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Scarliles/honesty #69

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commented out some flaky tests in tree which now fail. correct covera…

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Scarliles/honesty #69

commented out some flaky tests in tree which now fail. correct covera…
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Azure Pipelines / neurodata.scikit-learn failed Dec 31, 2024 in 34m 12s

Build #20241231.4 had test failures

2 errors / 0 warnings

Details

Tests

  • Failed: 60 (0.16%)
  • Passed: 35,612 (93.16%)
  • Other: 2,556 (6.69%)
  • Total: 38,228

Annotations

Check failure on line 7699 in Build log

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@azure-pipelines azure-pipelines / neurodata.scikit-learn

Build log #L7699 

Bash exited with code '1'.

Check failure on line 1506 in Build log

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@azure-pipelines azure-pipelines / neurodata.scikit-learn

Build log #L1506 

Bash exited with code '1'.

Check failure on line 1 in test_missing_value_is_predictive[ExtraTreesRegressor]

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@azure-pipelines azure-pipelines / neurodata.scikit-learn

test_missing_value_is_predictive[ExtraTreesRegressor] 

AssertionError
Raw output 
Forest = <class 'sklearn.ensemble._forest.ExtraTreesRegressor'>

    @pytest.mark.parametrize(
        "Forest",
        [
            RandomForestClassifier,
            RandomForestRegressor,
            ExtraTreesRegressor,
            ExtraTreesClassifier,
        ],
    )
    def test_missing_value_is_predictive(Forest):
        """Check that the forest learns when missing values are only present for
        a predictive feature."""
        rng = np.random.RandomState(0)
        n_samples = 300
        expected_score = 0.75
    
        X_non_predictive = rng.standard_normal(size=(n_samples, 10))
        y = rng.randint(0, high=2, size=n_samples)
    
        # Create a predictive feature using `y` and with some noise
        X_random_mask = rng.choice([False, True], size=n_samples, p=[0.95, 0.05])
        y_mask = y.astype(bool)
        y_mask[X_random_mask] = ~y_mask[X_random_mask]
    
        predictive_feature = rng.standard_normal(size=n_samples)
        predictive_feature[y_mask] = np.nan
        assert np.isnan(predictive_feature).any()
    
        X_predictive = X_non_predictive.copy()
        X_predictive[:, 5] = predictive_feature
    
        (
            X_predictive_train,
            X_predictive_test,
            X_non_predictive_train,
            X_non_predictive_test,
            y_train,
            y_test,
        ) = train_test_split(X_predictive, X_non_predictive, y, random_state=0)
        forest_predictive = Forest(random_state=0).fit(X_predictive_train, y_train)
        forest_non_predictive = Forest(random_state=0).fit(X_non_predictive_train, y_train)
    
        predictive_test_score = forest_predictive.score(X_predictive_test, y_test)
    
>       assert predictive_test_score >= expected_score
E       AssertionError

Forest     = <class 'sklearn.ensemble._forest.ExtraTreesRegressor'>
X_non_predictive = array([[ 1.76405235,  0.40015721,  0.97873798, ..., -0.15135721,
        -0.10321885,  0.4105985 ],
       [ 0.1440435...144, -1.57760707],
       [-0.34056003, -1.30322008,  0.46675065, ..., -0.19240421,
        -1.21251574, -0.08059852]])
X_non_predictive_test = array([[ 3.25799614e-01,  1.24769521e-01, -1.01267312e+00,
        -1.02729688e+00,  3.23356531e-01, -1.36939112e+00,
... 5.42610783e-01,  7.15938893e-01,
        -2.99461286e+00,  8.80937561e-01,  1.80813181e+00,
         4.36638475e-01]])
X_non_predictive_train = array([[ 0.94231431, -2.00512543,  0.75504968, ...,  0.39756522,
        -1.02285504, -1.150692  ],
       [ 0.2804417...406,  0.49600095],
       [-0.83963245, -1.3682451 ,  1.5612796 , ..., -0.25631689,
         0.46079433, -0.40098616]])
X_predictive = array([[ 1.76405235,  0.40015721,  0.97873798, ..., -0.15135721,
        -0.10321885,  0.4105985 ],
       [ 0.1440435...144, -1.57760707],
       [-0.34056003, -1.30322008,  0.46675065, ..., -0.19240421,
        -1.21251574, -0.08059852]])
X_predictive_test = array([[ 3.25799614e-01,  1.24769521e-01, -1.01267312e+00,
        -1.02729688e+00,  3.23356531e-01, -1.71864988e+00,
... 5.42610783e-01,  2.08233171e-01,
        -2.99461286e+00,  8.80937561e-01,  1.80813181e+00,
         4.36638475e-01]])
X_predictive_train = array([[ 0.94231431, -2.00512543,  0.75504968, ...,  0.39756522,
        -1.02285504, -1.150692  ],
       [ 0.2804417...406,  0.49600095],
       [-0.83963245, -1.3682451 ,  1.5612796 , ..., -0.25631689,
         0.46079433, -0.40098616]])
X_random_mask = array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False,...False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False])
expected_score = 0.75
forest_non_predictive = ExtraTreesRegressor(random_state=0)
forest_predictive = ExtraTreesRegressor(random_state=0)
n_samples  = 300
predictive_feature = array([        nan,         nan,         nan,         nan, -0.33406295,
               nan, -0.83677104,         nan, ...   nan,         nan, -0.

Check failure on line 1 in test_estimators[ExtraTreeClassifier()-check_no_attributes_set_in_init]

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@azure-pipelines azure-pipelines / neurodata.scikit-learn

test_estimators[ExtraTreeClassifier()-check_no_attributes_set_in_init] 

AssertionError: Estimator ExtraTreeClassifier should not set any attribute apart from parameters during init. Found attributes ['postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'].
Raw output 
estimator = ExtraTreeClassifier()
check = functools.partial(<function check_no_attributes_set_in_init at 0x7f6583504720>, 'ExtraTreeClassifier')
request = <FixtureRequest for <Function test_estimators[ExtraTreeClassifier()-check_no_attributes_set_in_init]>>

    @parametrize_with_checks(list(_tested_estimators()))
    def test_estimators(estimator, check, request):
        # Common tests for estimator instances
        with ignore_warnings(
            category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning)
        ):
>           check(estimator)

check      = functools.partial(<function check_no_attributes_set_in_init at 0x7f6583504720>, 'ExtraTreeClassifier')
estimator  = ExtraTreeClassifier()
request    = <FixtureRequest for <Function test_estimators[ExtraTreeClassifier()-check_no_attributes_set_in_init]>>

../1/s/sklearn/tests/test_common.py:127: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../1/s/sklearn/utils/_testing.py:147: in wrapper
    return fn(*args, **kwargs)
        args       = ('ExtraTreeClassifier', ExtraTreeClassifier())
        fn         = <function check_no_attributes_set_in_init at 0x7f6583504680>
        kwargs     = {}
        self       = _IgnoreWarnings(record=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'ExtraTreeClassifier', estimator_orig = ExtraTreeClassifier()

    @ignore_warnings(category=FutureWarning)
    def check_no_attributes_set_in_init(name, estimator_orig):
        """Check setting during init."""
        try:
            # Clone fails if the estimator does not store
            # all parameters as an attribute during init
            estimator = clone(estimator_orig)
        except AttributeError:
            raise AttributeError(
                f"Estimator {name} should store all parameters as an attribute during init."
            )
    
        if hasattr(type(estimator).__init__, "deprecated_original"):
            return
    
        init_params = _get_args(type(estimator).__init__)
        parents_init_params = [
            param
            for params_parent in (_get_args(parent) for parent in type(estimator).__mro__)
            for param in params_parent
        ]
    
        # Test for no setting apart from parameters during init
        invalid_attr = set(vars(estimator)) - set(init_params) - set(parents_init_params)
        # Ignore private attributes
        invalid_attr = set([attr for attr in invalid_attr if not attr.startswith("_")])
>       assert not invalid_attr, (
            "Estimator %s should not set any attribute apart"
            " from parameters during init. Found attributes %s."
            % (name, sorted(invalid_attr))
        )
E       AssertionError: Estimator ExtraTreeClassifier should not set any attribute apart from parameters during init. Found attributes ['postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'].

estimator  = ExtraTreeClassifier()
estimator_orig = ExtraTreeClassifier()
init_params = ['self', 'criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', ...]
invalid_attr = {'postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'}
name       = 'ExtraTreeClassifier'
parents_init_params = ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', ...]

../1/s/sklearn/utils/estimator_checks.py:3183: AssertionError

Check failure on line 1 in test_estimators[DecisionTreeClassifier()-check_no_attributes_set_in_init]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / neurodata.scikit-learn

test_estimators[DecisionTreeClassifier()-check_no_attributes_set_in_init] 

AssertionError: Estimator DecisionTreeClassifier should not set any attribute apart from parameters during init. Found attributes ['postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'].
Raw output 
estimator = DecisionTreeClassifier()
check = functools.partial(<function check_no_attributes_set_in_init at 0x7f4c96ee85e0>, 'DecisionTreeClassifier')
request = <FixtureRequest for <Function test_estimators[DecisionTreeClassifier()-check_no_attributes_set_in_init]>>

    @parametrize_with_checks(list(_tested_estimators()))
    def test_estimators(estimator, check, request):
        # Common tests for estimator instances
        with ignore_warnings(
            category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning)
        ):
>           check(estimator)

check      = functools.partial(<function check_no_attributes_set_in_init at 0x7f4c96ee85e0>, 'DecisionTreeClassifier')
estimator  = DecisionTreeClassifier()
request    = <FixtureRequest for <Function test_estimators[DecisionTreeClassifier()-check_no_attributes_set_in_init]>>

../1/s/sklearn/tests/test_common.py:127: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../1/s/sklearn/utils/_testing.py:147: in wrapper
    return fn(*args, **kwargs)
        args       = ('DecisionTreeClassifier', DecisionTreeClassifier())
        fn         = <function check_no_attributes_set_in_init at 0x7f4c96ee8540>
        kwargs     = {}
        self       = _IgnoreWarnings(record=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'DecisionTreeClassifier', estimator_orig = DecisionTreeClassifier()

    @ignore_warnings(category=FutureWarning)
    def check_no_attributes_set_in_init(name, estimator_orig):
        """Check setting during init."""
        try:
            # Clone fails if the estimator does not store
            # all parameters as an attribute during init
            estimator = clone(estimator_orig)
        except AttributeError:
            raise AttributeError(
                f"Estimator {name} should store all parameters as an attribute during init."
            )
    
        if hasattr(type(estimator).__init__, "deprecated_original"):
            return
    
        init_params = _get_args(type(estimator).__init__)
        parents_init_params = [
            param
            for params_parent in (_get_args(parent) for parent in type(estimator).__mro__)
            for param in params_parent
        ]
    
        # Test for no setting apart from parameters during init
        invalid_attr = set(vars(estimator)) - set(init_params) - set(parents_init_params)
        # Ignore private attributes
        invalid_attr = set([attr for attr in invalid_attr if not attr.startswith("_")])
>       assert not invalid_attr, (
            "Estimator %s should not set any attribute apart"
            " from parameters during init. Found attributes %s."
            % (name, sorted(invalid_attr))
        )
E       AssertionError: Estimator DecisionTreeClassifier should not set any attribute apart from parameters during init. Found attributes ['postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'].

estimator  = DecisionTreeClassifier()
estimator_orig = DecisionTreeClassifier()
init_params = ['self', 'criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', ...]
invalid_attr = {'postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'}
name       = 'DecisionTreeClassifier'
parents_init_params = ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', ...]

../1/s/sklearn/utils/estimator_checks.py:3183: AssertionError

Check failure on line 1 in test_estimators[ExtraTreeRegressor()-check_no_attributes_set_in_init]

See this annotation in the file changed.

@azure-pipelines azure-pipelines / neurodata.scikit-learn

test_estimators[ExtraTreeRegressor()-check_no_attributes_set_in_init] 

AssertionError: Estimator ExtraTreeRegressor should not set any attribute apart from parameters during init. Found attributes ['postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'].
Raw output 
estimator = ExtraTreeRegressor()
check = functools.partial(<function check_no_attributes_set_in_init at 0x7f6583504720>, 'ExtraTreeRegressor')
request = <FixtureRequest for <Function test_estimators[ExtraTreeRegressor()-check_no_attributes_set_in_init]>>

    @parametrize_with_checks(list(_tested_estimators()))
    def test_estimators(estimator, check, request):
        # Common tests for estimator instances
        with ignore_warnings(
            category=(FutureWarning, ConvergenceWarning, UserWarning, LinAlgWarning)
        ):
>           check(estimator)

check      = functools.partial(<function check_no_attributes_set_in_init at 0x7f6583504720>, 'ExtraTreeRegressor')
estimator  = ExtraTreeRegressor()
request    = <FixtureRequest for <Function test_estimators[ExtraTreeRegressor()-check_no_attributes_set_in_init]>>

../1/s/sklearn/tests/test_common.py:127: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../1/s/sklearn/utils/_testing.py:147: in wrapper
    return fn(*args, **kwargs)
        args       = ('ExtraTreeRegressor', ExtraTreeRegressor())
        fn         = <function check_no_attributes_set_in_init at 0x7f6583504680>
        kwargs     = {}
        self       = _IgnoreWarnings(record=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'ExtraTreeRegressor', estimator_orig = ExtraTreeRegressor()

    @ignore_warnings(category=FutureWarning)
    def check_no_attributes_set_in_init(name, estimator_orig):
        """Check setting during init."""
        try:
            # Clone fails if the estimator does not store
            # all parameters as an attribute during init
            estimator = clone(estimator_orig)
        except AttributeError:
            raise AttributeError(
                f"Estimator {name} should store all parameters as an attribute during init."
            )
    
        if hasattr(type(estimator).__init__, "deprecated_original"):
            return
    
        init_params = _get_args(type(estimator).__init__)
        parents_init_params = [
            param
            for params_parent in (_get_args(parent) for parent in type(estimator).__mro__)
            for param in params_parent
        ]
    
        # Test for no setting apart from parameters during init
        invalid_attr = set(vars(estimator)) - set(init_params) - set(parents_init_params)
        # Ignore private attributes
        invalid_attr = set([attr for attr in invalid_attr if not attr.startswith("_")])
>       assert not invalid_attr, (
            "Estimator %s should not set any attribute apart"
            " from parameters during init. Found attributes %s."
            % (name, sorted(invalid_attr))
        )
E       AssertionError: Estimator ExtraTreeRegressor should not set any attribute apart from parameters during init. Found attributes ['postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'].

estimator  = ExtraTreeRegressor()
estimator_orig = ExtraTreeRegressor()
init_params = ['self', 'criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', ...]
invalid_attr = {'postsplit_conditions', 'presplit_conditions', 'splitter_listeners', 'tree_build_listeners'}
name       = 'ExtraTreeRegressor'
parents_init_params = ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', ...]

../1/s/sklearn/utils/estimator_checks.py:3183: AssertionError
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