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Python library for Bayesian Optimization.

Project description

A python library for Bayesian Optimization.

Setup BOlib

  • Create and activate virtualenv (for python2) or venv (for python3)

# for python3
python3 -m venv .env
# or for python2
python2 -m virtualenv .env

source .env/bin/activate
  • Upgrade pip

python -m pip install --upgrade pip
  • Install BOlib package

python -m pip install bolib
  • Matplotlib requires to install a backend to work interactively (See https://matplotlib.org/faq/virtualenv_faq.html). The easiest solution is to install the Tk framework, which can be found as python-tk (or python3-tk) on certain Linux distributions.

Use BOlib

  • Import BOlib to use it in your python script.

import bolib
  • Some well-known objetive functions have been included.

of = bolib.ofs.Branin()

of.evaluate([1.0, 1.0])  # 27.702905548512433
  • To use Bayesian Optimization we need a probabilistic model. In this example we will use Gaussian Processes.

import gplib

model = gplib.GP(
    mean_function=gplib.mea.Fixed(),
    covariance_function=gplib.ker.SquaredExponential(ls=([1.] * of.d))
)

metric = gplib.me.LML()

fitting_method = gplib.fit.MultiStart(
    obj_fun=metric.fold_measure,
    max_fun_call=300,
    nested_fit_method=gplib.fit.LocalSearch(
        obj_fun=metric.fold_measure,
        max_fun_call=75,
        method='Powell'
    )
)

validation = gplib.dm.Full()
  • Bayesian Optimization also needs an acquisition function.

af = bolib.afs.ExpectedImprovement()
  • Finally, we can initialize our optimization model and start the optimization process.

bo = bolib.methods.BayesianOptimization(
    model, fitting_method, validation, af
)

bo.set_seed(seed=1)

x0 = bo.random_sample(of.get_bounds(), batch_size=10)

bo.minimize(
    of.evaluate, x0,
    bounds=of.get_bounds(),
    tol=1e-5,
    maxiter=of.get_max_eval(),
    disp=True
)
  • BOlib is also Scipy compatible.

import scipy.optimize as spo

bo.set_seed(seed=1)

x0 = bo.random_sample(of.get_bounds(), batch_size=5)

result = spo.minimize(
    of.evaluate,
    x0,
    bounds=of.get_bounds(),
    method=bo.minimize,
    tol=1e-5,
    options={
        'maxiter': of.get_max_eval(),
        'disp': True
    }
)
  • There are more examples in examples/ directory. Check them out!

Develop BOlib

  • Download the repository using git

git clone https://gitlab.com/ibaidev/bolib.git
cd bolib
git config user.email 'MAIL'
git config user.name 'NAME'
git config credential.helper 'cache --timeout=300'
git config push.default simple
  • Update API documentation

source ./.env/bin/activate
pip install Sphinx
cd docs/
sphinx-apidoc -f -o ./ ../bolib

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