
===============
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>`_.
