cmf_krr_ecvr_mem: External n-fold cross-validation with reshuffling in memory
In conmolfields: Continuous molecular fields

External n-fold cross-validation with reshuffling in memory

1	cmf_krr_ecvr_mem(nreshuffles = 10, y, kernels, mfields = c("q", "vdw", "logp", "abra", "abrb"), print_ecvr = TRUE, plot_ecvr = TRUE, seed = -1, ...)

`nreshuffles`
`y`
`kernels`
`mfields`
`print_ecvr`
`plot_ecvr`
`seed`
`...`

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (nreshuffles = 10, y, kernels, mfields = c("q", "vdw", 
    "logp", "abra", "abrb"), print_ecvr = TRUE, plot_ecvr = TRUE, 
    seed = -1, ...) 
{
    if (seed >= 0) 
        set.seed(seed)
    y_init <- y
    kernels_init <- kernels
    ncomp <- length(y)
    oldnum <- integer(10)
    YPred <- array(dim = c(ncomp, nreshuffles))
    Q2ecv_array <- double(nreshuffles)
    RMSEecv_array <- double(nreshuffles)
    permutations <- list()
    models <- list()
    indexes <- list()
    for (p in 1:nreshuffles) {
        if (print_ecvr) {
            cat(sprintf("reshuffle %d of %d\n", p, nreshuffles))
            flush.console()
        }
        permutation <- sample(1:ncomp, ncomp)
        for (i in 1:ncomp) oldnum[permutation[i]] <- i
        y_perm <- y_init[permutation]
        kernels_perm <- cmf_permute_kernels(kernels_init, permutation, 
            mfields)
        res_perm <- cmf_krr_ecv_mem(y = y_perm, kernels = kernels_perm, 
            mfields = mfields, ...)
        YPred[, p] <- res_perm$y_pred_ecv[oldnum]
        Q2ecv_array[p] <- res_perm$Q2ecv
        RMSEecv_array[p] <- res_perm$RMSEecv
        models <- c(models, res_perm$models)
        indexes <- c(indexes, res_perm$indexes)
        last <- length(permutations)
        for (ifold in 1:nfolds) permutations[[last + ifold]] <- permutation
    }
    y_pred_mean <- rowMeans(YPred)
    y_pred_sd <- double(ncomp)
    for (i in 1:ncomp) y_pred_sd[i] <- sd(YPred[i, ])
    Q2ecv_mean <- mean(Q2ecv_array)
    Q2ecv_sd <- sd(Q2ecv_array)
    RMSEecv_mean <- mean(RMSEecv_array)
    RMSEecv_sd <- sd(RMSEecv_array)
    regr <- regr_param(y_pred_mean, y)
    Q2ecv_aggr <- regr$R2
    RMSEecv_aggr <- regr$RMSE
    RMSEecv_aggr_pc <- regr$RMSE_pc
    nmodels <- length(models)
    if (print_ecvr) {
        cat(sprintf("Q2ecv_aggr=%.6f RMSEecv_aggr=%.6f (%g%%)\n", 
            Q2ecv_aggr, RMSEecv_aggr, RMSEecv_aggr_pc))
        cat(sprintf("Q2ecv_mean=%.6f Q2ecv_sd=%.6f RMSEecv_mean=%.6f RMSEecv_sd=%.6f\n", 
            Q2ecv_mean, Q2ecv_sd, RMSEecv_mean, RMSEecv_sd))
        flush.console()
    }
    if (plot_ecvr) {
        cinf_plotxy(y_pred_mean, y, xlab = "Predicted", ylab = "Experiment", 
            main = "Scatter Plot for External Cross-Validations with Reshuffles")
        abline(coef = c(0, 1))
    }
    list(Q2ecv_aggr = Q2ecv_aggr, RMSEecv_aggr = RMSEecv_aggr, 
        RMSEecv_aggr_pc = RMSEecv_aggr_pc, YPred = YPred, y_exp = y, 
        y_pred_mean = y_pred_mean, y_pred_sd = y_pred_sd, Q2ecv_array = Q2ecv_array, 
        Q2ecv_mean = Q2ecv_mean, Q2ecv_sd = Q2ecv_sd, RMSEecv_array = RMSEecv_array, 
        RMSEecv_mean = RMSEecv_mean, RMSEecv_sd = RMSEecv_sd, 
        nmodels = nmodels, permutations = permutations, models = models, 
        indexes = indexes, mfields = mfields)
  }