tuneR Grid vs Random Search Mock Benchmark

tuneR_bench.R

# Mocking a hyperparameter tuning process

simulate_model_eval <- function(params) {
  # Simulate evaluation time (e.g., matrix multiplication)
  mat <- matrix(rnorm(100 * 100), nrow = 100)
  res <- solve(mat %*% t(mat) + diag(100))
  # Return dummy accuracy
  return(runif(1, 0.8, 0.95))
}

grid_search <- function(combinations) {
  best_acc <- 0
  for(i in 1:combinations) {
    acc <- simulate_model_eval(i)
    if (acc > best_acc) best_acc <- acc
  }
  return(best_acc)
}

random_search <- function(combinations, n_random) {
  best_acc <- 0
  for(i in sample(1:combinations, n_random)) {
    acc <- simulate_model_eval(i)
    if (acc > best_acc) best_acc <- acc
  }
  return(best_acc)
}

# Run Benchmark
start_time <- Sys.time()
acc_grid <- grid_search(1000)
grid_time <- Sys.time() - start_time

start_time <- Sys.time()
acc_random <- random_search(1000, 250)
random_time <- Sys.time() - start_time

cat(sprintf("Grid Search (1000 evals): %.2f sec, Max Accuracy: %.4f\n", as.numeric(grid_time, units="secs"), acc_grid))
cat(sprintf("Random Search (250 evals): %.2f sec, Max Accuracy: %.4f\n", as.numeric(random_time, units="secs"), acc_random))