R/predict.deeptree.R

In RajeshB24/deepdive: Deep Learning for General Purpose

#' Predict Function for Deeptree
#'
#' @param object deeptree model object
#' @param newData pass dataframe for prediction
#' @param treeLeaves  Pass vector with tree leaves if fit outside deeptree. default NA.
#' @param stackPred   Pass stackPred of prediction data if it was passed in deeptree
#' @param ... further arguments passed to or from other methods.
#'
#'
#' @return returns predictions vector or dataframe
#' @export
#' @export predict.deeptree
#' @importFrom  data.table rbindlist
#' @import rpart
#' @importFrom treeClust rpart.predict.leaves
#' @importFrom graphics barplot
#' @importFrom stats formula predict runif
#'

predict.deeptree= function (object, newData, treeLeaves = NA, stackPred = NA, ...)
{
  row.names(newData) <- 1:nrow(newData)
  if (!inherits(object, "deeptree"))
    stop("Not a legitimate \"deeptree\" object")
  useStackPred = object$useStackPred
  if (length(useStackPred) < length(object$treeLeavesGroup)) {
    useStackPred <- rep(useStackPred, length(object$treeLeavesGroup))
  }
  if (sum(useStackPred) > 0) {
    if (is.na(sum(stackPred))) {
      warning("stackPred is required")
    }
  }
  treeLeavesGroup <- object[["treeLeavesGroup"]]
  modelGroup <- object[["modelGroup"]]
  xCols = object[["xColList"]]
  if (object[["preBuiltTree"]]) {
    if (is.na(treeLeaves)) {
      warning("missing treeLeaves attribute")
    }
  }else {
    treeLeaves = rpart.predict.leaves(object[["treeMod"]],
                                      newData)
  }
  newDataSplit <- lapply(treeLeavesGroup[treeLeavesGroup %in%
                                           unique(treeLeaves)], function(tp) {
                                             sdata <- data.frame(newData[treeLeaves == tp, ])
                                             names(sdata) <- names(newData)
                                             sdata
                                           })
  if (sum(useStackPred) > 0) {
    stackPred <- lapply(treeLeavesGroup, function(tp) {
      stackPred[treeLeaves == tp, ]
    })
  }


  modelGroup = modelGroup[ treeLeavesGroup %in% unique(treeLeaves)]

  predlist <- lapply(1:length(unique(treeLeaves)), function(mg) {
    if (!useStackPred[[mg]]) {
      if (class(modelGroup[[mg]]) == "deepnet") {
        if (length(xCols[[mg]]) == 1) {
          predict.deepnet(modelGroup[[mg]], data.frame(newDataSplit[[mg]][,
                                                                          xCols[[mg]]]))
        }
        else {
          predict.deepnet(modelGroup[[mg]], newDataSplit[[mg]][,
                                                               xCols[[mg]]])
        }
      }
      else {
        ypred = data.frame(ypred = rep(modelGroup[[mg]],
                                       nrow(newDataSplit[[mg]])))
        ypred
      }
    }
    else {
      stackPred[[mg]]
    }
  })
  predlist <- lapply(1:length(predlist), function(pl) {
    data.frame(cbind(predlist[[pl]], rowname = as.numeric(row.names(newDataSplit[[pl]]))))
  })
  ypred <- data.frame(rbindlist(predlist, use.names = T, fill = T))
  ypred[is.na(ypred)] = 0
  ypred <- ypred[order(ypred$rowname), ]

  row.names(ypred) <- 1:nrow(ypred)

  if (object$modelType == "regress") {
    names(ypred)[1] <- "ypred"
  }
  else if (object$modelType == "multiClass") {
    ypred$ypred <- stringr::str_remove_all(ypred$ypred, "y..s.._")
    names(ypred) <- stringr::str_remove_all(names(ypred),
                                            "y..s.._")
    if (length(unique(ypred$ypred)) > sum(!names(ypred) %in%
                                          c("rowname", "ypred"))) {
      for (coli in unique(ypred$ypred)[!unique(ypred$ypred) %in%
                                       names(ypred)]) {
        ypred[, coli] <- ifelse(ypred$ypred == coli,
                                1, 0)
      }
    }
    colnames <- names(ypred)[!names(ypred) %in% "rowname"]
    ypred <- data.frame(ypred[, !names(ypred) %in% "rowname"])
    names(ypred) <- colnames
    ProbCols <- names(ypred)[!names(ypred) %in% "ypred"]
    ypred$ProbSum <- rowSums(ypred[, ProbCols])
    for (icol in 1:length(ProbCols)) {
      ypred[, icol] <- ifelse(ypred$ProbSum == 0 & as.character(ypred[,
                                                                      icol]) == as.character(ypred$ypred), 1, as.character(ypred[,
                                                                                                                                 icol]))
    }
  }
  if (object$modelType == "regress") {
    ypred <- ypred["ypred"]
  }
  else {
    finColNames <- names(ypred)
    finColNames <- finColNames[!finColNames %in% c("rowname",
                                                   "ProbSum")]
    ypred = ypred[, finColNames]
    names(ypred) <- finColNames
  }
  return(data.frame(ypred))
}