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]

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)

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

OrderedDict([(1.0, {'y': 1.000099990001, 'waiting_time': 0.06392822910800966}),
             (0.0, {'y': 1.0, 'waiting_time': 0.2530178395681285}),
             (-1.0,
              {'y': -0.9999000099990001, 'waiting_time': 0.33221870731406444}),
             (0.5,
              {'y': 0.5003998400639744, 'waiting_time': 0.16272948847547097}),
             (-0.5,
              {'y': -0.4996001599360256, 'waiting_time': 0.36718057373251}),
             (-0.25,
              {'y': -0.24840255591054314,
               'waiting_time': 0.09811788562229484}),
             (-0.75,
              {'y': -0.7498222538215429, 'waiting_time': 0.45245506946012826}),
             (0.75,
              {'y': 0.7501777461784571, 'waiting_time': 0.5246337673470752}),
             (-0.125,
              {'y': -0.11864069952305246, 'waiting_time': 0.7080499103121634}),
             (0.25,
              {'y': 0.2515974440894569, 'waiting_time': 0.6798988172242958}),
             (-0.0625,
              {'y': -0.0375390015600624, 'waiting_time': 0.8551396042736303}),
             (0.125,
              {'y': 0.13135930047694755, 'waiting_time': 0.31042176323965187}),
             (0.0625,
              {'y': 0.0874609984399376, 'waiting_time': 0.5660631652031622}),
             (-0.03125,
              {'y': 0.06163824383164006, 'waiting_time': 0.8669316769251552}),
             (0.03125,
              {'y': 0.12413824383164006, 'waiting_time': 0.5390540780656408}),
             (-0.015625,
              {'y': 0.27495388762769585, 'waiting_time': 0.3913704971671935}),
             (-0.0078125,
              {'y': 0.6131699135839903, 'waiting_time': 0.07690079720157894}),
             (0.0078125,
              {'y': 0.6287949135839903, 'waiting_time': 0.5109819635638173}),
             (0.015625,
              {'y': 0.30620388762769585, 'waiting_time': 0.9727963297524818}),
             (0.00390625,
              {'y': 0.8715190438995976, 'waiting_time': 0.21900922236910159}),
             (-0.00390625,
              {'y': 0.8637065438995976, 'waiting_time': 0.6615372553215338}),
             (-0.625,
              {'y': -0.6247440655192271, 'waiting_time': 0.10953751483536844}),
             (-0.375,
              {'y': -0.3742893942085628, 'waiting_time': 0.29037278122612253}),
             (0.875,
              {'y': 0.8751305951875673, 'waiting_time': 0.6492109518708672}),
             (-0.875,
              {'y': -0.8748694048124327, 'waiting_time': 0.9320762432234413}),
             (0.625,
              {'y': 0.6252559344807729, 'waiting_time': 0.3354232666865796}),
             (0.375,
              {'y': 0.3757106057914372, 'waiting_time': 0.8177847796472785}),
             (-0.01171875,
              {'y': 0.40963707758975415, 'waiting_time': 0.8792849627692795}),
             (0.01171875,
              {'y': 0.43307457758975415, 'waiting_time': 0.36116923294700787}),
             (0.005859375,
              {'y': 0.7502821111533918, 'waiting_time': 0.19318660293985845}),
             (-0.0234375,
              {'y': 0.1305706215220334, 'waiting_time': 0.08229678085336822}),
             (-0.005859375,
              {'y': 0.7385633611533918, 'waiting_time': 0.8737614703835795}),
             (-0.009765625,
              {'y': 0.5020904154886127, 'waiting_time': 0.5497656606446031})])

Tutorial DataSaver#

Tutorial `DataSaver`#