663136aa08
* Add feature score support for linear model. * Port R interface to the new implementation. * Add linear model support in Python. Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
143 lines
6.6 KiB
R
143 lines
6.6 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 <- xgb.Booster.complete(model)
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config <- jsonlite::fromJSON(xgb.config(model))
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if (config$learner$gradient_booster$name == "gblinear") {
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args <- list(importance_type = "weight", feature_names = feature_names)
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results <- .Call(
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XGBoosterFeatureScore_R, model$handle, jsonlite::toJSON(args, auto_unbox = TRUE, null = "null")
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)
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names(results) <- c("features", "shape", "weight")
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n_classes <- if (length(results$shape) == 2) { results$shape[2] } else { 0 }
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importance <- if (n_classes == 0) {
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data.table(Feature = results$features, Weight = results$weight)[order(-abs(Weight))]
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} else {
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data.table(
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Feature = rep(results$features, each = n_classes), Weight = results$weight, Class = seq_len(n_classes) - 1
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)[order(Class, -abs(Weight))]
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}
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} else {
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concatenated <- list()
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output_names <- vector()
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for (importance_type in c("weight", "gain", "cover")) {
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args <- list(importance_type = importance_type, feature_names = feature_names)
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results <- .Call(
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XGBoosterFeatureScore_R, model$handle, jsonlite::toJSON(args, auto_unbox = TRUE, null = "null")
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)
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names(results) <- c("features", "shape", importance_type)
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concatenated[
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switch(importance_type, "weight" = "Frequency", "gain" = "Gain", "cover" = "Cover")
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] <- results[importance_type]
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output_names <- results$features
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}
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importance <- data.table(
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Feature = output_names,
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Gain = concatenated$Gain / sum(concatenated$Gain),
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Cover = concatenated$Cover / sum(concatenated$Cover),
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Frequency = concatenated$Frequency / sum(concatenated$Frequency)
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)[order(Gain, decreasing = TRUE)]
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}
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importance
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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", "Class"))
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