Source code for bambi.priors.likelihood

from .prior import Prior

from ..utils import multilinify, spacify

    "Normal": {"params": ("mu", "sigma"), "parent": "mu", "args": ("sigma",)},
    "Bernoulli": {"params": ("p",), "parent": "p", "args": None},
    "Beta": {"params": ("mu", "kappa"), "parent": "mu", "args": ("kappa",)},
    "Binomial": {"params": ("p",), "parent": "p", "args": None},
    "Poisson": {"params": ("mu",), "parent": "mu", "args": None},
    "StudentT": {"params": ("mu", "lam"), "args": ("lam", "nu")},
    "NegativeBinomial": {"params": ("mu", "alpha"), "parent": "mu", "args": ("alpha",)},
    "Gamma": {"params": ("mu", "alpha"), "parent": "mu", "args": ("alpha",)},
    "Wald": {"params": ("mu", "lam"), "parent": "mu", "args": ("lam",)},

[docs]class Likelihood: """Representation of a Likelihood function for a Bambi model. 'parent' must not be in 'kwargs'. 'parent' is inferred from the 'name' if it is a known name Parameters ---------- name: str Name of the likelihood function. Must be a valid PyMC3 distribution name. parent: str Optional specification of the name of the mean parameter in the likelihood. This is the parameter whose transformation is modeled by the linear predictor. kwargs Keyword arguments that indicate prior distributions for auxiliary parameters in the likelihood. """ DISTRIBUTIONS = DISTRIBUTIONS def __init__(self, name, parent=None, pps=None, **kwargs): if name in self.DISTRIBUTIONS: = name self.parent = self._get_parent(parent) self.priors = self._check_priors(kwargs) self.pps = pps else: # On your own risk = name # Check priors passed are in fact of class Prior check_all_are_priors(kwargs) self.priors = kwargs self.parent = parent self.pps = pps def _get_parent(self, parent): if parent is None: parent = self.DISTRIBUTIONS[]["parent"] elif parent not in self.DISTRIBUTIONS[]["params"]: raise ValueError( f"'{parent}' is not a valid parameter for the likelihood '{}'" ) return parent def _check_priors(self, priors): args = self.DISTRIBUTIONS[]["args"] # The function requires priors but none were passed if priors == {} and args is not None: raise ValueError(f"'{}' requires priors for the parameters {args}.") # The function does not require priors, but at least one was passed if priors != {} and args is None: raise ValueError(f"'{}' does not require any additional prior.") # The function requires priors, priors were passed, but they differ from the required if priors and args: difference = set(args) - set(priors) if len(difference) > 0: raise ValueError(f"'{}' misses priors for the parameters {difference}") # And check priors passed are in fact of class Prior check_all_are_priors(priors) return priors def __str__(self): args = [f"name: {}", f"parent: {self.parent}", f"priors: {self.priors}"] return f"{self.__class__.__name__}({spacify(multilinify(args))}\n)" def __repr__(self): return self.__str__()
def check_all_are_priors(priors): if any(not isinstance(prior, (Prior, int, float)) for prior in priors.values()): raise ValueError("Prior distributions must be a 'Prior' instance or a numeric value")