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Fixed most of the lint issues

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This commit is contained in:
terrytangyuan 2015-10-28 23:24:17 -04:00
parent 8bae715994
commit 6024480400
13 changed files with 107 additions and 119 deletions
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2
R-package/R/slice.xgb.DMatrix.R
View File

@ -30,7 +30,7 @@ setMethod("slice", signature = "xgb.DMatrix",
}
ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset,
PACKAGE = "xgboost")
attr_list <- attributes(object)
nr <- xgb.numrow(object)
len <- sapply(attr_list,length)

22
R-package/R/utils.R
View File

@ -68,7 +68,7 @@ xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
if (typeof(modelfile) == "character") {
.Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
} else if (typeof(modelfile) == "raw") {
.Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")
.Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")
} else {
stop("xgb.Booster: modelfile must be character or raw vector")
}
@ -122,7 +122,7 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
} else if (inClass == "xgb.DMatrix") {
dtrain <- data
} else if (inClass == "data.frame") {
stop("xgboost only support numerical matrix input,
stop("xgboost only support numerical matrix input,
use 'data.frame' to transform the data.")
} else {
stop("xgboost: Invalid input of data")
@ -156,12 +156,10 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
}
if (is.null(obj)) {
.Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain,
.Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain,
PACKAGE = "xgboost")
} else {
pred <- predict(booster, dtrain)
gpair <- obj(pred, dtrain)
succ <- xgb.iter.boost(booster, dtrain, gpair)
}
return(TRUE)
}
@ -189,9 +187,9 @@ xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL, prediction = F
}
evnames <- append(evnames, names(w))
}
msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist,
msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist,
evnames, PACKAGE = "xgboost")
} else {
} else {
msg <- paste("[", iter, "]", sep="")
for (j in 1:length(watchlist)) {
w <- watchlist[j]
@ -247,7 +245,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
if (length(unique(y)) <= 5) y <- factor(y)
}
folds <- xgb.createFolds(y, nfold)
} else {
} else {
# make simple non-stratified folds
kstep <- length(randidx) %/% nfold
folds <- list()
@ -282,7 +280,7 @@ xgb.cv.aggcv <- function(res, showsd = TRUE) {
kv <- strsplit(header[i], ":")[[1]]
ret <- paste(ret, "\t", kv[1], ":", sep="")
stats <- c()
stats[1] <- as.numeric(kv[2])
stats[1] <- as.numeric(kv[2])
for (j in 2:length(res)) {
tkv <- strsplit(res[[j]][i], ":")[[1]]
stats[j] <- as.numeric(tkv[2])
@ -311,8 +309,8 @@ xgb.createFolds <- function(y, k = 10)
## is too small, we just do regular unstratified
## CV
cuts <- floor(length(y) / k)
if(cuts < 2) cuts <- 2
if(cuts > 5) cuts <- 5
if (cuts < 2) cuts <- 2
if (cuts > 5) cuts <- 5
y <- cut(y,
unique(stats::quantile(y, probs = seq(0, 1, length = cuts))),
include.lowest = TRUE)
@ -324,7 +322,7 @@ xgb.createFolds <- function(y, k = 10)
y <- factor(as.character(y))
numInClass <- table(y)
foldVector <- vector(mode = "integer", length(y))
## For each class, balance the fold allocation as far
## as possible, then resample the remainder.
## The final assignment of folds is also randomized.

33
R-package/R/xgb.cv.R
View File

@ -118,7 +118,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
for (mc in metrics) {
params <- append(params, list("eval_metric"=mc))
}
# customized objective and evaluation metric interface
if (!is.null(params$objective) && !is.null(obj))
stop("xgb.cv: cannot assign two different objectives")
@ -134,7 +134,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
feval <- params$eval_metric
params[['eval_metric']] <- NULL
}
# Early Stopping
if (!is.null(early.stop.round)){
if (!is.null(feval) && is.null(maximize))
@ -149,7 +149,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
maximize <- TRUE
}
}
if (maximize) {
bestScore <- 0
} else {
@ -157,11 +157,11 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
}
bestInd <- 0
earlyStopflag <- FALSE
if (length(metrics) > 1)
warning('Only the first metric is used for early stopping process.')
}
xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
obj_type <- params[['objective']]
mat_pred <- FALSE
@ -181,7 +181,6 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
msg <- list()
for (k in 1:nfold) {
fd <- xgb_folds[[k]]
succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
}
ret <- xgb.cv.aggcv(msg, showsd)
@ -189,13 +188,13 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
if(verbose)
if (0 == (i - 1L) %% print.every.n)
cat(ret, "\n", sep="")
# early_Stopping
if (!is.null(early.stop.round)){
score <- strsplit(ret,'\\s+')[[1]][1 + length(metrics) + 2]
score <- strsplit(score,'\\+|:')[[1]][[2]]
score <- as.numeric(score)
if ((maximize && score > bestScore) || (!maximize && score < bestScore)) {
if ( (maximize && score > bestScore) || (!maximize && score < bestScore)) {
bestScore <- score
bestInd <- i
} else {
@ -206,9 +205,8 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
}
}
}
}
if (prediction) {
for (k in 1:nfold) {
fd <- xgb_folds[[k]]
@ -225,24 +223,23 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
}
}
}
colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
colnamesMean <- paste(colnames, "mean")
if(showsd) colnamesStd <- paste(colnames, "std")
colnames <- c()
if(showsd) for(i in 1:length(colnamesMean)) colnames <- c(colnames, colnamesMean[i], colnamesStd[i])
else colnames <- colnamesMean
type <- rep(x = "numeric", times = length(colnames))
dt <- utils::read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
split <- str_split(string = history, pattern = "\t")
for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist(list(dt, .), use.names = F, fill = F)}
for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist( list( dt, .), use.names = F, fill = F)}
if (prediction) {
return(list(dt = dt,pred = predictValues))
return( list( dt = dt,pred = predictValues))
}
return(dt)
}

10
R-package/R/xgb.importance.R
View File

@ -66,8 +66,8 @@
#' 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.")
}
@ -98,7 +98,7 @@ xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = N
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
@ -109,9 +109,9 @@ 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
}

44
R-package/R/xgb.model.dt.tree.R
View File

@ -57,7 +57,7 @@
#' @export
xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, text = NULL, n_first_tree = NULL){
if (!class(feature_names) %in% c("character", "NULL")) {
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)) {
@ -97,15 +97,15 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
allTrees <- data.table()
anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
for(i in 1:n_round){
for (i in 1:n_round){
tree <- text[(position[i] + 1):(position[i + 1] - 1)]
# avoid tree made of a leaf only (no split)
if(length(tree) < 2) next
treeID <- i - 1
notLeaf <- str_match(tree, "leaf") %>% is.na
leaf <- notLeaf %>% not %>% tree[.]
branch <- notLeaf %>% tree[.]
@ -129,37 +129,37 @@ xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model
coverBranch <- extract(branch, "cover=\\d*\\.*\\d*")
coverLeaf <- extract(leaf, "cover=\\d*\\.*\\d*")
dt <- data.table(ID = c(idBranch, idLeaf), Feature = c(featureBranch, featureLeaf), Split = c(splitBranch, splitLeaf), Yes = c(yesBranch, yesLeaf), No = c(noBranch, noLeaf), Missing = c(missingBranch, missingLeaf), Quality = c(qualityBranch, qualityLeaf), Cover = c(coverBranch, coverLeaf))[order(ID)][,Tree := treeID]
allTrees <- rbindlist(list(allTrees, dt), use.names = T, fill = F)
}
yes <- allTrees[!is.na(Yes), Yes]
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "Yes.Feature",
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "Yes.Feature",
value = allTrees[ID %in% yes, Feature])
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "Yes.Cover",
j = "Yes.Cover",
value = allTrees[ID %in% yes, Cover])
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "Yes.Quality",
j = "Yes.Quality",
value = allTrees[ID %in% yes, Quality])
no <- allTrees[!is.na(No), No]
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "No.Feature",
j = "No.Feature",
value = allTrees[ID %in% no, Feature])
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "No.Cover",
j = "No.Cover",
value = allTrees[ID %in% no, Cover])
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "No.Quality",
set(allTrees, i = which(allTrees[, Feature] != "Leaf"),
j = "No.Quality",
value = allTrees[ID %in% no, Quality])
allTrees
}

12
R-package/R/xgb.plot.importance.R
View File

@ -30,7 +30,7 @@
#'
#' @export
xgb.plot.importance <- function(importance_matrix = NULL, numberOfClusters = c(1:10)){
if (!"data.table" %in% class(importance_matrix)) {
if (!"data.table" %in% class(importance_matrix)) {
stop("importance_matrix: Should be a data.table.")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
@ -42,13 +42,13 @@ xgb.plot.importance <- function(importance_matrix = NULL, numberOfClusters = c(1
# To avoid issues in clustering when co-occurences are used
importance_matrix <- importance_matrix[, .(Gain = sum(Gain)), by = Feature]
clusters <- suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain], numberOfClusters))
importance_matrix[,"Cluster" := clusters$cluster %>% as.character]
plot <- ggplot2::ggplot(importance_matrix, ggplot2::aes(x=stats::reorder(Feature, Gain), y = Gain, width= 0.05), environment = environment()) + ggplot2::geom_bar(ggplot2::aes(fill=Cluster), stat="identity", position="identity") + ggplot2::coord_flip() + ggplot2::xlab("Features") + ggplot2::ylab("Gain") + ggplot2::ggtitle("Feature importance") + ggplot2::theme(plot.title = ggplot2::element_text(lineheight=.9, face="bold"), panel.grid.major.y = ggplot2::element_blank() )
return(plot)
plot <- ggplot2::ggplot(importance_matrix, ggplot2::aes(x=stats::reorder(Feature, Gain), y = Gain, width = 0.05), environment = environment()) + ggplot2::geom_bar(ggplot2::aes(fill=Cluster), stat="identity", position="identity") + ggplot2::coord_flip() + ggplot2::xlab("Features") + ggplot2::ylab("Gain") + ggplot2::ggtitle("Feature importance") + ggplot2::theme(plot.title = ggplot2::element_text(lineheight=.9, face="bold"), panel.grid.major.y = ggplot2::element_blank() )
return(plot)
}
# Avoid error messages during CRAN check.

41
R-package/R/xgb.plot.tree.R
View File

@ -54,40 +54,39 @@
#'
#' @export
#'
xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, n_first_tree = NULL, CSSstyle = NULL, width = NULL, height = NULL){
xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, n_first_tree = NULL, CSSstyle = NULL, width = NULL, height = NULL){
if (!(class(CSSstyle) %in% c("character", "NULL") && length(CSSstyle) <= 1)) {
stop("style: Has to be a character vector of size 1.")
}
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 (!requireNamespace("DiagrammeR", quietly = TRUE)) {
stop("DiagrammeR package is required for xgb.plot.tree", call. = FALSE)
}
if(is.null(model)){
allTrees <- xgb.model.dt.tree(feature_names = feature_names, filename_dump = filename_dump, n_first_tree = n_first_tree)
allTrees <- xgb.model.dt.tree(feature_names = feature_names, filename_dump = filename_dump, n_first_tree = n_first_tree)
} else {
allTrees <- xgb.model.dt.tree(feature_names = feature_names, model = model, n_first_tree = n_first_tree)
allTrees <- xgb.model.dt.tree(feature_names = feature_names, model = model, n_first_tree = n_first_tree)
}
allTrees[Feature!="Leaf" ,yesPath:= paste(ID,"(", Feature, "<br/>Cover: ", Cover, "<br/>Gain: ", Quality, ")-->|< ", Split, "|", Yes, ">", Yes.Feature, "]", sep = "")]
allTrees[Feature!="Leaf" ,noPath:= paste(ID,"(", Feature, ")-->|>= ", Split, "|", No, ">", No.Feature, "]", sep = "")]
allTrees[Feature != "Leaf" ,yesPath := paste(ID,"(", Feature, "<br/>Cover: ", Cover, "<br/>Gain: ", Quality, ")-->|< ", Split, "|", Yes, ">", Yes.Feature, "]", sep = "")]
allTrees[Feature != "Leaf" ,noPath := paste(ID,"(", Feature, ")-->|>= ", Split, "|", No, ">", No.Feature, "]", sep = "")]
if(is.null(CSSstyle)){
CSSstyle <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px"
}
yes <- allTrees[Feature!="Leaf", c(Yes)] %>% paste(collapse = ",") %>% paste("class ", ., " greenNode", sep = "")
no <- allTrees[Feature!="Leaf", c(No)] %>% paste(collapse = ",") %>% paste("class ", ., " redNode", sep = "")
path <- allTrees[Feature!="Leaf", c(yesPath, noPath)] %>% .[order(.)] %>% paste(sep = "", collapse = ";") %>% paste("graph LR", .,collapse = "", sep = ";") %>% paste(CSSstyle, yes, no, sep = ";")
CSSstyle <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px"
}
yes <- allTrees[Feature != "Leaf", c(Yes)] %>% paste(collapse = ",") %>% paste("class ", ., " greenNode", sep = "")
no <- allTrees[Feature != "Leaf", c(No)] %>% paste(collapse = ",") %>% paste("class ", ., " redNode", sep = "")
path <- allTrees[Feature != "Leaf", c(yesPath, noPath)] %>% .[order(.)] %>% paste(sep = "", collapse = ";") %>% paste("graph LR", .,collapse = "", sep = ";") %>% paste(CSSstyle, yes, no, sep = ";")
DiagrammeR::mermaid(path, width, height)
}

2
R-package/R/xgb.save.R
View File

@ -29,4 +29,4 @@ xgb.save <- function(model, fname) {
stop("xgb.save: the input must be xgb.Booster. Use xgb.DMatrix.save to save
xgb.DMatrix object.")
return(FALSE)
}
}

26
R-package/R/xgb.train.R
View File

@ -120,9 +120,9 @@
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
#' @export
#'
xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
obj = NULL, feval = NULL, verbose = 1, print.every.n=1L,
early.stop.round = NULL, maximize = NULL,
early.stop.round = NULL, maximize = NULL,
save_period = 0, save_name = "xgboost.model", ...) {
dtrain <- data
if (typeof(params) != "list") {
@ -139,14 +139,14 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
if (length(watchlist) != 0 && verbose == 0) {
warning('watchlist is provided but verbose=0, no evaluation information will be printed')
}
dot.params <- list(...)
nms.params <- names(params)
nms.dot.params <- names(dot.params)
if (length(intersect(nms.params,nms.dot.params))>0)
if (length(intersect(nms.params,nms.dot.params)) > 0)
stop("Duplicated term in parameters. Please check your list of params.")
params <- append(params, dot.params)
# customized objective and evaluation metric interface
if (!is.null(params$objective) && !is.null(obj))
stop("xgb.train: cannot assign two different objectives")
@ -162,7 +162,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
feval <- params$eval_metric
params$eval_metric <- NULL
}
# Early stopping
if (!is.null(early.stop.round)){
if (!is.null(feval) && is.null(maximize))
@ -179,25 +179,22 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
maximize <- TRUE
}
}
if (maximize) {
bestScore <- 0
} else {
bestScore <- Inf
}
bestInd <- 0
earlyStopflag <- FALSE
if (length(watchlist) > 1)
warning('Only the first data set in watchlist is used for early stopping process.')
}
handle <- xgb.Booster(params, append(watchlist, dtrain))
bst <- xgb.handleToBooster(handle)
print.every.n <- max( as.integer(print.every.n), 1L)
for (i in 1:nrounds) {
succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
if (length(watchlist) != 0) {
msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
if (0 == ( (i - 1) %% print.every.n))
@ -206,12 +203,11 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
{
score <- strsplit(msg,':|\\s+')[[1]][3]
score <- as.numeric(score)
if ((maximize && score > bestScore) || (!maximize && score < bestScore)) {
if ( (maximize && score > bestScore) || (!maximize && score < bestScore)) {
bestScore <- score
bestInd <- i
} else {
if (i - bestInd >= early.stop.round) {
earlyStopflag <- TRUE
cat('Stopping. Best iteration:',bestInd)
break
}
@ -230,4 +226,4 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
bst$bestInd <- bestInd
}
return(bst)
}
}

16
R-package/R/xgboost.R
View File

@ -59,28 +59,26 @@
#'
#' @export
#'
xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
params = list(), nrounds,
xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
params = list(), nrounds,
verbose = 1, print.every.n = 1L, early.stop.round = NULL,
maximize = NULL, save_period = 0, save_name = "xgboost.model", ...) {
dtrain <- xgb.get.DMatrix(data, label, missing, weight)
params <- append(params, list(...))
if (verbose > 0) {
watchlist <- list(train = dtrain)
} else {
watchlist <- list()
}
bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose, print.every.n=print.every.n,
early.stop.round = early.stop.round, maximize = maximize,
save_period = save_period, save_name = save_name)
return(bst)
}
}
#' Training part from Mushroom Data Set
#'
#' This data set is originally from the Mushroom data set,

14
R-package/tests/testthat/test_custom_objective.R
View File

@ -7,10 +7,10 @@ test_that("custom objective works", {
data(agaricus.test, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
watchlist <- list(eval = dtest, train = dtrain)
num_round <- 2
logregobj <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
preds <- 1 / (1 + exp(-preds))
@ -23,15 +23,15 @@ test_that("custom objective works", {
err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
return(list(metric = "error", value = err))
}
param <- list(max.depth=2, eta=1, nthread = 2, silent=1,
param <- list(max.depth=2, eta=1, nthread = 2, silent=1,
objective=logregobj, eval_metric=evalerror)
bst <- xgb.train(param, dtrain, num_round, watchlist)
expect_equal(class(bst), "xgb.Booster")
expect_equal(length(bst$raw), 1064)
attr(dtrain, 'label') <- getinfo(dtrain, 'label')
logregobjattr <- function(preds, dtrain) {
labels <- attr(dtrain, 'label')
preds <- 1 / (1 + exp(-preds))
@ -39,7 +39,7 @@ test_that("custom objective works", {
hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess))
}
param <- list(max.depth=2, eta=1, nthread = 2, silent = 1,
param <- list(max.depth=2, eta=1, nthread = 2, silent = 1,
objective = logregobjattr, eval_metric = evalerror)
bst <- xgb.train(param, dtrain, num_round, watchlist)
expect_equal(class(bst), "xgb.Booster")

2
R-package/tests/testthat/test_lint.R
View File

@ -23,5 +23,5 @@ test_that("Code Lint", {
trailing_blank_lines_linter=lintr::trailing_blank_lines_linter,
trailing_whitespace_linter=lintr::trailing_whitespace_linter
)
lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
# lintr::expect_lint_free(linters=my_linters) # uncomment this if you want to check code quality
})

2
R-package/tests/testthat/test_poisson_regression.R
View File

@ -9,5 +9,5 @@ test_that("poisson regression works", {
expect_equal(class(bst), "xgb.Booster")
pred <- predict(bst,as.matrix(mtcars[, -11]))
expect_equal(length(pred), 32)
sqrt(mean((pred - mtcars[,11]) ^ 2))
sqrt(mean( (pred - mtcars[,11]) ^ 2))
})