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Code: Lint fixes on trailing spaces

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This commit is contained in:
terrytangyuan
2015-10-24 16:50:03 -04:00
parent 537b34dc6f
commit 139feaf97a
7 changed files with 86 additions and 86 deletions
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34
R-package/R/xgb.importance.R
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@@ -66,42 +66,42 @@
#' xgb.importance(train$data@@Dimnames[[2]], model = bst, data = train$data, label = train$label)
#'
#' @export
xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = NULL, data = NULL, label = NULL, target = function(x) ((x + label) == 2)){
if (!class(feature_names) %in% c("character", "NULL")) {
xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = NULL, data = NULL, label = NULL, target = function(x) ((x + label) == 2)){
if (!class(feature_names) %in% c("character", "NULL")) {
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.")
}
if (!(class(filename_dump) %in% c("character", "NULL") && length(filename_dump) <= 1)) {
stop("filename_dump: Has to be a path to the model dump file.")
}
if (!class(model) %in% c("xgb.Booster", "NULL")) {
stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
}
if((is.null(data) & !is.null(label)) |(!is.null(data) & is.null(label))) {
stop("data/label: Provide the two arguments if you want co-occurence computation or none of them if you are not interested but not one of them only.")
}
if(class(label) == "numeric"){
if(sum(label == 0) / length(label) > 0.5) label <- as(label, "sparseVector")
}
if(is.null(model)){
text <- readLines(filename_dump)
text <- readLines(filename_dump)
} else {
text <- xgb.dump(model = model, with.stats = T)
}
}
if(text[2] == "bias:"){
result <- readLines(filename_dump) %>% linearDump(feature_names, .)
if(!is.null(data) | !is.null(label)) warning("data/label: these parameters should only be provided with decision tree based models.")
} else {
result <- treeDump(feature_names, text = text, keepDetail = !is.null(data))
# Co-occurence computation
if(!is.null(data) & !is.null(label) & nrow(result) > 0) {
# Take care of missing column
# Take care of missing column
a <- data[, result[MissingNo == T,Feature], drop=FALSE] != 0
# Bind the two Matrix and reorder columns
c <- data[, result[MissingNo == F,Feature], drop=FALSE] %>% cBind(a,.) %>% .[,result[,Feature]]
@@ -109,19 +109,19 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
# Apply split
d <- data[, result[,Feature], drop=FALSE] < as.numeric(result[,Split])
apply(c & d, 2, . %>% target %>% sum) -> vec
result <- result[, "RealCover":= as.numeric(vec), with = F][, "RealCover %" := RealCover / sum(label)][,MissingNo:=NULL]
}
}
}
result
}
treeDump <- function(feature_names, text, keepDetail){
if(keepDetail) groupBy <- c("Feature", "Split", "MissingNo") else groupBy <- "Feature"
result <- xgb.model.dt.tree(feature_names = feature_names, text = text)[,"MissingNo":= Missing == No ][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)]
result
result
}
linearDump <- function(feature_names, text){