# 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 pandas as pd
from scipy import stats
import statsmodels
import statsmodels.api as sm
from statsmodels.iolib.table import SimpleTable
from statsmodels.stats.outliers_influence import variance_inflation_factor
from datetime import datetime
import itertools
from .bayes_factors import BayesFactor, bayesfactor_afbf
from .config import config
from .sig_tests import FTestResult
from .utils import Estimate, check_nan, cols_for_formula, fmt_p, formula_factor_ref_category, parse_patsy_term
def vif(df, formula=None, *, nan_policy='warn'):
"""
Calculate the variance inflation factor (VIF) for each variable in *df*
:param df: Data to calculate the VIF for
:type df: DataFrame
:param formula: If specified, calculate the VIF only for the variables in the formula
:type formula: str
:return: The variance inflation factor
:rtype: float
"""
if formula:
# Only consider columns in the formula
df = df[cols_for_formula(formula, df)]
# Check for/clean NaNs
df = check_nan(df, nan_policy)
# Convert all to float64 otherwise statsmodels chokes with "ufunc 'isfinite' not supported for the input types ..."
df = pd.get_dummies(df, drop_first=True) # Convert categorical dtypes
df = df.astype('float64') # Convert all other dtypes
# Add intercept column
orig_columns = list(df.columns)
df['Intercept'] = [1] * len(df)
vifs = {}
for i, col in enumerate(orig_columns):
vifs[col] = variance_inflation_factor(df, i)
return pd.Series(vifs)
# ----------
# Regression
class LikelihoodRatioTestResult:
"""
Result of a likelihood ratio test for regression
See :meth:`RegressionResult.lrtest_null`.
"""
def __init__(self, statistic, dof, pvalue):
#: Likelihood ratio test statistic (*float*)
self.statistic = statistic
#: Degrees of freedom for the likelihood ratio test statistic (*int*)
self.dof = dof
#: *p* value for the likelihood ratio test (*float*)
self.pvalue = pvalue
def __repr__(self):
if config.repr_is_summary:
return self.summary()
return super().__repr__()
def _repr_html_(self):
return 'LR({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=True))
def summary(self):
"""
Return a stringified summary of the likelihood ratio test
:rtype: str
"""
return 'LR({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=False))
class RegressionResult:
"""
Result of a regression
See :func:`yli.regress`.
"""
def __init__(self,
raw_result,
full_name, model_name, fit_method,
dep, nobs, dof_model, fitted_dt, cov_type,
terms,
llf, llnull,
dof_resid, rsquared, f_statistic,
exp
):
#: Raw result from statsmodels *model.fit*
self.raw_result = raw_result
# Information for display
#: Full name of the regression model type (*str*)
self.full_name = full_name
#: Short name of the regression model type (*str*)
self.model_name = model_name
#: Method for fitting the regression model (*str*)
self.fit_method = fit_method
# Basic fitted model information
#: Name of the dependent variable (*str*)
self.dep = dep
#: Number of observations (*int*)
self.nobs = nobs
#: Degrees of freedom for the model (*int*)
self.dof_model = dof_model
#: Date and time of fitting the model (Python *datetime*)
self.fitted_dt = fitted_dt
#: Method for computing the covariance matrix (*str*)
self.cov_type = cov_type
# Regression coefficients/p values
#: Coefficients and *p* values for each term in the model (*dict* of :class:`SingleTerm` or :class:`CategoricalTerm`)
self.terms = terms
# Model log-likelihood
#: Log-likelihood of fitted model (*float*)
self.llf = llf
#: Log-likelihood of null model (*float*)
self.llnull = llnull
# Extra statistics (not all regression models have these)
#: Degrees of freedom for the residuals (*int*; *None* if N/A)
self.dof_resid = dof_resid
#: *R*:sup:`2` statistic (*float*; *None* if N/A)
self.rsquared = rsquared
#: *F* statistic (*float*; *None* if N/A)
self.f_statistic = f_statistic
# Config for display style
#: See :func:`yli.regress`
self.exp = exp
@property
def pseudo_rsquared(self):
"""McFadden's pseudo *R*:sup:`2` statistic"""
return 1 - self.llf/self.llnull
def lrtest_null(self):
"""
Compute the likelihood ratio test comparing the model to the null model
:rtype: :class:`LikelihoodRatioTestResult`
"""
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
:rtype: :class:`yli.sig_tests.FTestResult`
"""
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 bayesfactor_beta_zero(self, term):
"""
Compute a Bayes factor testing the hypothesis that the given beta is 0
Uses the R *BFpack* library.
Requires the regression to be from statsmodels.
The term must be specified as the *raw name* from the statsmodels regression, available via :attr:`RegressionResult.raw_result`.
:param term: Raw name of the term to be tested
:type term: str
:rtype: :class:`yli.bayes_factors.BayesFactor`
"""
# FIXME: Allow specifying our renamed terms
# Get parameters required for AFBF
params = pd.Series({raw_name.replace('[', '_').replace(']', '_'): beta for raw_name, beta in self.raw_result.params.items()})
cov = self.raw_result.cov_params()
# Compute BF matrix
bf01 = bayesfactor_afbf(params, cov, self.nobs, '{} = 0'.format(term.replace('[', '_').replace(']', '_')))
bf01 = BayesFactor(bf01.factor, '0', '{} = 0'.format(term), '1', '{} ≠ 0'.format(term))
if bf01.factor >= 1:
return bf01
else:
return bf01.invert()
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(('Std. Errors:', 'Non-Robust' if self.cov_type == 'nonrobust' else self.cov_type.upper()))
# Right column
right_col = []
right_col.append(('No. Observations:', format(self.nobs, '.0f')))
right_col.append(('Df. Model:', format(self.dof_model, '.0f')))
if self.dof_resid:
right_col.append(('Df. Residuals:', format(self.dof_resid, '.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(f_result.pvalue, html=True, tabular=True)))
else:
right_col.append(('F:', format(f_result.statistic, '.2f')))
right_col.append(('p (F):', fmt_p(f_result.pvalue, html=False, tabular=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(lrtest_result.pvalue, html=True, tabular=True)))
else:
right_col.append(('p (LR):', fmt_p(lrtest_result.pvalue, html=False, tabular=True)))
return left_col, right_col
def __repr__(self):
if config.repr_is_summary:
return self.summary()
return super().__repr__()
def _repr_html_(self):
# Render header table
left_col, right_col = self._header_table(html=True)
out = '{} Results'.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 += '
'
# Render coefficients table
out += ' | {} | ({:g}% CI) | p |
|---|
'.format('exp(β)' if self.exp else 'β', (1-config.alpha)*100)
for term_name, term in self.terms.items():
if isinstance(term, SingleTerm):
# Single term
# Exponentiate beta if requested
beta = term.beta
if self.exp:
beta = np.exp(beta)
out += '| {} | {:.2f} | ({:.2f} | – | {:.2f}) | {} |
|---|
'.format(term_name, beta.point, beta.ci_lower, beta.ci_upper, fmt_p(term.pvalue, html=True, tabular=True))
elif isinstance(term, CategoricalTerm):
# Categorical term
out += '| {} | | | | | |
|---|
'.format(term_name)
# Render reference category
out += '| {} | Ref. | | | | |
'.format(term.ref_category)
# Loop over terms
for sub_term_name, sub_term in term.categories.items():
# Exponentiate beta if requested
beta = sub_term.beta
if self.exp:
beta = np.exp(beta)
out += '| {} | {:.2f} | ({:.2f} | – | {:.2f}) | {} |
'.format(sub_term_name, beta.point, beta.ci_lower, beta.ci_upper, fmt_p(sub_term.pvalue, html=True, tabular=True))
else:
raise Exception('Attempt to render unknown term type')
out += '
'
# TODO: Have a detailed view which shows SE, t/z, etc.
return out
def summary(self):
"""
Return a stringified summary of the regression model
:rtype: str
"""
# 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_name, term in self.terms.items():
if isinstance(term, SingleTerm):
# Single term
# Exponentiate beta if requested
beta = term.beta
if self.exp:
beta = np.exp(beta)
# Add some extra padding
table_data.append([term_name + ' ', format(beta.point, '.2f'), '({:.2f}'.format(beta.ci_lower), '-', '{:.2f})'.format(beta.ci_upper), ' ' + fmt_p(term.pvalue, html=False, tabular=True)])
elif isinstance(term, CategoricalTerm):
# Categorical term
table_data.append([term_name + ' ', '', '', '', '', ''])
# Render reference category
table_data.append(['{} '.format(term.ref_category), 'Ref.', '', '', '', ''])
# Loop over terms
for sub_term_name, sub_term in term.categories.items():
# Exponentiate beta if requested
beta = sub_term.beta
if self.exp:
beta = np.exp(beta)
table_data.append([sub_term_name + ' ', format(beta.point, '.2f'), '({:.2f}'.format(beta.ci_lower), '-', '{:.2f})'.format(beta.ci_upper), ' ' + fmt_p(sub_term.pvalue, html=False, tabular=True)])
else:
raise Exception('Attempt to render unknown term type')
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 ', '({:g}% CI)'.format((1-config.alpha)*100)) # 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
class SingleTerm:
"""A term in a :class:`RegressionResult` which is a single term"""
def __init__(self, raw_name, beta, pvalue):
#: Raw name of the term (*str*; e.g. in :attr:`RegressionResult.raw_result`)
self.raw_name = raw_name
#: :class:`yli.utils.Estimate` of the coefficient
self.beta = beta
#: *p* value for the coefficient (*float*)
self.pvalue = pvalue
class CategoricalTerm:
"""A group of terms in a :class:`RegressionResult` corresponding to a categorical variable"""
def __init__(self, categories, ref_category):
#: Terms for each of the categories, excluding the reference category (*dict* of :class:`SingleTerm`)
self.categories = categories
#: Name of the reference category (*str*)
self.ref_category = ref_category
def regress(
model_class, df, dep, formula, *,
nan_policy='warn',
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
):
"""
Fit a statsmodels regression model
:param model_class: Type of regression model to fit
:type model_class: statsmodels model class
:param df: Data to perform regression on
:type df: DataFrame
:param dep: Column in *df* for the dependent variable (numeric)
:type dep: str
:param formula: Patsy formula for the regression model
:type formula: str
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
:type nan_policy: str
:param model_kwargs: Keyword arguments to pass to *model_class* constructor
:type model_kwargs: dict
:param fit_kwargs: Keyword arguments to pass to *model.fit*
:type fit_kwargs: dict
:param family: See statsmodels *GLM* constructor
:param cov_type: See statsmodels *model.fit*
:param maxiter: See statsmodels *model.fit*
:param start_params: See statsmodels *model.fit*
:param bool_baselevels: Show reference categories for boolean independent variables even if reference category is *False*
:type bool_baselevels: bool
:param exp: Report exponentiated parameters rather than raw parameters, default (*None*) is to autodetect based on *model_class*
:type exp: bool
:rtype: :class:`yli.regress.RegressionResult`
"""
# Populate model_kwargs
if model_kwargs is None:
model_kwargs = {}
if family is not None:
model_kwargs['family'] = family
# Populate fit_kwargs
if fit_kwargs is None:
fit_kwargs = {}
if cov_type is not None:
fit_kwargs['cov_type'] = cov_type
if maxiter is not None:
fit_kwargs['maxiter'] = maxiter
if start_params is not None:
fit_kwargs['start_params'] = start_params
# Autodetect whether to exponentiate
if exp is None:
if model_class in (sm.Logit, sm.Poisson, PenalisedLogit):
exp = True
else:
exp = False
# 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')
# 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, **model_kwargs)
result = model.fit(disp=False, **fit_kwargs)
if isinstance(result, RegressionResult):
# Already processed!
result.exp = exp
return result
# Check convergence
if hasattr(result, 'mle_retvals') and not result.mle_retvals['converged']:
warnings.warn('Maximum likelihood estimation failed to converge. Check raw_result.mle_retvals.')
# Process terms
terms = {}
confint = result.conf_int(config.alpha)
for raw_name, raw_beta in result.params.items():
beta = Estimate(raw_beta, confint[0][raw_name], confint[1][raw_name])
# Rename terms
if raw_name == 'Intercept':
# Intercept term (single term)
term = '(Intercept)'
terms[term] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
else:
# Parse if required
factor, column, contrast = parse_patsy_term(formula, df, raw_name)
if contrast is not None:
# Categorical term
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])
else:
# Add a new categorical term if not exists
if column not in terms:
ref_category = formula_factor_ref_category(formula, df, factor)
terms[column] = CategoricalTerm({}, ref_category)
terms[column].categories[contrast] = SingleTerm(raw_name, beta, result.pvalues[raw_name])
else:
# Single term
terms[column] = SingleTerm(raw_name, beta, result.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()
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}
# Get full name to display
if model_class is sm.Logit:
full_name = 'Logistic Regression'
else:
full_name = '{} Regression'.format(model_class.__name__)
if fit_kwargs.get('cov_type', 'nonrobust') != 'nonrobust':
full_name = 'Robust {}'.format(full_name)
return RegressionResult(
result,
full_name, model_class.__name__, header_dict['Method:'],
dep, result.nobs, result.df_model, datetime.now(), result.cov_type,
terms,
result.llf, llnull,
getattr(result, 'df_resid', None), getattr(result, 'rsquared', None), getattr(result, 'fvalue', None),
exp
)
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
:param model_class: Type of regression model to fit
:type model_class: statsmodels model class
:param df: Data to perform regression on
:type df: DataFrame
:param dep: Column in *df* for the dependent variable (numeric)
:type dep: str
:param formula: Patsy formula for the regression model
:type formula: str
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
:type nan_policy: str
:param kwargs: Passed through to :func:`yli.regress`
:rtype: :class:`yli.regress.RegressionResult`
"""
# Check for/clean NaNs
# Do this once here so we only get 1 warning
df = df[[dep] + cols_for_formula(formula, df)]
df = check_nan(df, nan_policy)
# Perform logistic regression
logit_result = regress(sm.Logit, df, dep, formula, **kwargs)
logit_params = logit_result.raw_result.params
# Check convergence
if not logit_result.raw_result.mle_retvals['converged']:
return None
# Perform desired regression
return regress(model_class, df, dep, formula, start_params=logit_params, **kwargs)
# -----------------------------
# Penalised logistic regression
class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
"""
statsmodel-compatible model for computing Firth penalised logistic regression
Uses the R *logistf* library.
This class should be used in conjunction with :func:`yli.regress`.
"""
def fit(self, disp=False):
import rpy2.robjects as ro
import rpy2.robjects.packages
import rpy2.robjects.pandas2ri
# Assume data is already cleaned from regress()
df = self.data.frame.copy()
# Convert bool to int otherwise rpy2 chokes
df = df.replace({False: 0, True: 1})
# Import logistf
ro.packages.importr('logistf')
with ro.conversion.localconverter(ro.default_converter + ro.pandas2ri.converter):
with ro.local_context() as lc:
# Convert DataFrame to R
lc['df'] = df
# Transfer other parameters to R
lc['formula_'] = self.formula
lc['alpha'] = config.alpha
# Fit the model
model = ro.r('logistf(formula_, data=df, alpha=alpha)')
# TODO: Handle categorical terms?
terms = {t: SingleTerm(t, Estimate(b, ci0, ci1), p) for t, b, ci0, ci1, p in zip(model['terms'], model['coefficients'], model['ci.lower'], model['ci.upper'], model['prob'])}
return RegressionResult(
model,
'Penalised Logistic Regression', 'Logit', 'Penalised ML',
self.endog_names, model['n'][0], model['df'][0], datetime.now(), 'nonrobust',
terms,
model['loglik'][0], model['loglik'][1],
None, None, None,
None # Set exp in regress()
)