Table of Contents

Class RandomSampler

Namespace
OptunaSharp.Samplers
Assembly
OptunaSharp.dll

Sampler using random sampling. Port of optuna/samplers/_random.py: RandomSampler.

This sampler is based on independent sampling. See also BaseSampler for more details of 'independent sampling'.

public class RandomSampler : BaseSampler
Inheritance
RandomSampler
Inherited Members

Examples

var study = Optuna.CreateStudy(sampler: new RandomSampler());
study.Optimize(trial => Math.Pow(trial.SuggestFloat("x", -5, 5), 2), nTrials: 10);

Constructors

RandomSampler(int?)

Creates an independent sampler with a lazily initialized NumPy-compatible random stream.

public RandomSampler(int? seed = null)

Parameters

seed int?

Seed for random number generator.

Methods

InferRelativeSearchSpace(Study, FrozenTrial)

Infer the search space that will be used by relative sampling in the target trial.

This method is called right before SampleRelative(Study, FrozenTrial, OrderedDict<string, BaseDistribution>), and the search space returned by this method is passed to it. The parameters not contained in the search space will be sampled by using SampleIndependent(Study, FrozenTrial, string, BaseDistribution).

public override OrderedDict<string, BaseDistribution> InferRelativeSearchSpace(Study study, FrozenTrial trial)

Parameters

study Study

Study supplying trial history, directions, and storage.

trial FrozenTrial

Current trial snapshot; copy it before modifying its contents.

Returns

OrderedDict<string, BaseDistribution>

Parameter distributions eligible for joint sampling, in sampler-defined order.

ReseedRng()

Reseed sampler's random number generator.

Parallel optimization calls this hook before a worker's sequential trial loop. Concrete samplers refresh their random streams so workers do not deliberately reuse a seeded sequence. OptunaSharp workers share the study's sampler instance; this hook does not clone it or make concurrent sampling reproducible.

public override void ReseedRng()

SampleIndependent(Study, FrozenTrial, string, BaseDistribution)

Sample a parameter for a given distribution.

This method is called only for the parameters not contained in the search space returned by SampleRelative(Study, FrozenTrial, OrderedDict<string, BaseDistribution>). This method is suitable for sampling algorithms that do not use relationship between parameters such as random sampling and TPE.

public override object? SampleIndependent(Study study, FrozenTrial trial, string paramName, BaseDistribution paramDistribution)

Parameters

study Study

Study supplying trial history, directions, and storage.

trial FrozenTrial

Current trial snapshot; copy it before modifying its contents.

paramName string

Name of the parameter being suggested.

paramDistribution BaseDistribution

Distribution defining the parameter's domain and scale.

Returns

object

A value in the distribution's external representation, including null categorical choices.

Remarks

Optuna draws rng.uniform(trans.bounds[:, 0], trans.bounds[:, 1]) over the transform's bound rows (one row for a numerical parameter, one per choice for a categorical one) and untransforms the result. This draws the same rows one at a time through Uniform(NumPyRandom, double, double): each row is NumPy's low + (high - low) * U from the same uniform in the same order, so the value and the generator's next state are Optuna's bit for bit (pinned live by RandomSamplerParityTests). The array form's only other difference, raising before any draw when some range is infinite, cannot arise here: a numerical parameter has one row and every categorical row is [0, 1].

Why not the array form (plan 14): it built five NumSharp arrays per suggestion (the bounds, two column views, the draws and the untransform's float64 copy) and left them to the finalizer, which made GC pauses 47 % of a 40-parameter random search.

SampleRelative(Study, FrozenTrial, OrderedDict<string, BaseDistribution>)

Sample parameters in a given search space.

The trial invokes this method lazily when it first needs relative parameters and caches the result for its remaining suggestions. This method is suitable for sampling algorithms that use relationship between parameters such as Gaussian Process and CMA-ES.

public override OrderedDict<string, object?> SampleRelative(Study study, FrozenTrial trial, OrderedDict<string, BaseDistribution> searchSpace)

Parameters

study Study

Study supplying trial history, directions, and storage.

trial FrozenTrial

Current trial snapshot; copy it before modifying its contents.

searchSpace OrderedDict<string, BaseDistribution>

Distributions returned by InferRelativeSearchSpace(Study, FrozenTrial).

Returns

OrderedDict<string, object>

Jointly sampled external parameter values; omitted parameters use independent sampling.

See Also

Official Optuna RandomSampler reference.
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