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Fix/replace deprecated budget method #1604

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Fix/replace deprecated budget method #1604

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7b39ddd
Fix test to suppress warning on allocate_budget_to_maximize_response
caiquefortunato Mar 29, 2025
c7341e1
refactor(tests): replace deprecated method with optimize_budget in bu…
caiquefortunato Mar 29, 2025
739b8bd
Merge branch 'main' into fix/replace-deprecated-budget-method
cetagostini Apr 2, 2025
14f36cd
Merge branch 'main' into fix/replace-deprecated-budget-method
williambdean Apr 2, 2025
6d8af18
Merge branch 'main' into fix/replace-deprecated-budget-method
cetagostini Apr 2, 2025
e73e4de
Merge branch 'main' into fix/replace-deprecated-budget-method
caiquefortunato Apr 5, 2025
c0e3894
Merge branch 'main' into fix/replace-deprecated-budget-method
williambdean Apr 9, 2025
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6 changes: 1 addition & 5 deletions tests/mmm/test_mmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -571,21 +571,17 @@ def test_allocate_budget_to_maximize_response_bad_noise_level(
) -> None:
budget = 2.0
num_periods = 8
time_granularity = "weekly"
budget_bounds = optimizer_xarray_builder(
value=[[0.5, 1.2], [0.5, 1.5]],
channel=["channel_1", "channel_2"],
bound=["lower", "upper"],
)
noise_level = "bad_noise_level"

with pytest.raises(ValueError, match="noise_level must be a float"):
mmm_fitted.allocate_budget_to_maximize_response(
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Sorry, reading this better. Here we are doing the wrong fix. We should:

  1. Replace all the suite (not only this test) to work with the new class.
  2. We need to test the same scenarios, meaning, you need to test optimize budget and sample_response_distribution
  3. No parameters should be delete, the test should be handle differently.
  4. We need test for the method to be deprecated, and the new one. This because we still will hold the old method for at least one more release. So, best way should be to have test for both, and be sure non of them is doing something funny. IF we don't want to do this then we should deprecated already the old method. @williambdean Whats your take?

mmm_fitted.optimize_budget(
budget=budget,
time_granularity=time_granularity,
num_periods=num_periods,
budget_bounds=budget_bounds,
noise_level=noise_level,
)

@pytest.mark.parametrize(
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