Manuel Raimann
47b5f9cec8
- Add blanket `impl Objective<V> for Fn(&mut Trial) -> Result<V, E>` so closures work directly with `optimize` - Rewrite optimize, optimize_async, optimize_parallel to accept `impl Objective<V>` with before_trial/after_trial hooks - Remove optimize_with, optimize_with_async, optimize_with_parallel - Remove max_retries and retry logic from Objective trait - Add explicit closure type annotations for HRTB inference - Convert FnMut test closures to Fn via RefCell/Cell
79 lines
3.0 KiB
Rust
79 lines
3.0 KiB
Rust
//! Sampler comparison example — benchmarks Random, TPE, and Grid samplers on the same problem.
|
|
//!
|
|
//! Runs the Sphere function f(x, y) = x² + y² with each sampler and compares the best
|
|
//! value found. This shows how sampler choice affects optimization quality.
|
|
//!
|
|
//! Run with: `cargo run --example sampler_comparison`
|
|
|
|
use optimizer::prelude::*;
|
|
|
|
/// Shared objective function: Sphere function with global minimum at (0, 0).
|
|
/// Simple enough to solve well, but 2-D so samplers have room to differ.
|
|
fn sphere(x: f64, y: f64) -> f64 {
|
|
x.powi(2) + y.powi(2)
|
|
}
|
|
|
|
/// Run an optimization study and return the best value found.
|
|
fn run_study(study: Study<f64>, n_trials: usize) -> f64 {
|
|
// Use asymmetric ranges so the Grid sampler tracks each parameter independently.
|
|
let x = FloatParam::new(-5.0, 5.0).name("x");
|
|
let y = FloatParam::new(-3.0, 3.0).name("y");
|
|
|
|
study
|
|
.optimize(n_trials, |trial: &mut optimizer::Trial| {
|
|
let x_val = x.suggest(trial)?;
|
|
let y_val = y.suggest(trial)?;
|
|
Ok::<_, Error>(sphere(x_val, y_val))
|
|
})
|
|
.unwrap();
|
|
|
|
let best = study.best_trial().unwrap();
|
|
println!(
|
|
" Best trial #{:>3}: x = {:>7.4}, y = {:>7.4}, f(x,y) = {:.6}",
|
|
best.id,
|
|
best.get(&x).unwrap(),
|
|
best.get(&y).unwrap(),
|
|
best.value,
|
|
);
|
|
best.value
|
|
}
|
|
|
|
fn main() {
|
|
let n_trials: usize = 100;
|
|
println!("Comparing samplers on Sphere(x, y) = x² + y² ({n_trials} trials each)");
|
|
println!();
|
|
|
|
// --- Random sampler (baseline) ---
|
|
// Pure random search: samples uniformly at random. Fast but not guided.
|
|
println!("1. Random sampler:");
|
|
let random_best = run_study(Study::minimize(RandomSampler::with_seed(42)), n_trials);
|
|
|
|
// --- TPE sampler (Bayesian) ---
|
|
// Tree-structured Parzen Estimator: builds a probabilistic model of good vs bad
|
|
// regions and focuses sampling where improvements are likely.
|
|
println!("\n2. TPE sampler (Bayesian):");
|
|
let tpe = TpeSampler::builder()
|
|
.n_startup_trials(10) // random exploration for the first 10 trials
|
|
.n_ei_candidates(24) // candidates evaluated per Expected Improvement step
|
|
.gamma(0.25) // top 25% of trials define the "good" distribution
|
|
.seed(42)
|
|
.build()
|
|
.unwrap();
|
|
let tpe_best = run_study(Study::minimize(tpe), n_trials);
|
|
|
|
// --- Grid sampler (exhaustive) ---
|
|
// Evaluates evenly spaced grid points. Each parameter gets its own grid that
|
|
// is sampled in order, so n_points_per_param must be >= n_trials.
|
|
println!("\n3. Grid sampler (exhaustive):");
|
|
let grid = GridSearchSampler::builder()
|
|
.n_points_per_param(n_trials) // one grid point per trial per parameter
|
|
.build();
|
|
let grid_best = run_study(Study::minimize(grid), n_trials);
|
|
|
|
// --- Summary ---
|
|
println!("\n--- Summary ---");
|
|
println!(" Random : {random_best:.6}");
|
|
println!(" TPE : {tpe_best:.6}");
|
|
println!(" Grid : {grid_best:.6}");
|
|
}
|