Tutorial 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.

See also

The complete source code of this tutorial can be found in tutorial.SKOptLearner.ipynb

import adaptive
adaptive.notebook_extension()

import holoviews as hv
import numpy as np

We have wrapped the Optimizer class from 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. Although SKOptLearner can optimize functions of arbitrary dimensionality, we can only plot the learner if a 1D function is being learned.

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

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)

await runner.task  # This is not needed in a notebook environment!

runner.live_info()

%%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

Make live