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[R] Fix global feature importance and predict with 1 sample. (#7394)

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* [R] Fix global feature importance.

* Add implementation for tree index.  The parameter is not documented in C API since we
should work on porting the model slicing to R instead of supporting more use of tree
index.

* Fix the difference between "gain" and "total_gain".

* debug.

* Fix prediction.
This commit is contained in:
Jiaming Yuan
2021-11-05 10:07:00 +08:00
committed by GitHub
parent 48aff0eabd
commit c968217ca8
11 changed files with 119 additions and 49 deletions
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6
R-package/R/xgb.importance.R
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@@ -115,14 +115,14 @@ xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
} else {
concatenated <- list()
output_names <- vector()
for (importance_type in c("weight", "gain", "cover")) {
args <- list(importance_type = importance_type, feature_names = feature_names)
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$handle, jsonlite::toJSON(args, auto_unbox = TRUE, null = "null")
)
names(results) <- c("features", "shape", importance_type)
concatenated[
switch(importance_type, "weight" = "Frequency", "gain" = "Gain", "cover" = "Cover")
switch(importance_type, "weight" = "Frequency", "total_gain" = "Gain", "total_cover" = "Cover")
] <- results[importance_type]
output_names <- results$features
}