Manuel Raimann
74 lines
2.8 KiB
Rust
74 lines
2.8 KiB
Rust
//! Parameter types example — demonstrates all five parameter types and the derive feature.
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//!
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//! Shows `FloatParam`, `IntParam`, `CategoricalParam`, `BoolParam`, and `EnumParam`
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//! with `.name()` labels, `#[derive(Categorical)]` for enums, and typed access
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//! to results via `CompletedTrial::get()`.
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//!
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//! Run with: `cargo run --example parameter_types --features derive`
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use optimizer::prelude::*;
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use optimizer_derive::Categorical;
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/// Activation functions — `#[derive(Categorical)]` auto-generates the
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/// `Categorical` trait, mapping each variant to a sequential index.
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#[derive(Clone, Debug, Categorical)]
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enum Activation {
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Relu,
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Sigmoid,
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Tanh,
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Gelu,
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}
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fn main() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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// --- Define one of each parameter type, each with a human-readable .name() ---
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// Float: learning rate on a log scale (common for ML hyperparameters)
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let lr = FloatParam::new(1e-5, 1e-1).log_scale().name("lr");
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// Int: number of hidden layers (stepped by 1, the default)
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let n_layers = IntParam::new(1, 5).name("n_layers");
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// Categorical: optimizer algorithm chosen from a list of strings
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let optimizer = CategoricalParam::new(vec!["sgd", "adam", "rmsprop"]).name("optimizer");
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// Bool: whether to apply dropout
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let use_dropout = BoolParam::new().name("use_dropout");
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// Enum: activation function — uses #[derive(Categorical)] above
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let activation = EnumParam::<Activation>::new().name("activation");
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// --- Run the optimization ---
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study
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.optimize(30, |trial| {
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let lr_val = lr.suggest(trial)?;
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let layers = n_layers.suggest(trial)?;
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let opt = optimizer.suggest(trial)?;
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let dropout = use_dropout.suggest(trial)?;
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let act = activation.suggest(trial)?;
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// Simulated loss that depends on all parameters
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let loss = lr_val * f64::from(layers as i32)
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+ if opt == "adam" { -0.05 } else { 0.0 }
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+ if dropout { -0.02 } else { 0.0 }
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+ match act {
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Activation::Gelu => -0.03,
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Activation::Relu => -0.01,
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_ => 0.0,
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};
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Ok::<_, Error>(loss)
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})
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.unwrap();
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// --- Retrieve best trial and read back each parameter with typed .get() ---
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let best = study.best_trial().unwrap();
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println!("Best trial #{} — loss = {:.6}", best.id, best.value);
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println!(" lr = {:.6}", best.get(&lr).unwrap());
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println!(" n_layers = {}", best.get(&n_layers).unwrap());
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println!(" optimizer = {}", best.get(&optimizer).unwrap());
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println!(" use_dropout = {}", best.get(&use_dropout).unwrap());
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println!(" activation = {:?}", best.get(&activation).unwrap());
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}
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