Setup
Packages and options
library(NNbenchmark)
library(kableExtra)
options(scipen = 999)
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
MachineShop trainPredict arguments - inputs fmla, data
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(MachineShop)
MachineShop.method <- "none"
hyperParams.MachineShop <- function(...) {
return (list(iter=maxit2ndorder, trace=FALSE, linout=TRUE))
}
NNtrain.MachineShop <- function(x, y, dataxy, formula, neur, method, hyperParams, ...) {
hyper_params <- do.call(hyperParams, list(...))
trace <- hyper_params$trace
maxit <- hyper_params$iter
linout <- hyper_params$linout #linearoutpputunit
myNN <- MachineShop::NNetModel(size = neur, linout = linout, maxit = maxit,
trace=trace)
MachineShop::fit(formula, data = dataxy, model = myNN)
}
NNpredict.MachineShop <- function(object, x, ...)
as.numeric(predict(object, newdata=x, type="response"))
NNclose.MachineShop <- function()
if("package:MachineShop" %in% search())
detach("package:MachineShop", unload=TRUE)
MachineShop.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, MachineShop.method, "NNtrain.MachineShop", "hyperParams.MachineShop", "NNpredict.MachineShop",
NNsummary, "NNclose.MachineShop", NA, MachineShop.prepareZZ, nrep=5,
echo=TRUE, doplot=FALSE, echoreport=0,
pkgname="MachineShop", pkgfun="fit", csvfile=TRUE, rdafile=TRUE, odir=odir)
Launch package’s trainPredict
res <- trainPredict_1pkg(1:12, pkgname = "MachineShop", pkgfun = "fit", MachineShop.method,
prepareZZ.arg = MachineShop.prepareZZ, nrep = nrep, doplot = TRUE,
csvfile = TRUE, rdafile = TRUE, odir = odir, echo = FALSE)
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
|
0.3019
|
0.0912
|
0.2322
|
1.2867
|
0.07
|
|
mFriedman
|
0.0118
|
0.0001
|
0.0093
|
0.0359
|
0.08
|
|
mIshigami
|
0.5946
|
0.3536
|
0.4553
|
2.6373
|
0.14
|
|
mRef153
|
3.1128
|
9.6892
|
2.1804
|
11.7425
|
0.01
|
|
uDmod1
|
0.0438
|
0.0019
|
0.0353
|
0.1157
|
0.01
|
|
uDmod2
|
0.0429
|
0.0018
|
0.0342
|
0.1056
|
0.00
|
|
uDreyfus1
|
0.0017
|
0.0000
|
0.0013
|
0.0051
|
0.00
|
|
uDreyfus2
|
0.0906
|
0.0082
|
0.0724
|
0.2196
|
0.01
|
|
uGauss1
|
2.2373
|
5.0057
|
1.7396
|
6.9809
|
0.03
|
|
uGauss2
|
2.3754
|
5.6426
|
1.8697
|
7.6227
|
0.03
|
|
uGauss3
|
2.8198
|
7.9512
|
2.2059
|
7.6890
|
0.03
|
|
uNeuroOne
|
0.2830
|
0.0801
|
0.2313
|
0.5675
|
0.00
|
kable(t(apply(res, c(1,4), median)))%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
|
|
RMSE
|
MSE
|
MAE
|
WAE
|
time
|
|
mDette
|
0.48575
|
0.23610
|
0.36050
|
2.11030
|
0.085
|
|
mFriedman
|
0.02635
|
0.00075
|
0.02115
|
0.08195
|
0.100
|
|
mIshigami
|
0.74250
|
0.55420
|
0.55380
|
3.14845
|
0.160
|
|
mRef153
|
3.26040
|
10.63145
|
2.25895
|
13.99795
|
0.020
|
|
uDmod1
|
0.06545
|
0.00470
|
0.04950
|
0.19400
|
0.015
|
|
uDmod2
|
0.06050
|
0.00365
|
0.04790
|
0.13035
|
0.010
|
|
uDreyfus1
|
0.00230
|
0.00000
|
0.00180
|
0.00730
|
0.010
|
|
uDreyfus2
|
0.09065
|
0.00820
|
0.07250
|
0.22070
|
0.010
|
|
uGauss1
|
2.59080
|
6.71650
|
2.04905
|
8.41850
|
0.030
|
|
uGauss2
|
2.79355
|
7.85105
|
2.23895
|
8.85435
|
0.030
|
|
uGauss3
|
3.18900
|
10.17025
|
2.51305
|
9.85535
|
0.030
|
|
uNeuroOne
|
0.28300
|
0.08010
|
0.23130
|
0.56750
|
0.000
|