d5c143367d
* [R] do not remove zero coefficients from gblinear dump * [R] switch from stringr to stringi * fix #1399 * [R] separate ggplot backend, add base r graphics, cleanup, more plots, tests * add missing include in amalgamation - fixes building R package in linux * add forgotten file * [R] fix DESCRIPTION * [R] fix travis check issue and some cleanup
126 lines
5.5 KiB
R
126 lines
5.5 KiB
R
#' Plot feature importance as a bar graph
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#'
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#' Represents previously calculated feature importance as a bar graph.
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#' \code{xgb.plot.importance} uses base R graphics, while \code{xgb.ggplot.importance} uses the ggplot backend.
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#'
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#' @param importance_matrix a \code{data.table} returned by \code{\link{xgb.importance}}.
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#' @param top_n maximal number of top features to include into the plot.
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#' @param measure the name of importance measure to plot.
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#' When \code{NULL}, 'Gain' would be used for trees and 'Weight' would be used for gblinear.
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#' @param rel_to_first whether importance values should be represented as relative to the highest ranked feature.
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#' See Details.
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#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.
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#' When it is NULL, the existing \code{par('mar')} is used.
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#' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}.
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#' @param plot (base R barplot) whether a barplot should be produced.
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#' If FALSE, only a data.table is returned.
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#' @param n_clusters (ggplot only) a \code{numeric} vector containing the min and the max range
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#' of the possible number of clusters of bars.
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#' @param ... other parameters passed to \code{barplot} (except horiz, border, cex.names, names.arg, and las).
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#'
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#' @details
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#' The graph represents each feature as a horizontal bar of length proportional to the importance of a feature.
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#' Features are shown ranked in a decreasing importance order.
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#' It works for importances from both \code{gblinear} and \code{gbtree} models.
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#'
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#' When \code{rel_to_first = FALSE}, the values would be plotted as they were in \code{importance_matrix}.
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#' For gbtree model, that would mean being normalized to the total of 1
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#' ("what is feature's importance contribution relative to the whole model?").
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#' For linear models, \code{rel_to_first = FALSE} would show actual values of the coefficients.
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#' Setting \code{rel_to_first = TRUE} allows to see the picture from the perspective of
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#' "what is feature's importance contribution relative to the most important feature?"
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#'
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#' The ggplot-backend method also performs 1-D custering of the importance values,
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#' with bar colors coresponding to different clusters that have somewhat similar importance values.
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#'
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#' @return
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#' The \code{xgb.plot.importance} function creates a \code{barplot} (when \code{plot=TRUE})
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#' and silently returns a processed data.table with \code{n_top} features sorted by importance.
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#'
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#' The \code{xgb.ggplot.importance} function returns a ggplot graph which could be customized afterwards.
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#' E.g., to change the title of the graph, add \code{+ ggtitle("A GRAPH NAME")} to the result.
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#'
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#' @seealso
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#' \code{\link[graphics]{barplot}}.
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#'
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#' @examples
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#' data(agaricus.train)
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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
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#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
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#'
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#' importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
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#'
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#' xgb.plot.importance(importance_matrix, rel_to_first = TRUE, xlab = "Relative importance")
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#'
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#' (gg <- xgb.ggplot.importance(importance_matrix, measure = "Frequency", rel_to_first = TRUE))
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#' gg + ggplot2::ylab("Frequency")
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#'
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#' @rdname xgb.plot.importance
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#' @export
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xgb.plot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL,
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rel_to_first = FALSE, left_margin = 10, cex = NULL, plot = TRUE, ...) {
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check.deprecation(...)
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if (!"data.table" %in% class(importance_matrix)) {
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stop("importance_matrix: Should be a data.table.")
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}
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imp_names <- colnames(importance_matrix)
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if (is.null(measure)) {
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if (all(c("Feature", "Gain") %in% imp_names)) {
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measure <- "Gain"
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} else if (all(c("Feature", "Weight") %in% imp_names)) {
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measure <- "Weight"
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} else {
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stop("Importance matrix column names are not as expected!")
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}
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} else {
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if (!measure %in% imp_names)
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stop("Invalid `measure`")
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if (!"Feature" %in% imp_names)
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stop("Importance matrix column names are not as expected!")
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}
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# also aggregate, just in case when the values were not yet summed up by feature
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importance_matrix <- importance_matrix[, Importance := sum(get(measure)), by = Feature]
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# make sure it's ordered
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importance_matrix <- importance_matrix[order(-abs(Importance))]
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if (!is.null(top_n)) {
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top_n <- min(top_n, nrow(importance_matrix))
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importance_matrix <- head(importance_matrix, top_n)
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}
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if (rel_to_first) {
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importance_matrix[, Importance := Importance/max(abs(Importance))]
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}
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if (is.null(cex)) {
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cex <- 2.5/log2(1 + nrow(importance_matrix))
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}
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if (plot) {
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op <- par(no.readonly = TRUE)
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mar <- op$mar
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if (!is.null(left_margin))
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mar[2] <- left_margin
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par(mar = mar)
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# reverse the order of rows to have the highest ranked at the top
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importance_matrix[nrow(importance_matrix):1,
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barplot(Importance, horiz=TRUE, border=NA, cex.names=cex,
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names.arg=Feature, las=1, ...)]
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grid(NULL, NA)
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# redraw over the grid
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importance_matrix[nrow(importance_matrix):1,
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barplot(Importance, horiz=TRUE, border=NA, add=TRUE)]
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par(op)
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}
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invisible(importance_matrix)
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}
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# Avoid error messages during CRAN check.
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# The reason is that these variables are never declared
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# They are mainly column names inferred by Data.table...
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globalVariables(c("Feature", "Importance"))
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