syne_tune.optimizer.schedulers.searchers.bayesopt.models package

Subpackages

• syne_tune.optimizer.schedulers.searchers.bayesopt.models.cost package
  • Submodules
    • syne_tune.optimizer.schedulers.searchers.bayesopt.models.cost.cost_model module
      • CostValue
      • CostModel
    • syne_tune.optimizer.schedulers.searchers.bayesopt.models.cost.linear_cost_model module
    • syne_tune.optimizer.schedulers.searchers.bayesopt.models.cost.sklearn_cost_model module
      • ScikitLearnCostModel
      • UnivariateSplineCostModel

Submodules

• syne_tune.optimizer.schedulers.searchers.bayesopt.models.acqfunc_factory module
  • acquisition_function_factory()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.cost_fifo_model module
  • CostFixedResourcePredictor
    • CostFixedResourcePredictor.keys_predict()
    • CostFixedResourcePredictor.predict()
    • CostFixedResourcePredictor.backward_gradient()
    • CostFixedResourcePredictor.predict_mean_current_candidates()
    • CostFixedResourcePredictor.current_best()
  • CostEstimator
    • CostEstimator.get_params()
    • CostEstimator.set_params()
    • CostEstimator.fixed_resource
    • CostEstimator.set_fixed_resource()
    • CostEstimator.fit_from_state()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.estimator module
  • Estimator
    • Estimator.get_params()
    • Estimator.set_params()
    • Estimator.fit_from_state()
    • Estimator.debug_log
    • Estimator.configure_scheduler()
  • TransformedData
    • TransformedData.features
    • TransformedData.targets
    • TransformedData.mean
    • TransformedData.std
  • transform_state_to_data()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.gp_mcmc_model module
  • GaussProcMCMCEstimator
    • GaussProcMCMCEstimator.get_params()
    • GaussProcMCMCEstimator.set_params()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.gp_model module
  • GaussProcPredictor
    • GaussProcPredictor.hp_ranges_for_prediction()
    • GaussProcPredictor.predict()
    • GaussProcPredictor.backward_gradient()
    • GaussProcPredictor.does_mcmc()
    • GaussProcPredictor.posterior_states
  • GaussProcEstimator
    • GaussProcEstimator.debug_log
    • GaussProcEstimator.gpmodel
    • GaussProcEstimator.fit_from_state()
    • GaussProcEstimator.configure_scheduler()
  • GaussProcEmpiricalBayesEstimator
    • GaussProcEmpiricalBayesEstimator.get_params()
    • GaussProcEmpiricalBayesEstimator.set_params()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.gpiss_model module
  • GaussProcAdditivePredictor
    • GaussProcAdditivePredictor.predict()
    • GaussProcAdditivePredictor.backward_gradient()
    • GaussProcAdditivePredictor.does_mcmc()
    • GaussProcAdditivePredictor.posterior_states
  • GaussProcAdditiveEstimator
    • GaussProcAdditiveEstimator.debug_log
    • GaussProcAdditiveEstimator.get_params()
    • GaussProcAdditiveEstimator.set_params()
    • GaussProcAdditiveEstimator.fit_from_state()
    • GaussProcAdditiveEstimator.predictor_for_fantasy_samples()
    • GaussProcAdditiveEstimator.configure_scheduler()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.kernel_factory module
  • base_kernel_factory()
  • resource_kernel_factory()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.meanstd_acqfunc module
  • HeadWithGradient
    • HeadWithGradient.hval
    • HeadWithGradient.gradient
  • CurrentBestProvider
  • NoneCurrentBestProvider
  • ActiveMetricCurrentBestProvider
  • MeanStdAcquisitionFunction
    • MeanStdAcquisitionFunction.compute_acq()
    • MeanStdAcquisitionFunction.compute_acq_with_gradient()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.meanstd_acqfunc_impl module
  • EIAcquisitionFunction
    • EIAcquisitionFunction.debug_stats_message()
  • LCBAcquisitionFunction
  • EIpuAcquisitionFunction
  • ConstraintCurrentBestProvider
  • CEIAcquisitionFunction
  • get_quantiles()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_base module
  • BasePredictor
    • BasePredictor.filter_observed_data
    • BasePredictor.set_filter_observed_data()
    • BasePredictor.predict_mean_current_candidates()
    • BasePredictor.current_best()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_skipopt module
  • SkipOptimizationPredicate
    • SkipOptimizationPredicate.reset()
  • NeverSkipPredicate
  • AlwaysSkipPredicate
  • SkipPeriodicallyPredicate
    • SkipPeriodicallyPredicate.reset()
  • SkipNoMaxResourcePredicate
    • SkipNoMaxResourcePredicate.reset()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.model_transformer module
  • StateForModelConverter
    • StateForModelConverter.set_random_state()
  • ModelStateTransformer
    • ModelStateTransformer.state
    • ModelStateTransformer.use_single_model
    • ModelStateTransformer.estimator
    • ModelStateTransformer.skip_optimization
    • ModelStateTransformer.fit()
    • ModelStateTransformer.get_params()
    • ModelStateTransformer.set_params()
    • ModelStateTransformer.append_trial()
    • ModelStateTransformer.drop_pending_evaluation()
    • ModelStateTransformer.remove_observed_case()
    • ModelStateTransformer.label_trial()
    • ModelStateTransformer.filter_pending_evaluations()
    • ModelStateTransformer.mark_trial_failed()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.sklearn_model module
  • SKLearnPredictorWrapper
    • SKLearnPredictorWrapper.predict()
    • SKLearnPredictorWrapper.backward_gradient()
  • SKLearnEstimatorWrapper
    • SKLearnEstimatorWrapper.get_params()
    • SKLearnEstimatorWrapper.set_params()
    • SKLearnEstimatorWrapper.fit_from_state()
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.subsample_state_multi_fidelity module
  • cap_size_tuning_job_state()
  • SubsampleMultiFidelityStateConverter
    • SubsampleMultiFidelityStateConverter.set_random_state()
  • sparsify_tuning_job_state()
  • SubsampleMFDenseDataStateConverter
• syne_tune.optimizer.schedulers.searchers.bayesopt.models.subsample_state_single_fidelity module
  • cap_size_tuning_job_state()
  • SubsampleSingleFidelityStateConverter
    • SubsampleSingleFidelityStateConverter.set_random_state()