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[R] update doc; add drat repo

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This commit is contained in:
Qiang Kou 2016-01-24 11:28:55 -05:00
parent 1ab0c3c248
commit bdeb095a7d
5 changed files with 51 additions and 34 deletions
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2
R-package/vignettes/xgboost.Rnw
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@ -21,7 +21,7 @@ if (require('knitr')) opts_chunk$set(fig.width = 5, fig.height = 5, fig.align =
% %
<<prelim,echo=FALSE>>= <<prelim,echo=FALSE>>=
xgboost.version = '0.3-0' xgboost.version = '0.4-2'
@ @
% %

18
R-package/vignettes/xgboostPresentation.Rmd
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@ -49,18 +49,24 @@ It has several features:
### Github version ### Github version
For up-to-date version (highly recommended), install from *Github*: For weekly updated version (highly recommended), install from *Github*:
```{r installGithub, eval=FALSE} ```{r installGithub, eval=FALSE}
devtools::install_git('git://github.com/dmlc/xgboost', subdir='R-package') install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
``` ```
> *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first. > *Windows* user will need to install [Rtools](http://cran.r-project.org/bin/windows/Rtools/) first.
Cran version ### CRAN version
------------
As of 2015-03-13, ‘xgboost’ was removed from the CRAN repository.
The version 0.4-2 is on CRAN, and you can install it by:
```{r, eval=FALSE}
install.packages("xgboost")
```
Formerly available versions can be obtained from the CRAN [archive](http://cran.r-project.org/src/contrib/Archive/xgboost) Formerly available versions can be obtained from the CRAN [archive](http://cran.r-project.org/src/contrib/Archive/xgboost)

1
doc/R-package/Makefile
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@ -8,6 +8,7 @@ xgboostPresentation.md: $(PKGROOT)/vignettes/xgboostPresentation.Rmd
# General Rules for build rmarkdowns, need knitr # General Rules for build rmarkdowns, need knitr
%.md: %.md:
Rscript -e \ Rscript -e \
"require(methods);"\
"require(knitr);"\ "require(knitr);"\
"knitr::opts_knit\$$set(root.dir=\".\");"\ "knitr::opts_knit\$$set(root.dir=\".\");"\
"knitr::opts_chunk\$$set(fig.path=\"../web-data/xgboost/knitr/$(basename $@)-\");"\ "knitr::opts_chunk\$$set(fig.path=\"../web-data/xgboost/knitr/$(basename $@)-\");"\

47
doc/R-package/xgboostPresentation.md
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@ -49,19 +49,26 @@ It has several features:
### Github version ### Github version
For up-to-date version (highly recommended), install from *Github*: For weekly updated version (highly recommended), install from *Github*:
```r ```r
devtools::install_git('git://github.com/dmlc/xgboost', subdir='R-package') install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
``` ```
> *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first. > *Windows* user will need to install [Rtools](http://cran.r-project.org/bin/windows/Rtools/) first.
Cran version ### CRAN version
------------
As of 2015-03-13, ‘xgboost’ was removed from the CRAN repository.
The version 0.4-2 is on CRAN, and you can install it by:
```r
install.packages("xgboost")
```
Formerly available versions can be obtained from the CRAN [archive](http://cran.r-project.org/src/contrib/Archive/xgboost) Formerly available versions can be obtained from the CRAN [archive](http://cran.r-project.org/src/contrib/Archive/xgboost)
@ -115,11 +122,16 @@ str(train)
``` ```
## List of 2 ## List of 2
## $ data : ## $ data :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
``` ## .. ..@ i : int [1:143286] 2 6 8 11 18 20 21 24 28 32 ...
## .. ..@ p : int [1:127] 0 369 372 3306 5845 6489 6513 8380 8384 10991 ...
``` ## .. ..@ Dim : int [1:2] 6513 126
## Error in str.default(obj, ...): could not find function "is" ## .. ..@ Dimnames:List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:126] "cap-shape=bell" "cap-shape=conical" "cap-shape=convex" "cap-shape=flat" ...
## .. ..@ x : num [1:143286] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..@ factors : list()
## $ label: num [1:6513] 1 0 0 1 0 0 0 1 0 0 ...
``` ```
`label` is the outcome of our dataset meaning it is the binary *classification* we will try to predict. `label` is the outcome of our dataset meaning it is the binary *classification* we will try to predict.
@ -206,7 +218,8 @@ bstDense <- xgboost(data = as.matrix(train$data), label = train$label, max.depth
``` ```
``` ```
## Error in as.vector(data): no method for coercing this S4 class to a vector ## [0] train-error:0.046522
## [1] train-error:0.022263
``` ```
##### xgb.DMatrix ##### xgb.DMatrix
@ -254,9 +267,9 @@ bst <- xgboost(data = dtrain, max.depth = 2, eta = 1, nthread = 2, nround = 2, o
``` ```
``` ```
## [11:43:20] ../..//amalgamation/../src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=2 ## [11:41:01] amalgamation/../src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 6 extra nodes, 0 pruned nodes, max_depth=2
## [0] train-error:0.046522 ## [0] train-error:0.046522
## [11:43:20] ../..//amalgamation/../src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 0 pruned nodes, max_depth=2 ## [11:41:01] amalgamation/../src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 4 extra nodes, 0 pruned nodes, max_depth=2
## [1] train-error:0.022263 ## [1] train-error:0.022263
``` ```
@ -412,8 +425,8 @@ bst <- xgb.train(data=dtrain, booster = "gblinear", max.depth=2, nthread = 2, nr
``` ```
``` ```
## [0] train-error:0.019499 train-logloss:0.176561 test-error:0.018001 test-logloss:0.173835 ## [0] train-error:0.024720 train-logloss:0.184616 test-error:0.022967 test-logloss:0.184234
## [1] train-error:0.004760 train-logloss:0.068214 test-error:0.003104 test-logloss:0.065493 ## [1] train-error:0.004146 train-logloss:0.069885 test-error:0.003724 test-logloss:0.068081
``` ```
In this specific case, *linear boosting* gets sligtly better performance metrics than decision trees based algorithm. In this specific case, *linear boosting* gets sligtly better performance metrics than decision trees based algorithm.
@ -442,7 +455,7 @@ dtrain2 <- xgb.DMatrix("dtrain.buffer")
``` ```
``` ```
## [11:43:20] 6513x126 matrix with 143286 entries loaded from dtrain.buffer ## [11:41:01] 6513x126 matrix with 143286 entries loaded from dtrain.buffer
``` ```
```r ```r

17
doc/build.md
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@ -143,10 +143,12 @@ There are several ways to install the package:
## R Package Installation ## R Package Installation
You can install R package using devtools You can install R package from cran just like other packages, or you can install from our weekly updated drat repo:
```r ```r
devtools::install_git('git://github.com/dmlc/xgboost',subdir='R-package') install.packages("drat", repos="https://cran.rstudio.com")
drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
``` ```
@ -160,13 +162,9 @@ First follow [Building on OSX](#building-on-osx) to get the OpenMP enabled compi
Then inside R, run Then inside R, run
```R ```R
install.packages('xgboost/R-package/', repos=NULL, type='source') install.packages("drat", repos="https://cran.rstudio.com")
``` drat:::addRepo("dmlc")
install.packages("xgboost", repos="http://dmlc.ml/drat/", type = "source")
Or
```R
devtools::install_local('xgboost/', subdir = 'R-package') # you may use devtools
``` ```
## Trouble Shooting ## Trouble Shooting
@ -198,4 +196,3 @@ First follow [Building on OSX](#building-on-osx) to get the OpenMP enabled compi
```bash ```bash
git clone https://github.com/dmlc/xgboost --recursive git clone https://github.com/dmlc/xgboost --recursive
``` ```