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Unverified Commit 4c8418ea authored by Nikita Titov's avatar Nikita Titov Committed by GitHub
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[docs] R docs cleanup (#2375) 

* R docs cleanup

* regenerated R documentation files
parent df26b65d
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R-package/R/lgb.Dataset.R
View file @ 4c8418ea
......@@ -635,9 +635,9 @@ Dataset <- R6::R6Class(
)
)
#' Construct lgb.Dataset object
#' Construct \code{lgb.Dataset} object
#'
#' Construct lgb.Dataset object from dense matrix, sparse matrix
#' Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
#' or local file (that was created previously by saving an \code{lgb.Dataset}).
#'
#' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
......@@ -646,7 +646,7 @@ Dataset <- R6::R6Class(
#' @param colnames names of columns
#' @param categorical_feature categorical features
#' @param free_raw_data TRUE for need to free raw data after construct
#' @param info a list of information of the lgb.Dataset object
#' @param info a list of information of the \code{lgb.Dataset} object
#' @param ... other information to pass to \code{info} or parameters pass to \code{params}
#'
#' @return constructed dataset
......@@ -690,7 +690,7 @@ lgb.Dataset <- function(data,
#'
#' @param dataset \code{lgb.Dataset} object, training data
#' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
#' @param info a list of information of the lgb.Dataset object
#' @param info a list of information of the \code{lgb.Dataset} object
#' @param ... other information to pass to \code{info}.
#'
#' @return constructed dataset
......@@ -741,7 +741,7 @@ lgb.Dataset.construct <- function(dataset) {
}
#' Dimensions of an lgb.Dataset
#' Dimensions of an \code{lgb.Dataset}
#'
#' Returns a vector of numbers of rows and of columns in an \code{lgb.Dataset}.
#' @param x Object of class \code{lgb.Dataset}
......@@ -852,9 +852,9 @@ dimnames.lgb.Dataset <- function(x) {
#' Slice a dataset
#'
#' Get a new \code{lgb.Dataset} containing the specified rows of
#' original lgb.Dataset object
#' original \code{lgb.Dataset} object
#'
#' @param dataset Object of class "lgb.Dataset"
#' @param dataset Object of class \code{lgb.Dataset}
#' @param idxset a integer vector of indices of rows needed
#' @param ... other parameters (currently not used)
#' @return constructed sub dataset
......@@ -888,7 +888,7 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
}
#' Get information of an lgb.Dataset object
#' Get information of an \code{lgb.Dataset} object
#'
#' @param dataset Object of class \code{lgb.Dataset}
#' @param name the name of the information field to get (see details)
......@@ -901,8 +901,8 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
#' \itemize{
#' \item \code{label}: label lightgbm learn from ;
#' \item \code{weight}: to do a weight rescale ;
#' \item \code{group}: group size
#' \item \code{init_score}: initial score is the base prediction lightgbm will boost from ;
#' \item \code{group}: group size ;
#' \item \code{init_score}: initial score is the base prediction lightgbm will boost from.
#' }
#'
#' @examples
......@@ -937,9 +937,9 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
}
#' Set information of an lgb.Dataset object
#' Set information of an \code{lgb.Dataset} object
#'
#' @param dataset Object of class "lgb.Dataset"
#' @param dataset Object of class \code{lgb.Dataset}
#' @param name the name of the field to get
#' @param info the specific field of information to set
#' @param ... other parameters
......
R-package/R/lgb.prepare.R
View file @ 4c8418ea
#' Data preparator for LightGBM datasets (numeric)
#'
#' 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.
#' Attempts to prepare a clean dataset to prepare to put in a \code{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.
#'
#' @param data A data.frame or data.table to prepare.
#'
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#'
#' @examples
#' 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 ...
#'
#' \dontrun{
#' # When lightgbm package is installed, and you do not want to load it
......
R-package/R/lgb.prepare2.R
View file @ 4c8418ea
#' Data preparator for LightGBM datasets (integer)
#'
#' 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.
#' Attempts to prepare a clean dataset to prepare to put in a \code{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.
#'
#' @param data A data.frame or data.table to prepare.
#'
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#'
#' @examples
#' 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 ...
#'
#' # 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 ...
#' # $ 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 ...
#'
#' \dontrun{
#' # When lightgbm package is installed, and you do not want to load it
......
R-package/R/lgb.prepare_rules.R
View file @ 4c8418ea
#' Data preparator for LightGBM datasets with rules (numeric)
#'
#' 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.
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.
#'
#' @param data A data.frame or data.table to prepare.
#' @param rules A set of rules from the data preparator, if already used.
#'
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#'
#' @examples
#' 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$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
#' # Warning message:
#' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L, :
#' # invalid factor level, NA generated
#'
#' # Use conversion using known rules
#' # Unknown factors become 0, excellent for sparse datasets
......@@ -40,14 +25,11 @@
#'
#' # Unknown factor is now zero, perfect for sparse datasets
#' newer_iris$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$data)
#' # [1] TRUE
#'
#' # Can we test our own rules?
#' data(iris) # Erase iris dataset
......@@ -58,12 +40,6 @@
#' "virginica" = 1))
#' newest_iris <- lgb.prepare_rules(data = iris, rules = personal_rules)
#' str(newest_iris$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 ...
#'
#' @importFrom data.table set
#' @export
......
R-package/R/lgb.prepare_rules2.R
View file @ 4c8418ea
#' Data preparator for LightGBM datasets with rules (integer)
#'
#' 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.
#' Attempts to prepare a clean dataset to prepare to put in a \code{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.
#'
#' @param data A data.frame or data.table to prepare.
#' @param rules A set of rules from the data preparator, if already used.
#'
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#'
#' @examples
#' 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$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
#' # Warning message:
#' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L, :
#' # invalid factor level, NA generated
#'
#' # Use conversion using known rules
#' # Unknown factors become 0, excellent for sparse datasets
......@@ -40,14 +25,11 @@
#'
#' # Unknown factor is now zero, perfect for sparse datasets
#' newer_iris$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$data)
#' # [1] TRUE
#'
#' # Can we test our own rules?
#' data(iris) # Erase iris dataset
......@@ -58,12 +40,6 @@
#' "virginica" = 1L))
#' newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
#' str(newest_iris$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 ...
#'
#' @importFrom data.table set
#' @export
......
R-package/R/readRDS.lgb.Booster.R
View file @ 4c8418ea
#' readRDS for lgb.Booster models
#' readRDS for \code{lgb.Booster} models
#'
#' Attempts to load a model using RDS.
#'
#' @param file a connection or the name of the file where the R object is saved to or read from.
#' @param refhook a hook function for handling reference objects.
#'
#' @return lgb.Booster.
#' @return \code{lgb.Booster}.
#'
#' @examples
#' library(lightgbm)
......
R-package/R/saveRDS.lgb.Booster.R
View file @ 4c8418ea
#' saveRDS for lgb.Booster models
#' saveRDS for \code{lgb.Booster} models
#'
#' Attempts to save a model using RDS. Has an additional parameter (\code{raw}) which decides whether to save the raw model or not.
#'
......
R-package/man/dim.Rd
View file @ 4c8418ea
......@@ -2,7 +2,7 @@
% Please edit documentation in R/lgb.Dataset.R
\name{dim.lgb.Dataset}
\alias{dim.lgb.Dataset}
\title{Dimensions of an lgb.Dataset}
\title{Dimensions of an \code{lgb.Dataset}}
\usage{
\method{dim}{lgb.Dataset}(x, ...)
}
......
R-package/man/getinfo.Rd
View file @ 4c8418ea
......@@ -3,7 +3,7 @@
\name{getinfo}
\alias{getinfo}
\alias{getinfo.lgb.Dataset}
\title{Get information of an lgb.Dataset object}
\title{Get information of an \code{lgb.Dataset} object}
\usage{
getinfo(dataset, ...)
......@@ -20,7 +20,7 @@ getinfo(dataset, ...)
info data
}
\description{
Get information of an lgb.Dataset object
Get information of an \code{lgb.Dataset} object
}
\details{
The \code{name} field can be one of the following:
......@@ -28,8 +28,8 @@ The \code{name} field can be one of the following:
\itemize{
\item \code{label}: label lightgbm learn from ;
\item \code{weight}: to do a weight rescale ;
\item \code{group}: group size
\item \code{init_score}: initial score is the base prediction lightgbm will boost from ;
\item \code{group}: group size ;
\item \code{init_score}: initial score is the base prediction lightgbm will boost from.
}
}
\examples{
......
R-package/man/lgb.Dataset.Rd
View file @ 4c8418ea
......@@ -2,7 +2,7 @@
% Please edit documentation in R/lgb.Dataset.R
\name{lgb.Dataset}
\alias{lgb.Dataset}
\title{Construct lgb.Dataset object}
\title{Construct \code{lgb.Dataset} object}
\usage{
lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
categorical_feature = NULL, free_raw_data = TRUE, info = list(),
......@@ -21,7 +21,7 @@ lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
\item{free_raw_data}{TRUE for need to free raw data after construct}
\item{info}{a list of information of the lgb.Dataset object}
\item{info}{a list of information of the \code{lgb.Dataset} object}
\item{...}{other information to pass to \code{info} or parameters pass to \code{params}}
}
......@@ -29,7 +29,7 @@ lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
constructed dataset
}
\description{
Construct lgb.Dataset object from dense matrix, sparse matrix
Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
or local file (that was created previously by saving an \code{lgb.Dataset}).
}
\examples{
......
R-package/man/lgb.Dataset.create.valid.Rd
View file @ 4c8418ea
......@@ -11,7 +11,7 @@ lgb.Dataset.create.valid(dataset, data, info = list(), ...)
\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename}
\item{info}{a list of information of the lgb.Dataset object}
\item{info}{a list of information of the \code{lgb.Dataset} object}
\item{...}{other information to pass to \code{info}.}
}
......
R-package/man/lgb.Dataset.save.Rd
View file @ 4c8418ea
......@@ -18,7 +18,6 @@ passed dataset
Save \code{lgb.Dataset} to a binary file
}
\examples{
library(lightgbm)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
......
R-package/man/lgb.cv.Rd
View file @ 4c8418ea
......@@ -92,8 +92,8 @@ params <- list(objective = "regression", metric = "l2")
model <- lgb.cv(params,
dtrain,
10,
nfold = 5,
nfold = 3,
min_data = 1,
learning_rate = 1,
early_stopping_rounds = 10)
early_stopping_rounds = 5)
}
R-package/man/lgb.dump.Rd
View file @ 4c8418ea
......@@ -29,11 +29,11 @@ params <- list(objective = "regression", metric = "l2")
valids <- list(test = dtest)
model <- lgb.train(params,
dtrain,
100,
10,
valids,
min_data = 1,
learning_rate = 1,
early_stopping_rounds = 10)
early_stopping_rounds = 5)
json_model <- lgb.dump(model)
}
R-package/man/lgb.get.eval.result.Rd
View file @ 4c8418ea
......@@ -36,11 +36,10 @@ params <- list(objective = "regression", metric = "l2")
valids <- list(test = dtest)
model <- lgb.train(params,
dtrain,
100,
10,
valids,
min_data = 1,
learning_rate = 1,
early_stopping_rounds = 10)
early_stopping_rounds = 5)
lgb.get.eval.result(model, "test", "l2")
}
R-package/man/lgb.importance.Rd
View file @ 4c8418ea
......@@ -30,10 +30,9 @@ 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)
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, 10)
tree_imp1 <- lgb.importance(model, percentage = TRUE)
tree_imp2 <- lgb.importance(model, percentage = FALSE)
......
R-package/man/lgb.interprete.Rd
View file @ 4c8418ea
......@@ -44,7 +44,7 @@ params <- list(
, min_data_in_leaf = 1
, min_sum_hessian_in_leaf = 1
)
model <- lgb.train(params, dtrain, 20)
model <- lgb.train(params, dtrain, 10)
tree_interpretation <- lgb.interprete(model, test$data, 1:5)
......
R-package/man/lgb.load.Rd
View file @ 4c8418ea
......@@ -31,11 +31,11 @@ params <- list(objective = "regression", metric = "l2")
valids <- list(test = dtest)
model <- lgb.train(params,
dtrain,
100,
10,
valids,
min_data = 1,
learning_rate = 1,
early_stopping_rounds = 10)
early_stopping_rounds = 5)
lgb.save(model, "model.txt")
load_booster <- lgb.load(filename = "model.txt")
model_string <- model$save_model_to_string(NULL) # saves best iteration
......
R-package/man/lgb.model.dt.tree.Rd
View file @ 4c8418ea
......@@ -45,10 +45,9 @@ 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)
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, 10)
tree_dt <- lgb.model.dt.tree(model)
......
R-package/man/lgb.plot.importance.Rd
View file @ 4c8418ea
......@@ -43,7 +43,7 @@ params <- list(
, min_sum_hessian_in_leaf = 1
)
model <- lgb.train(params, dtrain, 20)
model <- lgb.train(params, dtrain, 10)
tree_imp <- lgb.importance(model, percentage = TRUE)
lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain")
......
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