(sklearn-dev) ➜  scikit-learn git:(main) ✗ SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all" pytest sklearn/_loss/tests/test_glm_distribution.py -k test_deviance_derivative
============================================================================================= test session starts ==============================================================================================
platform darwin -- Python 3.10.2, pytest-7.0.1, pluggy-1.0.0
rootdir: /Users/maren/open_source/scikit-learn, configfile: setup.cfg
plugins: xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 917 items / 17 deselected / 900 selected

sklearn/_loss/tests/test_glm_distribution.py ..............................................................................................................FF...F....................................... [ 17%]
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=================================================================================================== FAILURES ===================================================================================================
________________________________________________________________________________ test_deviance_derivative[12-GammaDistribution] ________________________________________________________________________________

family = <sklearn._loss.glm_distribution.GammaDistribution object at 0x142dc4e80>, global_random_seed = 12

    @pytest.mark.parametrize(
        "family",
        [
            NormalDistribution(),
            PoissonDistribution(),
            GammaDistribution(),
            InverseGaussianDistribution(),
            TweedieDistribution(power=-2.5),
            TweedieDistribution(power=-1),
            TweedieDistribution(power=1.5),
            TweedieDistribution(power=2.5),
            TweedieDistribution(power=-4),
        ],
        ids=lambda x: x.__class__.__name__,
    )
    def test_deviance_derivative(family, global_random_seed):
        """Test deviance derivative for different families."""
        rng = np.random.RandomState(global_random_seed)
        y_true = rng.rand(10)
        # make data positive
        y_true += np.abs(y_true.min()) + 1e-2

        y_pred = y_true + np.fmax(rng.rand(10), 0.0)

        dev = family.deviance(y_true, y_pred)
        assert isinstance(dev, float)
        dev_derivative = family.deviance_derivative(y_true, y_pred)
        assert dev_derivative.shape == y_pred.shape

        err = check_grad(
            lambda y_pred: family.deviance(y_true, y_pred),
            lambda y_pred: family.deviance_derivative(y_true, y_pred),
            y_pred,
        ) / np.linalg.norm(dev_derivative)
>       assert abs(err) < 1e-6
E       assert 1.262523602238264e-06 < 1e-06
E        +  where 1.262523602238264e-06 = abs(1.262523602238264e-06)

sklearn/_loss/tests/test_glm_distribution.py:123: AssertionError
___________________________________________________________________________ test_deviance_derivative[12-InverseGaussianDistribution] ___________________________________________________________________________

family = <sklearn._loss.glm_distribution.InverseGaussianDistribution object at 0x142dc4ee0>, global_random_seed = 12

    @pytest.mark.parametrize(
        "family",
        [
            NormalDistribution(),
            PoissonDistribution(),
            GammaDistribution(),
            InverseGaussianDistribution(),
            TweedieDistribution(power=-2.5),
            TweedieDistribution(power=-1),
            TweedieDistribution(power=1.5),
            TweedieDistribution(power=2.5),
            TweedieDistribution(power=-4),
        ],
        ids=lambda x: x.__class__.__name__,
    )
    def test_deviance_derivative(family, global_random_seed):
        """Test deviance derivative for different families."""
        rng = np.random.RandomState(global_random_seed)
        y_true = rng.rand(10)
        # make data positive
        y_true += np.abs(y_true.min()) + 1e-2

        y_pred = y_true + np.fmax(rng.rand(10), 0.0)

        dev = family.deviance(y_true, y_pred)
        assert isinstance(dev, float)
        dev_derivative = family.deviance_derivative(y_true, y_pred)
        assert dev_derivative.shape == y_pred.shape

        err = check_grad(
            lambda y_pred: family.deviance(y_true, y_pred),
            lambda y_pred: family.deviance_derivative(y_true, y_pred),
            y_pred,
        ) / np.linalg.norm(dev_derivative)
>       assert abs(err) < 1e-6
E       assert 2.712845682880818e-06 < 1e-06
E        +  where 2.712845682880818e-06 = abs(2.712845682880818e-06)

sklearn/_loss/tests/test_glm_distribution.py:123: AssertionError
______________________________________________________________________________ test_deviance_derivative[12-TweedieDistribution3] _______________________________________________________________________________

family = <sklearn._loss.glm_distribution.TweedieDistribution object at 0x142dc5060>, global_random_seed = 12

    @pytest.mark.parametrize(
        "family",
        [
            NormalDistribution(),
            PoissonDistribution(),
            GammaDistribution(),
            InverseGaussianDistribution(),
            TweedieDistribution(power=-2.5),
            TweedieDistribution(power=-1),
            TweedieDistribution(power=1.5),
            TweedieDistribution(power=2.5),
            TweedieDistribution(power=-4),
        ],
        ids=lambda x: x.__class__.__name__,
    )
    def test_deviance_derivative(family, global_random_seed):
        """Test deviance derivative for different families."""
        rng = np.random.RandomState(global_random_seed)
        y_true = rng.rand(10)
        # make data positive
        y_true += np.abs(y_true.min()) + 1e-2

        y_pred = y_true + np.fmax(rng.rand(10), 0.0)

        dev = family.deviance(y_true, y_pred)
        assert isinstance(dev, float)
        dev_derivative = family.deviance_derivative(y_true, y_pred)
        assert dev_derivative.shape == y_pred.shape

        err = check_grad(
            lambda y_pred: family.deviance(y_true, y_pred),
            lambda y_pred: family.deviance_derivative(y_true, y_pred),
            y_pred,
        ) / np.linalg.norm(dev_derivative)
>       assert abs(err) < 1e-6
E       assert 2.415069957951342e-06 < 1e-06
E        +  where 2.415069957951342e-06 = abs(2.415069957951342e-06)

sklearn/_loss/tests/test_glm_distribution.py:123: AssertionError
================================================================================= 3 failed, 897 passed, 17 deselected in 0.69s =================================================================================