# Construct a Booster from cachelist
# internal utility function
xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
  if (typeof(cachelist) != "list") {
    stop("xgb.Booster only accepts list of DMatrix as cachelist")
  }
  for (dm in cachelist) {
    if (class(dm) != "xgb.DMatrix") {
      stop("xgb.Booster only accepts list of DMatrix as cachelist")
    }
  }
  handle <- .Call("XGBoosterCreate_R", cachelist, PACKAGE = "xgboost")
  if (!is.null(modelfile)) {
    if (typeof(modelfile) == "character") {
      .Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
    } else if (typeof(modelfile) == "raw") {
      .Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")
    } else {
      stop("modelfile must be character or raw vector")
    }
  }
  class(handle) <- "xgb.Booster.handle"
  if (length(params) > 0) {
    xgb.parameters(handle) <- params
  }
  return(handle)
}

# Convert xgb.Booster.handle to xgb.Booster
# internal utility function
xgb.handleToBooster <- function(handle, raw = NULL)
{
  bst <- list(handle = handle, raw = raw)
  class(bst) <- "xgb.Booster"
  return(bst)
}

# Return a verified to be valid handle out of either xgb.Booster.handle or xgb.Booster
# internal utility function
xgb.get.handle <- function(object) {
  handle <- switch(class(object)[1],
    xgb.Booster = object$handle,
    xgb.Booster.handle = object,
    stop("argument must be of either xgb.Booster or xgb.Booster.handle class")
  )
  if (is.null(handle) | .Call("XGCheckNullPtr_R", handle, PACKAGE="xgboost")) {
    stop("invalid xgb.Booster.handle")
  }
  handle
}

# Check whether an xgb.Booster object is complete
# internal utility function
xgb.Booster.check <- function(bst, saveraw = TRUE)
{
  isnull <- is.null(bst$handle)
  if (!isnull) {
    isnull <- .Call("XGCheckNullPtr_R", bst$handle, PACKAGE="xgboost")
  }
  if (isnull) {
    bst$handle <- xgb.Booster(modelfile = bst$raw)
  } else {
    if (is.null(bst$raw) && saveraw)
      bst$raw <- xgb.save.raw(bst$handle)
  }
  return(bst)
}

#' Predict method for eXtreme Gradient Boosting model
#' 
#' Predicted values based on either xgboost model or model handle object.
#' 
#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}
#' @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.
#' @param ... Parameters pass to \code{predict.xgb.Booster}
#' 
#' @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$data)
#' @rdname predict.xgb.Booster
#' @export
predict.xgb.Booster <- function(object, newdata, missing = NA,
    outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
  if (class(object) != "xgb.Booster"){
    stop("predict: model in prediction must be of class xgb.Booster")
  } else {
    object <- xgb.Booster.check(object, saveraw = FALSE)
  }
  if (class(newdata) != "xgb.DMatrix") {
    newdata <- xgb.DMatrix(newdata, missing = missing)
  }
  if (is.null(ntreelimit)) {
    ntreelimit <- 0
  } else {
    if (ntreelimit < 1){
      stop("predict: ntreelimit must be equal to or greater than 1")
    }
  }
  option <- 0
  if (outputmargin) {
    option <- option + 1
  }
  if (predleaf) {
    option <- option + 2
  }
  ret <- .Call("XGBoosterPredict_R", object$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)
}

#' @rdname predict.xgb.Booster
#' @export
predict.xgb.Booster.handle <- function(object, ...) {

  bst <- xgb.handleToBooster(object)

  ret <- predict(bst, ...)
  return(ret)
}


#' Accessors for serializable attributes of a model.
#'
#' These methods allow to manipulate the key-value attribute strings of an xgboost model.
#'
#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}.
#' @param name a non-empty character string specifying which attribute is to be accessed.
#' @param value a value of an attribute for \code{xgb.attr<-}; for \code{xgb.attributes<-} 
#'        it's a list (or an object coercible to a list) with the names of attributes to set 
#'        and the elements corresponding to attribute values. 
#'        Non-character values are converted to character.
#'        When attribute value is not a scalar, only the first index is used.
#'        Use \code{NULL} to remove an attribute.
#'
#' @details
#' The primary purpose of xgboost model attributes is to store some meta-data about the model.
#' Note that they are a separate concept from the object attributes in R.
#' Specifically, they refer to key-value strings that can be attached to an xgboost model,
#' stored together with the model's binary representation, and accessed later 
#' (from R or any other interface).
#' In contrast, any R-attribute assigned to an R-object of \code{xgb.Booster} class
#' would not be saved by \code{xgb.save} because an xgboost model is an external memory object
#' and its serialization is handled extrnally.
#' Also, setting an attribute that has the same name as one of xgboost's parameters wouldn't 
#' change the value of that parameter for a model. 
#' Use \code{\link{`xgb.parameters<-`}} to set or change model parameters.
#' 
#' The attribute setters would usually work more efficiently for \code{xgb.Booster.handle}
#' than for \code{xgb.Booster}, since only just a handle (pointer) would need to be copied.
#' 
#' The \code{xgb.attributes<-} setter either updates the existing or adds one or several attributes, 
#' but doesn't delete the existing attributes which don't have their names in \code{names(attributes)}.
#' 
#' @return
#' \code{xgb.attr} returns either a string value of an attribute 
#' or \code{NULL} if an attribute wasn't stored in a model.
#' 
#' \code{xgb.attributes} returns a list of all attribute stored in a model 
#' or \code{NULL} if a model has no stored attributes.
#' 
#' @examples
#' data(agaricus.train, package='xgboost')
#' train <- agaricus.train
#'
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#'                eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
#'
#' xgb.attr(bst, "my_attribute") <- "my attribute value"
#' print(xgb.attr(bst, "my_attribute"))
#' xgb.attributes(bst) <- list(a = 123, b = "abc")
#'
#' xgb.save(bst, 'xgb.model')
#' bst1 <- xgb.load('xgb.model')
#' print(xgb.attr(bst1, "my_attribute"))
#' print(xgb.attributes(bst1))
#' 
#' # deletion:
#' xgb.attr(bst1, "my_attribute") <- NULL
#' print(xgb.attributes(bst1))
#' xgb.attributes(bst1) <- list(a = NULL, b = NULL)
#' print(xgb.attributes(bst1))
#' 
#' @rdname xgb.attr
#' @export
xgb.attr <- function(object, name) {
  if (is.null(name) || nchar(as.character(name[1])) == 0) stop("invalid attribute name")
  handle <- xgb.get.handle(object)
  .Call("XGBoosterGetAttr_R", handle, as.character(name[1]), PACKAGE="xgboost")
}

#' @rdname xgb.attr
#' @export
`xgb.attr<-` <- function(object, name, value) {
  if (is.null(name) || nchar(as.character(name[1])) == 0) stop("invalid attribute name")
  handle <- xgb.get.handle(object)
  if (!is.null(value)) {
    # Coerce the elements to be scalar strings.
    # Q: should we warn user about non-scalar elements?
    value <- as.character(value[1])
  }
  .Call("XGBoosterSetAttr_R", handle, as.character(name[1]), value, PACKAGE="xgboost")
  if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
    object$raw <- xgb.save.raw(object$handle)
  }
  object
}

#' @rdname xgb.attr
#' @export
xgb.attributes <- function(object) {
  handle <- xgb.get.handle(object)
  attr_names <- .Call("XGBoosterGetAttrNames_R", handle, PACKAGE="xgboost")
  if (is.null(attr_names)) return(NULL)
  res <- lapply(attr_names, function(x) {
    .Call("XGBoosterGetAttr_R", handle, x, PACKAGE="xgboost")
  })
  names(res) <- attr_names
  res
}

#' @rdname xgb.attr
#' @export
`xgb.attributes<-` <- function(object, value) {
  a <- as.list(value)
  if (is.null(names(a)) || any(nchar(names(a)) == 0)) {
    stop("attribute names cannot be empty strings")
  }
  # Coerce the elements to be scalar strings.
  # Q: should we warn a user about non-scalar elements?
  a <- lapply(a, function(x) {
    if (is.null(x)) return(NULL)
    as.character(x[1])
  })
  handle <- xgb.get.handle(object)
  for (i in seq_along(a)) {
    .Call("XGBoosterSetAttr_R", handle, names(a[i]), a[[i]], PACKAGE="xgboost")
  }
  if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
    object$raw <- xgb.save.raw(object$handle)
  }
  object
}

#' Accessors for model parameters.
#'
#' Only the setter for xgboost parameters is currently implemented.
#'
#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}.
#' @param value a list (or an object coercible to a list) with the names of parameters to set
#'        and the elements corresponding to parameter values.
#'
#' @details
#' Note that the setter would usually work more efficiently for \code{xgb.Booster.handle}
#' than for \code{xgb.Booster}, since only just a handle would need to be copied.
#' 
#' @examples
#' data(agaricus.train, package='xgboost')
#' train <- agaricus.train
#'
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#'                eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
#'
#' xgb.parameters(bst) <- list(eta = 0.1)
#' 
#' @rdname xgb.parameters
#' @export
`xgb.parameters<-` <- function(object, value) {
  if (length(value) == 0) return(object)
  p <- as.list(value)
  if (is.null(names(p)) || any(nchar(names(p)) == 0)) {
    stop("parameter names cannot be empty strings")
  }
  names(p) <- gsub("\\.", "_", names(p))
  p <- lapply(p, function(x) as.character(x)[1])
  handle <- xgb.get.handle(object)
  for (i in seq_along(p)) {
    .Call("XGBoosterSetParam_R", handle, names(p[i]), p[[i]], PACKAGE = "xgboost")
  }
  if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
    object$raw <- xgb.save.raw(object$handle)
  }
  object
}