wehub-resource-sync
96 lines
2.8 KiB
R
96 lines
2.8 KiB
R
% 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,
|
|
label = NULL,
|
|
weight = NULL,
|
|
group = NULL,
|
|
init_score = NULL,
|
|
params = list()
|
|
)
|
|
}
|
|
\arguments{
|
|
\item{dataset}{\code{lgb.Dataset} object, training data}
|
|
|
|
\item{data}{a \code{matrix} object, a \code{dgCMatrix} object,
|
|
a character representing a path to a text file (CSV, TSV, or LibSVM),
|
|
or a character representing a path to a binary \code{Dataset} file}
|
|
|
|
\item{label}{vector of labels to use as the target variable}
|
|
|
|
\item{weight}{numeric vector of sample weights}
|
|
|
|
\item{group}{used for learning-to-rank tasks. An integer vector describing how to
|
|
group rows together as ordered results from the same set of candidate results
|
|
to be ranked. For example, if you have a 100-document dataset with
|
|
\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups,
|
|
where the first 10 records are in the first group, records 11-30 are in the
|
|
second group, etc.}
|
|
|
|
\item{init_score}{initial score is the base prediction lightgbm will boost from}
|
|
|
|
\item{params}{a list of parameters. See
|
|
\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{
|
|
The "Dataset Parameters" section of the documentation} for a list of parameters
|
|
and valid values. If this is an empty list (the default), the validation Dataset
|
|
will have the same parameters as the Dataset passed to argument \code{dataset}.}
|
|
}
|
|
\value{
|
|
constructed dataset
|
|
}
|
|
\description{
|
|
Construct validation data according to training data
|
|
}
|
|
\examples{
|
|
\donttest{
|
|
\dontshow{setLGBMthreads(2L)}
|
|
\dontshow{data.table::setDTthreads(1L)}
|
|
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)
|
|
|
|
# parameters can be changed between the training data and validation set,
|
|
# for example to account for training data in a text file with a header row
|
|
# and validation data in a text file without it
|
|
train_file <- tempfile(pattern = "train_", fileext = ".csv")
|
|
write.table(
|
|
data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))
|
|
, file = train_file
|
|
, sep = ","
|
|
, col.names = TRUE
|
|
, row.names = FALSE
|
|
, quote = FALSE
|
|
)
|
|
|
|
valid_file <- tempfile(pattern = "valid_", fileext = ".csv")
|
|
write.table(
|
|
data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))
|
|
, file = valid_file
|
|
, sep = ","
|
|
, col.names = FALSE
|
|
, row.names = FALSE
|
|
, quote = FALSE
|
|
)
|
|
|
|
dtrain <- lgb.Dataset(
|
|
data = train_file
|
|
, params = list(has_header = TRUE)
|
|
)
|
|
dtrain$construct()
|
|
|
|
dvalid <- lgb.Dataset(
|
|
data = valid_file
|
|
, params = list(has_header = FALSE)
|
|
)
|
|
dvalid$construct()
|
|
}
|
|
}
|