Xopt

Documentation

Name	Downloads	Version	Platforms

Flexible optimization of arbitrary problems in Python.

The goal of this package is to provide advanced algorithmic support for arbitrary optimization problems (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:
  • Genetic algorithms
    • cnsga Continuous NSGA-II with constraints
  • Bayesian optimization (BO) algorithms:
    • upper_confidence_bound BO using Upper Confidence Bound acquisition function (w/ or w/o constraints, serial or parallel)
    • expected_improvement BO using Expected Improvement acquisition function (w/ or w/o constraints, serial or parallel)
    • mobo Multi-objective BO (w/ or w/o constraints, serial or parallel)
    • bayesian_exploration Autonomous function characterization using Bayesian Exploration
    • mggpo Parallelized hybrid Multi-Generation Multi-Objective Bayesian optimization
    • multi_fidelity Multi-fidelity single or multi objective optimization
    • BAX Bayesian algorithm execution using virtual measurements
    • BO customization:
      • Trust region BO
      • Heteroskedastic noise specification
      • Multiple acquisition function optimization stratigies
  • extremum_seeking Extremum seeking time-dependent optimization
  • rcds Robust Conjugate Direction Search (RCDS)
  • neldermead Nelder-Mead Simplex
• Sampling algorithms:
  • random Uniform random sampling
• 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 or experimental measurement via an evaluate function.

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

Configuring an Xopt run

Xopt runs can be specified via a YAML file or dictonary input. This requires generator, evaluator, and vocs to be specified, along with optional general options such as max_evaluations. An example to run a multi-objective optimiation of a user-defined function my_function is:

generator:
    name: cnsga
    population_size: 64
    population_file: test.csv
    output_path: .

evaluator:
    function: my_function
    function_kwargs:
      my_arguments: 42

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]
    constants: {a: dummy_constant}

max_evaluations: 6400    

Xopt can also be used through a simple Python interface.

import math

from xopt.vocs import VOCS
from xopt.evaluator import Evaluator
from xopt.generators.bayesian import UpperConfidenceBoundGenerator
from xopt import Xopt

# define variables and function objectives
vocs = VOCS(
    variables={"x": [0, 2 * math.pi]},
    objectives={"f": "MINIMIZE"},
)

# define the function to optimize
def sin_function(input_dict):
    return {"f": math.sin(input_dict["x"])}

# create Xopt evaluator, generator, and Xopt objects
evaluator = Evaluator(function=sin_function)
generator = UpperConfidenceBoundGenerator(vocs=vocs)
X = Xopt(evaluator=evaluator, generator=generator, vocs=vocs)

# call X.random_evaluate() to generate + evaluate 3 initial points
X.random_evaluate(3)

# run optimization for 10 steps
for i in range(10):
    X.step()

# view collected data
print(X.data)

Defining an evaluation function

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

def evaluate(inputs: dict) -> dict:
    """ your code here """

Evaluate functions must accept a dictionary object that at least has the keys specified in variables, constants 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.

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

Citing Xopt

If you use Xopt for your research, please consider adding the following citation to your publications.

R. Roussel., et al., "Xopt: A simplified framework for optimization of accelerator problems using advanced algorithms", 
in Proc. IPAC'23, Venezia.doi:https://doi.org/10.18429/JACoW-14th International Particle Accelerator Conference-THPL164

BibTex entry:

@inproceedings{Xopt,
	title        = {Xopt: A simplified framework for optimization of accelerator problems using advanced algorithms},
	author       = {R. Roussel and A. Edelen and A. Bartnik and C. Mayes},
	year         = 2023,
	month        = {05},
	booktitle    = {Proc. IPAC'23},
	publisher    = {JACoW Publishing, Geneva, Switzerland},
	series       = {IPAC'23 - 14th International Particle Accelerator Conference},
	number       = 14,
	pages        = {4796--4799},
	doi          = {doi:10.18429/jacow-ipac2023-thpl164},
	isbn         = {978-3-95450-231-8},
	issn         = {2673-5490},
	url          = {https://indico.jacow.org/event/41/contributions/2556},
	paper        = {THPL164},
	venue        = {Venezia},
	language     = {english}
}

Particular versions of Xopt can be cited from Zenodo

Developers

Clone this repository with a truncated git history (recommended):

git clone --depth=1 https://github.com/ChristopherMayes/Xopt.git

Or, clone this repository with the full git history (> 970 MB):

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 .

Install pre-commit hooks:

pre-commit install

The pre-commit hooks perform autoformatting and report style-compliance errors.

• ufmt formats files w.r.t. black a strict style enforcer, and μsort, which sorts imports in Python modules.
• flake8 confirms compliance. Occasionally black misses long-line comments/docstrings and they require manual format.

Pre-commit runs the hooks against your files. If the commit fails, correct the reported errors and then re-add the file with git add my_file.py.

VSCode

The source control integration packaged with VSCode requires additional configuration. Git commands are run in the integrated terminal, which does not inherit the Python interpreter configured with the VSCode project thus breaking the pre-commit hooks. The integration terminal can be configured to use the conda Python environment by including a .env file in your project repository:

#!/usr/bin/bash 
source /path/to/xopt-dev/bin/activate