From f25c7290e1353f00c7018c60888c28893a047c8e Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Tue, 8 Mar 2022 00:22:59 +0000
Subject: [PATCH] Resolve "Service Desk (from dreardon@swin.edu.au): Passing
**kwargs inputs to models via likelihood functions"
---
bilby/core/likelihood.py | 29 +++++++++++++++--------------
1 file changed, 15 insertions(+), 14 deletions(-)
diff --git a/bilby/core/likelihood.py b/bilby/core/likelihood.py
index 8e8d0ebc5..5abe4c2d8 100644
--- a/bilby/core/likelihood.py
+++ b/bilby/core/likelihood.py
@@ -108,13 +108,14 @@ class Analytical1DLikelihood(Likelihood):
value is given).
"""
- def __init__(self, x, y, func):
+ def __init__(self, x, y, func, **kwargs):
parameters = infer_parameters_from_function(func)
- super(Analytical1DLikelihood, self).__init__(dict.fromkeys(parameters))
+ super(Analytical1DLikelihood, self).__init__(dict())
self.x = x
self.y = y
self._func = func
- self._function_keys = list(self.parameters.keys())
+ self._function_keys = [key for key in parameters if key not in kwargs]
+ self.kwargs = kwargs
def __repr__(self):
return self.__class__.__name__ + '(x={}, y={}, func={})'.format(self.x, self.y, self.func.__name__)
@@ -164,11 +165,11 @@ class Analytical1DLikelihood(Likelihood):
@property
def residual(self):
""" Residual of the function against the data. """
- return self.y - self.func(self.x, **self.model_parameters)
+ return self.y - self.func(self.x, **self.model_parameters, **self.kwargs)
class GaussianLikelihood(Analytical1DLikelihood):
- def __init__(self, x, y, func, sigma=None):
+ def __init__(self, x, y, func, sigma=None, **kwargs):
"""
A general Gaussian likelihood for known or unknown noise - the model
parameters are inferred from the arguments of function
@@ -190,7 +191,7 @@ class GaussianLikelihood(Analytical1DLikelihood):
to that for `x` and `y`.
"""
- super(GaussianLikelihood, self).__init__(x=x, y=y, func=func)
+ super(GaussianLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
self.sigma = sigma
# Check if sigma was provided, if not it is a parameter
@@ -229,7 +230,7 @@ class GaussianLikelihood(Analytical1DLikelihood):
class PoissonLikelihood(Analytical1DLikelihood):
- def __init__(self, x, y, func):
+ def __init__(self, x, y, func, **kwargs):
"""
A general Poisson likelihood for a rate - the model parameters are
inferred from the arguments of function, which provides a rate.
@@ -251,10 +252,10 @@ class PoissonLikelihood(Analytical1DLikelihood):
fixed value is given).
"""
- super(PoissonLikelihood, self).__init__(x=x, y=y, func=func)
+ super(PoissonLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
def log_likelihood(self):
- rate = self.func(self.x, **self.model_parameters)
+ rate = self.func(self.x, **self.model_parameters, **self.kwargs)
if not isinstance(rate, np.ndarray):
raise ValueError(
"Poisson rate function returns wrong value type! "
@@ -286,7 +287,7 @@ class PoissonLikelihood(Analytical1DLikelihood):
class ExponentialLikelihood(Analytical1DLikelihood):
- def __init__(self, x, y, func):
+ def __init__(self, x, y, func, **kwargs):
"""
An exponential likelihood function.
@@ -302,10 +303,10 @@ class ExponentialLikelihood(Analytical1DLikelihood):
value is given). The model should return the expected mean of
the exponential distribution for each data point.
"""
- super(ExponentialLikelihood, self).__init__(x=x, y=y, func=func)
+ super(ExponentialLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
def log_likelihood(self):
- mu = self.func(self.x, **self.model_parameters)
+ mu = self.func(self.x, **self.model_parameters, **self.kwargs)
if np.any(mu < 0.):
return -np.inf
return -np.sum(np.log(mu) + (self.y / mu))
@@ -328,7 +329,7 @@ class ExponentialLikelihood(Analytical1DLikelihood):
class StudentTLikelihood(Analytical1DLikelihood):
- def __init__(self, x, y, func, nu=None, sigma=1.):
+ def __init__(self, x, y, func, nu=None, sigma=1, **kwargs):
"""
A general Student's t-likelihood for known or unknown number of degrees
of freedom, and known or unknown scale (which tends toward the standard
@@ -357,7 +358,7 @@ class StudentTLikelihood(Analytical1DLikelihood):
Set the scale of the distribution. If not given then this defaults
to 1, which specifies a standard (central) Student's t-distribution
"""
- super(StudentTLikelihood, self).__init__(x=x, y=y, func=func)
+ super(StudentTLikelihood, self).__init__(x=x, y=y, func=func, **kwargs)
self.nu = nu
self.sigma = sigma
--
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