from textwrap import indent
import numpy as np
from scipy.stats import multivariate_normal
from scipy.stats._distn_infrastructure import rv_continuous_frozen
from scipy.stats._multivariate import (
multivariate_normal_frozen,
multivariate_t_frozen,
)
from pyvbmc.formatting import full_repr
from pyvbmc.priors import Prior
[docs]
class SciPy(Prior):
"""Wrapper class for `scipy.stats` distributions.
Attributes
----------
D : int
The dimension of the prior distribution.
distribution : scipy.stats._multivariate.multivariate_normal_frozen\
or scipy.stats._multivariate.multivariate_t_frozen\
or scipy.stats._distn_infrastructure.rv_continuous_frozen
The underlying `scipy.stats` distributions.
"""
[docs]
def __init__(self, distribution):
"""Initialize `pyvbmc.priors.Prior` from `scipy.stats` distribution(s).
Parameters
----------
distribution : scipy.stats.multivariate_normal\
or scipy.stats.multivariate_t\
or scipy.stats.rv_continuous\
The underlying `scipy.stats` distributions. Should be a
multivariate normal distribution, a multivariate t distribution, or
a univariate continuous distribution (for 1-D models).
Raises
------
TypeError
If the provided distribution is not of the appropriate type.
"""
if isinstance(distribution, multivariate_normal_frozen) or isinstance(
distribution, multivariate_t_frozen
):
x = np.atleast_1d(distribution.rvs(1))
self.D = len(x)
self.a = np.full(self.D, -np.inf)
self.b = np.full(self.D, np.inf)
elif isinstance(distribution, rv_continuous_frozen):
self.D = 1
self.a = np.atleast_1d(distribution.a)
self.b = np.atleast_1d(distribution.b)
else:
raise TypeError(
f'A SciPy prior should be initialized from a "frozen" multivariate normal, multivariate t, or univariate SciPy distribution, but got `distribution` of type {type(distribution)}.'
)
self.distribution = distribution
def _log_pdf(self, x):
"""Compute the log-pdf of the multivariate uniform-box prior.
Parameters
----------
x : np.ndarray
The array of input point(s), of dimension `(D,)` or `(n,D)`, where
`D` is the distribution dimension.
Returns
-------
log_pdf : np.ndarray
The log-density of the prior at the input point(s), of dimension
`(n, 1)`.
"""
n, D = x.shape
log_pdf = self.distribution.logpdf(x).reshape((n, 1))
return log_pdf
def sample(self, n):
"""Sample random variables from the uniform-box distribution.
Parameters
----------
n : int
The number of points to sample.
Returns
-------
rvs : np.ndarray
The samples points, of shape `(n, D)`, where `D` is the dimension.
"""
rvs = self.distribution.rvs(n).reshape((n, self.D))
return rvs
@classmethod
def _generic(cls, D=1):
"""Return a generic instance of the class (used for tests)."""
return SciPy(multivariate_normal(np.zeros(D)))
def _support(self):
"""Returns the support of the distribution.
Used to test that the distribution integrates to one, so it is also
acceptable to return a box which bounds the support of the
distribution.
Returns
-------
a, b : tuple(np.ndarray, np.ndarray)
A tuple of lower and upper bounds of the support, such that
[``a[i]``, ``b[i]``] bounds the support of the `i`th marginal.
"""
return self.a, self.b
def __str__(self):
"""Print a string summary."""
return "SciPy prior:" + indent(
f"""
dimension = {self.D},
lower bounds = {self.a},
upper bounds = {self.b},
distribution(s) = {self.distribution}""",
" ",
)
def __repr__(self, arr_size_thresh=10, expand=False):
"""Construct a detailed string summary.
Parameters
----------
arr_size_thresh : float, optional
If ``obj`` is an array whose product of dimensions is less than
``arr_size_thresh``, print the full array. Otherwise print only the
shape. Default `10`.
expand : bool, optional
If ``expand`` is `False`, then describe any complex child
attributes of the object by their name and memory location.
Otherwise, recursively expand the child attributes into their own
representations. Default `False`.
Returns
-------
string : str
The string representation of ``self``.
"""
return full_repr(
self,
"SciPy",
order=[
"D",
"a",
"b",
"distribution",
],
expand=expand,
arr_size_thresh=arr_size_thresh,
)
[docs]
def is_valid_scipy_dist(obj):
"""Assess whether ``obj`` is a valid SciPy distribution for a PyVBMC prior.
A valid SciPy distribution is a frozen multivariate normal, multivariate t,
or continuous univariate distribution.
Parameters
----------
obj : any
The object to type-check.
Returns
-------
is_valid : bool
"""
return (
isinstance(obj, multivariate_normal_frozen)
or isinstance(obj, multivariate_t_frozen)
or isinstance(obj, rv_continuous_frozen)
)
