a375ad2822
* [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
100 lines
4.1 KiB
R
100 lines
4.1 KiB
R
#' Importance of features in a model.
|
|
#'
|
|
#' Creates a \code{data.table} of feature importances in a model.
|
|
#'
|
|
#' @param feature_names character vector of feature names. If the model already
|
|
#' contains feature names, those would be used when \code{feature_names=NULL} (default value).
|
|
#' Non-null \code{feature_names} could be provided to override those in the model.
|
|
#' @param model object of class \code{xgb.Booster}.
|
|
#' @param data deprecated.
|
|
#' @param label deprecated.
|
|
#' @param target deprecated.
|
|
#'
|
|
#' @details
|
|
#'
|
|
#' This function works for both linear and tree models.
|
|
#'
|
|
#' For linear models, the importance is the absolute magnitude of linear coefficients.
|
|
#' For that reason, in order to obtain a meaningful ranking by importance for a linear model,
|
|
#' the features need to be on the same scale (which you also would want to do when using either
|
|
#' L1 or L2 regularization).
|
|
#'
|
|
#' @return
|
|
#'
|
|
#' For a tree model, a \code{data.table} with the following columns:
|
|
#' \itemize{
|
|
#' \item \code{Features} names of the features used in the model;
|
|
#' \item \code{Gain} represents fractional contribution of each feature to the model based on
|
|
#' the total gain of this feature's splits. Higher percentage means a more important
|
|
#' predictive feature.
|
|
#' \item \code{Cover} metric of the number of observation related to this feature;
|
|
#' \item \code{Frequency} percentage representing the relative number of times
|
|
#' a feature have been used in trees.
|
|
#' }
|
|
#'
|
|
#' A linear model's importance \code{data.table} has only two columns:
|
|
#' \itemize{
|
|
#' \item \code{Features} names of the features used in the model;
|
|
#' \item \code{Weight} the linear coefficient of this feature.
|
|
#' }
|
|
#'
|
|
#' If you don't provide or \code{model} doesn't have \code{feature_names},
|
|
#' index of the features will be used instead. Because the index is extracted from the model dump
|
|
#' (based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
|
|
#'
|
|
#' @examples
|
|
#'
|
|
#' data(agaricus.train, package='xgboost')
|
|
#'
|
|
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
|
|
#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
|
|
#'
|
|
#' xgb.importance(model = bst)
|
|
#'
|
|
#' @export
|
|
xgb.importance <- function(feature_names = NULL, model = NULL,
|
|
data = NULL, label = NULL, target = NULL){
|
|
|
|
if (!(is.null(data) && is.null(label) && is.null(target)))
|
|
warning("xgb.importance: parameters 'data', 'label' and 'target' are deprecated")
|
|
|
|
if (!inherits(model, "xgb.Booster"))
|
|
stop("model: must be an object of class xgb.Booster")
|
|
|
|
if (is.null(feature_names) && !is.null(model$feature_names))
|
|
feature_names <- model$feature_names
|
|
|
|
if (!(is.null(feature_names) || is.character(feature_names)))
|
|
stop("feature_names: Has to be a character vector")
|
|
|
|
model_text_dump <- xgb.dump(model = model, with_stats = TRUE)
|
|
|
|
# linear model
|
|
if(model_text_dump[2] == "bias:"){
|
|
weights <- which(model_text_dump == "weight:") %>%
|
|
{model_text_dump[(. + 1):length(model_text_dump)]} %>%
|
|
as.numeric
|
|
if(is.null(feature_names))
|
|
feature_names <- seq(to = length(weights))
|
|
if (length(feature_names) != length(weights))
|
|
stop("feature_names has less elements than there are features used in the model")
|
|
result <- data.table(Feature = feature_names, Weight = weights)[order(-abs(Weight))]
|
|
} else {
|
|
# tree model
|
|
result <- xgb.model.dt.tree(feature_names = feature_names, text = model_text_dump)[
|
|
Feature != "Leaf", .(Gain = sum(Quality),
|
|
Cover = sum(Cover),
|
|
Frequency = .N), by = Feature][
|
|
,`:=`(Gain = Gain / sum(Gain),
|
|
Cover = Cover / sum(Cover),
|
|
Frequency = Frequency / sum(Frequency))][
|
|
order(Gain, decreasing = TRUE)]
|
|
}
|
|
result
|
|
}
|
|
|
|
# Avoid error messages during CRAN check.
|
|
# The reason is that these variables are never declared
|
|
# They are mainly column names inferred by Data.table...
|
|
globalVariables(c(".", ".N", "Gain", "Cover", "Frequency", "Feature"))
|