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.
The complete source code of this tutorial can be found in :jupyter-downloadtutorial.SKOptLearner
import adaptive adaptive.notebook_extension(_inline_js=False)
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