Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add a new Weight and Gain column.

Browse Source 
Update documentation.
This commit is contained in:
El Potaeto 2014-12-30 12:16:13 +01:00
parent 78813d8f78
commit 3694772bde
1 changed files with 16 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
R-package/R/xgb.importance.R
View File

@ -3,7 +3,7 @@
#' Read a xgboost model in text file format. #' Read a xgboost model in text file format.
#' Can be tree or linear model (text dump of linear model are only supported in dev version of Xgboost for now). #' Can be tree or linear model (text dump of linear model are only supported in dev version of Xgboost for now).
#' #'
#' Return a data.table of the features with their weight. #' Return a data.table of the features used in the model with their average gain (and their weight for boosted tree model)in the model.
#' #' #' #'
#' @importFrom data.table data.table #' @importFrom data.table data.table
#' @importFrom magrittr %>% #' @importFrom magrittr %>%
@ -12,6 +12,19 @@
#' @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}. #' @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}.
#' @param filename_dump the path to the text file storing the model. #' @param filename_dump the path to the text file storing the model.
#' #'
#' @details
#' This is the function to understand the model trained (and through your model, your data).
#' Results are returned for both linear and tree models.
#'
#' \code{data.table} is returned by the function.
#' There are 3 columns :
#' \itemize{
#' \item \code{Features} name of the features as provided in \code{feature_names} or already present in the model dump.
#' \item \code{Gain} contribution of each feature to the model. For boosted tree model, each gain of each feature of each tree is taken into account, then average per feature to give a vision of the entire model. Highest percentage means most important feature regarding the \code{label} used for the training.
#' \item \code{Weight} percentage representing the relative number of times a feature have been taken into trees. \code{Gain} should be prefered to search the most important feature. For boosted linear model, this column has no meaning.
#' }
#'
#'
#' @examples #' @examples
#' data(agaricus.train, package='xgboost') #' data(agaricus.train, package='xgboost')
#' data(agaricus.test, package='xgboost') #' data(agaricus.test, package='xgboost')
@ -45,7 +58,7 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL){
result result
} }
treeDump <- function(feature_names, text){ treeDump <- function(feature_names, text){
featureVec <- c() featureVec <- c()
gainVec <- c() gainVec <- c()
for(line in text){ for(line in text){
@ -59,7 +72,7 @@ treeDump <- function(feature_names, text){
featureVec %<>% as.numeric %>% {c =.+1; feature_names[c]} #+1 because in R indexing start with 1 instead of 0. featureVec %<>% as.numeric %>% {c =.+1; feature_names[c]} #+1 because in R indexing start with 1 instead of 0.
} }
#1. Reduce, 2. %, 3. reorder - bigger top, 4. remove temp col #1. Reduce, 2. %, 3. reorder - bigger top, 4. remove temp col
data.table(Feature = featureVec, Weight = gainVec)[,sum(Weight), by = Feature][, Weight:= V1 /sum(V1)][order(-rank(Weight))][,-2,with=F] data.table(Feature = featureVec, Weight = gainVec)[,list(sum(Weight), .N), by = Feature][, Gain:= V1 /sum(V1)][,Weight:= N / sum(N)][order(-rank(Gain))][,-c(2,3), with = F]
} }
linearDump <- function(feature_names, text){ linearDump <- function(feature_names, text){