Tutorial DataSaver
#
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
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 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)
await runner.task # This is not needed in a notebook environment!
runner.live_info()
runner.live_plot(plotter=lambda l: l.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
OrderedDict([(-1,
{'y': -0.9999000099990001, 'waiting_time': 0.5954382992013811}),
(1, {'y': 1.000099990001, 'waiting_time': 0.7132958164303117}),
(-0.5,
{'y': -0.4996001599360256, 'waiting_time': 0.12333111274840314}),
(0.0, {'y': 1.0, 'waiting_time': 0.4118500213346865}),
(0.5,
{'y': 0.5003998400639744, 'waiting_time': 0.3516196572080439}),
(-0.375,
{'y': -0.3742893942085628, 'waiting_time': 0.11571634114339047}),
(-0.125,
{'y': -0.11864069952305246, 'waiting_time': 0.4690237847633568}),
(-0.25,
{'y': -0.24840255591054314, 'waiting_time': 0.9923765994959743}),
(-0.0625,
{'y': -0.0375390015600624, 'waiting_time': 0.9796475585319789}),
(-0.75,
{'y': -0.7498222538215429, 'waiting_time': 0.78548666196635}),
(-0.03125,
{'y': 0.06163824383164006, 'waiting_time': 0.3812421473077766}),
(0.75,
{'y': 0.7501777461784571, 'waiting_time': 0.40776597297340034}),
(-0.015625,
{'y': 0.27495388762769585, 'waiting_time': 0.06920704602458938}),
(-0.0078125,
{'y': 0.6131699135839903, 'waiting_time': 0.10925327333928614}),
(-0.00390625,
{'y': 0.8637065438995976, 'waiting_time': 0.3430798258609171}),
(0.25,
{'y': 0.2515974440894569, 'waiting_time': 0.5363054383700058}),
(0.125,
{'y': 0.13135930047694755, 'waiting_time': 0.03987457699355723}),
(-0.625,
{'y': -0.6247440655192271, 'waiting_time': 0.4966860162028137}),
(0.0625,
{'y': 0.0874609984399376, 'waiting_time': 0.6419506205451319}),
(0.015625,
{'y': 0.30620388762769585, 'waiting_time': 0.3019608727779236}),
(0.03125,
{'y': 0.12413824383164006, 'waiting_time': 0.7460654015083211}),
(0.0078125,
{'y': 0.6287949135839903, 'waiting_time': 0.2564744526159317}),
(0.00390625,
{'y': 0.8715190438995976, 'waiting_time': 0.23390204718710406}),
(-0.875,
{'y': -0.8748694048124327, 'waiting_time': 0.9914501306701633}),
(0.875,
{'y': 0.8751305951875673, 'waiting_time': 0.8385091909726031}),
(0.375,
{'y': 0.3757106057914372, 'waiting_time': 0.5304844898619397}),
(-0.01171875,
{'y': 0.40963707758975415,
'waiting_time': 0.014682308961453061}),
(0.625,
{'y': 0.6252559344807729, 'waiting_time': 0.994417720029674}),
(-0.005859375,
{'y': 0.7385633611533918, 'waiting_time': 0.20769787603995693}),
(0.005859375,
{'y': 0.7502821111533918, 'waiting_time': 0.054722489918124895}),
(0.01171875,
{'y': 0.43307457758975415, 'waiting_time': 0.747941290840191}),
(-0.0234375,
{'y': 0.1305706215220334, 'waiting_time': 0.45660671026322075}),
(0.009765625,
{'y': 0.5216216654886127, 'waiting_time': 0.21740852974796432}),
(-0.009765625,
{'y': 0.5020904154886127, 'waiting_time': 0.5942564634166133})])
- 1
This notebook can be downloaded as
tutorial.DataSaver.ipynb
andtutorial.DataSaver.md
.