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fix iris multiclass problem

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This commit is contained in:
hetong 2014-09-05 19:22:27 -07:00
parent 2b170ecda4
commit d776e0fdf5
11 changed files with 15 additions and 14 deletions
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2
R-package/R/getinfo.xgb.DMatrix.R
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@ -6,7 +6,7 @@ setClass('xgb.DMatrix')
#' #'
#' @examples #' @examples
#' data(iris) #' 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]) #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
#' labels <- getinfo(dtrain, "label") #' labels <- getinfo(dtrain, "label")
#' @rdname getinfo #' @rdname getinfo

7
R-package/R/predict.xgb.Booster.R
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@ -11,11 +11,12 @@ setClass("xgb.Booster")
#' value of sum of functions, when outputmargin=TRUE, the prediction is #' value of sum of functions, when outputmargin=TRUE, the prediction is
#' untransformed margin value. In logistic regression, outputmargin=T will #' untransformed margin value. In logistic regression, outputmargin=T will
#' output value before logistic transformation. #' 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. #' @param ntreelimit limit number of trees used in prediction, this parameter is
#' set it to be value bigger than 0. It will use all trees by default. #' only valid for gbtree, but not for gblinear. set it to be value bigger
#' than 0. It will use all trees by default.
#' @examples #' @examples
#' data(iris) #' 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])) #' pred <- predict(bst, as.matrix(iris[,1:4]))
#' @export #' @export
#' #'

2
R-package/R/slice.xgb.DMatrix.R
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@ -8,7 +8,7 @@ setClass('xgb.DMatrix')
#' #'
#' @examples #' @examples
#' data(iris) #' 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]) #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
#' dsub <- slice(dtrain, 1:3) #' dsub <- slice(dtrain, 1:3)
#' @rdname slice #' @rdname slice

2
R-package/R/xgb.DMatrix.R
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@ -12,7 +12,7 @@
#' #'
#' @examples #' @examples
#' data(iris) #' 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]) #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
#' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') #' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix')
#' dtrain <- xgb.DMatrix('iris.xgb.DMatrix') #' dtrain <- xgb.DMatrix('iris.xgb.DMatrix')

2
R-package/R/xgb.DMatrix.save.R
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@ -7,7 +7,7 @@
#' #'
#' @examples #' @examples
#' data(iris) #' 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]) #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
#' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix') #' xgb.DMatrix.save(dtrain, 'iris.xgb.DMatrix')
#' dtrain <- xgb.DMatrix('iris.xgb.DMatrix') #' dtrain <- xgb.DMatrix('iris.xgb.DMatrix')

2
R-package/R/xgb.dump.R
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@ -13,7 +13,7 @@
#' #'
#' @examples #' @examples
#' data(iris) #' 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') #' xgb.dump(bst, 'iris.xgb.model.dump')
#' @export #' @export
#' #'

2
R-package/R/xgb.load.R
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@ -6,7 +6,7 @@
#' #'
#' @examples #' @examples
#' data(iris) #' 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') #' xgb.save(bst, 'iris.xgb.model')
#' bst <- xgb.load('iris.xgb.model') #' bst <- xgb.load('iris.xgb.model')
#' pred <- predict(bst, as.matrix(iris[,1:4])) #' pred <- predict(bst, as.matrix(iris[,1:4]))

2
R-package/R/xgb.save.R
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@ -7,7 +7,7 @@
#' #'
#' @examples #' @examples
#' data(iris) #' 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') #' xgb.save(bst, 'iris.xgb.model')
#' bst <- xgb.load('iris.xgb.model') #' bst <- xgb.load('iris.xgb.model')
#' pred <- predict(bst, as.matrix(iris[,1:4])) #' pred <- predict(bst, as.matrix(iris[,1:4]))

2
R-package/R/xgb.train.R
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@ -44,7 +44,7 @@
#' #'
#' @examples #' @examples
#' data(iris) #' 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]) #' dtrain <- xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
#' dtest <- dtrain #' dtest <- dtrain
#' watchlist <- list(eval = dtest, train = dtrain) #' watchlist <- list(eval = dtest, train = dtrain)

2
R-package/R/xgboost.R
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@ -34,7 +34,7 @@
#' #'
#' @examples #' @examples
#' data(iris) #' 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])) #' pred <- predict(bst, as.matrix(iris[,1:4]))
#' @export #' @export
#' #'

4
R-package/vignettes/xgboost.Rnw
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@ -80,7 +80,7 @@ In this section, we will illustrate some common usage of \verb@xgboost@.
<<Training and prediction with iris>>= <<Training and prediction with iris>>=
library(xgboost) library(xgboost)
data(iris) 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) nrounds = 5)
xgb.save(bst, 'model.save') xgb.save(bst, 'model.save')
bst = xgb.load('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: We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object:
<<xgb.DMatrix>>= <<xgb.DMatrix>>=
iris.mat <- as.matrix(iris[,1:4]) 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) diris <- xgb.DMatrix(iris.mat, label = iris.label)
class(diris) class(diris)
getinfo(diris,'label') getinfo(diris,'label')