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SMAC3 Documentation

v2.0.0a1
  • Installation
  • Package Overview
  • Getting Started
  • Minimal Example
  • Examples
    • Basics
      • Synthetic Function
      • Support Vector Machine with Cross-Validation
      • Ask-and-Tell
      • Custom Callback
      • User Priors over the Optimum
    • Multi-Fidelity and Multi-Instances
      • Multi-Layer Perceptron Using Multiple Epochs
      • Stochastic Gradient Descent On Multiple Datasets
    • Multi-Objective
      • 2D Schaffer Function with Objective Weights
      • ParEGO
    • Command-Line Interface
      • Call Target Function From Script
  • Advanced Usage
    • Components
    • Multi-Fidelity Optimization
    • Multi-Objective Optimization
    • Optimization across Instances
    • Ask-and-Tell Interface
    • Command-Line Interface
    • Stopping Criteria
    • Logging
    • Parallelism
    • Continue a Run
    • Reproducability
  • API References
    • smac.facade
      • smac.facade.abstract_facade
      • smac.facade.algorithm_configuration_facade
      • smac.facade.blackbox_facade
      • smac.facade.boing_facade
      • smac.facade.hyperband_facade
      • smac.facade.hyperparameter_optimization_facade
      • smac.facade.multi_fidelity_facade
      • smac.facade.random_facade
    • smac.main
      • smac.main.base_smbo
      • smac.main.boing
      • smac.main.smbo
      • smac.main.turbo
    • smac.model
      • smac.model.abstract_model
      • smac.model.gaussian_process
        • smac.model.gaussian_process.abstract_gaussian_process
        • smac.model.gaussian_process.gaussian_process
        • smac.model.gaussian_process.gpytorch_gaussian_process
        • smac.model.gaussian_process.kernels
        • smac.model.gaussian_process.mcmc_gaussian_process
        • smac.model.gaussian_process.priors
      • smac.model.multi_objective_model
      • smac.model.random_forest
        • smac.model.random_forest.abstract_random_forest
        • smac.model.random_forest.random_forest
      • smac.model.random_model
    • smac.acquisition
      • smac.acquisition.function
        • smac.acquisition.function.abstract_acquisition_function
        • smac.acquisition.function.confidence_bound
        • smac.acquisition.function.expected_improvement
        • smac.acquisition.function.integrated_acquisition_function
        • smac.acquisition.function.prior_acqusition_function
        • smac.acquisition.function.probability_improvement
        • smac.acquisition.function.thompson
      • smac.acquisition.maximizer
        • smac.acquisition.maximizer.abstract_acqusition_maximizer
        • smac.acquisition.maximizer.differential_evolution
        • smac.acquisition.maximizer.helpers
        • smac.acquisition.maximizer.local_and_random_search
        • smac.acquisition.maximizer.local_search
        • smac.acquisition.maximizer.random_search
    • smac.intensifier
      • smac.intensifier.abstract_intensifier
      • smac.intensifier.abstract_parallel_intensifier
      • smac.intensifier.hyperband
      • smac.intensifier.hyperband_worker
      • smac.intensifier.intensifier
      • smac.intensifier.stages
      • smac.intensifier.successive_halving
      • smac.intensifier.successive_halving_worker
    • smac.initial_design
      • smac.initial_design.abstract_initial_design
      • smac.initial_design.default_design
      • smac.initial_design.factorial_design
      • smac.initial_design.latin_hypercube_design
      • smac.initial_design.random_design
      • smac.initial_design.sobol_design
    • smac.random_design
      • smac.random_design.abstract_random_design
      • smac.random_design.annealing_design
      • smac.random_design.modulus_design
      • smac.random_design.probability_design
    • smac.runner
      • smac.runner.abstract_runner
      • smac.runner.abstract_serial_runner
      • smac.runner.dask_runner
      • smac.runner.exceptions
      • smac.runner.target_function_runner
      • smac.runner.target_function_script_runner
    • smac.runhistory
      • smac.runhistory.dataclasses
      • smac.runhistory.encoder
        • smac.runhistory.encoder.abstract_encoder
        • smac.runhistory.encoder.boing_encoder
        • smac.runhistory.encoder.eips_encoder
        • smac.runhistory.encoder.encoder
        • smac.runhistory.encoder.inverse_scaled_encoder
        • smac.runhistory.encoder.log_encoder
        • smac.runhistory.encoder.log_scaled_encoder
        • smac.runhistory.encoder.scaled_encoder
        • smac.runhistory.encoder.sqrt_scaled_encoder
      • smac.runhistory.enumerations
      • smac.runhistory.runhistory
    • smac.multi_objective
      • smac.multi_objective.abstract_multi_objective_algorithm
      • smac.multi_objective.aggregation_strategy
      • smac.multi_objective.parego
    • smac.utils
      • smac.utils.configspace
      • smac.utils.data_structures
      • smac.utils.logging
      • smac.utils.multi_objective
      • smac.utils.subspaces
        • smac.utils.subspaces.boing_subspace
        • smac.utils.subspaces.turbo_subspace
    • smac.scenario
    • smac.callback
  • References
  • Glossary
  • F.A.Q.
  • License
On this page
  • Classes
  • Interfaces
    • LocalSearch
      • LocalSearch.meta

smac.acquisition.maximizer.local_search¶

Classes¶

LocalSearch(configspace[, ...])

Implementation of SMAC's local search.

Interfaces¶

class smac.acquisition.maximizer.local_search.LocalSearch(configspace, acquisition_function=None, challengers=5000, max_steps=None, n_steps_plateau_walk=10, vectorization_min_obtain=2, vectorization_max_obtain=64, seed=0)[source]¶

Bases: AbstractAcquisitionMaximizer

Implementation of SMAC’s local search.

Parameters:
  • configspace (ConfigurationSpace) –

  • acquisition_function (AbstractAcquisitionFunction) –

  • challengers (int, defaults to 5000) – Number of challengers.

  • max_steps (int | None, defaults to None) – Maximum number of iterations that the local search will perform.

  • n_steps_plateau_walk (int, defaults to 10) – Number of steps during a plateau walk before local search terminates.

  • vectorization_min_obtain (int, defaults to 2) – Minimal number of neighbors to obtain at once for each local search for vectorized calls. Can be tuned to reduce the overhead of SMAC.

  • vectorization_max_obtain (int, defaults to 64) – Maximal number of neighbors to obtain at once for each local search for vectorized calls. Can be tuned to reduce the overhead of SMAC.

  • seed (int, defaults to 0) –

property meta: dict[str, Any]¶

Returns the meta data of the created object.

Return type:

dict[str, Any]

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© Copyright Copyright 2022, Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass and Frank Hutter.

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