Sampler engine structure #280 - #287
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rozyczko
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[area] fitting
damskii9992
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I am ALMOST done reviewing this behemoth of a PR. Still need to look at sampler_dream.py and the tests. but the day is almost over and I figured I should send off these comments now.
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You will need to change the tutorial in this PR too . . . |
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Please also remove all the "Optional" typehints in the code and replace them with | None as is the new convention.
| if weights is None: | ||
| raise ValueError( | ||
| 'weights must not be None for Bayesian sampling. Pass ' | ||
| 'measurement weights (e.g. ``1 / sigma``) matching x and y.' | ||
| ) |
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Why allow None in the typehint signature then?
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Why allow None weights and None entries in weights if the Dream sampler doesn't allow them?
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| def __init__( | ||
| self, | ||
| fitter: 'Fitter', |
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Wasn't a big part of this re-factoring done to also ensure that you didn't need a Fitter to create a Sampler?
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The refactor decoupled sampling from the minimizer, not from the Fitter. Fitter still has reshaping and fit-function wrapping.
One thing: __init__ requires hasattr(fitter, 'minimizer') but the minimizer is never used. I'll fix that.
| def validate_run_settings(samples: int, burn: int, thin: int) -> None: | ||
| """Validate the DREAM run settings. | ||
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| Parameters | ||
| ---------- | ||
| samples : int | ||
| Number of raw samples to draw; must be a positive integer. | ||
| burn : int | ||
| Burn-in generations to discard; must be a non-negative integer. | ||
| thin : int | ||
| Thinning interval; must be a positive integer. | ||
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| Raises | ||
| ------ | ||
| ValueError | ||
| If any value is out of range or not an integer. | ||
| """ | ||
| # bool is a subclass of int, so ``samples=True`` would otherwise pass as | ||
| # ``samples=1``; these checks are strict (``10.0`` is rejected), so | ||
| # booleans must be rejected too. | ||
| if not isinstance(samples, int) or isinstance(samples, bool) or samples <= 0: | ||
| raise ValueError('samples must be a positive integer.') | ||
| if not isinstance(burn, int) or isinstance(burn, bool) or burn < 0: | ||
| raise ValueError('burn must be a non-negative integer.') | ||
| if not isinstance(thin, int) or isinstance(thin, bool) or thin < 1: | ||
| raise ValueError('thin must be a positive integer.') |
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Why factor this into a separate module? It's only used by the DreamSampler, so shouldn't it live there?
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Also, it is only used in 1 place, so why even factor it out at all? It's more readable to just have the code where it is used, if it is only used once . . .
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moved to samples/validation.py, dropped from bumps_utils/init.py, exported from samplers/init.py
| validate_run_settings(samples, burn, thin) | ||
| validate_arrays(x, y, weights) |
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Should our validations belong in the Sampler class or here? Right now you validate in both places . . .
I think validations should belong in Sampler as long as they aren't engine-specific. Which means the engines can assume that whatever is passed to them is valid.
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It is not actually duplicated. Sampler checks numeric/non-scalar/non-empty; validate_arrays checks shape match, finiteness, positive weights.
They are more complementary than dupes. And validate_arrays cannot move into Sampler regardless, since all three minimizers call it from Fitter.fit...
No changes needed. I will need to change the tutorials for the |
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Core refactoring and shared logic:
EngineBase(engine_base.py) as a new base class for all evaluation engines (minimizers and samplers), with centralized parameter handling, fit function wrapping, and evaluation logic.MinimizerBaseso it inherits fromEngineBase. Cleaned up the constructor and removed duplicated parameter caching logic.Bumps integration and utilities:
bumps_utils/problem.py, includingbuild_curve_problem,to_bumps_parameter,parameter_names, andparameter_snapshot. These are now imported and exposed inbumps_utils/__init__.py.bumps_utils/validation.py) with functions for validating run settings and input arraysAPI changes and deprecations:
mcmc_sample()now lives onSamplerclass directly. The method now routes calls toSampler(...).sample(...)progress_callbacksignature inMinimizerBase.fitto clarify that its return value is ignored and to align with the new shared engine base.