import warnings import numpy as np import pandas as pd import pytest from numpy.testing import assert_equal from statsmodels.iolib.summary2 import summary_col from statsmodels.tools.tools import add_constant from statsmodels.regression.linear_model import OLS class TestSummaryLatex(object): def test_summarycol(self): # Test for latex output of summary_col object desired = r''' \begin{table} \caption{} \label{} \begin{center} \begin{tabular}{lll} \hline & y I & y II \\ \hline const & 7.7500 & 12.4231 \\ & (1.1058) & (3.1872) \\ x1 & -0.7500 & -1.5769 \\ & (0.2368) & (0.6826) \\ R-squared & 0.7697 & 0.6401 \\ R-squared Adj. & 0.6930 & 0.5202 \\ \hline \end{tabular} \end{center} \end{table} ''' x = [1, 5, 7, 3, 5] x = add_constant(x) y1 = [6, 4, 2, 7, 4] y2 = [8, 5, 0, 12, 4] reg1 = OLS(y1, x).fit() reg2 = OLS(y2, x).fit() actual = summary_col([reg1, reg2]).as_latex() actual = '\n%s\n' % actual assert_equal(desired, actual) def test_summarycol_float_format(self): # Test for latex output of summary_col object desired = r""" ========================== y I y II -------------------------- const 7.7 12.4 (1.1) (3.2) x1 -0.7 -1.6 (0.2) (0.7) R-squared 0.8 0.6 R-squared Adj. 0.7 0.5 ========================== Standard errors in parentheses. """ # noqa:W291 x = [1, 5, 7, 3, 5] x = add_constant(x) y1 = [6, 4, 2, 7, 4] y2 = [8, 5, 0, 12, 4] reg1 = OLS(y1, x).fit() reg2 = OLS(y2, x).fit() actual = summary_col([reg1, reg2], float_format='%0.1f').as_text() actual = '%s\n' % actual assert_equal(actual, desired) starred = summary_col([reg1, reg2], stars=True, float_format='%0.1f') assert "7.7***" in str(starred) assert "12.4**" in str(starred) assert "12.4***" not in str(starred) def test_summarycol_drop_omitted(self): # gh-3702 x = [1, 5, 7, 3, 5] x = add_constant(x) x2 = np.concatenate([x, np.array([[3], [9], [-1], [4], [0]])], 1) y1 = [6, 4, 2, 7, 4] y2 = [8, 5, 0, 12, 4] reg1 = OLS(y1, x).fit() reg2 = OLS(y2, x2).fit() actual = summary_col([reg1, reg2], regressor_order=['const', 'x1'], drop_omitted=True) assert 'x2' not in str(actual) actual = summary_col([reg1, reg2], regressor_order=['x1'], drop_omitted=False) assert 'const' in str(actual) assert 'x2' in str(actual) def test_summary_col_ordering_preserved(self): # gh-3767 x = [1, 5, 7, 3, 5] x = add_constant(x) x2 = np.concatenate([x, np.array([[3], [9], [-1], [4], [0]])], 1) x2 = pd.DataFrame(x2, columns=['const', 'b', 'a']) y1 = [6, 4, 2, 7, 4] y2 = [8, 5, 0, 12, 4] reg1 = OLS(y1, x2).fit() reg2 = OLS(y2, x2).fit() info_dict = {'R2': lambda x: '{:.3f}'.format(int(x.rsquared)), 'N': lambda x: '{0:d}'.format(int(x.nobs))} original = actual = summary_col([reg1, reg2], float_format='%0.4f') actual = summary_col([reg1, reg2], regressor_order=['a', 'b'], float_format='%0.4f', info_dict=info_dict) variables = ('const', 'b', 'a') for line in str(original).split('\n'): for variable in variables: if line.startswith(variable): assert line in str(actual) def test_OLSsummary(self): # Test that latex output of regular OLS output still contains # multiple tables x = [1, 5, 7, 3, 5] x = add_constant(x) y1 = [6, 4, 2, 7, 4] reg1 = OLS(y1, x).fit() with warnings.catch_warnings(): warnings.simplefilter("ignore") actual = reg1.summary().as_latex() string_to_find = r'''\end{tabular} \begin{tabular}''' result = string_to_find in actual assert (result is True) def test_ols_summary_rsquared_label(): # Check that the "uncentered" label is correctly added after rsquared x = [1, 5, 7, 3, 5, 2, 5, 3] y = [6, 4, 2, 7, 4, 9, 10, 2] reg_with_constant = OLS(y, add_constant(x)).fit() r2_str = 'R-squared:' with pytest.warns(UserWarning): assert r2_str in str(reg_with_constant.summary2()) with pytest.warns(UserWarning): assert r2_str in str(reg_with_constant.summary()) reg_without_constant = OLS(y, x, hasconst=False).fit() r2_str = 'R-squared (uncentered):' with pytest.warns(UserWarning): assert r2_str in str(reg_without_constant.summary2()) with pytest.warns(UserWarning): assert r2_str in str(reg_without_constant.summary()) def test_summary_col_r2(): # GH 6578 y = [1, 1, 4, 2] * 4 x = add_constant([1, 2, 3, 4] * 4) mod = OLS(endog=y, exog=x).fit() table = summary_col(results=mod) assert "R-squared " in str(table) assert "R-squared Adj." in str(table)