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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
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examples/parameter_api.rs
+16 -15
View File
@@ -1,7 +1,4 @@
use optimizer::parameter::{
BoolParam, CategoricalParam, EnumParam, FloatParam, IntParam, Parameter,
};
use optimizer::{Direction, Study};
use optimizer::prelude::*;
use optimizer_derive::Categorical;
#[derive(Clone, Debug, Categorical)]
@@ -15,13 +12,13 @@ fn main() {
let study: Study<f64> = Study::new(Direction::Minimize);
// Define parameters outside the objective function
let lr_param = FloatParam::new(1e-5, 1e-1).log_scale();
let n_layers_param = IntParam::new(1, 5);
let units_param = IntParam::new(32, 512).step(32);
let optimizer_param = CategoricalParam::new(vec!["sgd", "adam", "rmsprop"]);
let activation_param = EnumParam::<Activation>::new();
let batch_size_param = IntParam::new(16, 256).log_scale();
let use_dropout_param = BoolParam::new();
let lr_param = FloatParam::new(1e-5, 1e-1).name("lr").log_scale();
let n_layers_param = IntParam::new(1, 5).name("n_layers");
let units_param = IntParam::new(32, 512).name("units").step(32);
let optimizer_param = CategoricalParam::new(vec!["sgd", "adam", "rmsprop"]).name("optimizer");
let activation_param = EnumParam::<Activation>::new().name("activation");
let batch_size_param = IntParam::new(16, 256).name("batch_size").log_scale();
let use_dropout_param = BoolParam::new().name("use_dropout");
study
.optimize(20, |trial| {
@@ -43,13 +40,17 @@ fn main() {
trial.id()
);
Ok::<_, optimizer::Error>(loss)
Ok::<_, Error>(loss)
})
.unwrap();
let best = study.best_trial().unwrap();
println!("\nBest trial: value={:.4}", best.value);
for (id, label) in &best.param_labels {
println!(" {}: {:?}", label, best.params[id]);
}
println!(" lr: {:.6}", best.get(&lr_param).unwrap());
println!(" n_layers: {}", best.get(&n_layers_param).unwrap());
println!(" units: {}", best.get(&units_param).unwrap());
println!(" optimizer: {}", best.get(&optimizer_param).unwrap());
println!(" activation: {:?}", best.get(&activation_param).unwrap());
println!(" batch_size: {}", best.get(&batch_size_param).unwrap());
println!(" use_dropout: {}", best.get(&use_dropout_param).unwrap());
}