BOlib
=====
A python library for Bayesian Optimization.
Setup BOlib
-----------
- Create and activate virtualenv (for python2) or
venv (for python3)
```bash
# for python3
python3 -m venv .env
# or for python2
python2 -m virtualenv .env
source .env/bin/activate
```
- Upgrade pip
```bash
python -m pip install --upgrade pip
```
- Install BOlib package
```bash
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.
```python
import bolib
```
- Some well-known objetive functions have been included.
```python
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.
```python
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.
```python
af = bolib.afs.ExpectedImprovement()
```
- Finally, we can initialize our optimization model and start the optimization process.
```python
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.
```python
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
```bash
source ./.env/bin/activate
pip install Sphinx
cd docs/
sphinx-apidoc -f -o ./ ../bolib
```
