Comments (3)
Hmmm, while in principal I agree, it opens the door for what is close
, i.e. should all Float
parameters then also accept a tolerance?
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In my use case, i.e. YAHPO, I just use the np.isclose with the default tolerance and if it's slightly out of the range, I clip the value.
Ofc, you cannot easily define what "close" means, but some packages seem to be affected by sampling from boundaries.
In that sense, all float parameters need to be (somehow) modified (optionally with warning).
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Just revisited this after finisihng #346. I think there's too many places where this isclose
check would introduce difficulties in value space. However we do make use of isclose
in vectorized space.
I'll close this with the statement that numbers provided in value space (as opposed to vectorized space) are given by the user and as such have the ability to choose their tolerance and what they'd like to do. However if you encounter issues like this in vectorized space, then please do raise another issue!
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Related Issues (20)
- Orders of Conjunctions result in unequal ConfigSpace HOT 1
- Allow to sample Lists of Hyperparameters
- TypeError: Expected float, got numpy.float64 when using Float HOT 7
- TypeError: Expected int, got numpy.ndarray when sampling OrdinalHyperparameter HOT 4
- Question: How to define the nested config space? HOT 6
- [Bug] UniformFloatHyperparameter Typing check bug HOT 3
- Maximum recursion while calling "__contains__" function of Configuration HOT 1
- [Bug] Ordinal Hyperparameter got Key Error when run `self.get_order` in `get_neighbors` HOT 1
- `HyperparameterNotFoundError` prohibits `.get` from functioning as expected HOT 1
- Inconsistent typing on quantization parameter for Float hyperparameters HOT 1
- Missing documentation on Forbidden Relations HOT 8
- Feature Request: basic scalar operations on Forbidden Relation constraints HOT 1
- Duplicate forbiddens in ConfigurationSpace HOT 1
- Importing a custom incomplete configuration space? HOT 4
- Read ForbiddenRelations clauses from json HOT 2
- [Feature Request] Support json read for Categorical with tuple entries. HOT 2
- TypeError: Expected float, got numpy.float64 (ConfigSpace)
- TypeError: Expected float, got numpy.float64 HOT 1
- `np.array` on `Configuration` returns names of HPs instead of values HOT 4
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