NNbenchmark Generic Function

1 Environment and datasets

1.1 Setup environment

library(NNbenchmark)
library(kableExtra)
options(scipen = 999)
if(dir.exists("D:/GSoC2020/Results/"))
{  
  odir <- "D:/GSoC2020/Results/"
}else if(dir.exists("~/Documents/recherche-enseignement/code/R/NNbenchmark-project/NNtempresult/"))
{  
  odir <- "~/Documents/recherche-enseignement/code/R/NNbenchmark-project/NNtempresult/"
}else
  odir <- "~"

nrep <- 5
maxit2ndorder  <-    200
maxit1storderA <-   1000
maxit1storderB <-   5000
maxit1storderC <-  10000
maxit1storderD <- 100000

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

2 Dedicated functions by packages

2.1 BNN Train/predict Function - arguments x,y

#library(BNN)
BNN.method <- "none"
hyperParams.BNN <- function(optim_method, ...) {
    return(list(iter = maxit1storderD, popN = 10, nCPUs = 10))
}
NNtrain.BNN <- function(x, y, dataxy, formula, neur, optim_method, hyperParams,...) {
    
    hyper_params <- do.call(hyperParams.BNN, list(BNN.method, ...))
    
    NNreg <- BNN::BNNsel(X = x,Y = y,hid_num = neur,train_num = (nrow(dataxy)-1), 
                  total_iteration = hyper_params$iter, popN = hyper_params$popN, 
                  nCPUs = hyper_params$nCPUs)
    
    return (NNreg)
}
NNpredict.BNN <- function(object, x, ...)
    c(object$fit, object$pred)
NNclose.BNN <- function()
  if("package:BNN" %in% search())
    detach("package:BNN", unload=TRUE)
BNN.prepareZZ <- list(xdmv = "m", ydmv = "v", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, BNN.method, "NNtrain.BNN", "hyperParams.BNN", "NNpredict.BNN", 
                               NNsummary, "NNclose.BNN", NA, BNN.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
                               pkgname="BNN", pkgfun="BNN", csvfile=TRUE, rdafile=TRUE, odir=odir)
if(FALSE)
res <- trainPredict_1mth1data(1, BNN.method[1], "NNtrain.BNN", "hyperParams.BNN", "NNpredict.BNN", 
                               NNsummary, BNN.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
                               pkgname="BNN", pkgfun="BNN", csvfile=TRUE, rdafile=TRUE, odir=odir)

3 Launch the NN package

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

4 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	0.9358	0.8757	0.7527	3.6542	21.95
mFriedman	0.0294	0.0009	0.0228	0.0841	25.38
mIshigami	1.6159	2.6112	1.1502	5.5863	22.84
mRef153	4.2381	17.9614	3.1880	14.2094	9.67
uDmod1	0.3700	0.1369	0.2988	0.9969	7.80
uDmod2	0.2968	0.0881	0.2527	0.6157	7.75
uDreyfus1	0.0122	0.0001	0.0097	0.0376	7.77
uDreyfus2	0.0950	0.0090	0.0730	0.2328	7.67
uGauss1	10.4421	109.0369	8.5235	28.9261	12.33
uGauss2	6.2237	38.7339	4.9376	16.5715	12.04
uGauss3	3.5014	12.2595	2.7198	10.1987	12.62
uNeuroOne	0.3405	0.1159	0.2831	0.7512	7.69

kable(t(apply(res, c(1,4), median)))%>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))

	RMSE	MSE	MAE	WAE	time
mDette	1.2636	1.5968	0.9405	6.8995	23.67
mFriedman	0.0320	0.0010	0.0260	0.0885	26.31
mIshigami	2.5337	6.4198	2.1835	6.6712	26.61
mRef153	4.2940	18.4383	3.2814	14.4490	10.15
uDmod1	0.3709	0.1376	0.2998	0.9979	7.82
uDmod2	0.2972	0.0884	0.2531	0.6169	7.80
uDreyfus1	0.0226	0.0005	0.0200	0.0542	7.85
uDreyfus2	0.1069	0.0114	0.0840	0.2642	7.80
uGauss1	10.4803	109.8361	8.5672	29.4434	12.64
uGauss2	7.1107	50.5615	4.9682	22.2460	12.26
uGauss3	3.8620	14.9154	2.9733	12.0896	12.97
uNeuroOne	0.3699	0.1368	0.3075	0.7961	7.74