#' Additional arguments may be passed to [numDeriv::grad] (for regression or survival tasks) or
#' [numDeriv::jacobian] (for classification tasks). Note that functions which are not smooth may
#' result in estimated derivatives of 0 (for points where the function does not change within +/- epsilon)
#' or estimates trending towards +/- infinity (at discontinuities).
#' Default is `FALSE`.
#' @param individual (`logical(1)`)\cr
#' Whether to plot the individual conditional expectation curves rather than the aggregated curve, i.e.,
#' rather than aggregating (using `fun`) the partial dependences of `features`, plot the
#' partial dependences of all observations in `data` across all values of the `features`.
#' The algorithm is developed in Goldstein, Kapelner, Bleich, and Pitkin (2015).
#' Default is `FALSE`.
#' @param fun `function`\cr
#'
#' A function which operates on the output on the predictions made on the `input` data. For regression
#' this means a numeric vector, and, e.g., for a multiclass classification problem, this migh instead be probabilities
#' which are returned as a numeric matrix. This argument can return vectors of arbitrary length, however,
#' if their length is greater than one, they must by named, e.g., `fun = mean` or
#' `fun = function(x) c("mean" = mean(x), "variance" = var(x))`.
#' The default is the mean, unless `obj` is classification with `predict.type = "response"`
#' in which case the default is the proportion of observations predicted to be in each class.
#' @param bounds (`numeric(2)`)\cr
#' The value (lower, upper) the estimated standard error is multiplied by to estimate the bound on a
#' confidence region for a partial dependence. Ignored if `predict.type != "se"` for the learner.
#' Default is the 2.5 and 97.5 quantiles (-1.96, 1.96) of the Gaussian distribution.
#' @param uniform (`logical(1)`)\cr
#' Whether or not the prediction grid for the `features` is a uniform grid of size `n[1]` or sampled with
#' replacement from the `input`.
#' Default is `TRUE`.
#' @param n (`integer21`)\cr
#' The first element of `n` gives the size of the prediction grid created for each feature.
#' The second element of `n` gives the size of the sample to be drawn without replacement from the `input` data.
#' Setting `n[2]` less than the number of rows in the `input` will decrease computation time.
#' The default for `n[1]` is 10, and the default for `n[2]` is the number of rows in the `input`.
#' @param ... additional arguments to be passed to [mmpf::marginalPrediction].
#' @return [PartialDependenceData]. A named list, which contains the partial dependence,
#' input data, target, features, task description, and other arguments controlling the type of
#' partial dependences made.
#'
#' Object members:
#' \item{data}{[data.frame]\cr
#' Has columns for the prediction: one column for regression and
#' survival analysis, and a column for class and the predicted probability for classification as well
#' as a a column for each element of `features`. If `individual = TRUE` then there is an
#' additional column `idx` which gives the index of the `data` that each prediction corresponds to.}
#' \item{task.desc}{[TaskDesc]\cr
#' Task description.}
#' \item{target}{Target feature for regression, target feature levels for classification,
#' survival and event indicator for survival.}
#' \item{features}{[character]\cr
#' Features argument input.}
#' \item{interaction}{(`logical(1)`)\cr
#' Whether or not the features were interacted (i.e. conditioning).}
#' \item{derivative}{(`logical(1)`)\cr
#' Whether or not the partial derivative was estimated.}
#' \item{individual}{(`logical(1)`)\cr
#' Whether the partial dependences were aggregated or the individual curves are retained.}
#' @references
#' Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. \dQuote{Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation.} Journal of Computational and Graphical Statistics. Vol. 24, No. 1 (2015): 44-65.
#'
#' Friedman, Jerome. \dQuote{Greedy Function Approximation: A Gradient Boosting Machine.} The Annals of Statistics. Vol. 29. No. 5 (2001): 1189-1232.
#' @examples
#' lrn = makeLearner("regr.svm")
#' fit = train(lrn, bh.task)
#' pd = generatePartialDependenceData(fit, bh.task, "lstat")
#' plotPartialDependence(pd, data = getTaskData(bh.task))
#'
#' lrn = makeLearner("classif.rpart", predict.type = "prob")
#' fit = train(lrn, iris.task)
#' pd = generatePartialDependenceData(fit, iris.task, "Petal.Width")
#' plotPartialDependence(pd, data = getTaskData(iris.task))
#' @export
generatePartialDependenceData = function(obj, input, features = NULL,
interaction = FALSE, derivative = FALSE, individual = FALSE,
fun = mean, bounds = c(qnorm(.025), qnorm(.975)),
uniform = TRUE, n = c(10, NA), ...) {
requirePackages("mmpf")
assertClass(obj, "WrappedModel")
if (obj$learner$predict.type == "se" & individual) {
stop("individual = TRUE not compatabile with predict.type = 'se'!")
}
if (obj$learner$predict.type == "se" & derivative) {
stop("derivative = TRUE is not compatible with predict.type = 'se'!")
}
if (!inherits(input, c("Task", "data.frame"))) {
stop("input must be a Task or a data.frame!")
}
if (inherits(input, "Task")) {
data = getTaskData(input)
td = input$task.desc
} else {
data = input
td = obj$task.desc
assertDataFrame(data, col.names = "unique", min.rows = 1L, min.cols = length(obj$features) + length(td$target))
assertSetEqual(colnames(data), c(obj$features, td$target), ordered = FALSE)
}
if (is.na(n[2])) {
n[2] = nrow(data)
}
if (is.null(features)) {
features = colnames(data)[!colnames(data) %in% td$target]
} else {
assertSubset(features, obj$features)
}
assertFlag(interaction)
assertFlag(derivative)
if (derivative & interaction) {
stop("interaction cannot be TRUE if derivative is TRUE.")
}
if (derivative) {
if (any(sapply(data[, features, drop = FALSE], class) %in% c("factor", "ordered", "character"))) {
stop("All features must be numeric to estimate set derivative = TRUE!")
}
}
se = Function = Class = patterns = NULL # nolint
assertFlag(individual)
if (individual) {
fun = identity
}
assertFunction(fun)
test.fun = fun(1:3)
if (length(test.fun) == 1L) {
multi.fun = FALSE
} else {
multi.fun = TRUE
if (is.null(names(test.fun)) & !individual) {
stop("If fun returns a vector it must be named.")
}
}
assertNumeric(bounds, len = 2L)
assertNumber(bounds[1], upper = 0)
assertNumber(bounds[2], lower = 0)
assertFlag(uniform)
assertCount(n[1], positive = TRUE)
assertCount(n[2], positive = TRUE)
if (n[2] > nrow(data)) {
stop("The number of points taken from the training data cannot exceed the number of training data points.")
}
if (td$type == "regr") {
target = td$target
} else if (td$type == "classif") {
if (length(td$class.levels) > 2L) {
target = td$class.levels
} else {
target = td$positive
}
} else {
target = "Risk"
}
if (!derivative) {
args = list(model = obj, data = data, uniform = uniform, aggregate.fun = fun,
predict.fun = getPrediction, n = n, ...)
out = parallelMap(mmpf::marginalPrediction,
vars = if (interaction) list(features) else as.list(features), more.args = args)
if (length(target) == 1L) {
out = lapply(out, function(x) {
feature = features[features %in% names(x)]
names(x) = stri_replace_all(names(x), target, regex = "^preds")
x = data.table(x)
if (individual) {
x = melt(x, id.vars = feature, variable.name = "n", value.name = target)
x[, n := stri_replace(n, "", regex = target)]
setnames(x, c(feature, if (individual) "n" else "Function", target), names(x))
} else {
x
}
})
}
} else {
points = lapply(features, function(x) mmpf::uniformGrid(data[[x]], n[1]))
names(points) = features
args = list(obj = obj, data = data, uniform = uniform, fun = fun,
n = n, points = points, target = target, individual = individual, ...)
if (individual) {
int.points = sample(seq_len(nrow(data)), n[2])
out = parallelMap(doDerivativeMarginalPrediction, x = features,
z = int.points, more.args = args)
} else {
out = parallelMap(doDerivativeMarginalPrediction, x = features, more.args = args)
}
}
out = rbindlist(out, fill = TRUE, use.names = TRUE)
if (length(target) == 1L) {
if (!multi.fun) {
setcolorder(out, c(names(out)[grepl(paste(target, "preds", sep = "|"),
names(out))], if (obj$learner$predict.type == "se") "se" else NULL, features))
setnames(out, names(out), c(target, if (obj$learner$predict.type == "se") "se" else NULL, features))
} else if (individual) {
setcolorder(out, c(target, "n", features))
} else {
setnames(out, names(out), stri_replace_all_fixed(names(out), "preds", ""))
out = melt(as.data.table(out), id.vars = features, variable.name = "Function",
value.name = target)
setcolorder(out, c(target, "Function", features))
}
} else {
if (!multi.fun) {
out = melt(as.data.table(out), measure.vars = target,
variable.name = if (td$type == "classif") "Class" else "Function",
value.name = if (td$type == "classif") "Probability" else "Prediction")
if (td$type == "classif") {
out[, Class := stri_replace_all_regex(Class, "^prob\\.", "")]
setcolorder(out, c("Class",
if (td$type == "classif") "Probability" else "Prediction", features))
} else {
out[, Function := stri_replace_all_regex(target, "^preds\\.", "")]
setcolorder(out, c("Function",
if (td$type == "classif") "Probability" else "Prediction", features))
}
} else if (individual) {
if (!derivative) {
out = melt(as.data.table(out), measure = patterns(target), variable.name = "n",
value.name = target)
}
out = melt(as.data.table(out), measure.vars = target,
variable.name = if (td$type == "classif") "Class" else "Target",
value.name = if (td$type == "classif") "Probability" else "Prediction")
setcolorder(out, c(if (td$type == "classif") "Class" else "Target",
if (td$type == "classif") "Probability" else "Prediction", "n", features))
} else {
out = melt(as.data.table(out), id.vars = c(features, if (individual) "n"),
variable.name = if (td$type == "classif") "Class" else "Function",
value.name = if (td$type == "classif") "Probability" else "Prediction")
if (td$type == "classif") {
x = stri_split_regex(out$Class, "\\.", n = 2, simplify = TRUE)
## checking to see if there is detritus, e.g., preds.class or something
id = apply(x, 2, function(z) length(unique(z)) > 1L)
if (!all(id)) {
out[, "Class"] = x[, id]
setcolorder(out, c("Class", "Probability", features))
} else {
out[, c("Class", "Function") := lapply(1:2, function(i) x[, i])]
out[, Function := stri_replace_all_regex(Function, "^preds\\.", "")]
setcolorder(out, c("Class", "Function", "Probability", features))
}
} else {
out[, Function := stri_replace_all_regex(Function, "^preds\\.", "")]
setcolorder(out, c("Class", "Function", "Prediction", features))
}
}
}
# for se, compute upper and lower bounds
if (obj$learner$predict.type == "se") {
x = outer(out$se, bounds) + out[[target]]
out[, c("lower", "upper") := lapply(1:2, function(i) x[, i])]
out[, se := NULL]
target = c("lower", target, "upper")
setcolorder(out, c(target, features))
}
colnames(out) = make.names(colnames(out))
features = make.names(features)
target = make.names(features)
makeS3Obj("PartialDependenceData",
data = out,
task.desc = td,
target = target,
features = features,
derivative = derivative,
interaction = interaction,
individual = individual)
}
## second layer wrapper for numDeriv grad and jacobian use with marginal prediction
doDerivativeMarginalPrediction = function(x, z = sample(seq_len(nrow(data)), n[2]),
target, points, obj, data, uniform, fun, n, individual, ...) {
requirePackages("numDeriv", why = "PartialDependenceData", default.method = "load")
if (length(target) == 1L) {
ret = cbind(numDeriv::grad(numDerivWrapper,
x = points[[x]], model = obj, data = data,
uniform = uniform, aggregate.fun = fun, vars = x,
int.points = z,
predict.fun = getPrediction, n = n, target = target,
individual = individual, ...),
points[[x]], if (individual) z)
} else {
out = lapply(points[[x]], function(x.value) {
t(numDeriv::jacobian(numDerivWrapper, x = x.value, model = obj, data = data,
uniform = uniform, aggregate.fun = fun, vars = x, int.points = z,
predict.fun = getPrediction, n = n, target = target,
individual = individual, ...))
})
out = do.call("rbind", out)
ret = cbind(out, points[[x]], if (individual) z)
}
ret = as.data.table(ret)
setnames(ret, names(ret), c(target, x, if (individual) "n"))
ret
}
# grad and jacobian both need to take a vector along with ...
# and they return either a vector or a matrix
# so i need to pass the points as that x, and then extract the appropriate
# vector or matrix from marginalPrediction
numDerivWrapper = function(points, vars, individual, target, ...) {
args = list(...)
args$points = list(points)
names(args$points) = vars
args$vars = vars
out = do.call(mmpf::marginalPrediction, args)
as.matrix(out[, which(names(out) != vars), with = FALSE])
}
#' @export
print.PartialDependenceData = function(x, ...) {
catf("PartialDependenceData")
catf("Task: %s", x$task.desc$id)
catf("Features: %s", stri_paste(x$features, collapse = ", ", sep = " "))
catf("Target: %s", stri_paste(x$target, collapse = ", ", sep = " "))
catf("Derivative: %s", x$derivative)
catf("Interaction: %s", x$interaction)
catf("Individual: %s", x$individual)
printHead(x$data, ...)
}
#' @title Plot a partial dependence with ggplot2.
#' @description
#' Plot a partial dependence from [generatePartialDependenceData] using ggplot2.
#'
#' @family partial_dependence
#' @family plot
#'
#' @param obj [PartialDependenceData]\cr
#' Generated by [generatePartialDependenceData].
#' @param geom (`charater(1)`)\cr
#' The type of geom to use to display the data. Can be \dQuote{line} or \dQuote{tile}.
#' For tiling at least two features must be used with `interaction = TRUE` in the call to
#' [generatePartialDependenceData]. This may be used in conjuction with the
#' `facet` argument if three features are specified in the call to
#' [generatePartialDependenceData].
#' Default is \dQuote{line}.
#' @param facet (`character(1)`)\cr
#' The name of a feature to be used for facetting.
#' This feature must have been an element of the `features` argument to
#' [generatePartialDependenceData] and is only applicable when said argument had length
#' greater than 1.
#' The feature must be a factor or an integer.
#' If [generatePartialDependenceData] is called with the `interaction` argument `FALSE`
#' (the default) with argument `features` of length greater than one, then `facet` is ignored and
#' each feature is plotted in its own facet.
#' Default is `NULL`.
#' @template arg_facet_nrow_ncol
#' @param p (`numeric(1)`)\cr
#' If `individual = TRUE` then `sample` allows the user to sample without replacement
#' from the output to make the display more readable. Each row is sampled with probability `p`.
#' Default is `1`.
#' @param data ([data.frame])\cr
#' Data points to plot. Usually the training data. For survival and binary classification tasks a rug plot
#' wherein ticks represent failures or instances of the positive class are shown. For regression tasks
#' points are shown. For multiclass classification tasks ticks are shown and colored according to their class.
#' Both the features and the target must be included.
#' Default is `NULL`.
#' @template ret_gg2
#' @export
plotPartialDependence = function(obj, geom = "line", facet = NULL, facet.wrap.nrow = NULL,
facet.wrap.ncol = NULL, p = 1, data = NULL) {
assertClass(obj, "PartialDependenceData")
assertChoice(geom, c("tile", "line"))
if (obj$interaction & length(obj$features) > 2L & geom != "tile") {
stop("Cannot plot more than 2 features together with line plots.")
}
if (geom == "tile") {
if (!obj$interaction) {
stop("obj argument created by generatePartialDependenceData was called with interaction = FALSE!")
}
}
if (!is.null(data)) {
assertDataFrame(data, col.names = "unique", min.rows = 1L,
min.cols = length(obj$features) + length(obj$td$target))
assertSubset(obj$features, colnames(data), empty.ok = FALSE)
}
if (!is.null(facet)) {
assertChoice(facet, obj$features)
if (!length(obj$features) %in% 2:3) {
stop("obj argument created by generatePartialDependenceData must be called with two or three features to use this argument!")
}
if (!obj$interaction) {
stop("obj argument created by generatePartialDependenceData must be called with interaction = TRUE to use this argument!")
}
features = obj$features[which(obj$features != facet)]
if (is.factor(obj$data[[facet]])) {
obj$data[[facet]] = stri_paste(facet, "=", obj$data[[facet]], sep = " ")
} else if (is.character(obj$data[[facet]])) {
obj$data[[facet]] = stri_paste(facet, "=", as.factor(obj$data[[facet]]), sep = " ")
} else if (is.numeric(obj$data[[facet]])) {
obj$data[[facet]] = stri_paste(facet, "=", as.factor(signif(obj$data[[facet]], 3L)), sep = " ")
} else {
stop("Invalid input to facet arg. Must refer to a numeric/integer, character, or facet feature.")
}
scales = "fixed"
} else {
features = obj$features
if (length(features) > 1L & !(length(features) == 2L & geom == "tile")) {
facet = "Feature"
scales = "free_x"
} else {
scales = "fixed"
}
}
# detect if there was a multi-output function used in which case
# there should be a column named function which needs to be facetted
if ("Function" %in% colnames(obj$data)) {
facet = c(facet, "Function")
}
# sample from individual partial dependence estimates
if (p != 1) {
assertNumber(p, lower = 0, upper = 1, finite = TRUE)
if (!obj$individual) {
stop("obj argument created by generatePartialDependenceData must be called with individual = TRUE to use this argument!")
}
rows = unique(obj$data$idx)
id = sample(rows, size = floor(p * length(rows)))
obj$data = obj$data[which(obj$data$idx %in% id), ]
}
if (obj$task.desc$type %in% c("regr", "classif")) {
if (obj$task.desc$type == "classif" && length(obj$task.desc$class.levels) <= 2L) {
target = obj$task.desc$positive
} else {
target = obj$task.desc$target
}
} else {
target = "Risk"
}
# are there bounds compatible with a ribbon plot?
bounds = all(c("lower", "upper") %in% colnames(obj$data) & obj$task.desc$type %in% c("surv", "regr") &
length(features) < 3L & geom == "line")
if (geom == "line") {
# find factors and cast them to numerics so that we can melt
idx = which(sapply(obj$data, class) == "factor" & colnames(obj$data) %in% features)
# explicit casting previously done implicitly by reshape2::melt.data.frame
for (id in idx) obj$data[, id] = as.numeric(obj$data[[id]])
# melt the features but leave everything else alone
obj$data = setDF(melt(data.table(obj$data),
id.vars = colnames(obj$data)[!colnames(obj$data) %in% features],
variable = "Feature", value.name = "Value", na.rm = TRUE, variable.factor = TRUE))
# when individual is false plot variable value against the target
if (!obj$individual) {
# for regression/survival this is a simple line plot
if (obj$task.desc$type %in% c("regr", "surv") |
(obj$task.desc$type == "classif" & length(obj$task.desc$class.levels) <= 2L)) {
plt = ggplot(obj$data, aes_string("Value", target)) +
geom_line(color = ifelse(is.null(data), "black", "red")) + geom_point()
} else { # for classification create different colored lines
plt = ggplot(obj$data, aes_string("Value", "Probability", group = "Class", color = "Class")) +
geom_line() + geom_point()
}
} else { # if individual is true make the lines semi-transparent
if (obj$task.desc$type %in% c("regr", "surv") |
(obj$task.desc$type == "classif" & length(obj$task.desc$class.levels) <= 2L)) {
plt = ggplot(obj$data, aes_string("Value", target, group = "n")) +
geom_line(alpha = .25, color = ifelse(is.null(data), "black", "red")) + geom_point()
} else {
plt = ggplot(obj$data, aes_string("Value", "Probability", group = "idx", color = "Class")) +
geom_line(alpha = .25) + geom_point()
}
}
# if there is only one feature than melting was redundant (but cleaner code)
# so rename the x-axis using the feature name. rename target only if it was a vector
# since in this case the target name isn't passed through
if (length(features) == 1L) {
if (obj$task.desc$type %in% c("regr", "surv")) {
plt = plt + labs(x = features, y = target)
} else {
plt = plt + labs(x = features)
}
}
# ribbon bounds from se estimation
if (bounds) {
plt = plt + geom_ribbon(aes_string(ymin = "lower", ymax = "upper"), alpha = .5)
}
# labels added to for derivative plots
if (obj$derivative) {
plt = plt + ylab(stri_paste(target, "(derivative)", sep = " "))
}
} else { ## tiling
if (obj$task.desc$type == "classif") {
target = "Probability"
facet = "Class"
if ("Function" %in% obj$data) {
facet = c(facet, "Function")
}
scales = "free"
}
plt = ggplot(obj$data, aes_string(x = features[1], y = features[2], fill = target))
plt = plt + geom_raster(aes_string(fill = target))
# labels for ICE plots
if (obj$derivative) {
plt = plt + scale_fill_continuous(guide = guide_colorbar(title = stri_paste(target, "(derivative)", sep = " ")))
}
}
# facetting which is either passed in by the user, the features column when interaction = FALSE and length(features) > 1
# and/or when fun outputs a vector (then facetting on the Function column)
if (!is.null(facet)) {
if (length(facet) == 1L) {
plt = plt + facet_wrap(as.formula(stri_paste("~", facet)), scales = scales,
nrow = facet.wrap.nrow, ncol = facet.wrap.ncol)
} else {
plt = plt + facet_wrap(as.formula(stri_paste(facet[2], "~", facet[1])), scales = scales,
nrow = facet.wrap.nrow, ncol = facet.wrap.ncol)
} # facet ordering is reversed deliberately to handle len = 1 case!
}
# data overplotting
if (!is.null(data)) {
data = data[, colnames(data) %in% c(obj$features, obj$task.desc$target)]
if (!is.null(facet)) {
feature.facet = facet[facet %in% obj$features]
fun.facet = facet[!facet %in% feature.facet]
if (length(fun.facet) > 0L && (fun.facet == "Feature" || !feature.facet %in% obj$features)) {
data = melt(as.data.table(data), id.vars = c(obj$task.desc$target, feature.facet),
variable = "Feature", value.name = "Value", na.rm = TRUE, variable.factor = TRUE)
}
if (length(feature.facet) > 0) {
if (!is.factor(data[[feature.facet]])) {
data[[feature.facet]] = stri_paste(feature.facet, "=", as.factor(signif(data[[feature.facet]], 2)), sep = " ")
} else {
data[[feature.facet]] = stri_paste(feature.facet, "=", data[[feature.facet]], sep = " ")
}
}
if (length(fun.facet) > 0L && "Function" %in% fun.facet) {
data = mmpf::cartesianExpand(data, data.frame("Function" = unique(obj$data$Function)))
}
}
if (geom == "line") {
if (obj$task.desc$type %in% c("classif", "surv")) {
if (obj$task.desc$type == "classif") {
if (!is.na(obj$task.desc$positive)) {
plt = plt + geom_rug(aes_string(plt$labels$x, color = obj$task.desc$target),
data[data[[obj$task.desc$target]] == obj$task.desc$positive, ],
alpha = .25, inherit.aes = FALSE)
} else {
plt = plt + geom_rug(aes_string(plt$labels$x), data, alpha = .25, inherit.aes = FALSE)
}
} else {
plt = plt + geom_rug(aes_string(plt$labels$x),
data[data[[obj$task.desc$target[2]]], ],
alpha = .25, inherit.aes = FALSE)
}
} else {
plt = plt + geom_point(aes_string(plt$labels$x, obj$task.desc$target),
data, alpha = .25, inherit.aes = FALSE)
}
} else {
plt = plt + geom_point(aes_string(plt$labels$x, plt$labels$y), data, alpha = .25, inherit.aes = FALSE)
}
}
plt
}
berndbischl/mlr documentation built on Jan. 6, 2023, 12:45 p.m.
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Defines functions plotPartialDependence print.PartialDependenceData numDerivWrapper doDerivativeMarginalPrediction generatePartialDependenceData
Documented in generatePartialDependenceData plotPartialDependence
#' @title Generate partial dependence.
#' @importFrom data.table data.table melt
#'
#' @description
#' Estimate how the learned prediction function is affected by one or more features.
#' For a learned function f(x) where x is partitioned into x_s and x_c, the partial dependence of
#' f on x_s can be summarized by averaging over x_c and setting x_s to a range of values of interest,
#' estimating E_(x_c)(f(x_s, x_c)). The conditional expectation of f at observation i is estimated similarly.
#' Additionally, partial derivatives of the marginalized function w.r.t. the features can be computed.
#'
#' @family partial_dependence
#' @family generate_plot_data
#' @aliases PartialDependenceData
#'
#' @param obj ([WrappedModel])\cr
#' Result of [train].
#' @param input ([data.frame] | [Task])\cr
#' Input data.
#' @param features [character]\cr
#' A vector of feature names contained in the training data.
#' If not specified all features in the `input` will be used.
#' @param interaction (`logical(1)`)\cr
#' Whether the `features` should be interacted or not. If `TRUE` then the Cartesian product of the
#' prediction grid for each feature is taken, and the partial dependence at each unique combination of
#' values of the features is estimated. Note that if the length of `features` is greater than two,
#' [plotPartialDependence] cannot be used.
#' If `FALSE` each feature is considered separately. In this case `features` can be much longer
#' than two.
#' Default is `FALSE`.
#' @param derivative (`logical(1)`)\cr
#' Whether or not the partial derivative of the learned function with respect to the features should be
#' estimated. If `TRUE` `interaction` must be `FALSE`. The partial derivative of individual
#' observations may be estimated. Note that computation time increases as the learned prediction function
#' is evaluated at `gridsize` points * the number of points required to estimate the partial derivative.
#' Additional arguments may be passed to [numDeriv::grad] (for regression or survival tasks) or
#' [numDeriv::jacobian] (for classification tasks). Note that functions which are not smooth may
#' result in estimated derivatives of 0 (for points where the function does not change within +/- epsilon)
#' or estimates trending towards +/- infinity (at discontinuities).
#' Default is `FALSE`.
#' @param individual (`logical(1)`)\cr
#' Whether to plot the individual conditional expectation curves rather than the aggregated curve, i.e.,
#' rather than aggregating (using `fun`) the partial dependences of `features`, plot the
#' partial dependences of all observations in `data` across all values of the `features`.
#' The algorithm is developed in Goldstein, Kapelner, Bleich, and Pitkin (2015).
#' Default is `FALSE`.
#' @param fun `function`\cr
#'
#' A function which operates on the output on the predictions made on the `input` data. For regression
#' this means a numeric vector, and, e.g., for a multiclass classification problem, this migh instead be probabilities
#' which are returned as a numeric matrix. This argument can return vectors of arbitrary length, however,
#' if their length is greater than one, they must by named, e.g., `fun = mean` or
#' `fun = function(x) c("mean" = mean(x), "variance" = var(x))`.
#' The default is the mean, unless `obj` is classification with `predict.type = "response"`
#' in which case the default is the proportion of observations predicted to be in each class.
#' @param bounds (`numeric(2)`)\cr
#' The value (lower, upper) the estimated standard error is multiplied by to estimate the bound on a
#' confidence region for a partial dependence. Ignored if `predict.type != "se"` for the learner.
#' Default is the 2.5 and 97.5 quantiles (-1.96, 1.96) of the Gaussian distribution.
#' @param uniform (`logical(1)`)\cr
#' Whether or not the prediction grid for the `features` is a uniform grid of size `n[1]` or sampled with
#' replacement from the `input`.
#' Default is `TRUE`.
#' @param n (`integer21`)\cr
#' The first element of `n` gives the size of the prediction grid created for each feature.
#' The second element of `n` gives the size of the sample to be drawn without replacement from the `input` data.
#' Setting `n[2]` less than the number of rows in the `input` will decrease computation time.
#' The default for `n[1]` is 10, and the default for `n[2]` is the number of rows in the `input`.
#' @param ... additional arguments to be passed to [mmpf::marginalPrediction].
#' @return [PartialDependenceData]. A named list, which contains the partial dependence,
#' input data, target, features, task description, and other arguments controlling the type of
#' partial dependences made.
#'
#' Object members:
#' \item{data}{[data.frame]\cr
#' Has columns for the prediction: one column for regression and
#' survival analysis, and a column for class and the predicted probability for classification as well
#' as a a column for each element of `features`. If `individual = TRUE` then there is an
#' additional column `idx` which gives the index of the `data` that each prediction corresponds to.}
#' \item{task.desc}{[TaskDesc]\cr
#' Task description.}
#' \item{target}{Target feature for regression, target feature levels for classification,
#' survival and event indicator for survival.}
#' \item{features}{[character]\cr
#' Features argument input.}
#' \item{interaction}{(`logical(1)`)\cr
#' Whether or not the features were interacted (i.e. conditioning).}
#' \item{derivative}{(`logical(1)`)\cr
#' Whether or not the partial derivative was estimated.}
#' \item{individual}{(`logical(1)`)\cr
#' Whether the partial dependences were aggregated or the individual curves are retained.}
#' @references
#' Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. \dQuote{Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation.} Journal of Computational and Graphical Statistics. Vol. 24, No. 1 (2015): 44-65.
#'
#' Friedman, Jerome. \dQuote{Greedy Function Approximation: A Gradient Boosting Machine.} The Annals of Statistics. Vol. 29. No. 5 (2001): 1189-1232.
#' @examples
#' lrn = makeLearner("regr.svm")
#' fit = train(lrn, bh.task)
#' pd = generatePartialDependenceData(fit, bh.task, "lstat")
#' plotPartialDependence(pd, data = getTaskData(bh.task))
#'
#' lrn = makeLearner("classif.rpart", predict.type = "prob")
#' fit = train(lrn, iris.task)
#' pd = generatePartialDependenceData(fit, iris.task, "Petal.Width")
#' plotPartialDependence(pd, data = getTaskData(iris.task))
#' @export
generatePartialDependenceData = function(obj, input, features = NULL,
interaction = FALSE, derivative = FALSE, individual = FALSE,
fun = mean, bounds = c(qnorm(.025), qnorm(.975)),
uniform = TRUE, n = c(10, NA), ...) {
requirePackages("mmpf")
assertClass(obj, "WrappedModel")
if (obj$learner$predict.type == "se" & individual) {
stop("individual = TRUE not compatabile with predict.type = 'se'!")
}
if (obj$learner$predict.type == "se" & derivative) {
stop("derivative = TRUE is not compatible with predict.type = 'se'!")
}
if (!inherits(input, c("Task", "data.frame"))) {
stop("input must be a Task or a data.frame!")
}
if (inherits(input, "Task")) {
data = getTaskData(input)
td = input$task.desc
} else {
data = input
td = obj$task.desc
assertDataFrame(data, col.names = "unique", min.rows = 1L, min.cols = length(obj$features) + length(td$target))
assertSetEqual(colnames(data), c(obj$features, td$target), ordered = FALSE)
}
if (is.na(n[2])) {
n[2] = nrow(data)
}
if (is.null(features)) {
features = colnames(data)[!colnames(data) %in% td$target]
} else {
assertSubset(features, obj$features)
}
assertFlag(interaction)
assertFlag(derivative)
if (derivative & interaction) {
stop("interaction cannot be TRUE if derivative is TRUE.")
}
if (derivative) {
if (any(sapply(data[, features, drop = FALSE], class) %in% c("factor", "ordered", "character"))) {
stop("All features must be numeric to estimate set derivative = TRUE!")
}
}
se = Function = Class = patterns = NULL # nolint
assertFlag(individual)
if (individual) {
fun = identity
}
assertFunction(fun)
test.fun = fun(1:3)
if (length(test.fun) == 1L) {
multi.fun = FALSE
} else {
multi.fun = TRUE
if (is.null(names(test.fun)) & !individual) {
stop("If fun returns a vector it must be named.")
}
}
assertNumeric(bounds, len = 2L)
assertNumber(bounds[1], upper = 0)
assertNumber(bounds[2], lower = 0)
assertFlag(uniform)
assertCount(n[1], positive = TRUE)
assertCount(n[2], positive = TRUE)
if (n[2] > nrow(data)) {
stop("The number of points taken from the training data cannot exceed the number of training data points.")
}
if (td$type == "regr") {
target = td$target
} else if (td$type == "classif") {
if (length(td$class.levels) > 2L) {
target = td$class.levels
} else {
target = td$positive
}
} else {
target = "Risk"
}
if (!derivative) {
args = list(model = obj, data = data, uniform = uniform, aggregate.fun = fun,
predict.fun = getPrediction, n = n, ...)
out = parallelMap(mmpf::marginalPrediction,
vars = if (interaction) list(features) else as.list(features), more.args = args)
if (length(target) == 1L) {
out = lapply(out, function(x) {
feature = features[features %in% names(x)]
names(x) = stri_replace_all(names(x), target, regex = "^preds")
x = data.table(x)
if (individual) {
x = melt(x, id.vars = feature, variable.name = "n", value.name = target)
x[, n := stri_replace(n, "", regex = target)]
setnames(x, c(feature, if (individual) "n" else "Function", target), names(x))
} else {
x
}
})
}
} else {
points = lapply(features, function(x) mmpf::uniformGrid(data[[x]], n[1]))
names(points) = features
args = list(obj = obj, data = data, uniform = uniform, fun = fun,
n = n, points = points, target = target, individual = individual, ...)
if (individual) {
int.points = sample(seq_len(nrow(data)), n[2])
out = parallelMap(doDerivativeMarginalPrediction, x = features,
z = int.points, more.args = args)
} else {
out = parallelMap(doDerivativeMarginalPrediction, x = features, more.args = args)
}
}
out = rbindlist(out, fill = TRUE, use.names = TRUE)
if (length(target) == 1L) {
if (!multi.fun) {
setcolorder(out, c(names(out)[grepl(paste(target, "preds", sep = "|"),
names(out))], if (obj$learner$predict.type == "se") "se" else NULL, features))
setnames(out, names(out), c(target, if (obj$learner$predict.type == "se") "se" else NULL, features))
} else if (individual) {
setcolorder(out, c(target, "n", features))
} else {
setnames(out, names(out), stri_replace_all_fixed(names(out), "preds", ""))
out = melt(as.data.table(out), id.vars = features, variable.name = "Function",
value.name = target)
setcolorder(out, c(target, "Function", features))
}
} else {
if (!multi.fun) {
out = melt(as.data.table(out), measure.vars = target,
variable.name = if (td$type == "classif") "Class" else "Function",
value.name = if (td$type == "classif") "Probability" else "Prediction")
if (td$type == "classif") {
out[, Class := stri_replace_all_regex(Class, "^prob\\.", "")]
setcolorder(out, c("Class",
if (td$type == "classif") "Probability" else "Prediction", features))
} else {
out[, Function := stri_replace_all_regex(target, "^preds\\.", "")]
setcolorder(out, c("Function",
if (td$type == "classif") "Probability" else "Prediction", features))
}
} else if (individual) {
if (!derivative) {
out = melt(as.data.table(out), measure = patterns(target), variable.name = "n",
value.name = target)
}
out = melt(as.data.table(out), measure.vars = target,
variable.name = if (td$type == "classif") "Class" else "Target",
value.name = if (td$type == "classif") "Probability" else "Prediction")
setcolorder(out, c(if (td$type == "classif") "Class" else "Target",
if (td$type == "classif") "Probability" else "Prediction", "n", features))
} else {
out = melt(as.data.table(out), id.vars = c(features, if (individual) "n"),
variable.name = if (td$type == "classif") "Class" else "Function",
value.name = if (td$type == "classif") "Probability" else "Prediction")
if (td$type == "classif") {
x = stri_split_regex(out$Class, "\\.", n = 2, simplify = TRUE)
## checking to see if there is detritus, e.g., preds.class or something
id = apply(x, 2, function(z) length(unique(z)) > 1L)
if (!all(id)) {
out[, "Class"] = x[, id]
setcolorder(out, c("Class", "Probability", features))
} else {
out[, c("Class", "Function") := lapply(1:2, function(i) x[, i])]
out[, Function := stri_replace_all_regex(Function, "^preds\\.", "")]
setcolorder(out, c("Class", "Function", "Probability", features))
}
} else {
out[, Function := stri_replace_all_regex(Function, "^preds\\.", "")]
setcolorder(out, c("Class", "Function", "Prediction", features))
}
}
}
# for se, compute upper and lower bounds
if (obj$learner$predict.type == "se") {
x = outer(out$se, bounds) + out[[target]]
out[, c("lower", "upper") := lapply(1:2, function(i) x[, i])]
out[, se := NULL]
target = c("lower", target, "upper")
setcolorder(out, c(target, features))
}
colnames(out) = make.names(colnames(out))
features = make.names(features)
target = make.names(features)
makeS3Obj("PartialDependenceData",
data = out,
task.desc = td,
target = target,
features = features,
derivative = derivative,
interaction = interaction,
individual = individual)
}
## second layer wrapper for numDeriv grad and jacobian use with marginal prediction
doDerivativeMarginalPrediction = function(x, z = sample(seq_len(nrow(data)), n[2]),
target, points, obj, data, uniform, fun, n, individual, ...) {
requirePackages("numDeriv", why = "PartialDependenceData", default.method = "load")
if (length(target) == 1L) {
ret = cbind(numDeriv::grad(numDerivWrapper,
x = points[[x]], model = obj, data = data,
uniform = uniform, aggregate.fun = fun, vars = x,
int.points = z,
predict.fun = getPrediction, n = n, target = target,
individual = individual, ...),
points[[x]], if (individual) z)
} else {
out = lapply(points[[x]], function(x.value) {
t(numDeriv::jacobian(numDerivWrapper, x = x.value, model = obj, data = data,
uniform = uniform, aggregate.fun = fun, vars = x, int.points = z,
predict.fun = getPrediction, n = n, target = target,
individual = individual, ...))
})
out = do.call("rbind", out)
ret = cbind(out, points[[x]], if (individual) z)
}
ret = as.data.table(ret)
setnames(ret, names(ret), c(target, x, if (individual) "n"))
ret
}
# grad and jacobian both need to take a vector along with ...
# and they return either a vector or a matrix
# so i need to pass the points as that x, and then extract the appropriate
# vector or matrix from marginalPrediction
numDerivWrapper = function(points, vars, individual, target, ...) {
args = list(...)
args$points = list(points)
names(args$points) = vars
args$vars = vars
out = do.call(mmpf::marginalPrediction, args)
as.matrix(out[, which(names(out) != vars), with = FALSE])
}
#' @export
print.PartialDependenceData = function(x, ...) {
catf("PartialDependenceData")
catf("Task: %s", x$task.desc$id)
catf("Features: %s", stri_paste(x$features, collapse = ", ", sep = " "))
catf("Target: %s", stri_paste(x$target, collapse = ", ", sep = " "))
catf("Derivative: %s", x$derivative)
catf("Interaction: %s", x$interaction)
catf("Individual: %s", x$individual)
printHead(x$data, ...)
}
#' @title Plot a partial dependence with ggplot2.
#' @description
#' Plot a partial dependence from [generatePartialDependenceData] using ggplot2.
#'
#' @family partial_dependence
#' @family plot
#'
#' @param obj [PartialDependenceData]\cr
#' Generated by [generatePartialDependenceData].
#' @param geom (`charater(1)`)\cr
#' The type of geom to use to display the data. Can be \dQuote{line} or \dQuote{tile}.
#' For tiling at least two features must be used with `interaction = TRUE` in the call to
#' [generatePartialDependenceData]. This may be used in conjuction with the
#' `facet` argument if three features are specified in the call to
#' [generatePartialDependenceData].
#' Default is \dQuote{line}.
#' @param facet (`character(1)`)\cr
#' The name of a feature to be used for facetting.
#' This feature must have been an element of the `features` argument to
#' [generatePartialDependenceData] and is only applicable when said argument had length
#' greater than 1.
#' The feature must be a factor or an integer.
#' If [generatePartialDependenceData] is called with the `interaction` argument `FALSE`
#' (the default) with argument `features` of length greater than one, then `facet` is ignored and
#' each feature is plotted in its own facet.
#' Default is `NULL`.
#' @template arg_facet_nrow_ncol
#' @param p (`numeric(1)`)\cr
#' If `individual = TRUE` then `sample` allows the user to sample without replacement
#' from the output to make the display more readable. Each row is sampled with probability `p`.
#' Default is `1`.
#' @param data ([data.frame])\cr
#' Data points to plot. Usually the training data. For survival and binary classification tasks a rug plot
#' wherein ticks represent failures or instances of the positive class are shown. For regression tasks
#' points are shown. For multiclass classification tasks ticks are shown and colored according to their class.
#' Both the features and the target must be included.
#' Default is `NULL`.
#' @template ret_gg2
#' @export
plotPartialDependence = function(obj, geom = "line", facet = NULL, facet.wrap.nrow = NULL,
facet.wrap.ncol = NULL, p = 1, data = NULL) {
assertClass(obj, "PartialDependenceData")
assertChoice(geom, c("tile", "line"))
if (obj$interaction & length(obj$features) > 2L & geom != "tile") {
stop("Cannot plot more than 2 features together with line plots.")
}
if (geom == "tile") {
if (!obj$interaction) {
stop("obj argument created by generatePartialDependenceData was called with interaction = FALSE!")
}
}
if (!is.null(data)) {
assertDataFrame(data, col.names = "unique", min.rows = 1L,
min.cols = length(obj$features) + length(obj$td$target))
assertSubset(obj$features, colnames(data), empty.ok = FALSE)
}
if (!is.null(facet)) {
assertChoice(facet, obj$features)
if (!length(obj$features) %in% 2:3) {
stop("obj argument created by generatePartialDependenceData must be called with two or three features to use this argument!")
}
if (!obj$interaction) {
stop("obj argument created by generatePartialDependenceData must be called with interaction = TRUE to use this argument!")
}
features = obj$features[which(obj$features != facet)]
if (is.factor(obj$data[[facet]])) {
obj$data[[facet]] = stri_paste(facet, "=", obj$data[[facet]], sep = " ")
} else if (is.character(obj$data[[facet]])) {
obj$data[[facet]] = stri_paste(facet, "=", as.factor(obj$data[[facet]]), sep = " ")
} else if (is.numeric(obj$data[[facet]])) {
obj$data[[facet]] = stri_paste(facet, "=", as.factor(signif(obj$data[[facet]], 3L)), sep = " ")
} else {
stop("Invalid input to facet arg. Must refer to a numeric/integer, character, or facet feature.")
}
scales = "fixed"
} else {
features = obj$features
if (length(features) > 1L & !(length(features) == 2L & geom == "tile")) {
facet = "Feature"
scales = "free_x"
} else {
scales = "fixed"
}
}
# detect if there was a multi-output function used in which case
# there should be a column named function which needs to be facetted
if ("Function" %in% colnames(obj$data)) {
facet = c(facet, "Function")
}
# sample from individual partial dependence estimates
if (p != 1) {
assertNumber(p, lower = 0, upper = 1, finite = TRUE)
if (!obj$individual) {
stop("obj argument created by generatePartialDependenceData must be called with individual = TRUE to use this argument!")
}
rows = unique(obj$data$idx)
id = sample(rows, size = floor(p * length(rows)))
obj$data = obj$data[which(obj$data$idx %in% id), ]
}
if (obj$task.desc$type %in% c("regr", "classif")) {
if (obj$task.desc$type == "classif" && length(obj$task.desc$class.levels) <= 2L) {
target = obj$task.desc$positive
} else {
target = obj$task.desc$target
}
} else {
target = "Risk"
}
# are there bounds compatible with a ribbon plot?
bounds = all(c("lower", "upper") %in% colnames(obj$data) & obj$task.desc$type %in% c("surv", "regr") &
length(features) < 3L & geom == "line")
if (geom == "line") {
# find factors and cast them to numerics so that we can melt
idx = which(sapply(obj$data, class) == "factor" & colnames(obj$data) %in% features)
# explicit casting previously done implicitly by reshape2::melt.data.frame
for (id in idx) obj$data[, id] = as.numeric(obj$data[[id]])
# melt the features but leave everything else alone
obj$data = setDF(melt(data.table(obj$data),
id.vars = colnames(obj$data)[!colnames(obj$data) %in% features],
variable = "Feature", value.name = "Value", na.rm = TRUE, variable.factor = TRUE))
# when individual is false plot variable value against the target
if (!obj$individual) {
# for regression/survival this is a simple line plot
if (obj$task.desc$type %in% c("regr", "surv") |
(obj$task.desc$type == "classif" & length(obj$task.desc$class.levels) <= 2L)) {
plt = ggplot(obj$data, aes_string("Value", target)) +
geom_line(color = ifelse(is.null(data), "black", "red")) + geom_point()
} else { # for classification create different colored lines
plt = ggplot(obj$data, aes_string("Value", "Probability", group = "Class", color = "Class")) +
geom_line() + geom_point()
}
} else { # if individual is true make the lines semi-transparent
if (obj$task.desc$type %in% c("regr", "surv") |
(obj$task.desc$type == "classif" & length(obj$task.desc$class.levels) <= 2L)) {
plt = ggplot(obj$data, aes_string("Value", target, group = "n")) +
geom_line(alpha = .25, color = ifelse(is.null(data), "black", "red")) + geom_point()
} else {
plt = ggplot(obj$data, aes_string("Value", "Probability", group = "idx", color = "Class")) +
geom_line(alpha = .25) + geom_point()
}
}
# if there is only one feature than melting was redundant (but cleaner code)
# so rename the x-axis using the feature name. rename target only if it was a vector
# since in this case the target name isn't passed through
if (length(features) == 1L) {
if (obj$task.desc$type %in% c("regr", "surv")) {
plt = plt + labs(x = features, y = target)
} else {
plt = plt + labs(x = features)
}
}
# ribbon bounds from se estimation
if (bounds) {
plt = plt + geom_ribbon(aes_string(ymin = "lower", ymax = "upper"), alpha = .5)
}
# labels added to for derivative plots
if (obj$derivative) {
plt = plt + ylab(stri_paste(target, "(derivative)", sep = " "))
}
} else { ## tiling
if (obj$task.desc$type == "classif") {
target = "Probability"
facet = "Class"
if ("Function" %in% obj$data) {
facet = c(facet, "Function")
}
scales = "free"
}
plt = ggplot(obj$data, aes_string(x = features[1], y = features[2], fill = target))
plt = plt + geom_raster(aes_string(fill = target))
# labels for ICE plots
if (obj$derivative) {
plt = plt + scale_fill_continuous(guide = guide_colorbar(title = stri_paste(target, "(derivative)", sep = " ")))
}
}
# facetting which is either passed in by the user, the features column when interaction = FALSE and length(features) > 1
# and/or when fun outputs a vector (then facetting on the Function column)
if (!is.null(facet)) {
if (length(facet) == 1L) {
plt = plt + facet_wrap(as.formula(stri_paste("~", facet)), scales = scales,
nrow = facet.wrap.nrow, ncol = facet.wrap.ncol)
} else {
plt = plt + facet_wrap(as.formula(stri_paste(facet[2], "~", facet[1])), scales = scales,
nrow = facet.wrap.nrow, ncol = facet.wrap.ncol)
} # facet ordering is reversed deliberately to handle len = 1 case!
}
# data overplotting
if (!is.null(data)) {
data = data[, colnames(data) %in% c(obj$features, obj$task.desc$target)]
if (!is.null(facet)) {
feature.facet = facet[facet %in% obj$features]
fun.facet = facet[!facet %in% feature.facet]
if (length(fun.facet) > 0L && (fun.facet == "Feature" || !feature.facet %in% obj$features)) {
data = melt(as.data.table(data), id.vars = c(obj$task.desc$target, feature.facet),
variable = "Feature", value.name = "Value", na.rm = TRUE, variable.factor = TRUE)
}
if (length(feature.facet) > 0) {
if (!is.factor(data[[feature.facet]])) {
data[[feature.facet]] = stri_paste(feature.facet, "=", as.factor(signif(data[[feature.facet]], 2)), sep = " ")
} else {
data[[feature.facet]] = stri_paste(feature.facet, "=", data[[feature.facet]], sep = " ")
}
}
if (length(fun.facet) > 0L && "Function" %in% fun.facet) {
data = mmpf::cartesianExpand(data, data.frame("Function" = unique(obj$data$Function)))
}
}
if (geom == "line") {
if (obj$task.desc$type %in% c("classif", "surv")) {
if (obj$task.desc$type == "classif") {
if (!is.na(obj$task.desc$positive)) {
plt = plt + geom_rug(aes_string(plt$labels$x, color = obj$task.desc$target),
data[data[[obj$task.desc$target]] == obj$task.desc$positive, ],
alpha = .25, inherit.aes = FALSE)
} else {
plt = plt + geom_rug(aes_string(plt$labels$x), data, alpha = .25, inherit.aes = FALSE)
}
} else {
plt = plt + geom_rug(aes_string(plt$labels$x),
data[data[[obj$task.desc$target[2]]], ],
alpha = .25, inherit.aes = FALSE)
}
} else {
plt = plt + geom_point(aes_string(plt$labels$x, obj$task.desc$target),
data, alpha = .25, inherit.aes = FALSE)
}
} else {
plt = plt + geom_point(aes_string(plt$labels$x, plt$labels$y), data, alpha = .25, inherit.aes = FALSE)
}
}
plt
}
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