
[kill_producers] MODE='train': disabled 1 producers
........................................................................ [ 27%]
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==================================== ERRORS ====================================
_________ ERROR at setup of test_fit_result_property_distinct_objects __________

    @pytest.fixture(scope="module")
    def fitted_model() -> Model:
        model = Model(make_config("regression"))
>       model.fit(data=make_regression_df(n=120))

tests/test_core/test_result_isolation.py:28: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d6e7c50>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3eff6d90>, <lightgbm.callback._EarlyStoppingC...valuationCallback object at 0x7def3ee16450>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 10, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_____ ERROR at setup of test_fit_result_property_returns_independent_copy ______

    @pytest.fixture(scope="module")
    def fitted_model() -> Model:
        model = Model(make_config("regression"))
>       model.fit(data=make_regression_df(n=120))

tests/test_core/test_result_isolation.py:28: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d6e7c50>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3eff6d90>, <lightgbm.callback._EarlyStoppingC...valuationCallback object at 0x7def3ee16450>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 10, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
___ ERROR at setup of test_fit_result_shares_trained_estimators_by_reference ___

    @pytest.fixture(scope="module")
    def fitted_model() -> Model:
        model = Model(make_config("regression"))
>       model.fit(data=make_regression_df(n=120))

tests/test_core/test_result_isolation.py:28: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d6e7c50>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3eff6d90>, <lightgbm.callback._EarlyStoppingC...valuationCallback object at 0x7def3ee16450>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 10, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-Float32] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-Float64] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-Int64] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-Int64_na] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
___ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-Int8] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-UInt32] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
___ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-bool] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-bool_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-boolean] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-category] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-complex128] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-datetime] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-datetime_tz] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-float16] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-float32] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-float64] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-float64_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-int16] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-int32] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-int64] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-int64_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
___ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-int8] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-interval] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-longdouble] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-object_num] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-period] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-sparse_float] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
___ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-str] ____

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-string_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-string_python] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-timedelta] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-uint64] __

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[str-uint8] ___

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-Float32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-Float64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-Int64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-Int64_na] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-Int8] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-UInt32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-bool] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-bool_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-boolean] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-category] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-complex128] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-datetime] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-datetime_tz] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-float16] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-float32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-float64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-float64_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-int16] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-int32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-int64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-int64_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-int8] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-interval] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-longdouble] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-object_num] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-period] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-sparse_float] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-str] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-string_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-string_python] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-timedelta] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-uint64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[object_num-uint8] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-Float32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-Float64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-Int64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-Int64_na] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-Int8] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-UInt32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-bool] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-bool_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-boolean] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-category] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-complex128] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-datetime] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-datetime_tz] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-float16] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-float32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-float64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-float64_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-int16] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-int32] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-int64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-int64_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-int8] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-interval] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-longdouble] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-object_num] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-period] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-sparse_float] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-str] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-string_pyarrow] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-string_python] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-timedelta] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-uint64] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_ ERROR at setup of test_categorical_at_fit_is_not_dtype_checked[category-uint8] _

    @pytest.fixture(scope="module")
    def categorical_models() -> dict[str, Model]:
>       return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:161: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:161: in <dictcomp>
    return {fit: _fit(fit) for fit in _CATEGORICAL_FITS}
                 ^^^^^^^^^
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d744290>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d7460d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d7473d0>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[Float32] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[Float64] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[Int64] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[Int64_na] ________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__________ ERROR at setup of test_numeric_at_fit_arrival_matrix[Int8] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[UInt32] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__________ ERROR at setup of test_numeric_at_fit_arrival_matrix[bool] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
______ ERROR at setup of test_numeric_at_fit_arrival_matrix[bool_pyarrow] ______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[boolean] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[category] ________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_______ ERROR at setup of test_numeric_at_fit_arrival_matrix[complex128] _______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[datetime] ________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
______ ERROR at setup of test_numeric_at_fit_arrival_matrix[datetime_tz] _______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[float16] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[float32] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[float64] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
____ ERROR at setup of test_numeric_at_fit_arrival_matrix[float64_pyarrow] _____

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[int16] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[int32] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[int64] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_____ ERROR at setup of test_numeric_at_fit_arrival_matrix[int64_pyarrow] ______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__________ ERROR at setup of test_numeric_at_fit_arrival_matrix[int8] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
________ ERROR at setup of test_numeric_at_fit_arrival_matrix[interval] ________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_______ ERROR at setup of test_numeric_at_fit_arrival_matrix[longdouble] _______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_______ ERROR at setup of test_numeric_at_fit_arrival_matrix[object_num] _______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[period] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
______ ERROR at setup of test_numeric_at_fit_arrival_matrix[sparse_float] ______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
__________ ERROR at setup of test_numeric_at_fit_arrival_matrix[str] ___________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_____ ERROR at setup of test_numeric_at_fit_arrival_matrix[string_pyarrow] _____

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_____ ERROR at setup of test_numeric_at_fit_arrival_matrix[string_python] ______

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_______ ERROR at setup of test_numeric_at_fit_arrival_matrix[timedelta] ________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[uint64] _________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
_________ ERROR at setup of test_numeric_at_fit_arrival_matrix[uint8] __________

    @pytest.fixture(scope="module")
    def float_model() -> Model:
>       return _fit("float64")
               ^^^^^^^^^^^^^^^

tests/test_features/test_column_dtype_check.py:123: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/test_features/test_column_dtype_check.py:113: in _fit
    model.fit(data=df)
lizyml/core/model.py:278: in fit
    fit_result = cv_trainer.fit(
lizyml/training/cv_trainer.py:123: in fit
    estimator = self._fit_estimator(
lizyml/training/cv_trainer.py:258: in _fit_estimator
    estimator.fit(
lizyml/estimators/lgbm/adapter.py:230: in fit
    self._model = lgb.train(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

a = ({'bagging_fraction': 0.7, 'bagging_freq': 10, 'boosting': 'gbdt', 'feature_fraction': 0.7, ...}, <lightgbm.basic.Dataset object at 0x7def3d885610>)
k = {'callbacks': [<lightgbm.callback._LogEvaluationCallback object at 0x7def3d8858d0>, <lightgbm.callback._EarlyStoppingC...EvaluationCallback object at 0x7def3d885c50>], 'feval': None, 'keep_training_booster': True, 'num_boost_round': 5, ...}

    def boom(*a: object, **k: object):
>       raise ProducerRan(f"producer disabled for the hollowness check: {label}")
E       kill_producers.ProducerRan: producer disabled for the hollowness check: lightgbm.train (adapter alias)

docs/audits/2026-09-defect-discovery/instruments/kill_producers.py:49: ProducerRan
=============================== warnings summary ===============================
tests/test_core/test_param_domain.py:146
  /tmp/claude-1000/-home-rem-repos-LizyML/e1612d43-a3df-4ff3-849b-8f1591799376/scratchpad/base1d4/tests/test_core/test_param_domain.py:146: RuntimeWarning: overflow encountered in cast
    atoms.append(dtype(value))

tests/test_core/test_fit_params_override.py: 8 warnings
tests/test_core/test_refusal_matrix.py: 3 warnings
tests/test_tuning/test_dimension_consumption.py: 3 warnings
  /tmp/claude-1000/-home-rem-repos-LizyML/e1612d43-a3df-4ff3-849b-8f1591799376/scratchpad/base1d4/lizyml/core/_model_tuning.py:204: UserWarning: task='binary' with split.method='kfold' does not preserve class distribution. Consider using 'stratified_kfold' instead.
    splitter = build_splitter(

tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-Float32]
tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[Float32]
  /tmp/claude-1000/-home-rem-repos-LizyML/e1612d43-a3df-4ff3-849b-8f1591799376/scratchpad/base1d4/lizyml/core/model.py:254: UserWarning: task='binary' with split.method='kfold' does not preserve class distribution. Consider using 'stratified_kfold' instead.
    splitter = build_splitter(

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================== PASSES ====================================
_________________ test_each_input_is_named_by_its_own_surface __________________
------------------------------ Captured log call -------------------------------
WARNING  lizyml.model:model.py:211 event='fit.post_tune' run_id=f9337acd-96ca-48e6-97e6-8dc1bcd2c414 msg='fit() after tune() reuses the tuning CV splits; reported OOF metrics are optimistically biased (BLUEPRINT 11.6). Use a held-out set for an unbiased estimate.'
____ test_a_restored_tuning_result_is_refused_too[check_param_names-best0] _____
------------------------------ Captured log call -------------------------------
WARNING  lizyml.model:model.py:211 event='fit.post_tune' run_id=c757a72b-5dbb-43e4-b980-d8eb771d7942 msg='fit() after tune() reuses the tuning CV splits; reported OOF metrics are optimistically biased (BLUEPRINT 11.6). Use a held-out set for an unbiased estimate.'
____ test_a_restored_tuning_result_is_refused_too[check_param_values-best1] ____
------------------------------ Captured log call -------------------------------
WARNING  lizyml.model:model.py:211 event='fit.post_tune' run_id=757ddf9b-a563-40a6-bbfb-05177941b004 msg='fit() after tune() reuses the tuning CV splits; reported OOF metrics are optimistically biased (BLUEPRINT 11.6). Use a held-out set for an unbiased estimate.'
_ test_a_restored_tuning_result_is_refused_too[check_training_managed_overrides-best2] _
------------------------------ Captured log call -------------------------------
WARNING  lizyml.model:model.py:211 event='fit.post_tune' run_id=62baf361-9868-4a1e-8dbd-d4d9534b6637 msg='fit() after tune() reuses the tuning CV splits; reported OOF metrics are optimistically biased (BLUEPRINT 11.6). Use a held-out set for an unbiased estimate.'
_ test_a_restored_tuning_result_is_refused_too[check_duplicate_identities-best3] _
------------------------------ Captured log call -------------------------------
WARNING  lizyml.model:model.py:211 event='fit.post_tune' run_id=b2c71193-910e-4a94-be99-9255b9842b3c msg='fit() after tune() reuses the tuning CV splits; reported OOF metrics are optimistically biased (BLUEPRINT 11.6). Use a held-out set for an unbiased estimate.'
_____ test_a_restored_tuning_result_is_refused_too[normalise_params-best4] _____
------------------------------ Captured log call -------------------------------
WARNING  lizyml.model:model.py:211 event='fit.post_tune' run_id=768afcfa-8ec2-4607-8488-9df6fa6feb0b msg='fit() after tune() reuses the tuning CV splits; reported OOF metrics are optimistically biased (BLUEPRINT 11.6). Use a held-out set for an unbiased estimate.'
=========================== short test summary info ============================
PASSED tests/test_calibration/test_beta_calibration.py::TestBetaCrossFit::test_cross_fit_leakage
PASSED tests/test_calibration/test_calibration_failed_optimisation.py::test_cross_fit_names_c_final
PASSED tests/test_calibration/test_calibration_failed_optimisation.py::test_default_settings_still_fit[beta]
PASSED tests/test_calibration/test_calibration_failed_optimisation.py::test_default_settings_still_fit[platt]
PASSED tests/test_calibration/test_calibration_failed_optimisation.py::test_refit_failing_before_minimize_does_not_keep_the_earlier_fit[beta]
PASSED tests/test_calibration/test_calibration_failed_optimisation.py::test_refit_failing_before_minimize_does_not_keep_the_earlier_fit[platt]
PASSED tests/test_calibration/test_calibration_param_contract.py::test_beta_default_fit_emits_no_warning
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[boundary_table]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[calibration_plot]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[confusion_matrix]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[evaluate]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[evaluate_table]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[export]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[export_code]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[importance]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[importance_plot]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[load]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[params_table]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[plot_learning_curve]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[plot_oof_distribution]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[predict]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[probability_histogram_plot]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[residuals]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[residuals_plot]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[roc_curve_plot]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[split_summary]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[tuning_plot]
PASSED tests/test_calibration/test_calibration_param_names.py::test_non_training_entry_points_really_do_not_train[tuning_table]
PASSED tests/test_calibration/test_platt_mle.py::test_default_fit_emits_no_warning
PASSED tests/test_calibration/test_platt_mle.py::test_target_smoothing_can_be_turned_off_and_changes_the_fit
PASSED tests/test_config/test_schema.py::TestEnvOverride::test_override_training_seed
PASSED tests/test_core/test_calibration_params_reach.py::test_fit_refuses_before_training[platt-params0]
PASSED tests/test_core/test_calibration_params_reach.py::test_fit_refuses_before_training[platt-params1]
PASSED tests/test_core/test_calibration_params_reach.py::test_fit_refuses_before_training[beta-params2]
PASSED tests/test_core/test_calibration_params_reach.py::test_fit_refuses_before_training[beta-params3]
PASSED tests/test_core/test_calibration_params_reach.py::test_fit_refuses_before_training[platt-params4]
PASSED tests/test_core/test_calibration_params_reach.py::test_fit_refuses_before_training[beta-params5]
PASSED tests/test_core/test_fit_params_override.py::test_a_duplicate_spelling_carrying_equal_values_is_refused_before_training
PASSED tests/test_core/test_fit_params_override.py::test_a_hostile_value_is_refused_beside_a_second_spelling_too[round 16]
PASSED tests/test_core/test_fit_params_override.py::test_a_hostile_value_is_refused_beside_a_second_spelling_too[round 18]
PASSED tests/test_core/test_fit_params_override.py::test_a_hostile_value_is_refused_beside_a_second_spelling_too[round 19 conflicting]
PASSED tests/test_core/test_fit_params_override.py::test_a_hostile_value_is_refused_beside_a_second_spelling_too[round 19 equivalent]
PASSED tests/test_core/test_fit_params_override.py::test_a_hostile_value_is_refused_beside_a_second_spelling_too[round 20]
PASSED tests/test_core/test_fit_params_override.py::test_a_search_dimension_a_training_setting_controls_is_refused[seed]
PASSED tests/test_core/test_fit_params_override.py::test_a_search_dimension_a_training_setting_controls_is_refused[early_stopping_round]
PASSED tests/test_core/test_fit_params_override.py::test_a_smart_name_in_fit_params_is_reported_as_smart
PASSED tests/test_core/test_fit_params_override.py::test_an_objective_alias_gets_the_same_task_check
PASSED tests/test_core/test_fit_params_override.py::test_an_unknown_name_in_fit_params_is_refused_before_training
PASSED tests/test_core/test_fit_params_override.py::test_each_input_is_named_by_its_own_surface
PASSED tests/test_core/test_fit_params_override.py::test_the_alias_review_round_2_measured_is_refused
PASSED tests/test_core/test_fit_params_override.py::test_the_objects_rounds_16_to_20_found_never_reach_training[round 16]
PASSED tests/test_core/test_fit_params_override.py::test_the_objects_rounds_16_to_20_found_never_reach_training[round 18]
PASSED tests/test_core/test_fit_params_override.py::test_the_objects_rounds_16_to_20_found_never_reach_training[round 19 conflicting]
PASSED tests/test_core/test_fit_params_override.py::test_the_objects_rounds_16_to_20_found_never_reach_training[round 19 equivalent]
PASSED tests/test_core/test_fit_params_override.py::test_the_objects_rounds_16_to_20_found_never_reach_training[round 20]
PASSED tests/test_core/test_fit_params_override.py::test_the_refusal_names_the_fit_params_surface
PASSED tests/test_core/test_fit_params_override.py::test_two_search_dimensions_naming_one_parameter_are_refused
PASSED tests/test_core/test_fit_params_override.py::test_two_spellings_in_the_config_are_refused_before_training
PASSED tests/test_core/test_fit_params_override.py::test_two_spellings_of_one_value_in_the_config_are_refused
PASSED tests/test_core/test_fit_params_override.py::test_two_spellings_with_different_values_are_refused
PASSED tests/test_core/test_param_domain.py::test_a_caller_class_cannot_claim_to_be_a_numpy_type
PASSED tests/test_core/test_param_domain.py::test_an_accepted_type_is_not_by_itself_a_writable_value
PASSED tests/test_core/test_param_domain.py::test_the_exit_assertion_is_called_at_every_place_that_trains
PASSED tests/test_core/test_param_domain.py::test_the_exit_assertion_refuses_a_value_that_skipped_the_surfaces
PASSED tests/test_core/test_param_domain_cycles.py::test_cyclic_mapping_is_a_controlled_training_refusal[False]
PASSED tests/test_core/test_param_domain_cycles.py::test_cyclic_mapping_is_a_controlled_training_refusal[True]
PASSED tests/test_core/test_refusal_matrix.py::test_a_restored_tuning_result_is_refused_too[check_param_names-best0]
PASSED tests/test_core/test_refusal_matrix.py::test_a_restored_tuning_result_is_refused_too[check_param_values-best1]
PASSED tests/test_core/test_refusal_matrix.py::test_a_restored_tuning_result_is_refused_too[check_training_managed_overrides-best2]
PASSED tests/test_core/test_refusal_matrix.py::test_a_restored_tuning_result_is_refused_too[check_duplicate_identities-best3]
PASSED tests/test_core/test_refusal_matrix.py::test_a_restored_tuning_result_is_refused_too[normalise_params-best4]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell0]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell1]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell2]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell3]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell4]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell5]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell6]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell7]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell8]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell9]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell10]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell11]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell12]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell13]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell14]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell15]
PASSED tests/test_core/test_refusal_matrix.py::test_each_wired_cell_refuses_before_training[cell16]
PASSED tests/test_core/test_seed_propagation.py::TestBuildSplitterSeedResolution::test_default_inherits_training_seed_backward_compat
PASSED tests/test_core/test_seed_propagation.py::TestBuildSplitterSeedResolution::test_explicit_random_state_overrides_training_seed
PASSED tests/test_core/test_seed_propagation.py::TestBuildSplitterSeedResolution::test_training_seed_propagates_to_splitter
PASSED tests/test_core/test_train_components.py::TestMergeParams::test_fit_args_override_tune_best
PASSED tests/test_e2e/test_public_api.py::TestPublicImports::test_fit_result_importable
PASSED tests/test_estimators/test_lightgbm_parameter_names.py::test_every_route_into_the_estimator_is_inventoried
PASSED tests/test_estimators/test_lightgbm_parameter_names.py::test_loading_an_artifact_is_not_blocked_by_the_gate
PASSED tests/test_estimators/test_lightgbm_parameter_names.py::test_model_params_names_are_lightgbm_names[regression]
PASSED tests/test_estimators/test_lightgbm_parameter_names.py::test_model_params_names_are_lightgbm_names[binary]
PASSED tests/test_estimators/test_lightgbm_parameter_names.py::test_model_params_names_are_lightgbm_names[multiclass]
PASSED tests/test_estimators/test_literal_parameter_reads.py::test_every_literal_parameter_read_is_declared
PASSED tests/test_estimators/test_param_behavioral_effect.py::TestSmartParamsBehavior::test_feature_weights_resolves_to_an_ordered_list
PASSED tests/test_estimators/test_provider_choice_apis.py::TestObjectiveChoicesContent::test_aligned_with_task_compatible_objectives
PASSED tests/test_features/test_categorical_encoder_edge.py::TestCategoricalEncoder::test_fit_all_na_column
PASSED tests/test_features/test_categorical_encoder_edge.py::TestCategoricalEncoder::test_fit_missing_column
PASSED tests/test_features/test_categorical_encoder_edge.py::TestCategoricalEncoder::test_fit_object_dtype
PASSED tests/test_features/test_categorical_encoder_edge.py::TestCategoricalEncoder::test_transform_before_fit
PASSED tests/test_features/test_feature_pipeline.py::TestCategoricalEncoder::test_fit_records_categories
PASSED tests/test_features/test_feature_pipeline.py::TestCategoricalEncoder::test_transform_restricts_to_training_categories
PASSED tests/test_features/test_feature_pipeline.py::TestNativeFeaturePipelineBasic::test_fit_records_feature_names
PASSED tests/test_features/test_feature_pipeline.py::TestNativeFeaturePipelineBasic::test_fit_transform_consistent
PASSED tests/test_persistence/test_export_load_errors.py::TestLoadErrors::test_corrupt_fit_result_pkl
PASSED tests/test_splitters/test_splitters.py::TestGroupTimeSeriesSplitter::test_max_train_size
PASSED tests/test_training/test_blocked_group_inner_valid.py::TestStratifiedTimeHoldout::test_regression_fallback_raises_when_ratio_consumes_all
PASSED tests/test_tuning/test_default_space.py::TestDefaultSpace::test_training_dims_ranges
PASSED tests/test_tuning/test_dimension_consumption.py::test_unconsumed_training_dimension_refused[seed]
PASSED tests/test_tuning/test_dimension_consumption.py::test_unconsumed_training_dimension_refused[n_jobs]
PASSED tests/test_tuning/test_dimension_consumption.py::test_unconsumed_training_dimension_refused[unknown_setting]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping_round-model-merge]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping_round-model-replace]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping_round-space-merge]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping_round-space-replace]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping-model-merge]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping-model-replace]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping-space-merge]
PASSED tests/test_tuning/test_space_merge.py::test_resolved_training_conflict_refused_before_study[early_stopping-space-replace]
ERROR tests/test_core/test_result_isolation.py::test_fit_result_property_distinct_objects
ERROR tests/test_core/test_result_isolation.py::test_fit_result_property_returns_independent_copy
ERROR tests/test_core/test_result_isolation.py::test_fit_result_shares_trained_estimators_by_reference
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-Float32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-Float64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-Int64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-Int64_na]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-Int8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-UInt32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-bool]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-bool_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-boolean]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-category]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-complex128]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-datetime]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-datetime_tz]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-float16]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-float32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-float64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-float64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-int16]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-int32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-int64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-int64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-int8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-interval]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-longdouble]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-object_num]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-period]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-sparse_float]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-str]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-string_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-string_python]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-timedelta]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-uint64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[str-uint8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-Float32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-Float64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-Int64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-Int64_na]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-Int8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-UInt32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-bool]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-bool_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-boolean]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-category]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-complex128]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-datetime]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-datetime_tz]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-float16]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-float32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-float64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-float64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-int16]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-int32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-int64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-int64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-int8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-interval]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-longdouble]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-object_num]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-period]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-sparse_float]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-str]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-string_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-string_python]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-timedelta]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-uint64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[object_num-uint8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-Float32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-Float64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-Int64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-Int64_na]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-Int8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-UInt32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-bool]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-bool_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-boolean]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-category]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-complex128]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-datetime]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-datetime_tz]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-float16]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-float32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-float64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-float64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-int16]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-int32]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-int64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-int64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-int8]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-interval]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-longdouble]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-object_num]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-period]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-sparse_float]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-str]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-string_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-string_python]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-timedelta]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-uint64]
ERROR tests/test_features/test_column_dtype_check.py::test_categorical_at_fit_is_not_dtype_checked[category-uint8]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[Float32]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[Float64]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[Int64]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[Int64_na]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[Int8]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[UInt32]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[bool]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[bool_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[boolean]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[category]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[complex128]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[datetime]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[datetime_tz]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[float16]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[float32]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[float64]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[float64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[int16]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[int32]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[int64]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[int64_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[int8]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[interval]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[longdouble]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[object_num]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[period]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[sparse_float]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[str]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[string_pyarrow]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[string_python]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[timedelta]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[uint64]
ERROR tests/test_features/test_column_dtype_check.py::test_numeric_at_fit_arrival_matrix[uint8]
124 passed, 17 warnings, 135 errors in 3.40s
