[CORE] The update process for a tree model, and its application to feature importance (#1670)

* [CORE] allow updating trees in an existing model

* [CORE] in refresh updater, allow keeping old leaf values and update stats only

* [R-package] xgb.train mod to allow updating trees in an existing model

* [R-package] added check for nrounds when is_update

* [CORE] merge parameter declaration changes; unify their code style

* [CORE] move the update-process trees initialization to Configure; rename default process_type to 'default'; fix the trees and trees_to_update sizes comparison check

* [R-package] unit tests for the update process type

* [DOC] documentation for process_type parameter; improved docs for updater, Gamma and Tweedie; added some parameter aliases; metrics indentation and some were non-documented

* fix my sloppy merge conflict resolutions

* [CORE] add a TreeProcessType enum

* whitespace fix

R-package/R/xgb.train.R

@@ -284,7 +284,9 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
   # Sort the callbacks into categories
   cb <- categorize.callbacks(callbacks)
 
-  
+  # The tree updating process would need slightly different handling
+  is_update <- NVL(params[['process_type']], '.') == 'update'
+
   # Construct a booster (either a new one or load from xgb_model)
   handle <- xgb.Booster(params, append(watchlist, dtrain), xgb_model)
   bst <- xgb.handleToBooster(handle)
@@ -294,17 +296,20 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
   num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)
 
   # When the 'xgb_model' was set, find out how many boosting iterations it has
-  niter_skip <- 0
+  niter_init <- 0
   if (!is.null(xgb_model)) {
-    niter_skip <- as.numeric(xgb.attr(bst, 'niter')) + 1
-    if (length(niter_skip) == 0) {
-      niter_skip <- xgb.ntree(bst) %/% (num_parallel_tree * num_class)
+    niter_init <- as.numeric(xgb.attr(bst, 'niter')) + 1
+    if (length(niter_init) == 0) {
+      niter_init <- xgb.ntree(bst) %/% (num_parallel_tree * num_class)
     }
   }
+  if(is_update && nrounds > niter_init)
+    stop("nrounds cannot be larger than ", niter_init, " (nrounds of xgb_model)")
 
   # TODO: distributed code
   rank <- 0
   
+  niter_skip <- ifelse(is_update, 0, niter_init)
   begin_iteration <- niter_skip + 1
   end_iteration <- niter_skip + nrounds
   
@@ -337,6 +342,7 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
       nrow(evaluation_log) > 0) {
     # include the previous compatible history when available
     if (class(xgb_model) == 'xgb.Booster' &&
+        !is_update &&
         !is.null(xgb_model$evaluation_log) &&
         all.equal(colnames(evaluation_log),
                   colnames(xgb_model$evaluation_log))) {