[python] tests fail on scikit-learn 1.6 nightlies

Description

The Python package's tests fail with the latest scikit-learn nightlies (v1.6.dev0).

================= 7 failed, 103 passed, 14 warnings in 19.91s ==================

All the failures appear to be from the estimator checks scikit-learn ships to help projects test compliance with scikit-learn API expectations. Stuff like this:

E AssertionError: XGBRegressor.predict() does not check for consistency between input number E of features with XGBRegressor.fit(), via the n_features_in_ attribute.

full logs (click me)

```text ============================= test session starts ============================== platform darwin -- Python 3.11.9, pytest-8.2.2, pluggy-1.5.0 rootdir: /Users/jlamb/repos/xgboost/tests configfile: pytest.ini plugins: cov-5.0.0, hypothesis-6.115.2 collected 110 items tests/python/test_with_sklearn.py ...................................... [ 34%] .....................F.......F.F.F.............F...............FF....... [100%] =================================== FAILURES =================================== _ test_estimator_reg[XGBRegressor(base_score=None,booster=None,callbacks=None,colsample_bylevel=None,colsample_bynode=None,colsample_bytree=None,device=None,early_stopping_rounds=None,enable_categorical=False,eval_metric=None,feature_types=None,gamma=None,grow_policy=None,importance_type=None,interaction_constraints=None,learning_rate=None,max_bin=None,max_cat_threshold=None,max_cat_to_onehot=None,max_delta_step=None,max_depth=None,max_leaves=None,min_child_weight=None,missing=nan,monotone_constraints=None,multi_strategy=None,n_estimators=None,n_jobs=None,num_parallel_tree=None,random_state=None,...)-check_n_features_in_after_fitting] _ ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:3974: in check_n_features_in_after_fitting callable_method(X_bad) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/sklearn.py:1225: in predict predts = self.get_booster().inplace_predict( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:2642: in inplace_predict raise ValueError( E ValueError: Feature shape mismatch, expected: 4, got 1 The above exception was the direct cause of the following exception: tests/python/test_with_sklearn.py:1349: in test_estimator_reg check(estimator) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:140: in wrapper return fn(*args, **kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:3971: in check_n_features_in_after_fitting with raises( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:1076: in __exit__ raise AssertionError(err_msg) from exc_value E AssertionError: `XGBRegressor.predict()` does not check for consistency between input number E of features with XGBRegressor.fit(), via the `n_features_in_` attribute. E You might want to use `sklearn.utils.validation.validate_data` instead E of `check_array` in `XGBRegressor.fit()` and XGBRegressor.predict()`. This can be done E like the following: E from sklearn.utils.validation import validate_data E ... E class MyEstimator(BaseEstimator): E ... E def fit(self, X, y): E X, y = validate_data(self, X, y, ...) E ... E return self E ... E def predict(self, X): E X = validate_data(self, X, ..., reset=False) E ... E return X _ test_estimator_reg[XGBRegressor(base_score=None,booster=None,callbacks=None,colsample_bylevel=None,colsample_bynode=None,colsample_bytree=None,device=None,early_stopping_rounds=None,enable_categorical=False,eval_metric=None,feature_types=None,gamma=None,grow_policy=None,importance_type=None,interaction_constraints=None,learning_rate=None,max_bin=None,max_cat_threshold=None,max_cat_to_onehot=None,max_delta_step=None,max_depth=None,max_leaves=None,min_child_weight=None,missing=nan,monotone_constraints=None,multi_strategy=None,n_estimators=None,n_jobs=None,num_parallel_tree=None,random_state=None,...)-check_complex_data] _ ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:1239: in check_complex_data estimator.fit(X, y) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/sklearn.py:1118: in fit train_dmatrix, evals = _wrap_evaluation_matrices( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/sklearn.py:605: in _wrap_evaluation_matrices train_dmatrix = create_dmatrix( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/sklearn.py:1040: in _create_dmatrix return QuantileDMatrix( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:1636: in __init__ self._init( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:1695: in _init it.reraise() ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:618: in reraise raise exc # pylint: disable=raising-bad-type ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:599: in _handle_exception return fn() ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:686: in return self._handle_exception(lambda: int(self.next(input_data)), 0) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/data.py:1479: in next input_data(**self.kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:674: in input_data dispatch_proxy_set_data(self.proxy, new, cat_codes) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/data.py:1559: in dispatch_proxy_set_data proxy._ref_data_from_array(data) # pylint: disable=W0212 ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:1509: in _ref_data_from_array _check_call( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:297: in _check_call raise XGBoostError(py_str(_LIB.XGBGetLastError())) E xgboost.core.XGBoostError: [23:42:00] /Users/jlamb/repos/xgboost/src/c_api/../data/array_interface.h:499: Complex floating point-1 is not supported. E Stack trace: E [bt] (0) 1 libxgboost.dylib 0x0000000148b1e448 dmlc::LogMessageFatal::~LogMessageFatal() + 124 E [bt] (1) 2 libxgboost.dylib 0x0000000148b32684 xgboost::ArrayInterface<2, false>::AssignType(xgboost::StringView) + 1272 E [bt] (2) 3 libxgboost.dylib 0x0000000148b31e64 xgboost::ArrayInterface<2, false>::Initialize(std::__1::map, std::__1::allocator>, xgboost::Json, std::__1::less, std::__1::allocator, std::__1::allocator> const, xgboost::Json>>> const&) + 408 E [bt] (3) 4 libxgboost.dylib 0x0000000148ccea50 xgboost::data::ArrayAdapter::ArrayAdapter(xgboost::StringView) + 148 E [bt] (4) 5 libxgboost.dylib 0x0000000148cce664 xgboost::data::DMatrixProxy::SetArrayData(xgboost::StringView) + 72 E [bt] (5) 6 libxgboost.dylib 0x0000000148b2a8c0 XGProxyDMatrixSetDataDense + 136 E [bt] (6) 7 libffi.8.dylib 0x0000000105ebc04c ffi_call_SYSV + 76 E [bt] (7) 8 libffi.8.dylib 0x0000000105eb974c ffi_call_int + 1208 E [bt] (8) 9 _ctypes.cpython-311-darwin.so 0x0000000105e38988 _ctypes_callproc + 1208 The above exception was the direct cause of the following exception: tests/python/test_with_sklearn.py:1349: in test_estimator_reg check(estimator) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:1238: in check_complex_data with raises(ValueError, match="Complex data not supported"): ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:1076: in __exit__ raise AssertionError(err_msg) from exc_value E AssertionError: The error message should contain one of the following patterns: E Complex data not supported E Got [23:42:00] /Users/jlamb/repos/xgboost/src/c_api/../data/array_interface.h:499: Complex floating point-1 is not supported. E Stack trace: E [bt] (0) 1 libxgboost.dylib 0x0000000148b1e448 dmlc::LogMessageFatal::~LogMessageFatal() + 124 E [bt] (1) 2 libxgboost.dylib 0x0000000148b32684 xgboost::ArrayInterface<2, false>::AssignType(xgboost::StringView) + 1272 E [bt] (2) 3 libxgboost.dylib 0x0000000148b31e64 xgboost::ArrayInterface<2, false>::Initialize(std::__1::map, std::__1::allocator>, xgboost::Json, std::__1::less, std::__1::allocator, std::__1::allocator> const, xgboost::Json>>> const&) + 408 E [bt] (3) 4 libxgboost.dylib 0x0000000148ccea50 xgboost::data::ArrayAdapter::ArrayAdapter(xgboost::StringView) + 148 E [bt] (4) 5 libxgboost.dylib 0x0000000148cce664 xgboost::data::DMatrixProxy::SetArrayData(xgboost::StringView) + 72 E [bt] (5) 6 libxgboost.dylib 0x0000000148b2a8c0 XGProxyDMatrixSetDataDense + 136 E [bt] (6) 7 libffi.8.dylib 0x0000000105ebc04c ffi_call_SYSV + 76 E [bt] (7) 8 libffi.8.dylib 0x0000000105eb974c ffi_call_int + 1208 E [bt] (8) 9 _ctypes.cpython-311-darwin.so 0x0000000105e38988 _ctypes_callproc + 1208 _ test_estimator_reg[XGBRegressor(base_score=None,booster=None,callbacks=None,colsample_bylevel=None,colsample_bynode=None,colsample_bytree=None,device=None,early_stopping_rounds=None,enable_categorical=False,eval_metric=None,feature_types=None,gamma=None,grow_policy=None,importance_type=None,interaction_constraints=None,learning_rate=None,max_bin=None,max_cat_threshold=None,max_cat_to_onehot=None,max_delta_step=None,max_depth=None,max_leaves=None,min_child_weight=None,missing=nan,monotone_constraints=None,multi_strategy=None,n_estimators=None,n_jobs=None,num_parallel_tree=None,random_state=None,...)-check_estimators_empty_data_messages] _ tests/python/test_with_sklearn.py:1349: in test_estimator_reg check(estimator) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:140: in wrapper return fn(*args, **kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:1830: in check_estimators_empty_data_messages with raises(ValueError, err_msg=err_msg): ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:1059: in __exit__ raise AssertionError(err_msg) E AssertionError: The estimator XGBRegressor does not raise a ValueError when an empty data is used to train. Perhaps use check_array in train. _ test_estimator_reg[XGBRegressor(base_score=None,booster=None,callbacks=None,colsample_bylevel=None,colsample_bynode=None,colsample_bytree=None,device=None,early_stopping_rounds=None,enable_categorical=False,eval_metric=None,feature_types=None,gamma=None,grow_policy=None,importance_type=None,interaction_constraints=None,learning_rate=None,max_bin=None,max_cat_threshold=None,max_cat_to_onehot=None,max_delta_step=None,max_depth=None,max_leaves=None,min_child_weight=None,missing=nan,monotone_constraints=None,multi_strategy=None,n_estimators=None,n_jobs=None,num_parallel_tree=None,random_state=None,...)-check_estimators_nan_inf] _ tests/python/test_with_sklearn.py:1349: in test_estimator_reg check(estimator) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:140: in wrapper return fn(*args, **kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:1867: in check_estimators_nan_inf with raises(ValueError, match=["inf", "NaN"], err_msg=error_string_fit): ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:1059: in __exit__ raise AssertionError(err_msg) E AssertionError: Estimator XGBRegressor doesn't check for NaN and inf in fit. _ test_estimator_reg[XGBRegressor(base_score=None,booster=None,callbacks=None,colsample_bylevel=None,colsample_bynode=None,colsample_bytree=None,device=None,early_stopping_rounds=None,enable_categorical=False,eval_metric=None,feature_types=None,gamma=None,grow_policy=None,importance_type=None,interaction_constraints=None,learning_rate=None,max_bin=None,max_cat_threshold=None,max_cat_to_onehot=None,max_delta_step=None,max_depth=None,max_leaves=None,min_child_weight=None,missing=nan,monotone_constraints=None,multi_strategy=None,n_estimators=None,n_jobs=None,num_parallel_tree=None,random_state=None,...)-check_supervised_y_2d] _ tests/python/test_with_sklearn.py:1349: in test_estimator_reg check(estimator) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:140: in wrapper return fn(*args, **kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:2799: in check_supervised_y_2d assert len(w) > 0, msg E AssertionError: expected 1 DataConversionWarning, got: _ test_estimator_reg[XGBRegressor(base_score=None,booster=None,callbacks=None,colsample_bylevel=None,colsample_bynode=None,colsample_bytree=None,device=None,early_stopping_rounds=None,enable_categorical=False,eval_metric=None,feature_types=None,gamma=None,grow_policy=None,importance_type=None,interaction_constraints=None,learning_rate=None,max_bin=None,max_cat_threshold=None,max_cat_to_onehot=None,max_delta_step=None,max_depth=None,max_leaves=None,min_child_weight=None,missing=nan,monotone_constraints=None,multi_strategy=None,n_estimators=None,n_jobs=None,num_parallel_tree=None,random_state=None,...)-check_fit2d_predict1d] _ ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:1345: in check_fit2d_predict1d getattr(estimator, method)(X[0]) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/sklearn.py:1225: in predict predts = self.get_booster().inplace_predict( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:2651: in inplace_predict _check_call( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:297: in _check_call raise XGBoostError(py_str(_LIB.XGBGetLastError())) E xgboost.core.XGBoostError: [23:42:04] /Users/jlamb/repos/xgboost/src/predictor/cpu_predictor.cc:789: Check failed: m->NumColumns() == model.learner_model_param->num_feature (1 vs. 3) : Number of columns in data must equal to trained model. E Stack trace: E [bt] (0) 1 libxgboost.dylib 0x0000000148b1e448 dmlc::LogMessageFatal::~LogMessageFatal() + 124 E [bt] (1) 2 libxgboost.dylib 0x0000000148dd3e10 void xgboost::predictor::CPUPredictor::DispatchedInplacePredict(std::__1::any const&, std::__1::shared_ptr, xgboost::gbm::GBTreeModel const&, float, xgboost::PredictionCacheEntry*, unsigned int, unsigned int) const + 344 E [bt] (2) 3 libxgboost.dylib 0x0000000148dca8d0 xgboost::predictor::CPUPredictor::InplacePredict(std::__1::shared_ptr, xgboost::gbm::GBTreeModel const&, float, xgboost::PredictionCacheEntry*, unsigned int, unsigned int) const + 1572 E [bt] (3) 4 libxgboost.dylib 0x0000000148cf83d0 xgboost::gbm::GBTree::InplacePredict(std::__1::shared_ptr, float, xgboost::PredictionCacheEntry*, int, int) const + 740 E [bt] (4) 5 libxgboost.dylib 0x0000000148d16c20 xgboost::LearnerImpl::InplacePredict(std::__1::shared_ptr, xgboost::PredictionType, float, xgboost::HostDeviceVector**, int, int) + 164 E [bt] (5) 6 libxgboost.dylib 0x0000000148ba0e58 InplacePredictImpl(std::__1::shared_ptr, char const*, xgboost::Learner*, unsigned long long const**, unsigned long long*, float const**) + 276 E [bt] (6) 7 libxgboost.dylib 0x0000000148ba14b4 XGBoosterPredictFromDense + 420 E [bt] (7) 8 libffi.8.dylib 0x0000000105ebc04c ffi_call_SYSV + 76 E [bt] (8) 9 libffi.8.dylib 0x0000000105eb974c ffi_call_int + 1208 The above exception was the direct cause of the following exception: tests/python/test_with_sklearn.py:1349: in test_estimator_reg check(estimator) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:140: in wrapper return fn(*args, **kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:1344: in check_fit2d_predict1d with raises(ValueError, match="Reshape your data"): ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/_testing.py:1076: in __exit__ raise AssertionError(err_msg) from exc_value E AssertionError: The error message should contain one of the following patterns: E Reshape your data E Got [23:42:04] /Users/jlamb/repos/xgboost/src/predictor/cpu_predictor.cc:789: Check failed: m->NumColumns() == model.learner_model_param->num_feature (1 vs. 3) : Number of columns in data must equal to trained model. E Stack trace: E [bt] (0) 1 libxgboost.dylib 0x0000000148b1e448 dmlc::LogMessageFatal::~LogMessageFatal() + 124 E [bt] (1) 2 libxgboost.dylib 0x0000000148dd3e10 void xgboost::predictor::CPUPredictor::DispatchedInplacePredict(std::__1::any const&, std::__1::shared_ptr, xgboost::gbm::GBTreeModel const&, float, xgboost::PredictionCacheEntry*, unsigned int, unsigned int) const + 344 E [bt] (2) 3 libxgboost.dylib 0x0000000148dca8d0 xgboost::predictor::CPUPredictor::InplacePredict(std::__1::shared_ptr, xgboost::gbm::GBTreeModel const&, float, xgboost::PredictionCacheEntry*, unsigned int, unsigned int) const + 1572 E [bt] (3) 4 libxgboost.dylib 0x0000000148cf83d0 xgboost::gbm::GBTree::InplacePredict(std::__1::shared_ptr, float, xgboost::PredictionCacheEntry*, int, int) const + 740 E [bt] (4) 5 libxgboost.dylib 0x0000000148d16c20 xgboost::LearnerImpl::InplacePredict(std::__1::shared_ptr, xgboost::PredictionType, float, xgboost::HostDeviceVector**, int, int) + 164 E [bt] (5) 6 libxgboost.dylib 0x0000000148ba0e58 InplacePredictImpl(std::__1::shared_ptr, char const*, xgboost::Learner*, unsigned long long const**, unsigned long long*, float const**) + 276 E [bt] (6) 7 libxgboost.dylib 0x0000000148ba14b4 XGBoosterPredictFromDense + 420 E [bt] (7) 8 libffi.8.dylib 0x0000000105ebc04c ffi_call_SYSV + 76 E [bt] (8) 9 libffi.8.dylib 0x0000000105eb974c ffi_call_int + 1208 _ test_estimator_reg[XGBRegressor(base_score=None,booster=None,callbacks=None,colsample_bylevel=None,colsample_bynode=None,colsample_bytree=None,device=None,early_stopping_rounds=None,enable_categorical=False,eval_metric=None,feature_types=None,gamma=None,grow_policy=None,importance_type=None,interaction_constraints=None,learning_rate=None,max_bin=None,max_cat_threshold=None,max_cat_to_onehot=None,max_delta_step=None,max_depth=None,max_leaves=None,min_child_weight=None,missing=nan,monotone_constraints=None,multi_strategy=None,n_estimators=None,n_jobs=None,num_parallel_tree=None,random_state=None,...)-check_requires_y_none] _ tests/python/test_with_sklearn.py:1349: in test_estimator_reg check(estimator) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:3889: in check_requires_y_none raise ve ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/sklearn/utils/estimator_checks.py:3886: in check_requires_y_none estimator.fit(X, None) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/sklearn.py:1145: in fit self._Booster = train( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:775: in inner_f return func(**kwargs) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/training.py:181: in train bst.update(dtrain, iteration=i, fobj=obj) ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:2218: in update _check_call( ../../miniforge3/envs/lgb-dev/lib/python3.11/site-packages/xgboost/core.py:297: in _check_call raise XGBoostError(py_str(_LIB.XGBGetLastError())) E xgboost.core.XGBoostError: [23:42:04] /Users/jlamb/repos/xgboost/src/objective/init_estimation.h:16: Check failed: info.labels.Shape(0) == info.num_row_ (0 vs. 100) : Invalid shape of labels. E Stack trace: E [bt] (0) 1 libxgboost.dylib 0x0000000148b1e448 dmlc::LogMessageFatal::~LogMessageFatal() + 124 E [bt] (1) 2 libxgboost.dylib 0x0000000148d7cc28 xgboost::obj::CheckInitInputs(xgboost::MetaInfo const&) + 200 E [bt] (2) 3 libxgboost.dylib 0x0000000148d7fa14 xgboost::obj::FitIntercept::InitEstimation(xgboost::MetaInfo const&, xgboost::linalg::Tensor*) const + 68 E [bt] (3) 4 libxgboost.dylib 0x0000000148d28f64 xgboost::LearnerConfiguration::InitBaseScore(xgboost::DMatrix const*) + 252 E [bt] (4) 5 libxgboost.dylib 0x0000000148d15a1c xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr) + 140 E [bt] (5) 6 libxgboost.dylib 0x0000000148b41b6c XGBoosterUpdateOneIter + 144 E [bt] (6) 7 libffi.8.dylib 0x0000000105ebc04c ffi_call_SYSV + 76 E [bt] (7) 8 libffi.8.dylib 0x0000000105eb974c ffi_call_int + 1208 E [bt] (8) 9 _ctypes.cpython-311-darwin.so 0x0000000105e38988 _ctypes_callproc + 1208 ================= 7 failed, 103 passed, 14 warnings in 19.76s ================== ```

Reproducible Example

On an M2 Mac, in a Python 3.11.9 conda environment, built the Python package from source.

cd python-package
pip install --no-deps .

Installed the latest scikit-learn nightlies.

pip uninstall --yes scikit-learn

pytest \
    --disable-warnings \
    --tb=short \
    -rs \
    ./tests/python/test_with_sklearn.py \
| tee ./out.txt

Saw the failures reported above.

Repeated that same process but with the latest release of scikit-learn.

pip uninstall --yes scikit-learn
pip install --no-deps 'scikit-learn==1.5.2'

All tests passed.

Notes

I found this while testing against this in-progress scikit-learn branch: https://github.com/scikit-learn/scikit-learn/pull/28901#discussion_r1748413039

@trivialfis @hcho3 I'd be happy to try to help with this over the next week if you'd like. I'm familiar with some of the changes in scikit-learn from this related work we've been doing in lightgbm:

dmlc / xgboost