Rename bootstrap_regress to regress_bootstrap and update
Improve performance by directly calling statsmodels regression Report bootstrap in model summary Add to documentation
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@ -10,6 +10,8 @@ Functions
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.. autofunction:: yli.regress
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.. autofunction:: yli.regress_bootstrap
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.. autofunction:: yli.vif
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Result classes
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@ -15,12 +15,11 @@
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from .bayes_factors import bayesfactor_afbf
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from .bootstrap import bootstrap_regress
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from .config import config
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from .descriptives import auto_descriptives
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from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
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from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
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from .regress import PenalisedLogit, logit_then_regress, regress, vif
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from .regress import PenalisedLogit, logit_then_regress, regress, regress_bootstrap, vif
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from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, ttest_ind
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def reload_me():
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103
yli/bootstrap.py
103
yli/bootstrap.py
@ -1,103 +0,0 @@
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# scipy-yli: Helpful SciPy utilities and recipes
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# Copyright © 2022 Lee Yingtong Li (RunasSudo)
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import numpy as np
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import pandas as pd
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import patsy
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from tqdm import tqdm
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from datetime import datetime
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from .regress import CategoricalTerm, RegressionResult, SingleTerm, regress
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from .utils import Estimate, check_nan, cols_for_formula, convert_pandas_nullable, fmt_p, formula_factor_ref_category, parse_patsy_term
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def bootstrap_regress(
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model_class, df, dep, formula, *,
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nan_policy='warn',
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samples=1000,
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**kwargs
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):
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"""
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Fit a statsmodels regression model, using bootstrapping to compute confidence intervals and *p* values
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:param model_class: See :func:`yli.regress`
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:param df: See :func:`yli.regress`
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:param dep: See :func:`yli.regress`
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:param formula: See :func:`yli.regress`
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:param nan_policy: See :func:`yli.regress`
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:param samples: Number of bootstrap samples to draw
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:type samples: int
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:param kwargs: See :func:`yli.regress`
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:rtype: :class:`yli.regress.RegressionResult`
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"""
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# Check for/clean NaNs
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# Following this, we pass nan_policy='raise' to assert no NaNs remaining
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df = df[[dep] + cols_for_formula(formula, df)]
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df = check_nan(df, nan_policy)
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# Ensure numeric type for dependent variable
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if df[dep].dtype != 'float64':
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df[dep] = df[dep].astype('float64')
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# Convert pandas nullable types for independent variables as this breaks statsmodels
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df = convert_pandas_nullable(df)
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# Precompute design matrices
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dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
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# Fit full model
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full_model = regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=dmatrices, **kwargs)
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# Cache reference categories
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ref_categories = {term_name: term.ref_category for term_name, term in full_model.terms.items() if isinstance(term, CategoricalTerm)}
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# Draw bootstrap samples and regress
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bootstrap_results = []
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for i in tqdm(range(samples)):
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#bootstrap_sample = df.sample(len(df), replace=True)
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#bootstrap_results.append(regress(model_class, bootstrap_sample, dep, formula, nan_policy='raise', _dmatrices=dmatrices, _ref_categories=ref_categories, **kwargs))
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bootstrap_rows = pd.Series(dmatrices[0].index).sample(len(df), replace=True)
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bootstrap_dmatrices = (dmatrices[0].loc[bootstrap_rows], dmatrices[1].loc[bootstrap_rows])
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bootstrap_results.append(regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=bootstrap_dmatrices, _ref_categories=ref_categories, **kwargs))
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# Combine bootstrap results
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terms = {}
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for term_name, term in full_model.terms.items():
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if isinstance(term, SingleTerm):
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bootstrap_betas = [r.terms[term_name].beta.point for r in bootstrap_results]
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bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
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bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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terms[term_name] = SingleTerm(term.raw_name, Estimate(term.beta.point, np.quantile(bootstrap_betas, 0.025), np.quantile(bootstrap_betas, 0.975)), bootstrap_pvalue)
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else:
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categories = {}
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for sub_term_name, sub_term in term.categories.items():
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bootstrap_betas = [r.terms[term_name].categories[sub_term_name].beta.point for r in bootstrap_results if sub_term_name in r.terms[term_name].categories]
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bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
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bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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categories[sub_term_name] = SingleTerm(sub_term.raw_name, Estimate(sub_term.beta.point, np.quantile(bootstrap_betas, 0.025), np.quantile(bootstrap_betas, 0.975)), bootstrap_pvalue)
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terms[term_name] = CategoricalTerm(categories, term.ref_category)
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return RegressionResult(
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None,
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full_model.full_name, full_model.model_name, full_model.fit_method,
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dep, full_model.nobs, full_model.dof_model, datetime.now(), full_model.cov_type,
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terms,
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full_model.llf, full_model.llnull,
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full_model.dof_resid, full_model.rsquared, full_model.f_statistic,
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full_model.exp
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)
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104
yli/regress.py
104
yli/regress.py
@ -22,6 +22,7 @@ import statsmodels
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import statsmodels.api as sm
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from statsmodels.iolib.table import SimpleTable
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from statsmodels.stats.outliers_influence import variance_inflation_factor
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from tqdm import tqdm
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from datetime import datetime
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import itertools
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@ -254,7 +255,7 @@ class RegressionResult:
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left_col.append(('Method:', self.fit_method))
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left_col.append(('Date:', self.fitted_dt.strftime('%Y-%m-%d')))
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left_col.append(('Time:', self.fitted_dt.strftime('%H:%M:%S')))
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left_col.append(('Std. Errors:', 'Non-Robust' if self.cov_type == 'nonrobust' else self.cov_type.upper()))
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left_col.append(('Std. Errors:', 'Non-Robust' if self.cov_type == 'nonrobust' else self.cov_type.upper() if self.cov_type.startswith('hc') else self.cov_type))
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# Right column
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right_col = []
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@ -440,7 +441,7 @@ def regress(
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family=None, # common model_kwargs
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cov_type=None, maxiter=None, start_params=None, # common fit_kwargs
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bool_baselevels=False, exp=None,
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_dmatrices=None, _ref_categories=None,
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_dmatrices=None,
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):
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"""
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Fit a statsmodels regression model
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@ -589,10 +590,7 @@ def regress(
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else:
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# Add a new categorical term if not exists
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if column not in terms:
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if _ref_categories is None:
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ref_category = formula_factor_ref_category(formula, df, factor)
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else:
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ref_category = _ref_categories[column]
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terms[column] = CategoricalTerm({}, ref_category)
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terms[column].categories[contrast] = SingleTerm(raw_name, beta, pvalues[raw_name])
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@ -640,6 +638,102 @@ def regress(
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exp
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)
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def regress_bootstrap(
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model_class, df, dep, formula, *,
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nan_policy='warn',
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model_kwargs=None, fit_kwargs=None,
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samples=1000,
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**kwargs
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):
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"""
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Fit a statsmodels regression model, using bootstrapping to compute confidence intervals and *p* values
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:param model_class: See :func:`yli.regress`
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:param df: See :func:`yli.regress`
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:param dep: See :func:`yli.regress`
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:param formula: See :func:`yli.regress`
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:param nan_policy: See :func:`yli.regress`
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:param model_kwargs: See :func:`yli.regress`
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:param fit_kwargs: See :func:`yli.regress`
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:param samples: Number of bootstrap samples to draw
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:type samples: int
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:param kwargs: Other arguments to pass to :func:`yli.regress`
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:rtype: :class:`yli.regress.RegressionResult`
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"""
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if model_kwargs is None:
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model_kwargs = {}
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if fit_kwargs is None:
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fit_kwargs = {}
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# Check for/clean NaNs
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# Following this, we pass nan_policy='raise' to assert no NaNs remaining
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df = df[[dep] + cols_for_formula(formula, df)]
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df = check_nan(df, nan_policy)
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# Ensure numeric type for dependent variable
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if df[dep].dtype != 'float64':
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df[dep] = df[dep].astype('float64')
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# Convert pandas nullable types for independent variables as this breaks statsmodels
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df = convert_pandas_nullable(df)
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# Precompute design matrices
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dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
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# Fit full model
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full_model = regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=dmatrices, model_kwargs=model_kwargs, fit_kwargs=fit_kwargs, **kwargs)
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# Initialise bootstrap_results
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bootstrap_results = {} # Dict mapping term raw names to bootstrap betas
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for term in full_model.terms.values():
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if isinstance(term, SingleTerm):
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bootstrap_results[term.raw_name] = []
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else:
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for sub_term in term.categories.values():
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bootstrap_results[sub_term.raw_name] = []
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# Draw bootstrap samples and regress
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dmatrices = dmatrices[0].join(dmatrices[1])
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for i in tqdm(range(samples)):
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bootstrap_rows = dmatrices.sample(len(df), replace=True)
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model = model_class(endog=bootstrap_rows.iloc[:,0], exog=bootstrap_rows.iloc[:,1:], **model_kwargs)
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model.formula = dep + ' ~ ' + formula
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result = model.fit(disp=False, **fit_kwargs)
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for raw_name, raw_beta in zip(model.exog_names, result._results.params):
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bootstrap_results[raw_name].append(raw_beta)
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# Combine bootstrap results
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terms = {}
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for term_name, term in full_model.terms.items():
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if isinstance(term, SingleTerm):
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bootstrap_betas = bootstrap_results[term.raw_name]
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bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
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bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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terms[term_name] = SingleTerm(term.raw_name, Estimate(term.beta.point, np.quantile(bootstrap_betas, config.alpha/2), np.quantile(bootstrap_betas, 1-config.alpha/2)), bootstrap_pvalue)
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else:
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categories = {}
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for sub_term_name, sub_term in term.categories.items():
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bootstrap_betas = bootstrap_results[sub_term.raw_name]
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bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
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bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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categories[sub_term_name] = SingleTerm(sub_term.raw_name, Estimate(sub_term.beta.point, np.quantile(bootstrap_betas, config.alpha/2), np.quantile(bootstrap_betas, 1-config.alpha/2)), bootstrap_pvalue)
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terms[term_name] = CategoricalTerm(categories, term.ref_category)
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return RegressionResult(
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None,
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full_model.full_name, full_model.model_name, full_model.fit_method,
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dep, full_model.nobs, full_model.dof_model, datetime.now(), 'Bootstrap',
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terms,
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full_model.llf, full_model.llnull,
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full_model.dof_resid, full_model.rsquared, full_model.f_statistic,
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full_model.exp
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)
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def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kwargs):
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"""
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Perform logistic regression, then use parameters as start parameters for desired regression
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