xopt 0.5.0

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Xopt

Documentation

Flexible optimization of arbitrary problems in Python.

The goal of this package is to provide advanced algorithmic support for arbitrary simulations/control systems with minimal required coding. Users can easily connect arbitrary evaluation functions to advanced algorithms with minimal coding with support for multi-threaded or MPI-enabled execution.

Currenty Xopt provides:

• optimization algorithms:
  • cnsga Continuous NSGA-II with constraints.
  • bayesian_optimization Single objective Bayesian optimization (w/ or w/o constraints, serial or parallel).
  • mobo Multi-objective Bayesian optimization (w/ or w/o constraints, serial or parallel).
  • bayesian_exploration Bayesian exploration.
  • multi_fidelity Multi-fidelity Single objective Bayesian optimization.
• sampling algorithms:
  • random sampler
• Convenient YAML/JSON based input format.
• Driver programs:
  • xopt.mpi.run Parallel MPI execution using this input format.

Xopt does not provide:

• your custom simulation via an evaluate function.

Current release info

Name	Downloads	Version	Platforms

Configuring an Xopt run

Xopt runs are specified via a dictionary that can be directly imported from a YAML file.

xopt: 
  output_path: .

algorithm:
  name: cnsga
  options: 
    max_generations: 50 
    population_size: 128
    crossover_probability: 0.9
    mutation_probability: 1.0
    selection: auto
    population: null
  
simulation: 
  name: test_TNK
  evaluate: xopt.tests.evaluators.TNK.evaluate_TNK  
  
vocs:
  variables: 
    x1: [0, 3.14159]
    x2: [0, 3.14159]
  objectives:
    y1: MINIMIZE 
    y2: MINIMIZE
  constraints:
    c1: [GREATER_THAN, 0]
    c2: [LESS_THAN, 0.5]
  linked_variables:
    x9: x1
  constants:
    a: dummy_constant

Using MPI

Example MPI run, with xopt.yaml as the only user-defined file:

mpirun -n 64 python -m mpi4py.futures -m xopt.mpi.run xopt.yaml

The complete configuration of a simulation optimization is given by a proper YAML file:

Defining evaluation function

Xopt can interface with arbitrary evaluate functions (defined in Python) with the following form:

evaluate(params[Dict]) -> Dict

Evaluate functions must accept a dictionary object that at least has the keys specified in variables, constants, linked_variables and returns a dictionary containing at least the keys contained in objectives, constraints. Extra dictionary keys are tracked and used in the evaluate function but are not modified by xopt.

Installing Xopt

Installing xopt from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge

Once the conda-forge channel has been enabled, xopt can be installed with:

conda install xopt

It is possible to list all of the versions of xopt available on your platform with:

conda search xopt --channel conda-forge

Developers

Clone this repository:

git clone https://github.com/ChristopherMayes/Xopt.git

Create an environment xopt-dev with all the dependencies:

conda env create -f environment.yml

Install as editable:

conda activate xopt-dev
pip install --no-dependencies -e .