Python library for Bayesian Optimization.
Project description
BOlib
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
- Update API documentation
source ./.env/bin/activate
pip install Sphinx
cd docs/
sphinx-apidoc -f -o ./ ../bolib
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
bolib-0.21.0.tar.gz
(22.1 kB
view hashes)
Built Distribution
bolib-0.21.0-py2.py3-none-any.whl
(34.3 kB
view hashes)
Close
Hashes for bolib-0.21.0-py2.py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | dd881606dc9073cc40c25231499b6c74b9451332e7658712d36016f8fb662dde |
|
| MD5 | da36cabea3d4b07ab4ab13cc8e8427d0 |
|
| BLAKE2b-256 | 008c8060c0a13e54a50c73d682627753e2dec066ace837676cdd18f8c9cfb6b6 |
