Table of Contents

Class BayesianOptimizationAutoML<T, TInput, TOutput>

Namespace
AiDotNet.AutoML
Assembly
AiDotNet.dll

Built-in AutoML strategy that uses a lightweight Bayesian-style surrogate to guide trial selection.

public sealed class BayesianOptimizationAutoML<T, TInput, TOutput> : BuiltInSupervisedAutoMLModelBase<T, TInput, TOutput>, IAutoMLModel<T, TInput, TOutput>, IFullModel<T, TInput, TOutput>, IModel<TInput, TOutput, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, TInput, TOutput>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, TInput, TOutput>>, IGradientComputable<T, TInput, TOutput>, IJitCompilable<T>

Type Parameters

T

The numeric type used for calculations.

TInput

The input data type.

TOutput

The output data type.

Inheritance
AutoMLModelBase<T, TInput, TOutput>
SupervisedAutoMLModelBase<T, TInput, TOutput>
BayesianOptimizationAutoML<T, TInput, TOutput>
Implements
IAutoMLModel<T, TInput, TOutput>
IFullModel<T, TInput, TOutput>
IModel<TInput, TOutput, ModelMetadata<T>>
IParameterizable<T, TInput, TOutput>
ICloneable<IFullModel<T, TInput, TOutput>>
IGradientComputable<T, TInput, TOutput>
Inherited Members
Extension Methods

Remarks

This implementation uses a pragmatic, production-friendly approach: - Use a bandit policy to allocate trials across candidate model families. - Use a kernel-weighted surrogate over observed trials to bias sampling toward promising regions.

For Beginners: Instead of trying totally random settings every time, this strategy learns from earlier trials and tries more settings similar to the best ones found so far.

Constructors

BayesianOptimizationAutoML(IModelEvaluator<T, TInput, TOutput>?, Random?) 

public BayesianOptimizationAutoML(IModelEvaluator<T, TInput, TOutput>? modelEvaluator = null, Random? random = null)

Parameters

modelEvaluator IModelEvaluator<T, TInput, TOutput>
random Random

Methods

CreateInstanceForCopy()

Factory method for creating a new instance for deep copy. Derived classes must implement this to return a new instance of themselves. This ensures each copy has its own collections and lock object.

protected override AutoMLModelBase<T, TInput, TOutput> CreateInstanceForCopy()

Returns

AutoMLModelBase<T, TInput, TOutput>

A fresh instance of the derived class with default parameters

Remarks

When implementing this method, derived classes should create a fresh instance with default parameters, and should not attempt to preserve runtime or initialization state from the original instance. The deep copy logic will transfer relevant state (trial history, search space, etc.) after construction.

SearchAsync(TInput, TOutput, TInput, TOutput, TimeSpan, CancellationToken)

Searches for the best model configuration

public override Task<IFullModel<T, TInput, TOutput>> SearchAsync(TInput inputs, TOutput targets, TInput validationInputs, TOutput validationTargets, TimeSpan timeLimit, CancellationToken cancellationToken = default)

Parameters

inputs TInput
targets TOutput
validationInputs TInput
validationTargets TOutput
timeLimit TimeSpan
cancellationToken CancellationToken

Returns

Task<IFullModel<T, TInput, TOutput>>

SuggestNextTrialAsync()

Suggests the next hyperparameters to try

public override Task<Dictionary<string, object>> SuggestNextTrialAsync()

Returns

Task<Dictionary<string, object>>
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