GitLab

Currently, the ProposeCandidatesGrid() function from Calibration_Michel() removes duplicated values after the creation of the table of proposed candidates grid using expand.grid() in order to compute all combinations of parameters.

In addition, the function is not clean because it use the DistribParamR external variable (but the result was not wrong).

Current function:

ProposeCandidatesGrid <- function(DistribParam) {
  NewCandidates <- expand.grid(lapply(seq_len(ncol(DistribParamR)), function(x) DistribParam[, x]))
  NewCandidates <- unique(NewCandidates) # to avoid duplicates when a parameter is set
  Output <- list(NewCandidates = NewCandidates)
}

Benchmarking:

It is more efficient to remove duplicated values on each parameter instead of the whole table.
In addition, it is not necessary to return a list.

## original one removing duplcated values on the whole data.frame (but DistribParamR replacedby DistribParam)
ProposeCandidatesGrid1 <- function(DistribParam) {
  NewCandidates <- expand.grid(lapply(seq_len(ncol(DistribParam)), function(x) DistribParam[, x]))
  NewCandidates <- unique(NewCandidates) # to avoid duplicates when a parameter is set
  Output <- list(NewCandidates = NewCandidates)
}

## original one removing duplicated values on the whole data.frame & returning data.frame instead of a list
ProposeCandidatesGrid2 <- function(DistribParam) {
  NewCandidates <- expand.grid(lapply(seq_len(ncol(DistribParam)), function(x) DistribParam[, x]))
  unique(NewCandidates) # to avoid duplicates when a parameter is set
}

## new one removing duplicated values on each parameter & returning a list
ProposeCandidatesGrid3 <- function(DistribParam) {
  NewCandidates <- expand.grid(lapply(seq_len(ncol(DistribParam)), function(x) unique(DistribParam[, x])))
  Output <- list(NewCandidates = NewCandidates)
}

## new one removing duplicated values on each parameter & returning data.frame instead of a list
ProposeCandidatesGrid4 <- function(DistribParam) {
  expand.grid(lapply(seq_len(ncol(DistribParam)), function(x) unique(DistribParam[, x])))
}

ParamT <- matrix(c(+3.60, -1.00, +3.30, -9.10, -0.90, +3.00,
                   +3.90, -0.50, +4.10, -8.70, +0.10, +4.00,
                   +4.50, +0.50, +5.00, -8.10, +1.10, +5.00), ncol = 6, byrow = TRUE)

microbenchmark(ProposeCandidatesGrid1(ParamT), 
               ProposeCandidatesGrid2(ParamT),
               ProposeCandidatesGrid3(ParamT),
               ProposeCandidatesGrid4(ParamT),
               times = 50L,
               unit = "milliseconds")

## Unit: milliseconds
##                            expr    min     lq     mean  median     uq    max neval
##  ProposeCandidatesGrid1(ParamT) 1.7160 1.8258 2.176492 1.85945 1.9305 5.4754    50
##  ProposeCandidatesGrid2(ParamT) 1.7570 1.8292 2.090968 1.88125 1.9109 5.5252    50
##  ProposeCandidatesGrid3(ParamT) 0.2275 0.2456 0.275084 0.26530 0.2881 0.5601    50
##  ProposeCandidatesGrid4(ParamT) 0.2337 0.2466 0.318084 0.26510 0.2856 2.7356    50

ParamT <- matrix(c(+1.9, +2.7, -1.1, -9.6, -5.5, +1.0, +3.4, -0.2, +4.5, +3.5, +4.3, -1.5, -4.2, -4.4,
                   +3.5, +3.4, -0.3, -9.2, -1.6, +2.0, +3.7, +0.0, +5.1, +5.3, +5.6, -0.5, -3.6, -2.7), 
                   ncol = 14, byrow = TRUE)

microbenchmark(ProposeCandidatesGrid1(ParamT), 
               ProposeCandidatesGrid2(ParamT),
               ProposeCandidatesGrid3(ParamT),
               ProposeCandidatesGrid4(ParamT),
               times = 50L,
               unit = "milliseconds")
               
## Unit: milliseconds
##                          expr     min      lq       mean   median       uq      max neval
## ProposeCandidatesGrid1(ParamT) 88.6652 92.7099 100.529286 96.37210 100.7031 169.4589    50
## ProposeCandidatesGrid2(ParamT) 84.6542 90.4932 104.274102 95.62470 109.4955 167.5425    50
## ProposeCandidatesGrid3(ParamT)  1.3705  1.4359   1.909450  1.50210   1.5678  15.6336    50
## ProposeCandidatesGrid4(ParamT)  1.3880  1.4408   1.503504  1.48280   1.5404   2.2734    50
}

ParamT <- matrix(c(+1.9, +2.7, -1.1, -9.6, -5.5, +1.0, +3.4, -0.2, +4.5, +3.5, +4.3, -1.5, -4.2, -4.4,
                   +3.5, +3.4, -0.3, -9.2, -1.6, +2.0, +3.7, +0.0, +5.1, +5.3, +5.6, -0.5, -3.6, -2.7,
                   +3.5, +3.4, -0.3, -9.2, -1.6, +4.0, +3.7, +0.0, +5.1, +5.3, +5.6, -0.5, -3.6, -2.7), 
                   ncol = 14, byrow = TRUE)
ParamT [3, ] <- Sacramento[3, ] + 1

microbenchmark(ProposeCandidatesGrid1(ParamT), 
               ProposeCandidatesGrid2(ParamT),
               ProposeCandidatesGrid3(ParamT),
               ProposeCandidatesGrid4(ParamT),
               times = 50L,
               unit = "milliseconds")

## Unit: milliseconds
##                            expr        min        lq      mean     median        uq       max neval
##  ProposeCandidatesGrid1(ParamT) 31527.7280 35190.369 36366.143 36351.8798 38167.122 40490.213    10
##  ProposeCandidatesGrid2(ParamT) 31889.5226 33820.050 35162.053 34758.2704 37117.132 38873.993    10
##  ProposeCandidatesGrid3(ParamT)   350.7097   371.400  1505.988   481.6215  2297.846  5384.131    10
##  ProposeCandidatesGrid4(ParamT)   347.6815   372.925  1410.104   427.5523  2170.164  4755.841    10