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': 0.4738923089288334}),
             (-1.0,
              {'y': -0.9999000099990001, 'waiting_time': 0.6590563336915942}),
             (0.0, {'y': 1.0, 'waiting_time': 0.5766681315676223}),
             (-0.75,
              {'y': -0.7498222538215429, 'waiting_time': 0.26686404673430053}),
             (-0.25,
              {'y': -0.24840255591054314, 'waiting_time': 0.1981273730618276}),
             (-0.5,
              {'y': -0.4996001599360256, 'waiting_time': 0.9221358607251033}),
             (0.5,
              {'y': 0.5003998400639744, 'waiting_time': 0.6895057665052392}),
             (-0.125,
              {'y': -0.11864069952305246, 'waiting_time': 0.9027813209278817}),
             (-0.0625,
              {'y': -0.0375390015600624, 'waiting_time': 0.09848857260970978}),
             (0.75,
              {'y': 0.7501777461784571, 'waiting_time': 0.28077389538378716}),
             (-0.03125,
              {'y': 0.06163824383164006, 'waiting_time': 0.25291890475360357}),
             (-0.015625,
              {'y': 0.27495388762769585, 'waiting_time': 0.06253961595525348}),
             (0.25,
              {'y': 0.2515974440894569, 'waiting_time': 0.5081475660257416}),
             (0.125,
              {'y': 0.13135930047694755, 'waiting_time': 0.21732447005432598}),
             (0.0625,
              {'y': 0.0874609984399376, 'waiting_time': 0.004553151820959167}),
             (-0.0078125,
              {'y': 0.6131699135839903, 'waiting_time': 0.5896255072212871}),
             (0.015625,
              {'y': 0.30620388762769585, 'waiting_time': 0.19671856367504803}),
             (0.03125,
              {'y': 0.12413824383164006, 'waiting_time': 0.8763116496754801}),
             (-0.00390625,
              {'y': 0.8637065438995976, 'waiting_time': 0.05475445173200455}),
             (0.0078125,
              {'y': 0.6287949135839903, 'waiting_time': 0.6142667635569115}),
             (-0.375,
              {'y': -0.3742893942085628, 'waiting_time': 0.5547229527409359}),
             (0.00390625,
              {'y': 0.8715190438995976, 'waiting_time': 0.6328955103162566}),
             (-0.625,
              {'y': -0.6247440655192271, 'waiting_time': 0.5013549366675127}),
             (-0.875,
              {'y': -0.8748694048124327, 'waiting_time': 0.6503826697562932}),
             (0.875,
              {'y': 0.8751305951875673, 'waiting_time': 0.3387711436711297}),
             (0.625,
              {'y': 0.6252559344807729, 'waiting_time': 0.3104978002663631}),
             (-0.01171875,
              {'y': 0.40963707758975415, 'waiting_time': 0.109238924703432}),
             (0.375,
              {'y': 0.3757106057914372, 'waiting_time': 0.4209196839988639}),
             (-0.005859375,
              {'y': 0.7385633611533918, 'waiting_time': 0.16121040842625733}),
             (0.01171875,
              {'y': 0.43307457758975415, 'waiting_time': 0.38514074391693376}),
             (-0.0234375,
              {'y': 0.1305706215220334, 'waiting_time': 0.023724163153835187}),
             (0.005859375,
              {'y': 0.7502821111533918, 'waiting_time': 0.6491468651704524}),
             (0.009765625,
              {'y': 0.5216216654886127, 'waiting_time': 0.02547431880204487}),
             (-0.009765625,
              {'y': 0.5020904154886127, 'waiting_time': 0.5776794515198178})])

Tutorial DataSaver#

Tutorial `DataSaver`#