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Remove silent param from demo and regenerate Rd files (#951)
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* Silent parameter does not exist anymore

* Fix #913

* Generate Rd files for R documentation
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@Laurae2 @guolinke
Laurae2 authored and guolinke committed Oct 6, 2017
1 parent 8b62e3b commit 97b9110
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21 changes: 21 additions & 0 deletions R-package/R/lgb.Booster.R
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Expand Up @@ -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) {
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58 changes: 29 additions & 29 deletions R-package/R/lgb.prepare.R
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Expand Up @@ -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
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60 changes: 30 additions & 30 deletions R-package/R/lgb.prepare2.R
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Original file line number Diff line number Diff line change
Expand Up @@ -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
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114 changes: 57 additions & 57 deletions R-package/R/lgb.prepare_rules.R
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Expand Up @@ -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
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