AverageLearner1D added

[AlvaroGI]

The AverageLearner1D has been added to the master branch (original code with design details and tests can be found here).
Tutorial notebook added (tutorial_averagelearner1d_aux.py contains some auxiliary functions for the tutorial).

Changes in existing files

The init.py files of adaptive/ and learner/, and notebook_integration.py were only modified to include the AverageLearner1D.

[basnijholt]

This is really great! I am super excited to use this 🎉

I have made some style changes and I left some small comments.

Todo:

• add tests
• add tutorial in the right format

[basnijholt]

Awesome!

[basnijholt]

When testing this I realized that the function is not deterministic (does not take a seed).

I am implementing that the function signature of the function-to-learn is: f(x_seed: Tuple[int, float]).

[akhmerov]

When testing this I realized that the function is not deterministic (does not take a seed).

Well spotted, thanks!

[basnijholt]

I am fixing the remaining (2) failing tests.

test_expected_loss_improvement_is_less_than_total_loss is failing because the loss_improvement for seed=0 is always set to np.inf and afterwards to 0.

e.g.:

learner.ask(10)

returns

([(0, -2),
  (1, -2),
  (2, -2),
  (3, -2),
  (4, -2),
  (5, -2),
  (6, -2),
  (7, -2),
  (8, -2),
  (9, -2)],
 [inf, 0, 0, 0, 0, 0, 0, 0, 0, 0])

for an empty learner.

With data:

With data it is:

([(40, -2),
  (41, -2),
  (42, -2),
  (43, -2),
  (44, -2),
  (45, -2),
  (46, -2),
  (47, -2),
  (48, -2),
  (49, -2)],
 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

[basnijholt]

I have fixed all the tests! 🎉

I have also enabled auto-merge. It's now in a working state, we can make more changes in subsequent PRs.

@AlvaroGI, really great work!

[AlvaroGI]

I have fixed all the tests! 🎉

I have also enabled auto-merge. It's now in a working state, we can make more changes in subsequent PRs.

@AlvaroGI, really great work!

That’s great news!! 🎉 Thank you for all your work, it looks so neat now! 😄