179 lines
6.3 KiB
R
179 lines
6.3 KiB
R
#' Feature importance
|
|
#'
|
|
#' Creates a `data.table` of feature importances.
|
|
#'
|
|
#' @param feature_names Character vector used to overwrite the feature names
|
|
#' of the model. The default is `NULL` (use original feature names).
|
|
#' @param model Object of class `xgb.Booster`.
|
|
#' @param trees An integer vector of tree indices that should be included
|
|
#' into the importance calculation (only for the "gbtree" booster).
|
|
#' The default (`NULL`) parses all trees.
|
|
#' It could be useful, e.g., in multiclass classification to get feature importances
|
|
#' for each class separately. *Important*: the tree index in XGBoost models
|
|
#' is zero-based (e.g., use `trees = 0:4` for the first five trees).
|
|
#' @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.
|
|
#' To obtain a meaningful ranking by importance for linear models, the features need to
|
|
#' be on the same scale (which is also recommended when using L1 or L2 regularization).
|
|
#'
|
|
#' @return A `data.table` with the following columns:
|
|
#'
|
|
#' For a tree model:
|
|
#' - `Features`: Names of the features used in the model.
|
|
#' - `Gain`: Fractional contribution of each feature to the model based on
|
|
#' the total gain of this feature's splits. Higher percentage means higher importance.
|
|
#' - `Cover`: Metric of the number of observation related to this feature.
|
|
#' - `Frequency`: Percentage of times a feature has been used in trees.
|
|
#'
|
|
#' For a linear model:
|
|
#' - `Features`: Names of the features used in the model.
|
|
#' - `Weight`: Linear coefficient of this feature.
|
|
#' - `Class`: Class label (only for multiclass models).
|
|
#'
|
|
#' If `feature_names` is not provided and `model` doesn't have `feature_names`,
|
|
#' the 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
|
|
#'
|
|
#' # binomial classification using "gbtree":
|
|
#' 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)
|
|
#'
|
|
#' # binomial classification using "gblinear":
|
|
#' bst <- xgboost(
|
|
#' data = agaricus.train$data,
|
|
#' label = agaricus.train$label,
|
|
#' booster = "gblinear",
|
|
#' eta = 0.3,
|
|
#' nthread = 1,
|
|
#' nrounds = 20,objective = "binary:logistic"
|
|
#' )
|
|
#'
|
|
#' xgb.importance(model = bst)
|
|
#'
|
|
#' # multiclass classification using "gbtree":
|
|
#' nclass <- 3
|
|
#' nrounds <- 10
|
|
#' mbst <- xgboost(
|
|
#' data = as.matrix(iris[, -5]),
|
|
#' label = as.numeric(iris$Species) - 1,
|
|
#' max_depth = 3,
|
|
#' eta = 0.2,
|
|
#' nthread = 2,
|
|
#' nrounds = nrounds,
|
|
#' objective = "multi:softprob",
|
|
#' num_class = nclass
|
|
#' )
|
|
#'
|
|
#' # all classes clumped together:
|
|
#' xgb.importance(model = mbst)
|
|
#'
|
|
#' # inspect importances separately for each class:
|
|
#' xgb.importance(
|
|
#' model = mbst, trees = seq(from = 0, by = nclass, length.out = nrounds)
|
|
#' )
|
|
#' xgb.importance(
|
|
#' model = mbst, trees = seq(from = 1, by = nclass, length.out = nrounds)
|
|
#' )
|
|
#' xgb.importance(
|
|
#' model = mbst, trees = seq(from = 2, by = nclass, length.out = nrounds)
|
|
#' )
|
|
#'
|
|
#' # multiclass classification using "gblinear":
|
|
#' mbst <- xgboost(
|
|
#' data = scale(as.matrix(iris[, -5])),
|
|
#' label = as.numeric(iris$Species) - 1,
|
|
#' booster = "gblinear",
|
|
#' eta = 0.2,
|
|
#' nthread = 1,
|
|
#' nrounds = 15,
|
|
#' objective = "multi:softprob",
|
|
#' num_class = nclass
|
|
#' )
|
|
#'
|
|
#' xgb.importance(model = mbst)
|
|
#'
|
|
#' @export
|
|
xgb.importance <- function(feature_names = NULL, model = NULL, trees = 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 <- xgb.Booster.complete(model)
|
|
config <- jsonlite::fromJSON(xgb.config(model))
|
|
if (config$learner$gradient_booster$name == "gblinear") {
|
|
args <- list(importance_type = "weight", feature_names = feature_names)
|
|
results <- .Call(
|
|
XGBoosterFeatureScore_R, model$handle, jsonlite::toJSON(args, auto_unbox = TRUE, null = "null")
|
|
)
|
|
names(results) <- c("features", "shape", "weight")
|
|
if (length(results$shape) == 2) {
|
|
n_classes <- results$shape[2]
|
|
} else {
|
|
n_classes <- 0
|
|
}
|
|
importance <- if (n_classes == 0) {
|
|
data.table(Feature = results$features, Weight = results$weight)[order(-abs(Weight))]
|
|
} else {
|
|
data.table(
|
|
Feature = rep(results$features, each = n_classes), Weight = results$weight, Class = seq_len(n_classes) - 1
|
|
)[order(Class, -abs(Weight))]
|
|
}
|
|
} else {
|
|
concatenated <- list()
|
|
output_names <- vector()
|
|
for (importance_type in c("weight", "total_gain", "total_cover")) {
|
|
args <- list(importance_type = importance_type, feature_names = feature_names, tree_idx = trees)
|
|
results <- .Call(
|
|
XGBoosterFeatureScore_R, model$handle, jsonlite::toJSON(args, auto_unbox = TRUE, null = "null")
|
|
)
|
|
names(results) <- c("features", "shape", importance_type)
|
|
concatenated[
|
|
switch(importance_type, "weight" = "Frequency", "total_gain" = "Gain", "total_cover" = "Cover")
|
|
] <- results[importance_type]
|
|
output_names <- results$features
|
|
}
|
|
importance <- data.table(
|
|
Feature = output_names,
|
|
Gain = concatenated$Gain / sum(concatenated$Gain),
|
|
Cover = concatenated$Cover / sum(concatenated$Cover),
|
|
Frequency = concatenated$Frequency / sum(concatenated$Frequency)
|
|
)[order(Gain, decreasing = TRUE)]
|
|
}
|
|
importance
|
|
}
|
|
|
|
# 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", "Class"))
|