Implement bootstrap_regress

This commit is contained in:
RunasSudo 2022-11-29 14:40:50 +11:00
parent e268f385be
commit e71a1aea12
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
3 changed files with 157 additions and 26 deletions

View File

@ -15,6 +15,7 @@
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from .bayes_factors import bayesfactor_afbf
from .bootstrap import bootstrap_regress
from .config import config
from .descriptives import auto_descriptives
from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist

103
yli/bootstrap.py Normal file
View File

@ -0,0 +1,103 @@
# 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
)

View File

@ -16,6 +16,7 @@
import numpy as np
import pandas as pd
import patsy
from scipy import stats
import statsmodels
import statsmodels.api as sm
@ -438,7 +439,8 @@ def regress(
model_kwargs=None, fit_kwargs=None,
family=None, # common model_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,
):
"""
Fit a statsmodels regression model
@ -526,19 +528,26 @@ def regress(
else:
exp = False
# Check for/clean NaNs
df = df[[dep] + cols_for_formula(formula, df)]
df = check_nan(df, nan_policy)
if _dmatrices is None:
# Check for/clean NaNs
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')
# 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)
# Convert pandas nullable types for independent variables as this breaks statsmodels
df = convert_pandas_nullable(df)
# Construct design matrix from formula
dmatrices = patsy.dmatrices(dep + ' ~ ' + formula, df, return_type='dataframe')
else:
dmatrices = _dmatrices
# Fit model
model = model_class.from_formula(formula=dep + ' ~ ' + formula, data=df, **model_kwargs)
model = model_class(endog=dmatrices[0], exog=dmatrices[1], **model_kwargs)
model.formula = dep + ' ~ ' + formula
result = model.fit(disp=False, **fit_kwargs)
if isinstance(result, RegressionResult):
@ -553,16 +562,20 @@ def regress(
# Process terms
terms = {}
confint = result.conf_int(config.alpha)
# Join term names manually because statsmodels wrapper is very slow
#confint = result.conf_int(config.alpha)
confint = {k: v for k, v in zip(model.exog_names, result._results.conf_int(config.alpha))}
pvalues = {k: v for k, v in zip(model.exog_names, result._results.pvalues)}
for raw_name, raw_beta in result.params.items():
beta = Estimate(raw_beta, confint[0][raw_name], confint[1][raw_name])
#for raw_name, raw_beta in result.params.items():
for raw_name, raw_beta in zip(model.exog_names, result._results.params):
beta = Estimate(raw_beta, confint[raw_name][0], confint[raw_name][1])
# Rename terms
if raw_name == 'Intercept':
# Intercept term (single term)
term = '(Intercept)'
terms[term] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
terms[term] = SingleTerm(raw_name, beta, pvalues[raw_name])
else:
# Parse if required
factor, column, contrast = parse_patsy_term(formula, df, raw_name)
@ -572,28 +585,42 @@ def regress(
if bool_baselevels is False and contrast == 'True' and set(df[column].unique()) == set([True, False]):
# Treat as single term
terms[column] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
else:
# Add a new categorical term if not exists
if column not in terms:
ref_category = formula_factor_ref_category(formula, df, factor)
if _ref_categories is None:
ref_category = formula_factor_ref_category(formula, df, factor)
else:
ref_category = _ref_categories[column]
terms[column] = CategoricalTerm({}, ref_category)
terms[column].categories[contrast] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
terms[column].categories[contrast] = SingleTerm(raw_name, beta, pvalues[raw_name])
else:
# Single term
terms[column] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
# 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()
# Construct null (intercept-only) model
#result_null = model_class.from_formula(formula=dep + ' ~ 1', data=df).fit()
dm_exog = pd.DataFrame(index=dmatrices[0].index)
dm_exog['Intercept'] = pd.Series(dtype='float64')
dm_exog['Intercept'].fillna(1, inplace=True)
result_null = model_class(endog=dmatrices[0], exog=dm_exog).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}
if model_class is sm.OLS:
method_name = 'Least Squares'
else:
# Parse raw regression results to get fit method
# Avoid this in general as it can be expensive to summarise all the post hoc tests, etc.
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}
method_name = header_dict['Method:']
# Get full name to display
if model_class is sm.Logit:
@ -605,7 +632,7 @@ def regress(
return RegressionResult(
result,
full_name, model_class.__name__, header_dict['Method:'],
full_name, model_class.__name__, method_name,
dep, result.nobs, result.df_model, datetime.now(), result.cov_type,
terms,
result.llf, llnull,
@ -697,7 +724,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
import rpy2.robjects.pandas2ri
# Assume data is already cleaned from regress()
df = self.data.frame.copy()
df = self.data.orig_endog.join(self.data.orig_exog)
# Convert bool to int otherwise rpy2 chokes
df = df.replace({False: 0, True: 1})