Manuel Raimann
95 lines
3.4 KiB
Rust
95 lines
3.4 KiB
Rust
//! Sampler comparison example — benchmarks Random, TPE, and Grid samplers on the same problem.
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//!
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//! Runs the Sphere function f(x, y) = x² + y² with each sampler and compares the best
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//! value found. This shows how sampler choice affects optimization quality.
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//!
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//! Run with: `cargo run --example sampler_comparison`
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use optimizer::prelude::*;
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/// Shared objective function: Sphere function with global minimum at (0, 0).
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/// Simple enough to solve well, but 2-D so samplers have room to differ.
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fn sphere(x: f64, y: f64) -> f64 {
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x.powi(2) + y.powi(2)
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}
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/// Run an optimization study and return the best value found.
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fn run_study(study: Study<f64>, n_trials: usize) -> f64 {
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// Use asymmetric ranges so the Grid sampler tracks each parameter independently.
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let x = FloatParam::new(-5.0, 5.0).name("x");
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let y = FloatParam::new(-3.0, 3.0).name("y");
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study
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.optimize(n_trials, |trial| {
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let x_val = x.suggest(trial)?;
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let y_val = y.suggest(trial)?;
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Ok::<_, Error>(sphere(x_val, y_val))
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})
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.unwrap();
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let best = study.best_trial().unwrap();
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println!(
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" Best trial #{:>3}: x = {:>7.4}, y = {:>7.4}, f(x,y) = {:.6}",
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best.id,
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best.get(&x).unwrap(),
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best.get(&y).unwrap(),
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best.value,
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);
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best.value
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}
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fn main() {
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let n_trials: usize = 100;
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println!("Comparing samplers on Sphere(x, y) = x² + y²");
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println!(" Search space: x ∈ [-5, 5], y ∈ [-3, 3]");
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println!(" Known minimum: f(0, 0) = 0");
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println!(" Trials per sampler: {n_trials}");
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println!();
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// --- Random sampler (baseline) ---
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// Pure random search: samples uniformly at random. Fast but not guided.
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println!("1. Random sampler:");
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let random_best = run_study(Study::minimize(RandomSampler::with_seed(42)), n_trials);
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// --- TPE sampler (Bayesian) ---
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// Tree-structured Parzen Estimator: builds a probabilistic model of good vs bad
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// regions and focuses sampling where improvements are likely.
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println!("\n2. TPE sampler (Bayesian):");
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let tpe = TpeSampler::builder()
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.n_startup_trials(10) // random exploration for the first 10 trials
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.n_ei_candidates(24) // candidates evaluated per Expected Improvement step
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.gamma(0.25) // top 25% of trials define the "good" distribution
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.seed(42)
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.build()
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.unwrap();
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let tpe_best = run_study(Study::minimize(tpe), n_trials);
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// --- Grid sampler (exhaustive) ---
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// Evaluates evenly spaced grid points. Each parameter gets its own grid that
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// is sampled in order, so n_points_per_param must be >= n_trials.
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println!("\n3. Grid sampler (exhaustive):");
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let grid = GridSearchSampler::builder()
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.n_points_per_param(n_trials) // one grid point per trial per parameter
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.build();
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let grid_best = run_study(Study::minimize(grid), n_trials);
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// --- Summary ---
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println!("\n--- Summary ---");
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println!(" Random : {random_best:.6}");
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println!(" TPE : {tpe_best:.6}");
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println!(" Grid : {grid_best:.6}");
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println!();
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// Find the winner
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let results = [
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("Random", random_best),
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("TPE", tpe_best),
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("Grid", grid_best),
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];
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let (winner, _) = results
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.iter()
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.min_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
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.unwrap();
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println!("Winner: {winner} (closest to known minimum of 0.0)");
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}
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