Main Complete + CSV Writing

main.py

@@ -9,18 +9,23 @@ import math
 import os
 import numpy as np
 from shutil import copyfile
-
-import gen_bn.gen_bn
-
-NETWORK_SIZE = 20
-NETWORK_TYPE = "polytree"
-NUM_SAMPLES = 200
-ALPHA = 0.75
+import argparse
+from csv import writer
+import re
 
 def main():
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("-s", "--size", dest = "NETWORK_SIZE", default = 5, type = int, help = "Number of nodes in the network")
+    parser.add_argument("-t", "--type", dest = "NETWORK_TYPE", default = "dag", help = "Type of network. dag or polytree")
+    parser.add_argument("-n", "--num", dest = "NUM_SAMPLES", default = 100, type = int, help = "Number of samples to take")
+    parser.add_argument("-a", "--alpha", dest = "ALPHA", default = 0.95, type = float, help = "Metropolis-Hastings split probabilities. Must be between 0 and 1.")
+
+    args = parser.parse_args()
     #Generate a new BN. Specify type and number of nodes in network
-    print("Generating a Bayesian Network of type", NETWORK_TYPE, "and with", NETWORK_SIZE, "nodes.")
-    gen_json(NETWORK_TYPE, NETWORK_SIZE)
+    print("Generating a Bayesian Network of type", args.NETWORK_TYPE, "and with", args.NETWORK_SIZE, "nodes.")
+    gen_json(args.NETWORK_TYPE, args.NETWORK_SIZE)
     
     #Get our BN
     bayes_net = import_bayes()
@@ -35,22 +40,50 @@ def main():
     print()
 
     #Get probability of query from LW given evidence
-    LW_prob = likelihood_weighting(X, E, bayes_net, NUM_SAMPLES)
-    print("Probability of", X, "given", E, "with the LW algorithm and", NUM_SAMPLES, "samples is:")
+    LW_prob = likelihood_weighting(X, E, bayes_net, args.NUM_SAMPLES)
+    print("Probability of", X, "given", E, "with the LW algorithm and", args.NUM_SAMPLES, "samples is:")
     print(LW_prob)
 
-    GS_prob, tmp = gibbs_sampling(X, E, bayes_net, NUM_SAMPLES)
+    GS_prob, tmp = gibbs_sampling(X, E, bayes_net, args.NUM_SAMPLES)
    
-    print("Probability of", X, "given", E, "with the GS algorithm and", NUM_SAMPLES, "samples is:")
+    print("Probability of", X, "given", E, "with the GS algorithm and", args.NUM_SAMPLES, "samples is:")
     print(GS_prob)
 
     #Get probability of query from MH given evidence
-    MH_prob = metropolis_hastings(X, E, bayes_net, NUM_SAMPLES, ALPHA)
-    print("Probability of", X, "given", E, "with the MH algorithm and", NUM_SAMPLES, "samples, and a", ALPHA*100, "/", 100-(ALPHA*100),  "split is:")
+    MH_prob = metropolis_hastings(X, E, bayes_net, args.NUM_SAMPLES, args.ALPHA)
+    print("Probability of", X, "given", E, "with the MH algorithm and", args.NUM_SAMPLES, "samples, and a", args.ALPHA*100, "/", 100-(args.ALPHA*100),  "split is:")
     print(MH_prob)
 
     query_var =  (list(X.items())[0][0])
-    run_exact(query_var)
+    query_val =  (list(X.values())[0])
+    exact_total = run_exact(query_var)
+
+    print("Probability of", X, "with the Variable Elimination algorithm is:")
+
+    #Do extremely sloppy string parsing that I'm too lazy to fix
+    if(query_val):
+        match = re.search(r'\bP_True\b', exact_total)
+        offset = 9
+    else:
+        match = re.search(r'\bP_False\b', exact_total)
+        offset = 10
+    start = match.span()[0]
+    if(query_val):
+        Exact_prob = exact_total[(start+offset):].splitlines()[0]
+        print(Exact_prob)
+    else:    
+        result = exact_total[(start+offset):]
+        res_split = result.split()
+        Exact_prob = res_split[0]
+        print(Exact_prob)
+
+    to_write = [args.NETWORK_TYPE, args.NETWORK_SIZE, args.ALPHA, args.NUM_SAMPLES, LW_prob, GS_prob, MH_prob, Exact_prob]
+    append_csv(to_write)
+
+def append_csv(list_of_ele):
+    with open('results.csv', 'a+', newline='') as file:
+        csv_writer = writer(file)
+        csv_writer.writerow(list_of_ele)
 
 def run_exact(query):
     #Get dir of exact_inference
@@ -63,7 +96,9 @@ def run_exact(query):
     copyfile("bn.json", dirname+'/bn.json')
     os.chdir("../exact_inference")
     #Run the exact_inference on the query variable
-    os.system('python exact_inference.py -f bn.json' + ' ' + '-q' + ' ' + str(query))
+    output = os.popen('python exact_inference.py -f bn.json' + ' ' + '-q' + ' ' + str(query)).read()
+    os.chdir("..")
+    return output
 
 #Generate a new BN.
 #Input: Type ("dag", or "polytree")

results.csv

@@ -0,0 +1,3 @@
+dag,10,0.95,100,0.0,0.0,0.0,0.9681714923847967
+dag,10,0.95,100,0.13113707604213187,0.91,0.95,0.8278012710087637
+dag,10,0.95,100,0.3552298068888378,0.88,0.0,0.1798664152088963