Computer Experiment LEarning Curve eXtrapolation
Tools for active learning on computer experiments, with support for learning curve extrapolation and progress forecasting.
Work in progress, nothing in here should be considered stable yet.
# you almost certainly need:
install.packages(c("mlr3learners", "DiceKriging"))
# Install celecx
remotes::install_github("mlr-org/celecx")Run active learning to explore an unknown function:
library("celecx")
library("mlr3")
library("mlr3learners") # for regr.km
# Define objective (unknown function to learn)
objective <- ObjectiveRFun$new(
fun = function(xs) list(y = sin(xs$x * pi)),
domain = ps(x = p_dbl(lower = 0, upper = 2)),
codomain = ps(y = p_dbl(tags = "learn"))
)
# Run active learning
result <- optimize_active(
objective = objective,
term_evals = 10L,
learner = lrn("regr.km", covtype = "matern5_2"),
se_method = "auto",
batch_size = 2L,
aqf_evals = 20L,
multipoint_method = "greedy"
)
# Access results
result$instance$archive$data # All evaluated points
xvals <- seq(0, 2, length.out = 100)
yvals.true <- objective$fun(list(x = xvals))$y
yvals.pred <- result$optimizer$surrogate$predict(data.table::data.table(x = xvals))
plot(xvals, yvals.true, col = "red", type = "l", xlab = "x", ylab = "y",
main = "Active Learning sin(x) with batch_size = 2")
lines(xvals, yvals.pred$mean, col = "blue")
lines(xvals, with(yvals.pred, mean + 1.96 * se), col = "blue", lty = 2)
lines(xvals, with(yvals.pred, mean - 1.96 * se), col = "blue", lty = 2)
text(y ~ x, labels = batch_nr, data = result$instance$archive$data, pos = 1)
MIT