Tutorial BalancingLearner
#
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. [1]
Show code cell content
import adaptive
adaptive.notebook_extension()
import random
from functools import partial
import holoviews as hv
import numpy as np
The balancing learner is a โmeta-learnerโ that takes a list of learners. When you request a point from the balancing learner, it will query all of its โchildrenโ to figure out which one will give the most improvement.
The balancing learner can for example be used to implement a poor-manโs 2D learner by using the Learner1D
.
def h(x, offset=0):
a = 0.01
return x + a**2 / (a**2 + (x - offset) ** 2)
learners = [
adaptive.Learner1D(partial(h, offset=random.uniform(-1, 1)), bounds=(-1, 1))
for i in range(10)
]
bal_learner = adaptive.BalancingLearner(learners)
runner = adaptive.Runner(bal_learner, loss_goal=0.01)
Show code cell content
await runner.task # This is not needed in a notebook environment!
runner.live_info()
def plotter(learner):
return hv.Overlay([L.plot() for L in learner.learners])
runner.live_plot(plotter=plotter, update_interval=0.1)
Often one wants to create a set of learner
s for a cartesian product of parameters.
For that particular case weโve added a classmethod
called from_product
.
See how it works below
from scipy.special import eval_jacobi
def jacobi(x, n, alpha, beta):
return eval_jacobi(n, alpha, beta, x)
combos = {
"n": [1, 2, 4, 8],
"alpha": np.linspace(0, 2, 3),
"beta": np.linspace(0, 1, 5),
}
learner = adaptive.BalancingLearner.from_product(
jacobi, adaptive.Learner1D, {"bounds": (0, 1)}, combos
)
runner = adaptive.BlockingRunner(learner, loss_goal=0.01)
# The `cdims` will automatically be set when using `from_product`, so
# `plot()` will return a HoloMap with correctly labeled sliders.
learner.plot().overlay("beta").grid().select(y=(-1, 3))