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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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19
R-package/R/xgb.importance.R
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@@ -2,14 +2,6 @@
#'
#' Create a \code{data.table} of the most important features of a model.
#'
#' @importFrom data.table data.table
#' @importFrom data.table setnames
#' @importFrom data.table :=
#' @importFrom magrittr %>%
#' @importFrom Matrix colSums
#' @importFrom Matrix cBind
#' @importFrom Matrix sparseVector
#'
#' @param feature_names names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#' @param model generated by the \code{xgb.train} function.
#' @param data the dataset used for the training step. Will be used with \code{label} parameter for co-occurence computation. More information in \code{Detail} part. This parameter is optional.
@@ -46,14 +38,13 @@
#' @examples
#' data(agaricus.train, package='xgboost')
#'
#' 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$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst)
#' xgb.importance(colnames(agaricus.train$data), model = bst)
#'
#' # Same thing with co-occurence computation this time
#' xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst, data = agaricus.train$data, label = agaricus.train$label)
#' xgb.importance(colnames(agaricus.train$data), model = bst, data = agaricus.train$data, label = agaricus.train$label)
#'
#' @export
xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, label = NULL, target = function(x) ( (x + label) == 2)){
@@ -84,7 +75,7 @@ xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, labe
data.table(Feature = feature_names, Weight = weights)
}
model.text.dump <- xgb.dump(model = model, with.stats = T)
model.text.dump <- xgb.dump(model = model, with_stats = T)
if(model.text.dump[2] == "bias:"){
result <- model.text.dump %>% linearDump(feature_names, .)