Source code for bambi.priors.scaler_mle

# pylint: disable=no-name-in-module
import sys

from os.path import dirname, join

import numpy as np
import pandas as pd

from scipy.special import hyp2f1
from statsmodels.genmod.generalized_linear_model import GLM
from import PerfectSeparationError

from .prior import Prior

[docs]class PriorScalerMLE: """Scale prior distributions parameters. Used internally. Based on """ # Default is 'wide'. The wide prior sigma is sqrt(1/3) = .577 on the partial # corr scale, which is the sigma of a flat prior over [-1,1]. names = {"narrow": 0.2, "medium": 0.4, "wide": 3 ** -0.5, "superwide": 0.8} def __init__(self, model, taylor): self.model = model # Equal to the design matrix for the common terms. Categorical are like "var[level]". # Q: What if the model does not have common effects? Not even intercepts. It doesn't work # right now. Should we flag it? Attempt to fix it? if model._design.common: = model._design.common.as_dataframe() else: = pd.DataFrame() self.has_intercept = model.intercept_term is not None self.priors = {} self.mle = None self.taylor = taylor # pylint: disable=unspecified-encoding with open(join(dirname(__file__), "config", "derivs.txt"), "r") as file: self.deriv = [next(file).strip("\n") for x in range(taylor + 1)] def get_intercept_stats(self, add_slopes=True): # Start with mean and variance of Y on the link scale mod = GLM(, exog=np.repeat(1, len(,, missing="drop" if self.model.dropna else "none", ).fit() mu = mod.params # Multiply SE by sqrt(N) to turn it into (approx.) sigma(Y) on link scale sigma = (mod.cov_params()[0] * len( ** 0.5 # Modify mu and sigma based on means and sigmas of slope priors. if self.model.common_terms and add_slopes: # prior["mu"] and prior["sigma"] have more than one value when the term is categoric. means = np.hstack([prior["mu"] for prior in self.priors.values()]) sigmas = np.hstack([prior["sigma"] for prior in self.priors.values()]) x_mean = np.hstack([self.model.terms[term].data.mean(axis=0) for term in self.priors]) mu -=, x_mean) sigma = (sigma ** 2 + ** 2, x_mean ** 2)) ** 0.5 return mu, sigma
[docs] def get_slope_stats(self, exog, name, values, sigma_corr, points=4, full_model=None): """ Parameters ---------- name: str Name of the term values: np.array Values of the term full_model: statsmodels.genmod.generalized_linear_model.GLM Statsmodels GLM to replace MLE model. For when ``'predictor'`` is not in the common part of the model. points : int Number of points to use for LL approximation. """ # Make sure 'name' is in 'exog' columns if full_model is None: full_model = self.mle # Get log-likelihood values from beta=0 to beta=MLE beta_mle = full_model.params[name].item() beta_seq = np.linspace(0, beta_mle, points) log_likelihood = get_llh(self.model, exog, full_model, name, values, beta_seq) coef_a, coef_b = get_llh_coeffs(log_likelihood, beta_mle, beta_seq) p, q = shape_params(sigma_corr) # Evaluate the derivatives of beta = f(correlation). # dict 'point' gives points about which to Taylor expand. # We want to expand about the mean (generally 0), but some of the derivatives # do not exist at 0. Evaluating at a point very close to 0 (e.g., .001) # generally gives good results, but the higher order the expansion, the # further from 0 we need to evaluate the derivatives, or they blow up. point = dict(zip(range(1, 14), 2 ** np.linspace(-1, 5, 13) / 100)) vals = dict(a=coef_a, b=coef_b, n=len(, r=point[self.taylor]) deriv = [eval(x, globals(), vals) for x in self.deriv] # pylint: disable=eval-used terms = [ compute_sigma(deriv, p, q, i, j) for i in range(1, self.taylor + 1) for j in range(1, self.taylor + 1) ] return np.array(terms).sum() ** 0.5
def scale_response(self): # Add cases for other families priors = if == "gaussian": if priors["sigma"].auto_scale: sigma = np.std( priors["sigma"] = Prior("HalfStudentT", nu=4, sigma=sigma)
[docs] def scale_common(self, term): """Scale common terms, excluding intercepts.""" # Defaults are only defined for Normal priors if != "Normal": return mu = [] sigma = [] sigma_corr = term.prior.scale for name, values in zip(term.levels, mu += [0] sigma += [ self.get_slope_stats(, name=name, values=values, sigma_corr=sigma_corr) ] # Save and set prior self.priors.update({ {"mu": mu, "sigma": sigma}}) term.prior.update(mu=np.array(mu), sigma=np.array(sigma))
def scale_intercept(self, term): # Default priors are only defined for Normal priors if != "Normal": return # Get prior mean and sigma for common intercept mu, sigma = self.get_intercept_stats() # Save and set prior term.prior.update(mu=mu, sigma=sigma) def scale_group_specific(self, term): # these default priors are only defined for HalfNormal priors if term.prior.args["sigma"].name != "HalfNormal": return sigma_corr = term.prior.scale # recreate the corresponding common effect data data_as_common = term.predictor # Handle intercepts if term.type == "intercept": _, sigma = self.get_intercept_stats() sigma *= sigma_corr # Handle slopes else: # Check whether the expr is also included as common term in the model. expr ="|")[0] term_levels_len = term.predictor.shape[1] # Handle case where there IS a corresponding common effect with same encoding if expr in self.priors and term_levels_len == len(self.priors[expr]["mu"]): sigma = self.priors[expr]["sigma"] # Handle case where there IS NOT a corresponding common effect else: if expr in self.priors and not term_levels_len == len(self.priors[expr]["mu"]): # Common effect is present, but with different encoding # Replace columns from the common term with those from the group specific term. exog =[expr].levels, axis=1) else: # Common effect is not present exog = # Append columns from 'data_as_common' df_to_append = pd.DataFrame(data_as_common) df_to_append.columns = [f"_name_{i}" for i in df_to_append.columns] exog = exog.join(df_to_append) # If there's intercept and the term is cell means, drop intercept to avoid # linear dependence in design matrix columns. if term.is_cell_means and self.has_intercept: exog = exog.drop("Intercept", axis=1) sigma = [] for name, values in zip(df_to_append.columns, data_as_common.T): full_model = GLM(, exog=exog,, missing="drop" if self.model.dropna else "none", ).fit() sigma += [ self.get_slope_stats( exog=exog, name=name, values=values, full_model=full_model, sigma_corr=sigma_corr, ) ] sigma = np.array(sigma) # Set the prior sigma. term.prior.args["sigma"].update(sigma=np.squeeze(np.atleast_1d(sigma))) def scale(self): # Classify all terms common = list(self.model.common_terms.values()) group_specific = list(self.model.group_specific_terms.values()) if self.has_intercept: intercept = [self.model.intercept_term] else: intercept = [] # Arrange them in the order in which they should be initialized terms = common + intercept + group_specific term_types = ( ["common"] * len(common) + ["intercept"] * len(intercept) + ["group_specific"] * len(group_specific) ) # Scale response self.scale_response() # Initialize terms in order for term, term_type in zip(terms, term_types): # Only scale priors if term or model is set to be auto scaled. # By default, use "wide". if not term.prior.auto_scale: continue if term.prior.scale is None: term.prior.scale = "wide" # Convert scale names to floats if isinstance(term.prior.scale, str): term.prior.scale = self.names[term.prior.scale] if self.mle is None: self.fit_mle() # Scale term with the appropiate method getattr(self, f"scale_{term_type}")(term)
[docs] def fit_mle(self): """Fits MLE of the common part of the model. This used to be called in the class instantiation, but there is no need to fit the GLM when there are no automatic priors. So this method is only called when needed. """ missing = "drop" if self.model.dropna else "none" try: self.mle = GLM(,,, missing=missing, ).fit() except PerfectSeparationError as error: msg = "Perfect separation detected, automatic priors are not available. " msg += "Please indicate priors manually." raise PerfectSeparationError(msg) from error except: msg = "Unexpected error when trying to compute automatic priors." msg += "Please indicate priors manually." print(msg, sys.exc_info()[0]) raise
def get_llh(model, exog, full_model, name, values, beta_seq): """ Parameters --------- model: bambi.Model exog: pandas.DataFrame name: str Name of the term for which we want to compute the llh values: np.array Values of the term for which we want to compute the llh beta_seq: np.array Sequence of values from to beta_mle. """ # True if there are other predictors appart from `predictor_name` if name not in exog.columns: raise ValueError("get_llh failed. Term name not in exog.") multiple_predictors = exog.shape[1] > 1 sm_family = if multiple_predictors: # Use statsmodels to _optimize_ the LL. Model is fitted 'points' times. glm_model = GLM(, exog=exog, family=sm_family) null_models = [glm_model.fit_constrained(f"{name}={beta}") for beta in beta_seq[:-1]] null_models = np.append(null_models, full_model) log_likelihood = np.array([x.llf for x in null_models]) else: # Use statsmodels to _evaluate_ the LL. Model is fitted 'points' times. log_likelihood = [ sm_family.loglike(np.squeeze(, beta * values) for beta in beta_seq[:-1] ] log_likelihood = np.append(log_likelihood, full_model.llf) return log_likelihood def moment(p, q, k): """Return central moments of rescaled beta distribution""" return (2 * p / (p + q)) ** k * hyp2f1(p, -k, p + q, (p + q) / p) def compute_sigma(deriv, p, q, i, j): """Compute and return the approximate sigma""" return ( 1 / np.math.factorial(i) * 1 / np.math.factorial(j) * deriv[i] * deriv[j] * (moment(p, q, i + j) - moment(p, q, i) * moment(p, q, j)) ) def get_llh_coeffs(llh, beta_mle, beta_seq): # compute params of quartic approximation to log-likelihood # c: intercept, d: shift parameter # a: quartic coefficient, b: quadratic coefficient # beta_mle: beta obtained via MLE # beta_seq: sequence from 0 to beta_mle intercept, shift_parameter = llh[-1], -beta_mle X = np.array([(beta_seq + shift_parameter) ** 4, (beta_seq + shift_parameter) ** 2]).T a, b = np.squeeze( # pylint: disable=invalid-name np.linalg.multi_dot([np.linalg.inv(, X)), X.T, (llh[:, None] - intercept)]) ) return a, b def shape_params(sigma_corr, mean=0.5): # m, v: mean and variance of beta distribution of correlations # p, q: corresponding shape parameters of beta distribution mean = 0.5 variance = sigma_corr ** 2 / 4 p = mean * (mean * (1 - mean) / variance - 1) q = (1 - mean) * (mean * (1 - mean) / variance - 1) return p, q