A fit whose estimator is "maf", "mdn" or "nsf" holds a torch module,
and a torch module is an external pointer. saveRDS() writes the pointer,
not the network: the file reloads without complaint and the object prints
normally, but the first call that touches the network fails with
external pointer is not valid. save_npe() and load_npe() are the
round trip that works.
Arguments
- fit
An
nsbi_npeobject fromnpe()ornpe_sequential().- path
File to write to (or read from). The convention is
.rds.
Details
save_npe() writes one .rds file holding the network's weights (via
torch::torch_save() on its state_dict) alongside everything else the fit
carries as ordinary R objects: the prior, the standardization centers and
scales, parameter and outcome names, the simulation count, and the training
history. load_npe() rebuilds the network from the recorded architecture
and restores the weights, returning an nsbi_npe that behaves exactly like
the one you trained.
A "linear_gaussian" fit holds no torch objects and round-trips through
saveRDS() unharmed; save_npe() accepts it anyway, so saving code does
not have to know which estimator was used.
Weights are saved, not code. A fit saved by one version of neuralsbi loads
into a later one as long as the estimator's architecture has not changed;
load_npe() reports the version that wrote the file when the rebuild fails.
Examples
prior <- prior_uniform(c(mu = -2, nu = -2), c(mu = 2, nu = 2))
simulator <- function(mu, nu) c(a = mu + rnorm(1, sd = 0.1),
b = nu + rnorm(1, sd = 0.1))
fit <- npe(prior, simulator, n_simulations = 500,
density_estimator = "linear_gaussian")
path <- tempfile(fileext = ".rds")
save_npe(fit, path)
fit2 <- load_npe(path)
sample(posterior(fit2, x_obs = c(0.8, 0.6)), 100)
#> <nsbi_samples> 100 draws x 2 parameters
#> support acceptance rate: 1.000
#> mu nu
#> [1,] 0.8409237 0.6299982
#> [2,] 0.7963170 0.5584969
#> [3,] 0.7290463 0.6580178
#> [4,] 0.6987101 0.6188983
#> [5,] 0.7956863 0.5846908
#> [6,] 0.8042810 0.4436697
unlink(path)