gpb.Dataset: Construct 'gpb.Dataset' object

In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

Construct gpb.Dataset object

Description

Construct gpb.Dataset object from dense matrix, sparse matrix or local file (that was created previously by saving an gpb.Dataset).

Usage

gpb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
  categorical_feature = NULL, free_raw_data = FALSE, info = list(), ...)

Arguments

data	a matrix object, a dgCMatrix object or a character representing a filename
params	a list of parameters. See the "Dataset Parameters" section of the parameter documentation for a list of parameters and valid values.
reference	reference dataset. When GPBoost creates a Dataset, it does some preprocessing like binning continuous features into histograms. If you want to apply the same bin boundaries from an existing dataset to new data, pass that existing Dataset to this argument.
colnames	names of columns
categorical_feature	categorical features. This can either be a character vector of feature names or an integer vector with the indices of the features (e.g. c(1L, 10L) to say "the first and tenth columns").
free_raw_data	GPBoost constructs its data format, called a "Dataset", from tabular data. By default, this Dataset object on the R side does keep a copy of the raw data. If you set free_raw_data = TRUE, no copy of the raw data is kept (this reduces memory usage)
info	a list of information of the gpb.Dataset object
...	other information to pass to info or parameters pass to params

Value

constructed dataset

Examples

data(agaricus.train, package = "gpboost")
train <- agaricus.train
dtrain <- gpb.Dataset(train$data, label = train$label)
data_file <- tempfile(fileext = ".data")
gpb.Dataset.save(dtrain, data_file)
dtrain <- gpb.Dataset(data_file)
gpb.Dataset.construct(dtrain)

gpboost documentation built on Oct. 24, 2023, 9:09 a.m.