1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
|
===============
Getting started
===============
.. currentmodule:: skopt
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 <http://docs.scipy.org/doc/scipy/reference/optimize.html>`_.
.. figure:: https://raw.githubusercontent.com/holgern/scikit-optimize/main/media/bo-objective.png
:alt: Approximated objective
Approximated objective function after 50 iterations of :class:`gp_minimize`.
Plot made using :class:`plots.plot_objective`.
Finding a minimum
=================
Find the minimum of the noisy function ``f(x)`` over the range ``-2 < x < 2``
with :class:`skopt`::
>>> import numpy as np
>>> from skopt import gp_minimize
>>> np.random.seed(123)
>>> 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)], n_calls=20)
>>> print("x*=%.2f f(x*)=%.2f" % (res.x[0], res.fun))
x*=0.85 f(x*)=-0.06
For more control over the optimization loop you can use the :class:`skopt.Optimizer`
class::
>>> from skopt import Optimizer
>>> opt = Optimizer([(-2.0, 2.0)])
>>>
>>> for i in range(20):
... suggested = opt.ask()
... y = f(suggested)
... res = opt.tell(suggested, y)
>>> print("x*=%.2f f(x*)=%.2f" % (res.x[0], res.fun))
x*=0.27 f(x*)=-0.15
For more read our :ref:`sphx_glr_auto_examples_bayesian-optimization.py` and the other
`examples <auto_examples/index.html>`_.
|
