[R-package] updated examples and removed dontrun guards on them in roxygen (#1626)

R-package/man/lgb.Dataset.construct.Rd

@@ -13,12 +13,10 @@ lgb.Dataset.construct(dataset)
 Construct Dataset explicitly
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 lgb.Dataset.construct(dtrain)
-}
 
 }

R-package/man/lgb.Dataset.create.valid.Rd

@@ -22,7 +22,6 @@ constructed dataset
 Construct validation data according to training data
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -30,6 +29,5 @@ 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)
-}
 
 }

R-package/man/lgb.Dataset.save.Rd

@@ -19,12 +19,10 @@ Save \code{lgb.Dataset} to a binary file
 }
 \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")
-}
 
 }

R-package/man/lgb.Dataset.set.categorical.Rd

@@ -18,7 +18,6 @@ passed dataset
 Set categorical feature of \code{lgb.Dataset}
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -26,6 +25,5 @@ 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)
-}
 
 }

R-package/man/lgb.Dataset.set.reference.Rd

@@ -18,7 +18,6 @@ passed dataset
 If you want to use validation data, you should set reference to training data
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package ="lightgbm")
 train <- agaricus.train
@@ -27,6 +26,5 @@ data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 dtest <- lgb.Dataset(test$data, test = train$label)
 lgb.Dataset.set.reference(dtest, dtrain)
-}
 
 }

R-package/man/lgb.cv.Rd

@@ -80,7 +80,6 @@ a trained model \code{lgb.CVBooster}.
 Cross validation logic used by LightGBM
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -94,4 +93,3 @@ model <- lgb.cv(params,
                 learning_rate = 1,
                 early_stopping_rounds = 10)
 }
-}

R-package/man/lgb.dump.Rd

@@ -18,7 +18,6 @@ json format of model
 Dump LightGBM model to json
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -36,6 +35,5 @@ model <- lgb.train(params,
                    learning_rate = 1,
                    early_stopping_rounds = 10)
 json_model <- lgb.dump(model)
-}
 
 }

R-package/man/lgb.get.eval.result.Rd

@@ -25,7 +25,6 @@ vector of evaluation result
 Get record evaluation result from booster
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -43,6 +42,5 @@ model <- lgb.train(params,
                    learning_rate = 1,
                    early_stopping_rounds = 10)
 lgb.get.eval.result(model, "test", "l2")
-}
 
 }

R-package/man/lgb.importance.Rd

@@ -24,13 +24,12 @@ For a tree model, a \code{data.table} with the following columns:
 Creates a \code{data.table} of feature importances in a model.
 }
 \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)
@@ -38,6 +37,5 @@ model <- lgb.train(params, dtrain, 20)
 
 tree_imp1 <- lgb.importance(model, percentage = TRUE)
 tree_imp2 <- lgb.importance(model, percentage = FALSE)
-}
 
 }

R-package/man/lgb.interprete.Rd

@@ -27,8 +27,6 @@ For multiclass classification, a \code{list} of \code{data.table} with the Featu
 Computes feature contribution components of rawscore prediction.
 }
 \examples{
-\dontrun{
-library(lightgbm)
 Sigmoid <- function(x) 1 / (1 + exp(-x))
 Logit <- function(x) log(x / (1 - x))
 data(agaricus.train, package = "lightgbm")
@@ -38,13 +36,16 @@ 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)
-}
 
 }

R-package/man/lgb.load.Rd

@@ -20,7 +20,6 @@ Load LightGBM takes in either a file path or model string
 If both are provided, Load will default to loading from file
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -41,6 +40,5 @@ lgb.save(model, "model.txt")
 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)
-}
 
 }

R-package/man/lgb.model.dt.tree.Rd

@@ -39,20 +39,17 @@ The columns of the \code{data.table} are:
 Parse a LightGBM model json dump into a \code{data.table} structure.
 }
 \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)
-}
 
 }

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

@@ -29,19 +29,3 @@ 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{
-\dontrun{
-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)
-model <- lgb.train(params, dtrain, 20)
-
-tree_imp <- lgb.importance(model, percentage = TRUE)
-lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain")
-}
-}

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

@@ -29,7 +29,6 @@ The graph represents each feature as a horizontal bar of length proportional to
 Features are shown ranked in a decreasing contribution order.
 }
 \examples{
-\dontrun{
 library(lightgbm)
 Sigmoid <- function(x) {1 / (1 + exp(-x))}
 Logit <- function(x) {log(x / (1 - x))}
@@ -40,7 +39,7 @@ setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label))
 data(agaricus.test, package = "lightgbm")
 test <- agaricus.test
 
-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)
@@ -49,4 +48,3 @@ model <- lgb.train(params, dtrain, 20)
 tree_interpretation <- lgb.interprete(model, test$data, 1:5)
 lgb.plot.interpretation(tree_interpretation[[1]], top_n = 10)
 }
-}

R-package/man/lgb.prepare.Rd

@@ -16,7 +16,6 @@ The cleaned dataset. It must be converted to a matrix format (\code{as.matrix})
 Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric without integers. Please use \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(iris)
 
@@ -46,6 +45,5 @@ str(lightgbm::lgb.prepare(data = iris))
 # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 # $ Species     : num  1 1 1 1 1 1 1 1 1 1 ...
-}
 
 }

R-package/man/lgb.prepare2.Rd

@@ -16,7 +16,6 @@ The cleaned dataset. It must be converted to a matrix format (\code{as.matrix})
 Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(iris)
 
@@ -28,7 +27,8 @@ str(iris)
 # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
 
-str(lgb.prepare2(data = iris)) # Convert all factors/chars to integer
+# Convert all factors/chars to integer
+str(lgb.prepare2(data = iris))
 # 'data.frame':	150 obs. of  5 variables:
 # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
@@ -48,5 +48,3 @@ str(lightgbm::lgb.prepare2(data = iris))
 # $ Species     : int  1 1 1 1 1 1 1 1 1 1 ...
 
 }
-
-}

R-package/man/lgb.prepare_rules.Rd

@@ -18,7 +18,6 @@ A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The
 Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(iris)
 
@@ -77,5 +76,3 @@ str(newest_iris$data) # SUCCESS!
 # $ Species     : num  3 3 3 3 3 3 3 3 3 3 ...
 
 }
-
-}

R-package/man/lgb.prepare_rules2.Rd

@@ -18,7 +18,6 @@ A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The
 Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). In addition, keeps rules created so you can convert other datasets using this converter. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(iris)
 
@@ -77,5 +76,3 @@ str(newest_iris$data) # SUCCESS!
 # $ Species     : int  3 3 3 3 3 3 3 3 3 3 ...
 
 }
-
-}

R-package/man/lgb.save.Rd

@@ -20,7 +20,6 @@ lgb.Booster
 Save LightGBM model
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -38,6 +37,5 @@ model <- lgb.train(params,
                    learning_rate = 1,
                    early_stopping_rounds = 10)
 lgb.save(model, "model.txt")
-}
 
 }

R-package/man/lgb.train.Rd

@@ -69,7 +69,6 @@ a trained booster model \code{lgb.Booster}.
 Logic to train with LightGBM
 }
 \examples{
-\dontrun{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -86,6 +85,5 @@ model <- lgb.train(params,
                    min_data = 1,
                    learning_rate = 1,
                    early_stopping_rounds = 10)
-}
 
 }