[python] tests fail on scikit-learn 1.6 nightlies #10896

jameslamb · 2024-10-16T04:49:28Z

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:

The text was updated successfully, but these errors were encountered:

trivialfis · 2024-10-18T19:06:38Z

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 · 2024-10-29T04:20:03Z

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

jameslamb · 2024-11-26T06:47:36Z

I have a patch that's working for me locally on a couple of different scikit-learn versions. Put up #11021 to get some feedback from CI.

I'll continue working on this there tomorrow.

jameslamb mentioned this issue Oct 16, 2024

FEAT allow metadata to be transformed in a Pipeline scikit-learn/scikit-learn#28901

Merged

jameslamb mentioned this issue Nov 12, 2024

[ci] [python-package] more errors from scikit-learn rearranging estimator tags microsoft/LightGBM#6717

Closed

jameslamb linked a pull request Nov 26, 2024 that will close this issue

Adapt to scikit-learn 1.6 estimator tag changes #11021

Open

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[python] tests fail on scikit-learn 1.6 nightlies #10896

[python] tests fail on scikit-learn 1.6 nightlies #10896

jameslamb commented Oct 16, 2024

trivialfis commented Oct 18, 2024 •

edited

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jameslamb commented Oct 29, 2024

jameslamb commented Nov 26, 2024

[python] tests fail on scikit-learn 1.6 nightlies #10896

[python] tests fail on scikit-learn 1.6 nightlies #10896

Comments

jameslamb commented Oct 16, 2024

Description

Reproducible Example

Notes

trivialfis commented Oct 18, 2024 • edited Loading

jameslamb commented Oct 29, 2024

jameslamb commented Nov 26, 2024

trivialfis commented Oct 18, 2024 •

edited

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