[R] maintenance Apr 2017 (#2237)

* [R] make sure things work for a single split model; fixes #2191

* [R] add option use_int_id to xgb.model.dt.tree

* [R] add example of exporting tree plot to a file

* [R] set save_period = NULL as default in xgboost() to be the same as in xgb.train; fixes #2182

* [R] it's a good practice after CRAN releases to bump up package version in dev

* [R] allow xgb.DMatrix construction from integer dense matrices

* [R] xgb.DMatrix: silent parameter; improve documentation

* [R] xgb.model.dt.tree code style changes

* [R] update NEWS with parameter changes

* [R] code safety & style; handle non-strict matrix and inherited classes of input and model; fixes #2242

* [R] change to x.y.z.p R-package versioning scheme and set version to 0.6.4.3

* [R] add an R package versioning section to the contributors guide

* [R] R-package/README.md: clean up the redundant old installation instructions, link the contributors guide

R-package/man/xgb.Booster.complete.Rd

@@ -28,8 +28,8 @@ E.g., when an \code{xgb.Booster} model is saved as an R object and then is loade
 its handle (pointer) to an internal xgboost model would be invalid. The majority of xgboost methods 
 should still work for such a model object since those methods would be using 
 \code{xgb.Booster.complete} internally. However, one might find it to be more efficient to call the  
-\code{xgb.Booster.complete} function once after loading a model as an R-object. That which would
-prevent further reconstruction (potentially, multiple times) of an internal booster model.
+\code{xgb.Booster.complete} function explicitely once after loading a model as an R-object.
+That would prevent further repeated implicit reconstruction of an internal booster model.
 }
 \examples{
 
@@ -41,6 +41,7 @@ saveRDS(bst, "xgb.model.rds")
 bst1 <- readRDS("xgb.model.rds")
 # the handle is invalid:
 print(bst1$handle)
+
 bst1 <- xgb.Booster.complete(bst1)
 # now the handle points to a valid internal booster model:
 print(bst1$handle)

R-package/man/xgb.DMatrix.Rd

@@ -2,23 +2,28 @@
 % Please edit documentation in R/xgb.DMatrix.R
 \name{xgb.DMatrix}
 \alias{xgb.DMatrix}
-\title{Contruct xgb.DMatrix object}
+\title{Construct xgb.DMatrix object}
 \usage{
-xgb.DMatrix(data, info = list(), missing = NA, ...)
+xgb.DMatrix(data, info = list(), missing = NA, silent = FALSE, ...)
 }
 \arguments{
-\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename}
+\item{data}{a \code{matrix} object (either numeric or integer), a \code{dgCMatrix} object, or a character 
+string representing a filename.}
 
-\item{info}{a list of information of the xgb.DMatrix object}
+\item{info}{a named list of additional information to store in the \code{xgb.DMatrix} object.
+See \code{\link{setinfo}} for the specific allowed kinds of}
 
-\item{missing}{Missing is only used when input is dense matrix, pick a float
-value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.}
+\item{missing}{a float value to represents missing values in data (used only when input is a dense matrix).
+It is useful when a 0 or some other extreme value represents missing values in data.}
 
-\item{...}{other information to pass to \code{info}.}
+\item{silent}{whether to suppress printing an informational message after loading from a file.}
+
+\item{...}{the \code{info} data could be passed directly as parameters, without creating an \code{info} list.}
 }
 \description{
-Contruct xgb.DMatrix object from dense matrix, sparse matrix 
-or local file (that was created previously by saving an \code{xgb.DMatrix}).
+Construct xgb.DMatrix object from either a dense matrix, a sparse matrix, or a local file.
+Supported input file formats are either a libsvm text file or a binary file that was created previously by
+\code{\link{xgb.DMatrix.save}}).
 }
 \examples{
 data(agaricus.train, package='xgboost')

R-package/man/xgb.dump.Rd

@@ -4,7 +4,7 @@
 \alias{xgb.dump}
 \title{Dump an xgboost model in text format.}
 \usage{
-xgb.dump(model = NULL, fname = NULL, fmap = "", with_stats = FALSE,
+xgb.dump(model, fname = NULL, fmap = "", with_stats = FALSE,
   dump_format = c("text", "json"), ...)
 }
 \arguments{

R-package/man/xgb.model.dt.tree.Rd

@@ -5,7 +5,7 @@
 \title{Parse a boosted tree model text dump}
 \usage{
 xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
-  trees = NULL, ...)
+  trees = NULL, use_int_id = FALSE, ...)
 }
 \arguments{
 \item{feature_names}{character vector of feature names. If the model already
@@ -24,6 +24,9 @@ It could be useful, e.g., in multiclass classification to get only
 the trees of one certain class. IMPORTANT: the tree index in xgboost models
 is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).}
 
+\item{use_int_id}{a logical flag indicating whether nodes in columns "Yes", "No", "Missing" should be
+represented as integers (when FALSE) or as "Tree-Node" character strings (when FALSE).}
+
 \item{...}{currently not used.}
 }
 \value{
@@ -32,9 +35,9 @@ A \code{data.table} with detailed information about model trees' nodes.
 The columns of the \code{data.table} are:
 
 \itemize{
- \item \code{Tree}: ID of a tree in a model (integer)
- \item \code{Node}: integer ID of a node in a tree (integer)
- \item \code{ID}: identifier of a node in a model (character)
+ \item \code{Tree}: integer ID of a tree in a model (zero-based index)
+ \item \code{Node}: integer ID of a node in a tree (zero-based index)
+ \item \code{ID}: character identifier of a node in a model (only when \code{use_int_id=FALSE})
  \item \code{Feature}: for a branch node, it's a feature id or name (when available);
              for a leaf note, it simply labels it as \code{'Leaf'}
  \item \code{Split}: location of the split for a branch node (split condition is always "less than")
@@ -44,7 +47,11 @@ The columns of the \code{data.table} are:
  \item \code{Quality}: either the split gain (change in loss) or the leaf value
  \item \code{Cover}: metric related to the number of observation either seen by a split
                      or collected by a leaf during training.
-}
+} 
+
+When \code{use_int_id=FALSE}, columns "Yes", "No", and "Missing" point to model-wide node identifiers
+in the "ID" column. When \code{use_int_id=TRUE}, those columns point to node identifiers from 
+the corresponding trees in the "Node" column.
 }
 \description{
 Parse a boosted tree model text dump into a \code{data.table} structure.
@@ -58,8 +65,9 @@ bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_dep
                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 
 (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
-# This bst has feature_names stored in it, so those would be used when 
-# the feature_names parameter is not provided:
+
+# This bst model already has feature_names stored with it, so those would be used when 
+# feature_names is not set:
 (dt <- xgb.model.dt.tree(model = bst))
 
 # How to match feature names of splits that are following a current 'Yes' branch:

R-package/man/xgb.plot.tree.Rd

@@ -24,7 +24,7 @@ IMPORTANT: the tree index in xgboost model is zero-based
 
 \item{render}{a logical flag for whether the graph should be rendered (see Value).}
 
-\item{show_node_id}{a logical flag for whether to include node id's in the graph.}
+\item{show_node_id}{a logical flag for whether to show node id's in the graph.}
 
 \item{...}{currently not used.}
 }
@@ -68,9 +68,17 @@ data(agaricus.train, package='xgboost')
 bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
                eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
 # plot all the trees
-xgb.plot.tree(feature_names = colnames(agaricus.train$data), model = bst)
-# plot only the first tree and include the node ID:
-xgb.plot.tree(feature_names = colnames(agaricus.train$data), model = bst,
-              trees = 0, show_node_id = TRUE)
+xgb.plot.tree(model = bst)
+# plot only the first tree and display the node ID:
+xgb.plot.tree(model = bst, trees = 0, show_node_id = TRUE)
+
+\dontrun{
+# Below is an example of how to save this plot to a file. 
+# Note that for `export_graph` to work, the DiagrammeRsvg and rsvg packages must also be installed.
+library(DiagrammeR)
+gr <- xgb.plot.tree(model=bst, trees=0:1, render=FALSE)
+export_graph(gr, 'tree.pdf', width=1500, height=1900)
+export_graph(gr, 'tree.png', width=1500, height=1900)
+}
 
 }

R-package/man/xgb.train.Rd

@@ -12,7 +12,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
 
 xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
   params = list(), nrounds, verbose = 1, print_every_n = 1L,
-  early_stopping_rounds = NULL, maximize = NULL, save_period = 0,
+  early_stopping_rounds = NULL, maximize = NULL, save_period = NULL,
   save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ...)
 }
 \arguments{