63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
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import numpy as np
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from numpy.testing import assert_allclose
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import pytest
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from statsmodels.regression.linear_model import WLS
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from statsmodels.regression._tools import _MinimalWLS
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class TestMinimalWLS:
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@classmethod
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def setup_class(cls):
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rs = np.random.RandomState(1234)
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cls.exog1 = rs.randn(200, 5)
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cls.endog1 = cls.exog1.sum(1) + rs.randn(200)
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cls.weights1 = 1.0 + np.sin(np.arange(200.0) / 100.0 * np.pi)
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cls.exog2 = rs.randn(50, 1)
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cls.endog2 = 0.3 * cls.exog2.ravel() + rs.randn(50)
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cls.weights2 = 1.0 + np.log(np.arange(1.0, 51.0))
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@pytest.mark.parametrize('check', [True, False])
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def test_equivalence_with_wls(self, check):
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res = WLS(self.endog1, self.exog1).fit()
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minres = _MinimalWLS(self.endog1, self.exog1,
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check_endog=check, check_weights=check).fit()
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assert_allclose(res.params, minres.params)
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assert_allclose(res.resid, minres.resid)
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res = WLS(self.endog2, self.exog2).fit()
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minres = _MinimalWLS(self.endog2, self.exog2,
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check_endog=check, check_weights=check).fit()
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assert_allclose(res.params, minres.params)
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assert_allclose(res.resid, minres.resid)
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res = WLS(self.endog1, self.exog1, weights=self.weights1).fit()
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minres = _MinimalWLS(self.endog1, self.exog1, weights=self.weights1,
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check_endog=check, check_weights=check).fit()
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assert_allclose(res.params, minres.params)
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assert_allclose(res.resid, minres.resid)
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res = WLS(self.endog2, self.exog2, weights=self.weights2).fit()
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minres = _MinimalWLS(self.endog2, self.exog2, weights=self.weights2,
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check_endog=check, check_weights=check).fit()
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assert_allclose(res.params, minres.params)
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assert_allclose(res.resid, minres.resid)
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@pytest.mark.parametrize('bad_value', [np.nan, np.inf])
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def test_inf_nan(self, bad_value):
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with pytest.raises(
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ValueError,
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match=r'detected in endog, estimation infeasible'):
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endog = self.endog1.copy()
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endog[0] = bad_value
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_MinimalWLS(endog, self.exog1, self.weights1,
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check_endog=True, check_weights=True).fit()
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with pytest.raises(
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ValueError,
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match=r'detected in weights, estimation infeasible'):
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weights = self.weights1.copy()
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weights[-1] = bad_value
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_MinimalWLS(self.endog1, self.exog1, weights,
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check_endog=True, check_weights=True).fit()
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