120 lines
3.2 KiB
Python
120 lines
3.2 KiB
Python
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'''examples to check summary, not converted to tests yet
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'''
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import numpy as np # noqa: F401
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import pytest
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from numpy.testing import assert_equal
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from statsmodels.datasets import macrodata
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from statsmodels.tools.tools import add_constant
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from statsmodels.regression.linear_model import OLS
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def test_escaped_variable_name():
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# Rename 'cpi' column to 'CPI_'
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data = macrodata.load().data
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data.rename(columns={'cpi': 'CPI_'}, inplace=True)
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mod = OLS.from_formula('CPI_ ~ 1 + np.log(realgdp)', data=data)
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res = mod.fit()
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assert 'CPI\\_' in res.summary().as_latex()
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assert 'CPI_' in res.summary().as_text()
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def test_wrong_len_xname(reset_randomstate):
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y = np.random.randn(100)
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x = np.random.randn(100, 2)
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res = OLS(y, x).fit()
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with pytest.raises(ValueError):
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res.summary(xname=['x1'])
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with pytest.raises(ValueError):
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res.summary(xname=['x1', 'x2', 'x3'])
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class TestSummaryLatex:
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def test__repr_latex_(self):
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desired = r'''
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\begin{center}
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\begin{tabular}{lcccccc}
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\toprule
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& \textbf{coef} & \textbf{std err} & \textbf{t} & \textbf{P$> |$t$|$} & \textbf{[0.025} & \textbf{0.975]} \\
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\midrule
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\textbf{const} & 7.2248 & 0.866 & 8.346 & 0.000 & 5.406 & 9.044 \\
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\textbf{x1} & -0.6609 & 0.177 & -3.736 & 0.002 & -1.033 & -0.289 \\
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\bottomrule
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\end{tabular}
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\end{center}
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'''
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x = [1, 5, 7, 3, 5, 5, 8, 3, 3, 4, 6, 4, 2, 7, 4, 2, 1, 9, 2, 6]
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x = add_constant(x)
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y = [6, 4, 2, 7, 4, 2, 1, 9, 2, 6, 1, 5, 7, 3, 5, 5, 8, 3, 3, 4]
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reg = OLS(y, x).fit()
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actual = reg.summary().tables[1]._repr_latex_()
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actual = '\n%s\n' % actual
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assert_equal(actual, desired)
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if __name__ == '__main__':
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from statsmodels.regression.tests.test_regression import TestOLS
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#def mytest():
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aregression = TestOLS()
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TestOLS.setup_class()
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results = aregression.res1
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r_summary = str(results.summary_old())
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print(r_summary)
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olsres = results
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print('\n\n')
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r_summary = str(results.summary())
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print(r_summary)
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print('\n\n')
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from statsmodels.discrete.tests.test_discrete import TestProbitNewton
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aregression = TestProbitNewton()
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TestProbitNewton.setup_class()
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results = aregression.res1
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r_summary = str(results.summary())
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print(r_summary)
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print('\n\n')
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probres = results
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from statsmodels.robust.tests.test_rlm import TestHampel
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aregression = TestHampel()
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#TestHampel.setup_class()
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results = aregression.res1
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r_summary = str(results.summary())
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print(r_summary)
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rlmres = results
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print('\n\n')
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from statsmodels.genmod.tests.test_glm import TestGlmBinomial
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aregression = TestGlmBinomial()
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#TestGlmBinomial.setup_class()
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results = aregression.res1
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r_summary = str(results.summary())
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print(r_summary)
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#print(results.summary2(return_fmt='latex'))
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#print(results.summary2(return_fmt='csv'))
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smry = olsres.summary()
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print(smry.as_csv())
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# import matplotlib.pyplot as plt
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# plt.plot(rlmres.model.endog,'o')
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# plt.plot(rlmres.fittedvalues,'-')
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#
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# plt.show()
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