diff --git a/tests/test_regress.py b/tests/test_regress.py new file mode 100644 index 0000000..1345b1c --- /dev/null +++ b/tests/test_regress.py @@ -0,0 +1,123 @@ +# scipy-yli: Helpful SciPy utilities and recipes +# Copyright © 2022 Lee Yingtong Li (RunasSudo) +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . + +from pytest import approx + +import numpy as np +import pandas as pd +import statsmodels.api as sm + +import yli + +def test_regress_ols_ol11_4(): + """Compare yli.regress for Ott & Longnecker (2016) example 11.4/11.7""" + + df = pd.DataFrame({ + 'SoilPh': [3.3, 3.4, 3.4, 3.5, 3.6, 3.6, 3.7, 3.7, 3.8, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 5.0, 5.1, 5.2], + 'GrowthRet': [17.78, 21.59, 23.84, 15.13, 23.45, 20.87, 17.78, 20.09, 17.78, 12.46, 14.95, 15.87, 17.45, 14.35, 14.64, 17.25, 12.57, 7.15, 7.50, 4.34] + }) + + result = yli.regress(sm.OLS, df, 'GrowthRet', 'SoilPh') + + assert result.dof_model == 1 + assert result.dof_resid == 18 + assert result.f_statistic == approx(52.01, abs=0.01) + assert result.ftest().pvalue < 0.0005 + assert result.rsquared == approx(0.7429, abs=0.0001) + + assert result.beta['Intercept'].point == approx(47.48, abs=0.01) + assert result.pvalues['Intercept'] < 0.0005 + assert result.beta['SoilPh'].point == approx(-7.86, abs=0.01) + assert result.pvalues['SoilPh'] < 0.0005 + + assert result.beta['SoilPh'].ci_lower == approx(-10.15, abs=0.01) + assert result.beta['SoilPh'].ci_upper == approx(-5.57, abs=0.01) + +def test_regress_ols_ol13_5(): + """Compare yli.regress for Ott & Longnecker (2016) chapter 13.5""" + + df = pd.DataFrame({ + 'C': [460.05, 452.99, 443.22, 652.32, 642.23, 345.39, 272.37, 317.21, 457.12, 690.19, 350.63, 402.59, 412.18, 495.58, 394.36, 423.32, 712.27, 289.66, 881.24, 490.88, 567.79, 665.99, 621.45, 608.8, 473.64, 697.14, 207.51, 288.48, 284.88, 280.36, 217.38, 270.71], + 'D': [68.58, 67.33, 67.33, 68, 68, 67.92, 68.17, 68.42, 68.42, 68.33, 68.58, 68.75, 68.42, 68.92, 68.92, 68.42, 69.5, 68.42, 69.17, 68.92, 68.75, 70.92, 69.67, 70.08, 70.42, 71.08, 67.25, 67.17, 67.83, 67.83, 67.25, 67.83], + 'T1': [14, 10, 10, 11, 11, 13, 12, 14, 15, 12, 12, 13, 15, 17, 13, 11, 18, 15, 15, 16, 11, 22, 16, 19, 19, 20, 13, 9, 12, 12, 13, 7], + 'T2': [46, 73, 85, 67, 78, 51, 50, 59, 55, 71, 64, 47, 62, 52, 65, 67, 60, 76, 67, 59, 70, 57, 59, 58, 44, 57, 63, 48, 63, 71, 72, 80], + 'S': [687, 1065, 1065, 1065, 1065, 514, 822, 457, 822, 792, 560, 790, 530, 1050, 850, 778, 845, 530, 1090, 1050, 913, 828, 786, 821, 538, 1130, 745, 821, 886, 886, 745, 886], + 'PR': [0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1], + 'NE': [1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + 'CT': [0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0], + 'BW': [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1], + 'N': [14, 1, 1, 12, 12, 3, 5, 1, 5, 2, 3, 6, 2, 7, 16, 3, 17, 2, 1, 8, 15, 20, 18, 3, 19, 21, 8, 7, 11, 11, 8, 11], + 'PT': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1] + }) + df['LNC'] = np.log(df['C']) + + result = yli.regress(sm.OLS, df, 'LNC', 'D + T1 + T2 + S + PR + NE + CT + BW + N + PT') + + assert result.dof_model == 10 + assert result.dof_resid == 21 + assert result.f_statistic == approx(13.28, abs=0.01) + assert result.ftest().pvalue < 0.00005 + assert result.rsquared == approx(0.8635, abs=0.0001) + + assert result.beta['Intercept'].point == approx(-10.63398, abs=0.00001) + assert result.pvalues['Intercept'] == approx(0.0766, abs=0.0001) + assert result.beta['D'].point == approx(0.22760, abs=0.00001) + assert result.pvalues['D'] == approx(0.0157, abs=0.0001) + assert result.beta['T1'].point == approx(0.00525, abs=0.00001) + assert result.pvalues['T1'] == approx(0.8161, abs=0.0001) + assert result.beta['T2'].point == approx(0.00561, abs=0.00001) + assert result.pvalues['T2'] == approx(0.2360, abs=0.0001) + assert result.beta['S'].point == approx(0.00088369, abs=0.00000001) + assert result.pvalues['S'] < 0.0001 + assert result.beta['PR'].point == approx(-0.10813, abs=0.00001) + assert result.pvalues['PR'] == approx(0.2094, abs=0.0001) + assert result.beta['NE'].point == approx(0.25949, abs=0.00001) + assert result.pvalues['NE'] == approx(0.0036, abs=0.0001) + assert result.beta['CT'].point == approx(0.11554, abs=0.00001) + assert result.pvalues['CT'] == approx(0.1150, abs=0.0001) + assert result.beta['BW'].point == approx(0.03680, abs=0.00001) + assert result.pvalues['BW'] == approx(0.7326, abs=0.0001) + assert result.beta['N'].point == approx(-0.01203, abs=0.00001) + assert result.pvalues['N'] == approx(0.1394, abs=0.0001) + assert result.beta['PT'].point == approx(-0.22197, abs=0.00001) + assert result.pvalues['PT'] == approx(0.1035, abs=0.0001) + +def test_regress_logit_ol12_23(): + """Compare yli.regress for Ott & Longnecker (2016) chapter 12.23""" + + df = pd.DataFrame({ + 'Unhealthy': [False, False, False, False, False, False, False, True, False, False, False, True, False, False, False, False, False, False, True, False, True, False, False, False, False, False, True, False, False, True, False, False], + 'Fibrinogen': [2.52, 2.46, 2.29, 3.15, 2.88, 2.29, 2.99, 2.38, 2.56, 3.22, 2.35, 3.53, 2.65, 2.15, 3.32, 2.23, 2.19, 2.21, 5.06, 2.68, 2.09, 2.54, 2.18, 2.68, 3.41, 3.15, 3.35, 2.60, 2.28, 3.93, 2.60, 3.34], + 'GammaGlobulin': [38, 36, 36, 36, 30, 31, 36, 37, 31, 38, 29, 46, 46, 31, 35, 37, 33, 37, 37, 34, 44, 28, 31, 39, 37, 39, 32, 38, 36, 32, 41, 30] + }) + + result = yli.regress(sm.Logit, df, 'Unhealthy', 'Fibrinogen + GammaGlobulin') + + # Some numerical differences as intercept term is very negative + lrtest_result = result.lrtest_null() + assert lrtest_result.statistic == approx(7.9138, rel=0.01) + assert lrtest_result.dof == 2 + assert lrtest_result.pvalue == approx(0.0191, rel=0.02) + + expbeta_fib = np.exp(result.beta['Fibrinogen']) + assert expbeta_fib.point == approx(6.756, rel=0.01) + assert expbeta_fib.ci_lower == approx(1.007, rel=0.01) + assert expbeta_fib.ci_upper == approx(45.308, rel=0.02) + + expbeta_gam = np.exp(result.beta['GammaGlobulin']) + assert expbeta_gam.point == approx(1.169, abs=0.001) + assert expbeta_gam.ci_lower == approx(0.924, abs=0.001) + assert expbeta_gam.ci_upper == approx(1.477, abs=0.001) diff --git a/yli/__init__.py b/yli/__init__.py index 25a978f..071c7e0 100644 --- a/yli/__init__.py +++ b/yli/__init__.py @@ -16,6 +16,7 @@ from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist from .fs import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted +from .regress import regress from .sig_tests import chi2, mannwhitney, ttest_ind def reload_me(): diff --git a/yli/regress.py b/yli/regress.py new file mode 100644 index 0000000..2a04a1a --- /dev/null +++ b/yli/regress.py @@ -0,0 +1,310 @@ +# scipy-yli: Helpful SciPy utilities and recipes +# Copyright © 2022 Lee Yingtong Li (RunasSudo) +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. +# +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see . + +import numpy as np +import patsy +from scipy import stats +import statsmodels.api as sm +from statsmodels.iolib.table import SimpleTable + +from datetime import datetime +import itertools + +from .utils import Estimate, check_nan, fmt_p_html, fmt_p_text + +def cols_for_formula(formula): + """Return the columns corresponding to the Patsy formula""" + + model_desc = patsy.ModelDesc.from_formula(formula) + cols = set() + for term in model_desc.rhs_termlist: + for factor in term.factors: + name = factor.name() + if '(' in name: + # FIXME: Is there a better way of doing this? + name = name[name.index('(')+1:name.index(')')] + + cols.add(name) + + return list(cols) + +# ---------- +# Regression + +class LikelihoodRatioTestResult: + """Result of a likelihood ratio test for regression""" + + def __init__(self, statistic, dof, pvalue): + self.statistic = statistic + self.dof = dof + self.pvalue = pvalue + + def _repr_html_(self): + return 'LR({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p_html(self.pvalue)) + + def summary(self): + return 'LR({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p_text(self.pvalue)) + +class FTestResult: + """Result of an F test for regression""" + + def __init__(self, statistic, dof_model, dof_resid, pvalue): + self.statistic = statistic + self.dof_model = dof_model + self.dof_resid = dof_resid + self.pvalue = pvalue + + def _repr_html_(self): + return 'F({}, {}) = {:.2f}; p {}'.format(self.dof_model, self.dof_resid, self.statistic, fmt_p_html(self.pvalue)) + + def summary(self): + return 'F({}, {}) = {:.2f}; p {}'.format(self.dof_model, self.dof_resid, self.statistic, fmt_p_text(self.pvalue)) + +class RegressionResult: + """ + Result of a regression + + llf/llnull: Log-likelihood of model/null model + """ + + def __init__(self, + raw_result, + full_name, model_name, fit_method, + dep, nobs, dof_model, fitted_dt, + beta, pvalues, + llf, llnull, + dof_resid, rsquared, f_statistic, + exp + ): + # A copy of the raw result so we can access it + self.raw_result = raw_result + + # Information for display + self.full_name = full_name + self.model_name = model_name + self.fit_method = fit_method + + # Basic fitted model information + self.dep = dep + self.nobs = nobs + self.dof_model = dof_model + self.fitted_dt = fitted_dt + + # Regression coefficients + self.beta = beta + self.pvalues = pvalues + + # Model log-likelihood + self.llf = llf + self.llnull = llnull + + # Extra statistics (not all regression models have these) + self.dof_resid = dof_resid + self.rsquared = rsquared + self.f_statistic = f_statistic + + # Config for display style + self.exp = exp + + @property + def pseudo_rsquared(self): + """McFadden's pseudo R-squared""" + + return 1 - self.llf/self.llnull + + def lrtest_null(self): + """Compute the likelihood ratio test comparing the model to the null model""" + + statistic = -2 * (self.llnull - self.llf) + pvalue = 1 - stats.chi2.cdf(statistic, self.dof_model) + + return LikelihoodRatioTestResult(statistic, self.dof_model, pvalue) + + def ftest(self): + """Perform the F test that all slopes are 0""" + + pvalue = 1 - stats.f(self.dof_model, self.dof_resid).cdf(self.f_statistic) + return FTestResult(self.f_statistic, self.dof_model, self.dof_resid, pvalue) + + def _header_table(self, html): + """Return the entries for the header table""" + + # Left column + left_col = [] + + left_col.append(('Dep. Variable:', self.dep)) + left_col.append(('Model:', self.model_name)) + left_col.append(('Method:', self.fit_method)) + left_col.append(('Date:', self.fitted_dt.strftime('%Y-%m-%d'))) + left_col.append(('Time:', self.fitted_dt.strftime('%H:%M:%S'))) + left_col.append(('No. Observations:', format(self.nobs, '.0f'))) + + # Right column + right_col = [] + + if self.dof_resid: + right_col.append(('Df. Residuals:', format(self.dof_resid, '.0f'))) + right_col.append(('Df. Model:', format(self.dof_model, '.0f'))) + if self.rsquared: + right_col.append(('R2:' if html else 'R²:', format(self.rsquared, '.2f'))) + else: + right_col.append(('Pseudo R2:' if html else 'Pseudo R²:', format(self.pseudo_rsquared, '.2f'))) + if self.f_statistic: + # Report the F test if available + f_result = self.ftest() + + if html: + right_col.append(('F:', format(f_result.statistic, '.2f'))) + right_col.append(('p (F):', fmt_p_html(f_result.pvalue, True))) + else: + right_col.append(('F:', format(f_result.statistic, '.2f'))) + right_col.append(('p (F):', fmt_p_text(f_result.pvalue, True))) + else: + # Otherwise report likelihood ratio test as overall test + lrtest_result = self.lrtest_null() + + right_col.append(('LL-Model:', format(self.llf, '.2f'))) + right_col.append(('LL-Null:', format(self.llnull, '.2f'))) + if html: + right_col.append(('p (LR):', fmt_p_html(lrtest_result.pvalue, True))) + else: + right_col.append(('p (LR):', fmt_p_text(lrtest_result.pvalue, True))) + + return left_col, right_col + + def _repr_html_(self): + # Render header table + left_col, right_col = self._header_table(html=True) + + out = ''.format(self.full_name) + for left_cell, right_cell in itertools.zip_longest(left_col, right_col): + out += ''.format( + left_cell[0] if left_cell else '', + left_cell[1] if left_cell else '', + right_cell[0] if right_cell else '', + right_cell[1] if right_cell else '' + ) + out += '
{} Results
{}{}{}{}
' + + # Render coefficients table + out += ''.format('exp(β)' if self.exp else 'β') + + for term, beta in self.beta.items(): + # Exponentiate if requested + if self.exp: + beta = np.exp(beta) + + out += ''.format(term, beta.point, beta.ci_lower, beta.ci_upper, fmt_p_html(self.pvalues[term], True)) + + out += '
{}(95% CI)p
{}{:.2f}({:.2f}–{:.2f}){}
' + + # TODO: Have a detailed view which shows SE, t/z, etc. + + return out + + def summary(self): + # Render header table + left_col, right_col = self._header_table(html=False) + + # Ensure equal sizes for SimpleTable + if len(right_col) > len(left_col): + left_col.extend([('', '')] * (len(right_col) - len(left_col))) + elif len(left_col) > len(right_col): + right_col.extend([('', '')] * (len(left_col) - len(right_col))) + + table1 = SimpleTable(np.concatenate([left_col, right_col], axis=1), title='{} Results'.format(self.full_name)) + table1.insert_stubs(2, [' | '] * len(left_col)) + + # Get rid of last line (merge with next table) + table1_lines = table1.as_text().splitlines(keepends=False) + out = '\n'.join(table1_lines[:-1]) + '\n' + + # Render coefficients table + table_data = [] + + for term, beta in self.beta.items(): + # Exponentiate if requested + if self.exp: + beta = np.exp(estimate) + + # Add some extra padding + table_data.append([term + ' ', format(beta.point, '.2f'), '({:.2f}'.format(beta.ci_lower), '-', '{:.2f})'.format(beta.ci_upper), ' ' + fmt_p_text(self.pvalues[term], True)]) + + table2 = SimpleTable(data=table_data, headers=['', 'exp(β)' if self.exp else 'β', '', '\ue000', '', ' p']) # U+E000 is in Private Use Area, mark middle of CI column + table2_text = table2.as_text().replace(' \ue000 ', '(95% CI)') # Render heading in the right spot + table2_lines = table2_text.splitlines(keepends=False) + + # Render divider line between 2 tables + max_table_len = max(len(table1_lines[-1]), len(table2_lines[-1])) + out += '=' * max_table_len + '\n' + + out += '\n'.join(table2_lines[1:]) + + return out + +def regress( + model_class, df, dep, formula, *, + nan_policy='warn', exp=None +): + """Fit a statsmodels regression model""" + + # Autodetect whether to exponentiate + if exp is None: + if model_class is sm.Logit: + exp = True + else: + exp = False + + # Check for/clean NaNs + df = df[[dep] + cols_for_formula(formula)] + df = check_nan(df, nan_policy) + + # Ensure numeric type for dependent variable + if df[dep].dtype != 'float64': + df[dep] = df[dep].astype('float64') + + # Convert pandas nullable types for independent variables + for col in df.columns: + if df[col].dtype == 'Int64': + df[col] = df[col].astype('float64') + + # Fit model + model = model_class.from_formula(formula=dep + ' ~ ' + formula, data=df) + result = model.fit() + + confint = result.conf_int() + beta = {t: Estimate(b, confint[0][t], confint[1][t]) for t, b in result.params.items()} + + # Fit null model (for llnull) + if hasattr(result, 'llnull'): + llnull = result.llnull + else: + result_null = model_class.from_formula(formula=dep + ' ~ 1', data=df).fit() + llnull = result_null.llf + + # Parse raw regression results (to get fit method) + header_entries = np.vectorize(str.strip)(np.concatenate(np.split(np.array(result.summary().tables[0].data), 2, axis=1))) + header_dict = {x[0]: x[1] for x in header_entries} + + return RegressionResult( + result, + 'Logistic Regression' if model_class is sm.Logit else '{} Regression'.format(model_class.__name__), model_class.__name__, header_dict['Method:'], + dep, result.nobs, result.df_model, datetime.now(), + beta, result.pvalues, + result.llf, llnull, + getattr(result, 'df_resid', None), getattr(result, 'rsquared', None), getattr(result, 'fvalue', None), + exp + )