Examples - Nevergrad for R

# Library and environment for Reticulate/Nevergrad.
library("reticulate")
conda_create("r-reticulate")
conda_install("r-reticulate", "nevergrad", pip=TRUE)
use_condaenv("r-reticulate")

# Only if you use parallelism.
library(doParallel)

# Choose your optimization method below.
optimizer_name <- "NgIohTuned"
# optimizer_name <- "DoubleFastGADiscreteOnePlusOne"
# optimizer_name <- "OnePlusOne"
# optimizer_name <- "DE"
# optimizer_name <- "RandomSearch"
# optimizer_name <- "TwoPointsDE"
# optimizer_name <- "Powell"
# optimizer_name <- "MetaModel"  CRASH !!!!
# optimizer_name <- "SQP"
# optimizer_name <- "Cobyla"
# optimizer_name <- "NaiveTBPSA"
# optimizer_name <- "DiscreteOnePlusOne"
# optimizer_name <- "cGA"
# optimizer_name <- "ScrHammersleySearch"

# Now we can play with Nevergrad as usual.
# We assume here that we have 17 continuous hyperparameters with values in [0, 1].
# We can do other instrumentations, as discussed below.
my_tuple <- tuple(17)
instrumentation <- ng$p$Array(shape=my_tuple)
instrumentation$set_bounds(0., 1.)
num_workers <- 3  # We want to be able to evaluate 3 hyperparametrizations simultaneously.
num_iterations <- 100 * num_workers  # Let us say we have a budget of 100xnum_workers hyperparameters to evaluate.

# Let us create a Nevergrad optimization method.
optimizer <-  ng$optimizers$registry[optimizer_name](instrumentation, budget=num_iterations, num_workers=num_workers)

# Dummy initializations.
nevergrad_hp <- 0
nevergrad_hp_val <- 0
score <- 0

for (i in 1:num_iterations) {
    for (j in 1:num_workers) {
       nevergrad_hp[j] <- optimizer$ask()
       nevergrad_hp_val[j] <- nevergrad_hp[j]$value
    }

    # Sequential version.
    # for (j in 1:num_workers) {  # In a perfect world this would be parallel.
    #    score[j] <- norm(nevergrad_hp_val[j]
    # }

    # Parallel version.
    # Actually this could be asynchronous, Nevergrad is ok for that, you do not have to
    # do the tell's in the same order as the ask's.
    registerDoParallel(cores=num_workers)
    getDoParWorkers()
    foreach(i=1:num_workers) %dopar% score[j] <- norm(nevergrad_hp_val[j])

    for (j in 1:num_workers) {
       optimizer$tell(nevergrad_hp[j], score[j])
    }
}
print(optimizer$recommend()$value)

Don’t forget the “pip=TRUE”. I wasted so much time because of this :-)

For other instrumentations (discrete variables, logarithmic continuous variables…), please check different instrumentations: <https://github.com/facebookresearch/nevergrad/blob/main/docs/parametrization.rst>. Or for simple examples for machine learning machine learning <https://github.com/facebookresearch/nevergrad/blob/main/docs/machinelearning.rst>.