https://github.com/pymc-devs/pymc3
Tip revision: 2583b7f834f2012bc4a462a576b956cfca722a7c authored by Thomas Wiecki on 15 July 2022, 10:34:28 UTC
Bump version to 4.1.3
Bump version to 4.1.3
Tip revision: 2583b7f
censored.py
# Copyright 2020 The PyMC Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
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)
"""
@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)
@classmethod
def ndim_supp(cls, *dist_params):
return 0
@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
@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