Remove silent param from demo and regenerate Rd files (#951)

* Silent parameter does not exist anymore

* Fix Microsoft/LightGBM#913

* Generate Rd files for R documentation

R-package/R/lgb.Booster.R

@@ -833,6 +833,27 @@ lgb.dump <- function(booster, num_iteration = NULL){
 #' 
 #' @return vector of evaluation result
 #' 
+#' @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)
+#' params <- list(objective = "regression", metric = "l2")
+#' valids <- list(test = dtest)
+#' model <- lgb.train(params,
+#'                    dtrain,
+#'                    100,
+#'                    valids,
+#'                    min_data = 1,
+#'                    learning_rate = 1,
+#'                    early_stopping_rounds = 10)
+#' lgb.get.eval.result(model, "test", "l2")
+#' }
+#' 
 #' @rdname lgb.get.eval.result
 #' @export
 lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) {

R-package/R/lgb.prepare.R

@@ -8,35 +8,35 @@
 #' 
 #' @examples
 #' \dontrun{
-#'   library(lightgbm)
-#'   data(iris)
-#'   
-#'   str(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 ...
-#'   # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#'   
-#'   str(lgb.prepare(data = iris)) # Convert all factors/chars to numeric
-#'   # '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 ...
-#'   # $ 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 ...
-#'   
-#'   # When lightgbm package is installed, and you do not want to load it
-#'   # You can still use the function!
-#'   lgb.unloader()
-#'   str(lightgbm::lgb.prepare(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 ...
-#'   # $ 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 ...
+#' library(lightgbm)
+#' data(iris)
+#' 
+#' str(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 ...
+#' # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+#' 
+#' str(lgb.prepare(data = iris)) # Convert all factors/chars to numeric
+#' # '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 ...
+#' # $ 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 ...
+#' 
+#' # When lightgbm package is installed, and you do not want to load it
+#' # You can still use the function!
+#' lgb.unloader()
+#' str(lightgbm::lgb.prepare(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 ...
+#' # $ 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 ...
 #' }
 #' 
 #' @export

R-package/R/lgb.prepare2.R

@@ -8,36 +8,36 @@
 #' 
 #' @examples
 #' \dontrun{
-#'   library(lightgbm)
-#'   data(iris)
-#'   
-#'   str(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 ...
-#'   # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#'   
-#'   str(lgb.prepare2(data = iris)) # Convert all factors/chars to integer
-#'   # '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 ...
-#'   # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
-#'   
-#'   # When lightgbm package is installed, and you do not want to load it
-#'   # You can still use the function!
-#'   lgb.unloader()
-#'   str(lightgbm::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 ...
-#'   # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
-#'   
+#' library(lightgbm)
+#' data(iris)
+#' 
+#' str(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 ...
+#' # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+#' 
+#' str(lgb.prepare2(data = iris)) # Convert all factors/chars to integer
+#' # '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 ...
+#' # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
+#' 
+#' # When lightgbm package is installed, and you do not want to load it
+#' # You can still use the function!
+#' lgb.unloader()
+#' str(lightgbm::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 ...
+#' # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
+#' 
 #' }
 #' 
 #' @export

R-package/R/lgb.prepare_rules.R

@@ -9,63 +9,63 @@
 #' 
 #' @examples
 #' \dontrun{
-#'   library(lightgbm)
-#'   data(iris)
-#'   
-#'   str(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 ...
-#'   # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#'   
-#'   new_iris <- lgb.prepare_rules(data = iris) # Autoconverter
-#'   str(new_iris$data)
-#'   # '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 ...
-#'   # $ 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 ...
-#'   
-#'   data(iris) # Erase iris dataset
-#'   iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
-#'   # Warning message:
-#'   In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
-#'     invalid factor level, NA generated
-#'   
-#'   # Use conversion using known rules
-#'   # Unknown factors become 0, excellent for sparse datasets
-#'   newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$rules)
-#'   
-#'   # Unknown factor is now zero, perfect for sparse datasets
-#'   newer_iris$data[1, ] # Species became 0 as it is an unknown factor
-#'   #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-#'   # 1          5.1         3.5          1.4         0.2       0
-#'   
-#'   newer_iris$data[1, 5] <- 1 # Put back real initial value
-#'   
-#'   # Is the newly created dataset equal? YES!
-#'   all.equal(new_iris$data, newer_iris$data)
-#'   # [1] TRUE
-#'   
-#'   # Can we test our own rules?
-#'   data(iris) # Erase iris dataset
-#'   
-#'   # We remapped values differently
-#'   personal_rules <- list(Species = c("setosa" = 3,
-#'                                      "versicolor" = 2,
-#'                                      "virginica" = 1))
-#'   newest_iris <- lgb.prepare_rules(data = iris, rules = personal_rules)
-#'   str(newest_iris$data) # SUCCESS!
-#'   # '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 ...
-#'   # $ 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  3 3 3 3 3 3 3 3 3 3 ...
-#'   
+#' library(lightgbm)
+#' data(iris)
+#' 
+#' str(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 ...
+#' # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+#' 
+#' new_iris <- lgb.prepare_rules(data = iris) # Autoconverter
+#' str(new_iris$data)
+#' # '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 ...
+#' # $ 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 ...
+#' 
+#' data(iris) # Erase iris dataset
+#' iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
+#' # Warning message:
+#' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
+#' #  invalid factor level, NA generated
+#' 
+#' # Use conversion using known rules
+#' # Unknown factors become 0, excellent for sparse datasets
+#' newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$rules)
+#' 
+#' # Unknown factor is now zero, perfect for sparse datasets
+#' newer_iris$data[1, ] # Species became 0 as it is an unknown factor
+#' #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+#' # 1          5.1         3.5          1.4         0.2       0
+#' 
+#' newer_iris$data[1, 5] <- 1 # Put back real initial value
+#' 
+#' # Is the newly created dataset equal? YES!
+#' all.equal(new_iris$data, newer_iris$data)
+#' # [1] TRUE
+#' 
+#' # Can we test our own rules?
+#' data(iris) # Erase iris dataset
+#' 
+#' # We remapped values differently
+#' personal_rules <- list(Species = c("setosa" = 3,
+#'                                    "versicolor" = 2,
+#'                                    "virginica" = 1))
+#' newest_iris <- lgb.prepare_rules(data = iris, rules = personal_rules)
+#' str(newest_iris$data) # SUCCESS!
+#' # '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 ...
+#' # $ 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  3 3 3 3 3 3 3 3 3 3 ...
+#' 
 #' }
 #' 
 #' @export

R-package/R/lgb.prepare_rules2.R

@@ -9,63 +9,63 @@
 #' 
 #' @examples
 #' \dontrun{
-#'   library(lightgbm)
-#'   data(iris)
-#'   
-#'   str(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 ...
-#'   # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#'   
-#'   new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
-#'   str(new_iris$data)
-#'   # '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 ...
-#'   # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
-#'   
-#'   data(iris) # Erase iris dataset
-#'   iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
-#'   # Warning message:
-#'   In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
-#'     invalid factor level, NA generated
-#'   
-#'   # Use conversion using known rules
-#'   # Unknown factors become 0, excellent for sparse datasets
-#'   newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
-#'   
-#'   # Unknown factor is now zero, perfect for sparse datasets
-#'   newer_iris$data[1, ] # Species became 0 as it is an unknown factor
-#'   #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-#'   # 1          5.1         3.5          1.4         0.2       0
-#'   
-#'   newer_iris$data[1, 5] <- 1 # Put back real initial value
-#'   
-#'   # Is the newly created dataset equal? YES!
-#'   all.equal(new_iris$data, newer_iris$data)
-#'   # [1] TRUE
-#'   
-#'   # Can we test our own rules?
-#'   data(iris) # Erase iris dataset
-#'   
-#'   # We remapped values differently
-#'   personal_rules <- list(Species = c("setosa" = 3L,
-#'                                      "versicolor" = 2L,
-#'                                      "virginica" = 1L))
-#'   newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
-#'   str(newest_iris$data) # SUCCESS!
-#'   # '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 ...
-#'   # $ 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     : int  3 3 3 3 3 3 3 3 3 3 ...
-#'   
+#' library(lightgbm)
+#' data(iris)
+#' 
+#' str(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 ...
+#' # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+#' 
+#' new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
+#' str(new_iris$data)
+#' # '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 ...
+#' # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
+#' 
+#' data(iris) # Erase iris dataset
+#' iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
+#' # Warning message:
+#' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
+#' #  invalid factor level, NA generated
+#' 
+#' # Use conversion using known rules
+#' # Unknown factors become 0, excellent for sparse datasets
+#' newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
+#' 
+#' # Unknown factor is now zero, perfect for sparse datasets
+#' newer_iris$data[1, ] # Species became 0 as it is an unknown factor
+#' #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+#' # 1          5.1         3.5          1.4         0.2       0
+#' 
+#' newer_iris$data[1, 5] <- 1 # Put back real initial value
+#' 
+#' # Is the newly created dataset equal? YES!
+#' all.equal(new_iris$data, newer_iris$data)
+#' # [1] TRUE
+#' 
+#' # Can we test our own rules?
+#' data(iris) # Erase iris dataset
+#' 
+#' # We remapped values differently
+#' personal_rules <- list(Species = c("setosa" = 3L,
+#'                                    "versicolor" = 2L,
+#'                                    "virginica" = 1L))
+#' newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
+#' str(newest_iris$data) # SUCCESS!
+#' # '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 ...
+#' # $ 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     : int  3 3 3 3 3 3 3 3 3 3 ...
+#' 
 #' }
 #' 
 #' @export

R-package/R/readRDS.lgb.Booster.R

@@ -9,24 +9,24 @@
 #' 
 #' @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)
-#'   params <- list(objective = "regression", metric = "l2")
-#'   valids <- list(test = dtest)
-#'   model <- lgb.train(params,
-#'                      dtrain,
-#'                      100,
-#'                      valids,
-#'                      min_data = 1,
-#'                      learning_rate = 1,
-#'                      early_stopping_rounds = 10)
-#'   saveRDS.lgb.Booster(model, "model.rds")
-#'   new_model <- readRDS.lgb.Booster("model.rds")
+#' 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)
+#' params <- list(objective = "regression", metric = "l2")
+#' valids <- list(test = dtest)
+#' model <- lgb.train(params,
+#'                    dtrain,
+#'                    100,
+#'                    valids,
+#'                    min_data = 1,
+#'                    learning_rate = 1,
+#'                    early_stopping_rounds = 10)
+#' saveRDS.lgb.Booster(model, "model.rds")
+#' new_model <- readRDS.lgb.Booster("model.rds")
 #' }
 #' 
 #' @export

R-package/R/saveRDS.lgb.Booster.R

@@ -14,23 +14,23 @@
 #' 
 #' @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)
-#'   params <- list(objective = "regression", metric = "l2")
-#'   valids <- list(test = dtest)
-#'   model <- lgb.train(params,
-#'                      dtrain,
-#'                      100,
-#'                      valids,
-#'                      min_data = 1,
-#'                      learning_rate = 1,
-#'                      early_stopping_rounds = 10)
-#'   saveRDS.lgb.Booster(model, "model.rds")
+#' 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)
+#' params <- list(objective = "regression", metric = "l2")
+#' valids <- list(test = dtest)
+#' model <- lgb.train(params,
+#'                    dtrain,
+#'                    100,
+#'                    valids,
+#'                    min_data = 1,
+#'                    learning_rate = 1,
+#'                    early_stopping_rounds = 10)
+#' saveRDS.lgb.Booster(model, "model.rds")
 #' }
 #' 
 #' @export

R-package/demo/boost_from_prediction.R

@@ -16,7 +16,6 @@ print("Start running example to start from a initial prediction")
 param <- list(num_leaves = 4,
               learning_rate = 1,
               nthread = 2,
-              silent = 1,
               objective = "binary")
 bst <- lgb.train(param, dtrain, 1, valids = valids)
 

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

@@ -24,3 +24,25 @@ vector of evaluation result
 \description{
 Get record evaluation result from booster
 }
+\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)
+params <- list(objective = "regression", metric = "l2")
+valids <- list(test = dtest)
+model <- lgb.train(params,
+                   dtrain,
+                   100,
+                   valids,
+                   min_data = 1,
+                   learning_rate = 1,
+                   early_stopping_rounds = 10)
+lgb.get.eval.result(model, "test", "l2")
+}
+
+}

R-package/man/lgb.prepare.Rd

@@ -17,35 +17,35 @@ Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors
 }
 \examples{
 \dontrun{
-  library(lightgbm)
-  data(iris)
-  
-  str(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 ...
-  # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-  
-  str(lgb.prepare(data = iris)) # Convert all factors/chars to numeric
-  # '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 ...
-  # $ 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 ...
-  
-  # When lightgbm package is installed, and you do not want to load it
-  # You can still use the function!
-  lgb.unloader()
-  str(lightgbm::lgb.prepare(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 ...
-  # $ 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 ...
+library(lightgbm)
+data(iris)
+
+str(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 ...
+# $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+
+str(lgb.prepare(data = iris)) # Convert all factors/chars to numeric
+# '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 ...
+# $ 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 ...
+
+# When lightgbm package is installed, and you do not want to load it
+# You can still use the function!
+lgb.unloader()
+str(lightgbm::lgb.prepare(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 ...
+# $ 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

@@ -17,36 +17,36 @@ Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors
 }
 \examples{
 \dontrun{
-  library(lightgbm)
-  data(iris)
-  
-  str(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 ...
-  # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-  
-  str(lgb.prepare2(data = iris)) # Convert all factors/chars to integer
-  # '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 ...
-  # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
-  
-  # When lightgbm package is installed, and you do not want to load it
-  # You can still use the function!
-  lgb.unloader()
-  str(lightgbm::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 ...
-  # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
-  
+library(lightgbm)
+data(iris)
+
+str(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 ...
+# $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+
+str(lgb.prepare2(data = iris)) # Convert all factors/chars to integer
+# '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 ...
+# $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
+
+# When lightgbm package is installed, and you do not want to load it
+# You can still use the function!
+lgb.unloader()
+str(lightgbm::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 ...
+# $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
+
 }
 
 }

R-package/man/lgb.prepare_rules.Rd

@@ -19,63 +19,63 @@ Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors
 }
 \examples{
 \dontrun{
-  library(lightgbm)
-  data(iris)
-  
-  str(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 ...
-  # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-  
-  new_iris <- lgb.prepare_rules(data = iris) # Autoconverter
-  str(new_iris$data)
-  # '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 ...
-  # $ 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 ...
-  
-  data(iris) # Erase iris dataset
-  iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
-  # Warning message:
-  In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
-    invalid factor level, NA generated
-  
-  # Use conversion using known rules
-  # Unknown factors become 0, excellent for sparse datasets
-  newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$rules)
-  
-  # Unknown factor is now zero, perfect for sparse datasets
-  newer_iris$data[1, ] # Species became 0 as it is an unknown factor
-  #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-  # 1          5.1         3.5          1.4         0.2       0
-  
-  newer_iris$data[1, 5] <- 1 # Put back real initial value
-  
-  # Is the newly created dataset equal? YES!
-  all.equal(new_iris$data, newer_iris$data)
-  # [1] TRUE
-  
-  # Can we test our own rules?
-  data(iris) # Erase iris dataset
-  
-  # We remapped values differently
-  personal_rules <- list(Species = c("setosa" = 3,
-                                     "versicolor" = 2,
-                                     "virginica" = 1))
-  newest_iris <- lgb.prepare_rules(data = iris, rules = personal_rules)
-  str(newest_iris$data) # SUCCESS!
-  # '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 ...
-  # $ 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  3 3 3 3 3 3 3 3 3 3 ...
-  
+library(lightgbm)
+data(iris)
+
+str(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 ...
+# $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+
+new_iris <- lgb.prepare_rules(data = iris) # Autoconverter
+str(new_iris$data)
+# '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 ...
+# $ 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 ...
+
+data(iris) # Erase iris dataset
+iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
+# Warning message:
+# In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
+#  invalid factor level, NA generated
+
+# Use conversion using known rules
+# Unknown factors become 0, excellent for sparse datasets
+newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$rules)
+
+# Unknown factor is now zero, perfect for sparse datasets
+newer_iris$data[1, ] # Species became 0 as it is an unknown factor
+#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+# 1          5.1         3.5          1.4         0.2       0
+
+newer_iris$data[1, 5] <- 1 # Put back real initial value
+
+# Is the newly created dataset equal? YES!
+all.equal(new_iris$data, newer_iris$data)
+# [1] TRUE
+
+# Can we test our own rules?
+data(iris) # Erase iris dataset
+
+# We remapped values differently
+personal_rules <- list(Species = c("setosa" = 3,
+                                   "versicolor" = 2,
+                                   "virginica" = 1))
+newest_iris <- lgb.prepare_rules(data = iris, rules = personal_rules)
+str(newest_iris$data) # SUCCESS!
+# '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 ...
+# $ 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  3 3 3 3 3 3 3 3 3 3 ...
+
 }
 
 }

R-package/man/lgb.prepare_rules2.Rd

@@ -19,63 +19,63 @@ Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors
 }
 \examples{
 \dontrun{
-  library(lightgbm)
-  data(iris)
-  
-  str(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 ...
-  # $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-  
-  new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
-  str(new_iris$data)
-  # '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 ...
-  # $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
-  
-  data(iris) # Erase iris dataset
-  iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
-  # Warning message:
-  In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
-    invalid factor level, NA generated
-  
-  # Use conversion using known rules
-  # Unknown factors become 0, excellent for sparse datasets
-  newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
-  
-  # Unknown factor is now zero, perfect for sparse datasets
-  newer_iris$data[1, ] # Species became 0 as it is an unknown factor
-  #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-  # 1          5.1         3.5          1.4         0.2       0
-  
-  newer_iris$data[1, 5] <- 1 # Put back real initial value
-  
-  # Is the newly created dataset equal? YES!
-  all.equal(new_iris$data, newer_iris$data)
-  # [1] TRUE
-  
-  # Can we test our own rules?
-  data(iris) # Erase iris dataset
-  
-  # We remapped values differently
-  personal_rules <- list(Species = c("setosa" = 3L,
-                                     "versicolor" = 2L,
-                                     "virginica" = 1L))
-  newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
-  str(newest_iris$data) # SUCCESS!
-  # '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 ...
-  # $ 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     : int  3 3 3 3 3 3 3 3 3 3 ...
-  
+library(lightgbm)
+data(iris)
+
+str(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 ...
+# $ 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     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
+
+new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
+str(new_iris$data)
+# '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 ...
+# $ 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     : int  1 1 1 1 1 1 1 1 1 1 ...
+
+data(iris) # Erase iris dataset
+iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
+# Warning message:
+# In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
+#  invalid factor level, NA generated
+
+# Use conversion using known rules
+# Unknown factors become 0, excellent for sparse datasets
+newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
+
+# Unknown factor is now zero, perfect for sparse datasets
+newer_iris$data[1, ] # Species became 0 as it is an unknown factor
+#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+# 1          5.1         3.5          1.4         0.2       0
+
+newer_iris$data[1, 5] <- 1 # Put back real initial value
+
+# Is the newly created dataset equal? YES!
+all.equal(new_iris$data, newer_iris$data)
+# [1] TRUE
+
+# Can we test our own rules?
+data(iris) # Erase iris dataset
+
+# We remapped values differently
+personal_rules <- list(Species = c("setosa" = 3L,
+                                   "versicolor" = 2L,
+                                   "virginica" = 1L))
+newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
+str(newest_iris$data) # SUCCESS!
+# '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 ...
+# $ 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     : int  3 3 3 3 3 3 3 3 3 3 ...
+
 }
 
 }

R-package/man/readRDS.lgb.Booster.Rd

@@ -19,24 +19,24 @@ Attemps to load a model using RDS.
 }
 \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)
-  params <- list(objective = "regression", metric = "l2")
-  valids <- list(test = dtest)
-  model <- lgb.train(params,
-                     dtrain,
-                     100,
-                     valids,
-                     min_data = 1,
-                     learning_rate = 1,
-                     early_stopping_rounds = 10)
-  saveRDS.lgb.Booster(model, "model.rds")
-  new_model <- readRDS.lgb.Booster("model.rds")
+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)
+params <- list(objective = "regression", metric = "l2")
+valids <- list(test = dtest)
+model <- lgb.train(params,
+                   dtrain,
+                   100,
+                   valids,
+                   min_data = 1,
+                   learning_rate = 1,
+                   early_stopping_rounds = 10)
+saveRDS.lgb.Booster(model, "model.rds")
+new_model <- readRDS.lgb.Booster("model.rds")
 }
 
 }

R-package/man/saveRDS.lgb.Booster.Rd

@@ -30,23 +30,23 @@ Attemps to save a model using RDS. Has an additional parameter (\code{raw}) whic
 }
 \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)
-  params <- list(objective = "regression", metric = "l2")
-  valids <- list(test = dtest)
-  model <- lgb.train(params,
-                     dtrain,
-                     100,
-                     valids,
-                     min_data = 1,
-                     learning_rate = 1,
-                     early_stopping_rounds = 10)
-  saveRDS.lgb.Booster(model, "model.rds")
+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)
+params <- list(objective = "regression", metric = "l2")
+valids <- list(test = dtest)
+model <- lgb.train(params,
+                   dtrain,
+                   100,
+                   valids,
+                   min_data = 1,
+                   learning_rate = 1,
+                   early_stopping_rounds = 10)
+saveRDS.lgb.Booster(model, "model.rds")
 }
 
 }