[R-package] fixed examples in lgb.plot.importance (#1635)

R-package/R/lgb.plot.importance.R

 #' Plot feature importance as a bar graph
-#' 
+#'
 #' Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.
-#' 
+#'
 #' @param tree_imp a \code{data.table} returned by \code{\link{lgb.importance}}.
 #' @param top_n maximal number of top features to include into the plot.
 #' @param measure the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".
 #' @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 importance of a feature.
 #' Features are shown ranked in a decreasing importance order.
-#' 
+#'
 #' @return
 #' The \code{lgb.plot.importance} function creates a \code{barplot}
 #' and silently returns a processed data.table with \code{top_n} features sorted by defined importance.
-#' 
+#'
 #' @examples
-# data(agaricus.train, package = "lightgbm")
-# train <- agaricus.train
-# dtrain <- lgb.Dataset(train$data, label = train$label)
-# 
-# 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)
-# 
-# tree_imp <- lgb.importance(model, percentage = TRUE)
-# lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain")
+#' data(agaricus.train, package = "lightgbm")
+#' train <- agaricus.train
+#' dtrain <- lgb.Dataset(train$data, label = train$label)
+#'
+#' 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)
+#'
+#' tree_imp <- lgb.importance(model, percentage = TRUE)
+#' lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain")
 #' @importFrom graphics barplot par
 #' @export
 lgb.plot.importance <- function(tree_imp,
@@ -41,28 +41,28 @@ lgb.plot.importance <- function(tree_imp,
                                 measure = "Gain",
                                 left_margin = 10,
                                 cex = NULL) {
-  
+
   # Check for measurement (column names) correctness
   measure <- match.arg(measure, choices = c("Gain", "Cover", "Frequency"), several.ok = FALSE)
-  
+
   # Get top N importance (defaults to 10)
   top_n <- min(top_n, nrow(tree_imp))
-  
+
   # Parse importance
   tree_imp <- tree_imp[order(abs(get(measure)), decreasing = TRUE),][seq_len(top_n),]
-  
+
   # Attempt to setup a correct cex
   if (is.null(cex)) {
     cex <- 2.5 / log2(1 + top_n)
   }
-  
+
   # Refresh plot
   op <- graphics::par(no.readonly = TRUE)
   on.exit(graphics::par(op))
-  
+
   # Do some magic plotting
   graphics::par(mar = op$mar %>% magrittr::inset(., 2, left_margin))
-  
+
   # Do plot
   tree_imp[.N:1,
            graphics::barplot(
@@ -75,8 +75,8 @@ lgb.plot.importance <- function(tree_imp,
                cex.names = cex,
                las = 1
            )]
-  
+
   # Return invisibly
   invisible(tree_imp)
-  
+
 }

R-package/man/lgb.plot.importance.Rd

@@ -29,3 +29,22 @@ Plot previously calculated feature importance: Gain, Cover and Frequency, as a b
 The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature.
 Features are shown ranked in a decreasing importance order.
 }
+\examples{
+data(agaricus.train, package = "lightgbm")
+train <- agaricus.train
+dtrain <- lgb.Dataset(train$data, label = train$label)
+
+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)
+
+tree_imp <- lgb.importance(model, percentage = TRUE)
+lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain")
+}