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_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
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:
if counter==1:
break
print "Processed ",counter," out of: ",len(monitoring_marks)
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,'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:
print "WARNING: bad (not enough present) pattern or too high trash threshold! STOPPING."
break
[pdf , tl, dk]= smplr.make_pd_general_kickout_default_limited(fdict_Furer, trash_list, default_key)
#with open('make_pdf_general_kickout_default_MARTIN.csv','w') as f:
# for k in pdf.keys():
# f.write(str(k)+';'+str(pdf[k])+'\n')
furer_results_KLD.append(su.avg_kld(smplr.transform_to_ptable(pde), smplr.transform_to_ptable(pdf)))
#print "KLD: ",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)))
print "Writing to: ",snapshot_directory_file
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")
print "KLD: ",str(numpy.mean(furer_results_KLD))
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]
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
print "Old stdev"
print sum_of_squares[counter]
print sum_of_embeddings[counter]
a=Decimal(sum_of_squares[counter])-(Decimal(math.pow(sum_of_embeddings[counter], 2))/Decimal(float(nr_iterations[counter])))
print a
stdeviation=math.sqrt(a/Decimal(float((nr_iterations[counter]-1))))
else:
print "here"
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 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 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)))
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 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_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
print "NR ITERATIONS: ",nr_iterations
print "sum_of_embeddings: ",sum_of_embeddings_vers1
print "sum_of_squares: ",sum_of_squares_vers1
snapshot_inits=[]
for i in range(repetitions):
snapshot_inits.append(0)
counter_duration=0
counter=0
interval=0
acc_nr_emb=0
acc_nr_emb_minus_average=0
nr_emb_per_interval=[]
for time_snapshot in monitoring_marks:
interval+=1
randnode_results_KLD = []
randnode_results_bhatta = []
randnode_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,'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_old(fdict_exhaustive, trash_factor=0.01)
if len(pde) < 1:
print "WARNING: bad (not enough present) pattern or too high trash threshold! STOPPING."
break
print "random vertex counts: "
#for k in fdict_limited.keys():
# print k,fdict_limited[k]
[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)))
print "Writing to: ",snapshot_directory_file
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")
print "KLD: ",str(numpy.mean(randnode_results_KLD))
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])*(experiments.globals.nr_root_nodes)
print "HALOOO:", Decimal(sum_number_of_embeddings[counter])
old=False
if avg<0:
print "Handling old data structures"
#this means we handle the old version
avg=float(sum_of_embeddings_random_old[counter])/nr_iterations[counter]
old=True
#nr_emb_per_interval.append(avg)
#print "Nr emb per interval: ",nr_emb_per_interval
#resultfile.write('average of embeddings w.r.t sampling iterations:' + str(avg)+"\n")
#print "Avg old method: ",avg
#acc_nr_emb+=Decimal(avg)
#print "Interval: ",interval," Accnr emg: ",acc_nr_emb
#avg_emb_Jan=acc_nr_emb/(interval)
#acc_nr_emb_minus_average=0
#for i in xrange(0,interval):
# acc_nr_emb_minus_average+=math.pow((Decimal(nr_emb_per_interval[i])-Decimal(avg_emb_Jan)),2)
#std_Jan=math.sqrt(acc_nr_emb_minus_average)/(interval)
#print "STDEV Jan: ",std_Jan
#print "Average Nr emb: Jan Method: ",avg_emb_Jan
sum1=Decimal(sum_of_the_square_embeddings[counter])
sum2=Decimal(sum_number_of_embeddings[counter])
#variance=(Decimal(sum1)-Decimal(sum2))/(nr_iterations[counter]-1)
print "sum 1: ",sum1
print "sum 2: ",sum2
#print "Variance: ",variance
#stdev1=math.sqrt(variance)
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:
print "Handling old data structures"
#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))
print "STDEV: ",stdev
print "STDEV 2: ",stdev2
print "Nr embeddingS: ",avg
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")
#resultfile.write('overal nr. of embeddings:' + str(sum_of_embeddings[counter])+"\n")
#a=sum_of_squares[counter]-((math.pow(sum_of_embeddings[counter], 2)/nr_iterations[counter]))
#stdeviation=math.sqrt(a/(nr_iterations[counter]-1))
#a1=sum_of_squares_vers1[counter]-((math.pow(sum_of_embeddings_vers1[counter], 2)/nr_iterations[counter]))
#stdeviation1=math.sqrt(a1/(nr_iterations[counter]-1))
#resultfile.write('stdeviation of # embeddings:' + str(stdeviation)+"\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:
print "WARNING: bad (not enough present) pattern or too high trash threshold! STOPPING."
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"
print "RESULT FILE NAME: ",result_file_name
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()