[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
This commit is contained in:
Vadim Khotilovich
2017-05-02 00:51:34 -05:00
committed by Tong He
parent d769b6bcb5
commit a375ad2822
29 changed files with 351 additions and 246 deletions

View File

@@ -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: