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xgb.model.dt.tree up to x100 faster

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This commit is contained in:
Vadim Khotilovich
2016-04-13 02:32:00 -05:00
parent 49bbd72d08
commit be65949ba2
4 changed files with 96 additions and 150 deletions
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49
R-package/man/xgb.model.dt.tree.Rd
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@@ -8,48 +8,47 @@ xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
n_first_tree = NULL)
}
\arguments{
\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If the model already contains feature names, this argument should be \code{NULL} (default value).}
\item{feature_names}{character vector of feature names. If the model already
contains feature names, this argument should be \code{NULL} (default value)}
\item{model}{object created by the \code{xgb.train} function.}
\item{model}{object of class \code{xgb.Booster}}
\item{text}{\code{character} vector generated by the \code{xgb.dump} function. Model dump must include the gain per feature and per tree (parameter \code{with.stats = TRUE} in function \code{xgb.dump}).}
\item{text}{\code{character} vector previously generated by the \code{xgb.dump}
function (where parameter \code{with.stats = TRUE} should have been set).}
\item{n_first_tree}{limit the plot to the \code{n} first trees. If set to \code{NULL}, all trees of the model are plotted. Performance can be low depending of the size of the model.}
\item{n_first_tree}{limit the parsing to the \code{n} first trees.
If set to \code{NULL}, all trees of the model are parsed.}
}
\value{
A \code{data.table} of the features used in the model with their gain, cover and few other information.
}
\description{
Parse a boosted tree model text dump and return a \code{data.table}.
}
\details{
General function to convert a text dump of tree model to a \code{data.table}.
The purpose is to help user to explore the model and get a better understanding of it.
A \code{data.table} with detailed information about model trees' nodes.
The columns of the \code{data.table} are:
\itemize{
\item \code{ID}: unique identifier of a node ;
\item \code{Feature}: feature used in the tree to operate a split. When Leaf is indicated, it is the end of a branch ;
\item \code{Split}: value of the chosen feature where is operated the split ;
\item \code{Yes}: ID of the feature for the next node in the branch when the split condition is met ;
\item \code{No}: ID of the feature for the next node in the branch when the split condition is not met ;
\item \code{Missing}: ID of the feature for the next node in the branch for observation where the feature used for the split are not provided ;
\item \code{Quality}: it's the gain related to the split in this specific node ;
\item \code{Cover}: metric to measure the number of observation affected by the split ;
\item \code{Tree}: ID of the tree. It is included in the main ID ;
\item \code{Yes.Feature}, \code{No.Feature}, \code{Yes.Cover}, \code{No.Cover}, \code{Yes.Quality} and \code{No.Quality}: data related to the pointer in \code{Yes} or \code{No} column ;
\item \code{Tree}: ID of a tree in a model
\item \code{Node}: ID of a node in a tree
\item \code{ID}: unique identifier of a node in a model
\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")
\item \code{Yes}: ID of the next node when the split condition is met
\item \code{No}: ID of the next node when the split condition is not met
\item \code{Missing}: ID of the next node when branch value is missing
\item \code{Quality}: either the split gain or the leaf value
\item \code{Cover}: metric related to the number of observation either seen by a split split
or collected by a leaf during training.
}
}
\description{
Parse a boosted tree model text dump into a \code{data.table} structure.
}
\examples{
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
# agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.model.dt.tree(feature_names = agaricus.train$data@Dimnames[[2]], model = bst)
xgb.model.dt.tree(colnames(agaricus.train$data), bst)
}