gavindiaz/rust-optimizer
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat: unify optimize and optimize_with via blanket Objective impl

Browse Source 
- 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
This commit is contained in:
Manuel Raimann
2026-02-12 13:09:14 +01:00
parent c20a53dfba
commit 47b5f9cec8
41 changed files with 316 additions and 793 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/sampler/random.rs
+23 -15
View File
@@ -1,3 +1,5 @@
use std::cell::RefCell;
use optimizer::parameter::{CategoricalParam, FloatParam, IntParam, Parameter};
use optimizer::sampler::random::RandomSampler;
use optimizer::{Direction, Error, Study};
@@ -7,18 +9,20 @@ fn test_random_sampler_uniform_float_distribution() {
let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(42));
let n_samples = 1000;
let mut samples = Vec::with_capacity(n_samples);
let samples = RefCell::new(Vec::with_capacity(n_samples));
let x_param = FloatParam::new(0.0, 1.0);
study
.optimize(n_samples, |trial| {
.optimize(n_samples, |trial: &mut optimizer::Trial| {
let x = x_param.suggest(trial)?;
samples.push(x);
samples.borrow_mut().push(x);
Ok::<_, Error>(x)
})
.unwrap();
let mut samples = samples.into_inner();
// All samples should be in range
for &s in &samples {
assert!((0.0..=1.0).contains(&s), "sample {s} out of range [0, 1]");
@@ -44,19 +48,20 @@ fn test_random_sampler_uniform_int_distribution() {
let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(123));
let n_samples = 5000;
let mut counts = [0u32; 10]; // counts for values 1-10
let counts = RefCell::new([0u32; 10]); // counts for values 1-10
let n_param = IntParam::new(1, 10);
study
.optimize(n_samples, |trial| {
.optimize(n_samples, |trial: &mut optimizer::Trial| {
let n = n_param.suggest(trial)?;
assert!((1..=10).contains(&n), "sample {n} out of range [1, 10]");
counts[(n - 1) as usize] += 1;
counts.borrow_mut()[(n - 1) as usize] += 1;
Ok::<_, Error>(n as f64)
})
.unwrap();
let counts = counts.into_inner();
let expected = n_samples as f64 / 10.0;
for (i, &count) in counts.iter().enumerate() {
let diff = (count as f64 - expected).abs() / expected;
@@ -76,20 +81,21 @@ fn test_random_sampler_uniform_categorical_distribution() {
let study: Study<f64> = Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(456));
let n_samples = 2000;
let mut counts = [0u32; 4];
let counts = RefCell::new([0u32; 4]);
let choices = ["a", "b", "c", "d"];
let cat_param = CategoricalParam::new(choices.to_vec());
study
.optimize(n_samples, |trial| {
.optimize(n_samples, |trial: &mut optimizer::Trial| {
let choice = cat_param.suggest(trial)?;
let idx = choices.iter().position(|&c| c == choice).unwrap();
counts[idx] += 1;
counts.borrow_mut()[idx] += 1;
Ok::<_, Error>(idx as f64)
})
.unwrap();
let counts = counts.into_inner();
let expected = n_samples as f64 / 4.0;
for (i, &count) in counts.iter().enumerate() {
let diff = (count as f64 - expected).abs() / expected;
@@ -111,28 +117,30 @@ fn test_random_sampler_reproducibility() {
let study2: Study<f64> =
Study::with_sampler(Direction::Minimize, RandomSampler::with_seed(999));
let mut values1 = Vec::new();
let mut values2 = Vec::new();
let values1 = RefCell::new(Vec::new());
let values2 = RefCell::new(Vec::new());
let x_param1 = FloatParam::new(0.0, 100.0);
let x_param2 = FloatParam::new(0.0, 100.0);
study1
.optimize(100, |trial| {
.optimize(100, |trial: &mut optimizer::Trial| {
let x = x_param1.suggest(trial)?;
values1.push(x);
values1.borrow_mut().push(x);
Ok::<_, Error>(x)
})
.unwrap();
study2
.optimize(100, |trial| {
.optimize(100, |trial: &mut optimizer::Trial| {
let x = x_param2.suggest(trial)?;
values2.push(x);
values2.borrow_mut().push(x);
Ok::<_, Error>(x)
})
.unwrap();
let values1 = values1.into_inner();
let values2 = values2.into_inner();
for (i, (v1, v2)) in values1.iter().zip(values2.iter()).enumerate() {
assert_eq!(
v1, v2,