[R-package] removed horizontal whitespace (fixes #1642) (#1651)

* [R-package] removed horizontal whitespace (fixes #1642)

* [R-package] fixed missing newline in test file

R-package/R/callback.R

@@ -16,74 +16,74 @@ CB_ENV <- R6::R6Class(
 )
 
 cb.reset.parameters <- function(new_params) {
-  
+
   # Check for parameter list
   if (!is.list(new_params)) {
     stop(sQuote("new_params"), " must be a list")
   }
-  
+
   # Deparse parameter list
   pnames  <- gsub("\\.", "_", names(new_params))
   nrounds <- NULL
 
   # Run some checks in the beginning
   init <- function(env) {
-    
+
     # Store boosting rounds
     nrounds <<- env$end_iteration - env$begin_iteration + 1
-    
+
     # Check for model environment
     if (is.null(env$model)) { stop("Env should have a ", sQuote("model")) }
-    
+
     # Some parameters are not allowed to be changed,
     # since changing them would simply wreck some chaos
     not_allowed <- c("num_class", "metric", "boosting_type")
     if (any(pnames %in% not_allowed)) {
       stop("Parameters ", paste0(pnames[pnames %in% not_allowed], collapse = ", "), " cannot be changed during boosting")
     }
-    
+
     # Check parameter names
     for (n in pnames) {
-      
+
       # Set name
       p <- new_params[[n]]
-      
+
       # Check if function for parameter
       if (is.function(p)) {
-        
+
         # Check if requires at least two arguments
         if (length(formals(p)) != 2) {
           stop("Parameter ", sQuote(n), " is a function but not of two arguments")
         }
-        
+
         # Check if numeric or character
       } else if (is.numeric(p) || is.character(p)) {
-        
+
         # Check if length is matching
         if (length(p) != nrounds) {
           stop("Length of ", sQuote(n), " has to be equal to length of ", sQuote("nrounds"))
         }
-        
+
       } else {
-        
+
         stop("Parameter ", sQuote(n), " is not a function or a vector")
-        
+
       }
-      
+
     }
-    
+
   }
 
   callback <- function(env) {
-    
+
     # Check if rounds is null
     if (is.null(nrounds)) {
       init(env)
     }
-    
+
     # Store iteration
     i <- env$iteration - env$begin_iteration
-    
+
     # Apply list on parameters
     pars <- lapply(new_params, function(p) {
       if (is.function(p)) {
@@ -91,14 +91,14 @@ cb.reset.parameters <- function(new_params) {
       }
       p[i]
     })
-    
+
     # To-do check pars
     if (!is.null(env$model)) {
       env$model$reset_parameter(pars)
     }
-    
+
   }
-  
+
   attr(callback, "call") <- match.call()
   attr(callback, "is_pre_iteration") <- TRUE
   attr(callback, "name") <- "cb.reset.parameters"
@@ -107,258 +107,258 @@ cb.reset.parameters <- function(new_params) {
 
 # Format the evaluation metric string
 format.eval.string <- function(eval_res, eval_err = NULL) {
-  
+
   # Check for empty evaluation string
   if (is.null(eval_res) || length(eval_res) == 0) {
     stop("no evaluation results")
   }
-  
+
   # Check for empty evaluation error
   if (!is.null(eval_err)) {
     sprintf("%s\'s %s:%g+%g", eval_res$data_name, eval_res$name, eval_res$value, eval_err)
   } else {
     sprintf("%s\'s %s:%g", eval_res$data_name, eval_res$name, eval_res$value)
   }
-  
+
 }
 
 merge.eval.string <- function(env) {
-  
+
   # Check length of evaluation list
   if (length(env$eval_list) <= 0) {
     return("")
   }
-  
+
   # Get evaluation
   msg <- list(sprintf("[%d]:", env$iteration))
-  
+
   # Set if evaluation error
   is_eval_err <- length(env$eval_err_list) > 0
-  
+
   # Loop through evaluation list
   for (j in seq_along(env$eval_list)) {
-    
+
     # Store evaluation error
     eval_err <- NULL
     if (is_eval_err) {
       eval_err <- env$eval_err_list[[j]]
     }
-    
+
     # Set error message
     msg <- c(msg, format.eval.string(env$eval_list[[j]], eval_err))
-    
+
   }
-  
+
   # Return tabulated separated message
   paste0(msg, collapse = "\t")
-  
+
 }
 
 cb.print.evaluation <- function(period = 1) {
-  
+
   # Create callback
   callback <- function(env) {
-    
+
     # Check if period is at least 1 or more
     if (period > 0) {
-      
+
       # Store iteration
       i <- env$iteration
-      
+
       # Check if iteration matches moduo
       if ((i - 1) %% period == 0 || is.element(i, c(env$begin_iteration, env$end_iteration ))) {
-        
+
         # Merge evaluation string
         msg <- merge.eval.string(env)
-        
+
         # Check if message is existing
         if (nchar(msg) > 0) {
           cat(merge.eval.string(env), "\n")
         }
-        
+
       }
-      
+
     }
-    
+
   }
-  
+
   # Store attributes
   attr(callback, "call") <- match.call()
   attr(callback, "name") <- "cb.print.evaluation"
-  
+
   # Return callback
   callback
-  
+
 }
 
 cb.record.evaluation <- function() {
-  
+
   # Create callback
   callback <- function(env) {
-    
+
     # Return empty if empty evaluation list
     if (length(env$eval_list) <= 0) {
       return()
     }
-    
+
     # Set if evaluation error
     is_eval_err <- length(env$eval_err_list) > 0
-    
+
     # Check length of recorded evaluation
     if (length(env$model$record_evals) == 0) {
-      
+
       # Loop through each evaluation list element
       for (j in seq_along(env$eval_list)) {
-        
+
         # Store names
         data_name <- env$eval_list[[j]]$data_name
         name <- env$eval_list[[j]]$name
         env$model$record_evals$start_iter <- env$begin_iteration
-        
+
         # Check if evaluation record exists
         if (is.null(env$model$record_evals[[data_name]])) {
           env$model$record_evals[[data_name]] <- list()
         }
-        
+
         # Create dummy lists
         env$model$record_evals[[data_name]][[name]] <- list()
         env$model$record_evals[[data_name]][[name]]$eval <- list()
         env$model$record_evals[[data_name]][[name]]$eval_err <- list()
-        
+
       }
-      
+
     }
-    
+
     # Loop through each evaluation list element
     for (j in seq_along(env$eval_list)) {
-      
+
       # Get evaluation data
       eval_res <- env$eval_list[[j]]
       eval_err <- NULL
       if (is_eval_err) {
         eval_err <- env$eval_err_list[[j]]
       }
-      
+
       # Store names
       data_name <- eval_res$data_name
       name <- eval_res$name
-      
+
       # Store evaluation data
       env$model$record_evals[[data_name]][[name]]$eval <- c(env$model$record_evals[[data_name]][[name]]$eval, eval_res$value)
       env$model$record_evals[[data_name]][[name]]$eval_err <- c(env$model$record_evals[[data_name]][[name]]$eval_err, eval_err)
-      
+
     }
-    
+
   }
-  
+
   # Store attributes
   attr(callback, "call") <- match.call()
   attr(callback, "name") <- "cb.record.evaluation"
-  
+
   # Return callback
   callback
-  
+
 }
 
 cb.early.stop <- function(stopping_rounds, verbose = TRUE) {
-  
+
   # Initialize variables
   factor_to_bigger_better <- NULL
   best_iter <- NULL
   best_score <- NULL
   best_msg <- NULL
   eval_len <- NULL
-  
+
   # Initalization function
   init <- function(env) {
-    
+
     # Store evaluation length
     eval_len <<- length(env$eval_list)
-    
+
     # Early stopping cannot work without metrics
     if (eval_len == 0) {
       stop("For early stopping, valids must have at least one element")
     }
-    
+
     # Check if verbose or not
     if (isTRUE(verbose)) {
       cat("Will train until there is no improvement in ", stopping_rounds, " rounds.\n\n", sep = "")
     }
-    
+
     # Maximization or minimization task
     factor_to_bigger_better <<- rep.int(1.0, eval_len)
     best_iter <<- rep.int(-1, eval_len)
     best_score <<- rep.int(-Inf, eval_len)
     best_msg <<- list()
-    
+
     # Loop through evaluation elements
     for (i in seq_len(eval_len)) {
-      
+
       # Prepend message
       best_msg <<- c(best_msg, "")
-      
+
       # Check if maximization or minimization
       if (!env$eval_list[[i]]$higher_better) {
         factor_to_bigger_better[i] <<- -1.0
       }
-      
+
     }
-    
+
   }
-  
+
   # Create callback
   callback <- function(env, finalize = FALSE) {
-    
+
     # Check for empty evaluation
     if (is.null(eval_len)) {
       init(env)
     }
-    
+
     # Store iteration
     cur_iter <- env$iteration
-    
+
     # Loop through evaluation
     for (i in seq_len(eval_len)) {
-      
+
       # Store score
       score <- env$eval_list[[i]]$value * factor_to_bigger_better[i]
-      
+
       # Check if score is better
       if (score > best_score[i]) {
-        
+
         # Store new scores
         best_score[i] <<- score
         best_iter[i] <<- cur_iter
-        
+
         # Prepare to print if verbose
         if (verbose) {
           best_msg[[i]] <<- as.character(merge.eval.string(env))
         }
-        
+
       } else {
-        
+
         # Check if early stopping is required
         if (cur_iter - best_iter[i] >= stopping_rounds) {
-          
+
           # Check if model is not null
           if (!is.null(env$model)) {
             env$model$best_score <- best_score[i]
             env$model$best_iter <- best_iter[i]
           }
-          
+
           # Print message if verbose
           if (isTRUE(verbose)) {
-            
+
             cat("Early stopping, best iteration is:", "\n")
             cat(best_msg[[i]], "\n")
-            
+
           }
-          
+
           # Store best iteration and stop
           env$best_iter <- best_iter[i]
           env$met_early_stop <- TRUE
         }
-        
+
       }
       if (!isTRUE(env$met_early_stop) && cur_iter == env$end_iteration) {
         # Check if model is not null
@@ -366,58 +366,58 @@ cb.early.stop <- function(stopping_rounds, verbose = TRUE) {
           env$model$best_score <- best_score[i]
           env$model$best_iter <- best_iter[i]
         }
-        
+
         # Print message if verbose
         if (isTRUE(verbose)) {
           cat("Did not meet early stopping, best iteration is:", "\n")
           cat(best_msg[[i]], "\n")
         }
-        
+
         # Store best iteration and stop
         env$best_iter <- best_iter[i]
         env$met_early_stop <- TRUE
       }
     }
   }
-  
+
   # Set attributes
   attr(callback, "call") <- match.call()
   attr(callback, "name") <- "cb.early.stop"
-  
+
   # Return callback
   callback
-  
+
 }
 
 # Extract callback names from the list of callbacks
 callback.names <- function(cb_list) { unlist(lapply(cb_list, attr, "name")) }
 
 add.cb <- function(cb_list, cb) {
-  
+
   # Combine two elements
   cb_list <- c(cb_list, cb)
-  
+
   # Set names of elements
   names(cb_list) <- callback.names(cb_list)
-  
+
   # Check for existence
   if ("cb.early.stop" %in% names(cb_list)) {
-    
+
     # Concatenate existing elements
     cb_list <- c(cb_list, cb_list["cb.early.stop"])
-    
+
     # Remove only the first one
     cb_list["cb.early.stop"] <- NULL
-    
+
   }
-  
+
   # Return element
   cb_list
-  
+
 }
 
 categorize.callbacks <- function(cb_list) {
-  
+
   # Check for pre-iteration or post-iteration
   list(
     pre_iter = Filter(function(x) {
@@ -429,5 +429,5 @@ categorize.callbacks <- function(cb_list) {
         is.null(pre) || !pre
       }, cb_list)
   )
-  
+
 }

R-package/R/lgb.Dataset.R

@@ -393,11 +393,11 @@ Dataset <- R6::R6Class(
 
       # Check for info name and handle
       if (is.null(private$info[[name]])) {
-        
+
         if (lgb.is.null.handle(private$handle)){
           stop("Cannot perform getinfo before constructing Dataset.")
         }
-        
+
         # Get field size of info
         info_len <- 0L
         info_len <- lgb.call("LGBM_DatasetGetFieldSize_R",
@@ -850,7 +850,7 @@ dimnames.lgb.Dataset <- function(x) {
 #'
 #' Get a new \code{lgb.Dataset} containing the specified rows of
 #' original lgb.Dataset object
-#' 
+#'
 #' @param dataset Object of class "lgb.Dataset"
 #' @param idxset a integer vector of indices of rows needed
 #' @param ... other parameters (currently not used)

R-package/R/lgb.cv.R

@@ -24,11 +24,11 @@ CVBooster <- R6::R6Class(
 #' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
 #' @param label vector of response values. Should be provided only when data is an R-matrix.
 #' @param weight vector of response values. If not NULL, will set to dataset
-#' @param obj objective function, can be character or custom objective function. Examples include 
+#' @param 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}
 #' @param eval evaluation function, can be (list of) character or custom eval function
-#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} 
+#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals}
 #' @param showsd \code{boolean}, whether to show standard deviation of cross validation
 #' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
 #'        by the values of outcome labels.
@@ -45,15 +45,15 @@ CVBooster <- R6::R6Class(
 #'            \itemize{
 #'                \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
 #'                \item{num_leaves}{number of leaves in one tree. defaults to 127}
-#'                \item{max_depth}{Limit the max depth for tree model. This is used to deal with 
+#'                \item{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{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 
+#'                                   the number of real CPU cores, not the number of threads (most
 #'                                   CPU using hyper-threading to generate 2 threads per CPU core).}
 #'            }
-#' 
+#'
 #' @return a trained model \code{lgb.CVBooster}.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
@@ -70,7 +70,7 @@ CVBooster <- R6::R6Class(
 #' @export
 lgb.cv <- function(params = list(),
                    data,
-                   nrounds = 10, 
+                   nrounds = 10,
                    nfold = 3,
                    label = NULL,
                    weight = NULL,
@@ -88,7 +88,7 @@ lgb.cv <- function(params = list(),
                    early_stopping_rounds = NULL,
                    callbacks = list(),
                    ...) {
-  
+
   # Setup temporary variables
   addiction_params <- list(...)
   params <- append(params, addiction_params)
@@ -101,31 +101,31 @@ lgb.cv <- function(params = list(),
   if (nrounds <= 0) {
     stop("nrounds should be greater than zero")
   }
-  
+
   # Check for objective (function or not)
   if (is.function(params$objective)) {
     fobj <- params$objective
     params$objective <- "NONE"
   }
-  
+
   # Check for loss (function or not)
   if (is.function(eval)) {
     feval <- eval
   }
-  
+
   # Check for parameters
   lgb.check.params(params)
-  
+
   # Init predictor to empty
   predictor <- NULL
-  
+
   # Check for boosting from a trained model
   if (is.character(init_model)) {
     predictor <- Predictor$new(init_model)
   } else if (lgb.is.Booster(init_model)) {
     predictor <- init_model$to_predictor()
   }
-  
+
   # Set the iteration to start from / end to (and check for boosting from a trained model, again)
   begin_iteration <- 1
   if (!is.null(predictor)) {
@@ -138,7 +138,7 @@ lgb.cv <- function(params = list(),
   } else {
     end_iteration <- begin_iteration + nrounds - 1
   }
-  
+
   # Check for training dataset type correctness
   if (!lgb.is.Dataset(data)) {
     if (is.null(label)) {
@@ -146,49 +146,49 @@ lgb.cv <- function(params = list(),
     }
     data <- lgb.Dataset(data, label = label)
   }
-  
+
   # Check for weights
   if (!is.null(weight)) {
     data$setinfo("weight", weight)
   }
-  
+
   # Update parameters with parsed parameters
   data$update_params(params)
-  
+
   # Create the predictor set
   data$.__enclos_env__$private$set_predictor(predictor)
-  
+
   # Write column names
   if (!is.null(colnames)) {
     data$set_colnames(colnames)
   }
-  
+
   # Write categorical features
   if (!is.null(categorical_feature)) {
     data$set_categorical_feature(categorical_feature)
   }
-  
+
   # Construct datasets, if needed
   data$construct()
-  
+
   # Check for folds
   if (!is.null(folds)) {
-    
+
     # Check for list of folds or for single value
     if (!is.list(folds) || length(folds) < 2) {
       stop(sQuote("folds"), " must be a list with 2 or more elements that are vectors of indices for each CV-fold")
     }
-    
+
     # Set number of folds
     nfold <- length(folds)
-    
+
   } else {
-    
+
     # Check fold value
     if (nfold <= 1) {
       stop(sQuote("nfold"), " must be > 1")
     }
-    
+
     # Create folds
     folds <- generate.cv.folds(nfold,
                                nrow(data),
@@ -196,19 +196,19 @@ lgb.cv <- function(params = list(),
                                getinfo(data, "label"),
                                getinfo(data, "group"),
                                params)
-    
+
   }
-  
+
   # Add printing log callback
   if (verbose > 0 && eval_freq > 0) {
     callbacks <- add.cb(callbacks, cb.print.evaluation(eval_freq))
   }
-  
+
   # Add evaluation log callback
   if (record) {
     callbacks <- add.cb(callbacks, cb.record.evaluation())
   }
-  
+
   # Check for early stopping passed as parameter when adding early stopping callback
   early_stop <- c("early_stopping_round", "early_stopping_rounds", "early_stopping")
   if (any(names(params) %in% early_stop)) {
@@ -222,10 +222,10 @@ lgb.cv <- function(params = list(),
       }
     }
   }
-  
+
   # Categorize callbacks
   cb <- categorize.callbacks(callbacks)
-  
+
   # Construct booster using a list apply, check if requires group or not
   if (!is.list(folds[[1]])) {
     bst_folds <- lapply(seq_along(folds), function(k) {
@@ -254,107 +254,107 @@ lgb.cv <- function(params = list(),
       list(booster = booster)
     })
   }
-  
-  
+
+
   # Create new booster
   cv_booster <- CVBooster$new(bst_folds)
-  
+
   # Callback env
   env <- CB_ENV$new()
   env$model <- cv_booster
   env$begin_iteration <- begin_iteration
   env$end_iteration <- end_iteration
-  
+
   # Start training model using number of iterations to start and end with
   for (i in seq.int(from = begin_iteration, to = end_iteration)) {
-    
+
     # Overwrite iteration in environment
     env$iteration <- i
     env$eval_list <- list()
-    
+
     # Loop through "pre_iter" element
     for (f in cb$pre_iter) {
       f(env)
     }
-    
+
     # Update one boosting iteration
     msg <- lapply(cv_booster$boosters, function(fd) {
       fd$booster$update(fobj = fobj)
       fd$booster$eval_valid(feval = feval)
     })
-    
+
     # Prepare collection of evaluation results
     merged_msg <- lgb.merge.cv.result(msg)
-    
+
     # Write evaluation result in environment
     env$eval_list <- merged_msg$eval_list
-    
+
     # Check for standard deviation requirement
     if(showsd) {
       env$eval_err_list <- merged_msg$eval_err_list
     }
-    
+
     # Loop through env
     for (f in cb$post_iter) {
       f(env)
     }
-    
+
     # Check for early stopping and break if needed
     if (env$met_early_stop) break
-    
+
   }
-  
+
   # Return booster
   return(cv_booster)
-  
+
 }
 
 # Generates random (stratified if needed) CV folds
 generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
-  
+
   # Check for group existence
   if (is.null(group)) {
-    
+
     # Shuffle
     rnd_idx <- sample.int(nrows)
-    
+
     # Request stratified folds
     if (isTRUE(stratified) && params$objective %in% c("binary", "multiclass") && length(label) == length(rnd_idx)) {
-      
+
       y <- label[rnd_idx]
       y <- factor(y)
       folds <- lgb.stratified.folds(y, nfold)
-      
+
     } else {
-      
+
       # Make simple non-stratified folds
       folds <- list()
-      
+
       # Loop through each fold
       for (i in seq_len(nfold)) {
         kstep <- length(rnd_idx) %/% (nfold - i + 1)
         folds[[i]] <- rnd_idx[seq_len(kstep)]
         rnd_idx <- rnd_idx[-seq_len(kstep)]
       }
-      
+
     }
-    
+
   } else {
-    
+
     # When doing group, stratified is not possible (only random selection)
     if (nfold > length(group)) {
       stop("\n\tYou requested too many folds for the number of available groups.\n")
     }
-    
+
     # Degroup the groups
     ungrouped <- inverse.rle(list(lengths = group, values = seq_along(group)))
-    
+
     # Can't stratify, shuffle
     rnd_idx <- sample.int(length(group))
-    
+
     # Make simple non-stratified folds
     folds <- list()
-    
+
     # Loop through each fold
     for (i in seq_len(nfold)) {
       kstep <- length(rnd_idx) %/% (nfold - i + 1)
@@ -362,12 +362,12 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
                          group = rnd_idx[seq_len(kstep)])
       rnd_idx <- rnd_idx[-seq_len(kstep)]
     }
-    
+
   }
-  
+
   # Return folds
   return(folds)
-  
+
 }
 
 # Creates CV folds stratified by the values of y.
@@ -375,7 +375,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
 # by always returning an unnamed list of fold indices.
 #' @importFrom stats quantile
 lgb.stratified.folds <- function(y, k = 10) {
-  
+
   ## Group the numeric data based on their magnitudes
   ## and sample within those groups.
   ## When the number of samples is low, we may have
@@ -385,51 +385,51 @@ lgb.stratified.folds <- function(y, k = 10) {
   ## At most, we will use quantiles. If the sample
   ## is too small, we just do regular unstratified CV
   if (is.numeric(y)) {
-    
+
     cuts <- length(y) %/% k
     if (cuts < 2) { cuts <- 2 }
     if (cuts > 5) { cuts <- 5 }
     y <- cut(y,
              unique(stats::quantile(y, probs = seq.int(0, 1, length.out = cuts))),
              include.lowest = TRUE)
-    
+
   }
-  
+
   if (k < length(y)) {
-    
+
     ## Reset levels so that the possible levels and
     ## the levels in the vector are the same
     y <- factor(as.character(y))
     numInClass <- table(y)
     foldVector <- vector(mode = "integer", length(y))
-    
+
     ## For each class, balance the fold allocation as far
     ## as possible, then resample the remainder.
     ## The final assignment of folds is also randomized.
-    
+
     for (i in seq_along(numInClass)) {
-      
+
       ## Create a vector of integers from 1:k as many times as possible without
       ## going over the number of samples in the class. Note that if the number
       ## of samples in a class is less than k, nothing is producd here.
       seqVector <- rep(seq_len(k), numInClass[i] %/% k)
-      
+
       ## Add enough random integers to get  length(seqVector) == numInClass[i]
       if (numInClass[i] %% k > 0) {
         seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
       }
-      
+
       ## Shuffle the integers for fold assignment and assign to this classes's data
       foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)
-      
+
     }
-    
+
   } else {
-    
+
     foldVector <- seq(along = y)
-    
+
   }
-  
+
   # Return data
   out <- split(seq(along = y), foldVector)
   names(out) <- NULL
@@ -437,53 +437,53 @@ lgb.stratified.folds <- function(y, k = 10) {
 }
 
 lgb.merge.cv.result <- function(msg, showsd = TRUE) {
-  
+
   # Get CV message length
   if (length(msg) == 0) {
     stop("lgb.cv: size of cv result error")
   }
-  
+
   # Get evaluation message length
   eval_len <- length(msg[[1]])
-  
+
   # Is evaluation message empty?
   if (eval_len == 0) {
     stop("lgb.cv: should provide at least one metric for CV")
   }
-  
+
   # Get evaluation results using a list apply
   eval_result <- lapply(seq_len(eval_len), function(j) {
     as.numeric(lapply(seq_along(msg), function(i) {
       msg[[i]][[j]]$value }))
   })
-  
+
   # Get evaluation
   ret_eval <- msg[[1]]
-  
+
   # Go through evaluation length items
   for (j in seq_len(eval_len)) {
     ret_eval[[j]]$value <- mean(eval_result[[j]])
   }
-  
+
   # Preinit evaluation error
   ret_eval_err <- NULL
-  
+
   # Check for standard deviation
   if (showsd) {
-    
+
     # Parse standard deviation
     for (j in seq_len(eval_len)) {
       ret_eval_err <- c(ret_eval_err,
                         sqrt(mean(eval_result[[j]] ^ 2) - mean(eval_result[[j]]) ^ 2))
     }
-    
+
     # Convert to list
     ret_eval_err <- as.list(ret_eval_err)
-    
+
   }
-  
+
   # Return errors
   list(eval_list = ret_eval,
        eval_err_list = ret_eval_err)
-  
+
 }

R-package/R/lgb.importance.R

 #' Compute feature importance in a model
-#' 
+#'
 #' Creates a \code{data.table} of feature importances in a model.
-#' 
+#'
 #' @param model object of class \code{lgb.Booster}.
 #' @param percentage whether to show importance in relative percentage.
-#' 
+#'
 #' @return
-#' 
+#'
 #' For a tree model, a \code{data.table} with the following columns:
 #' \itemize{
 #'   \item \code{Feature} Feature names in the model.
@@ -14,7 +14,7 @@
 #'   \item \code{Cover} The number of observation related to this feature.
 #'   \item \code{Frequency} The number of times a feature splited in trees.
 #' }
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
@@ -29,20 +29,20 @@
 #'
 #' tree_imp1 <- lgb.importance(model, percentage = TRUE)
 #' tree_imp2 <- lgb.importance(model, percentage = FALSE)
-#' 
+#'
 #' @importFrom magrittr %>% %T>% extract
 #' @importFrom data.table :=
 #' @export
 lgb.importance <- function(model, percentage = TRUE) {
-  
+
   # Check if model is a lightgbm model
   if (!inherits(model, "lgb.Booster")) {
     stop("'model' has to be an object of class lgb.Booster")
   }
-  
+
   # Setup importance
   tree_dt <- lgb.model.dt.tree(model)
-  
+
   # Extract elements
   tree_imp <- tree_dt %>%
     magrittr::extract(.,
@@ -51,15 +51,15 @@ lgb.importance <- function(model, percentage = TRUE) {
                       by = "split_feature") %T>%
     data.table::setnames(., old = "split_feature", new = "Feature") %>%
     magrittr::extract(., i = order(Gain, decreasing = TRUE))
-  
+
   # Check if relative values are requested
   if (percentage) {
     tree_imp[, ":="(Gain = Gain / sum(Gain),
                     Cover = Cover / sum(Cover),
                     Frequency = Frequency / sum(Frequency))]
   }
-  
+
   # Return importance table
   return(tree_imp)
-  
+
 }

R-package/R/lgb.interprete.R

 #' Compute feature contribution of prediction
-#' 
+#'
 #' Computes feature contribution components of rawscore prediction.
-#' 
+#'
 #' @param model object of class \code{lgb.Booster}.
 #' @param data a matrix object or a dgCMatrix object.
 #' @param idxset a integer vector of indices of rows needed.
 #' @param num_iteration number of iteration want to predict with, NULL or <= 0 means use best iteration.
-#' 
+#'
 #' @return
-#' 
+#'
 #' For regression, binary classification and lambdarank model, a \code{list} of \code{data.table} with the following columns:
 #' \itemize{
 #'   \item \code{Feature} Feature names in the model.
 #'   \item \code{Contribution} The total contribution of this feature's splits.
 #' }
 #' For multiclass classification, a \code{list} of \code{data.table} with the Feature column and Contribution columns to each class.
-#' 
+#'
 #' @examples
 #' Sigmoid <- function(x) 1 / (1 + exp(-x))
 #' Logit <- function(x) log(x / (1 - x))
@@ -25,7 +25,7 @@
 #' setinfo(dtrain, "init_score", rep(Logit(mean(train$label)), length(train$label)))
 #' data(agaricus.test, package = "lightgbm")
 #' test <- agaricus.test
-#' 
+#'
 #' params <- list(
 #'     objective = "binary"
 #'     , learning_rate = 0.01
@@ -35,9 +35,9 @@
 #'     , min_sum_hessian_in_leaf = 1
 #' )
 #' model <- lgb.train(params, dtrain, 20)
-#' 
+#'
 #' tree_interpretation <- lgb.interprete(model, test$data, 1:5)
-#' 
+#'
 #' @importFrom data.table as.data.table
 #' @importFrom magrittr %>% %T>%
 #' @export
@@ -45,16 +45,16 @@ lgb.interprete <- function(model,
                            data,
                            idxset,
                            num_iteration = NULL) {
-  
+
   # Get tree model
   tree_dt <- lgb.model.dt.tree(model, num_iteration)
-  
+
   # Check number of classes
   num_class <- model$.__enclos_env__$private$num_class
-  
+
   # Get vector list
   tree_interpretation_dt_list <- vector(mode = "list", length = length(idxset))
-  
+
   # Get parsed predictions of data
   leaf_index_mat_list <- model$predict(data[idxset, , drop = FALSE],
                                        num_iteration = num_iteration,
@@ -62,63 +62,63 @@ lgb.interprete <- function(model,
     t(.) %>%
     data.table::as.data.table(.) %>%
     lapply(., FUN = function(x) matrix(x, ncol = num_class, byrow = TRUE))
-  
+
   # Get list of trees
   tree_index_mat_list <- lapply(leaf_index_mat_list,
                                 FUN = function(x) matrix(seq_len(length(x)) - 1, ncol = num_class, byrow = TRUE))
-  
+
   # Sequence over idxset
   for (i in seq_along(idxset)) {
     tree_interpretation_dt_list[[i]] <- single.row.interprete(tree_dt, num_class, tree_index_mat_list[[i]], leaf_index_mat_list[[i]])
   }
-  
+
   # Return interpretation list
   return(tree_interpretation_dt_list)
-  
+
 }
 
 #' @importFrom data.table data.table
 single.tree.interprete <- function(tree_dt,
                                    tree_id,
                                    leaf_id) {
-  
+
   # Match tree id
   single_tree_dt <- tree_dt[tree_index == tree_id, ]
-  
+
   # Get leaves
   leaf_dt <- single_tree_dt[leaf_index == leaf_id, .(leaf_index, leaf_parent, leaf_value)]
-  
+
   # Get nodes
   node_dt <- single_tree_dt[!is.na(split_index), .(split_index, split_feature, node_parent, internal_value)]
-  
+
   # Prepare sequences
   feature_seq <- character(0)
   value_seq <- numeric(0)
-  
+
   # Get to root from leaf
   leaf_to_root <- function(parent_id, current_value) {
-    
+
     # Store value
     value_seq <<- c(current_value, value_seq)
-    
+
     # Check for null parent id
     if (!is.na(parent_id)) {
-      
+
       # Not null means existing node
       this_node <- node_dt[split_index == parent_id, ]
       feature_seq <<- c(this_node[["split_feature"]], feature_seq)
       leaf_to_root(this_node[["node_parent"]], this_node[["internal_value"]])
-      
+
     }
-    
+
   }
-  
+
   # Perform leaf to root conversion
   leaf_to_root(leaf_dt[["leaf_parent"]], leaf_dt[["leaf_value"]])
-  
+
   # Return formatted data.table
   data.table::data.table(Feature = feature_seq, Contribution = diff.default(value_seq))
-  
+
 }
 
 #' @importFrom data.table rbindlist
@@ -126,7 +126,7 @@ single.tree.interprete <- function(tree_dt,
 multiple.tree.interprete <- function(tree_dt,
                                      tree_index,
                                      leaf_index) {
-  
+
   # Apply each trees
   mapply(single.tree.interprete,
          tree_id = tree_index, leaf_id = leaf_index,
@@ -135,52 +135,52 @@ multiple.tree.interprete <- function(tree_dt,
     data.table::rbindlist(., use.names = TRUE) %>%
     magrittr::extract(., j = .(Contribution = sum(Contribution)), by = "Feature") %>%
     magrittr::extract(., i = order(abs(Contribution), decreasing = TRUE))
-  
+
 }
 
 #' @importFrom data.table set setnames
 single.row.interprete <- function(tree_dt, num_class, tree_index_mat, leaf_index_mat) {
-  
+
   # Prepare vector list
   tree_interpretation <- vector(mode = "list", length = num_class)
-  
+
   # Loop throughout each class
   for (i in seq_len(num_class)) {
-    
+
     tree_interpretation[[i]] <- multiple.tree.interprete(tree_dt, tree_index_mat[,i], leaf_index_mat[,i]) %T>% {
-      
+
       # Number of classes larger than 1 requires adjustment
       if (num_class > 1) {
         data.table::setnames(., old = "Contribution", new = paste("Class", i - 1))
       }
     }
-    
+
   }
-  
+
   # Check for numbe rof classes larger than 1
   if (num_class == 1) {
-    
+
     # First interpretation element
     tree_interpretation_dt <- tree_interpretation[[1]]
-    
+
   } else {
-    
+
     # Full interpretation elements
     tree_interpretation_dt <- Reduce(f = function(x, y) merge(x, y, by = "Feature", all = TRUE),
                                      x = tree_interpretation)
-    
+
     # Loop throughout each tree
     for (j in 2:ncol(tree_interpretation_dt)) {
-      
+
       data.table::set(tree_interpretation_dt,
                       i = which(is.na(tree_interpretation_dt[[j]])),
                       j = j,
                       value = 0)
-      
+
     }
-    
+
   }
-  
+
   # Return interpretation tree
   return(tree_interpretation_dt)
 }

R-package/R/lgb.prepare.R

 #' Data preparator for LightGBM datasets (numeric)
 #'
 #' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric without integers. Please use \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
-#' 
+#'
 #' @param data A data.frame or data.table to prepare.
-#' 
+#'
 #' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
-#' 
+#'
 #' str(iris)
 #' # 'data.frame':	150 obs. of  5 variables:
 #' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
@@ -17,7 +17,7 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#' 
+#'
 #' str(lgb.prepare(data = iris)) # Convert all factors/chars to numeric
 #' # 'data.frame':	150 obs. of  5 variables:
 #' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
@@ -25,7 +25,7 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : num  1 1 1 1 1 1 1 1 1 1 ...
-#' 
+#'
 #' # When lightgbm package is installed, and you do not want to load it
 #' # You can still use the function!
 #' lgb.unloader()
@@ -36,57 +36,57 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : num  1 1 1 1 1 1 1 1 1 1 ...
-#' 
+#'
 #' @export
 lgb.prepare <- function(data) {
-  
+
   # data.table not behaving like data.frame
   if ("data.table" %in% class(data)) {
-    
+
     # Get data classes
     list_classes <- sapply(data, class)
-    
+
     # Convert characters to factors only (we can change them to numeric after)
     is_char <- which(list_classes == "character")
     if (length(is_char) > 0) {
       data[, (is_char) := lapply(.SD, function(x) {as.numeric(as.factor(x))}), .SDcols = is_char]
     }
-    
+
     # Convert factors to numeric (integer is more efficient actually)
     is_fact <- c(which(list_classes == "factor"), is_char)
     if (length(is_fact) > 0) {
       data[, (is_fact) := lapply(.SD, function(x) {as.numeric(x)}), .SDcols = is_fact]
     }
-    
+
   } else {
-    
+
     # Default routine (data.frame)
     if ("data.frame" %in% class(data)) {
-      
+
       # Get data classes
       list_classes <- sapply(data, class)
-      
+
       # Convert characters to factors to numeric (integer is more efficient actually)
       is_char <- which(list_classes == "character")
       if (length(is_char) > 0) {
         data[is_char] <- lapply(data[is_char], function(x) {as.numeric(as.factor(x))})
       }
-      
+
       # Convert factors to numeric (integer is more efficient actually)
       is_fact <- which(list_classes == "factor")
       if (length(is_fact) > 0) {
         data[is_fact] <- lapply(data[is_fact], function(x) {as.numeric(x)})
       }
-      
+
     } else {
-      
+
       # What do you think you are doing here? Throw error.
       stop("lgb.prepare2: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
-      
+
     }
-    
+
   }
-  
+
   return(data)
-  
+
 }

R-package/R/lgb.prepare2.R

 #' Data preparator for LightGBM datasets (integer)
 #'
 #' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
-#' 
+#'
 #' @param data A data.frame or data.table to prepare.
-#' 
+#'
 #' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
-#' 
+#'
 #' str(iris)
 #' # 'data.frame':	150 obs. of  5 variables:
 #' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
@@ -17,7 +17,7 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#' 
+#'
 #' # Convert all factors/chars to integer
 #' str(lgb.prepare2(data = iris))
 #' # 'data.frame':	150 obs. of  5 variables:
@@ -26,7 +26,7 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : int  1 1 1 1 1 1 1 1 1 1 ...
-#' 
+#'
 #' # When lightgbm package is installed, and you do not want to load it
 #' # You can still use the function!
 #' lgb.unloader()
@@ -37,57 +37,57 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : int  1 1 1 1 1 1 1 1 1 1 ...
-#' 
+#'
 #' @export
 lgb.prepare2 <- function(data) {
-  
+
   # data.table not behaving like data.frame
   if (inherits(data, "data.table")) {
-    
+
     # Get data classes
     list_classes <- vapply(data, class, character(1))
-    
+
     # Convert characters to factors only (we can change them to numeric after)
     is_char <- which(list_classes == "character")
     if (length(is_char) > 0) {
       data[, (is_char) := lapply(.SD, function(x) {as.integer(as.factor(x))}), .SDcols = is_char]
     }
-    
+
     # Convert factors to numeric (integer is more efficient actually)
     is_fact <- c(which(list_classes == "factor"), is_char)
     if (length(is_fact) > 0) {
       data[, (is_fact) := lapply(.SD, function(x) {as.integer(x)}), .SDcols = is_fact]
     }
-    
+
   } else {
-    
+
     # Default routine (data.frame)
     if (inherits(data, "data.frame")) {
-      
+
       # Get data classes
       list_classes <- vapply(data, class, character(1))
-      
+
       # Convert characters to factors to numeric (integer is more efficient actually)
       is_char <- which(list_classes == "character")
       if (length(is_char) > 0) {
         data[is_char] <- lapply(data[is_char], function(x) {as.integer(as.factor(x))})
       }
-      
+
       # Convert factors to numeric (integer is more efficient actually)
       is_fact <- which(list_classes == "factor")
       if (length(is_fact) > 0) {
         data[is_fact] <- lapply(data[is_fact], function(x) {as.integer(x)})
       }
-      
+
     } else {
-      
+
       # What do you think you are doing here? Throw error.
       stop("lgb.prepare: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
-      
+
     }
-    
+
   }
-  
+
   return(data)
-  
+
 }

R-package/R/lgb.prepare_rules.R

 #' Data preparator for LightGBM datasets with rules (numeric)
 #'
 #' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.
-#' 
+#'
 #' @param data A data.frame or data.table to prepare.
 #' @param rules A set of rules from the data preparator, if already used.
-#' 
+#'
 #' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
-#' 
+#'
 #' str(iris)
 #' # 'data.frame':	150 obs. of  5 variables:
 #' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
@@ -18,7 +18,7 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#' 
+#'
 #' new_iris <- lgb.prepare_rules(data = iris) # Autoconverter
 #' str(new_iris$data)
 #' # 'data.frame':	150 obs. of  5 variables:
@@ -27,31 +27,31 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : num  1 1 1 1 1 1 1 1 1 1 ...
-#' 
+#'
 #' data(iris) # Erase iris dataset
 #' iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
 #' # Warning message:
 #' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
 #' #  invalid factor level, NA generated
-#' 
+#'
 #' # Use conversion using known rules
 #' # Unknown factors become 0, excellent for sparse datasets
 #' newer_iris <- lgb.prepare_rules(data = iris, rules = new_iris$rules)
-#' 
+#'
 #' # Unknown factor is now zero, perfect for sparse datasets
 #' newer_iris$data[1, ] # Species became 0 as it is an unknown factor
 #' #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
 #' # 1          5.1         3.5          1.4         0.2       0
-#' 
+#'
 #' newer_iris$data[1, 5] <- 1 # Put back real initial value
-#' 
+#'
 #' # Is the newly created dataset equal? YES!
 #' all.equal(new_iris$data, newer_iris$data)
 #' # [1] TRUE
-#' 
+#'
 #' # Can we test our own rules?
 #' data(iris) # Erase iris dataset
-#' 
+#'
 #' # We remapped values differently
 #' personal_rules <- list(Species = c("setosa" = 3,
 #'                                    "versicolor" = 2,
@@ -64,43 +64,43 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : num  3 3 3 3 3 3 3 3 3 3 ...
-#' 
+#'
 #' @importFrom data.table set
 #' @export
 lgb.prepare_rules <- function(data, rules = NULL) {
-  
+
   # data.table not behaving like data.frame
   if (inherits(data, "data.table")) {
-    
+
     # Must use existing rules
     if (!is.null(rules)) {
-      
+
       # Loop through rules
       for (i in names(rules)) {
-        
+
         data.table::set(data, j = i, value = unname(rules[[i]][data[[i]]]))
         data[[i]][is.na(data[[i]])] <- 0 # Overwrite NAs by 0s
-        
+
       }
-      
+
     } else {
-      
+
       # Get data classes
       list_classes <- vapply(data, class, character(1))
-      
+
       # Map characters/factors
       is_fix <- which(list_classes %in% c("character", "factor"))
       rules <- list()
-      
+
       # Need to create rules?
       if (length(is_fix) > 0) {
-        
+
         # Go through all characters/factors
         for (i in is_fix) {
-          
+
           # Store column elsewhere
           mini_data <- data[[i]]
-          
+
           # Get unique values
           if (is.factor(mini_data)) {
             mini_unique <- levels(mini_data) # Factor
@@ -110,55 +110,55 @@ lgb.prepare_rules <- function(data, rules = NULL) {
             mini_unique <- as.factor(unique(mini_data)) # Character
             mini_numeric <- as.numeric(mini_unique) # No respect of ordinality
           }
-          
+
           # Create rules
           indexed <- colnames(data)[i] # Index value
           rules[[indexed]] <- mini_numeric # Numeric content
           names(rules[[indexed]]) <- mini_unique # Character equivalent
-          
+
           # Apply to real data column
           data.table::set(data, j = i, value = unname(rules[[indexed]][mini_data]))
-          
+
         }
-        
+
       }
-      
+
     }
-    
+
   } else {
-    
+
     # Must use existing rules
     if (!is.null(rules)) {
-      
+
       # Loop through rules
       for (i in names(rules)) {
-        
+
         data[[i]] <- unname(rules[[i]][data[[i]]])
         data[[i]][is.na(data[[i]])] <- 0 # Overwrite NAs by 0s
-        
+
       }
-      
+
     } else {
-      
+
       # Default routine (data.frame)
       if (inherits(data, "data.frame")) {
-        
+
         # Get data classes
         list_classes <- vapply(data, class, character(1))
-        
+
         # Map characters/factors
         is_fix <- which(list_classes %in% c("character", "factor"))
         rules <- list()
-        
+
         # Need to create rules?
         if (length(is_fix) > 0) {
-          
+
           # Go through all characters/factors
           for (i in is_fix) {
-            
+
             # Store column elsewhere
             mini_data <- data[[i]]
-            
+
             # Get unique values
             if (is.factor(mini_data)) {
               mini_unique <- levels(mini_data) # Factor
@@ -168,30 +168,30 @@ lgb.prepare_rules <- function(data, rules = NULL) {
               mini_unique <- as.factor(unique(mini_data)) # Character
               mini_numeric <- as.numeric(mini_unique) # No respect of ordinality
             }
-            
+
             # Create rules
             indexed <- colnames(data)[i] # Index value
             rules[[indexed]] <- mini_numeric # Numeric content
             names(rules[[indexed]]) <- mini_unique # Character equivalent
-            
+
             # Apply to real data column
             data[[i]] <- unname(rules[[indexed]][mini_data])
-            
+
           }
-          
+
         }
-        
+
       } else {
-        
+
         # What do you think you are doing here? Throw error.
         stop("lgb.prepare: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
-        
+
       }
-      
+
     }
-    
+
   }
-  
+
   return(list(data = data, rules = rules))
-  
+
 }

R-package/R/lgb.prepare_rules2.R

 #' Data preparator for LightGBM datasets with rules (integer)
 #'
 #' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). In addition, keeps rules created so you can convert other datasets using this converter. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
-#' 
+#'
 #' @param data A data.frame or data.table to prepare.
 #' @param rules A set of rules from the data preparator, if already used.
-#' 
+#'
 #' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
-#' 
+#'
 #' str(iris)
 #' # 'data.frame':	150 obs. of  5 variables:
 #' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
@@ -18,7 +18,7 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
-#' 
+#'
 #' new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
 #' str(new_iris$data)
 #' # 'data.frame':	150 obs. of  5 variables:
@@ -27,31 +27,31 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : int  1 1 1 1 1 1 1 1 1 1 ...
-#' 
+#'
 #' data(iris) # Erase iris dataset
 #' iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
 #' # Warning message:
 #' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
 #' #  invalid factor level, NA generated
-#' 
+#'
 #' # Use conversion using known rules
 #' # Unknown factors become 0, excellent for sparse datasets
 #' newer_iris <- lgb.prepare_rules2(data = iris, rules = new_iris$rules)
-#' 
+#'
 #' # Unknown factor is now zero, perfect for sparse datasets
 #' newer_iris$data[1, ] # Species became 0 as it is an unknown factor
 #' #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
 #' # 1          5.1         3.5          1.4         0.2       0
-#' 
+#'
 #' newer_iris$data[1, 5] <- 1 # Put back real initial value
-#' 
+#'
 #' # Is the newly created dataset equal? YES!
 #' all.equal(new_iris$data, newer_iris$data)
 #' # [1] TRUE
-#' 
+#'
 #' # Can we test our own rules?
 #' data(iris) # Erase iris dataset
-#' 
+#'
 #' # We remapped values differently
 #' personal_rules <- list(Species = c("setosa" = 3L,
 #'                                    "versicolor" = 2L,
@@ -64,43 +64,43 @@
 #' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 #' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 #' # $ Species     : int  3 3 3 3 3 3 3 3 3 3 ...
-#' 
+#'
 #' @importFrom data.table set
 #' @export
 lgb.prepare_rules2 <- function(data, rules = NULL) {
-  
+
   # data.table not behaving like data.frame
   if (inherits(data, "data.table")) {
-    
+
     # Must use existing rules
     if (!is.null(rules)) {
-      
+
       # Loop through rules
       for (i in names(rules)) {
-        
+
         data.table::set(data, j = i, value = unname(rules[[i]][data[[i]]]))
         data[[i]][is.na(data[[i]])] <- 0L # Overwrite NAs by 0s as integer
-        
+
       }
-      
+
     } else {
-      
+
       # Get data classes
       list_classes <- vapply(data, class, character(1))
-      
+
       # Map characters/factors
       is_fix <- which(list_classes %in% c("character", "factor"))
       rules <- list()
-      
+
       # Need to create rules?
       if (length(is_fix) > 0) {
-        
+
         # Go through all characters/factors
         for (i in is_fix) {
-          
+
           # Store column elsewhere
           mini_data <- data[[i]]
-          
+
           # Get unique values
           if (is.factor(mini_data)) {
             mini_unique <- levels(mini_data) # Factor
@@ -109,55 +109,55 @@ lgb.prepare_rules2 <- function(data, rules = NULL) {
             mini_unique <- as.factor(unique(mini_data)) # Character
             mini_numeric <- as.integer(mini_unique) # No respect of ordinality
           }
-          
+
           # Create rules
           indexed <- colnames(data)[i] # Index value
           rules[[indexed]] <- mini_numeric # Numeric content
           names(rules[[indexed]]) <- mini_unique # Character equivalent
-          
+
           # Apply to real data column
           data.table::set(data, j = i, value = unname(rules[[indexed]][mini_data]))
-          
+
         }
-        
+
       }
-      
+
     }
 
   } else {
-    
+
     # Must use existing rules
     if (!is.null(rules)) {
-      
+
       # Loop through rules
       for (i in names(rules)) {
-        
+
         data[[i]] <- unname(rules[[i]][data[[i]]])
         data[[i]][is.na(data[[i]])] <- 0L # Overwrite NAs by 0s as integer
-        
+
       }
-      
+
     } else {
-      
+
       # Default routine (data.frame)
       if (inherits(data, "data.frame")) {
-        
+
         # Get data classes
         list_classes <- vapply(data, class, character(1))
-        
+
         # Map characters/factors
         is_fix <- which(list_classes %in% c("character", "factor"))
         rules <- list()
-        
+
         # Need to create rules?
         if (length(is_fix) > 0) {
-          
+
           # Go through all characters/factors
           for (i in is_fix) {
-            
+
             # Store column elsewhere
             mini_data <- data[[i]]
-            
+
             # Get unique values
             if (is.factor(mini_data)) {
               mini_unique <- levels(mini_data) # Factor
@@ -166,30 +166,30 @@ lgb.prepare_rules2 <- function(data, rules = NULL) {
               mini_unique <- as.factor(unique(mini_data)) # Character
               mini_numeric <- as.integer(mini_unique) # No respect of ordinality
             }
-            
+
             # Create rules
             indexed <- colnames(data)[i] # Index value
             rules[[indexed]] <- mini_numeric # Numeric content
             names(rules[[indexed]]) <- mini_unique # Character equivalent
-            
+
             # Apply to real data column
             data[[i]] <- unname(rules[[indexed]][mini_data])
-            
+
           }
-          
+
         }
-        
+
       } else {
-        
+
         # What do you think you are doing here? Throw error.
         stop("lgb.prepare: you provided ", paste(class(data), collapse = " & "), " but data should have class data.frame")
-        
+
       }
-      
+
     }
-    
+
   }
-  
+
   return(list(data = data, rules = rules))
-  
+
 }

R-package/R/lgb.train.R

@@ -3,11 +3,11 @@
 #' @description Logic to train with LightGBM
 #' @inheritParams lgb_shared_params
 #' @param valids a list of \code{lgb.Dataset} objects, used for validation
-#' @param obj objective function, can be character or custom objective function. Examples include 
+#' @param 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}
 #' @param eval evaluation function, can be (a list of) character or custom eval function
-#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} 
+#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals}
 #' @param colnames feature names, if not null, will use this to overwrite the names in dataset
 #' @param categorical_feature list of str or int
 #'        type int represents index,
@@ -17,14 +17,14 @@
 #'            \itemize{
 #'                \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
 #'                \item{num_leaves}{number of leaves in one tree. defaults to 127}
-#'                \item{max_depth}{Limit the max depth for tree model. This is used to deal with 
+#'                \item{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{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 
+#'                                   the number of real CPU cores, not the number of threads (most
 #'                                   CPU using hyper-threading to generate 2 threads per CPU core).}
 #'            }
 #' @return a trained booster model \code{lgb.Booster}.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
@@ -42,7 +42,7 @@
 #'                    min_data = 1,
 #'                    learning_rate = 1,
 #'                    early_stopping_rounds = 10)
-#' 
+#'
 #' @export
 lgb.train <- function(params = list(),
                       data,
@@ -60,7 +60,7 @@ lgb.train <- function(params = list(),
                       callbacks = list(),
                       reset_data = FALSE,
                       ...) {
-  
+
   # Setup temporary variables
   additional_params <- list(...)
   params <- append(params, additional_params)
@@ -69,7 +69,7 @@ lgb.train <- function(params = list(),
   params <- lgb.check.eval(params, eval)
   fobj <- NULL
   feval <- NULL
-  
+
   if (nrounds <= 0) {
     stop("nrounds should be greater than zero")
   }
@@ -79,25 +79,25 @@ lgb.train <- function(params = list(),
     fobj <- params$objective
     params$objective <- "NONE"
   }
-  
+
   # Check for loss (function or not)
   if (is.function(eval)) {
     feval <- eval
   }
-  
+
   # Check for parameters
   lgb.check.params(params)
-  
+
   # Init predictor to empty
   predictor <- NULL
-  
+
   # Check for boosting from a trained model
   if (is.character(init_model)) {
     predictor <- Predictor$new(init_model)
   } else if (lgb.is.Booster(init_model)) {
     predictor <- init_model$to_predictor()
   }
-  
+
   # Set the iteration to start from / end to (and check for boosting from a trained model, again)
   begin_iteration <- 1
   if (!is.null(predictor)) {
@@ -110,89 +110,89 @@ lgb.train <- function(params = list(),
   } else {
     end_iteration <- begin_iteration + nrounds - 1
   }
-  
-  
+
+
   # Check for training dataset type correctness
   if (!lgb.is.Dataset(data)) {
     stop("lgb.train: data only accepts lgb.Dataset object")
   }
-  
+
   # Check for validation dataset type correctness
   if (length(valids) > 0) {
-    
+
     # One or more validation dataset
-    
+
     # Check for list as input and type correctness by object
     if (!is.list(valids) || !all(vapply(valids, lgb.is.Dataset, logical(1)))) {
       stop("lgb.train: valids must be a list of lgb.Dataset elements")
     }
-    
+
     # Attempt to get names
     evnames <- names(valids)
-    
+
     # Check for names existance
     if (is.null(evnames) || !all(nzchar(evnames))) {
       stop("lgb.train: each element of the valids must have a name tag")
     }
   }
-  
+
   # Update parameters with parsed parameters
   data$update_params(params)
-  
+
   # Create the predictor set
   data$.__enclos_env__$private$set_predictor(predictor)
-  
+
   # Write column names
   if (!is.null(colnames)) {
     data$set_colnames(colnames)
   }
-  
+
   # Write categorical features
   if (!is.null(categorical_feature)) {
     data$set_categorical_feature(categorical_feature)
   }
-  
+
   # Construct datasets, if needed
   data$construct()
   vaild_contain_train <- FALSE
   train_data_name <- "train"
   reduced_valid_sets <- list()
-  
+
   # Parse validation datasets
   if (length(valids) > 0) {
-    
+
     # Loop through all validation datasets using name
     for (key in names(valids)) {
-      
+
       # Use names to get validation datasets
       valid_data <- valids[[key]]
-      
+
       # Check for duplicate train/validation dataset
       if (identical(data, valid_data)) {
         vaild_contain_train <- TRUE
         train_data_name <- key
         next
       }
-      
+
       # Update parameters, data
       valid_data$update_params(params)
       valid_data$set_reference(data)
       reduced_valid_sets[[key]] <- valid_data
-      
+
     }
-    
+
   }
-  
+
   # Add printing log callback
   if (verbose > 0 && eval_freq > 0) {
     callbacks <- add.cb(callbacks, cb.print.evaluation(eval_freq))
   }
-  
+
   # Add evaluation log callback
   if (record && length(valids) > 0) {
     callbacks <- add.cb(callbacks, cb.record.evaluation())
   }
-  
+
   # Check for early stopping passed as parameter when adding early stopping callback
   early_stop <- c("early_stopping_round", "early_stopping_rounds", "early_stopping")
   if (any(names(params) %in% early_stop)) {
@@ -206,83 +206,83 @@ lgb.train <- function(params = list(),
       }
     }
   }
-  
+
   # "Categorize" callbacks
   cb <- categorize.callbacks(callbacks)
-  
+
   # Construct booster with datasets
   booster <- Booster$new(params = params, train_set = data)
   if (vaild_contain_train) { booster$set_train_data_name(train_data_name) }
   for (key in names(reduced_valid_sets)) {
     booster$add_valid(reduced_valid_sets[[key]], key)
   }
-  
+
   # Callback env
   env <- CB_ENV$new()
   env$model <- booster
   env$begin_iteration <- begin_iteration
   env$end_iteration <- end_iteration
-  
+
   # Start training model using number of iterations to start and end with
   for (i in seq.int(from = begin_iteration, to = end_iteration)) {
-    
+
     # Overwrite iteration in environment
     env$iteration <- i
     env$eval_list <- list()
-    
+
     # Loop through "pre_iter" element
     for (f in cb$pre_iter) {
       f(env)
     }
-    
+
     # Update one boosting iteration
     booster$update(fobj = fobj)
-    
+
     # Prepare collection of evaluation results
     eval_list <- list()
-    
+
     # Collection: Has validation dataset?
     if (length(valids) > 0) {
-      
+
       # Validation has training dataset?
       if (vaild_contain_train) {
         eval_list <- append(eval_list, booster$eval_train(feval = feval))
       }
-      
+
       # Has no validation dataset
       eval_list <- append(eval_list, booster$eval_valid(feval = feval))
     }
-    
+
     # Write evaluation result in environment
     env$eval_list <- eval_list
-    
+
     # Loop through env
     for (f in cb$post_iter) {
       f(env)
     }
-    
+
     # Check for early stopping and break if needed
     if (env$met_early_stop) break
-    
+
   }
-  
+
   # Check for booster model conversion to predictor model
   if (reset_data) {
-    
+
     # Store temporarily model data elsewhere
     booster_old <- list(best_iter = booster$best_iter,
                         best_score = booster$best_score,
                         record_evals = booster$record_evals)
-    
+
     # Reload model
     booster <- lgb.load(model_str = booster$save_model_to_string())
     booster$best_iter <- booster_old$best_iter
     booster$best_score <- booster_old$best_score
     booster$record_evals <- booster_old$record_evals
-    
+
   }
-  
+
   # Return booster
   return(booster)
-  
+
 }

R-package/R/lgb.unloader.R

 #' LightGBM unloading error fix
 #'
 #' Attempts to unload LightGBM packages so you can remove objects cleanly without having to restart R. This is useful for instance if an object becomes stuck for no apparent reason and you do not want to restart R to fix the lost object.
-#' 
+#'
 #' @param restore Whether to reload \code{LightGBM} immediately after detaching from R. Defaults to \code{TRUE} which means automatically reload \code{LightGBM} once unloading is performed.
 #' @param wipe Whether to wipe all \code{lgb.Dataset} and \code{lgb.Booster} from the global environment. Defaults to \code{FALSE} which means to not remove them.
 #' @param envir The environment to perform wiping on if \code{wipe == TRUE}. Defaults to \code{.GlobalEnv} which is the global environment.
-#' 
+#'
 #' @return NULL invisibly.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
@@ -28,16 +28,16 @@
 #' lgb.unloader(restore = FALSE, wipe = FALSE, envir = .GlobalEnv)
 #' rm(model, dtrain, dtest) # Not needed if wipe = TRUE
 #' gc() # Not needed if wipe = TRUE
-#' 
+#'
 #' library(lightgbm)
 #' # Do whatever you want again with LightGBM without object clashing
-#' 
+#'
 #' @export
 lgb.unloader <- function(restore = TRUE, wipe = FALSE, envir = .GlobalEnv) {
-  
+
   # Unload package
   try(detach("package:lightgbm", unload = TRUE), silent = TRUE)
-  
+
   # Should we wipe variables? (lgb.Booster, lgb.Dataset)
   if (wipe) {
     boosters <- Filter(function(x) inherits(get(x, envir = envir), "lgb.Booster"), ls(envir = envir))
@@ -45,12 +45,12 @@ lgb.unloader <- function(restore = TRUE, wipe = FALSE, envir = .GlobalEnv) {
     rm(list = c(boosters, datasets), envir = envir)
     gc(verbose = FALSE)
   }
-  
+
   # Load package back?
   if (restore) {
     library(lightgbm)
   }
-  
+
   invisible()
-  
+
 }

R-package/R/lightgbm.R

@@ -29,21 +29,21 @@ NULL
 #' @param ... Additional arguments passed to \code{\link{lgb.train}}. For example
 #'     \itemize{
 #'        \item{valids}{a list of \code{lgb.Dataset} objects, used for validation}
-#'        \item{obj}{objective function, can be character or custom objective function. Examples include 
+#'        \item{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{eval}{evaluation function, can be (a list of) character or custom eval function}
 #'        \item{record}{Boolean, TRUE will record iteration message to \code{booster$record_evals}}
 #'        \item{colnames}{feature names, if not null, will use this to overwrite the names in dataset}
 #'        \item{categorical_feature}{list of str or int. type int represents index, type str represents feature names}
-#'        \item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model 
+#'        \item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model
 #'                          into a predictor model which frees up memory and the original datasets}
 #'         \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
 #'         \item{num_leaves}{number of leaves in one tree. defaults to 127}
-#'         \item{max_depth}{Limit the max depth for tree model. This is used to deal with 
+#'         \item{max_depth}{Limit the max depth for tree model. This is used to deal with
 #'                          overfit when #data is small. Tree still grow by leaf-wise.}
 #'          \item{num_threads}{Number of threads for LightGBM. For the best speed, set this to
-#'                             the number of real CPU cores, not the number of threads (most 
+#'                             the number of real CPU cores, not the number of threads (most
 #'                             CPU using hyper-threading to generate 2 threads per CPU core).}
 #'     }
 #' @export
@@ -59,7 +59,7 @@ lightgbm <- function(data,
                      init_model = NULL,
                      callbacks = list(),
                      ...) {
-  
+
   # Set data to a temporary variable
   dtrain <- data
   if (nrounds <= 0) {
@@ -75,15 +75,15 @@ lightgbm <- function(data,
   if (verbose > 0) {
     valids$train = dtrain
   }
-  
+
   # Train a model using the regular way
   bst <- lgb.train(params, dtrain, nrounds, 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)
 }
@@ -152,7 +152,7 @@ NULL
 #'
 #' @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
 #'

R-package/R/readRDS.lgb.Booster.R

 #' readRDS for lgb.Booster models
 #'
 #' Attempts to load a model using RDS.
-#' 
+#'
 #' @param file a connection or the name of the file where the R object is saved to or read from.
 #' @param refhook a hook function for handling reference objects.
-#' 
+#'
 #' @return lgb.Booster.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
@@ -26,31 +26,31 @@
 #'                    early_stopping_rounds = 10)
 #' saveRDS.lgb.Booster(model, "model.rds")
 #' new_model <- readRDS.lgb.Booster("model.rds")
-#' 
+#'
 #' @export
 readRDS.lgb.Booster <- function(file = "", refhook = NULL) {
-  
+
   # Read RDS file
   object <- readRDS(file = file, refhook = refhook)
-  
+
   # Check if object has the model stored
   if (!is.na(object$raw)) {
-    
+
     # Create temporary model for the model loading
     object2 <- lgb.load(model_str = object$raw)
-    
+
     # Restore best iteration and recorded evaluations
     object2$best_iter <- object$best_iter
     object2$record_evals <- object$record_evals
-    
+
     # Return newly loaded object
     return(object2)
-    
+
   } else {
-    
+
     # Return RDS loaded object
     return(object)
-    
+
   }
-  
+
 }

R-package/R/saveRDS.lgb.Booster.R

 #' saveRDS for lgb.Booster models
 #'
 #' Attempts to save a model using RDS. Has an additional parameter (\code{raw}) which decides whether to save the raw model or not.
-#' 
+#'
 #' @param object R object to serialize.
 #' @param file a connection or the name of the file where the R object is saved to or read from.
 #' @param ascii a logical. If TRUE or NA, an ASCII representation is written; otherwise (default), a binary one is used. See the comments in the help for save.
@@ -9,9 +9,9 @@
 #' @param compress a logical specifying whether saving to a named file is to use "gzip" compression, or one of \code{"gzip"}, \code{"bzip2"} or \code{"xz"} to indicate the type of compression to be used. Ignored if file is a connection.
 #' @param refhook a hook function for handling reference objects.
 #' @param raw whether to save the model in a raw variable or not, recommended to leave it to \code{TRUE}.
-#' 
+#'
 #' @return NULL invisibly.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' data(agaricus.train, package = "lightgbm")
@@ -40,13 +40,13 @@ saveRDS.lgb.Booster <- function(object,
                                 compress = TRUE,
                                 refhook = NULL,
                                 raw = TRUE) {
-  
+
   # Check if object has a raw value (and if the user wants to store the raw)
   if (is.na(object$raw) && raw) {
-    
+
     # Save model
     object$save()
-    
+
     # Save RDS
     saveRDS(object,
             file = file,
@@ -54,12 +54,12 @@ saveRDS.lgb.Booster <- function(object,
             version = version,
             compress = compress,
             refhook = refhook)
-    
+
     # Free model from memory
     object$raw <- NA
-    
+
   } else {
-    
+
     # Save as usual
     saveRDS(object,
             file = file,
@@ -67,7 +67,7 @@ saveRDS.lgb.Booster <- function(object,
             version = version,
             compress = compress,
             refhook = refhook)
-    
+
   }
-  
+
 }

R-package/R/utils.R

@@ -11,18 +11,18 @@ lgb.is.null.handle <- function(x) {
 }
 
 lgb.encode.char <- function(arr, len) {
-  
+
   if (!is.raw(arr)) {
     stop("lgb.encode.char: Can only encode from raw type") # Not an object of type raw
   }
   rawToChar(arr[seq_len(len)]) # Return the conversion of raw type to character type
-  
+
 }
 
 lgb.call <- function(fun_name, ret, ...) {
   # Set call state to a zero value
   call_state <- 0L
-  
+
   # Check for a ret call
   if (!is.null(ret)) {
     call_state <- .Call(fun_name, ..., ret, call_state, PACKAGE = "lib_lightgbm") # Call with ret
@@ -38,7 +38,7 @@ lgb.call <- function(fun_name, ret, ...) {
     act_len <- 0L
     err_msg <- raw(buf_len)
     err_msg <- .Call("LGBM_GetLastError_R", buf_len, act_len, err_msg, PACKAGE = "lib_lightgbm")
-    
+
     # Check error buffer
     if (act_len > buf_len) {
       buf_len <- act_len
@@ -49,169 +49,169 @@ lgb.call <- function(fun_name, ret, ...) {
                         err_msg,
                         PACKAGE = "lib_lightgbm")
     }
-    
+
     # Return error
     stop("api error: ", lgb.encode.char(err_msg, act_len))
 
   }
 
   return(ret)
-  
+
 }
 
 lgb.call.return.str <- function(fun_name, ...) {
-  
+
   # Create buffer
   buf_len <- as.integer(1024 * 1024)
   act_len <- 0L
   buf <- raw(buf_len)
-  
+
   # Call buffer
   buf <- lgb.call(fun_name, ret = buf, ..., buf_len, act_len)
-  
+
   # Check for buffer content
   if (act_len > buf_len) {
     buf_len <- act_len
     buf <- raw(buf_len)
     buf <- lgb.call(fun_name, ret = buf, ..., buf_len, act_len)
   }
-  
+
   # Return encoded character
   return(lgb.encode.char(buf, act_len))
-  
+
 }
 
 lgb.params2str <- function(params, ...) {
-  
+
   # Check for a list as input
   if (!is.list(params)) {
     stop("params must be a list")
   }
-  
+
   # Split parameter names
   names(params) <- gsub("\\.", "_", names(params))
-  
+
   # Merge parameters from the params and the dots-expansion
   dot_params <- list(...)
   names(dot_params) <- gsub("\\.", "_", names(dot_params))
-  
+
   # Check for identical parameters
   if (length(intersect(names(params), names(dot_params))) > 0) {
     stop("Same parameters in ", sQuote("params"), " and in the call are not allowed. Please check your ", sQuote("params"), " list")
   }
-  
+
   # Merge parameters
   params <- c(params, dot_params)
-  
+
   # Setup temporary variable
   ret <- list()
-  
+
   # Perform key value join
   for (key in names(params)) {
-    
+
     # Join multi value first
     val <- paste0(format(params[[key]], scientific = FALSE), collapse = ",")
     if (nchar(val) <= 0) next # Skip join
-    
+
     # Join key value
     pair <- paste0(c(key, val), collapse = "=")
     ret <- c(ret, pair)
-    
+
   }
-  
+
   # Check ret length
   if (length(ret) == 0) {
-    
+
     # Return empty string
     lgb.c_str("")
-    
+
   } else {
-    
+
     # Return string separated by a space per element
     lgb.c_str(paste0(ret, collapse = " "))
-    
+
   }
-  
+
 }
 
 lgb.c_str <- function(x) {
-  
+
   # Perform character to raw conversion
   ret <- charToRaw(as.character(x))
   ret <- c(ret, as.raw(0))
   ret
-  
+
 }
 
 lgb.check.r6.class <- function(object, name) {
-  
+
   # Check for non-existence of R6 class or named class
   all(c("R6", name) %in% class(object))
-  
+
 }
 
 lgb.check.params <- function(params) {
-  
+
   # To-do
   params # Currently return params because this is not finalized
-  
+
 }
 
 lgb.check.obj <- function(params, obj) {
-  
+
   # List known objectives in a vector
   OBJECTIVES <- c("regression", "regression_l1", "regression_l2", "mean_squared_error", "mse", "l2_root", "root_mean_squared_error", "rmse",
-                  "mean_absolute_error", "mae", "quantile", 
-                  "huber", "fair", "poisson", "binary", "lambdarank", 
+                  "mean_absolute_error", "mae", "quantile",
+                  "huber", "fair", "poisson", "binary", "lambdarank",
                   "multiclass", "softmax", "multiclassova", "multiclass_ova", "ova", "ovr",
                   "xentropy", "cross_entropy", "xentlambda", "cross_entropy_lambda", "mean_absolute_percentage_error", "mape",
                   "gamma", "tweedie")
-  
+
   # Check whether the objective is empty or not, and take it from params if needed
   if (!is.null(obj)) { params$objective <- obj }
-  
+
   # Check whether the objective is a character
   if (is.character(params$objective)) {
-    
+
     # If the objective is a character, check if it is a known objective
     if (!(params$objective %in% OBJECTIVES)) {
-      
+
       # Interrupt on unknown objective name
       stop("lgb.check.obj: objective name error should be one of (", paste0(OBJECTIVES, collapse = ", "), ")")
-      
+
     }
-    
+
   } else if (!is.function(params$objective)) {
-    
+
     # If objective is not a character nor a function, then stop
     stop("lgb.check.obj: objective should be a character or a function")
-    
+
   }
-  
+
   # Return parameters
   return(params)
-  
+
 }
 
 lgb.check.eval <- function(params, eval) {
-  
+
   # Check if metric is null, if yes put a list instead
   if (is.null(params$metric)) {
     params$metric <- list()
   }
-  
+
   # Check if evaluation metric is null, if not then append it
   if (!is.null(eval)) {
-    
+
     # Append metric if character or list
     if (is.character(eval) || is.list(eval)) {
-      
+
       # Append metrics
       params$metric <- append(params$metric, eval)
-      
+
     }
-    
-  } 
+
+  }
   # Return parameters
   return(params)
 }

R-package/demo/categorical_features_prepare.R

@@ -5,7 +5,7 @@ library(data.table)
 library(lightgbm)
 
 # Load data and look at the structure
-# 
+#
 # Classes 'data.table' and 'data.frame':	4521 obs. of  17 variables:
 # $ age      : int  30 33 35 30 59 35 36 39 41 43 ...
 # $ job      : chr  "unemployed" "services" "management" "management" ...
@@ -30,7 +30,7 @@ str(bank)
 # We must now transform the data to fit in LightGBM
 # For this task, we use lgb.prepare
 # The function transforms the data into a fittable data
-# 
+#
 # Classes 'data.table' and 'data.frame':	4521 obs. of  17 variables:
 # $ age      : int  30 33 35 30 59 35 36 39 41 43 ...
 # $ job      : chr  "unemployed" "services" "management" "management" ...

R-package/demo/categorical_features_rules.R

@@ -5,7 +5,7 @@ library(data.table)
 library(lightgbm)
 
 # Load data and look at the structure
-# 
+#
 # Classes 'data.table' and 'data.frame':	4521 obs. of  17 variables:
 # $ age      : int  30 33 35 30 59 35 36 39 41 43 ...
 # $ job      : chr  "unemployed" "services" "management" "management" ...
@@ -34,7 +34,7 @@ bank_test <- bank[4001:4521, ]
 # We must now transform the data to fit in LightGBM
 # For this task, we use lgb.prepare
 # The function transforms the data into a fittable data
-# 
+#
 # Classes 'data.table' and 'data.frame':	521 obs. of  17 variables:
 # $ age      : int  53 36 58 26 34 55 55 34 41 38 ...
 # $ job      : num  1 10 10 9 10 2 2 3 3 4 ...

R-package/demo/multiclass_custom_objective.R

@@ -36,35 +36,35 @@ preds_builtin <- predict(model_builtin, test[, 1:4], rawscore = TRUE)
 # User defined objective function, given prediction, return gradient and second order gradient
 custom_multiclass_obj = function(preds, dtrain) {
     labels = getinfo(dtrain, "label")
-    
+
     # preds is a matrix with rows corresponding to samples and colums corresponding to choices
     preds = matrix(preds, nrow = length(labels))
-    
+
     # to prevent overflow, normalize preds by row
     preds = preds - apply(preds, 1, max)
     prob = exp(preds) / rowSums(exp(preds))
-    
+
     # compute gradient
     grad = prob
     grad[cbind(1:length(labels), labels + 1)] = grad[cbind(1:length(labels), labels + 1)] - 1
-    
+
     # compute hessian (approximation)
     hess = 2 * prob * (1 - prob)
-    
+
     return(list(grad = grad, hess = hess))
 }
 
-# define custom metric 
+# define custom metric
 custom_multiclass_metric = function(preds, dtrain) {
     labels = getinfo(dtrain, "label")
     preds = matrix(preds, nrow = length(labels))
     preds = preds - apply(preds, 1, max)
     prob = exp(preds) / rowSums(exp(preds))
-    
+
     return(list(name = "error",
                 value = -mean(log(prob[cbind(1:length(labels), labels + 1)])),
                 higher_better = FALSE))
-    
+
 }
 
 model_custom <- lgb.train(list(),

R-package/man/lgb.cv.Rd

@@ -23,7 +23,7 @@ lgb.cv(params = list(), data, nrounds = 10, nfold = 3, label = NULL,
 
 \item{weight}{vector of response values. If not NULL, will set to dataset}
 
-\item{obj}{objective function, can be character or custom objective function. Examples include 
+\item{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}}
 
@@ -66,10 +66,10 @@ List of callback functions that are applied at each iteration.}
 \itemize{
     \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
     \item{num_leaves}{number of leaves in one tree. defaults to 127}
-    \item{max_depth}{Limit the max depth for tree model. This is used to deal with 
+    \item{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{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 
+                       the number of real CPU cores, not the number of threads (most
                        CPU using hyper-threading to generate 2 threads per CPU core).}
 }}
 }