User guide¶ Campaigns Creating a campaign Basic creation Creation from a JSON config Getting recommendations Basics Caching of recommendations Adding measurements Serialization Further information Constraints Continuous Constraints ContinuousLinearEqualityConstraint ContinuousLinearInequalityConstraint Conditions ThresholdCondition SubSelectionCondition Discrete Constraints DiscreteExcludeConstraint DiscreteSumConstraint and DiscreteProductConstraint DiscreteNoLabelDuplicatesConstraint DiscreteLinkedParametersConstraint DiscreteDependenciesConstraint DiscretePermutationInvarianceConstraint DiscreteCustomConstraint Objective Supported Optimization Modes SINGLE DESIRABILITY 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 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 The role of TaskParameter Seeing Transfer Learning in Action