def test_converge(mc_num_list, profil, test_time):
for mc_num in mc_num_list:
reslist = [] ;
for t in range(test_time):
print "-------------mc num is:", mc_num , "----------------";
line = [] ;
lines = causal_test(filename, mc_num, 0) ;
for k in range(1,11):
print k, ;
#lines = causal_test(filename, mc_num, k) ;
final_mc_file = "converge_file_better/res_1000000.txt" ;
ground_truth_lines = [x.strip() for x in open(final_mc_file).readlines()] ;
res = evaluate(ground_truth_lines, lines, k, perm_num=10000) ;
print res[0] ;
line.append(float(res[0])) ;
#print line ;
reslist.append(line) ;
print [float(sum(col))/len(col) for col in zip(*reslist)] ;
import timeit
from random_causal_test import run_mc ;
from TargetProvenance import get_tuple_list, readProvenance ;
from BasicFunction import get_targets_karate, get_mc_result_list ;
from main_ndcg import evaluate ;
def causal_test(pro_file_name, mc_num, k):
targetnodelist = get_targets_karate() ;
targetlist = readProvenance(filename, targetnodelist) ;
res_dict = run_mc(targetlist, mc_num, k) ;
return get_mc_result_list(res_dict, targetlist, get_tuple_list(targetlist)) ;
if __name__ == "__main__":
filename = "provenance_dataset/karate_provenance.txt";
mc_num = 100 ;
k = 1 ;
start = timeit.default_timer() ;
lines = causal_test(filename, mc_num, k) ;
stop = timeit.default_timer() ;
print stop-start ;
#for line in lines:
#print " ".join(line) ;
final_mc_file = "converge_file_better/res_1000000.txt" ;
ground_truth_lines = [x.strip() for x in open(final_mc_file).readlines()] ;
res = evaluate(ground_truth_lines, lines, k, perm_num=1000) ;
