- Rename stale "GridSearchSampler" panic message to "GridSampler"
- Assert concurrency > 0 in optimize_parallel to prevent deadlock
- Fix inaccurate comment in CSV export (empty for all non-complete trials, not just pruned)
Resolve symlinks and `../` traversals best-effort in `new()` and
`open()`, falling back to the original path if canonicalization fails
(e.g. file doesn't exist yet).
- Remove stale #[allow(dead_code)] and #[allow(too_many_lines)] attributes
- Replace #[allow(dead_code)] with #[cfg(test)] for test-only methods
- Delete unused fill_remaining_independent() and ParamMeta.dist field
- Extract sample_float/int/categorical helpers from TpeSampler, MotpeSampler,
and SamplingEngine to shorten match-heavy sample() methods
- Extract try_fit_kdes() helper to consolidate repeated fallback blocks
- Refactor sample_tpe_float/int to take &FloatDistribution/&IntDistribution
instead of destructured args, removing #[allow(too_many_arguments)]
Fix direct path references for in-scope items (GammaStrategy,
CompletedTrial) and convert feature-gated items to plain backtick
formatting so docs build cleanly without --all-features.
- Add sampler/common.rs with distribution helpers (internal_bounds,
from_internal, to_internal, sample_random) used by 8 samplers
- Add sampler/tpe/common.rs with TPE sampling functions
(sample_tpe_float, sample_tpe_int, sample_tpe_categorical)
shared by TpeSampler, MultivariateTpeSampler, and MotpeSampler
- Remove ~940 lines of near-identical code across sampler modules
- Use Vec::with_capacity() in param_importance() and fanova_with_config()
where iteration count is known or bounded
- Fix flaky MultivariateTpeSampler doctest by increasing trials from 30
to 50
- Add Trial::into_completed() and into_multi_objective_trial() to move
fields instead of cloning 5 HashMaps/Vecs per trial completion
- Fire after_trial callback before pushing to storage, eliminating the
clone-from-storage pattern at all 4 call sites
- Optimize top_trials(n) to sort indices and clone only N trials instead
of cloning all completed trials
- Remove unused set_complete/set_pruned methods
- Replace Mutex<fastrand::Rng> with a stored seed + MurmurHash3 mixer
in RandomSampler, TpeSampler, and MotpeSampler so parallel workers
no longer serialize on a shared lock
- Add mix_seed() and distribution_fingerprint() to rng_util for
deterministic per-call RNG derivation from (seed, trial_id, distribution)
- Include an AtomicU64 call counter to disambiguate parameters that
share the same distribution within a trial
- Add `CompletedTrial::validate()` checking all f64 fields are finite
- Call validate after deserializing each trial in journal storage
- Make `distribution` and `param` modules public (types already in public fields)
- Add blanket `impl Objective<V> for Fn(&mut Trial) -> Result<V, E>`
so closures work directly with `optimize`
- Rewrite optimize, optimize_async, optimize_parallel to accept
`impl Objective<V>` with before_trial/after_trial hooks
- Remove optimize_with, optimize_with_async, optimize_with_parallel
- Remove max_retries and retry logic from Objective trait
- Add explicit closure type annotations for HRTB inference
- Convert FnMut test closures to Fn via RefCell/Cell
- Delete 20 duplicate tests already covered by parameter_tests.rs
- Move 11 pure Trial unit tests into src/trial.rs
- Split remaining 84 integration tests into tests/study/ (9 modules)
- Group sampler tests into tests/sampler/ (7 modules)
- Group pruner tests into tests/pruner/ (2 modules)
- Sampler, pruner, storage, parameter, and multi_objective types are no
longer re-exported at the crate root; access via module paths instead
(e.g. optimizer::sampler::TpeSampler, optimizer::parameter::FloatParam)
- Prelude continues to re-export everything for convenience
- Add module-level pub use re-exports in sampler/mod.rs and parameter.rs
- Update derive macro to reference optimizer::parameter::Categorical
- Rename sampler::differential_evolution to sampler::de
- Fix all downstream imports in tests, benches, and doctests
- Fix all rustdoc intra-doc links to use explicit module paths
Extract shared evolutionary algorithm infrastructure (genetic operators,
candidate management, Das-Dennis reference points) from NSGA-II into a
new genetic.rs module, then build two new multi-objective samplers on top:
- NSGA-III: reference-point-based niching for well-distributed fronts
on 3+ objective problems (Das-Dennis structured points, normalization,
perpendicular distance association, niching selection)
- MOEA/D: decomposition-based optimization with three scalarization
methods (Tchebycheff, WeightedSum, PBI), weight-vector neighborhoods,
and neighborhood-based mating selection
Both implement MultiObjectiveSampler with builder pattern, seeded RNG,
and SBX crossover / polynomial mutation via the shared genetic module.
Replace the internal Vec<CompletedTrial<V>> with a pluggable Storage<V>
trait. MemoryStorage is the default (no behavior change for existing
users). Behind the `journal` feature flag, JournalStorage persists
trials to a JSONL file with fs2 file locking for multi-process safety.
- Storage<V> trait with push(), trials_arc(), refresh() methods
- MemoryStorage<V> wraps Arc<RwLock<Vec<CompletedTrial<V>>>>
- JournalStorage<V> appends JSON lines with exclusive file locks
- Study::with_sampler_and_storage() general constructor
- Study::with_journal() convenience constructor (journal feature)
- Refresh from storage on create_trial() for multi-process discovery
- MSRV bumped to 1.89
Population-based optimizer with mutation + crossover. Supports three
strategies (Rand1, Best1, CurrentToBest1), configurable F and CR,
and auto-sized populations. No extra dependencies needed.
fastrand is smaller, faster, and has no dependencies. Add rng_util
helper for f64 range generation since fastrand lacks a built-in
equivalent. Migrate all samplers, KDE modules, and fANOVA to use
fastrand's concrete Rng type instead of rand's trait-based generics.
Implement a classical Bayesian optimization sampler using a GP surrogate
with Matérn 5/2 kernel and Expected Improvement acquisition function.
Feature-gated behind `gp = ["dep:nalgebra"]`.
Implement functional ANOVA decomposition behind the `fanova` feature
flag. A self-contained random forest is trained on trial data, then
marginal predictions are used to compute per-parameter main effects
and pairwise interaction effects, normalized to sum to 1.0.
Adds Study::fanova() / fanova_with_config(), FanovaResult, and
FanovaConfig. Includes unit and integration tests covering dominant
parameters, interaction detection, consistency with correlation-based
importance, and error handling.
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).
Add to_csv(), export_csv(), and export_json() methods to Study for
exporting trial data to external visualization tools. CSV export works
without extra dependencies; JSON export requires the serde feature.
Extend TPE to handle multi-objective optimization using Pareto-based
splitting. MOTPE uses non-dominated sorting to define "good" (front 0)
vs "bad" (dominated) regions for the KDE models, replacing the
single-objective gamma-based split.
Add MultiObjectiveStudy for optimizing multiple objectives simultaneously,
backed by NSGA-II (Non-dominated Sorting Genetic Algorithm II) with SBX
crossover, polynomial mutation, and constraint-aware dominance.
New public API:
- MultiObjectiveStudy with optimize(), pareto_front(), ask()/tell()
- MultiObjectiveTrial with get(), is_feasible(), user attributes
- MultiObjectiveSampler trait for custom MO samplers
- Nsga2Sampler with builder for population size, crossover/mutation params
- ObjectiveDimensionMismatch error variant
Compute per-parameter importance scores by measuring the absolute
Spearman rank correlation between each parameter's values and the
objective across completed trials. Scores are normalized to sum to 1.0
and returned sorted by descending importance.
Add constraint support so that optimization problems with constraints
(e.g., "model size < 100MB") prefer feasible solutions. Convention:
constraint value <= 0.0 means feasible.
- Add constraint_values field to Trial with set_constraints/getter
- Add constraints field to CompletedTrial with is_feasible() method
- Propagate constraints through complete_trial/prune_trial
- Make best_trial() and top_trials() constraint-aware: feasible trials
rank above infeasible, infeasible ranked by total violation
Manuel Raimann