lightgbm.R

#' @name lgb_shared_params
#' @title Shared parameter docs
#' @description Parameter docs shared by \code{lgb.train}, \code{lgb.cv}, and \code{lightgbm}
#' @param callbacks List of callback functions that are applied at each iteration.
#' @param data a \code{lgb.Dataset} object, used for training
#' @param early_stopping_rounds int. Activates early stopping. Requires at least one validation data
#'                              and one metric. If there's more than one, will check all of them
#'                              except the training data. Returns the model with (best_iter + early_stopping_rounds).
#'                              If early stopping occurs, the model will have 'best_iter' field.
#' @param eval_freq evaluation output frequency, only effect when verbose > 0
#' @param init_model path of model file of \code{lgb.Booster} object, will continue training from this model
#' @param nrounds number of training rounds
#' @param params List of parameters
#' @param verbose verbosity for output, if <= 0, also will disable the print of evaluation during training
NULL

#' @name lightgbm
#' @title Train a LightGBM model
#' @description Simple interface for training a LightGBM model.
#' @inheritParams lgb_shared_params
#' @param label Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}}
#' @param weight vector of response values. If not NULL, will set to dataset
#' @param save_name File name to use when writing the trained model to disk. Should end in ".model".
#' @param ... Additional arguments passed to \code{\link{lgb.train}}. For example
#'     \itemize{
#'        \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation}
#'        \item{\code{obj}: objective function, can be character or custom objective function. Examples include
#'                   \code{regression}, \code{regression_l1}, \code{huber},
#'                    \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}}
#'        \item{\code{eval}: evaluation function, can be (a list of) character or custom eval function}
#'        \item{\code{record}: Boolean, TRUE will record iteration message to \code{booster$record_evals}}
#'        \item{\code{colnames}: feature names, if not null, will use this to overwrite the names in dataset}
#'        \item{\code{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. \code{c(1L, 10L)} to
#'                            say "the first and tenth columns").}
#'        \item{\code{reset_data}: Boolean, setting it to TRUE (not the default value) will transform the booster model
#'                          into a predictor model which frees up memory and the original datasets}
#'         \item{\code{boosting}: Boosting type. \code{"gbdt"}, \code{"rf"}, \code{"dart"} or \code{"goss"}.}
#'         \item{\code{num_leaves}: Maximum number of leaves in one tree.}
#'         \item{\code{max_depth}: Limit the max depth for tree model. This is used to deal with
#'                          overfit when #data is small. Tree still grow by leaf-wise.}
#'          \item{\code{num_threads}: Number of threads for LightGBM. For the best speed, set this to
#'                             the number of real CPU cores, not the number of threads (most
#'                             CPU using hyper-threading to generate 2 threads per CPU core).}
#'     }
#' @export
lightgbm <- function(data,
                     label = NULL,
                     weight = NULL,
                     params = list(),
                     nrounds = 10L,
                     verbose = 1L,
                     eval_freq = 1L,
                     early_stopping_rounds = NULL,
                     save_name = "lightgbm.model",
                     init_model = NULL,
                     callbacks = list(),
                     ...) {

  # Set data to a temporary variable
  dtrain <- data
  if (nrounds <= 0L) {
    stop("nrounds should be greater than zero")
  }
  # Check whether data is lgb.Dataset, if not then create lgb.Dataset manually
  if (!lgb.is.Dataset(dtrain)) {
    dtrain <- lgb.Dataset(data, label = label, weight = weight)
  }

  # Set validation as oneself
  valids <- list()
  if (verbose > 0L) {
    valids$train <- dtrain
  }

  # Train a model using the regular way
  bst <- lgb.train(
    params = params
    , data = dtrain
    , nrounds = nrounds
    , valids = valids
    , verbose = verbose
    , eval_freq = eval_freq
    , early_stopping_rounds = early_stopping_rounds
    , init_model = init_model
    , callbacks = callbacks
    , ...
  )

  # Store model under a specific name
  bst$save_model(save_name)

  # Return booster
  return(bst)
}

#' @name agaricus.train
#' @title Training part from Mushroom Data Set
#' @description This data set is originally from the Mushroom data set,
#'              UCI Machine Learning Repository.
#'              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.
#'
#' @docType data
#' @keywords datasets
#' @usage data(agaricus.train)
#' @format A list containing a label vector, and a dgCMatrix object with 6513
#' rows and 127 variables
NULL

#' @name agaricus.test
#' @title Test part from Mushroom Data Set
#' @description This data set is originally from the Mushroom data set,
#'              UCI Machine Learning Repository.
#'              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.
#'
#' @docType data
#' @keywords datasets
#' @usage data(agaricus.test)
#' @format A list containing a label vector, and a dgCMatrix object with 1611
#' rows and 126 variables
NULL

#' @name bank
#' @title Bank Marketing Data Set
#' @description This data set is originally from the Bank Marketing data set,
#'              UCI Machine Learning Repository.
#'
#'              It contains only the following: bank.csv with 10% of the examples and 17 inputs,
#'              randomly selected from 3 (older version of this dataset with less inputs).
#'
#' @references
#' http://archive.ics.uci.edu/ml/datasets/Bank+Marketing
#'
#' S. Moro, P. Cortez and P. Rita. (2014)
#' A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems
#'
#' @docType data
#' @keywords datasets
#' @usage data(bank)
#' @format A data.table with 4521 rows and 17 variables
NULL

# Various imports
#' @import methods
#' @importFrom R6 R6Class
#' @useDynLib lib_lightgbm , .registration = TRUE
NULL

# Suppress false positive warnings from R CMD CHECK about
# "unrecognized global variable"
globalVariables(c(
    "."
    , ".N"
    , ".SD"
    , "abs_contribution"
    , "Contribution"
    , "Cover"
    , "Feature"
    , "Frequency"
    , "Gain"
    , "internal_count"
    , "internal_value"
    , "leaf_index"
    , "leaf_parent"
    , "leaf_value"
    , "node_parent"
    , "split_feature"
    , "split_gain"
    , "split_index"
    , "tree_index"
))