3912f3de06
* update for 1.2.0 cran submission * recover cmakelists * fix unittest from the shap PR * trigger CI
79 lines
2.6 KiB
R
79 lines
2.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.shap.R
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\name{xgb.ggplot.shap.summary}
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\alias{xgb.ggplot.shap.summary}
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\alias{xgb.plot.shap.summary}
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\title{SHAP contribution dependency summary plot}
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\usage{
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xgb.ggplot.shap.summary(
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data,
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shap_contrib = NULL,
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features = NULL,
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top_n = 10,
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model = NULL,
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trees = NULL,
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target_class = NULL,
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approxcontrib = FALSE,
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subsample = NULL
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)
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xgb.plot.shap.summary(
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data,
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shap_contrib = NULL,
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features = NULL,
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top_n = 10,
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model = NULL,
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trees = NULL,
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target_class = NULL,
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approxcontrib = FALSE,
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subsample = NULL
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)
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}
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\arguments{
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\item{data}{data as a \code{matrix} or \code{dgCMatrix}.}
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\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above
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\code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.}
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\item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
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feature importance is calculated, and \code{top_n} high ranked features are taken.}
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\item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are taken.}
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\item{model}{an \code{xgb.Booster} model. It has to be provided when either \code{shap_contrib}
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or \code{features} is missing.}
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\item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
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\item{target_class}{is only relevant for multiclass models. When it is set to a 0-based class index,
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only SHAP contributions for that specific class are used.
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If it is not set, SHAP importances are averaged over all classes.}
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\item{approxcontrib}{passed to \code{\link{predict.xgb.Booster}} when \code{shap_contrib = NULL}.}
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\item{subsample}{a random fraction of data points to use for plotting. When it is NULL,
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it is set so that up to 100K data points are used.}
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}
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\value{
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A \code{ggplot2} object.
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}
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\description{
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Compare SHAP contributions of different features.
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}
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\details{
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A point plot (each point representing one sample from \code{data}) is
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produced for each feature, with the points plotted on the SHAP value axis.
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Each point (observation) is coloured based on its feature value. The plot
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hence allows us to see which features have a negative / positive contribution
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on the model prediction, and whether the contribution is different for larger
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or smaller values of the feature. We effectively try to replicate the
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\code{summary_plot} function from https://github.com/slundberg/shap.
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}
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\examples{
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# See \code{\link{xgb.plot.shap}}.
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}
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\seealso{
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\code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
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\url{https://github.com/slundberg/shap}
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}
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