[R] maintenance Nov 2017; SHAP plots (#2888)

* [R] fix predict contributions for data with no colnames

* [R] add a render parameter for xgb.plot.multi.trees; fixes #2628

* [R] update Rd's

* [R] remove unnecessary dep-package from R cmake install

* silence type warnings; readability

* [R] silence complaint about incomplete line at the end

* [R] initial version of xgb.plot.shap()

* [R] more work on xgb.plot.shap

* [R] enforce black font in xgb.plot.tree; fixes #2640

* [R] if feature names are available, check in predict that they are the same; fixes #2857

* [R] cran check and lint fixes

* remove tabs

* [R] add references; a test for plot.shap

R-package/R/xgb.Booster.R

@@ -150,7 +150,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #' Setting \code{predcontrib = TRUE} allows to calculate contributions of each feature to
 #' individual predictions. For "gblinear" booster, feature contributions are simply linear terms
 #' (feature_beta * feature_value). For "gbtree" booster, feature contributions are SHAP
-#' values (https://arxiv.org/abs/1706.06060) that sum to the difference between the expected output
+#' values (Lundberg 2017) that sum to the difference between the expected output
 #' of the model and the current prediction (where the hessian weights are used to compute the expectations).
 #' Setting \code{approxcontrib = TRUE} approximates these values following the idea explained
 #' in \url{http://blog.datadive.net/interpreting-random-forests/}.
@@ -172,6 +172,12 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
 #'
 #' @seealso
 #' \code{\link{xgb.train}}.
+#' 
+#' @references
+#'
+#' Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
+#'
+#' Scott M. Lundberg, Su-In Lee, "Consistent feature attribution for tree ensembles", \url{https://arxiv.org/abs/1706.06060}
 #'
 #' @examples
 #' ## binary classification:
@@ -265,6 +271,10 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   object <- xgb.Booster.complete(object, saveraw = FALSE)
   if (!inherits(newdata, "xgb.DMatrix"))
     newdata <- xgb.DMatrix(newdata, missing = missing)
+  if (!is.null(object[["feature_names"]]) &&
+      !is.null(colnames(newdata)) &&
+      !identical(object[["feature_names"]], colnames(newdata)))
+    stop("Feature names stored in `object` and `newdata` are different!")
   if (is.null(ntreelimit))
     ntreelimit <- NVL(object$best_ntreelimit, 0)
   if (NVL(object$params[['booster']], '') == 'gblinear')
@@ -292,7 +302,7 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
   } else if (predcontrib) {
     n_col1 <- ncol(newdata) + 1
     n_group <- npred_per_case / n_col1
-    dnames <- list(NULL, c(colnames(newdata), "BIAS"))
+    dnames <- if (!is.null(colnames(newdata))) list(NULL, c(colnames(newdata), "BIAS")) else NULL
     ret <- if (n_ret == n_row) {
       matrix(ret, ncol = 1, dimnames = dnames)
     } else if (n_group == 1) {