140 lines
6.3 KiB
R
140 lines
6.3 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 trees (only for the gbtree booster) an integer vector of tree indices that should be included
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#' into the importance calculation. If set to \code{NULL}, all trees of the model are parsed.
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#' It could be useful, e.g., in multiclass classification to get feature importances
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#' for each class separately. IMPORTANT: the tree index in xgboost models
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#' is zero-based (e.g., use \code{trees = 0:4} for first 5 trees).
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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 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{Weight} the linear coefficient of this feature;
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#' \item \code{Class} (only for multiclass models) class label.
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#' }
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#'
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#' If \code{feature_names} is not provided and \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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#' # binomial classification using gbtree:
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#' data(agaricus.train, package='xgboost')
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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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#' xgb.importance(model = bst)
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#'
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#' # binomial classification using gblinear:
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, booster = "gblinear",
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#' eta = 0.3, nthread = 1, nrounds = 20, objective = "binary:logistic")
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#' xgb.importance(model = bst)
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#'
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#' # multiclass classification using gbtree:
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#' nclass <- 3
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#' nrounds <- 10
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#' mbst <- xgboost(data = as.matrix(iris[, -5]), label = as.numeric(iris$Species) - 1,
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#' max_depth = 3, eta = 0.2, nthread = 2, nrounds = nrounds,
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#' objective = "multi:softprob", num_class = nclass)
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#' # all classes clumped together:
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#' xgb.importance(model = mbst)
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#' # inspect importances separately for each class:
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#' xgb.importance(model = mbst, trees = seq(from=0, by=nclass, length.out=nrounds))
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#' xgb.importance(model = mbst, trees = seq(from=1, by=nclass, length.out=nrounds))
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#' xgb.importance(model = mbst, trees = seq(from=2, by=nclass, length.out=nrounds))
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#'
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#' # multiclass classification using gblinear:
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#' mbst <- xgboost(data = scale(as.matrix(iris[, -5])), label = as.numeric(iris$Species) - 1,
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#' booster = "gblinear", eta = 0.2, nthread = 1, nrounds = 15,
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#' objective = "multi:softprob", num_class = nclass)
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#' xgb.importance(model = mbst)
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#'
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#' @export
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xgb.importance <- function(feature_names = NULL, model = NULL, trees = 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 (!inherits(model, "xgb.Booster"))
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stop("model: must 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 (!(is.null(feature_names) || is.character(feature_names)))
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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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num_class <- NVL(model$params$num_class, 1)
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if(is.null(feature_names))
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feature_names <- seq(to = length(weights) / num_class) - 1
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if (length(feature_names) * num_class != length(weights))
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stop("feature_names length does not match the number of features used in the model")
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result <- if (num_class == 1) {
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data.table(Feature = feature_names, Weight = weights)[order(-abs(Weight))]
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} else {
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data.table(Feature = rep(feature_names, each = num_class),
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Weight = weights,
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Class = 0:(num_class - 1))[order(Class, -abs(Weight))]
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}
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} else {
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# tree model
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result <- xgb.model.dt.tree(feature_names = feature_names,
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text = model_text_dump,
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trees = trees)[
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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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