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Implement Parameters API

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- Add `.name()` builder method on all 5 parameter types for custom labels
- Add `CompletedTrial::get(&param)` for typed parameter access
- Add `Display` impl on `ParamValue`
- Add prelude module at `optimizer::prelude::*`
- Shadow `_with_sampler` methods on `Study<f64>` so `optimize()` auto-uses
  the configured sampler; deprecate `_with_sampler` variants
- Use runtime `Any` downcasting with `trial_factory` to avoid E0592
- Update all examples and tests to use the new API

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Manuel Raimann
2026-02-06 18:54:55 +01:00
parent 55e50e6afb
commit 5dc81fa0ab
11 changed files with 479 additions and 499 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/async_tests.rs
+6 -4
View File
@@ -33,7 +33,7 @@ async fn test_optimize_async_basic() {
}
#[tokio::test]
async fn test_optimize_async_with_sampler() {
async fn test_optimize_async_with_tpe() {
let sampler = TpeSampler::builder()
.seed(42)
.n_startup_trials(5)
@@ -45,7 +45,7 @@ async fn test_optimize_async_with_sampler() {
let x_param = FloatParam::new(-5.0, 5.0);
study
.optimize_async_with_sampler(15, move |mut trial| {
.optimize_async(15, move |mut trial| {
let x_param = x_param.clone();
async move {
let x = x_param.suggest(&mut trial)?;
@@ -82,7 +82,7 @@ async fn test_optimize_parallel() {
}
#[tokio::test]
async fn test_optimize_parallel_with_sampler() {
async fn test_optimize_parallel_with_tpe() {
let sampler = TpeSampler::builder()
.seed(42)
.n_startup_trials(5)
@@ -95,7 +95,7 @@ async fn test_optimize_parallel_with_sampler() {
let y_param = FloatParam::new(-5.0, 5.0);
study
.optimize_parallel_with_sampler(15, 3, move |mut trial| {
.optimize_parallel(15, 3, move |mut trial| {
let x_param = x_param.clone();
let y_param = y_param.clone();
async move {
@@ -128,6 +128,7 @@ async fn test_optimize_async_all_failures() {
}
#[tokio::test]
#[allow(deprecated)]
async fn test_optimize_async_with_sampler_all_failures() {
let study: Study<f64> = Study::new(Direction::Minimize);
@@ -162,6 +163,7 @@ async fn test_optimize_parallel_all_failures() {
}
#[tokio::test]
#[allow(deprecated)]
async fn test_optimize_parallel_with_sampler_all_failures() {
let study: Study<f64> = Study::new(Direction::Minimize);
tests/integration.rs
+60 -21
View File
@@ -31,7 +31,7 @@ fn test_tpe_optimizes_quadratic_function() {
let x_param = FloatParam::new(-10.0, 10.0);
study
.optimize_with_sampler(50, |trial| {
.optimize(50, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>((x - 3.0).powi(2))
})
@@ -64,7 +64,7 @@ fn test_tpe_optimizes_multivariate_function() {
let y_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(100, |trial| {
.optimize(100, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(x * x + y * y)
@@ -96,7 +96,7 @@ fn test_tpe_maximization() {
let x_param = FloatParam::new(-10.0, 10.0);
study
.optimize_with_sampler(50, |trial| {
.optimize(50, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>(-(x - 2.0).powi(2) + 10.0)
})
@@ -231,7 +231,7 @@ fn test_random_sampler_reproducibility() {
let x_param2 = FloatParam::new(0.0, 100.0);
study1
.optimize_with_sampler(100, |trial| {
.optimize(100, |trial| {
let x = x_param1.suggest(trial)?;
values1.push(x);
Ok::<_, Error>(x)
@@ -239,7 +239,7 @@ fn test_random_sampler_reproducibility() {
.unwrap();
study2
.optimize_with_sampler(100, |trial| {
.optimize(100, |trial| {
let x = x_param2.suggest(trial)?;
values2.push(x);
Ok::<_, Error>(x)
@@ -516,7 +516,7 @@ fn test_tpe_with_categorical_parameter() {
// Optimization where the best choice depends on the categorical
study
.optimize_with_sampler(30, |trial| {
.optimize(30, |trial| {
let choice = model_param.suggest(trial)?;
let x = x_param.suggest(trial)?;
@@ -553,7 +553,7 @@ fn test_tpe_with_integer_parameters() {
// Minimize (n - 7)^2 where n in [1, 10]
study
.optimize_with_sampler(30, |trial| {
.optimize(30, |trial| {
let n = n_param.suggest(trial)?;
Ok::<_, Error>(((n - 7) as f64).powi(2))
})
@@ -730,7 +730,7 @@ fn test_study_set_sampler() {
let x_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(10, |trial| {
.optimize(10, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>(x * x)
})
@@ -787,6 +787,7 @@ fn test_optimize_with_callback_all_trials_fail() {
}
#[test]
#[allow(deprecated)]
fn test_optimize_with_sampler_all_trials_fail() {
let study: Study<f64> = Study::new(Direction::Minimize);
@@ -799,6 +800,7 @@ fn test_optimize_with_sampler_all_trials_fail() {
}
#[test]
#[allow(deprecated)]
fn test_optimize_with_callback_sampler_all_trials_fail() {
use std::ops::ControlFlow;
@@ -840,7 +842,7 @@ fn test_tpe_sampler_builder_default_trait() {
let x_param = FloatParam::new(0.0, 1.0);
study
.optimize_with_sampler(5, |trial| {
.optimize(5, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>(x)
})
@@ -857,7 +859,7 @@ fn test_tpe_sampler_default_trait() {
let x_param = FloatParam::new(0.0, 1.0);
study
.optimize_with_sampler(5, |trial| {
.optimize(5, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>(x)
})
@@ -879,7 +881,7 @@ fn test_tpe_with_fixed_kde_bandwidth() {
let x_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(20, |trial| {
.optimize(20, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>(x * x)
})
@@ -907,7 +909,7 @@ fn test_tpe_split_trials_with_two_trials() {
let x_param = FloatParam::new(0.0, 10.0);
study
.optimize_with_sampler(5, |trial| {
.optimize(5, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>(x)
})
@@ -928,7 +930,7 @@ fn test_tpe_with_log_scale_int() {
let batch_param = IntParam::new(1, 1024).log_scale();
study
.optimize_with_sampler(20, |trial| {
.optimize(20, |trial| {
let batch_size = batch_param.suggest(trial)?;
Ok::<_, Error>(((batch_size as f64).log2() - 5.0).powi(2))
})
@@ -951,7 +953,7 @@ fn test_tpe_with_step_distributions() {
let n_param = IntParam::new(0, 100).step(10);
study
.optimize_with_sampler(20, |trial| {
.optimize(20, |trial| {
let x = x_param.suggest(trial)?;
let n = n_param.suggest(trial)?;
Ok::<_, Error>((x - 5.0).powi(2) + ((n - 50) as f64).powi(2))
@@ -963,15 +965,16 @@ fn test_tpe_with_step_distributions() {
}
#[test]
#[allow(deprecated)]
fn test_create_trial_vs_create_trial_with_sampler() {
let sampler = RandomSampler::with_seed(42);
let study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
// create_trial() creates trial without sampler integration
// create_trial() creates trial with sampler integration for Study<f64>
let trial1 = study.create_trial();
assert_eq!(trial1.id(), 0);
// create_trial_with_sampler() creates trial with sampler
// create_trial_with_sampler() is deprecated but still works
let trial2 = study.create_trial_with_sampler();
assert_eq!(trial2.id(), 1);
@@ -1034,7 +1037,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
// First optimize with one parameter
study
.optimize_with_sampler(10, |trial| {
.optimize(10, |trial| {
let x = x_param.suggest(trial)?;
Ok::<_, Error>(x)
})
@@ -1042,7 +1045,7 @@ fn test_tpe_empty_good_or_bad_values_fallback() {
// Now try with a different parameter - TPE won't have history for "y"
study
.optimize_with_sampler(5, |trial| {
.optimize(5, |trial| {
let y = y_param.suggest(trial)?;
Ok::<_, Error>(y)
})
@@ -1084,7 +1087,7 @@ fn test_callback_sampler_early_stopping() {
let x_param = FloatParam::new(0.0, 10.0);
study
.optimize_with_callback_sampler(
.optimize_with_callback(
100,
|trial| {
let x = x_param.suggest(trial)?;
@@ -1197,7 +1200,7 @@ fn test_suggest_bool_with_tpe() {
let x_param = FloatParam::new(0.0, 10.0);
study
.optimize_with_sampler(20, |trial| {
.optimize(20, |trial| {
let use_large = use_large_param.suggest(trial)?;
let x = x_param.suggest(trial)?;
// The value depends on use_large flag
@@ -1293,7 +1296,7 @@ fn test_params_with_tpe() {
let n_param = IntParam::new(1, 10);
study
.optimize_with_sampler(30, |trial| {
.optimize(30, |trial| {
let x = x_param.suggest(trial)?;
let n = n_param.suggest(trial)?;
Ok::<_, Error>(x * x + (n as f64 - 5.0).powi(2))
@@ -1324,3 +1327,39 @@ fn test_single_value_float_range() {
"single-value range should return that value"
);
}
// =============================================================================
// Tests for new API features
// =============================================================================
#[test]
fn test_param_name() {
let param = FloatParam::new(0.0, 1.0).name("learning_rate");
let mut trial = Trial::new(0);
param.suggest(&mut trial).unwrap();
let labels = trial.param_labels();
let label = labels.values().next().unwrap();
assert_eq!(label, "learning_rate");
}
#[test]
fn test_completed_trial_get() {
let study: Study<f64> = Study::new(Direction::Minimize);
let x_param = FloatParam::new(-10.0, 10.0).name("x");
let n_param = IntParam::new(1, 10).name("n");
study
.optimize(5, |trial| {
let x = x_param.suggest(trial)?;
let n = n_param.suggest(trial)?;
Ok::<_, Error>(x * x + n as f64)
})
.unwrap();
let best = study.best_trial().unwrap();
let x_val: f64 = best.get(&x_param).unwrap();
let n_val: i64 = best.get(&n_param).unwrap();
assert!((-10.0..=10.0).contains(&x_val));
assert!((1..=10).contains(&n_val));
}
tests/multivariate_tpe_integration.rs
+10 -10
View File
@@ -55,7 +55,7 @@ fn test_multivariate_tpe_rosenbrock_finds_good_solution() {
let y_param = FloatParam::new(-2.0, 4.0);
study
.optimize_with_sampler(100, |trial| {
.optimize(100, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(rosenbrock(x, y))
@@ -90,7 +90,7 @@ fn test_independent_tpe_rosenbrock() {
let y_param = FloatParam::new(-2.0, 4.0);
study
.optimize_with_sampler(100, |trial| {
.optimize(100, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(rosenbrock(x, y))
@@ -133,7 +133,7 @@ fn test_multivariate_tpe_outperforms_on_correlated_problem() {
let y_param = FloatParam::new(-2.0, 4.0);
study
.optimize_with_sampler(n_trials, |trial| {
.optimize(n_trials, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(rosenbrock(x, y))
@@ -156,7 +156,7 @@ fn test_multivariate_tpe_outperforms_on_correlated_problem() {
let y_param = FloatParam::new(-2.0, 4.0);
study
.optimize_with_sampler(n_trials, |trial| {
.optimize(n_trials, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(rosenbrock(x, y))
@@ -218,7 +218,7 @@ fn test_multivariate_tpe_independent_problem() {
let y_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(50, |trial| {
.optimize(50, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(sphere(x, y))
@@ -250,7 +250,7 @@ fn test_independent_tpe_independent_problem() {
let y_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(50, |trial| {
.optimize(50, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(sphere(x, y))
@@ -290,7 +290,7 @@ fn test_both_samplers_work_on_independent_problem() {
let y_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(n_trials, |trial| {
.optimize(n_trials, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(sphere(x, y))
@@ -312,7 +312,7 @@ fn test_both_samplers_work_on_independent_problem() {
let y_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(n_trials, |trial| {
.optimize(n_trials, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(sphere(x, y))
@@ -360,7 +360,7 @@ fn test_multivariate_tpe_with_group_decomposition() {
let y_param = FloatParam::new(-5.0, 5.0);
study
.optimize_with_sampler(50, |trial| {
.optimize(50, |trial| {
let x = x_param.suggest(trial)?;
let y = y_param.suggest(trial)?;
Ok::<_, Error>(sphere(x, y))
@@ -396,7 +396,7 @@ fn test_multivariate_tpe_mixed_parameter_types() {
let mode_param = CategoricalParam::new(vec!["a", "b", "c"]);
study
.optimize_with_sampler(50, |trial| {
.optimize(50, |trial| {
let x = x_param.suggest(trial)?;
let n = n_param.suggest(trial)?;
let mode = mode_param.suggest(trial)?;