2b5b96d760
* [R] xgb.save must work when handle in nil but raw exists * [R] print.xgb.Booster should still print other info when handle is nil * [R] rename internal function xgb.Booster to xgb.Booster.handle to make its intent clear * [R] rename xgb.Booster.check to xgb.Booster.complete and make it visible; more docs * [R] storing evaluation_log should depend only on watchlist, not on verbose * [R] reduce the excessive chattiness of unit tests * [R] only disable some tests in windows when it's not 64-bit * [R] clean-up xgb.DMatrix * [R] test xgb.DMatrix loading from libsvm text file * [R] store feature_names in xgb.Booster, use them from utility functions * [R] remove non-functional co-occurence computation from xgb.importance * [R] verbose=0 is enough without a callback * [R] added forgotten xgb.Booster.complete.Rd; cran check fixes * [R] update installation instructions
98 lines
3.9 KiB
R
98 lines
3.9 KiB
R
#' Importance of features in a model.
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#'
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#' Creates a \code{data.table} of feature importances in a model.
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#'
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#' @param feature_names character vector of feature names. If the model already
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#' contains feature names, those would be used when \code{feature_names=NULL} (default value).
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#' Non-null \code{feature_names} could be provided to override those in the model.
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#' @param model object of class \code{xgb.Booster}.
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#' @param data deprecated.
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#' @param label deprecated.
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#' @param target deprecated.
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#'
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#' @details
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#'
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#' This function works for both linear and tree models.
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#'
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#' For linear models, the importance is the absolute magnitude of linear coefficients.
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#' For that reason, in order to obtain a meaningful ranking by importance for a linear model,
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#' the features need to be on the same scale (which you also would want to do when using either
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#' L1 or L2 regularization).
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#'
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#' @return
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#'
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#' For a tree model, a \code{data.table} with the following columns:
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#' \itemize{
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#' \item \code{Features} names of the features used in the model;
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#' \item \code{Gain} represents fractional contribution of each feature to the model based on
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#' the total gain of this feature's splits. Higher percentage means a more important
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#' predictive feature.
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#' \item \code{Cover} metric of the number of observation related to this feature;
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#' \item \code{Frequency} percentage representing the relative number of times
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#' a feature have been used in trees.
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#' }
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#'
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#' A linear model's importance \code{data.table} has only two columns:
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#' \itemize{
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#' \item \code{Features} names of the features used in the model;
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#' \item \code{Weight} the linear coefficient of this feature.
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#' }
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#'
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#' If you don't provide or \code{model} doesn't have \code{feature_names},
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#' index of the features will be used instead. Because the index is extracted from the model dump
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#' (based on C++ code), it starts at 0 (as in C/C++ or Python) instead of 1 (usual in R).
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#'
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#' @examples
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#'
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#' data(agaricus.train, package='xgboost')
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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
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#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
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#'
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#' xgb.importance(model = bst)
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#'
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#' @export
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xgb.importance <- function(feature_names = NULL, model = NULL,
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data = NULL, label = NULL, target = NULL){
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if (!(is.null(data) && is.null(label) && is.null(target)))
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warning("xgb.importance: parameters 'data', 'label' and 'target' are deprecated")
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if (class(model) != "xgb.Booster")
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stop("Either 'model' has to be an object of class xgb.Booster")
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if (is.null(feature_names) && !is.null(model$feature_names))
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feature_names <- model$feature_names
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if (!class(feature_names) %in% c("character", "NULL"))
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stop("feature_names: Has to be a character vector")
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model_text_dump <- xgb.dump(model = model, with_stats = TRUE)
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# linear model
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if(model_text_dump[2] == "bias:"){
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weights <- which(model_text_dump == "weight:") %>%
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{model_text_dump[(. + 1):length(model_text_dump)]} %>%
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as.numeric
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if(is.null(feature_names))
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feature_names <- seq(to = length(weights))
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result <- data.table(Feature = feature_names, Weight = weights)[order(-abs(Weight))]
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} else {
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# tree model
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result <- xgb.model.dt.tree(feature_names = feature_names, text = model_text_dump)[
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Feature != "Leaf", .(Gain = sum(Quality),
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Cover = sum(Cover),
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Frequency = .N), by = Feature][
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,`:=`(Gain = Gain / sum(Gain),
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Cover = Cover / sum(Cover),
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Frequency = Frequency / sum(Frequency))][
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order(Gain, decreasing = TRUE)]
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}
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result
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}
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# Avoid error messages during CRAN check.
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# The reason is that these variables are never declared
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# They are mainly column names inferred by Data.table...
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globalVariables(c(".", ".N", "Gain", "Cover", "Frequency", "Feature"))
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