NNbenchmark | monmlp

library(NNbenchmark)
library(monmlp)

library(stringr)
library(dplyr)
library(kableExtra)

0.1 Set Environment

options(scipen = 9999)
options("digits.secs" = 2)
timer  <- createTimer(verbose = FALSE)

0.2 Datasets to Test

NNdataSummary(NNdatasets)

0.3 NN Train Function

hyperParams <- function(optim_method) {
    
    if (!is.element(optim_method, c("BFGS", "Nelder-Mead"))) stop("Invalid Parameters.")
    if (optim_method == "BFGS") {iter <- 250} 
    if (optim_method == "Nelder-Mead") {iter <- 10000} 

    params <- paste0("method=", optim_method, "_iter=", iter)
    
    out <- list(iter = iter, params = params)
    
    return (out)
}



NNtrain <- function(x, y, hidden1, optim_method) {
    
    hyper_params <- hyperParams(optim_method)
    
    iter <- hyper_params$iter
    
    NNreg <- monmlp::monmlp.fit(x, y, 
                                hidden1 = neur, 
                                scale.y = TRUE,
                                method = optim_method, 
                                iter.max = iter)
    
    return(NNreg)
}

0.4 Main Loop

for (dset in names(NNdatasets)) {

    ## =============================================
    ## EXTRACT INFORMATION FROM THE SELECTED DATASET
    ## =============================================
    ds     <- NNdatasets[[dset]]$ds
    Z      <- NNdatasets[[dset]]$Z
    neur   <- NNdatasets[[dset]]$neur
    nparNN <- NNdatasets[[dset]]$nparNN
    fmlaNN <- NNdatasets[[dset]]$fmlaNN
    donotremove  <- c("dset", "dsets", "ds", "Z", "neur", "TF", "nrep", "timer",
                      "donotremove", "donotremove2")
    donotremove2 <- c("dset", "dsets") 

    ## ===================================================
    ## SELECT THE FORMAT REQUIRED BY THE PACKAGE/ALGORITHM
    ## d = data.frame, m = matrix, v = vector/numeric
    ## ATTACH THE OBJECTS CREATED (x, y, Zxy, ... )
    ## ===================================================
    ZZ     <- prepareZZ(Z, xdmv = "m", ydmv = "m", zdm = "d", scale = FALSE)
    attach(ZZ)

    ## =============================================
    ## SELECT THE PACKAGE USED FOR TRAINING
    ## nrep => SELECT THE NUMBER OF INDEPENDANT RUNS
    ## iter => SELECT THE MAX NUMBER OF ITERATIONS
    ## TF   => PLOT THE RESULTS
    ## =============================================

    
    nrep   <- 10
    TF     <- TRUE 

    method <- c("BFGS", "Nelder-Mead")
        
    for (m in method) {
        descr  <- paste(dset, "monmlp::monmlp.fit", m, sep = "_")

        ##AUTO
        Ypred  <- list()
        Rmse   <- numeric(length = nrep)
        Mae    <- numeric(length = nrep)
    
        for(i in 1:nrep){
            event      <- paste0(descr, sprintf("_%.2d", i))
            timer$start(event)
            #### ADJUST THE FOLLOWING LINES TO THE PACKAGE::ALGORITHM
            
            hyper_params <- hyperParams(optim_method = m)

            NNreg      <- tryCatch(  
                            NNtrain(x = x, y = y, hidden1 = neur, optim_method = m),
                            error = function(y) {lm(y ~ 0, data = Zxy)}
                          )    
            y_pred     <- tryCatch(
                            ym0 + ysd0*attr(NNreg, "y.pred", TF),
                            error = function(NNreg) rep(ym0, nrow(Zxy))
                          )    
            ####
            Ypred[[i]] <- y_pred
            Rmse[i]    <- funRMSE(y_pred, y0)
            Mae[i]     <- funMAE(y_pred, y0)
            timer$stop(event, RMSE = Rmse[i], MAE = Mae[i], params = hyper_params$params, printmsg = FALSE)
            
        }
        best <- which(Rmse == min(Rmse, na.rm = TRUE))[1]
        best ; Rmse[[best]]
        
        ## ================================================
        ## PLOT ALL MODELS AND THE MODEL WITH THE BEST RMSE
        ## par OPTIONS CAN BE IMPROVED FOR A BETTER DISPLAY
        ## ================================================
        op <- par(mfcol = c(1,2))
        plotNN(xory, y0, uni, TF, main = descr)
        for (i in 1:nrep){lipoNN(xory, Ypred[[i]], uni, TF, col = i, lwd = 1)}
        
        plotNN(xory, y0, uni, TF, main = descr)
        lipoNN(xory, Ypred[[best]], uni, TF, col = 4, lwd = 4)
        par(op)
    }


## ===========================
## DETACH ZZ - END OF THE LOOP
## ===========================
    detach(ZZ)
}

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0.5 Results

dfr0 <- getTimer(timer) 

dfr  <- data.frame(
    ds_pkg.fun_algo = stringr::str_sub(dfr0[ ,1], 1, -4),
    run     = stringr::str_sub(dfr0[ ,1], -2, -1),
    dfr0[, c("RMSE","MAE")],
    dataset = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "^\\w*_"), fixed("_"), ""),
    method = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "_\\w*_"), fixed("_"), ""),
    Elapsed = round(dfr0[ ,4], 5),
    params = dfr0$params
)


dfr %>%
    select(-c(dataset, method))

0.6 Best Results

dfr %>%
    group_by(dataset, method) %>%
    summarise(minRMSE = min(RMSE), meanRMSE = mean(RMSE), meanTime = mean(Elapsed)) %>%
    kable() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    collapse_rows(columns = 1:2, valign = "top")

dataset	method	minRMSE	meanRMSE	meanTime
mDette	BFGS	0.3672	0.69379	0.292
mDette	NA	2.4780	3.53268	1.431
mFriedman	BFGS	0.0100	0.01425	0.319
mFriedman	NA	0.0958	0.11126	1.562
mIshigami	BFGS	0.6984	1.04233	0.615
mIshigami	NA	2.7412	2.84730	2.368
mRef153	BFGS	3.2008	3.23077	0.164
mRef153	NA	3.8886	4.32239	0.625
uDmod1	BFGS	0.0513	0.08708	0.159
uDmod1	NA	0.1698	0.22934	0.540
uDmod2	BFGS	0.0554	0.07792	0.129
uDmod2	NA	0.0675	0.17828	0.410
uDreyfus1	BFGS	0.0210	0.03924	0.124
uDreyfus1	NA	0.0957	0.25176	0.228
uDreyfus2	BFGS	0.0923	0.12101	0.121
uDreyfus2	NA	0.1368	0.24596	0.229
uGauss1	BFGS	2.3436	4.70580	0.180
uGauss1	NA	7.7318	13.39978	0.706
uGauss2	BFGS	2.6113	4.52269	0.161
uGauss2	NA	6.0705	10.33067	0.493
uGauss3	BFGS	2.4877	3.43451	0.153
uGauss3	NA	4.7663	8.12385	0.527
uNeuroOne	BFGS	0.2830	0.28743	0.111
uNeuroOne	NA	0.2913	0.43092	0.158

clearNN(donotremove)

NNbenchmark | monmlp

Salsabila Mahdi

0.1 Set Environment

0.2 Datasets to Test

0.3 NN Train Function

0.4 Main Loop

0.5 Results

0.6 Best Results