Source: imbalanced-learn Version: 0.12.2-1 Severity: normal imbalanced-learn is failing tests with scipy 1.14 (from experimental) due to an API change
159s ␛[31m␛[1m___________ test_estimators_imblearn[AllKNN()-check_samplers_sparse] ___________␛[0m 159s 159s estimator = AllKNN() 159s check = functools.partial(<function check_samplers_sparse at 0x7f29b0afade0>, 'AllKNN') 159s request = <FixtureRequest for <Function test_estimators_imblearn[AllKNN()-check_samplers_sparse]>> 159s 159s @parametrize_with_checks(list(_tested_estimators())) 159s def test_estimators_imblearn(estimator, check, request): 159s # Common tests for estimator instances 159s with ignore_warnings( 159s category=( 159s FutureWarning, 159s ConvergenceWarning, 159s UserWarning, 159s FutureWarning, 159s ) 159s ): 159s _set_checking_parameters(estimator) 159s > check(estimator) 159s 159s ␛[1m␛[31mimblearn/tests/test_common.py␛[0m:71: 159s _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 159s 159s name = 'AllKNN', sampler_orig = AllKNN() 159s 159s def check_samplers_sparse(name, sampler_orig): 159s sampler = clone(sampler_orig) 159s # check that sparse matrices can be passed through the sampler leading to 159s # the same results than dense 159s X, y = sample_dataset_generator() 159s X_sparse = sparse.csr_matrix(X) 159s X_res_sparse, y_res_sparse = sampler.fit_resample(X_sparse, y) 159s sampler = clone(sampler) 159s X_res, y_res = sampler.fit_resample(X, y) 159s assert sparse.issparse(X_res_sparse) 159s > assert_allclose(X_res_sparse.A, X_res, rtol=1e-5) 159s ␛[1m␛[31mE AttributeError: 'csr_matrix' object has no attribute 'A'␛[0m 159s 159s ␛[1m␛[31mimblearn/utils/estimator_checks.py␛[0m:312: AttributeError test log at https://ci.debian.net/packages/i/imbalanced-learn/unstable/amd64/50977859/
