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add potentials to priors when running abc #4016

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Jul 27, 2020
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add potentials to priors when running abc #4016

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f60a62e
add potentials to priors when running abc
aloctavodia Jul 16, 2020
488f483
update release notes
aloctavodia Jul 17, 2020
71d6e70
add message about potentials being added to the prior term
aloctavodia Jul 20, 2020
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6 changes: 5 additions & 1 deletion pymc3/smc/smc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
import numpy as np
from scipy.special import logsumexp
from theano import function as theano_function
import theano.tensor as tt

from ..model import modelcontext, Point
from ..theanof import floatX, inputvars, make_shared_replacements, join_nonshared_inputs
Expand Down Expand Up @@ -100,9 +101,11 @@ def setup_kernel(self):
Set up the likelihood logp function based on the chosen kernel
"""
shared = make_shared_replacements(self.variables, self.model)
self.prior_logp_func = logp_forw([self.model.varlogpt], self.variables, shared)

if self.kernel.lower() == "abc":
factors = [var.logpt for var in self.model.free_RVs]
factors += [tt.sum(factor) for factor in self.model.potentials]
self.prior_logp_func = logp_forw([tt.sum(factors)], self.variables, shared)
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I'm a bit confused. Why doesn't model.logpt work? To me, it looks like the only difference is that the observed_RVs aren't included in your factors whereas they are in the model.logpt.

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SMC needs to evaluate the prior and likelihood terms idenpendently, the sampler progressively turns on the likelihood term. Additionally in ABC, there is no true likelihood. The likelihood is approximated as a discrepancy between observed and simulated data. That's why I am moving the potential to the prior, only for this type of model/sampler

simulator = self.model.observed_RVs[0]
distance = simulator.distribution.distance
sum_stat = simulator.distribution.sum_stat
Expand All @@ -120,6 +123,7 @@ def setup_kernel(self):
self.save_sim_data,
)
elif self.kernel.lower() == "metropolis":
self.prior_logp_func = logp_forw([self.model.varlogpt], self.variables, shared)
self.likelihood_logp_func = logp_forw([self.model.datalogpt], self.variables, shared)

def initialize_logp(self):
Expand Down
13 changes: 13 additions & 0 deletions pymc3/tests/test_smc.py
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Expand Up @@ -130,6 +130,14 @@ def abs_diff(eps, obs_data, sim_data):
observed=self.data,
)

with pm.Model() as self.SMABC_potential:
a = pm.Normal("a", mu=0, sigma=1)
b = pm.HalfNormal("b", sigma=1)
c = pm.Potential("c", pm.math.switch(a > 0, 0, -np.inf))
s = pm.Simulator(
"s", normal_sim, params=(a, b), sum_stat="sort", epsilon=1, observed=self.data
)

def test_one_gaussian(self):
with self.SMABC_test:
trace = pm.sample_smc(draws=1000, kernel="ABC")
Expand Down Expand Up @@ -157,6 +165,11 @@ def test_custom_dist_sum(self):
with self.SMABC_test2:
trace = pm.sample_smc(draws=1000, kernel="ABC")

def test_potential(self):
with self.SMABC_potential:
trace = pm.sample_smc(draws=1000, kernel="ABC")
assert np.all(trace["a"] >= 0)

def test_automatic_use_of_sort(self):
with pm.Model() as model:
s_g = pm.Simulator(
Expand Down