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_number_of_embeddings = []
sum_of_embeddings_vers1 = []
sum_of_squares_vers1 = []
sum_of_the_square_embeddings = []
sum_of_embeddings_random_old = []
sum_of_square_emb_random_old = []
nr_root_nodes = []
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 range(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_number_of_embeddings.append(
monitoring_reports[key_iter][i].sum_nr_embeddings)
sum_of_the_square_embeddings.append(
monitoring_reports[key_iter]
[i].sum_of_the_square_embeddings)
nr_root_nodes.append(
monitoring_reports[key_iter][i].nr_root_nodes)
try:
sum_of_embeddings_random_old.append(
monitoring_reports[key_iter]
[i].sum_number_of_embeddings_random)
sum_of_square_emb_random_old.append(
monitoring_reports[key_iter]
[i].sum_of_the_square_embeddings_random)
except:
continue
try:
sum_of_embeddings_vers1.append(
monitoring_reports[key_iter][i].sum_nr_embeddings_aux)
sum_of_squares_vers1.append(
monitoring_reports[key_iter]
[i].sum_of_the_square_embeddings_aux)
except:
continue
except IndexError:
break
snapshot_inits = []
for i in range(repetitions):
snapshot_inits.append(0)
counter_duration = 0
counter = 0
interval = 0
for time_snapshot in monitoring_marks:
interval += 1
randnode_results_KLD = []
randnode_results_bhatta = []
randnode_results_hellinger = []
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
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_old(fdict_exhaustive,
trash_factor=0.01)
if len(pde) < 1:
break
[pdl, tl, dk] = smplr.make_pd_general_kickout_default_limited_old(
fdict_limited, trash_list, default_key)
randnode_results_KLD.append(
su.avg_kld(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdl)))
randnode_results_bhatta.append(
su.avg_bhatta(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdl)))
randnode_results_hellinger.append(
su.avg_hellinger(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdl)))
resultfile = open(snapshot_directory_file, 'w')
resultfile.write('Random\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 random: " +
str(numpy.mean(randnode_results_KLD)) +
" with SSTD: " +
str(numpy.std(randnode_results_KLD, ddof=1)) + "\n")
resultfile.write("average bhatta on random: " +
str(numpy.mean(randnode_results_bhatta)) +
" with SSTD: " +
str(numpy.std(randnode_results_bhatta, ddof=1)) +
"\n")
resultfile.write("average hellinger on random: " +
str(numpy.mean(randnode_results_hellinger)) +
" with SSTD: " +
str(numpy.std(randnode_results_hellinger, ddof=1)) +
"\n")
resultfile.write(" " + "\n")
resultfile.write('-----DETAILED RESULTS-----' + "\n")
resultfile.write('random_results_KLD :' + str(randnode_results_KLD) +
"\n")
resultfile.write('random_results_bhatta :' +
str(randnode_results_bhatta) + "\n")
resultfile.write('random_results_hellinger :' +
str(randnode_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")
avg = (float(Decimal(sum_number_of_embeddings[counter])) /
nr_iterations[counter]) * (
approaches.globals_sampling.nr_root_nodes)
old = False
if avg < 0:
#this means we handle the old version
avg = float(
sum_of_embeddings_random_old[counter]) / nr_iterations[counter]
old = True
sum1 = Decimal(sum_of_the_square_embeddings[counter])
sum2 = Decimal(sum_number_of_embeddings[counter])
var = Decimal(sum1) - (Decimal(math.pow(Decimal(sum2), 2)) /
nr_iterations[counter])
stdev2 = math.sqrt(var / (nr_iterations[counter] - 1))
stdev = Decimal(stdev2) * Decimal(math.sqrt(nr_iterations[counter]))
if old:
#this means we handle the old version
variance = sum_of_square_emb_random_old[counter] / (
nr_iterations[counter])
stdev2 = math.sqrt(variance / (nr_iterations[counter] - 1))
resultfile.write('average of embeddings w.r.t sampling iterations:' +
str(avg) + "\n")
resultfile.write('stdeviation of # embeddings:' + str(stdev2) + "\n")
resultfile.write('2 stdeviation of # embeddings:' + str(stdev) + "\n")
counter += 1
resultfile.close()
counter_duration += 1
def report(rndicts, all_randnode_times, NLIMIT_values, plot_result_dict,
repetitions, detailed_result_path, output_path,
exhaustive_approach_result_file, pattern_file_name,
nr_embeddings_n_limits):
if (len(rndicts) == 0):
with open(os.path.join(output_path, 'no_results.info'), 'wb') as file:
file.write("No results for random - empty rndicts!")
pickout = open(os.path.join(output_path, 'rndicts.pickle'), 'wb')
pickle.dump(rndicts, pickout)
pickout.close()
pickout = open(os.path.join(output_path, 'all_randnode_times.pickle'),
'wb')
pickle.dump(all_randnode_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)
for nli in range(len(NLIMIT_values)):
plot_result_dict[NLIMIT_values[nli]] = {}
randnode_results_KLD = []
randnode_results_bhatta = []
randnode_results_hellinger = []
randnode_times = []
for i in range(repetitions):
emb = nr_embeddings_n_limits[nli]
randnode_times.append(all_randnode_times[i][nli])
fdict_limited = rndicts[i][nli]
smplr.smooth(fdict_limited,
fdict_exhaustive) # smoothing to avoid zeros
[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:
break
#print "pde length: ",len(pde)
[pdl, tl, dk] = smplr.make_pd_general_kickout_default_limited(
fdict_limited, trash_list, default_key)
#print "fdict exhaustive: ",len(fdict_exhaustive),"fdict limited",len(fdict_limited)
# new function also for limited ones : make_pd_general_kickout_default_limited(fdict, trash, default_key)
randnode_results_KLD.append(
su.avg_kld(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdl)))
randnode_results_bhatta.append(
su.avg_bhatta(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdl)))
randnode_results_hellinger.append(
su.avg_hellinger(smplr.transform_to_ptable(pde),
smplr.transform_to_ptable(pdl)))
plot_result_dict[NLIMIT_values[nli]]["randomnode_KLD"] = (
numpy.mean(randnode_results_KLD),
numpy.std(randnode_results_KLD, ddof=1))
plot_result_dict[NLIMIT_values[nli]]["randomnode_BHT"] = (
numpy.mean(randnode_results_bhatta),
numpy.std(randnode_results_bhatta, ddof=1))
plot_result_dict[NLIMIT_values[nli]]["randomnode_HEL"] = (
numpy.mean(randnode_results_hellinger),
numpy.std(randnode_results_hellinger, ddof=1))
# added to store and plot the times
plot_result_dict[NLIMIT_values[nli]]["randomnode_times"] = (
numpy.mean(randnode_times), numpy.std(randnode_times, ddof=1))
plot_result_dict[NLIMIT_values[nli]]["randomnode_times"] = (
numpy.mean(randnode_times), numpy.std(randnode_times, ddof=1))
result_file_name = detailed_result_path + "/" + "ultimex_ICDM_" + pattern_file_name + pattern_file_name + "." + str(
repetitions) + "x" + str(NLIMIT_values[nli]) + ".result"
resultfile = open(result_file_name, 'w')
resultfile.write('Random Vertex\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(randnode_results_KLD)) +
" with SSTD: " +
str(numpy.std(randnode_results_KLD, ddof=1)) + "\n")
resultfile.write("average average bhatta on randomnode: " +
str(numpy.mean(randnode_results_bhatta)) +
" with SSTD: " +
str(numpy.std(randnode_results_bhatta, ddof=1)) +
"\n")
resultfile.write("average average hellinger on randomnode: " +
str(numpy.mean(randnode_results_hellinger)) +
" with SSTD: " +
str(numpy.std(randnode_results_hellinger, ddof=1)) +
"\n")
resultfile.write(" " + "\n")
resultfile.write("Random node took per run on average: " +
str(numpy.mean(randnode_times)) + " seconds." + "\n")
resultfile.write('-----DETAILED RESULTS-----' + "\n")
resultfile.write('randnode_results_KLD :' + str(randnode_results_KLD) +
"\n")
resultfile.write('randnode_results_bhatta :' +
str(randnode_results_bhatta) + "\n")
resultfile.write('randnode_results_hellinger :' +
str(randnode_results_hellinger) + "\n")
resultfile.write('randnode_times :' + str(randnode_times) + "\n")
resultfile.write('Nr embeddings for limit: ' + str(emb))
resultfile.close()
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)):
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)
if len(pde) < 1:
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)
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, fdict_exhaustive):
# CREATE DIRECTORY THAT WILL CONTAIN RESULTS FOR EACH TIME INSTANCE
dict = {}
duration = []
begin = 0
nr_iterations = []
sum_of_embeddings = []
sum_of_squares = []
embeddings_estimate = []
sum_of_root_node_emb = []
sum_of_squares_root_node_emb = []
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)
embeddings_estimate.append(
monitoring_reports[key_iter][i].embeddings_estimate)
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
snapshot_inits = []
for i in range(repetitions):
snapshot_inits.append(0)
counter_duration = 0
counter = 0
for time_snapshot in monitoring_marks:
if counter == 1:
break
furer_results_KLD = []
furer_results_bhatta = []
furer_results_hellinger = []
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)
# print "Exhaustive dict: ",pde
if len(pde) < 1:
break
[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)))
resultfile = open(snapshot_directory_file, 'w')
resultfile.write('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 average KLD on furer: " +
str(numpy.mean(furer_results_KLD)) + " with SSTD: " +
str(numpy.std(furer_results_KLD, ddof=1)) + "\n")
resultfile.write("average average bhatta on furer: " +
str(numpy.mean(furer_results_bhatta)) +
" with SSTD: " +
str(numpy.std(furer_results_bhatta, ddof=1)) + "\n")
resultfile.write("average average hellinger on furer: " +
str(numpy.mean(furer_results_hellinger)) +
" with SSTD: " +
str(numpy.std(furer_results_hellinger, ddof=1)) +
"\n")
resultfile.write(" " + "\n")
resultfile.write('-----DETAILED RESULTS-----' + "\n")
resultfile.write('furer_results_KLD : ' + str(furer_results_KLD) +
"\n")
resultfile.write('furer_results_bhatta : ' +
str(furer_results_bhatta) + "\n")
resultfile.write('furer_results_hellinger : ' +
str(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")
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]
# embeddings_estimate[counter]
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))))
resultfile.write('stdeviation of # embeddings: ' + str(stdeviation) +
"\n")
resultfile.close()
counter += 1
counter_duration += 1
def report_monitoring_my_version_online(
monitoring_marks, output_path, detailed_result_path,
monitoring_reports, exhaustive_approach_result_file, data_graph,
pattern, Plist, repetitions, pattern_file_name, fdict_exhaustive,
nr_non_observed_combinations):
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 range(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[i])
nr_iterations.append(monitoring_reports[i].nr_iterations)
sum_of_embeddings.append(monitoring_reports[i].sum_nr_embeddings)
sum_of_squares.append(
monitoring_reports[i].sum_of_the_square_embeddings)
sum_of_root_node_emb.append(
monitoring_reports[i].sum_nr_extra_embeddings)
sum_of_squares_root_node_emb.append(
monitoring_reports[i].sum_of_the_extra_square_embeddings)
except IndexError:
break
snapshot_inits = []
for i in range(repetitions):
snapshot_inits.append(0)
counter_duration = 0
counter = 0
average_klds = []
average_bhattas = []
average_hellingers = []
std_klds = []
std_bhattas = []
std_hellingers = []
avg_nodes_observed = []
nr_nodes_per_time_interval_per_runs = []
number_of_sampling_iterations = []
average_of_embeddings = []
stdevs = []
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)):
approaches.globals_sampling.nr_iterations = nr_iterations[i]
fdict_limited = fdicts_Furer[i]
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_my_version(
fdict_exhaustive)
if len(pde) < 1:
break
nr_possible_combinations = smplr.complete_combinations_1(
fdict_Furer, data_graph, pattern, Plist)
pdf = smplr.make_pd_general_kickout_default_limited_my_version(
fdict_Furer)
#print smplr.transform_to_ptable(pde)
#print smplr.transform_to_ptable(pdf)
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)))
average_klds.append(numpy.mean(false_furer_results_KLD))
std_klds.append(numpy.std(false_furer_results_KLD, ddof=1))
average_bhattas.append(numpy.mean(false_furer_results_bhatta))
std_bhattas.append(numpy.std(false_furer_results_bhatta, ddof=1))
average_hellingers.append(numpy.mean(false_furer_results_hellinger))
std_hellingers.append(numpy.std(false_furer_results_hellinger, ddof=1))
avg_nodes_observed.append(numpy.mean(observed_nodes))
number_of_sampling_iterations.append(nr_iterations[counter])
nr_nodes_per_time_interval_per_runs.append(
float((numpy.mean(observed_nodes_difference_per_snapshot) /
duration[counter_duration])))
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]
average_of_embeddings.append(nr_embeddings_temp)
stdeviation = numpy.nan
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))))
stdevs.append(stdeviation)
counter += 1
counter_duration += 1
return {
"average_klds": average_klds,
"average_bhattas": average_bhattas,
"average_hellingers": average_hellingers,
"std_klds": std_klds,
"std_bhattas": std_bhattas,
"std_hellingers": std_hellingers,
"avg_nodes_observed": avg_nodes_observed,
"nr_nodes_per_time_interval_per_runs":
nr_nodes_per_time_interval_per_runs,
"number_of_sampling_iterations": number_of_sampling_iterations,
"average_of_embeddings": average_of_embeddings,
"stdevs": stdevs
}
