From 4a0c97d81af3605291c904b87a68bfeb881b8889 Mon Sep 17 00:00:00 2001
From: Manuel Baum <mbaum@techfak.uni-bielefeld.de>
Date: Thu, 11 Jul 2013 18:56:27 +0200
Subject: [PATCH] fixed some bug in computation of acceptance probability in
MetropolisHastings, now works with restrained value ranges too. Also changed
sample_local in continuous node to direct sampling so there is no rejection
sampling anymore
---
examples/MCMC_Continuous.py | 21 +++---
primo/reasoning/ContinuousNode.py | 71 +++++++++++++++++---
primo/reasoning/Evidence.py | 21 +++---
primo/reasoning/MCMC.py | 15 +++--
primo/reasoning/MarkovChainSampler.py | 6 +-
primo/reasoning/__init__.py | 2 +-
primo/reasoning/density/LinearBeta.py | 28 +++++++-
primo/reasoning/density/LinearExponential.py | 14 +++-
primo/reasoning/density/LinearGauss.py | 15 ++++-
9 files changed, 144 insertions(+), 49 deletions(-)
diff --git a/examples/MCMC_Continuous.py b/examples/MCMC_Continuous.py
index b419255..42da8a7 100644
--- a/examples/MCMC_Continuous.py
+++ b/examples/MCMC_Continuous.py
@@ -43,10 +43,12 @@ diameter_parameters=LinearBetaParameters(-10.0,{age:4.0},10.0,{age:-4.0})
diameter.set_density_parameters(diameter_parameters)
-mcmc_ask=MCMC(bn,100000,convergence_test=ConvergenceTestSimpleCounting(50000))
+mcmc_ask=MCMC(bn,10000,convergence_test=ConvergenceTestSimpleCounting(5000))
print "------PriorMarginal:------"
+
+
pm=mcmc_ask.calculate_PriorMarginal([age],Gauss)
print pm
print "Ground truth: mu=0.5 C=[0.25]"
@@ -56,19 +58,20 @@ print pm
print ""
-evidence={age:EvEqual(2)}
-
-
-#print "ProbabilityOfEvidence: "
-#poe=mcmc_ask.calculate_PoE(evidence)
-#print poe
-
print "------PosteriorMarginal:------"
-pm=mcmc_ask.calculate_PosteriorMarginal([age,height],evidence,Gauss)
+pm=mcmc_ask.calculate_PosteriorMarginal([age,height],{age:EvEqual(2)},Gauss)
#pm=mcmc_ask.calculate_PosteriorMarginal([height],evidence,Gauss)
print "P(age,height|age=2):"
print pm
print "Ground truth: age=2, height=mu:1.9,C=0.3"
+print ""
+
+pm=mcmc_ask.calculate_PosteriorMarginal([age,height],{age:EvLower(0.1)},Gauss)
+#pm=mcmc_ask.calculate_PosteriorMarginal([height],evidence,Gauss)
+print "P(age,height|age<0.1):"
+print pm
+print "Ground truth: age=0:0.1, height=mu:-0.1:0.0,C=0.3"
+print ""
print "------PropabilityOfEvidence------"
poe=mcmc_ask.calculate_PoE({age:EvLower(0.347)})
diff --git a/primo/reasoning/ContinuousNode.py b/primo/reasoning/ContinuousNode.py
index 10c6ce1..02a67af 100644
--- a/primo/reasoning/ContinuousNode.py
+++ b/primo/reasoning/ContinuousNode.py
@@ -2,6 +2,7 @@
from primo.reasoning import RandomNode
import random
+import scipy
class ContinuousNode(RandomNode):
'''
@@ -53,18 +54,44 @@ class ContinuousNode(RandomNode):
reimplement it by constructing the integral over the normalvariate in the
intervalls allowed by the evidence and then generate a sample directly.
Currently this method has O(inf).'''
- std_walk=0.1
- v=random.normalvariate(x,std_walk)
+ std_walk=1.0
+
+ #intersect possible evidence-interval with value_range:
if self in evidence.keys():
- while not (evidence[self].is_compatible(v) and self.value_range[0]<v and v<self.value_range[1]):
- v=random.normalvariate(x,std_walk)
+ evidence_range=evidence[self].get_interval()
+ lower_limit=max(self.value_range[0],evidence_range[0])
+ upper_limit=min(self.value_range[1],evidence_range[1])
else:
- while not (self.value_range[0]<v and v<self.value_range[1]):
- v=random.normalvariate(x,std_walk)
- #print "REEEEEEEEEEEEEEEEEEEEEEEEEEEEEE"+self.name+" " +str(v)
- return v
+ lower_limit=self.value_range[0]
+ upper_limit=self.value_range[1]
+
+ if lower_limit==upper_limit:
+ v=lower_limit
+ if lower_limit>upper_limit:
+ raise Exception("Intersection of random variable's value_range and"
+ "allowed Interval for Evidence is empty - no sampling possible")
+
+ #v=random.normalvariate(x,std_walk)
+ #while not (lower_limit<=v and v<=upper_limit):
+ # v=random.normalvariate(x,std_walk)
+
+ #generate the actual sample
+ distribution=scipy.stats.norm(x, std_walk)
+ lower_cdf=distribution.cdf(lower_limit)
+ upper_cdf=distribution.cdf(upper_limit)
+
+ sample_in_integral=random.uniform(lower_cdf, upper_cdf)
+
+ sample=distribution.ppf(sample_in_integral)
- def sample_global(self, state):
+
+
+ a=scipy.stats.norm(self.value_range[0], std_walk).cdf(x)
+ b=scipy.stats.norm(self.value_range[0], std_walk).cdf(sample)
+ cdf_ratio = a/b
+ return sample,cdf_ratio
+
+ def sample_global(self, state, evidence):
'''
This method can be used to sample from this local distribution.
@@ -72,7 +99,31 @@ class ContinuousNode(RandomNode):
values of this nodes parents in this dict and the conditional
probability density will be adjusted accordingly.
'''
- proposal=self.cpd.sample_global(state)
+ #is there some evidence for this node?
+ if self in evidence.keys():
+ #if only one value is allowed we can return it immediatly
+ unique=evidence[self].get_unique_value()
+ if unique!=None:
+ return unique
+ #if a whole interval is allowed intersect it with this variable's
+ #value_range to get limits for sampling
+ else:
+ evidence_range=evidence[self].get_interval()
+ lower_limit=max(self.value_range[0],evidence_range[0])
+ upper_limit=min(self.value_range[1],evidence_range[1])
+ #without evidence this variable's value_range represents limits for sampling
+ else:
+ lower_limit=self.value_range[0]
+ upper_limit=self.value_range[1]
+ #check if only one value is allowed and in case return immediatly
+ if lower_limit==upper_limit:
+ return lower_limit
+ #check for empty interval
+ if lower_limit>upper_limit:
+ raise Exception("Intersection of random variable's value_range and"
+ "allowed Interval for Evidence is empty - no sampling possible")
+
+ proposal=self.cpd.sample_global(state,lower_limit,upper_limit)
return proposal
def get_probability(self, value, state):
diff --git a/primo/reasoning/Evidence.py b/primo/reasoning/Evidence.py
index 2946061..0974c9d 100644
--- a/primo/reasoning/Evidence.py
+++ b/primo/reasoning/Evidence.py
@@ -41,7 +41,7 @@ class EvidenceEqual(Evidence):
def get_unique_value(self):
return self.value
-class EvidenceIntervall(Evidence):
+class EvidenceInterval(Evidence):
'''
This class can be used to specify evidence that a variable has taken on
some value in a defined interval.
@@ -54,27 +54,26 @@ class EvidenceIntervall(Evidence):
def is_compatible(self, value):
return self.min_val <= value and value<=self.max_val
+ def get_interval(self):
+ return self.min_val,self.max_val
+
-class EvidenceLower(Evidence):
+class EvidenceLower(EvidenceInterval):
'''
This class can be used to specify evidence that a variable has taken on
some value lower than some threshold.
e.g. a<3
'''
def __init__(self,limit):
- self.limit=limit
-
- def is_compatible(self, value):
- return value<self.limit
+ super(EvidenceLower, self).__init__(float("-inf"),limit)
+
-class EvidenceHigher(Evidence):
+class EvidenceHigher(EvidenceInterval):
'''
This class can be used to specify evidence that a variable has taken on
some value higher than some threshold.
e.g. a>3
'''
def __init__(self,limit):
- self.limit=limit
-
- def is_compatible(self, value):
- return value>self.limit
+ super(EvidenceLower, self).__init__(limit,float("inf"))
+
diff --git a/primo/reasoning/MCMC.py b/primo/reasoning/MCMC.py
index a2b06a3..e2713a1 100644
--- a/primo/reasoning/MCMC.py
+++ b/primo/reasoning/MCMC.py
@@ -119,12 +119,13 @@ class MCMC(object):
'''
state={}
for var in self.bn.get_nodes_in_topological_sort():
- if var in evidence.keys():
- value=evidence[var].get_unique_value()
- if value == None:
- value=var.sample_global(state)
- state[var]=value
- else:
- state[var]=var.sample_global(state)
+# if var in evidence.keys():
+# value=evidence[var].get_unique_value()
+# if value == None:
+# value=var.sample_global(state,evidence)
+# state[var]=value
+# else:
+# state[var]=var.sample_global(state)
+ state[var]=var.sample_global(state, evidence)
return state
diff --git a/primo/reasoning/MarkovChainSampler.py b/primo/reasoning/MarkovChainSampler.py
index b2b792e..ced541f 100644
--- a/primo/reasoning/MarkovChainSampler.py
+++ b/primo/reasoning/MarkovChainSampler.py
@@ -101,12 +101,12 @@ class MetropolisHastingsTransitionModel(object):
for node in nodes_to_resample:
#propose a new value for this variable:
current_value = state[node]
- proposed_value = node.sample_local(current_value, extern_evidence)
+ proposed_value, cdf_ratio = node.sample_local(current_value, extern_evidence)
p_of_proposal_given_mb = self._compute_p_of_value_given_mb(network, state, node, proposed_value)
p_of_current_given_mb = self._compute_p_of_value_given_mb(network, state, node, current_value)
-
- acceptance_probability = min(1.0, p_of_proposal_given_mb/p_of_current_given_mb * 1.0/1.0)
+ #print "acceptance_probability = min(1.0, "+str(p_of_proposal_given_mb)+" / "+str(p_of_current_given_mb) + " * "+str(cdf_ratio)
+ acceptance_probability = min(1.0, p_of_proposal_given_mb/p_of_current_given_mb * cdf_ratio * 1.0/1.0)
if random.random() <= acceptance_probability:
state[node]=proposed_value
diff --git a/primo/reasoning/__init__.py b/primo/reasoning/__init__.py
index 298e06c..26d0de9 100644
--- a/primo/reasoning/__init__.py
+++ b/primo/reasoning/__init__.py
@@ -3,7 +3,7 @@ from DiscreteNode import DiscreteNode
from ContinuousNode import ContinuousNode
from ContinuousNodeFactory import ContinuousNodeFactory
-from Evidence import EvidenceEqual, EvidenceIntervall, EvidenceLower, EvidenceHigher
+from Evidence import EvidenceEqual, EvidenceInterval, EvidenceLower, EvidenceHigher
from MarkovChainSampler import MarkovChainSampler
from MarkovChainSampler import GibbsTransitionModel
diff --git a/primo/reasoning/density/LinearBeta.py b/primo/reasoning/density/LinearBeta.py
index d1bc190..0ba8bec 100644
--- a/primo/reasoning/density/LinearBeta.py
+++ b/primo/reasoning/density/LinearBeta.py
@@ -73,9 +73,31 @@ class LinearBeta(Density):
return self.output_scale*1.0/(1.0+math.exp(-x*self.input_scale))
- def sample_global(self, state):
+ def sample_global(self, state, lower_limit, upper_limit):
p=self._compute_p_given_parents(state)
q=self._compute_q_given_parents(state)
- value=random.betavariate(p,q)
+ #value=random.betavariate(p,q)
#print "Sampled:"+str(value)
- return value
+ #return value
+
+
+
+
+
+
+ #_lambda=self._compute_lambda_given_parents(state)
+ distribution=beta(p,q)
+
+ lower_cdf=distribution.cdf(lower_limit)
+ upper_cdf=distribution.cdf(upper_limit)
+
+ sample_in_integral=random.uniform(lower_cdf, upper_cdf)
+
+ sample=distribution.ppf(sample_in_integral)
+ return sample
+
+
+
+
+
+
diff --git a/primo/reasoning/density/LinearExponential.py b/primo/reasoning/density/LinearExponential.py
index 54c2e18..a9c0155 100644
--- a/primo/reasoning/density/LinearExponential.py
+++ b/primo/reasoning/density/LinearExponential.py
@@ -37,7 +37,6 @@ class LinearExponential(Density):
#Compute the offset for the density and displace the value accordingly
_lambda = self._compute_lambda_given_parents(dict(node_value_pairs))
- #print "lambda:"+str(_lambda)
#Evaluate the displaced density at value
return _lambda*math.exp(-_lambda*value)
@@ -49,8 +48,17 @@ class LinearExponential(Density):
_lambda=self.output_scale*1.0/(1.0+math.exp(-x*self.input_scale))
return _lambda
- def sample_global(self,state):
+ def sample_global(self,state, lower_limit, upper_limit):
_lambda=self._compute_lambda_given_parents(state)
- sample=random.expovariate(_lambda)
+ distribution=scipy.stats.expon(loc=0,scale=1.0/_lambda)
+
+ lower_cdf=distribution.cdf(lower_limit)
+ upper_cdf=distribution.cdf(upper_limit)
+
+ sample_in_integral=random.uniform(lower_cdf, upper_cdf)
+
+ sample=distribution.ppf(sample_in_integral)
+
+ #sample=random.expovariate(_lambda)
#print "EXPO-SAMPLE: "+str(sample)+" at lambda: "+str(_lambda)
return sample
diff --git a/primo/reasoning/density/LinearGauss.py b/primo/reasoning/density/LinearGauss.py
index f1ec5fb..9b9d676 100644
--- a/primo/reasoning/density/LinearGauss.py
+++ b/primo/reasoning/density/LinearGauss.py
@@ -65,6 +65,17 @@ class LinearGauss(Density):
x = x + self.b[node]*state[node]
return x
- def sample_global(self,state):
- return random.normalvariate(self._compute_offset_given_parents(state),self.var**0.5)
+ def sample_global(self,state,lower_limit,upper_limit):
+
+ distribution=scipy.stats.norm(self._compute_offset_given_parents(state), self.var**0.5)
+
+ lower_cdf=distribution.cdf(lower_limit)
+ upper_cdf=distribution.cdf(upper_limit)
+
+ sample_in_integral=random.uniform(lower_cdf, upper_cdf)
+
+ sample=distribution.ppf(sample_in_integral)
+
+
+ return sample
--
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