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Rename bootstrap_regress to regress_bootstrap and update

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Improve performance by directly calling statsmodels regression
Report bootstrap in model summary
Add to documentation
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RunasSudo 2022-11-29 23:00:14 +11:00
parent e71a1aea12
commit 645cb7a85e
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
4 changed files with 103 additions and 111 deletions
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2
docs/regress.rst
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@ -10,6 +10,8 @@ Functions
.. autofunction:: yli.regress .. autofunction:: yli.regress
.. autofunction:: yli.regress_bootstrap
.. autofunction:: yli.vif .. autofunction:: yli.vif
Result classes Result classes

3
yli/__init__.py
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@ -15,12 +15,11 @@
# along with this program. If not, see <https://www.gnu.org/licenses/>. # along with this program. If not, see <https://www.gnu.org/licenses/>.
from .bayes_factors import bayesfactor_afbf from .bayes_factors import bayesfactor_afbf
from .bootstrap import bootstrap_regress
from .config import config from .config import config
from .descriptives import auto_descriptives from .descriptives import auto_descriptives
from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted from .io import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
from .regress import PenalisedLogit, logit_then_regress, regress, vif from .regress import PenalisedLogit, logit_then_regress, regress, regress_bootstrap, vif
from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, ttest_ind from .sig_tests import anova_oneway, auto_univariable, chi2, mannwhitney, pearsonr, ttest_ind
def reload_me(): def reload_me():

103
yli/bootstrap.py
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@ -1,103 +0,0 @@
# 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 <https://www.gnu.org/licenses/>.
import numpy as np
import pandas as pd
import patsy
from tqdm import tqdm
from datetime import datetime
from .regress import CategoricalTerm, RegressionResult, SingleTerm, regress
from .utils import Estimate, check_nan, cols_for_formula, convert_pandas_nullable, fmt_p, formula_factor_ref_category, parse_patsy_term
def bootstrap_regress(
model_class, df, dep, formula, *,
nan_policy='warn',
samples=1000,
**kwargs
):
"""
Fit a statsmodels regression model, using bootstrapping to compute confidence intervals and *p* values
:param model_class: See :func:`yli.regress`
:param df: See :func:`yli.regress`
:param dep: See :func:`yli.regress`
:param formula: See :func:`yli.regress`
:param nan_policy: See :func:`yli.regress`
:param samples: Number of bootstrap samples to draw
:type samples: int
:param kwargs: See :func:`yli.regress`
:rtype: :class:`yli.regress.RegressionResult`
"""
# Check for/clean NaNs
# Following this, we pass nan_policy='raise' to assert no NaNs remaining
df = df[[dep] + cols_for_formula(formula, df)]
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 as this breaks statsmodels
df = convert_pandas_nullable(df)
# Precompute design matrices
dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
# Fit full model
full_model = regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=dmatrices, **kwargs)
# Cache reference categories
ref_categories = {term_name: term.ref_category for term_name, term in full_model.terms.items() if isinstance(term, CategoricalTerm)}
# Draw bootstrap samples and regress
bootstrap_results = []
for i in tqdm(range(samples)):
#bootstrap_sample = df.sample(len(df), replace=True)
#bootstrap_results.append(regress(model_class, bootstrap_sample, dep, formula, nan_policy='raise', _dmatrices=dmatrices, _ref_categories=ref_categories, **kwargs))
bootstrap_rows = pd.Series(dmatrices[0].index).sample(len(df), replace=True)
bootstrap_dmatrices = (dmatrices[0].loc[bootstrap_rows], dmatrices[1].loc[bootstrap_rows])
bootstrap_results.append(regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=bootstrap_dmatrices, _ref_categories=ref_categories, **kwargs))
# Combine bootstrap results
terms = {}
for term_name, term in full_model.terms.items():
if isinstance(term, SingleTerm):
bootstrap_betas = [r.terms[term_name].beta.point for r in bootstrap_results]
bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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)
else:
categories = {}
for sub_term_name, sub_term in term.categories.items():
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]
bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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)
terms[term_name] = CategoricalTerm(categories, term.ref_category)
return RegressionResult(
None,
full_model.full_name, full_model.model_name, full_model.fit_method,
dep, full_model.nobs, full_model.dof_model, datetime.now(), full_model.cov_type,
terms,
full_model.llf, full_model.llnull,
full_model.dof_resid, full_model.rsquared, full_model.f_statistic,
full_model.exp
)

104
yli/regress.py
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@ -22,6 +22,7 @@ import statsmodels
import statsmodels.api as sm import statsmodels.api as sm
from statsmodels.iolib.table import SimpleTable from statsmodels.iolib.table import SimpleTable
from statsmodels.stats.outliers_influence import variance_inflation_factor from statsmodels.stats.outliers_influence import variance_inflation_factor
from tqdm import tqdm
from datetime import datetime from datetime import datetime
import itertools import itertools
@ -254,7 +255,7 @@ class RegressionResult:
left_col.append(('Method:', self.fit_method)) left_col.append(('Method:', self.fit_method))
left_col.append(('Date:', self.fitted_dt.strftime('%Y-%m-%d'))) 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(('Time:', self.fitted_dt.strftime('%H:%M:%S')))
left_col.append(('Std. Errors:', 'Non-Robust' if self.cov_type == 'nonrobust' else self.cov_type.upper())) 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))
# Right column # Right column
right_col = [] right_col = []
@ -440,7 +441,7 @@ def regress(
family=None, # common model_kwargs family=None, # common model_kwargs
cov_type=None, maxiter=None, start_params=None, # common fit_kwargs cov_type=None, maxiter=None, start_params=None, # common fit_kwargs
bool_baselevels=False, exp=None, bool_baselevels=False, exp=None,
_dmatrices=None, _ref_categories=None, _dmatrices=None,
): ):
""" """
Fit a statsmodels regression model Fit a statsmodels regression model
@ -589,10 +590,7 @@ def regress(
else: else:
# Add a new categorical term if not exists # Add a new categorical term if not exists
if column not in terms: if column not in terms:
if _ref_categories is None:
ref_category = formula_factor_ref_category(formula, df, factor) ref_category = formula_factor_ref_category(formula, df, factor)
else:
ref_category = _ref_categories[column]
terms[column] = CategoricalTerm({}, ref_category) terms[column] = CategoricalTerm({}, ref_category)
terms[column].categories[contrast] = SingleTerm(raw_name, beta, pvalues[raw_name]) terms[column].categories[contrast] = SingleTerm(raw_name, beta, pvalues[raw_name])
@ -640,6 +638,102 @@ def regress(
exp exp
) )
def regress_bootstrap(
model_class, df, dep, formula, *,
nan_policy='warn',
model_kwargs=None, fit_kwargs=None,
samples=1000,
**kwargs
):
"""
Fit a statsmodels regression model, using bootstrapping to compute confidence intervals and *p* values
:param model_class: See :func:`yli.regress`
:param df: See :func:`yli.regress`
:param dep: See :func:`yli.regress`
:param formula: See :func:`yli.regress`
:param nan_policy: See :func:`yli.regress`
:param model_kwargs: See :func:`yli.regress`
:param fit_kwargs: See :func:`yli.regress`
:param samples: Number of bootstrap samples to draw
:type samples: int
:param kwargs: Other arguments to pass to :func:`yli.regress`
:rtype: :class:`yli.regress.RegressionResult`
"""
if model_kwargs is None:
model_kwargs = {}
if fit_kwargs is None:
fit_kwargs = {}
# Check for/clean NaNs
# Following this, we pass nan_policy='raise' to assert no NaNs remaining
df = df[[dep] + cols_for_formula(formula, df)]
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 as this breaks statsmodels
df = convert_pandas_nullable(df)
# Precompute design matrices
dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
# Fit full model
full_model = regress(model_class, df, dep, formula, nan_policy='raise', _dmatrices=dmatrices, model_kwargs=model_kwargs, fit_kwargs=fit_kwargs, **kwargs)
# Initialise bootstrap_results
bootstrap_results = {} # Dict mapping term raw names to bootstrap betas
for term in full_model.terms.values():
if isinstance(term, SingleTerm):
bootstrap_results[term.raw_name] = []
else:
for sub_term in term.categories.values():
bootstrap_results[sub_term.raw_name] = []
# Draw bootstrap samples and regress
dmatrices = dmatrices[0].join(dmatrices[1])
for i in tqdm(range(samples)):
bootstrap_rows = dmatrices.sample(len(df), replace=True)
model = model_class(endog=bootstrap_rows.iloc[:,0], exog=bootstrap_rows.iloc[:,1:], **model_kwargs)
model.formula = dep + ' ~ ' + formula
result = model.fit(disp=False, **fit_kwargs)
for raw_name, raw_beta in zip(model.exog_names, result._results.params):
bootstrap_results[raw_name].append(raw_beta)
# Combine bootstrap results
terms = {}
for term_name, term in full_model.terms.items():
if isinstance(term, SingleTerm):
bootstrap_betas = bootstrap_results[term.raw_name]
bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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)
else:
categories = {}
for sub_term_name, sub_term in term.categories.items():
bootstrap_betas = bootstrap_results[sub_term.raw_name]
bootstrap_pvalue = sum(1 for b in bootstrap_betas if b < 0) / len(bootstrap_betas)
bootstrap_pvalue = 2 * min(bootstrap_pvalue, 1 - bootstrap_pvalue)
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)
terms[term_name] = CategoricalTerm(categories, term.ref_category)
return RegressionResult(
None,
full_model.full_name, full_model.model_name, full_model.fit_method,
dep, full_model.nobs, full_model.dof_model, datetime.now(), 'Bootstrap',
terms,
full_model.llf, full_model.llnull,
full_model.dof_resid, full_model.rsquared, full_model.f_statistic,
full_model.exp
)
def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kwargs): def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kwargs):
""" """
Perform logistic regression, then use parameters as start parameters for desired regression Perform logistic regression, then use parameters as start parameters for desired regression