Refactor bootstrap regression API as method of RegressionResult
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@ -19,7 +19,7 @@ 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 OrdinalLogit, PenalisedLogit, logit_then_regress, regress, regress_bootstrap, vif
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from .regress import OrdinalLogit, PenalisedLogit, logit_then_regress, regress, 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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217
yli/regress.py
217
yli/regress.py
@ -125,7 +125,7 @@ class RegressionResult:
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"""
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def __init__(self,
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model_class, df, dep, formula, regress_kwargs,
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model_class, df, dep, formula, nan_policy, model_kwargs, fit_kwargs,
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raw_result,
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full_name, model_name, fit_method,
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nobs, dof_model, fitted_dt, cov_type,
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@ -136,16 +136,20 @@ class RegressionResult:
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exp
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):
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# Data about how model fitted
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#: statsmodels model class used for fitting the model
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#: See :func:`yli.regress`
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self.model_class = model_class
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#: Data fitted (*weakref* to *DataFrame*)
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self.df = df
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#: Name of the dependent variable (*str*)
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#: See :func:`yli.regress`
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self.dep = dep
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#: Formula for the model (*str*)
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#: See :func:`yli.regress`
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self.formula = formula
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#: Other kwargs passed to :func:`yli.regress` (*dict*)
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self.regress_kwargs = regress_kwargs
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#: See :func:`yli.regress`
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self.nan_policy = nan_policy
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#: See :func:`yli.regress`
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self.model_kwargs = model_kwargs
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#: See :func:`yli.regress`
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self.fit_kwargs = fit_kwargs
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#: Raw result from statsmodels *model.fit*
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self.raw_result = raw_result
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@ -260,9 +264,10 @@ class RegressionResult:
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dep = self.dep
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# Check for/clean NaNs
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# NaN warning/error will already have been handled in regress, so here we pass nan_policy='omit'
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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(self.formula, df)]
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df = check_nan(df, self.regress_kwargs['nan_policy'])
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df = check_nan(df, 'omit')
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# Ensure numeric type for dependent variable
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if df[dep].dtype != 'float64':
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@ -280,7 +285,7 @@ class RegressionResult:
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logit_models = []
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for upper_limit in sorted(df[dep].unique())[:-1]: # FIXME: Sort order
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dep_dichotomous = (df[dep] <= upper_limit).astype(int).reset_index(drop=True)
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logit_result = sm.Logit(dep_dichotomous, dmatrix_right).fit(disp=False, **self.regress_kwargs.get('fit_kwargs', {}))
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logit_result = sm.Logit(dep_dichotomous, dmatrix_right).fit(disp=False, **self.fit_kwargs)
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if not logit_result.mle_retvals['converged']:
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raise Exception('Maximum likelihood estimation failed to converge for {} <= {}. Check raw_result.mle_retvals.'.format(dep, upper_limit))
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@ -346,6 +351,91 @@ class RegressionResult:
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return BrantResult(wald_results)
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def bootstrap(self, samples=1000):
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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 samples: Number of bootstrap samples to draw
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:type samples: int
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:rtype: :class:`yli.regress.RegressionResult`
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"""
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df = self.df()
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if df is None:
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raise Exception('Referenced DataFrame has been dropped')
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dep = self.dep
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# Check for/clean NaNs
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# NaN warning/error will already have been handled in regress, so here we pass nan_policy='omit'
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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(self.formula, df)]
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df = check_nan(df, 'omit')
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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 + ' ~ ' + self.formula, df, return_type='dataframe')
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# Fit full model
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#full_model = regress(self.model_class, df, dep, self.formula, nan_policy='raise', _dmatrices=dmatrices, model_kwargs=self.model_kwargs, fit_kwargs=self.fit_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 self.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 = self.model_class(endog=bootstrap_rows.iloc[:,0], exog=bootstrap_rows.iloc[:,1:], **self.model_kwargs)
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model.formula = dep + ' ~ ' + self.formula
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result = model.fit(disp=False, **self.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 self.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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self.model_class, self.df, dep, self.formula, self.nan_policy, self.model_kwargs, self.fit_kwargs,
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None,
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self.full_name, self.model_name, self.fit_method,
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self.nobs, self.dof_model, datetime.now(), 'Bootstrap',
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terms,
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self.llf, self.llnull,
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self.dof_resid, self.rsquared, self.f_statistic,
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self.comments,
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self.exp
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)
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def _header_table(self, html):
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"""Return the entries for the header table"""
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@ -641,9 +731,6 @@ def regress(
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if start_params is not None:
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fit_kwargs['start_params'] = start_params
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# Collect kwargs to store in result later
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regress_kwargs = dict(nan_policy=nan_policy, fit_kwargs=fit_kwargs)
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# Autodetect whether to exponentiate
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if exp is None:
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if model_class in (sm.Logit, sm.Poisson, PenalisedLogit):
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@ -688,7 +775,9 @@ def regress(
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result.df = df_ref
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result.dep = dep
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result.formula = formula
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result.regress_kwargs = regress_kwargs
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result.nan_policy = nan_policy
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result.model_kwargs = model_kwargs
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result.fit_kwargs = fit_kwargs
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result.exp = exp
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return result
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@ -770,7 +859,7 @@ def regress(
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full_name = 'Robust {}'.format(full_name)
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return RegressionResult(
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model_class, df_ref, dep, formula, regress_kwargs,
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model_class, df_ref, dep, formula, nan_policy, model_kwargs, fit_kwargs,
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result,
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full_name, model_class.__name__, method_name,
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result.nobs, result.df_model, datetime.now(), getattr(result, 'cov_type', 'nonrobust'),
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@ -781,106 +870,6 @@ 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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df_ref = weakref.ref(df)
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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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model_class, df_ref, dep, formula, full_model.regress_kwargs,
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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.comments,
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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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@ -989,7 +978,7 @@ class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
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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'])}
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return RegressionResult(
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None, None, None, None, None, # Set in regress()
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None, None, None, None, None, None, None, # Set in regress()
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model,
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'Penalised Logistic Regression', 'Logit', 'Penalised ML',
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model['n'][0], model['df'][0], datetime.now(), 'nonrobust',
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