def martin_version_report(fdict_exhaustive, data_graph, pattern,
monitoring_marks, output_path, detailed_result_path,
monitoring_reports, exhaustive_approach_results_path,
Plist, nr, pattern_file_name):
print "LEN FDICT_EXHAUSTIVE (BEFORE): ", len(fdict_exhaustive)
size_fdict = len(fdict_exhaustive)
num_embeddings = 0
for k in fdict_exhaustive.keys():
num_embeddings = num_embeddings + fdict_exhaustive[k]
start_time = time.time()
nr_possible_combinations = smplr.complete_combinations(
fdict_exhaustive, data_graph, pattern,
Plist) # add zeros to all not present combinations
smplr.smooth(fdict_exhaustive, fdict_exhaustive)
report.report_monitoring(monitoring_marks, output_path,
detailed_result_path, monitoring_reports,
exhaustive_approach_results_path, data_graph,
pattern, Plist, nr, pattern_file_name,
fdict_exhaustive) #print monitoring_reports
print "ELAPSED TIME: ", time.time() - start_time
def report(output_path,detailed_result_path,fudicts,plot_result_dict,all_furer_times,exhaustive_approach_result_file,data_graph,pattern,Plist,NLIMIT_values,repetitions,pattern_file_name,fdict_exhaustive,iteration_counter_n_limit,n_limit_embeddings):
if (len(fudicts)==0):
with open(os.path.join(output_path,'no_results.info'), 'wb') as file:
file.write("No results for random - empty fudicts!")
pickout = open(os.path.join(output_path,'fudicts.pickle'), 'wb')
pickle.dump(fudicts, pickout)
pickout.close()
pickout = open(os.path.join(output_path,'all_furer_times.pickle'), 'wb')
pickle.dump(all_furer_times, pickout)
pickout.close()
picklename = os.path.join(exhaustive_approach_result_file,"fdict_exhaustive_%s.pickle" % pattern_file_name)
pickin = open(picklename, 'rb')
fdict_exhaustive = pickle.load(pickin)
smplr.complete_combinations(fdict_exhaustive, data_graph, pattern, Plist) # add zeros to all not present combinations
smplr.smooth(fdict_exhaustive, fdict_exhaustive) # Laplace smoothing also for the exhaustive
for nli in range(len(NLIMIT_values)):
print "ITERATION COUNTER FOR THIS LIMIT: ",iteration_counter_n_limit[nli]
print "REPORTING LIMIT: ",NLIMIT_values[nli]
plot_result_dict[NLIMIT_values[nli]] = {}
furer_results_KLD = []
furer_results_bhatta = []
furer_results_hellinger = []
furer_times = []
for i in range(repetitions):
furer_times.append(all_furer_times[i][nli])
fdict_limited = fudicts[i][nli]
smplr.smooth(fdict_limited, fdict_exhaustive) # smoothing to avoid zeros
fdict_Furer = fudicts[i][nli]
[pde, trash_list, default_key] = smplr.make_pd_general_kickout_default(fdict_exhaustive, trash_factor=0.01) # we remove rows where frequencies do not reach 1%
if len(pde) < 1:
print "WARNING: bad (not enough present) pattern or too high trash threshold! STOPPING."
break
emb=n_limit_embeddings[nli]
[pdl, tl, dk] = smplr.make_pd_general_kickout_default_limited(fdict_limited, trash_list, default_key)
[pdf , tl, dk]= smplr.make_pd_general_kickout_default_limited(fdict_Furer, trash_list, default_key)
print "Appending results ..."
furer_results_KLD.append(su.avg_kld(smplr.transform_to_ptable(pde), smplr.transform_to_ptable(pdf)))
furer_results_bhatta.append(su.avg_bhatta(smplr.transform_to_ptable(pde), smplr.transform_to_ptable(pdf)))
furer_results_hellinger.append(su.avg_hellinger(smplr.transform_to_ptable(pde), smplr.transform_to_ptable(pdf)))
plot_result_dict[NLIMIT_values[nli]]["furer_KLD"] = (numpy.mean(furer_results_KLD), numpy.std(furer_results_KLD, ddof=1))
plot_result_dict[NLIMIT_values[nli]]["furer_BHT"] = (numpy.mean(furer_results_bhatta), numpy.std(furer_results_bhatta, ddof=1))
plot_result_dict[NLIMIT_values[nli]]["furer_HEL"] = (numpy.mean(furer_results_hellinger), numpy.std(furer_results_hellinger, ddof=1))
plot_result_dict[NLIMIT_values[nli]]["furer_times"] = (numpy.mean(furer_times), numpy.std(furer_times, ddof=1))
result_file_name = detailed_result_path+"/"+"res_" + pattern_file_name + pattern_file_name+"."+str(repetitions) +"x"+str(NLIMIT_values[nli])+".result"
resultfile = open(result_file_name, 'w')
resultfile.write('Furer\n')
resultfile.write("experiment on graph: " + str(pattern_file_name) +" and pattern: "+pattern_file_name+"\n")
resultfile.write("NLIMIT: " + str(NLIMIT_values[nli]) +"\n")
resultfile.write("repetitions: " + str(repetitions) +"\n")
resultfile.write(" " +"\n")
resultfile.write("average average KLD on randomnode: " + str(numpy.mean(furer_results_KLD)) + " with SSTD: " + str(numpy.std(furer_results_KLD, ddof=1)) +"\n")
resultfile.write("average average bhatta on randomnode: " + str(numpy.mean(furer_results_bhatta)) + " with SSTD: " + str(numpy.std(furer_results_bhatta, ddof=1)) +"\n")
resultfile.write("average average hellinger on randomnode: " + str(numpy.mean(furer_results_hellinger)) + " with SSTD: " + str(numpy.std(furer_results_hellinger, ddof=1)) +"\n")
resultfile.write(" " +"\n")
resultfile.write("Random node took per run on average: " +str(numpy.mean(furer_times)) + " seconds." +"\n")
resultfile.write('-----DETAILED RESULTS-----' +"\n")
resultfile.write('randnode_results_KLD :' + str(furer_results_KLD) +"\n")
resultfile.write('randnode_results_bhatta :' + str(furer_results_bhatta) +"\n")
resultfile.write('randnode_results_hellinger :' + str(furer_results_hellinger) +"\n")
resultfile.write('randnode_times :' + str(furer_times) +"\n")
resultfile.write('Nr embeddings for limit: '+str(emb))
resultfile.close()
def report_monitoring(monitoring_marks, output_path, detailed_result_path,
monitoring_reports, exhaustive_approach_result_file,
data_graph, pattern, Plist, repetitions,
pattern_file_name):
#CREATE DIRECTORY THAT WILL CONTAINS RESULTS FOR EACH TIME INSTANCE
picklename = os.path.join(exhaustive_approach_result_file,
"fdict_exhaustive_%s.pickle" % pattern_file_name)
pickin = open(picklename, 'rb')
fdict_exhaustive = pickle.load(pickin)
smplr.complete_combinations(
fdict_exhaustive, data_graph, pattern,
Plist) # add zeros to all not present combinations
smplr.smooth(fdict_exhaustive,
fdict_exhaustive) # Laplace smoothing also for the exhaustive
dict = {}
duration = []
nr_iterations = []
sum_of_embeddings = []
sum_of_squares = []
sum_of_root_node_emb = []
sum_of_squares_root_node_emb = []
begin = 0
for time_int in monitoring_marks:
duration.append(time_int - begin)
begin = time_int
#the problem might be that some runs finished earlier, and some later.
for i in xrange(len(monitoring_marks)):
for key_iter in monitoring_reports.keys():
if not (monitoring_marks[i] in dict.keys()):
dict[monitoring_marks[i]] = []
try:
dict[monitoring_marks[i]].append(
monitoring_reports[key_iter][i])
nr_iterations.append(
monitoring_reports[key_iter][i].nr_iterations)
sum_of_embeddings.append(
monitoring_reports[key_iter][i].sum_nr_embeddings)
sum_of_squares.append(monitoring_reports[key_iter]
[i].sum_of_the_square_embeddings)
sum_of_root_node_emb.append(
monitoring_reports[key_iter][i].sum_nr_extra_embeddings)
sum_of_squares_root_node_emb.append(
monitoring_reports[key_iter]
[i].sum_of_the_extra_square_embeddings)
except IndexError:
break
print "NR ITERATIONS: ", nr_iterations
print "sum_of_embeddings: ", sum_of_embeddings
print "sum_of_squares: ", sum_of_squares
snapshot_inits = []
for i in range(repetitions):
snapshot_inits.append(0)
counter_duration = 0
counter = 0
for time_snapshot in monitoring_marks:
false_furer_results_KLD = []
false_furer_results_bhatta = []
false_furer_results_hellinger = []
false_furer_times = []
observed_nodes = []
observed_nodes_difference_per_snapshot = []
snapshot_directory_path = os.path.join(detailed_result_path, )
if not (os.path.exists(snapshot_directory_path)):
os.mkdir(snapshot_directory_path)
snapshot_directory_file = os.path.join(
snapshot_directory_path,
'res_time_' + str(time_snapshot) + '.info')
fdict_furer_temp = dict[time_snapshot]
fdicts_Furer = []
for f in fdict_furer_temp:
fdicts_Furer.append(f.current_fdict)
observed_nodes.append(f.number_of_observed_nodes)
if len(fdict_furer_temp) == 0:
continue
for i in range(len(fdict_furer_temp)):
fdict_limited = fdicts_Furer[i]
smplr.smooth(fdict_limited,
fdict_exhaustive) # smoothing to avoid zeros
fdict_Furer = fdicts_Furer[i]
observed_nodes_difference_per_snapshot.append(observed_nodes[i] -
snapshot_inits[i])
snapshot_inits[i] = observed_nodes[i]
[pde, trash_list, default_key
] = smplr.make_pd_general_kickout_default(fdict_exhaustive,
trash_factor=0.01)
if len(pde) < 1:
print "WARNING: bad (not enough present) pattern or too high trash threshold! STOPPING."
break
#[pdl, tl, dk] = smplr.make_pd_general_kickout_default_limited(fdict_limited, trash_list, default_key)
[pdf, tl, dk] = smplr.make_pd_general_kickout_default_limited(
fdict_Furer, trash_list, default_key)
false_furer_results_KLD.append(
su.avg_kld(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdf)))
false_furer_results_bhatta.append(
su.avg_bhatta(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdf)))
false_furer_results_hellinger.append(
su.avg_hellinger(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdf)))
print "Writing to: ", snapshot_directory_file
resultfile = open(snapshot_directory_file, 'w')
resultfile.write('False Furer\n')
resultfile.write("experiment on graph: " + str(pattern_file_name) +
" and pattern: " + pattern_file_name + "\n")
resultfile.write("repetitions (for this time snapshot): " +
str(repetitions) + "\n")
resultfile.write(" " + "\n")
resultfile.write("average KLD on false furer: " +
str(numpy.mean(false_furer_results_KLD)) +
" with SSTD: " +
str(numpy.std(false_furer_results_KLD, ddof=1)) +
"\n")
resultfile.write("average bhatta on false furer: " +
str(numpy.mean(false_furer_results_bhatta)) +
" with SSTD: " +
str(numpy.std(false_furer_results_bhatta, ddof=1)) +
"\n")
resultfile.write(
"average hellinger on false furer: " +
str(numpy.mean(false_furer_results_hellinger)) + " with SSTD: " +
str(numpy.std(false_furer_results_hellinger, ddof=1)) + "\n")
resultfile.write(" " + "\n")
resultfile.write('-----DETAILED RESULTS-----' + "\n")
resultfile.write('false_results_KLD :' + str(false_furer_results_KLD) +
"\n")
resultfile.write('false_results_bhatta :' +
str(false_furer_results_bhatta) + "\n")
resultfile.write('false_results_hellinger :' +
str(false_furer_results_hellinger) + "\n")
resultfile.write('avg #nodes observed :' +
str(numpy.mean(observed_nodes)) + "\n")
resultfile.write('# nodes per time interval per run:' + str(
(numpy.mean(observed_nodes_difference_per_snapshot) /
duration[counter_duration])) + "\n")
resultfile.write(
'avg difference of nodes observed from previous snapshot :' +
str(numpy.mean(observed_nodes_difference_per_snapshot)) + "\n")
resultfile.write(
"------------------------------------ Sampling info ------------------------------\n"
)
resultfile.write('number of sampling iterations : ' +
str(nr_iterations[counter]) + "\n")
resultfile.write('average of embeddings : ' +
str(sum_of_embeddings[counter] /
nr_iterations[counter]) + "\n")
#resultfile.write('average of embeddings w.r.t sampling iterations:' + str(sum_of_embeddings[counter]/float(nr_iterations[counter]))+"\n")
if sum_of_squares_root_node_emb[counter] == 0 and sum_of_root_node_emb[
counter] == 0:
nr_embeddings_temp = sum_of_embeddings[counter] / nr_iterations[
counter]
else:
nr_embeddings_temp = sum_of_root_node_emb[counter] / nr_iterations[
counter]
print "Writing to file: ", nr_embeddings_temp
resultfile.write('average of embeddings : ' + str(nr_embeddings_temp) +
"\n")
if sum_of_squares_root_node_emb[counter] == 0 and sum_of_root_node_emb[
counter] == 0:
#we do the old standard deviation
a = Decimal(sum_of_squares[counter]) - (
Decimal(math.pow(sum_of_embeddings[counter], 2)) /
Decimal(float(nr_iterations[counter])))
stdeviation = math.sqrt(
a / Decimal(float((nr_iterations[counter] - 1))))
else:
a = Decimal(sum_of_squares_root_node_emb[counter]) - (
Decimal(math.pow(sum_of_root_node_emb[counter], 2)) /
Decimal(float(nr_iterations[counter])))
stdeviation = math.sqrt(
a / Decimal(float((nr_iterations[counter] - 1))))
print "old stdev: ", stdeviation
resultfile.write('stdeviation of # embeddings: ' + str(stdeviation) +
"\n")
resultfile.close()
counter += 1
counter_duration += 1
def getStatistics_furer(fudicts, fdict_exhaustive, pattern, data_graph,
target_indices, targe_ids, head_node, target_nodes,
detailed_result_path, pattern_file_name):
#CREATE DIRECTORY THAT WILL CONTAIN RESULTS FOR EACH TIME INSTANCE
furer_results_KLD = []
furer_results_bhatta = []
furer_results_hellinger = []
furer_times = []
observed_nodes = []
observed_nodes_difference_per_snapshot = []
snapshot_directory_path = os.path.join(detailed_result_path)
if not (os.path.exists(snapshot_directory_path)):
os.mkdir(snapshot_directory_path)
snapshot_directory_file = os.path.join(snapshot_directory_path,
'statistics.info')
#filter out exhaustive dictionary
filtered_f_dict_exhaustive = {}
for key in fdict_exhaustive.keys():
new_key = ()
for target in target_indices:
new_key += (key[target - 1])
if not new_key in filtered_f_dict_exhaustive.keys():
filtered_f_dict_exhaustive[new_key] = 0
if new_key in filtered_f_dict_exhaustive.keys():
filtered_f_dict_exhaustive[new_key] += fdict_exhaustive[key]
#filter out furer dictionaries
filtered_fudicts = []
fudict_monitors = fudicts[0]
for dict in fudict_monitors:
temp_fudict = {}
for key in dict.keys():
new_key = ()
for target in target_indices:
new_key += (key[target - 1])
if not new_key in temp_fudict.keys():
temp_fudict[new_key] = 0
if new_key in temp_fudict.keys():
temp_fudict[new_key] += dict[key]
filtered_fudicts.append(temp_fudict)
smplr.complete_combinations(
filtered_f_dict_exhaustive, data_graph, pattern,
targe_ids) # add zeros to all not present combinations
smplr.smooth(filtered_f_dict_exhaustive, filtered_f_dict_exhaustive)
fdict_limited = filtered_fudicts[1]
smplr.smooth(fdict_limited, filtered_f_dict_exhaustive)
fdict_Furer = filtered_fudicts[1]
[pde, trash_list, default_key
] = smplr.make_pd_general_kickout_default(filtered_f_dict_exhaustive,
trash_factor=0.01)
[pdl, tl, dk
] = smplr.make_pd_general_kickout_default_limited(fdict_limited,
trash_list, default_key)
[pdf, tl, dk
] = smplr.make_pd_general_kickout_default_limited(fdict_Furer, trash_list,
default_key)
furer_results_KLD.append(
su.avg_kld(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdf)))
furer_results_bhatta.append(
su.avg_bhatta(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdf)))
furer_results_hellinger.append(
su.avg_hellinger(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdf)))