Predictor <- R6Class(
  "lgb.Predictor",
  cloneable = FALSE,
  public = list(
    finalize = function() {
      if (private$need_free_handle && !lgb.is.null.handle(private$handle)) {
        cat("free booster handle\n")
        lgb.call("LGBM_BoosterFree_R", ret = NULL, private$handle)
        private$handle <- NULL
      }
    },
    initialize = function(modelfile) {
      handle <- lgb.new.handle()
      if (is.character(modelfile)) {
        handle <- lgb.call("LGBM_BoosterCreateFromModelfile_R", ret = handle, lgb.c_str(modelfile))
        private$need_free_handle <- TRUE
      } else if (is(modelfile, "lgb.Booster.handle")) {
        handle <- modelfile
        private$need_free_handle <- FALSE
      } else {
        stop("lgb.Predictor: modelfile must be either a character filename or an lgb.Booster.handle")
      }
      class(handle) <- "lgb.Booster.handle"
      private$handle <- handle
    },
    current_iter = function() {
      cur_iter <- 0L
      lgb.call("LGBM_BoosterGetCurrentIteration_R",  ret = cur_iter, private$handle)
    },
    predict = function(data, num_iteration = NULL, rawscore = FALSE,
      predleaf = FALSE, header = FALSE, reshape = FALSE) {

      if (is.null(num_iteration)) { num_iteration <- -1 }
      num_row <- 0
      if (is.character(data)) {
        tmp_filename <- tempfile(pattern = "lightgbm_")
        on.exit(unlink(tmp_filename), add = TRUE)
        lgb.call("LGBM_BoosterPredictForFile_R", ret = NULL, private$handle, data,
          as.integer(header),
          as.integer(rawscore),
          as.integer(predleaf),
          as.integer(num_iteration),
          lgb.c_str(tmp_filename))
        preds   <- read.delim(tmp_filename, header = FALSE, seq = "\t")
        num_row <- nrow(preds)
        preds   <- as.vector(t(preds))
      } else {
        num_row <- nrow(data)
        npred   <- as.integer(0)
        npred   <- lgb.call("LGBM_BoosterCalcNumPredict_R", ret = npred,
          private$handle,
          as.integer(num_row),
          as.integer(rawscore),
          as.integer(predleaf),
          as.integer(num_iteration))
        # allocte space for prediction
        preds <- rep(0.0, npred)
        if (is.matrix(data)) {
          preds <- lgb.call("LGBM_BoosterPredictForMat_R", ret = preds,
            private$handle,
            data,
            as.integer(nrow(data)),
            as.integer(ncol(data)),
            as.integer(rawscore),
            as.integer(predleaf),
            as.integer(num_iteration))
        } else if (is(data, "dgCMatrix")) {
          preds <- lgb.call("LGBM_BoosterPredictForCSC_R", ret = preds,
            private$handle,
            data@p,
            data@i,
            data@x,
            length(data@p),
            length(data@x),
            nrow(data),
            as.integer(rawscore),
            as.integer(predleaf),
            as.integer(num_iteration))
        } else {
          stop("predict: cannot predict on data of class ", sQuote(class(data)))
        }
      }

      if (length(preds) %% num_row != 0) {
        stop("predict: prediction length ", sQuote(length(preds))," is not a multiple of nrows(data): ", sQuote(num_row))
      }
      npred_per_case <- length(preds) / num_row
      if (reshape && npred_per_case > 1) { preds <- matrix(preds, ncol = npred_per_case) }
      preds
    }
  ),
  private = list( handle = NULL, need_free_handle = FALSE )
)
