80 lines
4.0 KiB
R
80 lines
4.0 KiB
R
#' Show importance of features in a model
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#'
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#' Read a xgboost model text dump.
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#' Can be tree or linear model (text dump of linear model are only supported in dev version of \code{Xgboost} for now).
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#' 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.
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#'
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#' @importFrom data.table data.table
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#' @importFrom magrittr %>%
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#' @importFrom data.table :=
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#' @importFrom stringr str_extract
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#' @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}.
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#' @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}).
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#'
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#' @details
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#' This is the function to understand the model trained (and through your model, your data).
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#'
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#' Results are returned for both linear and tree models.
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#'
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#' \code{data.table} is returned by the function.
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#' There are 3 columns :
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#' \itemize{
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#' \item \code{Features} name of the features as provided in \code{feature_names} or already present in the model dump.
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#' \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.
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#' \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.
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#' }
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#'
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' data(agaricus.test, package='xgboost')
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#'
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#' #Both dataset are list with two items, a sparse matrix and labels (labels = outcome column which will be learned).
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#' #Each column of the sparse Matrix is a feature in one hot encoding format.
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#' train <- agaricus.train
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#' test <- agaricus.test
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#'
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nround = 2,objective = "binary:logistic")
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#' xgb.dump(bst, 'xgb.model.dump', with.stats = T)
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#'
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#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
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#' xgb.importance(agaricus.test$data@@Dimnames[[2]], 'xgb.model.dump')
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#'
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#' @export
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xgb.importance <- function(feature_names = NULL, filename_dump = NULL){
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if (!class(feature_names) %in% c("character", "NULL")) {
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stop("feature_names: Has to be a vector of character or NULL if the model dump already contains feature name. Look at this function documentation to see where to get feature names.")
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}
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if (class(filename_dump) != "character" & file.exists(filename_dump)) {
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stop("filename_dump: Has to be a path to the model dump file.")
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}
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text <- readLines(filename_dump)
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if(text[2] == "bias:"){
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result <- linearDump(feature_names, text)
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} else {
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result <- treeDump(feature_names, text)
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}
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result
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}
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treeDump <- function(feature_names, text){
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featureVec <- c()
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gainVec <- c()
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for(line in text){
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p <- str_extract(line, "\\[f.*<")
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if (!is.na(p)) {
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featureVec <- substr(p, 3, nchar(p)-1) %>% c(featureVec)
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gainVec <- str_extract(line, "gain.*,") %>% substr(x = ., 6, nchar(.)-1) %>% as.numeric %>% c(gainVec)
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}
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}
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if(!is.null(feature_names)) {
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featureVec %<>% as.numeric %>% {c =.+1; feature_names[c]} #+1 because in R indexing start with 1 instead of 0.
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}
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#1. Reduce, 2. %, 3. reorder - bigger top, 4. remove temp col
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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]
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}
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linearDump <- function(feature_names, text){
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which(text == "weight:") %>% {a=.+1;text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
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} |