[R-package] updated examples and removed dontrun guards on them in ro…

…xygen (#1626)
microsoft · Aug 31, 2018 · 029bcc4 · 029bcc4
1 parent abd7376
commit 029bcc4
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diff --git a/R-package/R/lgb.Booster.R b/R-package/R/lgb.Booster.R
@@ -633,7 +633,6 @@ Booster <- R6::R6Class(
 #' number of columns corresponding to the number of trees.
 #' 
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -651,7 +650,6 @@ Booster <- R6::R6Class(
 #'                    learning_rate = 1,
 #'                    early_stopping_rounds = 10)
 #' preds <- predict(model, test$data)
-#' }
 #' 
 #' @rdname predict.lgb.Booster
 #' @export
@@ -692,7 +690,6 @@ predict.lgb.Booster <- function(object,
 #' @return lgb.Booster
 #' 
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -713,7 +710,6 @@ predict.lgb.Booster <- function(object,
 #' load_booster <- lgb.load(filename = "model.txt")
 #' model_string <- model$save_model_to_string(NULL) # saves best iteration
 #' load_booster_from_str <- lgb.load(model_str = model_string)
-#' }
 #' 
 #' @rdname lgb.load
 #' @export
@@ -752,7 +748,6 @@ lgb.load <- function(filename = NULL, model_str = NULL){
 #' @return lgb.Booster
 #' 
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -770,7 +765,6 @@ lgb.load <- function(filename = NULL, model_str = NULL){
 #'                    learning_rate = 1,
 #'                    early_stopping_rounds = 10)
 #' lgb.save(model, "model.txt")
-#' }
 #' 
 #' @rdname lgb.save
 #' @export
@@ -801,7 +795,6 @@ lgb.save <- function(booster, filename, num_iteration = NULL){
 #' @return json format of model
 #' 
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -819,7 +812,6 @@ lgb.save <- function(booster, filename, num_iteration = NULL){
 #'                    learning_rate = 1,
 #'                    early_stopping_rounds = 10)
 #' json_model <- lgb.dump(model)
-#' }
 #' 
 #' @rdname lgb.dump
 #' @export
@@ -847,7 +839,6 @@ lgb.dump <- function(booster, num_iteration = NULL){
 #' @return vector of evaluation result
 #' 
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -865,7 +856,6 @@ lgb.dump <- function(booster, num_iteration = NULL){
 #'                    learning_rate = 1,
 #'                    early_stopping_rounds = 10)
 #' lgb.get.eval.result(model, "test", "l2")
-#' }
 #' 
 #' @rdname lgb.get.eval.result
 #' @export

diff --git a/R-package/R/lgb.Dataset.R b/R-package/R/lgb.Dataset.R
@@ -311,6 +311,7 @@ Dataset <- R6::R6Class(
       } else if (is.matrix(private$raw_data) || methods::is(private$raw_data, "dgCMatrix")) {
 
         # Check if dgCMatrix (sparse matrix column compressed)
+        # NOTE: requires Matrix package
         dim(private$raw_data)
 
       } else {
@@ -392,9 +393,11 @@ Dataset <- R6::R6Class(
 
       # Check for info name and handle
       if (is.null(private$info[[name]])) {
+
         if (lgb.is.null.handle(private$handle)){
-          stop("Cannot perform getinfo before construct Dataset.")
+          stop("Cannot perform getinfo before constructing Dataset.")
         }
+
         # Get field size of info
         info_len <- 0L
         info_len <- lgb.call("LGBM_DatasetGetFieldSize_R",
@@ -646,15 +649,13 @@ Dataset <- R6::R6Class(
 #' @return constructed dataset
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' lgb.Dataset.save(dtrain, "lgb.Dataset.data")
 #' dtrain <- lgb.Dataset("lgb.Dataset.data")
 #' lgb.Dataset.construct(dtrain)
-#' }
 #'
 #' @export
 lgb.Dataset <- function(data,
@@ -692,15 +693,13 @@ lgb.Dataset <- function(data,
 #' @return constructed dataset
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
-#' }
 #'
 #' @export
 lgb.Dataset.create.valid <- function(dataset, data, info = list(), ...) {
@@ -720,13 +719,11 @@ lgb.Dataset.create.valid <- function(dataset, data, info = list(), ...) {
 #' @param dataset Object of class \code{lgb.Dataset}
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' lgb.Dataset.construct(dtrain)
-#' }
 #'
 #' @export
 lgb.Dataset.construct <- function(dataset) {
@@ -754,7 +751,6 @@ lgb.Dataset.construct <- function(dataset) {
 #' be directly used with an \code{lgb.Dataset} object.
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -763,7 +759,6 @@ lgb.Dataset.construct <- function(dataset) {
 #' stopifnot(nrow(dtrain) == nrow(train$data))
 #' stopifnot(ncol(dtrain) == ncol(train$data))
 #' stopifnot(all(dim(dtrain) == dim(train$data)))
-#' }
 #'
 #' @rdname dim
 #' @export
@@ -793,7 +788,6 @@ dim.lgb.Dataset <- function(x, ...) {
 #' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -803,7 +797,6 @@ dim.lgb.Dataset <- function(x, ...) {
 #' colnames(dtrain)
 #' colnames(dtrain) <- make.names(1:ncol(train$data))
 #' print(dtrain, verbose = TRUE)
-#' }
 #'
 #' @rdname dimnames.lgb.Dataset
 #' @export
@@ -864,15 +857,14 @@ dimnames.lgb.Dataset <- function(x) {
 #' @return constructed sub dataset
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #'
 #' dsub <- lightgbm::slice(dtrain, 1:42)
+#' lgb.Dataset.construct(dsub)
 #' labels <- lightgbm::getinfo(dsub, "label")
-#' }
 #'
 #' @export
 slice <- function(dataset, ...) {
@@ -911,7 +903,6 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
 #' }
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -923,7 +914,6 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
 #'
 #' labels2 <- lightgbm::getinfo(dtrain, "label")
 #' stopifnot(all(labels2 == 1 - labels))
-#' }
 #'
 #' @export
 getinfo <- function(dataset, ...) {
@@ -963,7 +953,6 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
 #' }
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -975,7 +964,6 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
 #'
 #' labels2 <- lightgbm::getinfo(dtrain, "label")
 #' stopifnot(all.equal(labels2, 1 - labels))
-#' }
 #'
 #' @export
 setinfo <- function(dataset, ...) {
@@ -1003,15 +991,13 @@ setinfo.lgb.Dataset <- function(dataset, name, info, ...) {
 #' @return passed dataset
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' lgb.Dataset.save(dtrain, "lgb.Dataset.data")
 #' dtrain <- lgb.Dataset("lgb.Dataset.data")
 #' lgb.Dataset.set.categorical(dtrain, 1:2)
-#' }
 #'
 #' @rdname lgb.Dataset.set.categorical
 #' @export
@@ -1037,7 +1023,6 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
 #' @return passed dataset
 #'
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package ="lightgbm")
 #' train <- agaricus.train
@@ -1046,7 +1031,6 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
 #' test <- agaricus.test
 #' dtest <- lgb.Dataset(test$data, test = train$label)
 #' lgb.Dataset.set.reference(dtest, dtrain)
-#' }
 #'
 #' @rdname lgb.Dataset.set.reference
 #' @export
@@ -1070,13 +1054,11 @@ lgb.Dataset.set.reference <- function(dataset, reference) {
 #'
 #' @examples
 #'
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #' lgb.Dataset.save(dtrain, "data.bin")
-#' }
 #'
 #' @rdname lgb.Dataset.save
 #' @export

diff --git a/R-package/R/lgb.cv.R b/R-package/R/lgb.cv.R
@@ -55,7 +55,6 @@ CVBooster <- R6::R6Class(
 #' @return a trained model \code{lgb.CVBooster}.
 #' 
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
@@ -68,7 +67,6 @@ CVBooster <- R6::R6Class(
 #'                 min_data = 1,
 #'                 learning_rate = 1,
 #'                 early_stopping_rounds = 10)
-#' }
 #' @export
 lgb.cv <- function(params = list(),
                    data,

diff --git a/R-package/R/lgb.importance.R b/R-package/R/lgb.importance.R
@@ -16,21 +16,19 @@
 #' }
 #' 
 #' @examples
-#' \dontrun{
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #'
-#' params = list(objective = "binary",
+#' 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_imp1 <- lgb.importance(model, percentage = TRUE)
 #' tree_imp2 <- lgb.importance(model, percentage = FALSE)
-#' }
 #' 
 #' @importFrom magrittr %>% %T>%
 #' @importFrom data.table :=

diff --git a/R-package/R/lgb.interprete.R b/R-package/R/lgb.interprete.R
@@ -17,8 +17,6 @@
 #' For multiclass classification, a \code{list} of \code{data.table} with the Feature column and Contribution columns to each class.
 #' 
 #' @examples
-#' \dontrun{
-#' library(lightgbm)
 #' Sigmoid <- function(x) 1 / (1 + exp(-x))
 #' Logit <- function(x) log(x / (1 - x))
 #' data(agaricus.train, package = "lightgbm")
@@ -27,15 +25,18 @@
 #' 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)
+#' 
+#' 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_interpretation <- lgb.interprete(model, test$data, 1:5)
-#' }
 #' 
 #' @importFrom magrittr %>% %T>%
 #' @export

diff --git a/R-package/R/lgb.model.dt.tree.R b/R-package/R/lgb.model.dt.tree.R
@@ -30,21 +30,18 @@
 #' }
 #'
 #' @examples
-#' \dontrun{
-#' library(lightgbm)
 #'
 #' data(agaricus.train, package = "lightgbm")
 #' train <- agaricus.train
 #' dtrain <- lgb.Dataset(train$data, label = train$label)
 #'
-#' params = list(objective = "binary",
+#' 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_dt <- lgb.model.dt.tree(model)
-#' }
 #'
 #' @importFrom magrittr %>%
 #' @importFrom data.table := data.table rbindlist