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) ;