User Guide

• Campaigns
  • Creating a campaign
    • Basic creation
    • Creation from a JSON config
  • Getting recommendations
    • Basics
    • Caching of recommendations
  • Adding measurements
  • Serialization
  • Further information
• Active Learning
  • Local Uncertainty Reduction
  • Global Uncertainty Reduction
• Asynchronous Workflows
  • Marking Experiments as Pending
  • Adding Partial Results
• Constraints
  • Continuous Constraints
    • ContinuousLinearEqualityConstraint
    • ContinuousLinearInequalityConstraint
  • Conditions
    • ThresholdCondition
    • SubSelectionCondition
  • Discrete Constraints
    • DiscreteExcludeConstraint
    • DiscreteSumConstraint and DiscreteProductConstraint
    • DiscreteNoLabelDuplicatesConstraint
    • DiscreteLinkedParametersConstraint
    • DiscreteDependenciesConstraint
    • DiscretePermutationInvarianceConstraint
    • DiscreteCustomConstraint
• Environment Vars
  • Basic Instructions
  • Telemetry
  • Polars
  • Disk Caching
  • Floating Point Precision
• Objectives
  • SingleTargetObjective
  • DesirabilityObjective
• Parameters
  • Continuous Parameters
    • NumericalContinuousParameter
  • Discrete Parameters
    • NumericalDiscreteParameter
    • CategoricalParameter
    • SubstanceParameter
    • CustomDiscreteParameter
    • TaskParameter
• Recommenders
  • General Information
  • Pure Recommenders
    • Bayesian Recommenders
    • Clustering Recommenders
    • Sampling Recommenders
  • Meta Recommenders
• Search Spaces
  • Discrete Subspaces
    • Building from the Product of Parameter Values
    • Constructing from a Dataframe
    • Creating a Simplex-Bound Discrete Subspace
    • Representation of Data within Discrete Subspaces
    • Metadata
  • Continuous Subspaces
    • Using Explicit Bounds
    • Constructing from a Dataframe
  • Constructing Full Search Spaces
    • From the Default Constructor
    • Building from the Product of Parameter Values
    • Constructing from a Dataframe
  • Restricting Search Spaces Using Constraints
• Serialization
  • JSON de-/serialization
  • Deserialization from configuration strings
    • Basic string assembly
    • Using default values
    • Automatic field conversion
    • The type field
    • Using abbreviations
    • Nesting objects
    • Invoking alternative constructors
    • Dataframe deserialization
• Simulation
  • Terminology: What do we mean by “Simulation”?
  • The Lookup Functionality
    • Using a Dataframe
    • Using a Callable
  • Simulating a Single Experiment
  • Simulating Multiple Scenarios
  • Simulating Transfer Learning
• Surrogates
  • Available models
  • Using custom models
• Targets
  • NumericalTarget
    • MIN and MAX mode
    • MATCH mode
  • Limitations
• Transfer Learning
  • Unlocking Data Treasures Through Transfer Learning
  • The Role of the TaskParameter
  • Seeing Transfer Learning in Action