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

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

@@ -17,23 +17,23 @@
 #' 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,

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")
+}