v2.0.0b1 (unstable) v2.0.0a2 v2.0.0a1 v1.4.0

SMAC3 Documentation

v2.0.0b1
  • Installation
  • Package Overview
  • Getting Started
  • Minimal Example
  • Examples
    • Basics
      • Quadratic Function
      • Support Vector Machine with Cross-Validation
      • Ask-and-Tell
      • Custom Callback
      • Continue an Optimization
      • 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
    • Reproducibility
    • Optimizations
  • API References
    • smac.facade
      • smac.facade.abstract_facade
      • smac.facade.algorithm_configuration_facade
      • smac.facade.blackbox_facade
      • smac.facade.hyperband_facade
      • smac.facade.hyperparameter_optimization_facade
      • smac.facade.multi_fidelity_facade
      • smac.facade.random_facade
    • smac.main
      • smac.main.config_selector
      • smac.main.smbo
    • 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.hyperband
      • smac.intensifier.intensifier
      • smac.intensifier.successive_halving
    • 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.errors
      • 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.pareto_front
      • 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]¶

Return the meta-data of the created object.

previous

smac.acquisition.maximizer.local_and_random_search

next

smac.acquisition.maximizer.random_search

© Copyright Copyright 2023, Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass and Frank Hutter.

Created using Sphinx 6.1.3. Template is modified version of PyData Sphinx Theme.