Manuel Raimann
e359392a00
Add a `visualization` feature flag that generates self-contained HTML reports with interactive Plotly.js charts for offline visualization of optimization results. Charts include optimization history, slice plots, parallel coordinates, parameter importance, trial timeline, and intermediate values (learning curves).
46 lines
1.5 KiB
Rust
46 lines
1.5 KiB
Rust
use optimizer::prelude::*;
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fn main() {
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// Multi-parameter optimization with TPE sampler.
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let sampler = TpeSampler::builder().seed(42).build().unwrap();
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let mut study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
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study.set_pruner(MedianPruner::new(Direction::Minimize));
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let lr = FloatParam::new(1e-5, 1e-1)
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.log_scale()
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.name("learning_rate");
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let n_layers = IntParam::new(1, 5).name("n_layers");
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let dropout = FloatParam::new(0.0, 0.5).step(0.05).name("dropout");
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let batch_size = CategoricalParam::new(vec![16, 32, 64, 128]).name("batch_size");
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study
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.optimize(80, |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 drop = dropout.suggest(trial)?;
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let bs = batch_size.suggest(trial)?;
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// Simulate training with intermediate reporting.
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let mut loss = 1.0;
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for epoch in 0..10 {
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loss *= 0.7 + 0.3 * lr_val.ln().abs() / 12.0;
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loss += drop * 0.05;
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loss += (1.0 / bs as f64) * 0.1;
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loss -= layers as f64 * 0.02;
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trial.report(epoch, loss);
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if trial.should_prune() {
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return Err(TrialPruned.into());
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}
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}
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Ok::<_, Error>(loss)
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})
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.unwrap();
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println!("{}", study.summary());
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let path = "optimization_report.html";
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generate_html_report(&study, path).unwrap();
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println!("\nReport saved to {path}");
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}
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