Chimera Objective #455
Chimera Objective #455
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@AVHopp @AdrianSosic PR in draft mode, NOT for review |
baybe/objectives/chimera.py
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| @define(frozen=True, slots=False) | ||
| class ChimeraObjective(Objective): | ||
| """An objective scalarizing multiple targets using desirability values.""" |
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wrong docstring mentioning desirability
incude a link to the publication, see other code parts (eg edbo) for how to properly include links
baybe/objectives/chimera.py
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| ) | ||
| "The targets considered by the objective." | ||
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| targets_threshold_values: tuple[float, ...] = field( |
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targets_ in fornt is not necessary for either of the two threshold attributes
baybe/objectives/chimera.py
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| """The softness parameter regulating the Heaviside function.""" | ||
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| @targets_threshold_values.default | ||
| def _default_targets_threshold_values(self) -> tuple[float, ...]: |
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i think there are no rasonable defaults so these should not have defaults
apart from that the defaults you specified do not satisfy the requirements ge(0.0)
| arg = -value / softness | ||
| return np.exp(-np.logaddexp(0, arg)) | ||
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| def _hard_heaviside(self, value: float) -> float: |
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is the hard function really needed? I think the soft heavyside should just recover the hard one for an extreme value of softness. If so that can simply be sued wherever a hard heaviside is needed
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| @st.composite | ||
| def chimera_objectives(draw: st.DrawFn): | ||
| """Generate :class:`baybe.objectives.chimera.ChimeraObjective` and its reference.""" |
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"and its reference" is not accurate anymore
| targets = draw( | ||
| st.lists( | ||
| numerical_targets(intervals), | ||
| min_size=3, # At least 2 targets. |
| # Now draw threshold values one-by-one based on each threshold type. | ||
| threshold_values = [] | ||
| for i, tt in enumerate(threshold_types): | ||
| if tt == ThresholdType.ABSOLUTE: |
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for singletons, always use is and never ==
| return are_close | ||
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| @settings(max_examples=2) |
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i assume this is for your testing purposes and not supposed to be here genereally?
| @settings(max_examples=2) | ||
| @given(chimera_obj=chimera_objectives(), data=st.data()) | ||
| def test_chimera_merits(chimera_obj, data): | ||
| """Validating chimerra merits value with external reference.""" |
| absolutes=[ | ||
| tt.value.upper() == "ABSOLUTE" for tt in chimera_obj.threshold_types | ||
| ], | ||
| goals=[g.lower() for g in goals], |
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duplicated application of lower?
| ] | ||
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| target_vals = data.draw(data_frames(columns=columns)) | ||
| assume(target_vals.shape[0] >= 6) |
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does data_frames not have options to specify the amount of rows?
Also, why 6 specifically?
This PR introduces Chimera, a general-purpose achievement scalarizing function for multi-target optimization that allows users to establish a hierarchy of targets with relative or absolute thresholds for concurrent optimization.
This implementation includes a new objective class,
ChimeraObjective, which follows a similar approach to theDesirabilityObjective. It scalarizes multiple targets into a single score, termedChimera Merits, which is to be minimized.For further details, please refer to the following publication:
F. Häse, L.M. Roch, and A. Aspuru-Guzik. Chimera: enabling hierarchy-based multi-objective optimization for self-driving laboratories. Chemical Science 2018, 9(39), 7642-7655.
WIP:
ChimeraObjectiveimplementation.DesirabilityObjective.ChimeraObjective.