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[R-package] GPL2 dependency reduction and some fixes (#1401)

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* [R] do not remove zero coefficients from gblinear dump

* [R] switch from stringr to stringi

* fix #1399

* [R] separate ggplot backend, add base r graphics, cleanup, more plots, tests

* add missing include in amalgamation - fixes building R package in linux

* add forgotten file

* [R] fix DESCRIPTION

* [R] fix travis check issue and some cleanup
This commit is contained in:
Vadim Khotilovich
2016-07-27 02:05:04 -05:00
committed by Tong He
parent f6423056c0
commit d5c143367d
19 changed files with 548 additions and 312 deletions
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72
R-package/man/xgb.plot.deepness.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.plot.deepness.R
\name{xgb.plot.deepness}
% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.deepness.R
\name{xgb.ggplot.deepness}
\alias{xgb.ggplot.deepness}
\alias{xgb.plot.deepness}
\title{Plot model trees deepness}
\usage{
xgb.plot.deepness(model = NULL)
xgb.ggplot.deepness(model = NULL, which = c("2x1", "max.depth", "med.depth",
"med.weight"))
xgb.plot.deepness(model = NULL, which = c("2x1", "max.depth", "med.depth",
"med.weight"), plot = TRUE, ...)
}
\arguments{
\item{model}{dump generated by the \code{xgb.train} function.}
\item{model}{either an \code{xgb.Booster} model generated by the \code{xgb.train} function
or a data.table result of the \code{xgb.model.dt.tree} function.}
\item{which}{which distribution to plot (see details).}
\item{plot}{(base R barplot) whether a barplot should be produced.
If FALSE, only a data.table is returned.}
\item{...}{other parameters passed to \code{barplot} or \code{plot}.}
}
\value{
Two graphs showing the distribution of the model deepness.
Other than producing plots (when \code{plot=TRUE}), the \code{xgb.plot.deepness} function
silently returns a processed data.table where each row corresponds to a terminal leaf in a tree model,
and contains information about leaf's depth, cover, and weight (which is used in calculating predictions).
The \code{xgb.ggplot.deepness} silently returns either a list of two ggplot graphs when \code{which="2x1"}
or a single ggplot graph for the other \code{which} options.
}
\description{
Generate a graph to plot the distribution of deepness among trees.
Visualizes distributions related to depth of tree leafs.
\code{xgb.plot.deepness} uses base R graphics, while \code{xgb.ggplot.deepness} uses the ggplot backend.
}
\details{
Display both the number of \code{leaf} and the distribution of \code{weighted observations}
by tree deepness level.
The purpose of this function is to help the user to find the best trade-off to set
the \code{max_depth} and \code{min_child_weight} parameters according to the bias / variance trade-off.
See \link{xgb.train} for more information about these parameters.
The graph is made of two parts:
When \code{which="2x1"}, two distributions with respect to the leaf depth
are plotted on top of each other:
\itemize{
\item Count: number of leaf per level of deepness;
\item Weighted cover: noramlized weighted cover per leaf (weighted number of instances).
\item the distribution of the number of leafs in a tree model at a certain depth;
\item the distribution of average weighted number of observations ("cover")
ending up in leafs at certain depth.
}
Those could be helpful in determining sensible ranges of the \code{max_depth}
and \code{min_child_weight} parameters.
This function is inspired by the blog post \url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}
When \code{which="max.depth"} or \code{which="med.depth"}, plots of either maximum or median depth
per tree with respect to tree number are created. And \code{which="med.weight"} allows to see how
a tree's median absolute leaf weight changes through the iterations.
This function was inspired by the blog post
\url{http://aysent.github.io/2015/11/08/random-forest-leaf-visualization.html}.
}
\examples{
data(agaricus.train, package='xgboost')
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
min_child_weight = 50)
eta = 0.1, nthread = 2, nrounds = 50, objective = "binary:logistic",
subsample = 0.5, min_child_weight = 2)
xgb.plot.deepness(model = bst)
xgb.plot.deepness(bst)
xgb.ggplot.deepness(bst)
xgb.plot.deepness(bst, which='max.depth', pch=16, col=rgb(0,0,1,0.3), cex=2)
xgb.plot.deepness(bst, which='med.weight', pch=16, col=rgb(0,0,1,0.3), cex=2)
}
\seealso{
\code{\link{xgb.train}}, \code{\link{xgb.model.dt.tree}}.
}