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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. [^download]
:tags: [hide-cell]
import adaptive
adaptive.notebook_extension()
If the function that you want to learn returns a value along with some metadata, you can wrap your learner in an {class}adaptive.DataSaver.
In the following example the function to be learned returns its result and the execution time in a dictionary:
from operator import itemgetter
def f_dict(x):
"""The function evaluation takes roughly the time we `sleep`."""
import random
from time import sleep
waiting_time = random.random()
sleep(waiting_time)
a = 0.01
y = x + a**2 / (a**2 + x**2)
return {"y": y, "waiting_time": waiting_time}
# Create the learner with the function that returns a 'dict'
# This learner cannot be run directly, as Learner1D does not know what to do with the 'dict'
_learner = adaptive.Learner1D(f_dict, bounds=(-1, 1))
# Wrapping the learner with 'adaptive.DataSaver' and tell it which key it needs to learn
learner = adaptive.DataSaver(_learner, arg_picker=itemgetter("y"))
learner.learner is the original learner, so learner.learner.loss() will call the correct loss method.
runner = adaptive.Runner(learner, loss_goal=0.1)
:tags: [hide-cell]
await runner.task # This is not needed in a notebook environment!
runner.live_info()
runner.live_plot(plotter=lambda lrn: lrn.learner.plot(), update_interval=0.1)
Now the DataSavingLearner will have an dictionary attribute extra_data that has x as key and the data that was returned by learner.function as values.
learner.extra_data