Uploaded Test files

venv/Lib/site-packages/sklearn/_loss/tests/test_glm_distribution.py

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+# Authors: Christian Lorentzen <lorentzen.ch@gmail.com>
+#
+# License: BSD 3 clause
+import numpy as np
+from numpy.testing import (
+    assert_allclose,
+    assert_array_equal,
+)
+from scipy.optimize import check_grad
+import pytest
+
+from sklearn._loss.glm_distribution import (
+    TweedieDistribution,
+    NormalDistribution, PoissonDistribution,
+    GammaDistribution, InverseGaussianDistribution,
+    DistributionBoundary
+)
+
+
+@pytest.mark.parametrize(
+    'family, expected',
+    [(NormalDistribution(), [True, True, True]),
+     (PoissonDistribution(), [False, True, True]),
+     (TweedieDistribution(power=1.5), [False, True, True]),
+     (GammaDistribution(), [False, False, True]),
+     (InverseGaussianDistribution(), [False, False, True]),
+     (TweedieDistribution(power=4.5), [False, False, True])])
+def test_family_bounds(family, expected):
+    """Test the valid range of distributions at -1, 0, 1."""
+    result = family.in_y_range([-1, 0, 1])
+    assert_array_equal(result, expected)
+
+
+def test_invalid_distribution_bound():
+    dist = TweedieDistribution()
+    dist._lower_bound = 0
+    with pytest.raises(TypeError,
+                       match="must be of type DistributionBoundary"):
+        dist.in_y_range([-1, 0, 1])
+
+
+def test_tweedie_distribution_power():
+    msg = "distribution is only defined for power<=0 and power>=1"
+    with pytest.raises(ValueError, match=msg):
+        TweedieDistribution(power=0.5)
+
+    with pytest.raises(TypeError, match="must be a real number"):
+        TweedieDistribution(power=1j)
+
+    with pytest.raises(TypeError, match="must be a real number"):
+        dist = TweedieDistribution()
+        dist.power = 1j
+
+    dist = TweedieDistribution()
+    assert isinstance(dist._lower_bound, DistributionBoundary)
+
+    assert dist._lower_bound.inclusive is False
+    dist.power = 1
+    assert dist._lower_bound.value == 0.0
+    assert dist._lower_bound.inclusive is True
+
+
+@pytest.mark.parametrize(
+    'family, chk_values',
+    [(NormalDistribution(), [-1.5, -0.1, 0.1, 2.5]),
+     (PoissonDistribution(), [0.1, 1.5]),
+     (GammaDistribution(), [0.1, 1.5]),
+     (InverseGaussianDistribution(), [0.1, 1.5]),
+     (TweedieDistribution(power=-2.5), [0.1, 1.5]),
+     (TweedieDistribution(power=-1), [0.1, 1.5]),
+     (TweedieDistribution(power=1.5), [0.1, 1.5]),
+     (TweedieDistribution(power=2.5), [0.1, 1.5]),
+     (TweedieDistribution(power=-4), [0.1, 1.5])])
+def test_deviance_zero(family, chk_values):
+    """Test deviance(y,y) = 0 for different families."""
+    for x in chk_values:
+        assert_allclose(family.deviance(x, x), 0, atol=1e-9)
+
+
+@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):
+    """Test deviance derivative for different families."""
+    rng = np.random.RandomState(0)
+    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.)
+
+    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