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Updates from 1.2.0 cran submission (#6077)

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* update for 1.2.0 cran submission

* recover cmakelists

* fix unittest from the shap PR

* trigger CI
This commit is contained in:
Tong He
2020-09-02 20:50:23 +08:00
committed by GitHub
parent 9be969cc7a
commit 3912f3de06
23 changed files with 203 additions and 38 deletions
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2
R-package/R/utils.R
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@@ -349,6 +349,7 @@ NULL
#' # Save as a stand-alone file (JSON); load it with xgb.load()
#' xgb.save(bst, 'xgb.model.json')
#' bst2 <- xgb.load('xgb.model.json')
#' if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
#'
#' # Save as a raw byte vector; load it with xgb.load.raw()
#' xgb_bytes <- xgb.save.raw(bst)
@@ -364,6 +365,7 @@ NULL
#' obj2 <- readRDS('my_object.rds')
#' # Re-construct xgb.Booster object from the bytes
#' bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
#' if (file.exists('my_object.rds')) file.remove('my_object.rds')
#'
#' @name a-compatibility-note-for-saveRDS-save
NULL

2
R-package/R/xgb.cv.R
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@@ -79,7 +79,7 @@
#'
#' All observations are used for both training and validation.
#'
#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
#' Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
#'
#' @return
#' An object of class \code{xgb.cv.synchronous} with the following elements:

4
R-package/R/xgb.plot.shap.R
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@@ -200,9 +200,9 @@ xgb.plot.shap <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
#' @return A \code{ggplot2} object.
#' @export
#'
#' @examples See \code{\link{xgb.plot.shap}}.
#' @examples # See \code{\link{xgb.plot.shap}}.
#' @seealso \code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
#' \code{\url{https://github.com/slundberg/shap}}
#' \url{https://github.com/slundberg/shap}
xgb.plot.shap.summary <- function(data, shap_contrib = NULL, features = NULL, top_n = 10, model = NULL,
trees = NULL, target_class = NULL, approxcontrib = FALSE, subsample = NULL) {
# Only ggplot implementation is available.

10
R-package/R/xgb.train.R
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@@ -130,16 +130,16 @@
#' Note that when using a customized metric, only this single metric can be used.
#' The following is the list of built-in metrics for which Xgboost provides optimized implementation:
#' \itemize{
#' \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
#' \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{mlogloss} multiclass logloss. \url{http://wiki.fast.ai/index.php/Log_Loss}
#' \item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
#' \item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
#' \item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
#' \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#' By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
#' Different threshold (e.g., 0.) could be specified as "error@0."
#' \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
#' \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
#' \item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
#' \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
#' }
#'
#' The following callbacks are automatically created when certain parameters are set: