optimize_design: Generic optimizer that can be customized by user provided...

In designit: Blocking and Randomization for Experimental Design

Generic optimizer that can be customized by user provided functions for generating shuffles and progressing towards the minimal score

Description

Generic optimizer that can be customized by user provided functions for generating shuffles and progressing towards the minimal score

Usage

optimize_design(
  batch_container,
  samples = NULL,
  scoring = NULL,
  n_shuffle = NULL,
  shuffle_proposal_func = NULL,
  acceptance_func = accept_strict_improvement,
  aggregate_scores_func = identity,
  check_score_variance = TRUE,
  autoscale_scores = FALSE,
  autoscaling_permutations = 100,
  autoscale_useboxcox = TRUE,
  sample_attributes_fixed = FALSE,
  max_iter = 10000,
  min_delta = NA,
  quiet = FALSE
)

Arguments

batch_container	An instance of BatchContainer.
samples	A data.frame with sample information. Should be NULL if the BatchContainer already has samples in it.
scoring	Scoring function or a named list() of scoring functions.
n_shuffle	Vector of length 1 or larger, defining how many random sample swaps should be performed in each iteration. If length(n_shuffle)==1, this sets no limit to the number of iterations. Otherwise, the optimization stops if the swapping protocol is exhausted.
shuffle_proposal_func	A user defined function to propose the next shuffling of samples. Takes priority over n_shuffle if both are provided. The function is called with a BatchContainer bc and an integer parameter iteration for the current iteration number, allowing very flexible shuffling strategies. Mapper syntax is supported (see purrr::as_mapper()). The returned function must either return a list with fields srcand dst (for pairwise sample swapping) or a numeric vector with a complete re-assigned sample order.
acceptance_func	Alternative function to select a new score as the best one. Defaults to strict improvement rule, i.e. all elements of a score have to be smaller or equal in order to accept the solution as better. This may be replaced with an alternative acceptance function included in the package (e.g. mk_simanneal_acceptance_func()) or a user provided function. Mapper syntax is supported (see purrr::as_mapper()).
aggregate_scores_func	A function to aggregate multiple scores AFTER (potential) auto-scaling and BEFORE acceptance evaluation. If a function is passed, (multi-dimensional) scores will be transformed (often to a single double value) before calling the acceptance function. E.g., see first_score_only() or worst_score(). Note that particular acceptance functions may require aggregation of a score to a single scalar in order to work, see for example those generated by mk_simanneal_acceptance_func(). Mapper syntax is supported (see purrr::as_mapper()).
check_score_variance	Logical: if TRUE, scores will be checked for variability under sample permutation and the optimization is not performed if at least one subscore appears to have a zero variance.
autoscale_scores	Logical: if TRUE, perform a transformation on the fly to equally scale scores to a standard normal. This makes scores more directly comparable and easier to aggregate.
autoscaling_permutations	How many random sample permutations should be done to estimate autoscaling parameters. (Note: minimum will be 20, regardless of the specified value)
autoscale_useboxcox	Logical; if TRUE, use a boxcox transformation for the autoscaling if possible at all. Requires installation of the bestNormalize package.
sample_attributes_fixed	Logical; if TRUE, sample shuffle function may generate altered sample attributes at each iteration. This affects estimation of score distributions. (Parameter only relevant if shuffle function does introduce attributes!)
max_iter	Stop optimization after a maximum number of iterations, independent from other stopping criteria (user defined shuffle proposal or min_delta).
min_delta	If not NA, optimization is stopped as soon as successive improvement (i.e. euclidean distance between score vectors from current best and previously best solution) drops below min_delta.
quiet	If TRUE, suppress non-critical warnings or messages.

Value

A trace object

Examples

data("invivo_study_samples")
bc <- BatchContainer$new(
  dimensions = c("plate" = 2, "column" = 5, "row" = 6)
)
bc <- optimize_design(bc, invivo_study_samples,
  scoring = osat_score_generator("plate", "Sex"),
  max_iter = 100
)
plot_plate(bc$get_samples(), .col = Sex)

designit documentation built on May 29, 2024, 12:04 p.m.