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Updates from 1.2.0 cran submission (#6077)

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* update for 1.2.0 cran submission

* recover cmakelists

* fix unittest from the shap PR

* trigger CI
This commit is contained in:
Tong He
2020-09-02 20:50:23 +08:00
committed by GitHub
parent 9be969cc7a
commit 3912f3de06
23 changed files with 203 additions and 38 deletions
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2
R-package/man/a-compatibility-note-for-saveRDS-save.Rd
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@@ -43,6 +43,7 @@ bst2 <- xgb.load('xgb.model')
# Save as a stand-alone file (JSON); load it with xgb.load()
xgb.save(bst, 'xgb.model.json')
bst2 <- xgb.load('xgb.model.json')
if (file.exists('xgb.model.json')) file.remove('xgb.model.json')
# Save as a raw byte vector; load it with xgb.load.raw()
xgb_bytes <- xgb.save.raw(bst)
@@ -58,5 +59,6 @@ saveRDS(obj, 'my_object.rds')
obj2 <- readRDS('my_object.rds')
# Re-construct xgb.Booster object from the bytes
bst2 <- xgb.load.raw(obj2$xgb_model_bytes)
if (file.exists('my_object.rds')) file.remove('my_object.rds')
}

18
R-package/man/normalize.Rd Normal file
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@@ -0,0 +1,18 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.ggplot.R
\name{normalize}
\alias{normalize}
\title{Scale feature value to have mean 0, standard deviation 1}
\usage{
normalize(x)
}
\arguments{
\item{x}{Numeric vector}
}
\value{
Numeric vector with mean 0 and sd 1.
}
\description{
This is used to compare multiple features on the same plot.
Internal utility function
}

27
R-package/man/prepare.ggplot.shap.data.Rd Normal file
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@@ -0,0 +1,27 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.ggplot.R
\name{prepare.ggplot.shap.data}
\alias{prepare.ggplot.shap.data}
\title{Combine and melt feature values and SHAP contributions for sample
observations.}
\usage{
prepare.ggplot.shap.data(data_list, normalize = FALSE)
}
\arguments{
\item{data_list}{List containing 'data' and 'shap_contrib' returned by
\code{xgb.shap.data()}.}
\item{normalize}{Whether to standardize feature values to have mean 0 and
standard deviation 1 (useful for comparing multiple features on the same
plot). Default \code{FALSE}.}
}
\value{
A data.table containing the observation ID, the feature name, the
feature value (normalized if specified), and the SHAP contribution value.
}
\description{
Conforms to data format required for ggplot functions.
}
\details{
Internal utility function.
}

2
R-package/man/xgb.cv.Rd
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@@ -154,7 +154,7 @@ The cross-validation process is then repeated \code{nrounds} times, with each of
All observations are used for both training and validation.
Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
Adapted from \url{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
}
\examples{
data(agaricus.train, package='xgboost')

2
R-package/man/xgb.plot.shap.Rd
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@@ -131,6 +131,7 @@ bst <- xgboost(agaricus.train$data, agaricus.train$label, nrounds = 50,
xgb.plot.shap(agaricus.test$data, model = bst, features = "odor=none")
contr <- predict(bst, agaricus.test$data, predcontrib = TRUE)
xgb.plot.shap(agaricus.test$data, contr, model = bst, top_n = 12, n_col = 3)
xgb.ggplot.shap.summary(agaricus.test$data, contr, model = bst, top_n = 12) # Summary plot
# multiclass example - plots for each class separately:
nclass <- 3
@@ -149,6 +150,7 @@ xgb.plot.shap(x, model = mbst, trees = trees0 + 1, target_class = 1, top_n = 4,
n_col = 2, col = col, pch = 16, pch_NA = 17)
xgb.plot.shap(x, model = mbst, trees = trees0 + 2, target_class = 2, top_n = 4,
n_col = 2, col = col, pch = 16, pch_NA = 17)
xgb.ggplot.shap.summary(x, model = mbst, target_class = 0, top_n = 4) # Summary plot
}
\references{

78
R-package/man/xgb.plot.shap.summary.Rd Normal file
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@@ -0,0 +1,78 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.ggplot.R, R/xgb.plot.shap.R
\name{xgb.ggplot.shap.summary}
\alias{xgb.ggplot.shap.summary}
\alias{xgb.plot.shap.summary}
\title{SHAP contribution dependency summary plot}
\usage{
xgb.ggplot.shap.summary(
data,
shap_contrib = NULL,
features = NULL,
top_n = 10,
model = NULL,
trees = NULL,
target_class = NULL,
approxcontrib = FALSE,
subsample = NULL
)
xgb.plot.shap.summary(
data,
shap_contrib = NULL,
features = NULL,
top_n = 10,
model = NULL,
trees = NULL,
target_class = NULL,
approxcontrib = FALSE,
subsample = NULL
)
}
\arguments{
\item{data}{data as a \code{matrix} or \code{dgCMatrix}.}
\item{shap_contrib}{a matrix of SHAP contributions that was computed earlier for the above
\code{data}. When it is NULL, it is computed internally using \code{model} and \code{data}.}
\item{features}{a vector of either column indices or of feature names to plot. When it is NULL,
feature importance is calculated, and \code{top_n} high ranked features are taken.}
\item{top_n}{when \code{features} is NULL, top_n [1, 100] most important features in a model are taken.}
\item{model}{an \code{xgb.Booster} model. It has to be provided when either \code{shap_contrib}
or \code{features} is missing.}
\item{trees}{passed to \code{\link{xgb.importance}} when \code{features = NULL}.}
\item{target_class}{is only relevant for multiclass models. When it is set to a 0-based class index,
only SHAP contributions for that specific class are used.
If it is not set, SHAP importances are averaged over all classes.}
\item{approxcontrib}{passed to \code{\link{predict.xgb.Booster}} when \code{shap_contrib = NULL}.}
\item{subsample}{a random fraction of data points to use for plotting. When it is NULL,
it is set so that up to 100K data points are used.}
}
\value{
A \code{ggplot2} object.
}
\description{
Compare SHAP contributions of different features.
}
\details{
A point plot (each point representing one sample from \code{data}) is
produced for each feature, with the points plotted on the SHAP value axis.
Each point (observation) is coloured based on its feature value. The plot
hence allows us to see which features have a negative / positive contribution
on the model prediction, and whether the contribution is different for larger
or smaller values of the feature. We effectively try to replicate the
\code{summary_plot} function from https://github.com/slundberg/shap.
}
\examples{
# See \code{\link{xgb.plot.shap}}.
}
\seealso{
\code{\link{xgb.plot.shap}}, \code{\link{xgb.ggplot.shap.summary}},
\url{https://github.com/slundberg/shap}
}

29
R-package/man/xgb.shap.data.Rd Normal file
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@@ -0,0 +1,29 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/xgb.plot.shap.R
\name{xgb.shap.data}
\alias{xgb.shap.data}
\title{Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc.
Internal utility function.}
\usage{
xgb.shap.data(
data,
shap_contrib = NULL,
features = NULL,
top_n = 1,
model = NULL,
trees = NULL,
target_class = NULL,
approxcontrib = FALSE,
subsample = NULL,
max_observations = 1e+05
)
}
\value{
A list containing: 'data', a matrix containing sample observations
and their feature values; 'shap_contrib', a matrix containing the SHAP contribution
values for these observations.
}
\description{
Prepare data for SHAP plots. To be used in xgb.plot.shap, xgb.plot.shap.summary, etc.
Internal utility function.
}

10
R-package/man/xgb.train.Rd
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@@ -215,16 +215,16 @@ User may set one or several \code{eval_metric} parameters.
Note that when using a customized metric, only this single metric can be used.
The following is the list of built-in metrics for which Xgboost provides optimized implementation:
\itemize{
\item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss} multiclass logloss. \url{http://wiki.fast.ai/index.php/Log_Loss}
\item \code{rmse} root mean square error. \url{https://en.wikipedia.org/wiki/Root_mean_square_error}
\item \code{logloss} negative log-likelihood. \url{https://en.wikipedia.org/wiki/Log-likelihood}
\item \code{mlogloss} multiclass logloss. \url{https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html}
\item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
Different threshold (e.g., 0.) could be specified as "error@0."
\item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
\item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
\item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
\item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
\item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
\item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}
}
The following callbacks are automatically created when certain parameters are set: