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Add new tests for new functions

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pommedeterresautee
2015-11-30 15:04:17 +01:00
parent ad8766dfa4
commit 96c43cf197
5 changed files with 33 additions and 26 deletions
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29
R-package/R/xgb.importance.R
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@@ -76,14 +76,23 @@ xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, labe
if(class(label) == "numeric"){
if(sum(label == 0) / length(label) > 0.5) label <- as(label, "sparseVector")
}
text <- xgb.dump(model = model, with.stats = T)
if(text[2] == "bias:"){
result <- readLines(filename_dump) %>% linearDump(feature_names, .)
treeDump <- function(feature_names, text, keepDetail){
if(keepDetail) groupBy <- c("Feature", "Split", "MissingNo") else groupBy <- "Feature"
xgb.model.dt.tree(feature_names = feature_names, text = text)[,"MissingNo" := Missing == No ][Feature != "Leaf",.(Gain = sum(Quality), Cover = sum(Cover), Frequency = .N), by = groupBy, with = T][,`:=`(Gain = Gain / sum(Gain), Cover = Cover / sum(Cover), Frequency = Frequency / sum(Frequency))][order(Gain, decreasing = T)]
}
linearDump <- function(feature_names, text){
which(text == "weight:") %>% {a =. + 1; text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
}
model.text.dump <- xgb.dump(model = model, with.stats = T)
if(model.text.dump[2] == "bias:"){
result <- model.text.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))
result <- treeDump(feature_names, text = model.text.dump, keepDetail = !is.null(data))
# Co-occurence computation
if(!is.null(data) & !is.null(label) & nrow(result) > 0) {
@@ -102,17 +111,7 @@ xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, labe
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), Frequency = .N), by = groupBy, with = T][,`:=`(Gain = Gain / sum(Gain), Cover = Cover / sum(Cover), Frequency = Frequency / sum(Frequency))][order(Gain, decreasing = T)]
result
}
linearDump <- function(feature_names, text){
which(text == "weight:") %>% {a =. + 1; text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
}
# Avoid error messages during CRAN check.
# The reason is that these variables are never declared