[BUG] test_estimators_compatibility_sklearn fails

[penguinpee]

Describe the bug

Test test_estimators_compatibility_sklearn fails with AssertionError: Estimator SMOTEN didn't fail when fitted on sparse data but should have according to its tag self.input_tags.sparse=False. The tag is inconsistent and must be fixed.

This appears to be an issue with scikit-learn >= 1.6.1.

Expected Results

Test succeeds.

Actual Results

=================================== FAILURES ===================================
_ test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_sparse_tag] _

estimator = SMOTEN(random_state=42)
check = functools.partial(<function check_estimator_sparse_tag at 0x7f5dc05c6fc0>, 'SMOTEN')
request = <FixtureRequest for <Function test_estimators_compatibility_sklearn[SMOTEN(random_state=42)-check_estimator_sparse_tag]>>

    @parametrize_with_checks_sklearn(
        list(_tested_estimators()), expected_failed_checks=_get_expected_failed_checks
    )
    def test_estimators_compatibility_sklearn(estimator, check, request):
        _set_checking_parameters(estimator)
>       check(estimator)

imblearn/tests/test_common.py:46: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'SMOTEN', estimator_orig = SMOTEN(random_state=42)

    def check_estimator_sparse_tag(name, estimator_orig):
        """Check that estimator tag related with accepting sparse data is properly set."""
        if SPARSE_ARRAY_PRESENT:
            sparse_container = sparse.csr_array
        else:
            sparse_container = sparse.csr_matrix
        estimator = clone(estimator_orig)
    
        rng = np.random.RandomState(0)
        n_samples = 15 if name == "SpectralCoclustering" else 40
        X = rng.uniform(size=(n_samples, 3))
        X[X < 0.6] = 0
        y = rng.randint(0, 3, size=n_samples)
        X = _enforce_estimator_tags_X(estimator, X)
        y = _enforce_estimator_tags_y(estimator, y)
        X = sparse_container(X)
    
        tags = get_tags(estimator)
        if tags.input_tags.sparse:
            try:
                estimator.fit(X, y)  # should pass
            except Exception as e:
                err_msg = (
                    f"Estimator {name} raised an exception. "
                    f"The tag self.input_tags.sparse={tags.input_tags.sparse} "
                    "might not be consistent with the estimator's ability to "
                    "handle sparse data (i.e. controlled by the parameter `accept_sparse`"
                    " in `validate_data` or `check_array` functions)."
                )
                raise AssertionError(err_msg) from e
        else:
            err_msg = (
                f"Estimator {name} raised an exception. "
                "The estimator failed when fitted on sparse data in accordance "
                f"with its tag self.input_tags.sparse={tags.input_tags.sparse} "
                "but didn't raise the appropriate error: error message should "
                "state explicitly that sparse input is not supported if this is "
                "not the case, e.g. by using check_array(X, accept_sparse=False)."
            )
            try:
                estimator.fit(X, y)  # should fail with appropriate error
            except (ValueError, TypeError) as e:
                if re.search("[Ss]parse", str(e)):
                    # Got the right error type and mentioning sparse issue
                    return
                raise AssertionError(err_msg) from e
            except Exception as e:
                raise AssertionError(err_msg) from e
>           raise AssertionError(
                f"Estimator {name} didn't fail when fitted on sparse data "
                "but should have according to its tag "
                f"self.input_tags.sparse={tags.input_tags.sparse}. "
                f"The tag is inconsistent and must be fixed."
            )
E           AssertionError: Estimator SMOTEN didn't fail when fitted on sparse data but should have according to its tag self.input_tags.sparse=False. The tag is inconsistent and must be fixed.

Versions

System:
python: 3.13.2 (main, Feb 6 2025, 00:00:00) [GCC 15.0.1 20250204 (Red Hat 15.0.1-0)]
executable: /usr/bin/python3
machine: Linux-6.13.5-200.fc41.x86_64-x86_64-with-glibc2.41.9000

Python dependencies:
sklearn: 1.6.1
pip: 24.3.1
setuptools: 74.1.3
numpy: 2.2.4
scipy: 1.14.1
Cython: None
pandas: 2.2.3
matplotlib: None
joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
user_api: blas
internal_api: flexiblas
num_threads: 8
prefix: libflexiblas
filepath: /usr/lib64/libflexiblas.so.3.4
version: 3.4.5
available_backends: ['NETLIB', 'OPENBLAS-OPENMP']
loaded_backends: ['OPENBLAS-OPENMP']
current_backend: OPENBLAS-OPENMP

   user_api: blas

internal_api: openblas
num_threads: 8
prefix: libopenblas
filepath: /usr/lib64/libopenblaso-r0.3.29.so
version: 0.3.29
threading_layer: openmp
architecture: Sandybridge

   user_api: openmp

internal_api: openmp
num_threads: 8
prefix: libgomp
filepath: /usr/lib64/libgomp.so.1.0.0
version: None

[philipwilk]

Have also encountered this while packaging downstream for nix.