Improve model round-tripping #6342
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betatim
commented
Feb 20, 2025
| cuml_param_names = self._get_param_names() | ||
| for param, value in self._cpu_model.get_params().items(): | ||
| if param in cuml_param_names: | ||
| self.set_params(**{param: value}) |
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We can make this nicer, left it like this for now to illustrate the point. I think we need some translation mechanism, not just blindly copying things.
In the GPU -> CPU direction attributes are already copied, but probably also needs adjusting?
betatim
commented
Feb 20, 2025
| @@ -81,6 +82,8 @@ def assert_estimator_roundtrip( | |||
| # Convert back | |||
| roundtrip_model = type(cuml_model).from_sklearn(sklearn_model) | |||
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| assert original_params == roundtrip_model.get_params() | |||
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I think this should be true. Otherwise calling fit, which is possible (or should we disallow it? A la FrozenEstimator), will lead to very weird results.
betatim
commented
Feb 20, 2025
| @@ -115,7 +118,7 @@ def test_kmeans(random_state): | |||
| X, _ = make_blobs( | |||
| n_samples=50, n_features=2, centers=3, random_state=random_state | |||
| ) | |||
| original = KMeans(n_clusters=3, random_state=random_state) | |||
| original = KMeans(n_clusters=13, random_state=random_state) | |||
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Just a random value that isn't the default
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This PR is the result of investigating #6142
A short version of the story is that this roundtrip doesn't work:
This PR kinda fixes things, but really we need the hyper-parameter translator from the 0cc accelerator.
cc @dantegd