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missing feature management

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This commit is contained in:
El Potaeto 2015-02-17 23:27:02 +01:00
parent d4731e7b29
commit 08493c2b3d
2 changed files with 8 additions and 2 deletions
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1
R-package/NAMESPACE
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@ -22,6 +22,7 @@ importClassesFrom(Matrix,dgCMatrix)
importClassesFrom(Matrix,dgeMatrix)
importFrom(Ckmeans.1d.dp,Ckmeans.1d.dp)
importFrom(DiagrammeR,mermaid)
importFrom(Matrix,cBind)
importFrom(Matrix,colSums)
importFrom(data.table,":=")
importFrom(data.table,as.data.table)

9
R-package/R/xgb.importance.R
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@ -8,6 +8,7 @@
#' @importFrom data.table :=
#' @importFrom magrittr %>%
#' @importFrom Matrix colSums
#' @importFrom Matrix cBind
#'
#' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#'
@ -94,8 +95,12 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
# Co-occurence computation
if(!is.null(data) & !is.null(label) & nrow(result) > 0) {
a <- data[, result[,Feature],drop=FALSE] < as.numeric(result[,Split])
b <- data[, result[No == Missing,Feature],drop=FALSE] != 0
c <- data[, result[No != Missing,Feature],drop=FALSE]
d <- cBind(b,c) %>% .[,result[,Feature]]
((data[, result[,Feature],drop=FALSE] != 0) & (data[, result[,Feature],drop=FALSE] < as.numeric(result[,Split]))) %>% apply(., 2, . %>% target %>% sum) -> vec
apply(a & d, 2, . %>% target %>% sum) -> vec
result <- result[, "RealCover":= as.numeric(vec), with = F][, "RealCover %" := RealCover / sum(label)]
}
@ -104,7 +109,7 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
}
treeDump <- function(feature_names, text, keepDetail){
if(keepDetail) groupBy <- c("Feature", "Split") else groupBy <- "Feature"
if(keepDetail) groupBy <- c("Feature", "Split", "No", "Missing") else groupBy <- "Feature"
result <- xgb.model.dt.tree(feature_names = feature_names, text = text)[Feature!="Leaf",.(Gain = sum(Quality), Cover = sum(Cover), Frequence = .N), by = groupBy, with = T][,`:=`(Gain = Gain/sum(Gain), Cover = Cover/sum(Cover), Frequence = Frequence/sum(Frequence))][order(Gain, decreasing = T)]