BTB Logo
0.4.0
  • Overview
  • Installation
    • Stable Release
    • From source
  • Quickstart
    • Prepare a scoring function
    • Define the tunable hyperparameters
    • Start the searching process

User Guides

  • Tuning
    • Defining a Tuning Problem
    • What is a Hyperparameter?
    • Creating a HyperParam
      • BooleanHyperParam
      • CategoricalHyperParam
      • FloatHyperParam
      • IntHyperParam
    • What is Tunable?
      • Creating a Tunable
        • Using HyperParam instances
        • Using a dict representation
    • What is a Tuner?
    • Using a Tuner
      • Creating a Tuner
      • Propose
      • Record
      • Tuning loop example
    • Implemented tuners
      • Leaderboard
  • Selection
    • What is a Selection problem?
    • What is a Selector ?
    • Using a Selector
      • Creating a Selector
      • Select
    • Selection loop example
  • BTBSession
    • What is BTBSession
    • Creating a BTBSession
      • Prepare a scoring function
      • Define the tunable hyperparameters
      • Create BTBSession instance
    • Using BTBSession
      • Run
      • Exploring the result
      • Resume session
    • Fitting the best solution
  • Benchmarking BTB
    • The Benchmarking process
      • Challenges
        • Machine Learning Challenges
        • Mathematical Optimization Challenges
      • Tuning Functions
        • Tuning functions under comparison
        • To introduce a new Tuning Function:
    • Running the Benchmarking
      • Install
        • System Requierements
        • Python installation
      • Runnig the Benchmarking using python
      • Benchmark Arguments
        • Tuners
        • Challenges
        • Iterations
        • Storing the results
    • Our latest Results
    • Kubernetes
    • Credits
  • Kubernetes
    • Requirements
      • Kubernetes Cluster
      • Admin Access
      • Dask Function
    • Benchmark Configuration
      • Configuration Format
        • run
        • dask_cluster
        • output
      • Configuration Examples
    • Run a function on Kubernetes.
      • Usage Example
    • Results

Resources

  • API Reference
    • btb.selection package
      • btb.selection.best module
      • btb.selection.custom_selector module
      • btb.selection.hierarchical module
      • btb.selection.pure module
      • btb.selection.recent module
      • btb.selection.selector module
      • btb.selection.ucb1 module
      • btb.selection.uniform module
    • btb.tuning package
      • btb.tuning.acquisition package
        • btb.tuning.acquisition.base module
        • btb.tuning.acquisition.expected_improvement module
        • btb.tuning.acquisition.predicted_score module
      • btb.tuning.hyperparams package
        • btb.tuning.hyperparams.base module
        • btb.tuning.hyperparams.boolean module
        • btb.tuning.hyperparams.categorical module
        • btb.tuning.hyperparams.numerical module
      • btb.tuning.metamodels package
        • btb.tuning.metamodels.base module
        • btb.tuning.metamodels.gaussian_process module
      • btb.tuning.tuners package
        • btb.tuning.tuners.base module
        • btb.tuning.tuners.gaussian_process module
        • btb.tuning.tuners.uniform module
      • btb.tuning.tunable module
    • btb.session module
  • Contributing
  • History
  • Credits
BTB
  • Docs »
  • Overview: module code

All modules for which code is available

  • btb.selection.best
  • btb.selection.custom_selector
  • btb.selection.hierarchical
  • btb.selection.pure
  • btb.selection.recent
  • btb.selection.selector
  • btb.selection.ucb1
  • btb.selection.uniform
  • btb.session
  • btb.tuning.acquisition.base
  • btb.tuning.acquisition.expected_improvement
  • btb.tuning.acquisition.predicted_score
  • btb.tuning.hyperparams.base
  • btb.tuning.hyperparams.boolean
  • btb.tuning.hyperparams.categorical
  • btb.tuning.hyperparams.numerical
  • btb.tuning.metamodels.base
  • btb.tuning.metamodels.gaussian_process
  • btb.tuning.tunable
  • btb.tuning.tuners.base
  • btb.tuning.tuners.gaussian_process
  • btb.tuning.tuners.uniform
  • sklearn.gaussian_process._gpr

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