Raise NotFittedError from unfitted IsolationForest methods

[JulienAu]

Contributes to #8420 (Python interoperability and persistence: "Raise NotFittedError from unfitted estimator methods and remove the corresponding common-estimator-check xfail").

Description

Unfitted IsolationForest methods raised RuntimeError; scikit-learn's estimator contract (and its check_estimators_unfitted common check) expects sklearn.exceptions.NotFittedError. This change:

• converts the five unfitted-model raises in isolation_forest.pyx (predict, score_samples, as_treelite, as_nvforest, _score_samples_nvforest) from RuntimeError to NotFittedError, keeping the message unchanged;
• removes the check_estimators_unfitted xfail from test_sklearn_compatibility.py;
• updates the five corresponding assertions in test_isolation_forest.py.

NotFittedError subclasses ValueError and AttributeError, so any caller currently catching those broad types keeps working; only code catching RuntimeError specifically would notice, and the estimator is new in 26.08.

Verification

• Against the current cuml-cu13==26.08.00a171 nightly wheel (GTX 1650 Ti, WSL2), the three updated unfitted tests fail as expected with the old RuntimeError, and the remaining 82 tests in test_isolation_forest.py pass, so the assertions encode exactly the target behavior and nothing else in the suite is affected.
• ruff check / ruff format --check on the two test files and cython-lint on the .pyx are clean (remaining ruff findings are pre-existing on main, only shifted line numbers).
• I do not have a local CUDA toolchain to compile the modified .pyx; the change is a five-site exception-type swap plus one import, and CI's estimator-check job exercises check_estimators_unfitted directly.

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📝 Walkthrough

Summary by CodeRabbit

• Bug Fixes
  • IsolationForest now consistently raises the standard NotFittedError when prediction, scoring, or model export is attempted before fitting.
  • Improved compatibility with scikit-learn’s estimator validation checks.

Walkthrough

IsolationForest now uses sklearn’s NotFittedError for unfitted export, scoring, and prediction operations. Fitted-state checks use the native model and remain valid after unpickling. Tests and sklearn compatibility expectations were updated.

Changes

IsolationForest unfitted error handling

Layer / File(s)	Summary
Fitted-state validation and error handling python/cuml/cuml/ensemble/isolation_forest.pyx	IsolationForest defines fitted state through the native model. Export, scoring, and prediction paths now use check_is_fitted or raise NotFittedError.
Unfitted behavior validation python/cuml/tests/test_isolation_forest.py, python/cuml/tests/test_sklearn_compatibility.py	Tests now expect NotFittedError, and the sklearn compatibility exclusion was removed.

Estimated code review effort: 2 (Simple) | ~10 minutes

Merge Risk: ⚪ Minimal · up to d18df

This localized change updates unfitted IsolationForest methods to raise the expected exception type and aligns the related tests; no actionable merge-blocking risk remains after normal checks and review.

Suggested reviewers: betatim

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely describes the main change: unfitted IsolationForest methods now raise NotFittedError.
Description check	✅ Passed	The description directly explains the exception change, affected methods, tests, verification, and compatibility-check updates.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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[betatim]

Thanks for spotting this and making a PR!

Instead of hand rolling the "is this estimator fitted" check, the estimator should be using check_is_fitted(self). This raises the correct exception and we use it in all other estimators. We made the switch in #7868 (and follow up PRs).

We can also remove test_predict_before_fit_raises, I think the common check that you un-xfailed will take care of this.

The fact that the type of the exception changes is annoying for those already using this. However I'd consider it a bug fix and as such not a breaking change (no need for deprecation cycles, etc).

[JulienAu]

Thanks, done in 9e05e0e:

• predict and score_samples now call check_is_fitted(self) (via cuml.internals.validation, the Add cuml.internals.validation, check_is_fitted checks #7868 idiom). Since the public fit attributes survive unpickling while the native model does not, the estimator also defines __sklearn_check_is_fitted__ returning self._model is not None, so check_is_fitted stays correct for an unpickled model rather than passing on the surviving attributes and crashing downstream.
• Removed test_predict_before_fit_raises and test_score_samples_before_fit_raises; the un-xfailed check_estimators_unfitted covers both.
• as_treelite, as_nvforest, and _score_samples_nvforest keep their explicit guard on _treelite_model_bytes (raising NotFittedError): the serialized Treelite bytes survive pickling, so those exports still work on an unpickled model where the __sklearn_check_is_fitted__ condition is false. Happy to route them through check_is_fitted instead if you'd rather drop that post-unpickle behavior.

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Inline comments:
In `@python/cuml/cuml/ensemble/isolation_forest.pyx`:
- Around line 577-582: Rename __sklearn_check_is_fitted__ to the supported
__sklearn_is_fitted__ hook, and move `@mlfunc`(set_input_type=True) from the
zero-argument hook onto fit so decoration receives an array argument. Preserve
the native-model presence check and ensure fit continues recording the input
type.

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[betatim]

Thanks for the updates. While looking at them and thinking about why your solution looks different to what I was expecting I realised that I don't fully understand the intention of the pickling behaviour. In particular: why does an unpickled estimator not look and behave completely like an unfitted estimator? On main it looks like it mostly behaves like an unfitted estimator (fits with the warning) but some of the export methods work and as_sklearn does something strange (raise UnsupportedOnCPU, which I was not expecting to see there).

@dantegd can you explain a bit what your thinking was here regarding how an unpickled estimator should behave and what a user should/shouldn't be able to do with it?

The ideal outcome for me would be that we use check_is_fitted everywhere, including the remaining places where the code still has a custom check like here) and we do not need __sklearn_is_fitted__. I think we can achieve this by removing the fitted attributes in __getstate__ so that when you unpickle an estimator it is really unfitted (not semi-fitted).

[JulienAu]

That's a cleaner framing, thanks. Agreed: stripping the fitted attributes in __getstate__ so an unpickled estimator is genuinely unfitted is better than the __sklearn_is_fitted__ shim, and it would let plain check_is_fitted cover everything, including the export methods your diff link points at.

If it helps the discussion while @dantegd weighs in, my take would be to make the unpickled estimator fully unfitted. __getstate__ currently keeps _treelite_model_bytes (it only clears _model and _nvforest_model), which is why as_treelite and as_nvforest still work after a round trip today. Keeping those bytes just trades one semi-fitted state for another, so I'd drop them too and let check_is_fitted gate every method uniformly, unless the export-after-unpickle behaviour was a deliberate feature worth preserving. Happy to defer to whatever the intended contract is.

(Related: the as_sklearn surprise is _attrs_to_cpu raising UnsupportedOnCPU; that's the piece the fitted-model conversion in #8420 replaces with a real cuML -> sklearn sync.)

[csadorf]

@betatim The inability to serialize a fitted estimator is identified as a limitation and tracked in #8479 . I think raising UnsupportedOnCPU within the as_sklearn() method is a bug that slipped through review.