NNbenchmark | qrnn

library(NNbenchmark)
library(qrnn)

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("qrnn"))) stop("Invalid Parameters.")
    if (optim_method == "qrnn") {iter <- 2000} 
    
    params <- paste0("method=", optim_method, "_iter=", iter)
    
    out <- list(iter = iter, params = params)
    
    return (out)
}



NNtrain <- function(x, y, hidden_neur, optim_method) {
    
    hyper_params <- hyperParams(optim_method)
    
    iter <- hyper_params$iter
    
    NNreg <- qrnn::qrnn.fit(x, y, n.hidden = hidden_neur, iter.max = iter, n.trials = 1, init.range = c(-0.1, 0.1, -0.1, 0.1), trace = FALSE)
    
    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 = TRUE)
    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("qrnn")
        
    for (m in method) {
        
        descr  <- paste(dset, "qrnn::qrnn.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, hidden_neur = neur, optim_method = m),
                            error = function(y) {lm(y ~ 0, data = Zxy)}
                          )     
            y_pred     <- tryCatch(
                            ym0 + ysd0*qrnn::qrnn.predict(x, NNreg),
                            error = ym0
                          )     
            ####
            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)
}

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 = "middle")

dataset	method	minRMSE	meanRMSE	meanTime
mDette	qrnn	0.0653	0.62067	1.879
mFriedman		0.0042	0.02377	1.757
mIshigami		0.5054	0.87113	2.367
mRef153		3.3006	3.40601	0.603
uDmod1		0.0484	0.09395	1.179
uDmod2		0.0428	0.07350	1.032
uDreyfus1		0.0000	0.19439	0.585
uDreyfus2		0.0956	0.13560	1.153
uGauss1		2.2416	3.10190	1.881
uGauss2		2.3462	5.59926	1.333
uGauss3		2.2950	3.14083	1.468
uNeuroOne		0.2936	0.29387	0.171

clearNN(donotremove)