NNbenchmark (version 3.2.0)

NNtrainPredict: Generic Functions for Training and Predicting

Description

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.

Usage

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, ...)

Arguments

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

Value

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

Examples

# NOT RUN {
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)
  }

# }
# NOT RUN {
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)
# }
# NOT RUN {
# }