113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
# Authors: Christian Lorentzen <lorentzen.ch@gmail.com>
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#
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# License: BSD 3 clause
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import numpy as np
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from numpy.testing import (
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assert_allclose,
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assert_array_equal,
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)
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from scipy.optimize import check_grad
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import pytest
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from sklearn._loss.glm_distribution import (
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TweedieDistribution,
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NormalDistribution, PoissonDistribution,
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GammaDistribution, InverseGaussianDistribution,
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DistributionBoundary
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)
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@pytest.mark.parametrize(
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'family, expected',
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[(NormalDistribution(), [True, True, True]),
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(PoissonDistribution(), [False, True, True]),
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(TweedieDistribution(power=1.5), [False, True, True]),
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(GammaDistribution(), [False, False, True]),
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(InverseGaussianDistribution(), [False, False, True]),
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(TweedieDistribution(power=4.5), [False, False, True])])
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def test_family_bounds(family, expected):
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"""Test the valid range of distributions at -1, 0, 1."""
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result = family.in_y_range([-1, 0, 1])
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assert_array_equal(result, expected)
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def test_invalid_distribution_bound():
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dist = TweedieDistribution()
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dist._lower_bound = 0
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with pytest.raises(TypeError,
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match="must be of type DistributionBoundary"):
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dist.in_y_range([-1, 0, 1])
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def test_tweedie_distribution_power():
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msg = "distribution is only defined for power<=0 and power>=1"
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with pytest.raises(ValueError, match=msg):
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TweedieDistribution(power=0.5)
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with pytest.raises(TypeError, match="must be a real number"):
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TweedieDistribution(power=1j)
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with pytest.raises(TypeError, match="must be a real number"):
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dist = TweedieDistribution()
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dist.power = 1j
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dist = TweedieDistribution()
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assert isinstance(dist._lower_bound, DistributionBoundary)
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assert dist._lower_bound.inclusive is False
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dist.power = 1
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assert dist._lower_bound.value == 0.0
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assert dist._lower_bound.inclusive is True
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@pytest.mark.parametrize(
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'family, chk_values',
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[(NormalDistribution(), [-1.5, -0.1, 0.1, 2.5]),
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(PoissonDistribution(), [0.1, 1.5]),
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(GammaDistribution(), [0.1, 1.5]),
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(InverseGaussianDistribution(), [0.1, 1.5]),
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(TweedieDistribution(power=-2.5), [0.1, 1.5]),
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(TweedieDistribution(power=-1), [0.1, 1.5]),
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(TweedieDistribution(power=1.5), [0.1, 1.5]),
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(TweedieDistribution(power=2.5), [0.1, 1.5]),
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(TweedieDistribution(power=-4), [0.1, 1.5])])
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def test_deviance_zero(family, chk_values):
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"""Test deviance(y,y) = 0 for different families."""
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for x in chk_values:
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assert_allclose(family.deviance(x, x), 0, atol=1e-9)
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@pytest.mark.parametrize(
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'family',
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[NormalDistribution(),
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PoissonDistribution(),
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GammaDistribution(),
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InverseGaussianDistribution(),
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TweedieDistribution(power=-2.5),
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TweedieDistribution(power=-1),
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TweedieDistribution(power=1.5),
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TweedieDistribution(power=2.5),
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TweedieDistribution(power=-4)],
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ids=lambda x: x.__class__.__name__
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)
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def test_deviance_derivative(family):
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"""Test deviance derivative for different families."""
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rng = np.random.RandomState(0)
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y_true = rng.rand(10)
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# make data positive
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y_true += np.abs(y_true.min()) + 1e-2
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y_pred = y_true + np.fmax(rng.rand(10), 0.)
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dev = family.deviance(y_true, y_pred)
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assert isinstance(dev, float)
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dev_derivative = family.deviance_derivative(y_true, y_pred)
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assert dev_derivative.shape == y_pred.shape
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err = check_grad(
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lambda y_pred: family.deviance(y_true, y_pred),
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lambda y_pred: family.deviance_derivative(y_true, y_pred),
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y_pred,
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) / np.linalg.norm(dev_derivative)
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assert abs(err) < 1e-6
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