[Experimental] Enable multiobjective within optimizers #775
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facebook-github-bot
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jrapin
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Difficulty: High
Priority: Medium
Status: In Progess
Type: Enhancement
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...good luck, that one is super scary... |
jrapin
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Aug 3, 2020
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| # pylint: disable=redefined-outer-name,unsubscriptable-object,unused-variable,unused-import,reimported,import-outside-toplevel | ||
| def test_doc_multiobjective() -> None: |
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@teytaud below is an example of how that could work
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| # TODO the following is probably not good at all: | ||
| # -> +inf if no point is strictly better (but lower if it is) | ||
| if (stored_losses <= losses).all(): | ||
| distance_to_pareto = min(distance_to_pareto, min(losses - stored_losses)) |
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@teytaud any thought on this?
# TODO the following is probably not good at all:
# -> +inf if no point is strictly better (but lower if it is)
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| if (self._upper_bounds > -float("inf")).all() and (losses > self._upper_bounds).all(): | ||
| return float('inf') # Avoid uniformly worst points |
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@teytaud I've added this case to auto-bound:
if one point is uniformly bad compared to previous ones, do not include it
that makes the example work way better. Is it a problem though?
| remotely, and aggregate locally. This is what happens in the "minimize" method of optimizers. | ||
| """ | ||
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| def __init__(self, upper_bounds: tp.Optional[tp.ArrayLike] = None, auto_bound: int = 15) -> None: |
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the auto-bound number should probably depend on the dimension :s
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Todo:
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7 tasks
jrapin
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