NNtrainPredict: Generic Functions for Training and Predicting
In NNbenchmark: Datasets and Functions to Benchmark Neural Network Packages

Description Usage Arguments Value Examples

An implementation with do.call so that any neural network function that fits the format can be tested.

In trainPredict_1mth1data, a neural network is trained on one dataset and then used for predictions, with several functionalities. Then, the performance of the neural network is summarized.

trainPredict_1data serves as a wrapper function for trainPredict_1mth1data for multiple methods.

trainPredict_1pkg serves as a wrapper function for trainPredict_1mth1data for multiple datasets.

trainPredict_1mth1data(dset, method, trainFUN, hyperparamFUN, predictFUN,
  summaryFUN, prepareZZ.arg = list(), nrep = 5, doplot = FALSE,
  plot.arg = list(col1 = 1:nrep, lwd1 = 1, col2 = 4, lwd2 = 3), pkgname,
  pkgfun, csvfile = FALSE, rdafile = FALSE, odir = ".", echo = FALSE,
  echoreport = FALSE, appendcsv = TRUE, ...)

trainPredict_1data(dset, methodlist, trainFUN, hyperparamFUN, predictFUN,
  summaryFUN, closeFUN, startNN = NA, prepareZZ.arg = list(), nrep = 5,
  doplot = FALSE, plot.arg = list(), pkgname = "pkg", pkgfun = "train",
  csvfile = FALSE, rdafile = FALSE, odir = ".", echo = FALSE, ...)

trainPredict_1pkg(dsetnum, pkgname = "pkg", pkgfun = "train", methodvect,
  prepareZZ.arg = list(), summaryFUN, nrep = 5, doplot = FALSE,
  plot.arg = list(), csvfile = FALSE, rdafile = FALSE, odir = ".",
  echo = FALSE, appendcsv = TRUE, ...)

`dset`	a number or string indicating which dataset to use, see `NNdataSummary`
`method`	a method for a particular function
`trainFUN`	the training function used
`hyperparamFUN`	the function resulting in parameters needed for training
`predictFUN`	the prediction function used
`summaryFUN`	measure performance by observed and predicted y values, `NNsummary` is ready to use
`prepareZZ.arg`	list of arguments for `prepareZZ`
`nrep`	a number for how many times a neural network should be trained with a package/function
`doplot`	logical value, TRUE executes plots and FALSE does not
`plot.arg`	list of arguments for plots
`pkgname`	package name
`pkgfun`	name of the package function to train neural network
`csvfile`	logical value, adds summary to csv files per dataset if TRUE
`rdafile`	logical value, outputs rdafile of predictions and summary if TRUE
`odir`	output directory
`echo`	logical value, separates training between packages with some text and enables echoreport if TRUE
`echoreport`	logical value, detailed reports are printed (such as model summaries and str(data)) if TRUE, will not work if echo is FALSE
`appendcsv`	logical value, if `TRUE`, the csv output is appended to the csv file.
`...`	additional arguments
`methodlist`	list of methods per package/function
`closeFUN`	a function to detach packages or other necessary environment clearing
`startNN`	a function to start needed outside libraries, for example, h2o
`dsetnum`	a vector of numbers indicating which dataset to use in `NNdataSummary`
`methodvect`	vector of methods per package/function

An array with values as in NNsummary including each repetition, with options for plots and output files

nrep <- 2       
odir <- tempdir()

### Package with one method/optimization algorithm
library("brnn")
brnn.method <- "gaussNewton"
hyperParams.brnn <- function(optim_method, ...) {
  return(list(iter = 200))
  }
brnn.prepareZZ <- list(xdmv = "m", ydmv = "v", zdm = "d", scale = TRUE)

NNtrain.brnn   <- function(x, y, dataxy, formula, neur, optim_method, hyperParams,...) {
  hyper_params <- do.call(hyperParams.brnn, list(brnn.method))
  iter  <- hyper_params$iter
  
  NNreg <- brnn::brnn(x, y, neur, normalize = FALSE, epochs = iter, verbose = FALSE)
  return(NNreg)
  }
NNpredict.brnn <- function(object, x, ...) { predict(object, x) }
NNclose.brnn <- function(){
  if("package:brnn" %in% search())
    detach("package:brnn", unload=TRUE)
  }


res <- trainPredict_1pkg(1:2, pkgname = "brnn", pkgfun = "brnn", brnn.method,
                         prepareZZ.arg = brnn.prepareZZ, nrep = nrep, doplot = TRUE,
                         csvfile = FALSE, rdafile = FALSE, odir = odir, echo = FALSE)
                         
### Package with more than one method/optimization algorithm
library(validann)
validann.method <- c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN")
hyperParams.validann <- function(optim_method, ...) {
  if(optim_method == "Nelder-Mead")  { maxiter <- 10000 } 
  if(optim_method == "BFGS")         { maxiter <- 200   }
  if(optim_method == "CG")           { maxiter <- 1000  }
  if(optim_method == "L-BFGS-B")     { maxiter <- 200   }
  if(optim_method == "SANN")         { maxiter <- 1000  }
  return(list(iter = maxiter, method = optim_method, params))
  }
validann.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)

NNtrain.validann <- function(x, y, dataxy, formula, neur, optim_method, hyperParams, ...) {
  hyper_params <- do.call(hyperParams, list(optim_method, ...))
  iter <- hyper_params$iter
  method <- hyper_params$method
  
  NNreg <- validann::ann(x, y, size = neur, method = method, maxit = iter)
  return (NNreg)
  }
NNpredict.validann <- function(object, x, ...) { predict(object, x) }
NNclose.validann <- function() {
  if("package:validann" %in% search())
  detach("package:validann", unload=TRUE)
  }

res <- trainPredict_1pkg(1:2, pkgname = "validann", pkgfun = "ann", validann.method,
                         repareZZ.arg = validann.prepareZZ, nrep = nrep, doplot = FALSE,
                         csvfile = TRUE, rdafile = TRUE, odir = odir, echo = FALSE)