[R-package] remove unnecessary comments (#4383)

* [R-package] remove unnecessary comments

* Update R-package/R/lgb.Booster.R

R-package/R/lgb.Booster.R

@@ -145,12 +145,10 @@ Booster <- R6::R6Class(
     # Add validation data
     add_valid = function(data, name) {
 
-      # Check if data is lgb.Dataset
       if (!lgb.is.Dataset(data)) {
         stop("lgb.Booster.add_valid: Can only use lgb.Dataset as validation data")
       }
 
-      # Check if predictors are identical
       if (!identical(data$.__enclos_env__$private$predictor, private$init_predictor)) {
         stop(
           "lgb.Booster.add_valid: Failed to add validation data; "
@@ -158,7 +156,6 @@ Booster <- R6::R6Class(
         )
       }
 
-      # Check if names are character
       if (!is.character(name)) {
         stop("lgb.Booster.add_valid: Can only use characters as data name")
       }
@@ -170,7 +167,6 @@ Booster <- R6::R6Class(
         , data$.__enclos_env__$private$get_handle()
       )
 
-      # Store private information
       private$valid_sets <- c(private$valid_sets, data)
       private$name_valid_sets <- c(private$name_valid_sets, name)
       private$num_dataset <- private$num_dataset + 1L
@@ -180,7 +176,6 @@ Booster <- R6::R6Class(
 
     },
 
-    # Reset parameters of booster
     reset_parameter = function(params, ...) {
 
       if (methods::is(self$params, "list")) {
@@ -210,27 +205,22 @@ Booster <- R6::R6Class(
         }
       }
 
-      # Check if training set is not null
       if (!is.null(train_set)) {
 
-        # Check if training set is lgb.Dataset
         if (!lgb.is.Dataset(train_set)) {
           stop("lgb.Booster.update: Only can use lgb.Dataset as training data")
         }
 
-        # Check if predictors are identical
         if (!identical(train_set$predictor, private$init_predictor)) {
           stop("lgb.Booster.update: Change train_set failed, you should use the same predictor for these data")
         }
 
-        # Reset training data on booster
         .Call(
           LGBM_BoosterResetTrainingData_R
           , private$handle
           , train_set$.__enclos_env__$private$get_handle()
         )
 
-        # Store private train set
         private$train_set <- train_set
         private$train_set_version <- train_set$.__enclos_env__$private$version
 
@@ -249,7 +239,6 @@ Booster <- R6::R6Class(
 
       } else {
 
-        # Check if objective is function
         if (!is.function(fobj)) {
           stop("lgb.Booster.update: fobj should be a function")
         }
@@ -289,7 +278,6 @@ Booster <- R6::R6Class(
     # Return one iteration behind
     rollback_one_iter = function() {
 
-      # Return one iteration behind
       .Call(
         LGBM_BoosterRollbackOneIter_R
         , private$handle
@@ -346,7 +334,6 @@ Booster <- R6::R6Class(
     # Evaluate data on metrics
     eval = function(data, name, feval = NULL) {
 
-      # Check if dataset is lgb.Dataset
       if (!lgb.is.Dataset(data)) {
         stop("lgb.Booster.eval: Can only use lgb.Dataset to eval")
       }
@@ -360,7 +347,6 @@ Booster <- R6::R6Class(
         # Check for validation data
         if (length(private$valid_sets) > 0L) {
 
-          # Loop through each validation set
           for (i in seq_along(private$valid_sets)) {
 
             # Check for identical validation data with training data
@@ -406,15 +392,12 @@ Booster <- R6::R6Class(
     # Evaluation validation data
     eval_valid = function(feval = NULL) {
 
-      # Create ret list
       ret <- list()
 
-      # Check if validation is empty
       if (length(private$valid_sets) <= 0L) {
         return(ret)
       }
 
-      # Loop through each validation set
       for (i in seq_along(private$valid_sets)) {
         ret <- append(
           x = ret
@@ -429,12 +412,10 @@ Booster <- R6::R6Class(
     # Save model
     save_model = function(filename, num_iteration = NULL, feature_importance_type = 0L) {
 
-      # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
         num_iteration <- self$best_iter
       }
 
-      # Save booster model
       .Call(
         LGBM_BoosterSaveModel_R
         , private$handle
@@ -446,10 +427,8 @@ Booster <- R6::R6Class(
       return(invisible(self))
     },
 
-    # Save model to string
     save_model_to_string = function(num_iteration = NULL, feature_importance_type = 0L) {
 
-      # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
         num_iteration <- self$best_iter
       }
@@ -468,7 +447,6 @@ Booster <- R6::R6Class(
     # Dump model in memory
     dump_model = function(num_iteration = NULL, feature_importance_type = 0L) {
 
-      # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
         num_iteration <- self$best_iter
       }
@@ -495,11 +473,10 @@ Booster <- R6::R6Class(
                        reshape = FALSE,
                        ...) {
 
-      # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
         num_iteration <- self$best_iter
       }
-      # Check if start iteration is non existent
+
       if (is.null(start_iteration)) {
         start_iteration <- 0L
       }
@@ -565,7 +542,6 @@ Booster <- R6::R6Class(
       # Store data name
       data_name <- private$name_train_set
 
-      # Check for id bigger than 1
       if (idx > 1L) {
         data_name <- private$name_valid_sets[[idx - 1L]]
       }
@@ -609,14 +585,12 @@ Booster <- R6::R6Class(
     # Get evaluation information
     get_eval_info = function() {
 
-      # Check for evaluation names emptiness
       if (is.null(private$eval_names)) {
         eval_names <- .Call(
           LGBM_BoosterGetEvalNames_R
           , private$handle
         )
 
-        # Check names' length
         if (length(eval_names) > 0L) {
 
           # Parse and store privately names
@@ -635,7 +609,6 @@ Booster <- R6::R6Class(
 
     },
 
-    # Perform inner evaluation
     inner_eval = function(data_name, data_idx, feval = NULL) {
 
       # Check for unknown dataset (over the maximum provided range)
@@ -643,13 +616,10 @@ Booster <- R6::R6Class(
         stop("data_idx should not be greater than num_dataset")
       }
 
-      # Get evaluation information
       private$get_eval_info()
 
-      # Prepare return
       ret <- list()
 
-      # Check evaluation names existence
       if (length(private$eval_names) > 0L) {
 
         # Create evaluation values
@@ -661,7 +631,6 @@ Booster <- R6::R6Class(
           , tmp_vals
         )
 
-        # Loop through all evaluation names
         for (i in seq_along(private$eval_names)) {
 
           # Store evaluation and append to return
@@ -684,7 +653,6 @@ Booster <- R6::R6Class(
           stop("lgb.Booster.eval: feval should be a function")
         }
 
-        # Prepare data
         data <- private$train_set
 
         # Check if data to assess is existing differently
@@ -695,7 +663,6 @@ Booster <- R6::R6Class(
         # Perform function evaluation
         res <- feval(private$inner_predict(data_idx), data)
 
-        # Check for name correctness
         if (is.null(res$name) || is.null(res$value) ||  is.null(res$higher_better)) {
           stop("lgb.Booster.eval: custom eval function should return a
             list with attribute (name, value, higher_better)");
@@ -780,7 +747,6 @@ predict.lgb.Booster <- function(object,
     stop("predict.lgb.Booster: object should be an ", sQuote("lgb.Booster"))
   }
 
-  # Return booster predictions
   return(
     object$predict(
       data = data
@@ -995,12 +961,10 @@ lgb.dump <- function(booster, num_iteration = NULL) {
 #' @export
 lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) {
 
-  # Check if booster is booster
   if (!lgb.is.Booster(x = booster)) {
     stop("lgb.get.eval.result: Can only use ", sQuote("lgb.Booster"), " to get eval result")
   }
 
-  # Check if data and evaluation name are characters or not
   if (!is.character(data_name) || !is.character(eval_name)) {
     stop("lgb.get.eval.result: data_name and eval_name should be characters")
   }
@@ -1039,7 +1003,6 @@ lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_
     result <- booster$record_evals[[data_name]][[eval_name]][[.EVAL_ERR_KEY()]]
   }
 
-  # Check if iteration is non existant
   if (is.null(iters)) {
     return(as.numeric(result))
   }
@@ -1049,6 +1012,5 @@ lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_
   delta <- booster$record_evals$start_iter - 1.0
   iters <- iters - delta
 
-  # Return requested result
   return(as.numeric(result[iters]))
 }

R-package/R/lgb.Dataset.R

@@ -969,7 +969,6 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
     stop("slice.lgb.Dataset: input dataset should be an lgb.Dataset object")
   }
 
-  # Return sliced set
   return(invisible(dataset$slice(idxset = idxset, ...)))
 
 }
@@ -1076,7 +1075,6 @@ setinfo.lgb.Dataset <- function(dataset, name, info, ...) {
     stop("setinfo.lgb.Dataset: input dataset should be an lgb.Dataset object")
   }
 
-  # Set information
   return(invisible(dataset$setinfo(name = name, info = info)))
 }
 
@@ -1108,7 +1106,6 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
     stop("lgb.Dataset.set.categorical: input dataset should be an lgb.Dataset object")
   }
 
-  # Set categoricals
   return(invisible(dataset$set_categorical_feature(categorical_feature = categorical_feature)))
 
 }
@@ -1135,12 +1132,10 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
 #' @export
 lgb.Dataset.set.reference <- function(dataset, reference) {
 
-  # Check if dataset is not a dataset
   if (!lgb.is.Dataset(x = dataset)) {
     stop("lgb.Dataset.set.reference: input dataset should be an lgb.Dataset object")
   }
 
-  # Set reference
   return(invisible(dataset$set_reference(reference = reference)))
 }
 
@@ -1163,16 +1158,13 @@ lgb.Dataset.set.reference <- function(dataset, reference) {
 #' @export
 lgb.Dataset.save <- function(dataset, fname) {
 
-  # Check if dataset is not a dataset
   if (!lgb.is.Dataset(x = dataset)) {
     stop("lgb.Dataset.set: input dataset should be an lgb.Dataset object")
   }
 
-  # File-type is not matching
   if (!is.character(fname)) {
     stop("lgb.Dataset.set: fname should be a character or a file connection")
   }
 
-  # Store binary
   return(invisible(dataset$save_binary(fname = fname)))
 }

R-package/R/lgb.Predictor.R

@@ -30,7 +30,6 @@ Predictor <- R6::R6Class(
       private$params <- lgb.params2str(params = params)
       handle <- NULL
 
-      # Check if handle is a character
       if (is.character(modelfile)) {
 
         # Create handle on it

R-package/R/lgb.cv.R

@@ -189,7 +189,6 @@ lgb.cv <- function(params = list()
     , column_names = cnames
   )
 
-  # Check for weights
   if (!is.null(weight)) {
     data$setinfo(name = "weight", info = weight)
   }
@@ -210,7 +209,6 @@ lgb.cv <- function(params = list()
     data$set_categorical_feature(categorical_feature = categorical_feature)
   }
 
-  # Check for folds
   if (!is.null(folds)) {
 
     # Check for list of folds or for single value
@@ -218,12 +216,10 @@ lgb.cv <- function(params = list()
       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 <= 1L) {
       stop(sQuote("nfold"), " must be > 1")
     }
@@ -583,15 +579,12 @@ lgb.stratified.folds <- function(y, k) {
 
 lgb.merge.cv.result <- function(msg, showsd) {
 
-  # Get CV message length
   if (length(msg) == 0L) {
     stop("lgb.cv: size of cv result error")
   }
 
-  # Get evaluation message length
   eval_len <- length(msg[[1L]])
 
-  # Is evaluation message empty?
   if (eval_len == 0L) {
     stop("lgb.cv: should provide at least one metric for CV")
   }
@@ -606,7 +599,6 @@ lgb.merge.cv.result <- function(msg, showsd) {
   # get structure (name, higher_better, data_name)
   ret_eval <- msg[[1L]]
 
-  # Go through evaluation length items
   for (j in seq_len(eval_len)) {
     ret_eval[[j]]$value <- mean(eval_result[[j]])
   }
@@ -624,12 +616,10 @@ lgb.merge.cv.result <- function(msg, showsd) {
       )
     }
 
-    # Convert to list
     ret_eval_err <- as.list(ret_eval_err)
 
   }
 
-  # Return errors
   return(
     list(
       eval_list = ret_eval

R-package/R/lgb.interprete.R

@@ -78,7 +78,6 @@ lgb.interprete <- function(model,
     }
   )
 
-  # Sequence over idxset
   for (i in seq_along(idxset)) {
     tree_interpretation_dt_list[[i]] <- single.row.interprete(
       tree_dt = tree_dt
@@ -113,10 +112,8 @@ single.tree.interprete <- function(tree_dt,
   # 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
@@ -151,7 +148,6 @@ multiple.tree.interprete <- function(tree_dt,
                                      tree_index,
                                      leaf_index) {
 
-  # Apply each trees
   interp_dt <- data.table::rbindlist(
     l = mapply(
       FUN = single.tree.interprete

R-package/R/lgb.model.dt.tree.R

@@ -50,10 +50,8 @@
 #' @export
 lgb.model.dt.tree <- function(model, num_iteration = NULL) {
 
-  # Dump json model first
   json_model <- lgb.dump(booster = model, num_iteration = num_iteration)
 
-  # Parse json model second
   parsed_json_model <- jsonlite::fromJSON(
     txt = json_model
     , simplifyVector = TRUE
@@ -62,10 +60,10 @@ lgb.model.dt.tree <- function(model, num_iteration = NULL) {
     , flatten = FALSE
   )
 
-  # Parse tree model third
+  # Parse tree model
   tree_list <- lapply(parsed_json_model$tree_info, single.tree.parse)
 
-  # Combine into single data.table fourth
+  # Combine into single data.table
   tree_dt <- data.table::rbindlist(l = tree_list, use.names = TRUE)
 
   # Substitute feature index with the actual feature name

R-package/R/lgb.plot.interpretation.R

@@ -61,7 +61,6 @@ lgb.plot.interpretation <- function(tree_interpretation_dt,
                                     left_margin = 10L,
                                     cex = NULL) {
 
-  # Get number of columns
   num_class <- ncol(tree_interpretation_dt) - 1L
 
   # Refresh plot
@@ -82,7 +81,6 @@ lgb.plot.interpretation <- function(tree_interpretation_dt,
     )
   )
 
-  # Check for number of classes
   if (num_class == 1L) {
 
     # Only one class, plot straight away

R-package/R/lgb.train.R

@@ -188,7 +188,6 @@ lgb.train <- function(params = list(),
   # Parse validation datasets
   if (length(valids) > 0L) {
 
-    # Loop through all validation datasets using name
     for (key in names(valids)) {
 
       # Use names to get validation datasets

R-package/R/utils.R

@@ -21,7 +21,6 @@ lgb.is.null.handle <- function(x) {
 
 lgb.params2str <- function(params) {
 
-  # Check for a list as input
   if (!identical(class(params), "list")) {
     stop("params must be a list")
   }
@@ -29,7 +28,6 @@ lgb.params2str <- function(params) {
   # Split parameter names
   names(params) <- gsub("\\.", "_", names(params))
 
-  # Setup temporary variable
   ret <- list()
 
   # Perform key value join
@@ -54,7 +52,6 @@ lgb.params2str <- function(params) {
 
   }
 
-  # Check ret length
   if (length(ret) == 0L) {
     return("")
   }