diff --git a/rustuna_core/src/sampler.rs b/rustuna_core/src/sampler.rs index 6608872e..8f579eb0 100644 --- a/rustuna_core/src/sampler.rs +++ b/rustuna_core/src/sampler.rs @@ -54,6 +54,62 @@ impl RandomSampler { } } } + +fn round_to_step(value: f64, low: f64, high: f64, step: f64) -> f64 { + let mut stepped = low + ((value - low) / step).round() * step; + if stepped < low { + stepped = low; + } + if stepped > high { + stepped = high; + } + stepped +} + +fn sample_float_with_step( + rng: &mut StdRng, + low: f64, + high: f64, + step: Option, + log: bool, +) -> f64 { + match (step, log) { + (None, false) => rng.gen_range(low..high), + (None, true) => rng.gen_range(low.ln()..high.ln()).exp(), + (Some(step), false) => { + let max_index = ((high - low) / step).floor().max(0.0) as i64; + let index = rng.gen_range(0..=max_index); + low + (index as f64) * step + } + (Some(step), true) => { + let value = rng.gen_range(low.ln()..high.ln()).exp(); + round_to_step(value, low, high, step) + } + } +} + +fn sample_int_with_step(rng: &mut StdRng, low: i64, high: i64, step: i64, log: bool) -> f64 { + let low_f = low as f64; + let high_f = high as f64; + let step_f = step as f64; + if log { + let value = rng.gen_range(low_f.ln()..high_f.ln()).exp(); + let max_index = ((high_f - low_f) / step_f).floor().max(0.0) as i64; + let mut index = ((value - low_f) / step_f).round() as i64; + if index < 0 { + index = 0; + } + if index > max_index { + index = max_index; + } + low_f + (index as f64) * step_f + } else { + let max_index = ((high - low) / step).max(0); + let index = rng.gen_range(0..=max_index); + (low + index * step) as f64 + } +} + impl Sampler for RandomSampler { fn sample_independent( &mut self, @@ -63,24 +119,23 @@ impl Sampler for RandomSampler { distribution: &Distribution, ) -> Result { match distribution { - // TODO(c-bata): Support step and log Distribution::Float { low, high, - step: _, - log: _, + step, + log, } => { - let param_value = self.rng.gen_range(*low..*high); - Ok(param_value) + let value = sample_float_with_step(&mut self.rng, *low, *high, *step, *log); + Ok(value) } Distribution::Int { low, high, - step: _, - log: _, + step, + log, } => { - let param_value = self.rng.gen_range(*low..*high); - Ok(param_value as f64) + let value = sample_int_with_step(&mut self.rng, *low, *high, *step, *log); + Ok(value) } Distribution::Categorical { cardinality } => { let param_value = self.rng.gen_range(0..*cardinality); diff --git a/rustuna_importance/src/test_utils.rs b/rustuna_importance/src/test_utils.rs index dc6cffc4..59a7080d 100644 --- a/rustuna_importance/src/test_utils.rs +++ b/rustuna_importance/src/test_utils.rs @@ -20,7 +20,7 @@ fn single_objective(mut trial: Trial) -> Result> { let x3 = trial.suggest( "x3", &Distribution::Float { - low: 0.0, + low: 1.0, high: 3.0, step: Some(1.0), log: true, @@ -30,7 +30,7 @@ fn single_objective(mut trial: Trial) -> Result> { let x5 = trial.suggest( "x5", &Distribution::Int { - low: -3, + low: 1, high: 3, step: 1, log: true, @@ -54,7 +54,7 @@ fn multi_objective(mut trial: Trial) -> Result> { let x3 = trial.suggest( "x3", &Distribution::Float { - low: 0.0, + low: 0.1, high: 3.0, step: Some(1.0), log: true, @@ -64,7 +64,7 @@ fn multi_objective(mut trial: Trial) -> Result> { let x5 = trial.suggest( "x5", &Distribution::Int { - low: -3, + low: 1, high: 3, step: 1, log: true, diff --git a/rustuna_pyo3/rustuna/_rustuna.pyi b/rustuna_pyo3/rustuna/_rustuna.pyi index a2ca37e6..60389880 100644 --- a/rustuna_pyo3/rustuna/_rustuna.pyi +++ b/rustuna_pyo3/rustuna/_rustuna.pyi @@ -755,11 +755,18 @@ class Sampler: """Factory class for creating sampler instances.""" @classmethod - def tpe(cls, seed: int | None = None, multivariate: bool = True) -> Sampler: + def tpe( + cls, + seed: int | None = None, + n_startup_trials: int = 10, + multivariate: bool = True, + ) -> Sampler: """Create a Tree-structured Parzen Estimator sampler. Args: seed: Random seed. If None, a random seed is used. + n_startup_trials: Number of startup trials before using TPE. + multivariate: Whether to use multivariate TPE. Returns: A TPE sampler instance. diff --git a/rustuna_pyo3/src/sampler.rs b/rustuna_pyo3/src/sampler.rs index ed397f65..24f644c6 100644 --- a/rustuna_pyo3/src/sampler.rs +++ b/rustuna_pyo3/src/sampler.rs @@ -32,14 +32,11 @@ impl PySampler { n_startup_trials: usize, multivariate: bool, ) -> PyResult { - let rs_sampler = match seed { - Some(seed) => TpeSampler::from_config(TpeConfig { - seed: Some(seed), - n_startup_trials, - multivariate, - }), - None => TpeSampler::new(), - }; + let rs_sampler = TpeSampler::from_config(TpeConfig { + seed, + n_startup_trials, + multivariate, + }); Ok(PySampler { sampler: Arc::new(Mutex::new(rs_sampler)), kind: "tpe", diff --git a/rustuna_pyo3/tests/test_optuna_samplers.py b/rustuna_pyo3/tests/test_optuna_samplers.py new file mode 100644 index 00000000..cc3480da --- /dev/null +++ b/rustuna_pyo3/tests/test_optuna_samplers.py @@ -0,0 +1,22 @@ +from typing import Callable + +import pytest +from optuna.samplers import BaseSampler +from 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.into_iter() + .filter(|t| matches!(t.state_values, TrialStateValues::Complete(_))) + .collect(); let is_multi_objective = ctx.directions.len() > 1; let (pe_good, pe_poor) = if !is_multi_objective { @@ -399,7 +402,10 @@ impl Sampler for TpeSampler { let trials = guard.get_trials(ctx.study_id)?.clone(); drop(guard); - let complete_trials: Vec<_> = trials.into_iter().filter(|t| t.is_finished()).collect(); + let complete_trials: Vec<_> = trials + .into_iter() + .filter(|t| matches!(t.state_values, TrialStateValues::Complete(_))) + .collect(); if complete_trials.len() < self.n_startup_trials { let params = self.random_sampler @@ -428,7 +434,10 @@ impl Sampler for TpeSampler { let trials = guard.get_trials(ctx.study_id)?.clone(); drop(guard); - let complete_trials: Vec<_> = trials.into_iter().filter(|t| t.is_finished()).collect(); + let complete_trials: Vec<_> = trials + .into_iter() + .filter(|t| matches!(t.state_values, TrialStateValues::Complete(_))) + .collect(); if complete_trials.len() < self.n_startup_trials { let params = HashMap::new(); return Ok(params);