feat: add HTML visualization reports with Plotly.js charts

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).
This commit is contained in:
Manuel Raimann
2026-02-11 20:12:35 +01:00
parent 0e54356345
commit e359392a00
7 changed files with 716 additions and 0 deletions
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use optimizer::prelude::*;
fn main() {
// Multi-parameter optimization with TPE sampler.
let sampler = TpeSampler::builder().seed(42).build().unwrap();
let mut study: Study<f64> = Study::with_sampler(Direction::Minimize, sampler);
study.set_pruner(MedianPruner::new(Direction::Minimize));
let lr = FloatParam::new(1e-5, 1e-1)
.log_scale()
.name("learning_rate");
let n_layers = IntParam::new(1, 5).name("n_layers");
let dropout = FloatParam::new(0.0, 0.5).step(0.05).name("dropout");
let batch_size = CategoricalParam::new(vec![16, 32, 64, 128]).name("batch_size");
study
.optimize(80, |trial| {
let lr_val = lr.suggest(trial)?;
let layers = n_layers.suggest(trial)?;
let drop = dropout.suggest(trial)?;
let bs = batch_size.suggest(trial)?;
// Simulate training with intermediate reporting.
let mut loss = 1.0;
for epoch in 0..10 {
loss *= 0.7 + 0.3 * lr_val.ln().abs() / 12.0;
loss += drop * 0.05;
loss += (1.0 / bs as f64) * 0.1;
loss -= layers as f64 * 0.02;
trial.report(epoch, loss);
if trial.should_prune() {
return Err(TrialPruned.into());
}
}
Ok::<_, Error>(loss)
})
.unwrap();
println!("{}", study.summary());
let path = "optimization_report.html";
generate_html_report(&study, path).unwrap();
println!("\nReport saved to {path}");
}