feat: add BOHB sampler for budget-aware Bayesian optimization
BOHB combines TPE's model-guided sampling with Hyperband's budget-aware evaluation by conditioning the TPE model on trials at specific budget levels, producing better-calibrated parameter proposals.
This commit is contained in:
Manuel Raimann
@@ -0,0 +1,689 @@
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//! BOHB (Bayesian Optimization + `HyperBand`) sampler.
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//!
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//! BOHB combines TPE's model-guided sampling with Hyperband's budget-aware
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//! evaluation. Instead of building one global TPE model, BOHB conditions
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//! its TPE model on trials evaluated at a specific budget level, giving
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//! better-calibrated proposals for each rung of the Hyperband schedule.
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//!
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//! # How it works
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//!
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//! 1. Compute all Hyperband rung steps (budget levels) from the config.
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//! 2. On each `sample()` call, scan the history's `intermediate_values`
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//! to find the **largest budget level** with enough observations
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//! (`>= min_points_in_model`).
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//! 3. Build a filtered history where each trial's `value` is replaced
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//! with its intermediate value at that budget level.
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//! 4. Delegate to an internal [`TpeSampler`] for the actual sampling.
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//! 5. Fall back to random sampling if no budget level has enough data.
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//!
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//! # Examples
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//!
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//! ```
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//! use optimizer::sampler::bohb::BohbSampler;
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//! use optimizer::{Direction, Study};
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//!
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//! let bohb = BohbSampler::new();
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//! let pruner = bohb.matching_pruner(Direction::Minimize);
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//! let study: Study<f64> = Study::with_sampler_and_pruner(Direction::Minimize, bohb, pruner);
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//! ```
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//!
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//! Using the builder for custom configuration:
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//!
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//! ```
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//! use optimizer::sampler::bohb::BohbSampler;
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//!
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//! let bohb = BohbSampler::builder()
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//! .min_resource(1)
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//! .max_resource(81)
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//! .reduction_factor(3)
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//! .min_points_in_model(10)
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//! .seed(42)
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//! .build()
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//! .unwrap();
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//! ```
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use crate::distribution::Distribution;
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use crate::error::Result;
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use crate::param::ParamValue;
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use crate::pruner::HyperbandPruner;
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use crate::sampler::tpe::TpeSampler;
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use crate::sampler::{CompletedTrial, Sampler};
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use crate::types::Direction;
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/// A BOHB sampler that combines TPE with Hyperband budget awareness.
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///
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/// BOHB filters trial history by budget level before delegating to TPE,
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/// so the surrogate model is conditioned on trials evaluated at the same
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/// resource level. This produces better-calibrated parameter proposals
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/// than using a single global model across all budgets.
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///
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/// Use [`BohbSampler::matching_pruner`] to create a [`HyperbandPruner`]
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/// with matching parameters.
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pub struct BohbSampler {
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min_resource: u64,
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max_resource: u64,
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reduction_factor: u64,
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min_points_in_model: usize,
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tpe: TpeSampler,
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}
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impl BohbSampler {
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/// Creates a new BOHB sampler with default settings.
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///
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/// Defaults:
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/// - `min_resource`: 1
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/// - `max_resource`: 81
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/// - `reduction_factor`: 3
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/// - `min_points_in_model`: 10
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/// - TPE: default settings
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#[must_use]
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pub fn new() -> Self {
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Self {
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min_resource: 1,
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max_resource: 81,
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reduction_factor: 3,
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min_points_in_model: 10,
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tpe: TpeSampler::new(),
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}
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}
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/// Creates a builder for configuring a BOHB sampler.
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///
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/// # Examples
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///
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/// ```
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/// use optimizer::sampler::bohb::BohbSampler;
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///
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/// let sampler = BohbSampler::builder()
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/// .min_resource(1)
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/// .max_resource(27)
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/// .reduction_factor(3)
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/// .min_points_in_model(5)
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/// .seed(42)
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/// .build()
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/// .unwrap();
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/// ```
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#[must_use]
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pub fn builder() -> BohbSamplerBuilder {
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BohbSamplerBuilder::new()
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}
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/// Creates a [`HyperbandPruner`] with matching Hyperband parameters.
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///
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/// This ensures the pruner's budget schedule is consistent with the
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/// budget levels used by BOHB for model conditioning.
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#[must_use]
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pub fn matching_pruner(&self, direction: Direction) -> HyperbandPruner {
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HyperbandPruner::new()
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.min_resource(self.min_resource)
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.max_resource(self.max_resource)
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.reduction_factor(self.reduction_factor)
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.direction(direction)
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}
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/// Compute all unique budget levels (rung steps) across all Hyperband brackets.
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///
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/// Returns sorted ascending.
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#[allow(
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clippy::cast_precision_loss,
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clippy::cast_possible_truncation,
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clippy::cast_sign_loss
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)]
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fn all_budget_levels(&self) -> Vec<u64> {
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let eta = self.reduction_factor as f64;
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let ratio = self.max_resource as f64 / self.min_resource as f64;
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let s_max = (ratio.ln() / eta.ln()).floor() as u64;
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let mut levels = Vec::new();
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for bracket in 0..=s_max {
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let exponent = s_max.saturating_sub(bracket);
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let min_resource_bracket =
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(self.max_resource as f64 / eta.powi(exponent as i32)).ceil() as u64;
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let mut rung: u32 = 0;
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while let Some(power) = self.reduction_factor.checked_pow(rung) {
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let step = min_resource_bracket.saturating_mul(power);
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if step > self.max_resource {
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break;
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}
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levels.push(step);
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rung += 1;
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}
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}
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levels.sort_unstable();
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levels.dedup();
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levels
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}
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/// Build a filtered history for a specific budget level.
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///
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/// For each trial that has an intermediate value at the given budget step,
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/// creates a new `CompletedTrial` with `value` replaced by the intermediate
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/// value at that step.
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fn filter_history_for_budget(history: &[CompletedTrial], budget: u64) -> Vec<CompletedTrial> {
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history
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.iter()
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.filter_map(|trial| {
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trial
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.intermediate_values
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.iter()
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.find(|(step, _)| *step == budget)
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.map(|(_, iv)| CompletedTrial {
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id: trial.id,
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params: trial.params.clone(),
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distributions: trial.distributions.clone(),
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param_labels: trial.param_labels.clone(),
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value: *iv,
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intermediate_values: trial.intermediate_values.clone(),
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state: trial.state,
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user_attrs: trial.user_attrs.clone(),
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})
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})
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.collect()
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}
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}
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impl Default for BohbSampler {
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fn default() -> Self {
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Self::new()
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}
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}
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impl Sampler for BohbSampler {
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fn sample(
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&self,
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distribution: &Distribution,
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trial_id: u64,
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history: &[CompletedTrial],
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) -> ParamValue {
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// Find the largest budget level with enough observations
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let levels = self.all_budget_levels();
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for &budget in levels.iter().rev() {
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let count = history
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.iter()
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.filter(|t| {
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t.intermediate_values
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.iter()
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.any(|(step, _)| *step == budget)
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})
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.count();
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if count >= self.min_points_in_model {
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let filtered = Self::filter_history_for_budget(history, budget);
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return self.tpe.sample(distribution, trial_id, &filtered);
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}
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}
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// Not enough data at any budget level: delegate to TPE with empty history
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// which triggers its uniform-random startup behavior.
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self.tpe.sample(distribution, trial_id, &[])
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}
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}
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/// Builder for configuring a [`BohbSampler`].
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///
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/// # Examples
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///
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/// ```
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/// use optimizer::sampler::bohb::BohbSamplerBuilder;
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///
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/// let sampler = BohbSamplerBuilder::new()
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/// .min_resource(1)
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/// .max_resource(81)
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/// .reduction_factor(3)
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/// .gamma(0.15)
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/// .seed(42)
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/// .build()
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/// .unwrap();
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/// ```
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pub struct BohbSamplerBuilder {
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min_resource: u64,
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max_resource: u64,
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reduction_factor: u64,
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min_points_in_model: usize,
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tpe_builder: crate::sampler::tpe::TpeSamplerBuilder,
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}
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impl BohbSamplerBuilder {
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/// Creates a new builder with default settings.
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#[must_use]
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pub fn new() -> Self {
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Self {
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min_resource: 1,
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max_resource: 81,
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reduction_factor: 3,
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min_points_in_model: 10,
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tpe_builder: crate::sampler::tpe::TpeSamplerBuilder::new(),
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}
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}
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/// Sets the minimum resource (budget) per trial.
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///
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/// # Panics
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///
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/// Panics if `r` is 0.
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#[must_use]
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pub fn min_resource(mut self, r: u64) -> Self {
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assert!(r > 0, "min_resource must be > 0, got {r}");
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self.min_resource = r;
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self
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}
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/// Sets the maximum resource (budget) per trial.
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///
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/// # Panics
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///
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/// Panics if `r` is 0.
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#[must_use]
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pub fn max_resource(mut self, r: u64) -> Self {
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assert!(r > 0, "max_resource must be > 0, got {r}");
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self.max_resource = r;
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self
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}
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/// Sets the reduction factor (eta).
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///
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/// # Panics
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///
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/// Panics if `eta` is less than 2.
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#[must_use]
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pub fn reduction_factor(mut self, eta: u64) -> Self {
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assert!(eta >= 2, "reduction_factor must be >= 2, got {eta}");
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self.reduction_factor = eta;
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self
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}
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/// Sets the minimum number of observations at a budget level before
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/// BOHB uses TPE instead of random sampling.
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#[must_use]
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pub fn min_points_in_model(mut self, n: usize) -> Self {
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self.min_points_in_model = n;
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self
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}
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/// Sets a fixed gamma value for the internal TPE sampler.
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#[must_use]
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pub fn gamma(mut self, gamma: f64) -> Self {
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self.tpe_builder = self.tpe_builder.gamma(gamma);
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self
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}
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/// Sets a custom gamma strategy for the internal TPE sampler.
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#[must_use]
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pub fn gamma_strategy<G: crate::sampler::tpe::GammaStrategy + 'static>(
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mut self,
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strategy: G,
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) -> Self {
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self.tpe_builder = self.tpe_builder.gamma_strategy(strategy);
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self
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}
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/// Sets the number of EI candidates for the internal TPE sampler.
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#[must_use]
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pub fn n_ei_candidates(mut self, n: usize) -> Self {
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self.tpe_builder = self.tpe_builder.n_ei_candidates(n);
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self
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}
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/// Sets a fixed KDE bandwidth for the internal TPE sampler.
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#[must_use]
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pub fn kde_bandwidth(mut self, bandwidth: f64) -> Self {
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self.tpe_builder = self.tpe_builder.kde_bandwidth(bandwidth);
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self
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}
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/// Sets a seed for reproducible sampling.
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#[must_use]
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pub fn seed(mut self, seed: u64) -> Self {
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self.tpe_builder = self.tpe_builder.seed(seed);
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self
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}
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/// Builds the configured [`BohbSampler`].
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///
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/// # Errors
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///
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/// Returns an error if the TPE configuration is invalid (e.g. gamma
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/// not in (0, 1) or bandwidth not positive).
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pub fn build(self) -> Result<BohbSampler> {
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let tpe = self.tpe_builder.build()?;
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Ok(BohbSampler {
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min_resource: self.min_resource,
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max_resource: self.max_resource,
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reduction_factor: self.reduction_factor,
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min_points_in_model: self.min_points_in_model,
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tpe,
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})
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}
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}
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impl Default for BohbSamplerBuilder {
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fn default() -> Self {
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Self::new()
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}
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}
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#[cfg(test)]
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#[allow(clippy::cast_precision_loss)]
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mod tests {
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use std::collections::HashMap;
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use super::*;
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use crate::distribution::{FloatDistribution, IntDistribution};
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use crate::parameter::ParamId;
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use crate::types::TrialState;
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fn make_trial_with_intermediates(
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id: u64,
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value: f64,
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params: Vec<(ParamId, ParamValue, Distribution)>,
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intermediate_values: Vec<(u64, f64)>,
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) -> CompletedTrial {
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let mut param_map = HashMap::new();
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let mut dist_map = HashMap::new();
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for (param_id, pv, dist) in params {
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param_map.insert(param_id, pv);
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dist_map.insert(param_id, dist);
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}
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CompletedTrial {
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id,
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params: param_map,
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distributions: dist_map,
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param_labels: HashMap::new(),
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value,
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intermediate_values,
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state: TrialState::Complete,
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user_attrs: HashMap::new(),
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}
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}
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#[test]
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fn budget_levels_default() {
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let bohb = BohbSampler::new();
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let levels = bohb.all_budget_levels();
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// With min=1, max=81, eta=3:
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// bracket 0: 1, 3, 9, 27, 81
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// bracket 1: 3, 9, 27, 81
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// bracket 2: 9, 27, 81
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// bracket 3: 27, 81
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// bracket 4: 81
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// Unique sorted: [1, 3, 9, 27, 81]
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assert_eq!(levels, vec![1, 3, 9, 27, 81]);
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}
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#[test]
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fn budget_levels_eta2() {
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let bohb = BohbSampler::builder()
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.min_resource(1)
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.max_resource(16)
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.reduction_factor(2)
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.build()
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.unwrap();
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let levels = bohb.all_budget_levels();
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// s_max = floor(ln(16)/ln(2)) = 4
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// bracket 0: 1, 2, 4, 8, 16
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// bracket 1: 2, 4, 8, 16
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// bracket 2: 4, 8, 16
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// bracket 3: 8, 16
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// bracket 4: 16
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// Unique sorted: [1, 2, 4, 8, 16]
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assert_eq!(levels, vec![1, 2, 4, 8, 16]);
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}
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#[test]
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fn filter_history_selects_correct_budget() {
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let x_id = ParamId::new();
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let dist = Distribution::Float(FloatDistribution {
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low: 0.0,
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high: 1.0,
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log_scale: false,
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step: None,
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});
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let history = vec![
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make_trial_with_intermediates(
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0,
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0.5,
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vec![(x_id, ParamValue::Float(0.3), dist.clone())],
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vec![(1, 0.9), (3, 0.7), (9, 0.5)],
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),
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make_trial_with_intermediates(
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1,
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0.4,
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vec![(x_id, ParamValue::Float(0.6), dist.clone())],
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vec![(1, 0.8), (3, 0.4)],
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),
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make_trial_with_intermediates(
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2,
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0.3,
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vec![(x_id, ParamValue::Float(0.1), dist.clone())],
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vec![(1, 0.7)],
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),
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];
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// Budget 3: trials 0 and 1 have intermediate values at step 3
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let filtered = BohbSampler::filter_history_for_budget(&history, 3);
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assert_eq!(filtered.len(), 2);
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assert!((filtered[0].value - 0.7).abs() < f64::EPSILON);
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assert!((filtered[1].value - 0.4).abs() < f64::EPSILON);
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// Budget 9: only trial 0
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let filtered = BohbSampler::filter_history_for_budget(&history, 9);
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assert_eq!(filtered.len(), 1);
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assert!((filtered[0].value - 0.5).abs() < f64::EPSILON);
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// Budget 27: nobody
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let filtered = BohbSampler::filter_history_for_budget(&history, 27);
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assert!(filtered.is_empty());
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}
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#[test]
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fn matching_pruner_has_same_params() {
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let bohb = BohbSampler::builder()
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.min_resource(2)
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.max_resource(64)
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.reduction_factor(4)
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.build()
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.unwrap();
|
||||
let pruner = bohb.matching_pruner(Direction::Minimize);
|
||||
|
||||
// We can't directly inspect HyperbandPruner fields, but we can
|
||||
// verify it was created without panicking with the same params.
|
||||
// The pruner's rung steps should match BOHB's budget levels.
|
||||
// Just verify it doesn't panic.
|
||||
drop(pruner);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn fallback_to_random_when_insufficient_data() {
|
||||
let bohb = BohbSampler::builder()
|
||||
.min_points_in_model(10)
|
||||
.seed(42)
|
||||
.build()
|
||||
.unwrap();
|
||||
|
||||
let dist = Distribution::Float(FloatDistribution {
|
||||
low: 0.0,
|
||||
high: 1.0,
|
||||
log_scale: false,
|
||||
step: None,
|
||||
});
|
||||
|
||||
// Only 3 trials with intermediate values (< min_points_in_model=10)
|
||||
let x_id = ParamId::new();
|
||||
let history: Vec<CompletedTrial> = (0..3)
|
||||
.map(|i| {
|
||||
make_trial_with_intermediates(
|
||||
i,
|
||||
i as f64,
|
||||
vec![(x_id, ParamValue::Float(i as f64 / 3.0), dist.clone())],
|
||||
vec![(1, i as f64)],
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Should not panic, should sample within bounds
|
||||
for trial_id in 0..20 {
|
||||
let val = bohb.sample(&dist, trial_id, &history);
|
||||
if let ParamValue::Float(v) = val {
|
||||
assert!((0.0..=1.0).contains(&v));
|
||||
} else {
|
||||
panic!("Expected Float");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn uses_budget_level_when_enough_data() {
|
||||
let bohb = BohbSampler::builder()
|
||||
.min_points_in_model(5)
|
||||
.seed(42)
|
||||
.build()
|
||||
.unwrap();
|
||||
|
||||
let dist = Distribution::Float(FloatDistribution {
|
||||
low: 0.0,
|
||||
high: 10.0,
|
||||
log_scale: false,
|
||||
step: None,
|
||||
});
|
||||
|
||||
// Create 20 trials with intermediate values at budget 1.
|
||||
// Good trials have x near 2.0, bad trials have x far from 2.0.
|
||||
let x_id = ParamId::new();
|
||||
let history: Vec<CompletedTrial> = (0..20)
|
||||
.map(|i| {
|
||||
let x = i as f64 / 2.0;
|
||||
let iv_at_1 = (x - 2.0).powi(2);
|
||||
make_trial_with_intermediates(
|
||||
i,
|
||||
iv_at_1, // final value same as intermediate for simplicity
|
||||
vec![(x_id, ParamValue::Float(x), dist.clone())],
|
||||
vec![(1, iv_at_1)],
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Should use TPE on filtered history at budget 1
|
||||
let val = bohb.sample(&dist, 100, &history);
|
||||
if let ParamValue::Float(v) = val {
|
||||
assert!((0.0..=10.0).contains(&v), "Value {v} out of bounds");
|
||||
} else {
|
||||
panic!("Expected Float");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn prefers_largest_budget_level() {
|
||||
let bohb = BohbSampler::builder()
|
||||
.min_resource(1)
|
||||
.max_resource(9)
|
||||
.reduction_factor(3)
|
||||
.min_points_in_model(3)
|
||||
.seed(42)
|
||||
.build()
|
||||
.unwrap();
|
||||
|
||||
let dist = Distribution::Float(FloatDistribution {
|
||||
low: 0.0,
|
||||
high: 10.0,
|
||||
log_scale: false,
|
||||
step: None,
|
||||
});
|
||||
|
||||
// Budget levels: [1, 3, 9]
|
||||
assert_eq!(bohb.all_budget_levels(), vec![1, 3, 9]);
|
||||
|
||||
// Create 5 trials with intermediates at budget 1 and 3
|
||||
let x_id = ParamId::new();
|
||||
let history: Vec<CompletedTrial> = (0..5)
|
||||
.map(|i| {
|
||||
let x = i as f64;
|
||||
make_trial_with_intermediates(
|
||||
i,
|
||||
x,
|
||||
vec![(x_id, ParamValue::Float(x), dist.clone())],
|
||||
vec![(1, x * 2.0), (3, x)],
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
|
||||
// Budget 3 has 5 observations (>= 3), budget 9 has 0.
|
||||
// BOHB should pick budget 3 (largest with enough data).
|
||||
// The filtered history at budget 3 has values [0, 1, 2, 3, 4].
|
||||
let filtered_3 = BohbSampler::filter_history_for_budget(&history, 3);
|
||||
assert_eq!(filtered_3.len(), 5);
|
||||
let filtered_9 = BohbSampler::filter_history_for_budget(&history, 9);
|
||||
assert_eq!(filtered_9.len(), 0);
|
||||
|
||||
// Should sample successfully
|
||||
let val = bohb.sample(&dist, 100, &history);
|
||||
assert!(matches!(val, ParamValue::Float(_)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn builder_validates_tpe_params() {
|
||||
// Invalid gamma
|
||||
let result = BohbSampler::builder().gamma(1.5).build();
|
||||
assert!(result.is_err());
|
||||
|
||||
// Invalid bandwidth
|
||||
let result = BohbSampler::builder().kde_bandwidth(-1.0).build();
|
||||
assert!(result.is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "min_resource must be > 0")]
|
||||
fn builder_rejects_zero_min_resource() {
|
||||
let _ = BohbSampler::builder().min_resource(0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "max_resource must be > 0")]
|
||||
fn builder_rejects_zero_max_resource() {
|
||||
let _ = BohbSampler::builder().max_resource(0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
#[should_panic(expected = "reduction_factor must be >= 2")]
|
||||
fn builder_rejects_small_reduction_factor() {
|
||||
let _ = BohbSampler::builder().reduction_factor(1);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn int_distribution_works() {
|
||||
let bohb = BohbSampler::builder()
|
||||
.min_points_in_model(3)
|
||||
.seed(42)
|
||||
.build()
|
||||
.unwrap();
|
||||
|
||||
let dist = Distribution::Int(IntDistribution {
|
||||
low: 0,
|
||||
high: 100,
|
||||
log_scale: false,
|
||||
step: None,
|
||||
});
|
||||
|
||||
let x_id = ParamId::new();
|
||||
let history: Vec<CompletedTrial> = (0..10)
|
||||
.map(|i| {
|
||||
make_trial_with_intermediates(
|
||||
i,
|
||||
i as f64,
|
||||
vec![(x_id, ParamValue::Int(i.cast_signed() * 10), dist.clone())],
|
||||
vec![(1, i as f64)],
|
||||
)
|
||||
})
|
||||
.collect();
|
||||
|
||||
let val = bohb.sample(&dist, 100, &history);
|
||||
if let ParamValue::Int(v) = val {
|
||||
assert!((0..=100).contains(&v));
|
||||
} else {
|
||||
panic!("Expected Int");
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,5 +1,6 @@
|
||||
//! Sampler trait and implementations for parameter sampling.
|
||||
|
||||
pub mod bohb;
|
||||
#[cfg(feature = "cma-es")]
|
||||
pub mod cma_es;
|
||||
pub mod grid;
|
||||
|
||||
Reference in New Issue
Block a user