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, goal=lambda l: l.learner.loss() < 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': 0.2871670248928634}),
             (1, {'y': 1.000099990001, 'waiting_time': 0.8889756425411652}),
             (0.0, {'y': 1.0, 'waiting_time': 0.750274055399344}),
             (-0.75,
              {'y': -0.7498222538215429, 'waiting_time': 0.06915357621448237}),
             (-0.5,
              {'y': -0.4996001599360256, 'waiting_time': 0.26213254090758087}),
             (-0.25,
              {'y': -0.24840255591054314,
               'waiting_time': 0.06909623643704998}),
             (0.5,
              {'y': 0.5003998400639744, 'waiting_time': 0.5747532581068258}),
             (-0.125,
              {'y': -0.11864069952305246, 'waiting_time': 0.9711994173578742}),
             (0.75,
              {'y': 0.7501777461784571, 'waiting_time': 0.8284027041615553}),
             (-0.0625,
              {'y': -0.0375390015600624, 'waiting_time': 0.8362688290280648}),
             (0.25,
              {'y': 0.2515974440894569, 'waiting_time': 0.46634207190472}),
             (-0.03125,
              {'y': 0.06163824383164006, 'waiting_time': 0.4580469898222427}),
             (-0.015625,
              {'y': 0.27495388762769585, 'waiting_time': 0.0926580084903611}),
             (0.125,
              {'y': 0.13135930047694755, 'waiting_time': 0.7471784434053483}),
             (-0.0078125,
              {'y': 0.6131699135839903, 'waiting_time': 0.6606973966776399}),
             (0.03125,
              {'y': 0.12413824383164006, 'waiting_time': 0.05525946545550531}),
             (0.015625,
              {'y': 0.30620388762769585, 'waiting_time': 0.10182165367608498}),
             (0.0625,
              {'y': 0.0874609984399376, 'waiting_time': 0.8499854325554431}),
             (0.0078125,
              {'y': 0.6287949135839903, 'waiting_time': 0.4854471582474611}),
             (0.00390625,
              {'y': 0.8715190438995976, 'waiting_time': 0.7004201584531127}),
             (-0.00390625,
              {'y': 0.8637065438995976, 'waiting_time': 0.9555464691478341}),
             (-0.375,
              {'y': -0.3742893942085628, 'waiting_time': 0.5565430735181408}),
             (-0.875,
              {'y': -0.8748694048124327, 'waiting_time': 0.3154562100363819}),
             (-0.625,
              {'y': -0.6247440655192271, 'waiting_time': 0.9567299028927606}),
             (0.625,
              {'y': 0.6252559344807729, 'waiting_time': 0.17339657211905402}),
             (0.375,
              {'y': 0.3757106057914372, 'waiting_time': 0.1309954843140957}),
             (0.875,
              {'y': 0.8751305951875673, 'waiting_time': 0.4766984206374051}),
             (0.01171875,
              {'y': 0.43307457758975415,
               'waiting_time': 0.022380915645558885}),
             (-0.01171875,
              {'y': 0.40963707758975415, 'waiting_time': 0.17939698716894115}),
             (-0.005859375,
              {'y': 0.7385633611533918, 'waiting_time': 0.23352844571943}),
             (0.005859375,
              {'y': 0.7502821111533918, 'waiting_time': 0.2055606597157078}),
             (-0.0234375,
              {'y': 0.1305706215220334, 'waiting_time': 0.6564268811713541}),
             (-0.009765625,
              {'y': 0.5020904154886127, 'waiting_time': 0.8814949916535595})])

1: This notebook can be downloaded as tutorial.DataSaver.ipynb and tutorial.DataSaver.md.

Tutorial DataSaver#

Tutorial `DataSaver`#