Visualizer: 'R6' class representing a visualizer

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

R6 class representing a visualizer

Description

Visualizer which can visualize() outputs and/or evaluation metrics from Experiment runs.

Generally speaking, users won't directly interact with the Visualizer R6 class, but instead indirectly through create_visualizer() and the following Experiment helpers:

• add_visualizer()

• update_visualizer()

• remove_visualizer()

• get_visualizers()

• visualize_experiment()

Details

When visualizing or running the Experiment (see visualize_experiment() and run_experiment()), the named arguments fit_results, eval_results, and vary_params are automatically passed into the Visualizer function .viz_fun() and serve as placeholders for the fit_experiment() results, the evaluate_experiment() results, and the name of the varying parameter(s), respectively.

To visualize the performance of a method's fit and/or its evaluation metrics then, the Visualizer function .viz_fun() should take in the named arguments fit_results and/or eval_results. See fit_experiment() for details on the format of fit_results. See evaluate_experiment() for details on the format of eval_results. If the Visualizer is used within an Experiment with varying parameters, vary_params should be used as a stand in for the name of this varying parameter(s).

Public fields

name

The name of the Visualizer.

viz_fun

The user-defined visualization function.

viz_params

A (named) list of default parameters to input into the visualization function.

doc_options

List of options to control the aesthetics of the Visualizer's visualization in the knitted R Markdown report.

doc_show

Boolean indicating whether or not the resulting visualizations are shown in the R Markdown report.

Methods

Public methods

• Visualizer$new()

• Visualizer$visualize()

• Visualizer$print()

• Visualizer$clone()

---

Method new()

Initialize a new Visualizer object.

Usage

Visualizer$new(
  .viz_fun,
  .name = NULL,
  .doc_options = list(),
  .doc_show = TRUE,
  ...
)

Arguments

.viz_fun

The user-defined visualization function.

.name

(Optional) The name of the Visualizer.

.doc_options

(Optional) List of options to control the aesthetics of the Visualizer's visualization in the knitted R Markdown report. Currently, possible options are "height" and "width" (in inches). The argument must be specified by position or typed out in whole; no partial matching is allowed for this argument.

.doc_show

If TRUE (default), show the resulting visualization in the R Markdown report; if FALSE, hide output in the R Markdown report.

...

User-defined default arguments to pass into .viz_fun().

Returns

A new instance of Visualizer.

---

Method visualize()

Visualize the performance of methods and/or their evaluation metrics from the Experiment using the Visualizer and the provided parameters.

Usage

Visualizer$visualize(
  fit_results = NULL,
  eval_results = NULL,
  vary_params = NULL,
  ...
)

Arguments

fit_results

A tibble, as returned by fit_experiment().

eval_results

A list of result tibbles, as returned by evaluate_experiment().

vary_params

A vector of DGP or Method parameter names that are varied across in the Experiment.

...

Not used.

Returns

Result of Visualizer$viz_fun().

---

Method print()

Print the Visualizer in a nice format, showing the Visualizer's name, function, parameters, and R Markdown options.

Usage

Visualizer$print()

Returns

The original Visualizer object, invisibly.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Visualizer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

create_visualizer()

Examples

# create DGP
dgp_fun <- function(n, beta, rho, sigma) {
  cov_mat <- matrix(c(1, rho, rho, 1), byrow = TRUE, nrow = 2, ncol = 2)
  X <- MASS::mvrnorm(n = n, mu = rep(0, 2), Sigma = cov_mat)
  y <- X %*% beta + rnorm(n, sd = sigma)
  return(list(X = X, y = y))
}
dgp <- create_dgp(.dgp_fun = dgp_fun,
                  .name = "Linear Gaussian DGP",
                  n = 50, beta = c(1, 0), rho = 0, sigma = 1)

# create Method
lm_fun <- function(X, y, cols) {
  X <- X[, cols]
  lm_fit <- lm(y ~ X)
  pvals <- summary(lm_fit)$coefficients[-1, "Pr(>|t|)"] %>%
    setNames(paste(paste0("X", cols), "p-value"))
  return(pvals)
}
lm_method <- create_method(
  .method_fun = lm_fun,
  .name = "OLS",
  cols = c(1, 2)
)

# create an example Evaluator function
reject_prob_fun <- function(fit_results, vary_params = NULL, alpha = 0.05) {
  fit_results[is.na(fit_results)] <- 1
  group_vars <- c(".dgp_name", ".method_name", vary_params)
  eval_out <- fit_results %>%
    dplyr::group_by(across({{group_vars}})) %>%
    dplyr::summarise(
      n_reps = dplyr::n(),
      `X1 Reject Prob.` = mean(`X1 p-value` < alpha),
      `X2 Reject Prob.` = mean(`X2 p-value` < alpha)
    )
  return(eval_out)
}

reject_prob_eval <- Evaluator$new(.eval_fun = reject_prob_fun,
                                  .name = "Rejection Prob (alpha = 0.05)")

# create Experiment
experiment <- create_experiment() %>%
  add_dgp(dgp) %>%
  add_method(lm_method) %>%
  add_evaluator(reject_prob_eval) %>%
  add_vary_across(.dgp = dgp, rho = seq(0.91, 0.99, 0.02))

fit_results <- fit_experiment(experiment, n_reps=10)
eval_results <- evaluate_experiment(experiment, fit_results)

# create an example Visualizer function which takes fit_results as input
power_plot_fun <- function(fit_results, vary_params = NULL, col = "X1") {
  if (is.list(fit_results[[vary_params]])) {
    # deal with the case when we vary across a parameter that is vector-valued
    fit_results[[vary_params]] <- list_col_to_chr(
      fit_results[[vary_params]], name = vary_params, verbatim = TRUE
    )
  }
  plt <- ggplot2::ggplot(fit_results) +
    ggplot2::aes(x = .data[[paste(col, "p-value")]],
                 color = as.factor(.method_name)) +
    ggplot2::geom_abline(slope = 1, intercept = 0,
                         color = "darkgray", linetype = "solid", linewidth = 1) +
    ggplot2::stat_ecdf(size = 1) +
    ggplot2::scale_x_continuous(limits = c(0, 1)) +
    ggplot2::labs(x = "t", y = "P( p-value \u2264 t )",
                  linetype = "", color = "Method")
  if (!is.null(vary_params)) {
    plt <- plt + ggplot2::facet_wrap(~ .data[[vary_params]])
  }
  return(plt)
}

power_plot <- Visualizer$new(.viz_fun = power_plot_fun, .name = "Power")

power_plot$visualize(
  fit_results = fit_results, eval_results = eval_results, vary_params = "rho"
)

# create an example Visualizer function which takes eval_results as input
reject_prob_plot_fun <- function(eval_results, vary_params = NULL, eval_name) {
  eval_results_df <- eval_results[[eval_name]]
  if (is.list(eval_results_df[[vary_params]])) {
    # deal with the case when we vary across a parameter that is vector-valued
    eval_results_df[[vary_params]] <- list_col_to_chr(
      eval_results_df[[vary_params]], name = vary_params, verbatim = TRUE
    )
  }
  plt <- ggplot2::ggplot(eval_results_df) +
    ggplot2::aes(x = .data[[vary_params]], y = `X1 Reject Prob.`,
                 color = as.factor(.method_name),
                 fill = as.factor(.method_name)) +
    ggplot2::labs(x = vary_params, y = eval_name,
                  color = "Method", fill = "Method") +
    ggplot2::scale_y_continuous(limits = c(0, 1))
  if (is.numeric(eval_results_df[[vary_params]])) {
    plt <- plt +
      ggplot2::geom_line() +
      ggplot2::geom_point(size = 2)
  } else {
    plt <- plt +
      ggplot2::geom_bar(stat = "identity")
  }
  return(plt)
}

reject_prob_plot <- Visualizer$new(.viz_fun = reject_prob_plot_fun,
                                   .name = "Rejection Prob (alpha = 0.05) Plot",
                                   eval_name = "Rejection Prob (alpha = 0.05)")

reject_prob_plot$visualize(
  fit_results = fit_results, eval_results = eval_results, vary_params = "rho"
)

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