[python] tests fail on scikit-learn 1.6 nightlies

[jameslamb]

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)

============================= 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 <lambda>
    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::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, xgboost::Json, std::__1::less<void>, std::__1::allocator<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> 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::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, xgboost::Json, std::__1::less<void>, std::__1::allocator<std::__1::pair<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> 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<xgboost::data::ArrayAdapter, 64ul>(std::__1::any const&, std::__1::shared_ptr<xgboost::DMatrix>, 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::DMatrix>, 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<xgboost::DMatrix>, float, xgboost::PredictionCacheEntry*, int, int) const + 740
E     [bt] (4) 5   libxgboost.dylib                    0x0000000148d16c20 xgboost::LearnerImpl::InplacePredict(std::__1::shared_ptr<xgboost::DMatrix>, xgboost::PredictionType, float, xgboost::HostDeviceVector<float>**, int, int) + 164
E     [bt] (5) 6   libxgboost.dylib                    0x0000000148ba0e58 InplacePredictImpl(std::__1::shared_ptr<xgboost::DMatrix>, 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<xgboost::data::ArrayAdapter, 64ul>(std::__1::any const&, std::__1::shared_ptr<xgboost::DMatrix>, 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::DMatrix>, 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<xgboost::DMatrix>, float, xgboost::PredictionCacheEntry*, int, int) const + 740
E     [bt] (4) 5   libxgboost.dylib                    0x0000000148d16c20 xgboost::LearnerImpl::InplacePredict(std::__1::shared_ptr<xgboost::DMatrix>, xgboost::PredictionType, float, xgboost::HostDeviceVector<float>**, int, int) + 164
E     [bt] (5) 6   libxgboost.dylib                    0x0000000148ba0e58 InplacePredictImpl(std::__1::shared_ptr<xgboost::DMatrix>, 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<float, 1>*) 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<xgboost::DMatrix>) + 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: scikit-learn/scikit-learn#28901 (comment)

@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:

• [python-package] require scikit-learn>=0.24.2, make scikit-learn estimators compatible with scikit-learn>=1.6.0dev microsoft/LightGBM#6651
• [python-package] ignore scikit-learn 'check_sample_weight_equivalence' estimator check (fixes #6678) microsoft/LightGBM#6679

[trivialfis]

Thank you for sharing and volunteering!

I'd be happy to try to help with this over the next week if you'd like

Yes, please let me know if there's anything I can help. I can handle the C++ changes if needed; some checks are done inside libxgboost, and somehow, the error message requirements from sklearn are changed.

[jameslamb]

Ok thanks! Just confirming I am working on this, will hopefully have something to share soon.