[R-package] [docs] Simplified examples to cut example run time (fixes #2988) (#2989)

* [R-package] [docs] Simplified examles to cut example run time (fixes #2988)

* updated learning rates

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

@@ -22,7 +22,6 @@ constructed dataset
 Construct validation data according to training data
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

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

@@ -19,7 +19,6 @@ Please note that \code{init_score} is not saved in binary file.
              If you need it, please set it again after loading Dataset.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

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

@@ -21,7 +21,6 @@ Set the categorical features of an \code{lgb.Dataset} object. Use this function
              to tell LightGBM which features should be treated as categorical.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

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{
-library(lightgbm)
 data(agaricus.train, package ="lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)

R-package/man/lgb.cv.Rd

@@ -100,7 +100,6 @@ a trained model \code{lgb.CVBooster}.
 Cross validation logic used by LightGBM
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -108,10 +107,9 @@ params <- list(objective = "regression", metric = "l2")
 model <- lgb.cv(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , nfold = 3L
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
 )
 }

R-package/man/lgb.dump.Rd

@@ -18,6 +18,7 @@ json format of model
 Dump LightGBM model to json
 }
 \examples{
+\donttest{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -37,5 +38,5 @@ model <- lgb.train(
   , early_stopping_rounds = 5L
 )
 json_model <- lgb.dump(model)
-
+}
 }

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

@@ -33,7 +33,6 @@ Given a \code{lgb.Booster}, return evaluation results for a
 }
 \examples{
 # train a regression model
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -45,11 +44,10 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
 )
 
 # Examine valid data_name values

R-package/man/lgb.importance.Rd

@@ -24,20 +24,22 @@ For a tree model, a \code{data.table} with the following columns:
 Creates a \code{data.table} of feature importances in a model.
 }
 \examples{
-library(lightgbm)
 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 = 63L
+  , learning_rate = 0.1
   , max_depth = -1L
   , min_data_in_leaf = 1L
   , min_sum_hessian_in_leaf = 1.0
 )
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = 5L
+)
 
 tree_imp1 <- lgb.importance(model, percentage = TRUE)
 tree_imp2 <- lgb.importance(model, percentage = FALSE)

R-package/man/lgb.interprete.Rd

@@ -29,7 +29,6 @@ For regression, binary classification and lambdarank model, a \code{list} of \co
 Computes feature contribution components of rawscore prediction.
 }
 \examples{
-Sigmoid <- function(x) 1.0 / (1.0 + exp(-x))
 Logit <- function(x) log(x / (1.0 - x))
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -40,13 +39,16 @@ test <- agaricus.test
 
 params <- list(
     objective = "binary"
-    , learning_rate = 0.01
-    , num_leaves = 63L
+    , learning_rate = 0.1
     , max_depth = -1L
     , min_data_in_leaf = 1L
     , min_sum_hessian_in_leaf = 1.0
 )
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = 3L
+)
 
 tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
 

R-package/man/lgb.load.Rd

@@ -19,7 +19,7 @@ Load LightGBM takes in either a file path or model string.
               If both are provided, Load will default to loading from file
 }
 \examples{
-library(lightgbm)
+\donttest{
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -31,15 +31,15 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
+  , early_stopping_rounds = 3L
 )
 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.plot.importance.Rd

@@ -43,15 +43,17 @@ dtrain <- lgb.Dataset(train$data, label = train$label)
 
 params <- list(
     objective = "binary"
-    , learning_rate = 0.01
-    , num_leaves = 63L
-    , max_depth = -1L
+    , learning_rate = 0.1
     , min_data_in_leaf = 1L
     , min_sum_hessian_in_leaf = 1.0
 )
 
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+    params = params
+    , data = dtrain
+    , nrounds = 5L
+)
 
 tree_imp <- lgb.importance(model, percentage = TRUE)
-lgb.plot.importance(tree_imp, top_n = 10L, measure = "Gain")
+lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain")
 }

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

@@ -34,26 +34,41 @@ The graph represents each feature as a horizontal bar of length proportional to
 contribution of a feature. Features are shown ranked in a decreasing contribution order.
 }
 \examples{
-library(lightgbm)
-Sigmoid <- function(x) {1.0 / (1.0 + exp(-x))}
-Logit <- function(x) {log(x / (1.0 - x))}
+\donttest{
+Logit <- function(x) {
+  log(x / (1.0 - 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)))
+labels <- agaricus.train$label
+dtrain <- lgb.Dataset(
+  agaricus.train$data
+  , label = labels
+)
+setinfo(dtrain, "init_score", rep(Logit(mean(labels)), length(labels)))
+
 data(agaricus.test, package = "lightgbm")
-test <- agaricus.test
 
 params <- list(
   objective = "binary"
-  , learning_rate = 0.01
-  , num_leaves = 63L
+  , learning_rate = 0.1
   , max_depth = -1L
   , min_data_in_leaf = 1L
   , min_sum_hessian_in_leaf = 1.0
 )
-model <- lgb.train(params, dtrain, 10L)
+model <- lgb.train(
+  params = params
+  , data = dtrain
+  , nrounds = 5L
+)
 
-tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)
-lgb.plot.interpretation(tree_interpretation[[1L]], top_n = 10L)
+tree_interpretation <- lgb.interprete(
+  model = model
+  , data = agaricus.test$data
+  , idxset = 1L:5L
+)
+lgb.plot.interpretation(
+  tree_interpretation_dt = tree_interpretation[[1L]]
+  , top_n = 5L
+)
+}
 }

R-package/man/lgb.prepare.Rd

@@ -19,7 +19,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              \code{\link{lgb.prepare_rules}} if you want to apply this transformation to other datasets.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.prepare2.Rd

@@ -22,7 +22,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.prepare_rules.Rd

@@ -22,7 +22,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              so you can convert other datasets using this converter.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.prepare_rules2.Rd

@@ -25,7 +25,6 @@ Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
              Consider this as a half memory technique which is dangerous, especially for LightGBM.
 }
 \examples{
-library(lightgbm)
 data(iris)
 
 str(iris)

R-package/man/lgb.save.Rd

@@ -20,6 +20,7 @@ lgb.Booster
 Save LightGBM model
 }
 \examples{
+\donttest{
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
@@ -40,3 +41,4 @@ model <- lgb.train(
 )
 lgb.save(model, "model.txt")
 }
+}

R-package/man/lgb.train.Rd

@@ -83,7 +83,6 @@ a trained booster model \code{lgb.Booster}.
 Logic to train with LightGBM
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -95,10 +94,10 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
+  , early_stopping_rounds = 3L
 )
 }

R-package/man/lgb.unloader.Rd

@@ -26,7 +26,6 @@ Attempts to unload LightGBM packages so you can remove objects cleanly without
              apparent reason and you do not want to restart R to fix the lost object.
 }
 \examples{
-library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
@@ -38,11 +37,10 @@ valids <- list(test = dtest)
 model <- lgb.train(
   params = params
   , data = dtrain
-  , nrounds = 10L
+  , nrounds = 5L
   , valids = valids
   , min_data = 1L
   , learning_rate = 1.0
-  , early_stopping_rounds = 5L
 )
 
 \dontrun{

R-package/man/lgb_shared_params.Rd

@@ -4,7 +4,8 @@
 \alias{lgb_shared_params}
 \title{Shared parameter docs}
 \arguments{
-\item{callbacks}{List of callback functions that are applied at each iteration.}
+\item{callbacks}{list of callback functions
+List of callback functions that are applied at each iteration.}
 
 \item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}},
 may allow you to pass other types of data like \code{matrix} and then separately supply