Adds a tiny additive surface that turns any OptimizationResult into
a self-describing JSON file the heuropt-explorer webapp can load.
Real Pareto fronts have 50–200+ candidates spanning 2–7+ objectives;
reading them as numbers in a terminal scales badly. This commit
ships the heuropt-side of the explorer — the schema and the export
API. The webapp itself lives in a separate repo on its own cadence.
Three trait/type extensions, all with working defaults so existing
impls compile untouched:
- Objective gains optional `label: Option<String>` and
`unit: Option<String>` fields, plus fluent builders
`.with_label("Price").with_unit(\"\$k\")`. Existing
`Objective::minimize(name)` / `Objective::maximize(name)` are
unchanged. Both fields are #[serde(default,
skip_serializing_if = \"Option::is_none\")] so existing JSON
round-trips cleanly.
- Problem trait gains an optional
`fn decision_schema(&self) -> Vec<DecisionVariable>` with default
empty impl. Override it to provide pretty names / labels / units /
bounds for the explorer; the default produces fallback x[0],
x[1], … names. New DecisionVariable type at
`heuropt::core::DecisionVariable` with builder methods.
- New `heuropt::traits::AlgorithmInfo` trait with `name()`
(required) and `seed()` (default None). Every built-in algorithm
— all 33 — implements it. Separate from Optimizer<P> so
multi-fidelity Hyperband (which uses PartialProblem) implements
it uniformly.
The new explorer module:
- `heuropt::explorer::ExplorerExport` envelope with versioned
schema (SCHEMA_VERSION = 1).
- ExplorerCandidate per row, with front_rank from
non_dominated_sort attached at export time so downstream tools
don't re-derive it.
- ToDecisionValues adapter trait with provided impls for Vec<f64>,
Vec<bool>, Vec<usize>, Vec<i64>; custom decision types implement
one method.
- Free functions to_json / to_writer / to_file plus a builder API
(with_algorithm_info, with_problem_name, with_wall_clock,
with_timestamp).
- Gated on the existing `serde` feature, which now also pulls in
`serde_json` as a dep.
The example:
- `examples/pick_a_car.rs` — promotes the README's PickACar to a
real example, fully enriched with Objective labels/units and a
decision_schema. Runs NSGA-III for 200 generations, prints a
sample slice, writes pick_a_car.json. Gated on `serde`.
10 new explorer unit tests cover round-trip serde, fallback
decision-variable names, enriched export, AlgorithmInfo flow,
front-rank correctness, and the ToDecisionValues impls. Lib test
count went from 229 to 242.
Async coverage was incomplete in 0.7 (only RandomSearch and
DifferentialEvolution had run_async). 0.8 closes the gap: every one
of the 33 algorithms now exposes
run_async(&problem, concurrency).await, gated on the async feature.
- Population-based algorithms fan out per-generation evaluations
through evaluate_batch_async with concurrency-bounded
FuturesOrdered chunks.
- Steady-state algorithms (HillClimber, SimulatedAnnealing,
OnePlusOneEs, Paes, NelderMead) await each step sequentially;
they accept the concurrency parameter for API uniformity.
- TabuSearch fans out the K-neighbor batch each step.
- Surrogate algorithms (BayesianOpt, Tpe) batch the initial design
and await per-iteration acquisitions sequentially so the surrogate
can update between picks.
- Hyperband uses a new AsyncPartialProblem trait (mirroring
PartialProblem for multi-fidelity workloads) and a parallel
evaluate_batch_at_budget_async helper; each Successive-Halving
rung fans out its budgeted evaluations.
All paths preserve seeded determinism: RNG draws happen on the main
task in the same order as the sync path, and only the evaluations
are concurrent.
Adds a dedicated cookbook recipe at docs/book/src/cookbook/async.md
with a worked example (DifferentialEvolution under tokio) and
guidance on picking concurrency. Cross-references in SUMMARY.md
and cookbook.md are updated to surface the new recipe.
The follow-up docs commit reconciles the rest of the user guide
and README to describe the new feature; this commit is the bare
async surface.
Adds the headline async/await capability for IO-bound evaluations
(HTTP services, RPC clients, spawned subprocesses) — the
differentiator vs pymoo / hyperopt / MOEA Framework.
No public-API breaks for synchronous users. The new surface is
gated behind a new `async` feature flag.
- core::async_problem::AsyncProblem trait (async fn evaluate_async).
- algorithms::parallel_eval_async::evaluate_batch_async helper using
futures::stream::FuturesOrdered with concurrency-bounded chunks;
preserves input order so seeded determinism holds when evaluations
are themselves deterministic.
- run_async on RandomSearch and DifferentialEvolution.
- examples/async_eval.rs: simulated 20 ms remote service. concurrency=1
→ 4.2 s, concurrency=4 → 2.1 s (2× speedup).
Bumps Cargo.toml to 0.8.0; CHANGELOG entry covers the above plus a
note that 0.6.0/0.7.0 on crates.io are yanked experimentals and 0.8
picks up cleanly from 0.5.
Multi-fidelity optimization. Hyperband (Li et al. 2017) and its
foundation Successive Halving (Karnin et al. 2013) tune
hyperparameters by allocating *uneven* compute across configurations:
sample many cheap-to-evaluate-at-low-budget configs, then promote
the survivors to higher budgets. Crucial for ML hyperparameter
tuning where each evaluation is a partial training run.
This requires a new trait — `Problem::evaluate` is a single-shot
black box, but Hyperband needs to evaluate the SAME decision at
different fidelity budgets:
pub trait PartialProblem {
type Decision: Clone;
fn objectives(&self) -> ObjectiveSpace;
fn evaluate_at_budget(&self, decision: &Self::Decision,
budget: f64) -> Evaluation;
}
`PartialProblem` is intentionally NOT a sub-trait of `Problem`.
Implementors who already have a `Problem` and want their
`evaluate_at_budget` to ignore budget can write a one-line wrapper.
`Hyperband` is the optimizer:
pub struct HyperbandConfig {
max_budget: f64, eta: f64, max_brackets: usize, seed: u64,
}
pub struct Hyperband<I> { config, initializer, ... }
Single-objective only. The decision sampler is an `Initializer<D>` so
it works the same way as every other heuropt algorithm. Generic over
decision type.
The single trait users implement to describe an optimization problem:
associated `Decision: Clone` plus `objectives()` and `evaluate()`.
Both signatures match spec §8.1; `evaluate` takes `&self`.
Plain-data structs and the seeded Rng alias from spec §7. Each lives in
its own file under src/core/ with unit tests:
- Direction, Objective, ObjectiveSpace (with as_minimization negating
only Maximize axes)
- Evaluation (is_feasible == constraint_violation <= 0.0)
- Candidate<D>, Population<D> (concrete, public fields, From<Vec<...>>)
- OptimizationResult<D>
- type Rng = rand::rngs::StdRng + rng_from_seed, so no public trait is
generic over the RNG (spec §2.5)
All public types behind #[cfg_attr(feature = "serde", derive(...))] so
the optional feature wires up without changing the default surface.
swaits