From d776e0fdf54b5c86d8ac2547e09f54423d92fd34 Mon Sep 17 00:00:00 2001 From: hetong Date: Fri, 5 Sep 2014 19:22:27 -0700 Subject: [PATCH] fix iris multiclass problem --- R-package/R/getinfo.xgb.DMatrix.R | 2 +- R-package/R/predict.xgb.Booster.R | 7 ++++--- R-package/R/slice.xgb.DMatrix.R | 2 +- R-package/R/xgb.DMatrix.R | 2 +- R-package/R/xgb.DMatrix.save.R | 2 +- R-package/R/xgb.dump.R | 2 +- R-package/R/xgb.load.R | 2 +- R-package/R/xgb.save.R | 2 +- R-package/R/xgb.train.R | 2 +- R-package/R/xgboost.R | 2 +- R-package/vignettes/xgboost.Rnw | 4 ++-- 11 files changed, 15 insertions(+), 14 deletions(-) diff --git a/R-package/R/getinfo.xgb.DMatrix.R b/R-package/R/getinfo.xgb.DMatrix.R index 3a79fd2fb..2a7ae8e5e 100644 --- a/R-package/R/getinfo.xgb.DMatrix.R +++ b/R-package/R/getinfo.xgb.DMatrix.R @@ -6,7 +6,7 @@ setClass('xgb.DMatrix') #' #' @examples #' data(iris) -#' iris[,5] <- as.numeric(iris[,5]) +#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' labels <- getinfo(dtrain, "label") #' @rdname getinfo diff --git a/R-package/R/predict.xgb.Booster.R b/R-package/R/predict.xgb.Booster.R index 390ac689e..a41b26873 100644 --- a/R-package/R/predict.xgb.Booster.R +++ b/R-package/R/predict.xgb.Booster.R @@ -11,11 +11,12 @@ setClass("xgb.Booster") #' value of sum of functions, when outputmargin=TRUE, the prediction is #' untransformed margin value. In logistic regression, outputmargin=T will #' output value before logistic transformation. -#' @param ntreelimit limit number of trees used in prediction, this parameter is only valid for gbtree, but not for gblinear. -#' set it to be value bigger than 0. It will use all trees by default. +#' @param ntreelimit limit number of trees used in prediction, this parameter is +#' only valid for gbtree, but not for gblinear. set it to be value bigger +#' than 0. It will use all trees by default. #' @examples #' data(iris) -#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2) +#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' pred <- predict(bst, as.matrix(iris[,1:4])) #' @export #' diff --git a/R-package/R/slice.xgb.DMatrix.R b/R-package/R/slice.xgb.DMatrix.R index 8a93efc4d..72f94893a 100644 --- a/R-package/R/slice.xgb.DMatrix.R +++ b/R-package/R/slice.xgb.DMatrix.R @@ -8,7 +8,7 @@ setClass('xgb.DMatrix') #' #' @examples #' data(iris) -#' iris[,5] <- as.numeric(iris[,5]) +#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' dsub <- slice(dtrain, 1:3) #' @rdname slice diff --git a/R-package/R/xgb.DMatrix.R b/R-package/R/xgb.DMatrix.R index d52847ef2..3b320d73f 100644 --- a/R-package/R/xgb.DMatrix.R +++ b/R-package/R/xgb.DMatrix.R @@ -12,7 +12,7 @@ #' #' @examples #' data(iris) -#' iris[,5] <- as.numeric(iris[,5]) +#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') #' dtrain <- xgb.DMatrix('iris.xgb.DMatrix') diff --git a/R-package/R/xgb.DMatrix.save.R b/R-package/R/xgb.DMatrix.save.R index 4fcb71301..4f4f49399 100644 --- a/R-package/R/xgb.DMatrix.save.R +++ b/R-package/R/xgb.DMatrix.save.R @@ -7,7 +7,7 @@ #' #' @examples #' data(iris) -#' iris[,5] <- as.numeric(iris[,5]) +#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') #' dtrain <- xgb.DMatrix('iris.xgb.DMatrix') diff --git a/R-package/R/xgb.dump.R b/R-package/R/xgb.dump.R index 09406dc99..78fcf4d0b 100644 --- a/R-package/R/xgb.dump.R +++ b/R-package/R/xgb.dump.R @@ -13,7 +13,7 @@ #' #' @examples #' data(iris) -#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2) +#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' xgb.dump(bst, 'iris.xgb.model.dump') #' @export #' diff --git a/R-package/R/xgb.load.R b/R-package/R/xgb.load.R index 626c08d0d..54afe65dd 100644 --- a/R-package/R/xgb.load.R +++ b/R-package/R/xgb.load.R @@ -6,7 +6,7 @@ #' #' @examples #' data(iris) -#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2) +#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' xgb.save(bst, 'iris.xgb.model') #' bst <- xgb.load('iris.xgb.model') #' pred <- predict(bst, as.matrix(iris[,1:4])) diff --git a/R-package/R/xgb.save.R b/R-package/R/xgb.save.R index 64add0ca9..c211429ad 100644 --- a/R-package/R/xgb.save.R +++ b/R-package/R/xgb.save.R @@ -7,7 +7,7 @@ #' #' @examples #' data(iris) -#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2) +#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' xgb.save(bst, 'iris.xgb.model') #' bst <- xgb.load('iris.xgb.model') #' pred <- predict(bst, as.matrix(iris[,1:4])) diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R index 58a575d03..e5400829f 100644 --- a/R-package/R/xgb.train.R +++ b/R-package/R/xgb.train.R @@ -44,7 +44,7 @@ #' #' @examples #' data(iris) -#' iris[,5] <- as.numeric(iris[,5]) +#' iris[,5] <- as.numeric(iris[,5]=='setosa') #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5]) #' dtest <- dtrain #' watchlist <- list(eval = dtest, train = dtrain) diff --git a/R-package/R/xgboost.R b/R-package/R/xgboost.R index 6f4633fb8..dc8b17fa0 100644 --- a/R-package/R/xgboost.R +++ b/R-package/R/xgboost.R @@ -34,7 +34,7 @@ #' #' @examples #' data(iris) -#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2) +#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 2) #' pred <- predict(bst, as.matrix(iris[,1:4])) #' @export #' diff --git a/R-package/vignettes/xgboost.Rnw b/R-package/vignettes/xgboost.Rnw index 9ecceca17..45ab1a096 100644 --- a/R-package/vignettes/xgboost.Rnw +++ b/R-package/vignettes/xgboost.Rnw @@ -80,7 +80,7 @@ In this section, we will illustrate some common usage of \verb@xgboost@. <>= library(xgboost) data(iris) -bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), +bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'), nrounds = 5) xgb.save(bst, 'model.save') bst = xgb.load('model.save') @@ -121,7 +121,7 @@ training from initial prediction value, weighted training instance. We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object: <>= iris.mat <- as.matrix(iris[,1:4]) -iris.label <- as.numeric(iris[,5]) +iris.label <- as.numeric(iris[,5]=='setosa') diris <- xgb.DMatrix(iris.mat, label = iris.label) class(diris) getinfo(diris,'label')