Hendrik/xgboost
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add new Gain and Weight columns.

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documentation updated.
This commit is contained in:
El Potaeto 2014-12-30 12:32:58 +01:00
parent c754fd4ad0
commit c6f76fab56
1 changed files with 6 additions and 6 deletions
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12
R-package/R/xgb.importance.R
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@ -1,19 +1,19 @@
#' Show importance of features in a model
#'
#' 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).
#' Read a xgboost model text dump.
#' Can be tree or linear model (text dump of linear model are only supported in dev version of \code{Xgboost} for now).
#' 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.
#'
#' 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 magrittr %>%
#' @importFrom data.table :=
#' @importFrom stringr str_extract
#' @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. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).
#'
#' @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.
@ -72,7 +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.
}
#1. Reduce, 2. %, 3. reorder - bigger top, 4. remove temp col
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]
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){