Tutorial `~adaptive.SKOptLearner`
 ---------------------------------
 
 .. note::
    Because this documentation consists of static html, the ``live_plot``
    and ``live_info`` widget is not live. Download the notebook
    in order to see the real behaviour.
 
 .. seealso::
     The complete source code of this tutorial can be found in

     :jupyter-download:notebook:`tutorial.SKOptLearner`

 .. jupyter-execute::

     :hide-code:
 
     import adaptive
     adaptive.notebook_extension()
 
     import holoviews as hv
     import numpy as np
 
 We have wrapped the ``Optimizer`` class from
 `scikit-optimize <https://github.com/scikit-optimize/scikit-optimize>`__,
 to show how existing libraries can be integrated with ``adaptive``.
 
 The ``SKOptLearner`` attempts to “optimize” the given function ``g``
 (i.e. find the global minimum of ``g`` in the window of interest).
 
 Here we use the same example as in the ``scikit-optimize``
 `tutorial <https://github.com/scikit-optimize/scikit-optimize/blob/master/examples/ask-and-tell.ipynb>`__.
 Although ``SKOptLearner`` can optimize functions of arbitrary
 dimensionality, we can only plot the learner if a 1D function is being
 learned.
 

 .. jupyter-execute::

 
     def F(x, noise_level=0.1):
         return (np.sin(5 * x) * (1 - np.tanh(x ** 2))
                 + np.random.randn() * noise_level)
 

 .. jupyter-execute::

 
     learner = adaptive.SKOptLearner(F, dimensions=[(-2., 2.)],
                                     base_estimator="GP",
                                     acq_func="gp_hedge",
                                     acq_optimizer="lbfgs",
                                    )
     runner = adaptive.Runner(learner, ntasks=1, goal=lambda l: l.npoints > 40)
 

 .. jupyter-execute::

     :hide-code:
 
     await runner.task  # This is not needed in a notebook environment!
 

 .. jupyter-execute::

 
     runner.live_info()
 

 .. jupyter-execute::

 
     %%opts Overlay [legend_position='top']
     xs = np.linspace(*learner.space.bounds[0])
     to_learn = hv.Curve((xs, [F(x, 0) for x in xs]), label='to learn')
 
     runner.live_plot().relabel('prediction', depth=2) * to_learn