Add as_sklearn and from_sklearn APIs to serialize to CPU sklearn-estimators for supported models
#6102
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Why have the methods do both the conversion cuml<>sklearn and the serialisation? Having a way to convert to and from scikit-learn seems like a useful thing by itself. Maybe because you have your own way of serialising the model, or because you need a particular type of model or who-knows-what. So to serialise it you'd do something like How hard would it be to have Name bike shedding: if we don't save things to a file, how about If we do save to a file, then |
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dantegd
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to_sklearn and from_sklearn APIs to serialize to CPU sklearn-estimators for supported modelsas_sklearn and from_sklearn APIs to serialize to CPU sklearn-estimators for supported models
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@betatim just implemented your suggestions and changed the functionality to not save a file but return an estimator. I think the idea of |
| return self._cpu_model | ||
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| @classmethod | ||
| def from_sklearn(cls, model): |
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Would be great to have a global conversion table, so that we don't need to provide the class as a parameter.
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This is a class method, so we get the class from that, it's not something the user passes (like self in non class methods)
A global conversion table will be useful for a follow up to add cuml.from_sklearn library type of functionality though
| """ | ||
| estimator = cls() | ||
| estimator.import_cpu_model() | ||
| estimator._cpu_model = model |
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It could be interesting to add an optional parameter to this function to allow a deepcopy of the sklearn model.
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lol I asked the same thing before reading this suggestion :)
python/cuml/cuml/internals/base.pyx
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| self.import_cpu_model() | ||
| self.build_cpu_model() | ||
| self.gpu_to_cpu() | ||
| return self._cpu_model |
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Should we not return a deep copy here?
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For my education, why would we want to deepcopy? Mostly asking because in my experience 99% of cases where someone uses deepcopy there is something else that we can do instead or just not do it. Mostly Python "just works" without deepcopy'ing, hence my interest
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If we simply return a reference to the internal model, any modification to one (additional training or something else) would affect the other. This might create a situation in which the CPU and GPU attributes are out of sync in the cuML estimator. Or inversely, the sklearn estimator returned by the function might silently be updated by the cuML estimator.
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perhaps it should be a parameter, by default most users probably won't care about needing a deep copy, so I wouldn't do it by default, but if a user needs it then they can request it, what do you guys think?
| else: | ||
| raise ValueError(f"Serializer {format} not supported.") |
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Does click not take care of invalid values being passed :(
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It does, I added it by accident based on habits :P
| # Convert to sklearn estimator | ||
| sklearn_estimator = accelerated_estimator.as_sklearn() | ||
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| # Save using chosen format | ||
| with open(output, "wb") as f: | ||
| serializer.dump(sklearn_estimator, f) | ||
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| # Exit after conversion |
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Do we need the comments? They seem like they repeat what the code says. I like comments that explain why the code is the way it is, but I don't think we need that here as it is pretty straightforward
| @pytest.fixture | ||
| def random_state(): | ||
| return 42 |
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Do we really need this instead of using 42 in the tests directly?
We could have a global version of this that allows us to run the tests with several seeds, but maybe something to tackle in the future/new PR
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We don't really need it at all
Co-authored-by: Tim Head <[email protected]>
dantegd
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