Sequential model-based optimization toolbox.
Project description
Scikit-Optimize
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts.
The library is built on top of NumPy, SciPy and Scikit-Learn.
We do not perform gradient-based optimization. For gradient-based optimization algorithms look at scipy.optimize here.
Approximated objective function after 50 iterations of gp_minimize. Plot made using skopt.plots.plot_objective.
Important links
Static documentation - Static documentation
Example notebooks - can be found in the examples directory.
Issue tracker - https://github.com/scikit-optimize/scikit-optimize/issues
Install
The latest released version of scikit-optimize is v0.4, which you can install with:
pip install numpy # install numpy explicitly first pip install scikit-optimize
Getting started
Find the minimum of the noisy function f(x) over the range -2 < x < 2 with skopt:
import numpy as np
from skopt import gp_minimize
def f(x):
return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) *
np.random.randn() * 0.1)
res = gp_minimize(f, [(-2.0, 2.0)])For more control over the optimization loop you can use the skopt.Optimizer class:
from skopt import Optimizer
opt = Optimizer([(-2.0, 2.0)])
for i in range(20):
suggested = opt.ask()
y = f(suggested)
opt.tell(suggested, y)
print('iteration:', i, suggested, y)Read our introduction to bayesian optimization and the other examples.
Development
The library is still experimental and under heavy development. Checkout the next milestone for the plans for the next release or look at some easy issues to get started contributing.
The development version can be installed through:
git clone https://github.com/scikit-optimize/scikit-optimize.git cd scikit-optimize pip install -e.
Run all tests by executing pytest in the top level directory.
To only run the subset of tests with low run time, you can use pytest -m 'fast_test'. On the other hand pytest -m 'slow_test' is also possible. To exclude all slow running tests try pytest -m 'not slow_test'.
This is implemented using pytest attributes. If a tests runs longer than 1 second, it is marked as slow, else as fast.
All contributors are welcome!
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