fit_experiment: Fit an 'Experiment'.

In Yu-Group/simChef: Intensive Computational Experiments Made Easy

Fit an Experiment.

Description

Fit Methods in the Experiment across all DGPs for n_reps repetitions and return results from fits.

Usage

fit_experiment(
  experiment,
  n_reps = 1,
  parallel_strategy = c("reps"),
  future.globals = NULL,
  future.packages = NULL,
  future.seed = TRUE,
  use_cached = FALSE,
  return_all_cached_reps = FALSE,
  save = FALSE,
  checkpoint_n_reps = 0,
  verbose = 1,
  ...
)

Arguments

experiment	An Experiment object.
n_reps	The number of replicates of the Experiment for this run.
parallel_strategy	A vector with some combination of "reps", "dgps", or "methods". Determines how computation will be distributed across available resources. Currently only the default, "reps", is supported.
future.globals	Character vector of names in the global environment to pass to parallel workers. Passed as the argument of the same name to future.apply::future_lapply and related functions. To set for all runs of the experiment, use the same argument during initialization.
future.packages	Character vector of packages required by parallel workers. Passed as the argument of the same name to future.apply::future_lapply and related functions. To set for all runs of the experiment, use the same argument during initialization.
future.seed	Passed as the argument of the same name in future.apply::future_apply.
use_cached	Logical. If TRUE, find and return previously saved results. If cached results cannot be found, continue as if use_cached was FALSE.
return_all_cached_reps	Logical. If FALSE (default), returns only the fit results for the requested n_reps. If TRUE, returns fit results for the requested n_reps plus any additional cached replicates from the (DGP, Method) combinations in the Experiment.
save	If TRUE, save outputs to disk.
checkpoint_n_reps	The number of experiment replicates to compute before saving results to disk. If 0 (the default), no checkpoints are saved.
verbose	Level of verbosity. Default is 1, which prints out messages after major checkpoints in the experiment. If 2, prints additional debugging information for warnings and messages from user-defined functions (in addition to error debugging information). If 0, no messages are printed other than user-defined function error debugging information.
...	Additional ⁠future.*⁠ arguments to pass to future.apply functions. See future.apply::future_lapply() and future.apply::future_mapply().

Value

A tibble containing the results from fitting all Methods across all DGPs for n_reps repetitions. In addition to results columns, has columns named '.rep', '.dgp_name', '.method_name', and the vary_across parameter names if applicable.

Examples

## create toy DGPs, Methods, Evaluators, and Visualizers

# generate data from normal distribution with n samples
normal_dgp <- create_dgp(
  .dgp_fun = function(n) rnorm(n), .name = "Normal DGP", n = 10
)
# generate data from binomial distribution with n samples
bernoulli_dgp <- create_dgp(
  .dgp_fun = function(n) rbinom(n, 1, 0.5), .name = "Bernoulli DGP", n = 10
)

# compute mean of data
mean_method <- create_method(
  .method_fun = function(x) list(mean = mean(x)), .name = "Mean(x)"
)

# evaluate SD of mean(x) across simulation replicates
sd_mean_eval <- create_evaluator(
  .eval_fun = function(fit_results, vary_params = NULL) {
    group_vars <- c(".dgp_name", ".method_name", vary_params)
    fit_results %>%
      dplyr::group_by(dplyr::across(tidyselect::all_of(group_vars))) %>%
      dplyr::summarise(sd = sd(mean), .groups = "keep")
  },
  .name = "SD of Mean(x)"
)

# plot SD of mean(x) across simulation replicates
sd_mean_plot <- create_visualizer(
  .viz_fun = function(fit_results, eval_results, vary_params = NULL,
                      eval_name = "SD of Mean(x)") {
    if (!is.null(vary_params)) {
      add_aes <- ggplot2::aes(
        x = .data[[unique(vary_params)]], y = sd, color = .dgp_name
      )
    } else {
      add_aes <- ggplot2::aes(x = .dgp_name, y = sd)
    }
    plt <- ggplot2::ggplot(eval_results[[eval_name]]) +
      add_aes +
      ggplot2::geom_point()
    if (!is.null(vary_params)) {
      plt <- plt + ggplot2::geom_line()
    }
    return(plt)
  },
  .name = "SD of Mean(x) Plot"
)

# initialize experiment with toy DGPs, Methods, Evaluators, and Visualizers
experiment <- create_experiment(
  name = "Experiment Name",
  dgp_list = list(`Normal DGP` = normal_dgp, `Bernoulli DGP` = bernoulli_dgp),
  method_list = list(`Mean(x)` = mean_method),
  evaluator_list = list(`SD of Mean(x)` = sd_mean_eval),
  visualizer_list = list(`SD of Mean(x) Plot` = sd_mean_plot)
)

# initialize empty experiment with user-defined directory for saving results
experiment <- create_experiment(
  name = "Experiment Name",
  dgp_list = list(`Normal DGP` = normal_dgp, `Bernoulli DGP` = bernoulli_dgp),
  method_list = list(`Mean(x)` = mean_method),
  evaluator_list = list(`SD of Mean(x)` = sd_mean_eval),
  visualizer_list = list(`SD of Mean(x) Plot` = sd_mean_plot),
  save_dir = 'path/to/directory'
)

# initialize experiment with toy DGPs, Methods, Evaluators, and Visualizers
# using piping %>%
experiment <- create_experiment(name = "Experiment Name") %>%
  add_dgp(normal_dgp) %>%
  add_dgp(bernoulli_dgp) %>%
  add_method(mean_method) %>%
  add_evaluator(sd_mean_eval) %>%
  add_visualizer(sd_mean_plot)

# run experiment with 2 replicates
results <- run_experiment(experiment, n_reps = 2)
# uncomment below to view results
# results

# run experiment with varying number of samples n
experiment <- experiment %>%
  add_vary_across(
    .dgp = c("Normal DGP", "Bernoulli DGP"), n = c(1, 10)
  )
# run vary-across experiment with 2 replicates
results <- run_experiment(experiment, n_reps = 2)
# uncomment below to view results
# results

# `run_experiment()` above is equivalent to the following sequence of calls
fit_results <- fit_experiment(experiment, n_reps = 2)
eval_results <- evaluate_experiment(experiment, fit_results)
viz_results <- visualize_experiment(experiment, fit_results, eval_results)

# generate data from all DGPs (and vary across components) in experiment
data_out <- generate_data(experiment, n_reps = 1)

Yu-Group/simChef documentation built on March 25, 2024, 3:22 a.m.