Added all functions. Results not correct.
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BuildTools
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1
exact_inference
Submodule
1
exact_inference
Submodule
@ -0,0 +1 @@
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Subproject commit 9e3e13fe6b2b982a12a5a3f391f91107e95f13b8
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327
main.py
327
main.py
@ -6,37 +6,64 @@
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import json
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import random
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import math
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import sys
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import os
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#pwd = os.getcwd()
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#sys.path.append(pwd +'/gen-bn/')
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#sys.path.append("./gen-bn")
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import numpy as np
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from shutil import copyfile
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import gen_bn.gen_bn
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#from collections import defaultdict
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NETWORK_SIZE = 20
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NETWORK_TYPE = "polytree"
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NUM_SAMPLES = 200
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ALPHA = 0.75
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def main():
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#Generate a new BN. Specify type and number of nodes in network
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gen_json("dag", 5)
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print("Generating a Bayesian Network of type", NETWORK_TYPE, "and with", NETWORK_SIZE, "nodes.")
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gen_json(NETWORK_TYPE, NETWORK_SIZE)
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#Get our BN
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bayes_net = import_bayes()
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#Generate random evidence
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E = gen_ev(bayes_net)
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print("Generating random evidence:", E)
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#Generate a random query that is not an evidence variable in the form of {var : val}
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X = gen_query(E, bayes_net)
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#Get W from LW
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W = likelihood_weighting(X, E, bayes_net, 10)
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#Print if desired
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print("Generating random query:", X)
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print()
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for key, value in W.items():
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print(key, ' : ', value)
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#Get probability of query from LW given evidence
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LW_prob = likelihood_weighting(X, E, bayes_net, NUM_SAMPLES)
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print("Probability of", X, "given", E, "with the LW algorithm and", NUM_SAMPLES, "samples is:")
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print(LW_prob)
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GS_prob, tmp = gibbs_sampling(X, E, bayes_net, NUM_SAMPLES)
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print("Probability of", X, "given", E, "with the GS algorithm and", NUM_SAMPLES, "samples is:")
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print(GS_prob)
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#Get probability of query from MH given evidence
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MH_prob = metropolis_hastings(X, E, bayes_net, NUM_SAMPLES, ALPHA)
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print("Probability of", X, "given", E, "with the MH algorithm and", NUM_SAMPLES, "samples, and a", ALPHA*100, "/", 100-(ALPHA*100), "split is:")
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print(MH_prob)
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query_var = (list(X.items())[0][0])
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run_exact(query_var)
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def run_exact(query):
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#Get dir of exact_inference
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os.chdir("./exact_inference")
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dirname = os.getcwd()
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#Go back up and into the gen_bn folder
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os.chdir("..")
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os.chdir("./gen_bn")
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#copy the file to the exact_inference folder
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copyfile("bn.json", dirname+'/bn.json')
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os.chdir("../exact_inference")
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#Run the exact_inference on the query variable
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os.system('python exact_inference.py -f bn.json' + ' ' + '-q' + ' ' + str(query))
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#Generate a new BN.
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#Input: Type ("dag", or "polytree")
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@ -57,11 +84,11 @@ def import_bayes():
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def gen_ev(bayes_net):
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total_nodes = len(bayes_net)
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#Arbitrarily, let's only generate total_nodes/2 (rounded up) evidence variables at most, but at least 1
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num_ev = random.randint(0, int(math.ceil(total_nodes/2)))
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num_ev = random.randint(1, int(math.ceil(total_nodes/2)))
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fixed_ev = []
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#Go through and generate nodes that will be fixed
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for i in range(num_ev):
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fixed_var = random.randint(1, total_nodes-1)
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fixed_var = random.randint(0, total_nodes-1)
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if fixed_var not in fixed_ev:
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fixed_ev.append(fixed_var)
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#Now generate random values for the ev
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@ -82,7 +109,7 @@ def gen_query(ev, bayes_net):
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for e in ev_vars:
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if int(e) in possible_vars:
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possible_vars.remove(int(e))
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query = random.choice(possible_vars)
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query = str(random.choice(possible_vars))
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rand_prob = random.random()
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if rand_prob >= 0.5:
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val = True
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@ -91,7 +118,7 @@ def gen_query(ev, bayes_net):
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return {query : val}
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#Checks if node has parents
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#Checks if node has parents. If not, it is a root
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def is_root(node, BN):
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return (BN[node]["parents"]) == []
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@ -115,7 +142,7 @@ def get_parents(node, BN):
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#E is a dict in the form of {"Node" : Value}
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#Compute the estimate of P(X|e), where X is the query variable, and e is the observed value for variables E
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def likelihood_weighting(X, e, bayes_net, num_samples):
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def likelihood_weighting(X, e, bayes_net, num_samples, MH=0):
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W = {}
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for i in range(num_samples):
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@ -166,12 +193,18 @@ def likelihood_weighting(X, e, bayes_net, num_samples):
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#If the sample wasn't already in W, put it in there now
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if not written:
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W[len(W)] = {w : samples}
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return W
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#use for MH alg
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if(MH is not 0):
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return W
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prob = compute_prob(X, e, W)
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return prob
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#Return the probability of a node and the value dict, given the current evidence and fixed evidence
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#Uses recursion to pull probabilites and values down through the network
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def get_probability(node, samples, ev, bayes_net, w):
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def get_probability(node, samples, ev, bayes_net, w, G=0):
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parents = get_parents(node, bayes_net)
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for parent in parents:
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#If we already know the value of the parent, no need to reobtain
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@ -192,11 +225,15 @@ def get_probability(node, samples, ev, bayes_net, w):
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samples, prob, w = translate_ev(gparents, parent, ev, samples, bayes_net, w)
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#Now that we have all the parents' values, we can get the node value, probability, and update samples
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samples, prob, w = translate_ev(parents, node, ev, samples, bayes_net, w)
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samples, prob, w = translate_ev(parents, node, ev, samples, bayes_net, w, G)
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return samples, prob, w
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#Given a node and its parents, determine the node's value and it's probability
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def translate_ev(parents, node, ev, samples, bayes_net, w):
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def translate_ev(parents, node, ev, samples, bayes_net, w, G=0):
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#If G=1, it means we already set the state space to specific values.
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#Meaning, we only need to compute the prob of that happening, NOT set values
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#Call a different function instead
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#Sort in ascending order
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parents.sort()
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node = str(node)
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@ -227,13 +264,243 @@ def translate_ev(parents, node, ev, samples, bayes_net, w):
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w = w * table_prob
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return samples, table_prob, w
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def gibbs_sampling():
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print("Hello")
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def get_children(node, BN):
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children = []
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for x in range(len(BN)):
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if node in BN[str(x)]["parents"]:
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children.append(str(x))
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return children
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def metropolis_hastings():
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print("Hello")
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def product(nums):
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result = 1
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for num in nums:
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result *= num
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return result
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#Given a node and state_space values, get prob from CPT
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def get_cpt(node, samples, bayes_net):
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parents = bayes_net[str(node)]["parents"]
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parents.sort()
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value_list = []
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for parent in parents:
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value = samples[str(parent)]
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value_list.append(value)
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for i in range(2**len(parents)):
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#If the truth table matches the value combination we have
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if bayes_net[str(node)]["prob"][i][0] == value_list:
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return bayes_net[str(node)]["prob"][i][1]
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def mb(x_node, e, bayes_net, state_space):
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#x_node = (list(X.items())[0][0])
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#print("NODE IS", x_node)
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state_space[x_node] = True
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#print("True SS")
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#print(state_space)
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x_true = get_cpt(x_node, state_space, bayes_net) * product(get_cpt(child, state_space, bayes_net) for child in get_children(x_node, bayes_net))
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#print(x_true)
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state_space[x_node] = False
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#print("False SS")
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#print(state_space)
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x_false = get_cpt(x_node, state_space, bayes_net) * product(get_cpt(child, state_space, bayes_net) for child in get_children(x_node, bayes_net))
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#print(x_false)
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#print()
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return x_true, x_false
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#Given a query, the evidence, and a dict of samples, compute the prob of the query
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def compute_prob(X, e, W):
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#Combine the query and ev dicts for easier computation
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combined_dict = {**X, **e}
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#Initialize the probability of the query
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query_prob = 0
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ev_prob = 0
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#print(W)
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#Go through all the samples
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for i in range(len(W)):
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#Easy way to find all the instances where query var and ev match the samples - use subsets
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#Convert W[index].values() to a list, since Python 3 returns a "view" of the dictionary with .values()
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#Then remove the list aspect through [0], and obtain the values from the nested dict through .items()
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if combined_dict.items() <= list(W[i].values())[0].items():
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#Get the weight
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query_prob+=list(W[i].items())[0][0]
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#See if evidence is a subset
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if e.items() <= list(W[i].values())[0].items():
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ev_prob+=list(W[i].items())[0][0]
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if(ev_prob==0):
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return 0
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else:
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return (query_prob/ev_prob)
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def gibbs_sampling(X, e, bayes_net, num_samples):
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#State of the network, init'd with the query and ev
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#x = {**X, **e}
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x = {**e}
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#Counts for each value of X
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C = {}
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for j in range(len(bayes_net)):
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C[str(j)] = 0
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#Get a list of non-ev variables to make things easier
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Z = []
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for node in bayes_net:
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#if node not in e and node not in X:
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if node not in e:
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Z.append(node)
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#Initialize the rest of the network randomly
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for z in Z:
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rand_prob = random.random()
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if rand_prob >=0.5:
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x[z] = True
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else:
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x[z] = False
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for k in range(num_samples):
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zi = random.choice(Z)
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#Reuse the get_prob function, even though we don't care about maintaining a samples list or weight
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#samples, prob, w = get_probability(str(zi), {}, e, bayes_net, 0)
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z_true, z_false = mb(str(zi), e, bayes_net, x)
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#Gen a random number to determine the value based on probability
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value_prob = random.random()
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if value_prob >= z_true:
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x[zi] = True
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C[zi]+=1
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else:
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x[zi] = False
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for j in range(len(bayes_net)):
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C[str(j)]/=num_samples
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query_var = (list(X.items())[0][0])
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query_val = (list(X.values())[0])
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#print(C)
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if(query_val):
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GS_Prob = C[str(query_var)]
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else:
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GS_Prob = 1.00000 - C[str(query_var)]
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return GS_Prob, x
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#Function for using log-probabilities for preventing the underflow from MH as advised by Dr. Sen
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def compute_log(prob):
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return -0.5 * np.sum(prob ** 2)
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def acceptance(xprime, x, compute_log):
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return min(1, np.exp(compute_log(xprime) - compute_log(x)))
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def metropolis_hastings(X, e, bayes_net, num_samples, alpha):
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state_space = {**e}
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Z = []
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#Counts for each value of X
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W = {}
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C = {}
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for j in range(len(bayes_net)):
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C[str(j)] = 0
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for node in bayes_net:
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#if node not in e and node not in X:
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if node not in e:
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Z.append(node)
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#Initialize the rest of the network randomly
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for z in Z:
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rand_prob = random.random()
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if rand_prob >=0.5:
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state_space[z] = True
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else:
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state_space[z] = False
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#3 runs: 95% to run Gibbs for x', 5% for weighted sample. Then 85/15, 75/25
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#100% acceptance
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for num in range(num_samples):
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proposal_choice = random.random()
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#Weighted-Sample
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if(proposal_choice <= (1-alpha)):
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tmp = likelihood_weighting(X, e, bayes_net, 1, 1)
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sample = list(tmp[0].values())[0]
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#If this sample is already in W, don't add a new sample - only adjust the weight
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written = False
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for k in range(len(W)):
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#If sample is in W
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if sample in W[k].values():
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#Pull the weight that's associated with the sample
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key = list(W[k].items())[0][0]
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#Increment count
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new_key = key + 1
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#Store it all back into W
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W[k] = {new_key : sample}
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#Make note that we've already written to W in this loop, so we don't write it again
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written = True
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#If the sample wasn't already in W, put it in there now
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if not written:
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W[len(W)] = {1 : sample}
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#for key, value in W.items():
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# print(key, ' : ', value)
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#zi = random.choice(Z)
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#state_space, prob, w = get_probability(zi, state_space, bayes_net, 0)
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#Gibbs
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else:
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zi = random.choice(Z)
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#Reuse the get_prob function, even though we don't care about maintaining a samples list or weight
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#samples, prob, w = get_probability(str(zi), {}, e, bayes_net, 0)
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z_true, z_false = mb(str(zi), e, bayes_net, state_space)
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#Gen a random number to determine the value based on probability
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value_prob = random.random()
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if value_prob >= z_true:
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state_space[zi] = True
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C[zi]+=1
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else:
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state_space[zi] = False
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#If this sample is already in W, don't add a new sample - only adjust the weight
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written = False
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for tmp in range(len(W)):
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#If sample is in W
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if state_space in W[tmp].values():
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#Pull the weight that's associated with the sample
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key = list(W[tmp].items())[0][0]
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#Increment count
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new_key = key + 1
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#Store it all back into W
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W[tmp] = {new_key : state_space}
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#Make note that we've already written to W in this loop, so we don't write it again
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written = True
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#If the sample wasn't already in W, put it in there now
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if not written:
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W[len(W)] = {1 : state_space}
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#for key, value in W.items():
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# print(key, ' : ', value)
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#Combine the query and ev dicts for easier computation
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combined_dict = {**X, **e}
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#Initialize the probability of the query
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query_prob = 0
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#Go through all the samples
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for i in range(len(W)):
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#Easy way to find all the instances where query var and ev match the samples - use subsets
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#Convert W[index].values() to a list, since Python 3 returns a "view" of the dictionary with .values()
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#Then remove the list aspect through [0], and obtain the values from the nested dict through .items()
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if combined_dict.items() <= list(W[i].values())[0].items():
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#Get the weight
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query_prob+=list(W[i].items())[0][0]
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return (query_prob/num_samples)
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#MH_prob = compute_prob(X, e, W)
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#return MH_prob
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if __name__ == '__main__':
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main()
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