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
    • RandomSearch

smac.acquisition.maximizer.random_search¶

Classes¶

RandomSearch(configspace[, ...])

Get candidate solutions via random sampling of configurations.

Interfaces¶

class smac.acquisition.maximizer.random_search.RandomSearch(configspace, acquisition_function=None, challengers=5000, seed=0)[source]¶

Bases: AbstractAcquisitionMaximizer

Get candidate solutions via random sampling of configurations.

previous

smac.acquisition.maximizer.local_search

next

smac.intensifier

© 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.