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Improve model round-tripping #6342

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Improve model round-tripping #6342

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5 changes: 5 additions & 0 deletions python/cuml/cuml/internals/base.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -705,6 +705,11 @@ class UniversalBase(Base):
]
):
for attr in self.get_attr_names():
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?


# check presence of attribute
if hasattr(self._cpu_model, attr) or \
isinstance(getattr(type(self._cpu_model),
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5 changes: 4 additions & 1 deletion python/cuml/cuml/tests/test_sklearn_import_export.py
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Expand Up @@ -72,6 +72,7 @@ def assert_estimator_roundtrip(
else:
cuml_model.fit(X)

original_params = cuml_model.get_params()
# Convert to sklearn model
sklearn_model = cuml_model.as_sklearn()
check_is_fitted(sklearn_model)
Expand All @@ -81,6 +82,8 @@ def assert_estimator_roundtrip(
# Convert back
roundtrip_model = type(cuml_model).from_sklearn(sklearn_model)

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.


# Ensure roundtrip model is fitted
check_is_fitted(roundtrip_model)

Expand Down Expand Up @@ -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

assert_estimator_roundtrip(original, SkKMeans, X)


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