remove some files.

R-package/NAMESPACE

-# Generated by roxygen2: do not edit by hand
-
 S3method("dimnames<-",lgb.Dataset)
 S3method(dim,lgb.Dataset)
 S3method(dimnames,lgb.Dataset)

R-package/man/agaricus.test.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lightgbm.R
-\docType{data}
-\name{agaricus.test}
-\alias{agaricus.test}
-\title{Test part from Mushroom Data Set}
-\format{A list containing a label vector, and a dgCMatrix object with 1611
-rows and 126 variables}
-\usage{
-data(agaricus.test)
-}
-\description{
-This data set is originally from the Mushroom data set,
-UCI Machine Learning Repository.
-}
-\details{
-This data set includes the following fields:
-
-\itemize{
- \item \code{label} the label for each record
- \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
-}
-}
-\references{
-https://archive.ics.uci.edu/ml/datasets/Mushroom
-
-Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
-[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
-School of Information and Computer Science.
-}
-\keyword{datasets}
-

R-package/man/agaricus.train.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lightgbm.R
-\docType{data}
-\name{agaricus.train}
-\alias{agaricus.train}
-\title{Training part from Mushroom Data Set}
-\format{A list containing a label vector, and a dgCMatrix object with 6513
-rows and 127 variables}
-\usage{
-data(agaricus.train)
-}
-\description{
-This data set is originally from the Mushroom data set,
-UCI Machine Learning Repository.
-}
-\details{
-This data set includes the following fields:
-
-\itemize{
- \item \code{label} the label for each record
- \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 126 columns.
-}
-}
-\references{
-https://archive.ics.uci.edu/ml/datasets/Mushroom
-
-Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
-[http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
-School of Information and Computer Science.
-}
-\keyword{datasets}
-

R-package/man/dim.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{dim.lgb.Dataset}
-\alias{dim.lgb.Dataset}
-\title{Dimensions of an lgb.Dataset}
-\usage{
-\method{dim}{lgb.Dataset}(x, ...)
-}
-\arguments{
-\item{x}{Object of class \code{lgb.Dataset}}
-
-\item{...}{other parameters}
-}
-\value{
-a vector of numbers of rows and of columns
-}
-\description{
-Returns a vector of numbers of rows and of columns in an \code{lgb.Dataset}.
-}
-\details{
-Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
-be directly used with an \code{lgb.Dataset} object.
-}
-\examples{
-\dontrun{
-library(lightgbm)
-data(agaricus.train, package = "lightgbm")
-train <- agaricus.train
-dtrain <- lgb.Dataset(train$data, label = train$label)
-
-stopifnot(nrow(dtrain) == nrow(train$data))
-stopifnot(ncol(dtrain) == ncol(train$data))
-stopifnot(all(dim(dtrain) == dim(train$data)))
-}
-
-}
-

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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{dimnames.lgb.Dataset}
-\alias{dimnames.lgb.Dataset}
-\alias{dimnames<-.lgb.Dataset}
-\title{Handling of column names of \code{lgb.Dataset}}
-\usage{
-\method{dimnames}{lgb.Dataset}(x)
-
-\method{dimnames}{lgb.Dataset}(x) <- value
-}
-\arguments{
-\item{x}{object of class \code{lgb.Dataset}}
-
-\item{value}{a list of two elements: the first one is ignored
-and the second one is column names}
-}
-\description{
-Only column names are supported for \code{lgb.Dataset}, thus setting of
-row names would have no effect and returned row names would be NULL.
-}
-\details{
-Generic \code{dimnames} methods are used by \code{colnames}.
-Since row names are irrelevant, it is recommended to use \code{colnames} directly.
-}
-\examples{
-\dontrun{
-library(lightgbm)
-data(agaricus.train, package = "lightgbm")
-train <- agaricus.train
-dtrain <- lgb.Dataset(train$data, label = train$label)
-lgb.Dataset.construct(dtrain)
-dimnames(dtrain)
-colnames(dtrain)
-colnames(dtrain) <- make.names(1:ncol(train$data))
-print(dtrain, verbose = TRUE)
-}
-
-}
-

R-package/man/getinfo.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{getinfo}
-\alias{getinfo}
-\alias{getinfo.lgb.Dataset}
-\title{Get information of an lgb.Dataset object}
-\usage{
-getinfo(dataset, ...)
-
-\method{getinfo}{lgb.Dataset}(dataset, name, ...)
-}
-\arguments{
-\item{dataset}{Object of class \code{lgb.Dataset}}
-
-\item{...}{other parameters}
-
-\item{name}{the name of the information field to get (see details)}
-}
-\value{
-info data
-}
-\description{
-Get information of an lgb.Dataset object
-}
-\details{
-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 ;
-}
-}
-\examples{
-\dontrun{
-library(lightgbm)
-data(agaricus.train, package = "lightgbm")
-train <- agaricus.train
-dtrain <- lgb.Dataset(train$data, label = train$label)
-lgb.Dataset.construct(dtrain)
-
-labels <- lightgbm::getinfo(dtrain, "label")
-lightgbm::setinfo(dtrain, "label", 1 - labels)
-
-labels2 <- lightgbm::getinfo(dtrain, "label")
-stopifnot(all(labels2 == 1 - labels))
-}
-
-}
-

R-package/man/lgb.Dataset.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{lgb.Dataset}
-\alias{lgb.Dataset}
-\title{Construct lgb.Dataset object}
-\usage{
-lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
-  categorical_feature = NULL, free_raw_data = TRUE, info = list(), ...)
-}
-\arguments{
-\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename}
-
-\item{params}{a list of parameters}
-
-\item{reference}{reference dataset}
-
-\item{colnames}{names of columns}
-
-\item{categorical_feature}{categorical features}
-
-\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{...}{other information to pass to \code{info} or parameters pass to \code{params}}
-}
-\value{
-constructed dataset
-}
-\description{
-Construct lgb.Dataset object from dense matrix, sparse matrix
-or local file (that was created previously by saving an \code{lgb.Dataset}).
-}
-\examples{
-\dontrun{
-library(lightgbm)
-data(agaricus.train, package = "lightgbm")
-train <- agaricus.train
-dtrain <- lgb.Dataset(train$data, label = train$label)
-lgb.Dataset.save(dtrain, "lgb.Dataset.data")
-dtrain <- lgb.Dataset("lgb.Dataset.data")
-lgb.Dataset.construct(dtrain)
-}
-
-}
-

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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{lgb.Dataset.construct}
-\alias{lgb.Dataset.construct}
-\title{Construct Dataset explicitly}
-\usage{
-lgb.Dataset.construct(dataset)
-}
-\arguments{
-\item{dataset}{Object of class \code{lgb.Dataset}}
-}
-\description{
-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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{lgb.Dataset.create.valid}
-\alias{lgb.Dataset.create.valid}
-\title{Construct validation data}
-\usage{
-lgb.Dataset.create.valid(dataset, data, info = list(), ...)
-}
-\arguments{
-\item{dataset}{\code{lgb.Dataset} object, training data}
-
-\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{...}{other information to pass to \code{info}.}
-}
-\value{
-constructed dataset
-}
-\description{
-Construct validation data according to training data
-}
-\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)
-}
-
-}
-

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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{lgb.Dataset.save}
-\alias{lgb.Dataset.save}
-\title{Save \code{lgb.Dataset} to a binary file}
-\usage{
-lgb.Dataset.save(dataset, fname)
-}
-\arguments{
-\item{dataset}{object of class \code{lgb.Dataset}}
-
-\item{fname}{object filename of output file}
-}
-\value{
-passed dataset
-}
-\description{
-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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{lgb.Dataset.set.categorical}
-\alias{lgb.Dataset.set.categorical}
-\title{Set categorical feature of \code{lgb.Dataset}}
-\usage{
-lgb.Dataset.set.categorical(dataset, categorical_feature)
-}
-\arguments{
-\item{dataset}{object of class \code{lgb.Dataset}}
-
-\item{categorical_feature}{categorical features}
-}
-\value{
-passed dataset
-}
-\description{
-Set categorical feature of \code{lgb.Dataset}
-}
-\examples{
-\dontrun{
-library(lightgbm)
-data(agaricus.train, package = "lightgbm")
-train <- agaricus.train
-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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Dataset.R
-\name{lgb.Dataset.set.reference}
-\alias{lgb.Dataset.set.reference}
-\title{Set reference of \code{lgb.Dataset}}
-\usage{
-lgb.Dataset.set.reference(dataset, reference)
-}
-\arguments{
-\item{dataset}{object of class \code{lgb.Dataset}}
-
-\item{reference}{object of class \code{lgb.Dataset}}
-}
-\value{
-passed dataset
-}
-\description{
-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
-dtrain <- lgb.Dataset(train$data, label = train$label)
-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.dump.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Booster.R
-\name{lgb.dump}
-\alias{lgb.dump}
-\title{Dump LightGBM model to json}
-\usage{
-lgb.dump(booster, num_iteration = NULL)
-}
-\arguments{
-\item{booster}{Object of class \code{lgb.Booster}}
-
-\item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration}
-}
-\value{
-json format of model
-}
-\description{
-Dump LightGBM model to json
-}
-\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)
-json_model <- lgb.dump(model)
-}
-
-}
-

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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Booster.R
-\name{lgb.get.eval.result}
-\alias{lgb.get.eval.result}
-\title{Get record evaluation result from booster}
-\usage{
-lgb.get.eval.result(booster, data_name, eval_name, iters = NULL,
-  is_err = FALSE)
-}
-\arguments{
-\item{booster}{Object of class \code{lgb.Booster}}
-
-\item{data_name}{name of dataset}
-
-\item{eval_name}{name of evaluation}
-
-\item{iters}{iterations, NULL will return all}
-
-\item{is_err}{TRUE will return evaluation error instead}
-}
-\value{
-vector of evaluation result
-}
-\description{
-Get record evaluation result from booster
-}
-

R-package/man/lgb.importance.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.importance.R
-\name{lgb.importance}
-\alias{lgb.importance}
-\title{Compute feature importance in a model}
-\usage{
-lgb.importance(model, percentage = TRUE)
-}
-\arguments{
-\item{model}{object of class \code{lgb.Booster}.}
-
-\item{percentage}{whether to show importance in relative percentage.}
-}
-\value{
-For a tree model, a \code{data.table} with the following columns:
-\itemize{
-  \item \code{Feature} Feature names in the model.
-  \item \code{Gain} The total gain of this feature's splits.
-  \item \code{Cover} The number of observation related to this feature.
-  \item \code{Frequency} The number of times a feature splited in trees.
-}
-}
-\description{
-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",
-              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_imp1 <- lgb.importance(model, percentage = TRUE)
-tree_imp2 <- lgb.importance(model, percentage = FALSE)
-}
-
-}
-

R-package/man/lgb.interprete.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.interprete.R
-\name{lgb.interprete}
-\alias{lgb.interprete}
-\title{Compute feature contribution of prediction}
-\usage{
-lgb.interprete(model, data, idxset, num_iteration = NULL)
-}
-\arguments{
-\item{model}{object of class \code{lgb.Booster}.}
-
-\item{data}{a matrix object or a dgCMatrix object.}
-
-\item{idxset}{a integer vector of indices of rows needed.}
-
-\item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration.}
-}
-\value{
-For regression, binary classification and lambdarank model, a \code{list} of \code{data.table} with the following columns:
-\itemize{
-  \item \code{Feature} Feature names in the model.
-  \item \code{Contribution} The total contribution of this feature's splits.
-}
-For multiclass classification, a \code{list} of \code{data.table} with the Feature column and Contribution columns to each class.
-}
-\description{
-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")
-train <- agaricus.train
-dtrain <- lgb.Dataset(train$data, label = train$label)
-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)
-model <- lgb.train(params, dtrain, 20)
-
-tree_interpretation <- lgb.interprete(model, test$data, 1:5)
-}
-
-}
-

R-package/man/lgb.load.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.Booster.R
-\name{lgb.load}
-\alias{lgb.load}
-\title{Load LightGBM model}
-\usage{
-lgb.load(filename)
-}
-\arguments{
-\item{filename}{path of model file}
-}
-\value{
-booster
-}
-\description{
-Load LightGBM model from saved model file
-}
-\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.save(model, "model.txt")
-load_booster <- lgb.load("model.txt")
-}
-
-}
-

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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.model.dt.tree.R
-\name{lgb.model.dt.tree}
-\alias{lgb.model.dt.tree}
-\title{Parse a LightGBM model json dump}
-\usage{
-lgb.model.dt.tree(model, num_iteration = NULL)
-}
-\arguments{
-\item{model}{object of class \code{lgb.Booster}}
-}
-\value{
-A \code{data.table} with detailed information about model trees' nodes and leafs.
-
-The columns of the \code{data.table} are:
-
-\itemize{
- \item \code{tree_index}: ID of a tree in a model (integer)
- \item \code{split_index}: ID of a node in a tree (integer)
- \item \code{split_feature}: for a node, it's a feature name (character);
-                             for a leaf, it simply labels it as \code{"NA"}
- \item \code{node_parent}: ID of the parent node for current node (integer)
- \item \code{leaf_index}: ID of a leaf in a tree (integer)
- \item \code{leaf_parent}: ID of the parent node for current leaf (integer)
- \item \code{split_gain}: Split gain of a node
- \item \code{threshold}: Spliting threshold value of a node
- \item \code{decision_type}: Decision type of a node
- \item \code{internal_value}: Node value
- \item \code{internal_count}: The number of observation collected by a node
- \item \code{leaf_value}: Leaf value
- \item \code{leaf_count}: The number of observation collected by a leaf
-}
-}
-\description{
-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",
-              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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.plot.importance.R
-\name{lgb.plot.importance}
-\alias{lgb.plot.importance}
-\title{Plot feature importance as a bar graph}
-\usage{
-lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain",
-  left_margin = 10, cex = NULL)
-}
-\arguments{
-\item{tree_imp}{a \code{data.table} returned by \code{\link{lgb.importance}}.}
-
-\item{top_n}{maximal number of top features to include into the plot.}
-
-\item{measure}{the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".}
-
-\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.}
-
-\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
-}
-\value{
-The \code{lgb.plot.importance} function creates a \code{barplot}
-and silently returns a processed data.table with \code{top_n} features sorted by defined importance.
-}
-\description{
-Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.
-}
-\details{
-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

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lgb.plot.interpretation.R
-\name{lgb.plot.interpretation}
-\alias{lgb.plot.interpretation}
-\title{Plot feature contribution as a bar graph}
-\usage{
-lgb.plot.interpretation(tree_interpretation_dt, top_n = 10, cols = 1,
-  left_margin = 10, cex = NULL)
-}
-\arguments{
-\item{tree_interpretation_dt}{a \code{data.table} returned by \code{\link{lgb.interprete}}.}
-
-\item{top_n}{maximal number of top features to include into the plot.}
-
-\item{cols}{the column numbers of layout, will be used only for multiclass classification feature contribution.}
-
-\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.}
-
-\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.}
-}
-\value{
-The \code{lgb.plot.interpretation} function creates a \code{barplot}.
-}
-\description{
-Plot previously calculated feature contribution as a bar graph.
-}
-\details{
-The graph represents each feature as a horizontal bar of length proportional to the defined contribution of a feature.
-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))}
-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)))
-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)
-model <- lgb.train(params, dtrain, 20)
-
-tree_interpretation <- lgb.interprete(model, test$data, 1:5)
-lgb.plot.interpretation(tree_interpretation[[1]], top_n = 10)
-}
-
-}
-