Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Fix for CRAN Submission (#1826)

Browse Source 
* fix cran check

* change required R version because of utils::globalVariables

* temporary commit, monotone not working

* fix test

* fix doc

* fix doc

* fix cran note and warning

* improve checks

* fix urls
This commit is contained in:
Tong He
2016-12-02 20:19:03 -08:00
committed by Tianqi Chen
parent 27ca50e2c2
commit 2f3958a455
15 changed files with 40 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
R-package/R/xgb.create.features.R
View File

@@ -57,7 +57,8 @@
#' bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
#'
#' # Model accuracy without new features
#' accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
#' accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) /
#' length(agaricus.test$label)
#'
#' # Convert previous features to one hot encoding
#' new.features.train <- xgb.create.features(model = bst, agaricus.train$data)
@@ -70,10 +71,12 @@
#' bst <- xgb.train(params = param, data = new.dtrain, nrounds = nround, nthread = 2)
#'
#' # Model accuracy with new features
#' accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
#' accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) /
#' length(agaricus.test$label)
#'
#' # Here the accuracy was already good and is now perfect.
#' cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!\n"))
#' cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now",
#' accuracy.after, "!\n"))
#'
#' @export
xgb.create.features <- function(model, data, ...){

3
R-package/R/xgb.importance.R
View File

@@ -44,7 +44,8 @@
#' xgb.importance(colnames(agaricus.train$data), model = bst)
#'
#' # Same thing with co-occurence computation this time
#' xgb.importance(colnames(agaricus.train$data), 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)){

3
R-package/R/xgb.plot.deepness.R
View File

@@ -46,7 +46,8 @@
#'
#' data(agaricus.train, package='xgboost')
#'
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
#' # Change max_depth to a higher number to get a more significant result
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 6,
#' eta = 0.1, nthread = 2, nrounds = 50, objective = "binary:logistic",
#' subsample = 0.5, min_child_weight = 2)
#'

3
R-package/R/xgb.plot.multi.trees.R
View File

@@ -39,7 +39,8 @@
#' eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
#' min_child_weight = 50)
#'
#' p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train$data), features_keep = 3)
#' p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train$data),
#' features_keep = 3)
#' print(p)
#'
#' @export