Metadata-Version: 2.1
Name: design-bench
Version: 1.3
Summary: Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Home-page: https://github.com/brandontrabucco/design-bench
Author: Brandon Trabucco
Author-email: brandon@btrabucco.com
License: MIT
Download-URL: https://github.com/brandontrabucco/design-bench/archive/v1_3.tar.gz
Description: # Benchmarks for Data-Driven Offline Model-Based Optimization
        
        This repository contains several design benchmarks for model-based optimization. Our hope is that a common interface and stable nomenclature will encourage future research and comparability in model-based design.
        
        ## Available Tasks
        
        Current model-based design benchmarks (circa 2020) typically vary from paper-to-paper. For example, tasks employed by biologists differ strongly from those of interest to roboticists. We provide a common interface for tasks that span a wide-range of disciplines, from materials science, to reinforcement learning. We list these tasks below.
        
        * __Biology__: Protein Fluorescence: `design_bench.make('GFP-v0')`
        * __Chemistry__: Molecule Activity: `design_bench.make('MoleculeActivity-v0')`
        * __Materials Science__: Superconductor Critical Temperature: `design_bench.make('Superconductor-v0')`
        * __Robotics__: Hopper Controller: `design_bench.make('HopperController-v0')`
        * __Robotics__: Ant Morphology: `design_bench.make('AntMorphology-v0')`
        * __Robotics__: DKitty Morphology: `design_bench.make('DKittyMorphology-v0')`
        
        In addition, the following debugging tasks are provided.
        
        * __Debugging__: Quadratic Maximization: `design_bench.make('Quadratic-v0')`
        
        ## Setup
        
        You can install our benchmarks with the following command.
        
        ```bash
        pip install design-bench[all]
        ```
        
        If you do not have a MuJoCo License you can install the base benchmark.
        
        ```bash
        pip install design-bench
        ```
        
        ## Usage
        
        Every task inherits from the `design_bench.task.Task` class. This class provides access to attributes `task.x` and `task.y` that correspond to designs and labels as numpy arrays, respectively. In addition, every task implements a `task.score(x)` function that provides an (approximate) oracle predictor for `task.y`.
        
        ```python
        import design_bench
        task = design_bench.make('Superconductor-v0')
        x = task.x[:10]
        y = task.y[:10]
        oracle_y = task.score(x)
        ```
        
Keywords: Offline,Benchmark,Model-Based Optimization
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: all
