Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

remove inst/, improve vignette

Browse Source
This commit is contained in:
hetong
2014-09-06 23:05:21 -07:00
parent 50d77c72eb
commit cd35d88a03
6 changed files with 27 additions and 8437 deletions
Download Patch File Download Diff File Expand all files Collapse all files

51
R-package/vignettes/xgboost.Rnw
View File

@@ -80,12 +80,15 @@ Mushroom data is cited from UCI Machine Learning Repository. \citep{Bache+Lichma
<<Training and prediction with iris>>=
library(xgboost)
data(iris)
bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]=='setosa'),
nrounds = 5)
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1,
nround = 2, objective = "binary:logistic")
xgb.save(bst, 'model.save')
bst = xgb.load('model.save')
pred <- predict(bst, as.matrix(iris[,1:4]))
pred <- predict(bst, test$data)
@
\verb@xgboost@ is the main function to train a \verb@Booster@, i.e. a model.
@@ -102,17 +105,19 @@ The output looks like
\begin{verbatim}
booster[0]:
0:[f2<2.45] yes=1,no=2,missing=1
1:leaf=0.147059
2:[f3<1.65] yes=3,no=4,missing=3
3:leaf=0.464151
4:leaf=0.722449
0:[f28<1.00001] yes=1,no=2,missing=2
1:[f108<1.00001] yes=3,no=4,missing=4
3:leaf=1.85965
4:leaf=-1.94071
2:[f55<1.00001] yes=5,no=6,missing=6
5:leaf=-1.70044
6:leaf=1.71218
booster[1]:
0:[f2<2.45] yes=1,no=2,missing=1
1:leaf=0.103806
2:[f2<4.85] yes=3,no=4,missing=3
3:leaf=0.316341
4:leaf=0.510365
0:[f59<1.00001] yes=1,no=2,missing=2
1:leaf=-6.23624
2:[f28<1.00001] yes=3,no=4,missing=4
3:leaf=-0.96853
4:leaf=0.784718
\end{verbatim}
It is important to know \verb@xgboost@'s own data type: \verb@xgb.DMatrix@.
@@ -121,18 +126,16 @@ training from initial prediction value, weighted training instance.
We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object:
<<xgb.DMatrix>>=
iris.mat <- as.matrix(iris[,1:4])
iris.label <- as.numeric(iris[,5]=='setosa')
diris <- xgb.DMatrix(iris.mat, label = iris.label)
class(diris)
getinfo(diris,'label')
dtrain <- xgb.DMatrix(train$data, label = train$label)
class(dtrain)
head(getinfo(dtrain,'label'))
@
We can also save the matrix to a binary file. Then load it simply with
\verb@xgb.DMatrix@
<<save model>>=
xgb.DMatrix.save(diris, 'iris.xgb.DMatrix')
diris = xgb.DMatrix('iris.xgb.DMatrix')
xgb.DMatrix.save(dtrain, 'xgb.DMatrix')
dtrain = xgb.DMatrix('xgb.DMatrix')
@
\section{Advanced Examples}
@@ -157,11 +160,11 @@ evalerror <- function(preds, dtrain) {
return(list(metric = "MSE", value = err))
}
dtest <- slice(diris,1:100)
watchlist <- list(eval = dtest, train = diris)
dtest <- xgb.DMatrix(test$data, label = test$label)
watchlist <- list(eval = dtest, train = dtrain)
param <- list(max_depth = 2, eta = 1, silent = 1)
bst <- xgb.train(param, diris, nround = 2, watchlist, logregobj, evalerror)
bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
@
The gradient and second order gradient is required for the output of customized