NNSDR/simulations/simulations.R

100 lines
3.7 KiB
R

#!/usr/bin/env Rscript
library(MAVE)
library(CVarE)
Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
suppressPackageStartupMessages({
library(NNSDR)
})
## Parse script parameters
args <- parse.args(defaults = list(
# Simulation configuration
reps = 100, # Number of replications
dataset = 'M1', # Name (number) of the data set
# Neuronal Net. structure/definitions
hidden_units = 512L,
activation = 'relu',
trainable_reduction = TRUE,
# Neuronal Net. training
epochs = c(200L, 400L), # Number of training epochs for (`OPG`, Refinement)
batch_size = 32L,
initializer = 'fromOPG',
seed = 1390L
))
## Generate reference data (gets re-sampled for each replication)
# Generates a list with `X`, `Y`, `B` and `name`
ds <- dataset(args$dataset, n = 100)
## Build Dimension Reduction Neuronal Network model (matching the data)
nn <- nnsdr$new(
input_shapes = list(x = ncol(ds$X)),
d = ncol(ds$B),
hidden_units = args$hidden_units,
activation = args$activation,
trainable_reduction = args$trainable_reduction
)
## Open simulation log file, write simulation configuration and header
log <- file(format(Sys.time(), "results/sim_%Y%m%d_%H%M.csv"), "w", blocking = FALSE)
cat(paste('#', names(args), args, sep = ' ', collapse = '\n'), '\n',
'method,replication,dist.subspace,dist.grassmann,mse\n',
sep = '', file = log, append = TRUE)
## Set seed for sampling simulation data (NOT effecting the `NN` initialization)
set.seed(args$seed)
## Repeated simulation runs
for (rep in seq_len(args$reps)) {
## Re-sample seeded data for each simulation replication
with(dataset(ds$name), {
## Sample test dataset
ds.test <- dataset(ds$name, n = 1000)
## First the reference method `MAVE`
dr <- mave(Y ~ X, method = "meanMAVE")
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, dim = ncol(B)) - ds.test$Y)^2)
cat('"mave",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
## and the `OPG` method
dr <- mave(Y ~ X, method = "meanOPG")
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, dim = ncol(B)) - ds.test$Y)^2)
cat('"opg",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
## Next the `CVE` method
dr <- cve(Y ~ X, k = ncol(B))
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, k = ncol(B)) - ds.test$Y)^2)
cat('"cve",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
## Fit `NNSDR` model
nn$fit(X, Y, epochs = args$epochs,
batch_size = args$batch_size, initializer = args$initializer)
# `OPG` estimate
d.sub <- dist.subspace(B, coef(nn, 'OPG'), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(nn, 'OPG'))
cat('"nn.opg",', rep, ',', d.sub, ',', d.gra, ',', NA, '\n', sep = '',
file = log, append = TRUE)
# Refinement estimate
d.sub <- dist.subspace(B, coef(nn), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(nn))
mse <- mean((nn$predict(ds.test$X) - ds.test$Y)^2)
cat('"nn.ref",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
})
## Reset model
nn$reset()
}
## Finished, close simulation log file
close(log)