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BayBE documentation

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Documentation

Readme
User Guide
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Examples
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- Basics
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  - Campaign
  - Recommenders
- Searchspaces
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- Constraints Discrete
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  - Mixture
  - Dependency
  - Custom
  - Product/Sum
  - Exclusion
- Constraints Continuous
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  - Linear
  - Hybrid Space
- Multi Target
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  - Desirability
- Serialization
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- Custom Surrogates
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- Backtesting
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- Custom Hooks
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- Transfer Learning
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  - Backtesting
  - Basic Transfer Learning

baybe
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- baybe.acquisition
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  - baybe.acquisition.acqfs
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  - baybe.acquisition.base
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    - AcquisitionFunction
  - baybe.acquisition.partial
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    - PartialAcquisitionFunction
  - baybe.acquisition.utils
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    - baybe.acquisition.utils.convert_acqf
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- baybe.campaign
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  - Campaign
- baybe.constraints
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  - baybe.constraints.validation
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- baybe.exceptions
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- baybe.kernels
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- baybe.objective
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- baybe.objectives
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  - baybe.objectives.desirability
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    - DesirabilityObjective
  - baybe.objectives.enum
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    - Scalarizer
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    - SingleTargetObjective
- baybe.parameters
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  - baybe.parameters.categorical
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    - CategoricalParameter
    - TaskParameter
  - baybe.parameters.custom
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    - CustomDiscreteParameter
  - baybe.parameters.enum
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  - baybe.parameters.numerical
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    - NumericalContinuousParameter
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  - baybe.parameters.substance
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    - baybe.parameters.substance.Smiles
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  - baybe.parameters.utils
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    - baybe.parameters.utils.get_parameters_from_dataframe
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- baybe.priors
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    - Prior
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- baybe.recommenders
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    - RecommenderProtocol
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      - MetaRecommender
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  - baybe.recommenders.naive
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    - NaiveHybridSpaceRecommender
  - baybe.recommenders.pure
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      - PureRecommender
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        BayesianRecommender
      - baybe.recommenders.pure.bayesian.botorch
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        BotorchRecommender
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        baybe.recommenders.pure.bayesian.sequential_greedy.SequentialGreedyRecommender
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        NonPredictiveRecommender
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        GaussianMixtureClusteringRecommender
        
        KMeansClusteringRecommender
        
        PAMClusteringRecommender
        
        SKLearnClusteringRecommender
      - baybe.recommenders.pure.nonpredictive.sampling
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        FPSRecommender
        
        RandomRecommender
- baybe.scaler
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  - DefaultScaler
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- baybe.searchspace
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  - baybe.searchspace.continuous
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    - SubspaceContinuous
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- baybe.serialization
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- baybe.simulation
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    - baybe.simulation.scenarios.simulate_scenarios
  - baybe.simulation.transfer_learning
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- baybe.surrogates
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  - baybe.surrogates.base
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    - Surrogate
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  - baybe.surrogates.gaussian_process
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        GaussianProcessPreset
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        DefaultKernelFactory
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        EDBOKernelFactory
  - baybe.surrogates.linear
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    - BayesianLinearSurrogate
  - baybe.surrogates.naive
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    - MeanPredictionSurrogate
  - baybe.surrogates.ngboost
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    - NGBoostSurrogate
  - baybe.surrogates.random_forest
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    - RandomForestSurrogate
  - baybe.surrogates.utils
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  - baybe.surrogates.validation
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    - baybe.surrogates.validation.get_model_params_validator
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- baybe.targets
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  - baybe.targets.base
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    - Target
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    - TargetMode
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  - baybe.targets.numerical
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    - NumericalTarget
  - baybe.targets.transforms
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- baybe.telemetry
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- baybe.utils
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  - baybe.utils.augmentation
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  - baybe.utils.boolean
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  - baybe.utils.botorch_wrapper
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  - baybe.utils.torch
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Misc

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Backtesting¶

These examples demonstrate BayBE’s Backtesting capabilities.

Custom Analytical
Botorch Analytical
Multi Target
Hybrid
Full Lookup
Impute Mode
Full Initial Data

Example for full simulation loop using a custom analytical test function

Example for custom parameter passing in surrogate models

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