From 9c998750f710f0265489e5a65dbc127ca3a0d14d Mon Sep 17 00:00:00 2001
From: Matthew Pitkin <matthew.pitkin@ligo.org>
Date: Tue, 21 Aug 2018 22:35:23 +0100
Subject: [PATCH] Minor fixes
---
.../linear_regression_pymc3_custom_likelihood.py | 3 ---
tupak/core/sampler.py | 10 +++++-----
2 files changed, 5 insertions(+), 8 deletions(-)
diff --git a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
index 181f721ab..4d87b8110 100644
--- a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
+++ b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
@@ -89,7 +89,6 @@ class GaussianLikelihoodPyMC3(tupak.Likelihood):
"""
from tupak.core.sampler import Pymc3
- import theano.tensor as tt
if not isinstance(sampler, Pymc3):
raise ValueError("Sampler is not a tupak Pymc3 sampler object")
@@ -97,8 +96,6 @@ class GaussianLikelihoodPyMC3(tupak.Likelihood):
if not hasattr(sampler, 'pymc3_model'):
raise AttributeError("Sampler has not PyMC3 model attribute")
- pymc3 = sampler.external_sampler
-
with sampler.pymc3_model:
mdist = sampler.pymc3_priors['m']
cdist = sampler.pymc3_priors['c']
diff --git a/tupak/core/sampler.py b/tupak/core/sampler.py
index 5bde459a8..30690954c 100644
--- a/tupak/core/sampler.py
+++ b/tupak/core/sampler.py
@@ -1404,7 +1404,9 @@ class Pymc3(Sampler):
def like_(values):
for i, key in enumerate(pymc3_parameters):
pymc3_log_like.parameters[key] = values[i]
- return pymc3_log_like.loglikelihood()
+ return pymc3_log_like.log_likelihood()
+
+ pymc3 = self.external_sampler
with self.pymc3_model:
# check if it is a predefined likelhood function
@@ -1497,16 +1499,14 @@ class Pymc3(Sampler):
parameters = OrderedDict()
for key in self.pymc3_priors:
try:
- #self.likelihood.parameters[key] = tt.cast(self.pymc3_priors[key], 'float64')
- #self.likelihood.parameters[key] = self.pymc3_priors[key]
parameters[key] = self.pymc3_priors[key]
except KeyError:
raise KeyError("Unknown key '{}' when setting GravitationalWaveTransient likelihood".format(key))
- values = tt.as_tensor_variable(parameters.values())
+ values = tt.as_tensor_variable(list(parameters.values()))
pymc3_log_like = self.likelihood
pymc3_parameters = parameters.keys()
- pymc3.DensityDist('likelihood', _like, observed={'values': values})
+ pymc3.DensityDist('likelihood', lambda v: like_(v), observed={'values': values})
else:
raise ValueError("Unknown likelihood has been provided")
--
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