Source code for pymc.distributions.censored

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import aesara.tensor as at
import numpy as np

from aesara.scalar import Clip
from aesara.tensor import TensorVariable
from aesara.tensor.random.op import RandomVariable

from pymc.aesaraf import change_rv_size
from pymc.distributions.distribution import SymbolicDistribution, _moment
from pymc.util import check_dist_not_registered


[docs]class Censored(SymbolicDistribution): r""" Censored distribution The pdf of a censored distribution is .. math:: \begin{cases} 0 & \text{for } x < lower, \\ \text{CDF}(lower, dist) & \text{for } x = lower, \\ \text{PDF}(x, dist) & \text{for } lower < x < upper, \\ 1-\text{CDF}(upper, dist) & \text {for} x = upper, \\ 0 & \text{for } x > upper, \end{cases} Parameters ---------- dist: unnamed distribution Univariate distribution created via the `.dist()` API, which will be censored. This distribution must have a logcdf method implemented for sampling. .. warning:: dist will be cloned, rendering it independent of the one passed as input. lower: float or None Lower (left) censoring point. If `None` the distribution will not be left censored upper: float or None Upper (right) censoring point. If `None`, the distribution will not be right censored. Warnings -------- Continuous censored distributions should only be used as likelihoods. Continuous censored distributions are a form of discrete-continuous mixture and as such cannot be sampled properly without a custom step sampler. If you wish to sample such a distribution, you can add the latent uncensored distribution to the model and then wrap it in a :class:`~pymc.Deterministic` :func:`~pymc.math.clip`. Examples -------- .. code-block:: python with pm.Model(): normal_dist = pm.Normal.dist(mu=0.0, sigma=1.0) censored_normal = pm.Censored("censored_normal", normal_dist, lower=-1, upper=1) """
[docs] @classmethod def dist(cls, dist, lower, upper, **kwargs): if not isinstance(dist, TensorVariable) or not isinstance(dist.owner.op, RandomVariable): raise ValueError( f"Censoring dist must be a distribution created via the `.dist()` API, got {type(dist)}" ) if dist.owner.op.ndim_supp > 0: raise NotImplementedError( "Censoring of multivariate distributions has not been implemented yet" ) check_dist_not_registered(dist) return super().dist([dist, lower, upper], **kwargs)
[docs] @classmethod def ndim_supp(cls, *dist_params): return 0
[docs] @classmethod def rv_op(cls, dist, lower=None, upper=None, size=None): lower = at.constant(-np.inf) if lower is None else at.as_tensor_variable(lower) upper = at.constant(np.inf) if upper is None else at.as_tensor_variable(upper) # When size is not specified, dist may have to be broadcasted according to lower/upper dist_shape = size if size is not None else at.broadcast_shape(dist, lower, upper) dist = change_rv_size(dist, dist_shape) # Censoring is achieved by clipping the base distribution between lower and upper rv_out = at.clip(dist, lower, upper) # Reference nodes to facilitate identification in other classmethods, without # worring about possible dimshuffles rv_out.tag.dist = dist rv_out.tag.lower = lower rv_out.tag.upper = upper return rv_out
[docs] @classmethod def change_size(cls, rv, new_size, expand=False): dist = rv.tag.dist lower = rv.tag.lower upper = rv.tag.upper new_dist = change_rv_size(dist, new_size, expand=expand) return cls.rv_op(new_dist, lower, upper)
@_moment.register(Clip) def moment_censored(op, rv, dist, lower, upper): moment = at.switch( at.eq(lower, -np.inf), at.switch( at.isinf(upper), # lower = -inf, upper = inf 0, # lower = -inf, upper = x upper - 1, ), at.switch( at.eq(upper, np.inf), # lower = x, upper = inf lower + 1, # lower = x, upper = x (lower + upper) / 2, ), ) moment = at.full_like(dist, moment) return moment