зеркало из https://github.com/microsoft/LightGBM.git
51 строка
1.7 KiB
R
51 строка
1.7 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/lgb.Dataset.R
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\name{set_field}
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\alias{set_field}
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\alias{set_field.lgb.Dataset}
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\title{Set one attribute of a \code{lgb.Dataset} object}
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\usage{
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set_field(dataset, field_name, data)
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\method{set_field}{lgb.Dataset}(dataset, field_name, data)
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}
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\arguments{
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\item{dataset}{Object of class \code{lgb.Dataset}}
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\item{field_name}{String with the name of the attribute to set. One of the following.
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\itemize{
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\item \code{label}: label lightgbm learns from ;
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\item \code{weight}: to do a weight rescale ;
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\item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to
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group rows together as ordered results from the same set of candidate results to be ranked.
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For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)},
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that means that you have 6 groups, where the first 10 records are in the first group,
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records 11-30 are in the second group, etc.}
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\item \code{init_score}: initial score is the base prediction lightgbm will boost from.
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}}
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\item{data}{The data for the field. See examples.}
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}
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\value{
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The \code{lgb.Dataset} you passed in.
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}
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\description{
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Set one attribute of a \code{lgb.Dataset}
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}
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\examples{
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\donttest{
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\dontshow{setLGBMthreads(2L)}
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\dontshow{data.table::setDTthreads(1L)}
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data(agaricus.train, package = "lightgbm")
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train <- agaricus.train
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dtrain <- lgb.Dataset(train$data, label = train$label)
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lgb.Dataset.construct(dtrain)
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labels <- lightgbm::get_field(dtrain, "label")
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lightgbm::set_field(dtrain, "label", 1 - labels)
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labels2 <- lightgbm::get_field(dtrain, "label")
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stopifnot(all.equal(labels2, 1 - labels))
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}
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}
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