Add print method for BayesMallowsSMC2 objects

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(print,BayesMallowsSMC2)
 export(compute_sequentially)
 export(precompute_topological_sorts)
 export(set_hyperparameters)

R/print.R

@@ -0,0 +1,78 @@
+#' Print Method for BayesMallowsSMC2 Objects
+#'
+#' Prints a summary of a BayesMallowsSMC2 object returned by
+#' [compute_sequentially()].
+#'
+#' @param x An object of class \code{BayesMallowsSMC2}.
+#' @param ... Additional arguments (currently unused).
+#'
+#' @return Invisibly returns the input object \code{x}.
+#'
+#' @details
+#' The print method displays key information about the fitted Bayesian Mallows
+#' model, including:
+#' \itemize{
+#'   \item Number of particles
+#'   \item Number of timepoints
+#'   \item Number of items
+#'   \item Number of clusters
+#'   \item Log marginal likelihood
+#'   \item Final effective sample size (ESS)
+#'   \item Number of resampling events
+#' }
+#'
+#' @export
+#'
+#' @examples
+#' # Fit a model with complete rankings
+#' set.seed(123)
+#' mod <- compute_sequentially(
+#'   complete_rankings,
+#'   hyperparameters = set_hyperparameters(n_items = 5),
+#'   smc_options = set_smc_options(n_particles = 100, n_particle_filters = 1)
+#' )
+#'
+#' # Print the model
+#' print(mod)
+#'
+#' # or simply
+#' mod
+#'
+print.BayesMallowsSMC2 <- function(x, ...) {
+  # Basic validation
+  if (!inherits(x, "BayesMallowsSMC2")) {
+    stop("x must be an object of class 'BayesMallowsSMC2'")
+  }
+
+  required_fields <- c("alpha", "rho", "ESS", "resampling", "log_marginal_likelihood")
+  missing_fields <- setdiff(required_fields, names(x))
+  if (length(missing_fields) > 0) {
+    stop("Object is missing required fields: ", paste(missing_fields, collapse = ", "))
+  }
+
+  # Extract dimensions
+  n_particles <- ncol(x$alpha)
+  n_timepoints <- length(x$ESS)
+  n_items <- dim(x$rho)[1]
+  n_clusters <- nrow(x$alpha)
+
+  # Count resampling events
+  n_resampling_events <- sum(x$resampling)
+
+  # Create header
+  cat("BayesMallowsSMC2 Model\n")
+  cat(strrep("=", nchar("BayesMallowsSMC2 Model")), "\n\n", sep = "")
+
+  # Display basic information
+  cat(sprintf("%-25s %s\n", "Number of particles:", n_particles))
+  cat(sprintf("%-25s %s\n", "Number of timepoints:", n_timepoints))
+  cat(sprintf("%-25s %s\n", "Number of items:", n_items))
+  cat(sprintf("%-25s %s\n\n", "Number of clusters:", n_clusters))
+
+  # Display model fit information
+  cat(sprintf("%-25s %.2f\n", "Log marginal likelihood:", x$log_marginal_likelihood))
+  cat(sprintf("%-25s %.2f\n", "Final ESS:", x$ESS[n_timepoints]))
+  cat(sprintf("%-25s %d/%d\n", "Resampling events:", n_resampling_events, n_timepoints))
+
+  invisible(x)
+}

tests/testthat/test-print.R

@@ -0,0 +1,41 @@
+test_that("print method works for BayesMallowsSMC2 objects", {
+  set.seed(123)
+  mod <- compute_sequentially(
+    complete_rankings,
+    hyperparameters = set_hyperparameters(n_items = 5),
+    smc_options = set_smc_options(n_particles = 100, n_particle_filters = 1)
+  )
+
+  # Test that print method runs without error
+  expect_error(print(mod), NA)
+
+  # Test that print returns the object invisibly
+  expect_identical(print(mod), mod)
+
+  # Capture output and verify it contains expected content
+  output <- capture.output(print(mod))
+  expect_true(any(grepl("BayesMallowsSMC2 Model", output)))
+  expect_true(any(grepl("Number of particles:", output)))
+  expect_true(any(grepl("Number of timepoints:", output)))
+  expect_true(any(grepl("Number of items:", output)))
+  expect_true(any(grepl("Number of clusters:", output)))
+  expect_true(any(grepl("Log marginal likelihood:", output)))
+  expect_true(any(grepl("Final ESS:", output)))
+  expect_true(any(grepl("Resampling events:", output)))
+})
+
+test_that("print method works with partial rankings", {
+  set.seed(456)
+  mod <- compute_sequentially(
+    partial_rankings,
+    hyperparameters = set_hyperparameters(n_items = 5),
+    smc_options = set_smc_options(n_particles = 50, n_particle_filters = 1)
+  )
+
+  # Test that print method runs without error
+  expect_error(print(mod), NA)
+
+  # Capture output and verify it contains expected content
+  output <- capture.output(print(mod))
+  expect_true(any(grepl("BayesMallowsSMC2 Model", output)))
+})