Add new tests for new functions

R-package/R/xgb.importance.R

@@ -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:"){
+  treeDump <- function(feature_names, text, keepDetail){
-    result <- readLines(filename_dump) %>% linearDump(feature_names, .)
+    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

R-package/R/xgb.model.dt.tree.R

@@ -59,19 +59,19 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL, n
     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(model) != "xgb.Booster") {
+  if (class(model) != "xgb.Booster" & class(text) != "character") {
-    stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
+    "model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.\n" %>%
-  }
+      paste0("text: Has to be a vector of character or NULL if a path to the model dump has already been provided.") %>%
-
+      stop()
-  if (!class(text) %in% c("character", "NULL")) {
-    stop("text: Has to be a vector of character or NULL if a path to the model dump has already been provided.")
   }
 
   if (!class(n_first_tree) %in% c("numeric", "NULL") | length(n_first_tree) > 1) {
     stop("n_first_tree: Has to be a numeric vector of size 1.")
   }
 
-  text <- xgb.dump(model = model, with.stats = T)
+  if(is.null(text)){		
+    text <- xgb.dump(model = model, with.stats = T)
+  }
   
   position <- str_match(text, "booster") %>% is.na %>% not %>% which %>% c(length(text) + 1)
 

R-package/R/xgb.plot.multi.trees.R

@@ -45,7 +45,7 @@
 #'                  eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
 #'                  min_child_weight = 50)
 #'
-#' p <- xgb.plot.multi.trees(bst, agaricus.train$data@Dimnames[[2]], 3)
+#' p <- xgb.plot.multi.trees(model = bst, names = agaricus.train$data@Dimnames[[2]], 3)
 #' print(p)
 #'
 #' @export

R-package/man/xgb.plot.multi.trees.Rd

@@ -49,7 +49,7 @@ bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.dep
                  eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
                  min_child_weight = 50)
 
-p <- xgb.plot.multi.trees(bst, agaricus.train$data@Dimnames[[2]], 3)
+p <- xgb.plot.multi.trees(model = bst, names = agaricus.train$data@Dimnames[[2]], 3)
 print(p)
 
 }

R-package/tests/testthat/test_helpers.R

@@ -19,15 +19,23 @@ bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
 
 test_that("xgb.dump works", {
   capture.output(print(xgb.dump(bst)))
+  expect_true(xgb.dump(bst, 'xgb.model.dump', with.stats = T))
 })
 
 test_that("xgb.importance works", {
-  expect_true(xgb.dump(bst, 'xgb.model.dump', with.stats = T))
   importance <- xgb.importance(sparse_matrix@Dimnames[[2]], model = bst)
   expect_equal(dim(importance), c(7, 4))
   expect_equal(colnames(importance), c("Feature", "Gain", "Cover", "Frequency"))
 })
 
 test_that("xgb.plot.tree works", {
-  xgb.plot.tree(agaricus.train$data@Dimnames[[2]], model = bst)
+  xgb.plot.tree(names = agaricus.train$data@Dimnames[[2]], model = bst)
+})
+
+test_that("xgb.plot.deepness works", {
+  xgb.plot.deepness(model = bst)
+})
+
+test_that("xgb.plot.multi.trees works", {
+  xgb.plot.multi.trees(model = bst, names = agaricus.train$data@Dimnames[[2]], 3)
 })