1 Setup

1.1 Packages and options

library(NNbenchmark)
library(kableExtra)
options(scipen = 999)

1.2 Datasets to test

NNdataSummary(NNdatasets)
##      Dataset n_rows n_inputs n_neurons n_parameters
## 1     mDette    500        3         5           26
## 2  mFriedman    500        5         5           36
## 3  mIshigami    500        3        10           51
## 4    mRef153    153        5         3           22
## 5     uDmod1     51        1         6           19
## 6     uDmod2     51        1         5           16
## 7  uDreyfus1     51        1         3           10
## 8  uDreyfus2     51        1         3           10
## 9    uGauss1    250        1         5           16
## 10   uGauss2    250        1         4           13
## 11   uGauss3    250        1         4           13
## 12 uNeuroOne     51        1         2            7

1.3 EnsembleBase trainPredict arguments - inputs x, y

if(dir.exists("D:/GSoC2020/Results/2020run03/"))
{  
  odir <- "D:/GSoC2020/Results/2020run03/"
}else if(dir.exists("~/Documents/recherche-enseignement/code/R/NNbenchmark-project/NNtempresult/"))
{  
  odir <- "~/Documents/recherche-enseignement/code/R/NNbenchmark-project/NNtempresult/"
}else
  odir <- "~"

nrep <- 10
maxit2ndorder  <-    200
maxit1storderA <-   1000
maxit1storderB <-  10000
maxit1storderC <- 100000

#library(EnsembleBase)
EnsembleBase.method <- "none"
hyperParams.EnsembleBase <- function(optim_method, ...) {
    out <- list(iter = maxit2ndorder, decay = 0)
    return (out)
}
NNtrain.EnsembleBase <- function(x, y, dataxy, formula, neur, optim_method, hyperParams,...) {
    
    hyper_params <- do.call(hyperParams.EnsembleBase, list(EnsembleBase.method))
    
    iter <- hyper_params$iter ; decay <- hyper_params$decay
    
    NNreg <- EnsembleBase::Regression.Batch.Fit(make.configs("nnet", config.df = expand.grid(decay=decay,size=c(neur),maxit=iter)), formula, dataxy, ncores = 1)
    
    return (NNreg)
}
NNpredict.EnsembleBase <- function(object, x, ...)
    predict(object, x)
NNclose.EnsembleBase <- function()
  if("package:EnsembleBase" %in% search())
    detach("package:EnsembleBase", unload=TRUE)
EnsembleBase.prepareZZ <- list(xdmv = "m", ydmv = "v", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, EnsembleBase.method, "NNtrain.EnsembleBase", "hyperParams.EnsembleBase", "NNpredict.EnsembleBase", 
                               NNsummary, "NNclose.EnsembleBase", NA, EnsembleBase.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
                               pkgname="EnsembleBase", pkgfun="EnsembleBase", csvfile=TRUE, rdafile=TRUE, odir=odir)
if(FALSE)
res <- trainPredict_1mth1data(1, EnsembleBase.method[1], "NNtrain.EnsembleBase", "hyperParams.EnsembleBase", "NNpredict.EnsembleBase", 
                               NNsummary, EnsembleBase.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
                               pkgname="EnsembleBase", pkgfun="EnsembleBase", csvfile=TRUE, rdafile=TRUE, odir=odir)

2 Launch package’s trainPredict

res <- trainPredict_1pkg(1:12, pkgname = "EnsembleBase", pkgfun = "Regression.Batch.Fit", EnsembleBase.method,
  prepareZZ.arg = EnsembleBase.prepareZZ, nrep = nrep, doplot = TRUE,
  csvfile = TRUE, rdafile = TRUE, odir = odir, echo = FALSE)

3 Results

#print(res)
kable(t(apply(res, c(1,4), min)))%>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
RMSE MSE MAE WAE time
mDette 0.9162 0.8395 0.7103 4.3568 0.00
mFriedman 0.0251 0.0006 0.0164 0.1314 0.08
mIshigami 0.6717 0.4512 0.4711 3.6055 0.12
mRef153 3.4497 11.9005 2.4704 13.5906 0.01
uDmod1 0.0541 0.0029 0.0418 0.1344 0.00
uDmod2 0.0437 0.0019 0.0336 0.0947 0.00
uDreyfus1 0.0930 0.0086 0.0541 0.3124 0.00
uDreyfus2 0.1143 0.0131 0.0903 0.2712 0.01
uGauss1 2.5238 6.3698 1.9769 7.3391 0.03
uGauss2 2.6861 7.2151 2.0962 9.5462 0.03
uGauss3 2.3783 5.6563 1.8641 7.6236 0.03
uNeuroOne 0.2829 0.0800 0.2318 0.5451 0.00 
kable(t(apply(res, c(1,4), median)))%>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
RMSE MSE MAE WAE time
mDette 7.50495 97.76770 6.08405 26.30515 0.045
mFriedman 0.08125 0.00660 0.07255 0.18625 0.100
mIshigami 0.76470 0.58490 0.55145 3.82765 0.140
mRef153 3.73045 13.91750 2.68875 13.80425 0.020
uDmod1 0.06385 0.00405 0.04870 0.21480 0.010
uDmod2 0.06155 0.00380 0.04715 0.15210 0.010
uDreyfus1 0.10550 0.01125 0.06355 0.33680 0.000
uDreyfus2 0.14470 0.02095 0.10595 0.44615 0.020
uGauss1 2.76240 7.63095 2.14620 8.15580 0.030
uGauss2 3.14015 9.88260 2.50995 9.94470 0.030
uGauss3 3.72775 13.89630 2.76485 12.45400 0.030
uNeuroOne 0.28345 0.08035 0.23355 0.54985 0.000