Source code for bambi.families.likelihood

from collections import namedtuple

from bambi.priors import Prior
from bambi.utils import multilinify, spacify

DistSettings = namedtuple("DistSettings", ["params", "parent", "args"])

    "Bernoulli": DistSettings(params=("p",), parent="p", args=None),
    "Beta": DistSettings(params=("mu", "kappa"), parent="mu", args=("kappa",)),
    "Binomial": DistSettings(params=("p",), parent="p", args=None),
    "Categorical": DistSettings(params=("p",), parent="p", args=None),
    "Gamma": DistSettings(params=("mu", "alpha"), parent="mu", args=("alpha",)),
    "Multinomial": DistSettings(params=("p",), parent="p", args=None),
    "Normal": DistSettings(params=("mu", "sigma"), parent="mu", args=("sigma",)),
    "NegativeBinomial": DistSettings(params=("mu", "alpha"), parent="mu", args=("alpha",)),
    "Poisson": DistSettings(params=("mu",), parent="mu", args=None),
    "StudentT": DistSettings(params=("mu", "sigma"), parent="mu", args=("sigma", "nu")),
    "VonMises": DistSettings(params=("mu", "kappa"), parent="mu", args=("kappa",)),
    "Wald": DistSettings(params=("mu", "lam"), parent="mu", args=("lam",)),

[docs]class Likelihood: """Representation of a Likelihood function for a Bambi model. Notes: * ``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, **kwargs): if name in self.DISTRIBUTIONS: = name self.parent = parent self.priors = self._check_priors(kwargs) 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 @property def parent(self): return self._parent @parent.setter def parent(self, x): # Checks are made when using a known distribution if in self.DISTRIBUTIONS: if x is None: x = self.DISTRIBUTIONS[].parent elif x not in self.DISTRIBUTIONS[].params: raise ValueError(f"'{x}' is not a valid parameter for the likelihood '{}'") # Otherwise, no check is done. At your own risk! self._parent = x 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 priors.") # 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): 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): """Checks if values in the supplied dictionary are all valid prior objects An object is a valid prior if * It is an instance of bambi.priors.Prior * It is a number Parameters ---------- priors: dict A dictionary whose values are tested to be valid 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")