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[R] parameter style consistency

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This commit is contained in:
Vadim Khotilovich
2016-06-27 01:58:03 -05:00
parent 56bd442b31
commit e9eb34fabc
8 changed files with 63 additions and 94 deletions
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16
R-package/R/xgb.plot.importance.R
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@@ -2,9 +2,9 @@
#'
#' Read a data.table containing feature importance details and plot it (for both GLM and Trees).
#'
#' @importFrom magrittr %>%
#' @param importance_matrix a \code{data.table} returned by the \code{xgb.importance} function.
#' @param numberOfClusters a \code{numeric} vector containing the min and the max range of the possible number of clusters of bars.
#' @param n_clusters a \code{numeric} vector containing the min and the max range of the possible number of clusters of bars.
#' @param ... currently not used
#'
#' @return A \code{ggplot2} bar graph representing each feature by a horizontal bar. Longer is the bar, more important is the feature. Features are classified by importance and clustered by importance. The group is represented through the color of the bar.
#'
@@ -20,16 +20,16 @@
#' #(labels = outcome column which will be learned).
#' #Each column of the sparse Matrix is a feature in one hot encoding format.
#'
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
#'
#' #agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' importance_matrix <- xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst)
#' importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
#' xgb.plot.importance(importance_matrix)
#'
#' @export
xgb.plot.importance <-
function(importance_matrix = NULL, numberOfClusters = c(1:10)) {
function(importance_matrix = NULL, n_clusters = c(1:10), ...) {
check.deprecation(...)
if (!"data.table" %in% class(importance_matrix)) {
stop("importance_matrix: Should be a data.table.")
}
@@ -53,7 +53,7 @@ xgb.plot.importance <-
importance_matrix[, .(Gain.or.Weight = sum(get(y.axe.name))), by = Feature]
clusters <-
suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain.or.Weight], numberOfClusters))
suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain.or.Weight], n_clusters))
importance_matrix[,"Cluster":= clusters$cluster %>% as.character]
plot <-