62 lines
2.4 KiB
R
62 lines
2.4 KiB
R
#' Show importance of features in a model
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#'
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#' Read a xgboost model in text file format.
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#' Can be tree or linear model (text dump of linear model are only supported in dev version of Xgboost for now).
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#'
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#' Return a data.table of the features with their weight.
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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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#' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix.
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#' @param filename_dump the name of the text file.
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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 (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')
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#'
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#' #agaricus.test$data@@Dimnames[[2]] represents the column name of the sparse matrix.
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#' xgb.importance(agaricus.test$data@@Dimnames[[2]], 'xgb.model.dump')
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#'
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#' @export
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xgb.importance <- function(feature_names, filename_dump){
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if (class(feature_names) != "character") {
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stop("feature_names: Has to be a vector of character. See help to see where to get it.")
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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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result <- c()
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for(line in text){
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p <- regexec("\\[f.*\\]", line) %>% regmatches(line, .)
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if (length(p[[1]]) > 0) {
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splits <- sub("\\[f", "", p[[1]]) %>% sub("\\]", "", .) %>% strsplit("<") %>% .[[1]] %>% as.numeric
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result <- c(result, feature_names[splits[1]+ 1])
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}
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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 = result)[,.N, by = Feature][, Weight:= N /sum(N)][order(-rank(Weight))][,-2,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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} |