[R-package] added missing importFroms in NAMESPACE (#1627)

R-package/NAMESPACE

@@ -38,9 +38,11 @@ export(slice)
 import(methods)
 importFrom(R6,R6Class)
 importFrom(data.table,":=")
+importFrom(data.table,as.data.table)
 importFrom(data.table,data.table)
 importFrom(data.table,rbindlist)
 importFrom(data.table,set)
+importFrom(data.table,setnames)
 importFrom(graphics,barplot)
 importFrom(graphics,par)
 importFrom(jsonlite,fromJSON)
@@ -49,4 +51,5 @@ importFrom(magrittr,"%T>%")
 importFrom(magrittr,extract)
 importFrom(magrittr,inset)
 importFrom(methods,is)
+importFrom(stats,quantile)
 useDynLib(lib_lightgbm , .registration = TRUE)

R-package/R/lgb.cv.R

@@ -373,6 +373,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
 # Creates CV folds stratified by the values of y.
 # It was borrowed from caret::lgb.stratified.folds and simplified
 # 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

R-package/R/lgb.importance.R

@@ -30,7 +30,7 @@
 #' tree_imp1 <- lgb.importance(model, percentage = TRUE)
 #' tree_imp2 <- lgb.importance(model, percentage = FALSE)
 #' 
-#' @importFrom magrittr %>% %T>%
+#' @importFrom magrittr %>% %T>% extract
 #' @importFrom data.table :=
 #' @export
 lgb.importance <- function(model, percentage = TRUE) {

R-package/R/lgb.interprete.R

@@ -38,6 +38,7 @@
 #' 
 #' tree_interpretation <- lgb.interprete(model, test$data, 1:5)
 #' 
+#' @importFrom data.table as.data.table
 #' @importFrom magrittr %>% %T>%
 #' @export
 lgb.interprete <- function(model,
@@ -76,6 +77,7 @@ lgb.interprete <- function(model,
   
 }
 
+#' @importFrom data.table data.table
 single.tree.interprete <- function(tree_dt,
                                    tree_id,
                                    leaf_id) {
@@ -119,6 +121,8 @@ single.tree.interprete <- function(tree_dt,
   
 }
 
+#' @importFrom data.table rbindlist
+#' @importFrom magrittr %>% extract
 multiple.tree.interprete <- function(tree_dt,
                                      tree_index,
                                      leaf_index) {
@@ -134,6 +138,7 @@ multiple.tree.interprete <- function(tree_dt,
   
 }
 
+#' @importFrom data.table set setnames
 single.row.interprete <- function(tree_dt, num_class, tree_index_mat, leaf_index_mat) {
   
   # Prepare vector list

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

 #' Plot feature contribution as a bar graph
-#' 
+#'
 #' Plot previously calculated feature contribution as a bar graph.
-#' 
+#'
 #' @param tree_interpretation_dt a \code{data.table} returned by \code{\link{lgb.interprete}}.
 #' @param top_n maximal number of top features to include into the plot.
 #' @param cols the column numbers of layout, will be used only for multiclass classification feature contribution.
 #' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.
 #' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}.
-#' 
+#'
 #' @details
 #' The graph represents each feature as a horizontal bar of length proportional to the defined contribution of a feature.
 #' Features are shown ranked in a decreasing contribution order.
-#' 
+#'
 #' @return
 #' The \code{lgb.plot.interpretation} function creates a \code{barplot}.
-#' 
+#'
 #' @examples
 #' library(lightgbm)
 #' Sigmoid <- function(x) {1 / (1 + exp(-x))}
@@ -25,54 +25,56 @@
 #' 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, num_leaves = 63, max_depth = -1,
 #'               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
 #'               model <- lgb.train(params, dtrain, 20)
 #' model <- lgb.train(params, dtrain, 20)
-#' 
+#'
 #' tree_interpretation <- lgb.interprete(model, test$data, 1:5)
 #' lgb.plot.interpretation(tree_interpretation[[1]], top_n = 10)
+#' @importFrom data.table setnames
 #' @importFrom graphics barplot par
+#' @importFrom magrittr inset
 #' @export
 lgb.plot.interpretation <- function(tree_interpretation_dt,
                                     top_n = 10,
                                     cols = 1,
                                     left_margin = 10,
                                     cex = NULL) {
-  
+
   # Get number of columns
   num_class <- ncol(tree_interpretation_dt) - 1
-  
+
   # Refresh plot
   op <- graphics::par(no.readonly = TRUE)
   on.exit(graphics::par(op))
-  
+
   # Do some magic plotting
   graphics::par(mar = op$mar %>% magrittr::inset(., 1:3, c(3, left_margin, 2)))
-  
+
   # Check for number of classes
   if (num_class == 1) {
-    
+
     # Only one class, plot straight away
     multiple.tree.plot.interpretation(tree_interpretation_dt,
                                       top_n = top_n,
                                       title = NULL,
                                       cex = cex)
-    
+
   } else {
-    
+
     # More than one class, shape data first
     layout_mat <- matrix(seq.int(to = cols * ceiling(num_class / cols)),
                          ncol = cols, nrow = ceiling(num_class / cols))
-    
+
     # Shape output
     graphics::par(mfcol = c(nrow(layout_mat), ncol(layout_mat)))
-    
+
     # Loop throughout all classes
     for (i in seq_len(num_class)) {
-      
+
       # Prepare interpretation, perform T, get the names, and plot straight away
       tree_interpretation_dt[, c(1, i + 1), with = FALSE] %T>%
         data.table::setnames(., old = names(.), new = c("Feature", "Contribution")) %>%
@@ -80,24 +82,25 @@ lgb.plot.interpretation <- function(tree_interpretation_dt,
                                           top_n = top_n,
                                           title = paste("Class", i - 1),
                                           cex = cex)
-      
+
     }
   }
 }
 
+#' @importFrom graphics barplot
 multiple.tree.plot.interpretation <- function(tree_interpretation,
                                               top_n,
                                               title,
                                               cex) {
-  
+
   # Parse tree
   tree_interpretation <- tree_interpretation[order(abs(Contribution), decreasing = TRUE),][seq_len(min(top_n, .N)),]
-  
+
   # Attempt to setup a correct cex
   if (is.null(cex)) {
     cex <- 2.5 / log2(1 + top_n)
   }
-  
+
   # Do plot
   tree_interpretation[.N:1,
                       graphics::barplot(
@@ -110,8 +113,8 @@ multiple.tree.plot.interpretation <- function(tree_interpretation,
                           cex.names = cex,
                           las = 1
                       )]
-  
+
   # Return invisibly
   invisible(NULL)
-  
+
 }