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Add Feature Contribution Plot (#330)
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#' Plot feature contribution as a bar graph | ||
#' | ||
#' Plot previously calculated feature contribution as a bar graph. | ||
#' | ||
#' @param tree_interpretation_dt a \code{data.table} returned by \code{\link{lgb.interprete}}. | ||
#' @param top_n maximal number of top features to include into the plot. | ||
#' @param cols the column numbers of layout, will be used only for multiclass classification feature contribution. | ||
#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names. | ||
#' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}. | ||
#' | ||
#' @details | ||
#' The graph represents each feature as a horizontal bar of length proportional to the defined contribution of a feature. | ||
#' Features are shown ranked in a decreasing contribution order. | ||
#' | ||
#' @return | ||
#' The \code{lgb.plot.interpretation} function creates a \code{barplot} | ||
#' | ||
#' @examples | ||
#' | ||
#' Sigmoid <- function(x) 1 / (1 + exp(-x)) | ||
#' Logit <- function(x) log(x / (1 - x)) | ||
#' data(agaricus.train, package = 'lightgbm') | ||
#' train <- agaricus.train | ||
#' dtrain <- lgb.Dataset(train$data, label = train$label) | ||
#' setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label))) | ||
#' data(agaricus.test, package = 'lightgbm') | ||
#' test <- agaricus.test | ||
#' | ||
#' params = list(objective = "binary", | ||
#' learning_rate = 0.01, num_leaves = 63, max_depth = -1, | ||
#' min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1) | ||
#' model <- lgb.train(params, dtrain, 20) | ||
#' model <- lgb.train(params, dtrain, 20) | ||
#' | ||
#' tree_interpretation <- lgb.interprete(model, test$data, 1:5) | ||
#' lgb.plot.interpretation(tree_interpretation[[1]], top_n = 10) | ||
#' | ||
#' @export | ||
|
||
lgb.plot.interpretation <- function(tree_interpretation_dt, top_n = 10, cols = 1, left_margin = 10, cex = NULL) { | ||
num_class <- ncol(tree_interpretation_dt) - 1 | ||
op <- par(no.readonly = TRUE) | ||
on.exit(par(op)) | ||
par(mar = op$mar %>% magrittr::inset(., 1:3, c(3, left_margin, 2))) | ||
if (num_class == 1) { | ||
multiple.tree.plot.interpretation(tree_interpretation_dt, top_n = top_n, title = NULL, cex = cex) | ||
} else { | ||
layout_mat <- matrix(seq(1, cols * ceiling(num_class / cols)), | ||
ncol = cols, nrow = ceiling(num_class / cols)) | ||
par(mfcol = c(nrow(layout_mat), ncol(layout_mat))) | ||
for (i in seq_len(num_class)) { | ||
tree_interpretation_dt[, c(1, i + 1), with = FALSE] %T>% | ||
data.table::setnames(., old = names(.), new = c("Feature", "Contribution")) %>% | ||
multiple.tree.plot.interpretation(., top_n = top_n, title = paste("Class", i - 1), cex = cex) | ||
} | ||
} | ||
} | ||
|
||
multiple.tree.plot.interpretation <- function(tree_interpretation, top_n, title, cex) { | ||
tree_interpretation <- tree_interpretation[order(abs(Contribution), decreasing = TRUE),][1:min(top_n, .N),] | ||
if (is.null(cex)) { | ||
cex <- 2.5 / log2(1 + top_n) | ||
} | ||
tree_interpretation[.N:1, | ||
barplot(height = Contribution, names.arg = Feature, horiz = TRUE, | ||
col = ifelse(Contribution > 0, "firebrick", "steelblue"), | ||
border = NA, main = title, cex.names = cex, las = 1)] | ||
invisible(NULL) | ||
} |
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