getCores <- function(){
  cores <- NA
  if (requireNamespace("parallel", quietly = TRUE)){
    cores <- parallel::detectCores()
  }

  if (is.na(cores)){
    message("Unable to detect how many cores are available; defaulting to only using one. Feel free to override this by pre-specifying the cores argument.")
    cores <- 1
  }

  return(cores)
}

#' Train Random Forests
#'
#' Trains the random forest. The type of response the random forest can be
#' trained on varies depending on the \code{splitFinder},
#' \code{nodeResponseCombiner}, and the \code{forestResponseCombiner}
#' parameters. Make sure these are compatible with each other, and with the
#' response you plug in. \code{splitFinder} should work on the responses you are
#' providing; \code{nodeResponseCombiner} should combine these responses into
#' some intermediate product, and \code{forestResponseCombiner} combines these
#' intermediate products into the final output product.
#'
#' @param responses An R list of the responses. See \code{\link{CR_Response}}
#'   for an example function.
#' @param covariateData A data.frame containing only the columns of the
#'   covariates you wish to use in your training (unless you're using the
#'   \code{formula} version of \code{train}, in which case it should contain the
#'   response as well).
#' @param splitFinder A split finder that's used to score splits in the random
#'   forest training algorithm. See \code{\link{Competing Risk Split Finders}}
#'   or \code{\link{WeightedVarianceSplitFinder}}. If you don't specify one,
#'   this function tries to pick one based on the response. For
#'   \code{\link{CR_Response}} wihtout censor times, it will pick a
#'   \code{\link{LogRankSplitFinder}}; while if censor times were provided it
#'   will pick \code{\link{GrayLogRankSplitFinder}}; for integer or numeric
#'   responses it picks a \code{\link{WeightedVarianceSplitFinder}}.
#' @param nodeResponseCombiner A response combiner that's used to combine
#'   responses for each terminal node in a tree (regression example; average the
#'   observations in each tree into a single number). See
#'   \code{\link{CompetingRiskResponseCombiner}} or
#'   \code{\link{MeanResponseCombiner}}. If you don't specify one, this function
#'   tries to pick one based on the response. For \code{\link{CR_Response}} it
#'   picks a \code{\link{CompetingRiskResponseCombiner}}; for integer or numeric
#'   responses it picks a \code{\link{MeanResponseCombiner}}.
#' @param forestResponseCombiner A response combiner that's used to combine
#'   predictions across trees into one final result (regression example; average
#'   the prediction of each tree into a single number). See
#'   \code{\link{CompetingRiskFunctionCombiner}} or
#'   \code{\link{MeanResponseCombiner}}. If you don't specify one, this function
#'   tries to pick one based on the response. For \code{\link{CR_Response}} it
#'   picks a \code{\link{CompetingRiskFunctionCombiner}}; for integer or numeric
#'   responses it picks a \code{\link{MeanResponseCombiner}}.
#' @param ntree An integer that specifies how many trees should be trained.
#' @param numberOfSplits A tuning parameter specifying how many random splits
#'   should be tried for a covariate; a value of 0 means all splits will be
#'   tried (with an exception for factors, who might have too many splits to
#'   feasibly compute).
#' @param mtry A tuning parameter specifying how many covariates will be
#'   randomly chosen to be tried in the splitting process. This value must be at
#'   least 1.
#' @param nodeSize The algorithm will not attempt to split a node that has
#'   observations less than 2*\code{nodeSize}; this results in terminal nodes
#'   having a size of roughly \code{nodeSize} (true sizes may be both smaller or
#'   greater). This value must be at least 1.
#' @param maxNodeDepth This parameter is analogous to \code{nodeSize} in that it
#'   helps keep trees shorter; by default maxNodeDepth is an extremely high
#'   number and tree depth is controlled by \code{nodeSize}.
#' @param splitPureNodes This parameter determines whether the algorithm will
#'   split a pure node. If set to FALSE, then before every split it will check
#'   that every response is the same, and if so, not split. If set to TRUE it
#'   forgoes that check and just splits. Prediction accuracy won't change under
#'   any sensible \code{nodeResponseCombiner} as all terminal nodes from a split
#'   pure node should give the same prediction, so this parameter only affects
#'   performance. If your response is continuous you'll likely experience faster
#'   train times by setting it to TRUE. Default value is TRUE.
#' @param savePath If set, this parameter will save each tree of the random
#'   forest in this directory as the forest is trained. Use this parameter if
#'   you need to save memory while training. See also \code{\link{load_forest}}
#' @param savePath.overwrite This parameter controls the behaviour for what
#'   happens if \code{savePath} is pointing to an existing directory. If set to
#'   \code{warn} (default) then \code{train} refuses to proceed. If set to
#'   \code{delete} then all the contents in that folder are deleted for the new
#'   forest to be trained. Note that all contents are deleted, even those files
#'   not related to \code{largeRCRF}. Use only if you're sure it's safe. If set
#'   to \code{merge}, then the files describing the forest (such as its
#'   parameters) are overwritten but the saved trees are not. The algorithm
#'   assumes (without checking) that the existing trees are from a previous run
#'   and starts from where it left off. This option is useful if recovering from
#'   a crash.
#' @param cores This parameter specifies how many trees will be simultaneously
#'   trained. By default the package attempts to detect how many cores you have
#'   by using the \code{parallel} package, and using all of them. You may
#'   specify a lower number if you wish. It is not recommended to specify a
#'   number greater than the number of available cores as this will hurt
#'   performance with no available benefit.
#' @param randomSeed This parameter specifies a random seed if reproducible,
#'   deterministic forests are desired. The number o1
#' @export
#' @return A \code{JRandomForest} object. You may call \code{predict} or
#'   \code{print} on it.
#' @seealso \code{\link{predict.JRandomForest}}
#' @note If saving memory is a concern, you can replace \code{covariateData}
#'   with an environment containing one element called \code{data} as the actual
#'   dataset. After the data has been imported into Java, but before the forest
#'   training begins, the dataset in the environment is deleted, freeing up
#'   memory in R.
#' @examples
#' # Regression Example
#' x1 <- rnorm(1000)
#' x2 <- rnorm(1000)
#' y <- 1 + x1 + x2 + rnorm(1000)
#'
#' data <- data.frame(x1, x2, y)
#' forest <- train(y ~ x1 + x2, data, WeightedVarianceSplitFinder(), MeanResponseCombiner(), MeanResponseCombiner(), ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5)
#'
#' # Fix x2 to be 0
#' newData <- data.frame(x1 = seq(from=-2, to=2, by=0.5), x2 = 0)
#' ypred <- predict(forest, newData)
#'
#' plot(ypred ~ newData$x1, type="l")
#'
#' # Competing Risk Example
#' x1 <- abs(rnorm(1000))
#' x2 <- abs(rnorm(1000))
#'
#' T1 <- rexp(1000, rate=x1)
#' T2 <- rweibull(1000, shape=x1, scale=x2)
#' C <- rexp(1000)
#' u <- pmin(T1, T2, C)
#' delta <- ifelse(u==T1, 1, ifelse(u==T2, 2, 0))
#'
#' data <- data.frame(x1, x2)
#'
#' forest <- train(CompetingRiskResponses(delta, u) ~ x1 + x2, data,
#' LogRankSplitFinder(1:2), CompetingRiskResponseCombiner(1:2), CompetingRiskFunctionCombiner(1:2), ntree=100, numberOfSplits=5, mtry=1, nodeSize=10)
#' newData <- data.frame(x1 = c(-1, 0, 1), x2 = 0)
#' ypred <- predict(forest, newData)
train <- function(x, ...) UseMethod("train")



#' @rdname train
#' @export
train.default <- function(responses, covariateData, splitFinder = splitFinderDefault(responses), nodeResponseCombiner = nodeResponseCombinerDefault(responses), forestResponseCombiner = forestResponseCombinerDefault(responses), ntree, numberOfSplits, mtry, nodeSize, maxNodeDepth = 100000, splitPureNodes=TRUE, savePath=NULL, savePath.overwrite=c("warn", "delete", "merge"), cores = getCores(), randomSeed = NULL){
  
  # Some quick checks on parameters
  ntree <- as.integer(ntree)
  numberOfSplits <- as.integer(numberOfSplits)
  mtry <- as.integer(mtry)
  nodeSize <- as.integer(nodeSize)
  maxNodeDepth <- as.integer(maxNodeDepth)
  cores <- as.integer(cores)

  if (ntree <= 0){
    stop("ntree must be strictly positive.")
  }
  if (numberOfSplits < 0){
    stop("numberOfSplits cannot be negative.")
  }
  if (mtry <= 0){
    stop("mtry must be strictly positive. If you want to try all covariates, you can set it to be very large.")
  }
  if (nodeSize <= 0){
    stop("nodeSize must be strictly positive.")
  }
  if (maxNodeDepth <= 0){
    stop("maxNodeDepth must be strictly positive")
  }
  if (cores <= 0){
    stop("cores must be strictly positive")
  }
  
  if(is.null(savePath.overwrite) | length(savePath.overwrite)==0 | !(savePath.overwrite[1] %in% c("warn", "delete", "merge"))){
    stop("savePath.overwrite must be one of c(\"warn\", \"delete\", \"merge\")")
  }


  if(class(nodeResponseCombiner) != "ResponseCombiner"){
    stop("nodeResponseCombiner must be a ResponseCombiner")
  }
  if(class(splitFinder) != "SplitFinder"){
    stop("splitFinder must be a SplitFinder")
  }
  if(class(forestResponseCombiner) != "ResponseCombiner"){
    stop("forestResponseCombiner must be a ResponseCombiner")
  }
  
  if(class(covariateData)=="environment"){
    if(is.null(covariateData$data)){
      stop("When providing an environment with the dataset, the environment must contain an item called 'data'")
    }
    dataset <- loadData(covariateData$data, colnames(covariateData$data), responses)
    covariateData$data <- NULL # save memory, hopefully
    gc() # explicitly try to save memory
  }
  else{
    dataset <- loadData(covariateData, colnames(covariateData), responses)
  }

  
  
  treeTrainer <- createTreeTrainer(responseCombiner=nodeResponseCombiner,
                                   splitFinder=splitFinder,
                                   covariateList=dataset$covariateList,
                                   numberOfSplits=numberOfSplits,
                                   nodeSize=nodeSize,
                                   maxNodeDepth=maxNodeDepth,
                                   mtry=mtry,
                                   splitPureNodes=splitPureNodes)

  forestTrainer <- createForestTrainer(treeTrainer=treeTrainer,
                                       covariateList=dataset$covariateList,
                                       treeResponseCombiner=forestResponseCombiner,
                                       dataset=dataset$dataset,
                                       ntree=ntree,
                                       randomSeed=randomSeed,
                                       saveTreeLocation=savePath)
  
  params <- list(
    splitFinder=splitFinder,
    nodeResponseCombiner=nodeResponseCombiner,
    forestResponseCombiner=forestResponseCombiner,
    ntree=ntree,
    numberOfSplits=numberOfSplits,
    mtry=mtry,
    nodeSize=nodeSize,
    splitPureNodes=splitPureNodes,
    maxNodeDepth = maxNodeDepth,
    savePath=savePath
  )
  
  # We'll be saving an offline version of the forest
  if(!is.null(savePath)){
    
    if(file.exists(savePath)){ # we might have to remove the folder or display an error
      
      if(savePath.overwrite[1] == "warn"){
        stop(paste(savePath, "already exists; will not modify it. Please remove/rename it or set the savePath.overwrite to either 'delete' or 'merge'"))
      } else if(savePath.overwrite[1] == "delete"){
        unlink(savePath)
      }
      
    }

    if(savePath.overwrite[1] != "merge"){
      dir.create(savePath)
    }
    
    # First save forest components (so that if the training crashes mid-way through it can theoretically be recovered by the user)
    saveForestComponents(savePath, 
                         covariateList=dataset$covariateList,
                         params=params,
                         forestCall=match.call())
    
    if(cores > 1){
      .jcall(forestTrainer, "V", "trainParallelOnDisk", as.integer(cores))
    } else {
      .jcall(forestTrainer, "V", "trainSerialOnDisk")
    }
    
    # Need to now load forest trees back into memory
    forest.java <- .jcall(.class_DataUtils, makeResponse(.class_Forest), "loadForest", savePath, forestResponseCombiner$javaObject)
    
    
  }
  else{ # save directly into memory
    if(cores > 1){
      forest.java <- .jcall(forestTrainer, makeResponse(.class_Forest), "trainParallelInMemory", as.integer(cores))
    } else {
      forest.java <- .jcall(forestTrainer, makeResponse(.class_Forest), "trainSerialInMemory")
    }
  }
  

  

  forestObject <- list(call=match.call(), params=params, javaObject=forest.java, covariateList=dataset$covariateList)

  # TODO - remove redundant code if tests pass
  #forestObject$params <- list(
  #  splitFinder=splitFinder,
  #  nodeResponseCombiner=nodeResponseCombiner,
  #  forestResponseCombiner=forestResponseCombiner,
  #  ntree=ntree,
  #  numberOfSplits=numberOfSplits,
  #  mtry=mtry,
  #  nodeSize=nodeSize,
  #  splitPureNodes=splitPureNodes,
  #  maxNodeDepth = maxNodeDepth,
  #  savePath=savePath
  #)

  class(forestObject) <- "JRandomForest"
  return(forestObject)

}




#' @rdname train
#' @export
#' @param formula You may specify the response and covariates as a formula instead; make sure the response in the formula is still properly constructed; see \code{responses}
train.formula <- function(formula, covariateData, ...){
  
  # Having an R copy of the data loaded at the same time can be wasteful; we
  # also allow users to provide an environment of the data which gets removed
  # after being imported into Java
  env <- NULL
  if(class(covariateData) == "environment"){
    if(is.null(covariateData$data)){
      stop("When providing an environment with the dataset, the environment must contain an item called 'data'")
    }
    
    env <- covariateData
    covariateData <- env$data
  }

  yVar <- formula[[2]]

  responses <- NULL
  variablesToDrop <- character(0)
  
  # yVar is a call object; as.character(yVar) will be the different components, including the parameters.
  # if the length of yVar is > 1 then it's a function call. If the length is 1, and it's not in covariateData, 
  # then we also need to explicitly evaluate it
  if(class(yVar)=="call" || !(as.character(yVar) %in% colnames(covariateData))){
    # yVar is a function like CompetingRiskResponses
    responses <- eval(expr=yVar, envir=covariateData)
    
    if(class(formula[[3]]) == "name" && as.character(formula[[3]])=="."){
      # do any of the variables match data in covariateData? We need to track that so we can drop them later
      variablesToDrop <- as.character(yVar)[as.character(yVar) %in% names(covariateData)]
    }
    
    formula[[2]] <- NULL

  } else if(class(yVar)=="name"){ # and implicitly yVar is contained in covariateData
    variablesToDrop <- as.character(yVar)
  }

  # Includes responses which we may need to later cut out
  mf <- model.frame(formula=formula, data=covariateData, na.action=na.pass)

  if(is.null(responses)){
    responses <- model.response(mf)
  }

  # remove any response variables
  mf <- mf[,!(names(mf) %in% variablesToDrop), drop=FALSE]
  
  # If environment was provided instead of data
  if(!is.null(env)){
    env$data <- mf
    rm(covariateData)
    forest <- train.default(responses, env, ...)
  } else{
    forest <- train.default(responses, mf, ...)
  }
  

  
  forest$call <- match.call()
  forest$formula <- formula

  return(forest)
}

createForestTrainer <- function(treeTrainer, covariateList, treeResponseCombiner, dataset, ntree, randomSeed, saveTreeLocation){
  builderClassReturned <- makeResponse(.class_ForestTrainer_Builder)

  builder <- .jcall(.class_ForestTrainer, builderClassReturned, "builder")

  builder <- .jcall(builder, builderClassReturned, "treeTrainer", treeTrainer)
  builder <- .jcall(builder, builderClassReturned, "covariates", covariateList)
  builder <- .jcall(builder, builderClassReturned, "treeResponseCombiner", treeResponseCombiner$javaObject)
  builder <- .jcall(builder, builderClassReturned, "data", dataset)
  builder <- .jcall(builder, builderClassReturned, "ntree", as.integer(ntree))
  builder <- .jcall(builder, builderClassReturned, "displayProgress", TRUE)
  
  if(!is.null(randomSeed)){
    builder <- .jcall(builder, builderClassReturned, "randomSeed", .jlong(randomSeed))
  }
  else{
    builder <- .jcall(builder, builderClassReturned, "randomSeed", .jlong(as.integer(Sys.time())))
  }
  
  if(!is.null(saveTreeLocation)){
    builder <- .jcall(builder, builderClassReturned, "saveTreeLocation", saveTreeLocation)
  }
  

  forestTrainer <- .jcall(builder, makeResponse(.class_ForestTrainer), "build")
  return(forestTrainer)
}

createTreeTrainer <- function(responseCombiner, splitFinder, covariateList, numberOfSplits, nodeSize, maxNodeDepth, mtry, splitPureNodes){
  builderClassReturned <- makeResponse(.class_TreeTrainer_Builder)

  builder <- .jcall(.class_TreeTrainer, builderClassReturned, "builder")

  builder <- .jcall(builder, builderClassReturned, "responseCombiner", responseCombiner$javaObject)
  builder <- .jcall(builder, builderClassReturned, "splitFinder", splitFinder$javaObject)
  builder <- .jcall(builder, builderClassReturned, "covariates", covariateList)
  builder <- .jcall(builder, builderClassReturned, "numberOfSplits", as.integer(numberOfSplits))
  builder <- .jcall(builder, builderClassReturned, "nodeSize", as.integer(nodeSize))
  builder <- .jcall(builder, builderClassReturned, "maxNodeDepth", as.integer(maxNodeDepth))
  builder <- .jcall(builder, builderClassReturned, "mtry", as.integer(mtry))
  builder <- .jcall(builder, builderClassReturned, "checkNodePurity", !splitPureNodes)

  treeTrainer <- .jcall(builder, makeResponse(.class_TreeTrainer), "build")
  return(treeTrainer)
}