Construct lgb.Dataset
object from dense matrix, sparse matrix
or local file (that was created previously by saving an lgb.Dataset
).
lgb.Dataset( data, params = list(), reference = NULL, colnames = NULL, categorical_feature = NULL, free_raw_data = TRUE, label = NULL, weight = NULL, group = NULL, init_score = NULL )
data | a |
---|---|
params | a list of parameters. See The "Dataset Parameters" section of the documentation for a list of parameters and valid values. |
reference | reference dataset. When LightGBM 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 |
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.
|
free_raw_data | LightGBM constructs its data format, called a "Dataset", from tabular data.
By default, that Dataset object on the R side does not keep a copy of the raw data.
This reduces LightGBM's memory consumption, but it means that the Dataset object
cannot be changed after it has been constructed. If you'd prefer to be able to
change the Dataset object after construction, set |
label | vector of labels to use as the target variable |
weight | numeric vector of sample weights |
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
|
init_score | initial score is the base prediction lightgbm will boost from |
constructed dataset
# \donttest{ data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data_file <- tempfile(fileext = ".data") lgb.Dataset.save(dtrain, data_file) #> [LightGBM] [Info] Saving data to binary file /tmp/RtmpFi7Csu/filef0c20e7852.data dtrain <- lgb.Dataset(data_file) lgb.Dataset.construct(dtrain) #> [LightGBM] [Info] Load from binary file /tmp/RtmpFi7Csu/filef0c20e7852.data # }