data("multi_trt_day_samples")

multi_trt_day_samples |>
  count(Time, Treatment) |>
  gt::gt()

# Setting up the batch container
bc <- BatchContainer$new(
  dimensions = c(
    batch = ceiling(nrow(multi_trt_day_samples) / 8),
    run = 2, position = 4
  )
)

# Initial random assignment
bc <- assign_in_order(bc, multi_trt_day_samples)
bc
#> Batch container with 32 locations and 32 samples (assigned).
#>   Dimensions: batch, run, position

n_shuffle <- rep(c(32, 10, 2), c(100, 80, 20))
n_iterations <- length(n_shuffle)

set.seed(42) # should we have conventions for this?

scoring_f <- osat_score_generator(c("batch"), c("Treatment", "Time"))
bc <- optimize_design(
  bc,
  scoring = scoring_f,
  n_shuffle = n_shuffle,
  max_iter = n_iterations
) # default is 10000
#> Re-defined number of swaps to 16 in swapping function.
#> Checking variances of 1-dim. score vector.
#> ... (29.235) - OK
#> Initial score: 75
#> Achieved score: 25 at iteration 1
#> Achieved score: 21 at iteration 2
#> Achieved score: 15 at iteration 4
#> Achieved score: 13 at iteration 5
#> Achieved score: 11 at iteration 95
#> Achieved score: 9 at iteration 112
#> Achieved score: 5 at iteration 188

# copy batch container for second optimization
bc2 <- assign_in_order(bc)

n_iterations <- 200

set.seed(42) # should we have conventions for this?

bc2 <- optimize_design(
  bc2,
  scoring = scoring_f,
  shuffle_proposal = shuffle_with_constraints(
    src = TRUE,
    # batch needs to change for shuffle to be accepted
    dst = .src$batch != batch
  ),
  max_iter = n_iterations
)
#> Checking variances of 1-dim. score vector.
#> ... (26.382) - OK
#> Initial score: 75
#> Achieved score: 67 at iteration 1
#> Achieved score: 63 at iteration 2
#> Achieved score: 53 at iteration 3
#> Achieved score: 49 at iteration 4
#> Achieved score: 47 at iteration 5
#> Achieved score: 37 at iteration 6
#> Achieved score: 35 at iteration 9
#> Achieved score: 33 at iteration 11
#> Achieved score: 29 at iteration 13
#> Achieved score: 27 at iteration 14
#> Achieved score: 25 at iteration 19
#> Achieved score: 19 at iteration 20
#> Achieved score: 17 at iteration 25
#> Achieved score: 15 at iteration 36
#> Achieved score: 13 at iteration 40
#> Achieved score: 11 at iteration 49
#> Achieved score: 9 at iteration 57
#> Achieved score: 7 at iteration 79
#> Achieved score: 5 at iteration 126
#> Achieved score: 3 at iteration 180

qplot(
  x = bc2$trace$scores[[1]]$step,
  y = bc2$trace$scores[[1]]$score_1,
  main = str_glue("Final score={bc2$score(scoring_f)}"), geom = "point"
)

bc2$get_samples(assignment = TRUE) |>
  mutate(batch = factor(batch)) |>
  ggplot(aes(x = batch, fill = Treatment, alpha = factor(Time))) +
  geom_bar()
#> Warning: Using alpha for a discrete variable is not advised.

n_iterations <- 100

# new optimization function
scoring_f <- osat_score_generator(c("run"), c("Treatment", "Time"))
# like this the optimization score is wrong because it tries to optimize across Batches.
# Possible ways to go:
# - we'd need something like c("batch", batch/run") for optimize by batch and run within batch.
# - or we add "batch/run" to the constraints somehow.
bc$score(scoring_f)
#> score_1 
#>      16

bc <- optimize_design(
  bc,
  scoring = scoring_f,
  shuffle_proposal = shuffle_with_constraints(
    src = TRUE,
    # batch remains the same and run needs to change
    dst = batch == .src$batch & run != .src$run
  ),
  max_iter = n_iterations
)
#> Checking variances of 1-dim. score vector.
#> ... (36.071) - OK
#> Initial score: 16
#> Achieved score: 14 at iteration 2
#> Achieved score: 8 at iteration 4
#> Achieved score: 6 at iteration 10
#> Achieved score: 4 at iteration 11
#> Achieved score: 2 at iteration 16

qplot(
  x = bc$trace$scores[[1]]$step,
  y = bc$trace$scores[[1]]$score_1,
  color = factor(n_iterations),
  main = str_glue("Final score={bc$score(scoring_f)}"), geom = "point"
)