From d27bfb61b07bbe234bce9f7718f1b42420865d12 Mon Sep 17 00:00:00 2001
From: Vadim Khotilovich <khotilovich@gmail.com>
Date: Sun, 27 Mar 2016 19:17:13 -0500
Subject: [PATCH] consolidated DMatrix&Booster stuff into xgb.DMatrix.R &
 xgb.Booster.R

---
 R-package/R/getinfo.xgb.DMatrix.R             | 55 -------------
 R-package/R/nrow.xgb.DMatrix.R                | 19 -----
 R-package/R/predict.xgb.Booster.R             | 80 -------------------
 R-package/R/predict.xgb.Booster.handle.R      | 18 -----
 R-package/R/setinfo.xgb.DMatrix.R             | 37 ---------
 R-package/R/slice.xgb.DMatrix.R               | 44 ----------
 R-package/man/nrow-xgb.DMatrix-method.Rd      | 23 ------
 R-package/man/predict-xgb.Booster-method.Rd   | 53 ------------
 .../man/predict-xgb.Booster.handle-method.Rd  | 18 -----
 R-package/man/slice.Rd                        | 31 -------
 10 files changed, 378 deletions(-)
 delete mode 100644 R-package/R/getinfo.xgb.DMatrix.R
 delete mode 100644 R-package/R/nrow.xgb.DMatrix.R
 delete mode 100644 R-package/R/predict.xgb.Booster.R
 delete mode 100644 R-package/R/predict.xgb.Booster.handle.R
 delete mode 100644 R-package/R/setinfo.xgb.DMatrix.R
 delete mode 100644 R-package/R/slice.xgb.DMatrix.R
 delete mode 100644 R-package/man/nrow-xgb.DMatrix-method.Rd
 delete mode 100644 R-package/man/predict-xgb.Booster-method.Rd
 delete mode 100644 R-package/man/predict-xgb.Booster.handle-method.Rd
 delete mode 100644 R-package/man/slice.Rd

diff --git a/R-package/R/getinfo.xgb.DMatrix.R b/R-package/R/getinfo.xgb.DMatrix.R
deleted file mode 100644
index 3000a1e7d..000000000
--- a/R-package/R/getinfo.xgb.DMatrix.R
+++ /dev/null
@@ -1,55 +0,0 @@
-setClass('xgb.DMatrix')
-
-#' Get information of an xgb.DMatrix object
-#' 
-#' Get information of an xgb.DMatrix object
-#' 
-#' The information can be one of the following:
-#' 
-#' \itemize{
-#'     \item \code{label}: label Xgboost learn from ;
-#'     \item \code{weight}: to do a weight rescale ;
-#'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
-#'     \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
-#' }
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' labels <- getinfo(dtrain, 'label')
-#' setinfo(dtrain, 'label', 1-labels)
-#' labels2 <- getinfo(dtrain, 'label')
-#' stopifnot(all(labels2 == 1-labels))
-#' @rdname getinfo
-#' @export
-getinfo <- function(object, ...){
-    UseMethod("getinfo")
-}
-
-
-
-#' @param object Object of class \code{xgb.DMatrix}
-#' @param name the name of the field to get
-#' @param ... other parameters
-#' @rdname getinfo
-#' @method getinfo xgb.DMatrix
-setMethod("getinfo", signature = "xgb.DMatrix",
-          definition = function(object, name) {
-              if (typeof(name) != "character") {
-                  stop("xgb.getinfo: name must be character")
-              }
-              if (class(object) != "xgb.DMatrix") {
-                  stop("xgb.setinfo: first argument dtrain must be xgb.DMatrix")
-              }
-              if (name != "label" && name != "weight" &&
-                      name != "base_margin" && name != "nrow") {
-                  stop(paste("xgb.getinfo: unknown info name", name))
-              }
-              if (name != "nrow"){
-                  ret <- .Call("XGDMatrixGetInfo_R", object, name, PACKAGE = "xgboost")
-              } else {
-                  ret <- xgb.numrow(object)
-              }
-              return(ret)
-          })
diff --git a/R-package/R/nrow.xgb.DMatrix.R b/R-package/R/nrow.xgb.DMatrix.R
deleted file mode 100644
index 9ea039764..000000000
--- a/R-package/R/nrow.xgb.DMatrix.R
+++ /dev/null
@@ -1,19 +0,0 @@
-setGeneric("nrow")
-
-#' @title Number of xgb.DMatrix rows
-#' @description \code{nrow} return the number of rows present in the \code{xgb.DMatrix}.
-#' @param x Object of class \code{xgb.DMatrix}
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' stopifnot(nrow(dtrain) == nrow(train$data))
-#' 
-#' @export
-setMethod("nrow",
-          signature = "xgb.DMatrix",
-          definition = function(x) {
-            xgb.numrow(x)
-          }
-)
diff --git a/R-package/R/predict.xgb.Booster.R b/R-package/R/predict.xgb.Booster.R
deleted file mode 100644
index d608f3465..000000000
--- a/R-package/R/predict.xgb.Booster.R
+++ /dev/null
@@ -1,80 +0,0 @@
-setClass("xgb.Booster.handle")
-setClass("xgb.Booster",
-         slots = c(handle = "xgb.Booster.handle",
-                   raw = "raw"))
-
-#' Predict method for eXtreme Gradient Boosting model
-#' 
-#' Predicted values based on xgboost model object.
-#' 
-#' @param object Object of class "xgb.Boost"
-#' @param newdata takes \code{matrix}, \code{dgCMatrix}, local data file or 
-#'   \code{xgb.DMatrix}. 
-#' @param missing Missing is only used when input is dense matrix, pick a float 
-#'     value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
-#' @param outputmargin whether the prediction should be shown in the original
-#'   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 predleaf whether predict leaf index instead. If set to TRUE, the output will be a matrix object.
-#' 
-#' @details  
-#' The option \code{ntreelimit} purpose is to let the user train a model with lots 
-#' of trees but use only the first trees for prediction to avoid overfitting 
-#' (without having to train a new model with less trees).
-#' 
-#' The option \code{predleaf} purpose is inspired from §3.1 of the paper 
-#' \code{Practical Lessons from Predicting Clicks on Ads at Facebook}.
-#' The idea is to use the model as a generator of new features which capture non linear link 
-#' from original features.
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' data(agaricus.test, package='xgboost')
-#' train <- agaricus.train
-#' test <- agaricus.test
-#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
-#'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
-#' pred <- predict(bst, test$data)
-#' @export
-setMethod("predict", signature = "xgb.Booster",
-          definition = function(object, newdata, missing = NA,
-                                outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
-  if (class(object) != "xgb.Booster"){
-    stop("predict: model in prediction must be of class xgb.Booster")
-  } else {
-    object <- xgb.Booster.check(object, saveraw = FALSE)
-  }
-  if (class(newdata) != "xgb.DMatrix") {
-    newdata <- xgb.DMatrix(newdata, missing = missing)
-  }
-  if (is.null(ntreelimit)) {
-    ntreelimit <- 0
-  } else {
-    if (ntreelimit < 1){
-      stop("predict: ntreelimit must be equal to or greater than 1")
-    }
-  }
-  option <- 0
-  if (outputmargin) {
-    option <- option + 1
-  }
-  if (predleaf) {
-    option <- option + 2
-  }
-  ret <- .Call("XGBoosterPredict_R", object$handle, newdata, as.integer(option),
-               as.integer(ntreelimit), PACKAGE = "xgboost")
-  if (predleaf){
-      len <- getinfo(newdata, "nrow")
-      if (length(ret) == len){
-          ret <- matrix(ret,ncol = 1)
-      } else {
-          ret <- matrix(ret, ncol = len)
-          ret <- t(ret)
-      }
-  }
-  return(ret)
-})
diff --git a/R-package/R/predict.xgb.Booster.handle.R b/R-package/R/predict.xgb.Booster.handle.R
deleted file mode 100644
index 3e4013b75..000000000
--- a/R-package/R/predict.xgb.Booster.handle.R
+++ /dev/null
@@ -1,18 +0,0 @@
-#' Predict method for eXtreme Gradient Boosting model handle
-#' 
-#' Predicted values based on xgb.Booster.handle object.
-#' 
-#' @param object Object of class "xgb.Boost.handle"
-#' @param ... Parameters pass to \code{predict.xgb.Booster}
-#' 
-setMethod("predict", signature = "xgb.Booster.handle",
-          definition = function(object, ...) {
-  if (class(object) != "xgb.Booster.handle"){
-    stop("predict: model in prediction must be of class xgb.Booster.handle")
-  }
-
-  bst <- xgb.handleToBooster(object)
-
-  ret <- predict(bst, ...)
-  return(ret)
-})
diff --git a/R-package/R/setinfo.xgb.DMatrix.R b/R-package/R/setinfo.xgb.DMatrix.R
deleted file mode 100644
index 427de08d4..000000000
--- a/R-package/R/setinfo.xgb.DMatrix.R
+++ /dev/null
@@ -1,37 +0,0 @@
-#' Set information of an xgb.DMatrix object
-#' 
-#' Set information of an xgb.DMatrix object
-#' 
-#' It can be one of the following:
-#' 
-#' \itemize{
-#'     \item \code{label}: label Xgboost learn from ;
-#'     \item \code{weight}: to do a weight rescale ;
-#'     \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
-#'     \item \code{group}.
-#' }
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' labels <- getinfo(dtrain, 'label')
-#' setinfo(dtrain, 'label', 1-labels)
-#' labels2 <- getinfo(dtrain, 'label')
-#' stopifnot(all(labels2 == 1-labels))
-#' @rdname setinfo
-#' @export
-setinfo <- function(object, ...){
-  UseMethod("setinfo")
-}
-
-#' @param object Object of class "xgb.DMatrix"
-#' @param name the name of the field to get
-#' @param info the specific field of information to set
-#' @param ... other parameters
-#' @rdname setinfo
-#' @method setinfo xgb.DMatrix
-setMethod("setinfo", signature = "xgb.DMatrix",
-          definition = function(object, name, info) {
-            xgb.setinfo(object, name, info)
-          })
diff --git a/R-package/R/slice.xgb.DMatrix.R b/R-package/R/slice.xgb.DMatrix.R
deleted file mode 100644
index 4626c2b4d..000000000
--- a/R-package/R/slice.xgb.DMatrix.R
+++ /dev/null
@@ -1,44 +0,0 @@
-setClass('xgb.DMatrix')
-
-#' Get a new DMatrix containing the specified rows of
-#' orginal xgb.DMatrix object
-#'
-#' Get a new DMatrix containing the specified rows of
-#' orginal xgb.DMatrix object
-#' 
-#' @examples
-#' data(agaricus.train, package='xgboost')
-#' train <- agaricus.train
-#' dtrain <- xgb.DMatrix(train$data, label=train$label)
-#' dsub <- slice(dtrain, 1:3)
-#' @rdname slice
-#' @export
-slice <- function(object, ...){
-    UseMethod("slice")
-}
-
-#' @param object Object of class "xgb.DMatrix"
-#' @param idxset a integer vector of indices of rows needed
-#' @param ... other parameters
-#' @rdname slice
-#' @method slice xgb.DMatrix
-setMethod("slice", signature = "xgb.DMatrix",
-          definition = function(object, idxset, ...) {
-              if (class(object) != "xgb.DMatrix") {
-                  stop("slice: first argument dtrain must be xgb.DMatrix")
-              }
-              ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset,
-                           PACKAGE = "xgboost")
-
-              attr_list <- attributes(object)
-              nr <- xgb.numrow(object)
-              len <- sapply(attr_list,length)
-              ind <- which(len == nr)
-              if (length(ind) > 0) {
-                  nms <- names(attr_list)[ind]
-                  for (i in 1:length(ind)) {
-                    attr(ret,nms[i]) <- attr(object,nms[i])[idxset]
-                  }
-              }
-              return(structure(ret, class = "xgb.DMatrix"))
-          })
diff --git a/R-package/man/nrow-xgb.DMatrix-method.Rd b/R-package/man/nrow-xgb.DMatrix-method.Rd
deleted file mode 100644
index 1fd52b9c1..000000000
--- a/R-package/man/nrow-xgb.DMatrix-method.Rd
+++ /dev/null
@@ -1,23 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/nrow.xgb.DMatrix.R
-\docType{methods}
-\name{nrow,xgb.DMatrix-method}
-\alias{nrow,xgb.DMatrix-method}
-\title{Number of xgb.DMatrix rows}
-\usage{
-\S4method{nrow}{xgb.DMatrix}(x)
-}
-\arguments{
-\item{x}{Object of class \code{xgb.DMatrix}}
-}
-\description{
-\code{nrow} return the number of rows present in the \code{xgb.DMatrix}.
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-stopifnot(nrow(dtrain) == nrow(train$data))
-
-}
-
diff --git a/R-package/man/predict-xgb.Booster-method.Rd b/R-package/man/predict-xgb.Booster-method.Rd
deleted file mode 100644
index 341ced8c6..000000000
--- a/R-package/man/predict-xgb.Booster-method.Rd
+++ /dev/null
@@ -1,53 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/predict.xgb.Booster.R
-\docType{methods}
-\name{predict,xgb.Booster-method}
-\alias{predict,xgb.Booster-method}
-\title{Predict method for eXtreme Gradient Boosting model}
-\usage{
-\S4method{predict}{xgb.Booster}(object, newdata, missing = NA,
-  outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE)
-}
-\arguments{
-\item{object}{Object of class "xgb.Boost"}
-
-\item{newdata}{takes \code{matrix}, \code{dgCMatrix}, local data file or 
-\code{xgb.DMatrix}.}
-
-\item{missing}{Missing is only used when input is dense matrix, pick a float 
-value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.}
-
-\item{outputmargin}{whether the prediction should be shown in the original
-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.}
-
-\item{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.}
-
-\item{predleaf}{whether predict leaf index instead. If set to TRUE, the output will be a matrix object.}
-}
-\description{
-Predicted values based on xgboost model object.
-}
-\details{
-The option \code{ntreelimit} purpose is to let the user train a model with lots 
-of trees but use only the first trees for prediction to avoid overfitting 
-(without having to train a new model with less trees).
-
-The option \code{predleaf} purpose is inspired from §3.1 of the paper 
-\code{Practical Lessons from Predicting Clicks on Ads at Facebook}.
-The idea is to use the model as a generator of new features which capture non linear link 
-from original features.
-}
-\examples{
-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, nthread = 2, nround = 2,objective = "binary:logistic")
-pred <- predict(bst, test$data)
-}
-
diff --git a/R-package/man/predict-xgb.Booster.handle-method.Rd b/R-package/man/predict-xgb.Booster.handle-method.Rd
deleted file mode 100644
index 34454e555..000000000
--- a/R-package/man/predict-xgb.Booster.handle-method.Rd
+++ /dev/null
@@ -1,18 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/predict.xgb.Booster.handle.R
-\docType{methods}
-\name{predict,xgb.Booster.handle-method}
-\alias{predict,xgb.Booster.handle-method}
-\title{Predict method for eXtreme Gradient Boosting model handle}
-\usage{
-\S4method{predict}{xgb.Booster.handle}(object, ...)
-}
-\arguments{
-\item{object}{Object of class "xgb.Boost.handle"}
-
-\item{...}{Parameters pass to \code{predict.xgb.Booster}}
-}
-\description{
-Predicted values based on xgb.Booster.handle object.
-}
-
diff --git a/R-package/man/slice.Rd b/R-package/man/slice.Rd
deleted file mode 100644
index b17722115..000000000
--- a/R-package/man/slice.Rd
+++ /dev/null
@@ -1,31 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/slice.xgb.DMatrix.R
-\docType{methods}
-\name{slice}
-\alias{slice}
-\alias{slice,xgb.DMatrix-method}
-\title{Get a new DMatrix containing the specified rows of
-orginal xgb.DMatrix object}
-\usage{
-slice(object, ...)
-
-\S4method{slice}{xgb.DMatrix}(object, idxset, ...)
-}
-\arguments{
-\item{object}{Object of class "xgb.DMatrix"}
-
-\item{...}{other parameters}
-
-\item{idxset}{a integer vector of indices of rows needed}
-}
-\description{
-Get a new DMatrix containing the specified rows of
-orginal xgb.DMatrix object
-}
-\examples{
-data(agaricus.train, package='xgboost')
-train <- agaricus.train
-dtrain <- xgb.DMatrix(train$data, label=train$label)
-dsub <- slice(dtrain, 1:3)
-}
-