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import inspect
import os
import sys
import traceback
if __name__ == "__main__":
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
parentdir2 = os.path.dirname(parentdir)
sys.path.insert(0, parentdir)
sys.path.insert(0, parentdir2)
import time
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.algo.discovery.inductive import algorithm as inductive
from pm4py.algo.conformance.alignments.petri_net.variants import state_equation_a_star
from pm4py.algo.discovery.footprints import algorithm as footprints_discovery
from pm4py.algo.conformance.footprints import algorithm as footprints_conformance
from pm4py.algo.discovery.alpha import algorithm as alpha
from pm4py.algo.discovery.heuristics import algorithm as heuristics_miner
from pm4py.objects.conversion.process_tree import converter as pt_converter
from pm4py.algo.evaluation.replay_fitness import algorithm as fitness_evaluator
from pm4py.algo.evaluation.precision import algorithm as precision_evaluator
from pm4py.algo.evaluation.simplicity import algorithm as simplicity_evaluator
from pm4py.algo.evaluation.generalization import algorithm as generalization_evaluator
from pm4py.objects.log.util import insert_classifier
from pm4py.objects.petri_net.exporter import exporter as pnml_exporter
from pm4py.visualization.petri_net import visualizer as petri_vis
from pm4py.visualization.common.save import save as vis_save
from pm4py import util as pmutil
from pm4py.algo.analysis.woflan import algorithm as woflan
def get_elonged_string(stru):
nchar = 30
if len(stru) >= nchar:
return stru
return stru + " ".join([""] * (nchar - len(stru)))
def get_elonged_float(value):
stru = "%.3f" % value
return get_elonged_string(stru)
ENABLE_VISUALIZATIONS = False
ENABLE_VISUALIZATIONS_INDUCTIVE = False
ENABLE_ALIGNMENTS = False
ENABLE_PRECISION = True
ENABLE_PETRI_EXPORTING = False
ENABLE_PETRI_EXPORTING_DEBUG = True
CHECK_SOUNDNESS = True
WOFLAN_RETURN_ASAP = True
WOFLAN_PRINT_DIAGNOSTICS = True
WOFLAN_RETURN_DIAGNOSTICS = True
INDUCTIVE_MINER_VARIANT = inductive.Variants.IM_CLEAN
ALIGN_VARIANT = state_equation_a_star
logFolder = os.path.join("..", "compressed_input_data")
pnmlFolder = "pnml_folder"
pngFolder = "png_folder"
times_tokenreplay_alpha = {}
times_tokenreplay_imdf = {}
times_footprints_imdf = {}
times_alignments_imdf = {}
fitness_token_alpha = {}
fitness_token_imdf = {}
fitness_footprints_imdf = {}
fitness_align_imdf = {}
precision_alpha = {}
precision_imdf = {}
simplicity_alpha = {}
simplicity_imdf = {}
generalization_alpha = {}
generalization_imdf = {}
def write_report():
f = open("report.txt", "w")
f.write("\n\n")
f.write("Fitness on Alpha and Inductive models - measured by token-based replay and alignments\n")
f.write("----\n")
f.write(
get_elonged_string("log") + "\t" + get_elonged_string("fitness_token_alpha") + "\t" + get_elonged_string(
"times_tokenreplay_alpha") + "\t" + get_elonged_string(
"fitness_token_imdf") + "\t" + get_elonged_string("times_tokenreplay_imdf") + "\t" + get_elonged_string(
"fitness_footprints_imdf") + "\t" + get_elonged_string("times_footprints_imdf"))
if ENABLE_ALIGNMENTS:
f.write(
"\t" + get_elonged_string("fitness_align_imdf") + "\t" + get_elonged_string("times_alignments_imdf"))
f.write("\n")
for this_logname in precision_alpha:
# F.write("%s\t\t%.3f\t\t%.3f\n" % (logName, fitness_token_alpha[logName], fitness_token_imdf[logName]))
f.write(get_elonged_string(this_logname))
f.write("\t")
f.write(get_elonged_float(fitness_token_alpha[this_logname]))
f.write("\t")
f.write(get_elonged_float(times_tokenreplay_alpha[this_logname]))
f.write("\t")
f.write(get_elonged_float(fitness_token_imdf[this_logname]))
f.write("\t")
f.write(get_elonged_float(times_tokenreplay_imdf[this_logname]))
f.write("\t")
f.write(get_elonged_float(fitness_footprints_imdf[this_logname]))
f.write("\t")
f.write(get_elonged_float(times_footprints_imdf[this_logname]))
if ENABLE_ALIGNMENTS:
f.write("\t")
f.write(get_elonged_float(fitness_align_imdf[this_logname]))
f.write("\t")
f.write(get_elonged_float(times_alignments_imdf[this_logname]))
f.write("\n")
f.write("\n\n")
f.write("Precision measured by ETConformance where activated transitions are retrieved using token replay\n")
f.write("----\n")
f.write(get_elonged_string("log") + "\t" + get_elonged_string("precision_alpha") + "\t" + get_elonged_string(
"precision_imdf") + "\n")
for this_logname in precision_alpha:
f.write(get_elonged_string(this_logname))
f.write("\t")
f.write(get_elonged_float(precision_alpha[this_logname]))
f.write("\t")
f.write(get_elonged_float(precision_imdf[this_logname]))
f.write("\n")
f.write("\n\n")
f.write("Generalization based on token replay transition recall\n")
f.write("----\n")
f.write(
get_elonged_string("log") + "\t" + get_elonged_string("generalization_alpha") + "\t" + get_elonged_string(
"generalization_imdf") + "\n")
for this_logname in precision_alpha:
f.write(get_elonged_string(this_logname))
f.write("\t")
f.write(get_elonged_float(generalization_alpha[this_logname]))
f.write("\t")
f.write(get_elonged_float(generalization_imdf[this_logname]))
f.write("\n")
f.write("\n\n")
f.write("Simplicity based on inverse arc degree\n")
f.write("----\n")
f.write(get_elonged_string("log") + "\t" + get_elonged_string("simplicity_alpha") + "\t" + get_elonged_string(
"simplicity_imdf") + "\n")
for this_logname in precision_alpha:
f.write(get_elonged_string(this_logname))
f.write("\t")
f.write(get_elonged_float(simplicity_alpha[this_logname]))
f.write("\t")
f.write(get_elonged_float(simplicity_imdf[this_logname]))
f.write("\n")
f.write("\n")
f.close()
for logName in os.listdir(logFolder):
if "." in logName:
logNamePrefix = logName.split(".")[0]
logExtension = logName[len(logNamePrefix) + 1:]
print("\nelaborating " + logName)
logPath = os.path.join(logFolder, logName)
if "xes" in logExtension:
log = xes_importer.apply(logPath, variant=xes_importer.Variants.CHUNK_REGEX)
log, classifier_key = insert_classifier.search_act_class_attr(log, force_activity_transition_insertion=True)
print("loaded log")
activity_key = "concept:name"
if classifier_key is not None:
activity_key = classifier_key
parameters_discovery = {pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY: activity_key,
pmutil.constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY: activity_key}
t1 = time.time()
alpha_model, alpha_initial_marking, alpha_final_marking = alpha.apply(log, parameters=parameters_discovery)
if ENABLE_PETRI_EXPORTING:
pnml_exporter.export_net(alpha_model, alpha_initial_marking,
os.path.join(pnmlFolder, logNamePrefix + "_alpha.pnml"),
final_marking=alpha_final_marking)
t2 = time.time()
print("time interlapsed for calculating Alpha Model", (t2 - t1))
if CHECK_SOUNDNESS:
try:
res_woflan, diagn = woflan.apply(alpha_model, alpha_initial_marking, alpha_final_marking,
parameters={"return_asap_when_not_sound": WOFLAN_RETURN_ASAP,
"print_diagnostics": WOFLAN_PRINT_DIAGNOSTICS,
"return_diagnostics": WOFLAN_RETURN_DIAGNOSTICS})
print("alpha woflan", res_woflan)
except:
if ENABLE_PETRI_EXPORTING_DEBUG:
exce = traceback.format_exc()
pnml_exporter.export_net(alpha_model, alpha_initial_marking,
os.path.join(pnmlFolder, logNamePrefix + "_alpha.pnml"),
final_marking=alpha_final_marking)
F = open(logNamePrefix + "_alpha.txt", "w")
F.write(exce)
F.close()
t1 = time.time()
heu_model, heu_initial_marking, heu_final_marking = heuristics_miner.apply(log,
parameters=parameters_discovery)
if ENABLE_PETRI_EXPORTING:
pnml_exporter.export_net(heu_model, heu_initial_marking,
os.path.join(pnmlFolder, logNamePrefix + "_heuristics.pnml"),
final_marking=heu_final_marking)
t2 = time.time()
print("time interlapsed for calculating Heuristics Model", (t2 - t1))
if CHECK_SOUNDNESS:
try:
res_woflan, diagn = woflan.apply(heu_model, heu_initial_marking, heu_initial_marking,
parameters={"return_asap_when_not_sound": WOFLAN_RETURN_ASAP,
"print_diagnostics": WOFLAN_PRINT_DIAGNOSTICS,
"return_diagnostics": WOFLAN_RETURN_DIAGNOSTICS})
print("heuristics woflan", res_woflan)
except:
if ENABLE_PETRI_EXPORTING_DEBUG:
exce = traceback.format_exc()
pnml_exporter.export_net(heu_model, heu_initial_marking,
os.path.join(pnmlFolder, logNamePrefix + "_heuristics.pnml"),
final_marking=heu_final_marking)
F = open(logNamePrefix + "_heuristics.txt", "w")
F.write(exce)
F.close()
t1 = time.time()
tree = inductive.apply(log, parameters=parameters_discovery, variant=INDUCTIVE_MINER_VARIANT)
# print(tree)
inductive_model, inductive_im, inductive_fm = pt_converter.apply(tree,
variant=pt_converter.Variants.TO_PETRI_NET)
"""inductive_model, inductive_im, inductive_fm = inductive.apply(log, parameters=parameters_discovery,
variant=INDUCTIVE_MINER_VARIANT)"""
if ENABLE_PETRI_EXPORTING:
pnml_exporter.export_net(inductive_model, inductive_im,
os.path.join(pnmlFolder, logNamePrefix + "_inductive.pnml"),
final_marking=inductive_fm)
"""
generated_log = pt_semantics.generate_log(tree)
print("first trace of log", [x["concept:name"] for x in generated_log[0]])
"""
t2 = time.time()
print("time interlapsed for calculating Inductive Model", (t2 - t1))
if CHECK_SOUNDNESS:
res_woflan, diagn = woflan.apply(inductive_model, inductive_im, inductive_fm,
parameters={"return_asap_when_not_sound": WOFLAN_RETURN_ASAP,
"print_diagnostics": WOFLAN_PRINT_DIAGNOSTICS,
"return_diagnostics": WOFLAN_RETURN_DIAGNOSTICS})
print("inductive woflan", res_woflan)
parameters = {fitness_evaluator.Variants.TOKEN_BASED.value.Parameters.ACTIVITY_KEY: activity_key,
fitness_evaluator.Variants.TOKEN_BASED.value.Parameters.ATTRIBUTE_KEY: activity_key,
"align_variant": ALIGN_VARIANT,
"format": "png"}
t1 = time.time()
fitness_token_alpha[logName] = \
fitness_evaluator.apply(log, alpha_model, alpha_initial_marking, alpha_final_marking,
parameters=parameters, variant=fitness_evaluator.Variants.TOKEN_BASED)[
'perc_fit_traces']
print(str(time.time()) + " fitness_token_alpha for " + logName + " succeeded! " + str(
fitness_token_alpha[logName]))
t2 = time.time()
times_tokenreplay_alpha[logName] = t2 - t1
t1 = time.time()
fitness_token_imdf[logName] = \
fitness_evaluator.apply(log, inductive_model, inductive_im, inductive_fm, parameters=parameters,
variant=fitness_evaluator.Variants.TOKEN_BASED)[
'perc_fit_traces']
print(str(time.time()) + " fitness_token_inductive for " + logName + " succeeded! " + str(
fitness_token_imdf[logName]))
t2 = time.time()
times_tokenreplay_imdf[logName] = t2 - t1
t1 = time.time()
fp_log = footprints_discovery.apply(log, parameters=parameters)
fp_tree = footprints_discovery.apply(tree, parameters=parameters)
conf = footprints_conformance.apply(fp_log, fp_tree,
variant=footprints_conformance.Variants.TRACE_EXTENSIVE,
parameters=parameters)
# fitness_fp = float(len([x for x in conf if len(x) == 0])) / float(len(conf)) * 100.0 if conf else 0.0
fitness_fp = float(len([x for x in conf if x["is_footprints_fit"]])) / float(
len(conf)) * 100.0 if conf else 0.0
t2 = time.time()
fitness_footprints_imdf[logName] = fitness_fp
times_footprints_imdf[logName] = t2 - t1
if ENABLE_ALIGNMENTS:
t1 = time.time()
fitness_align_imdf[logName] = \
fitness_evaluator.apply(log, inductive_model, inductive_im, inductive_fm,
variant=fitness_evaluator.Variants.ALIGNMENT_BASED, parameters=parameters)[
'percFitTraces']
print(str(time.time()) + " fitness_token_align for " + logName + " succeeded! " + str(
fitness_align_imdf[logName]))
t2 = time.time()
times_alignments_imdf[logName] = t2 - t1
if ENABLE_PRECISION:
precision_alpha[logName] = precision_evaluator.apply(log, alpha_model, alpha_initial_marking,
alpha_final_marking,
variant=precision_evaluator.Variants.ETCONFORMANCE_TOKEN,
parameters=parameters)
else:
precision_alpha[logName] = 0.0
print(str(time.time()) + " precision_alpha for " + logName + " succeeded! " + str(precision_alpha[logName]))
generalization_alpha[logName] = generalization_evaluator.apply(log, alpha_model, alpha_initial_marking,
alpha_final_marking, parameters=parameters)
print(str(time.time()) + " generalization_alpha for " + logName + " succeeded! " + str(
generalization_alpha[logName]))
simplicity_alpha[logName] = simplicity_evaluator.apply(alpha_model, parameters=parameters)
print(
str(time.time()) + " simplicity_alpha for " + logName + " succeeded! " + str(simplicity_alpha[logName]))
if ENABLE_PRECISION:
precision_imdf[logName] = precision_evaluator.apply(log, inductive_model, inductive_im,
inductive_fm,
variant=precision_evaluator.Variants.ETCONFORMANCE_TOKEN,
parameters=parameters)
else:
precision_imdf[logName] = 0.0
print(str(time.time()) + " precision_imdf for " + logName + " succeeded! " + str(precision_imdf[logName]))
generalization_imdf[logName] = generalization_evaluator.apply(log, inductive_model, inductive_im,
inductive_fm, parameters=parameters)
print(str(time.time()) + " generalization_imdf for " + logName + " succeeded! " + str(
generalization_imdf[logName]))
simplicity_imdf[logName] = simplicity_evaluator.apply(inductive_model, parameters=parameters)
print(str(time.time()) + " simplicity_imdf for " + logName + " succeeded! " + str(simplicity_imdf[logName]))
write_report()
if ENABLE_VISUALIZATIONS:
try:
alpha_vis = petri_vis.apply(alpha_model, alpha_initial_marking, alpha_final_marking, log=log,
parameters=parameters, variant=petri_vis.Variants.FREQUENCY)
vis_save(alpha_vis, os.path.join(pngFolder, logNamePrefix + "_alpha.png"))
print(str(time.time()) + " alpha visualization for " + logName + " succeeded!")
except:
print(str(time.time()) + " alpha visualization for " + logName + " failed!")
traceback.print_exc()
try:
heuristics_vis = petri_vis.apply(heu_model, heu_initial_marking, heu_final_marking,
log=log, parameters=parameters,
variant=petri_vis.FREQUENCY_DECORATION)
vis_save(heuristics_vis, os.path.join(pngFolder, logNamePrefix + "_heuristics.png"))
print(str(time.time()) + " heuristics visualization for " + logName + " succeeded!")
except:
print(str(time.time()) + " heuristics visualization for " + logName + " failed!")
traceback.print_exc()
if ENABLE_VISUALIZATIONS or ENABLE_VISUALIZATIONS_INDUCTIVE:
try:
inductive_vis = petri_vis.apply(inductive_model, inductive_im, inductive_fm,
log=log, parameters=parameters,
variant=petri_vis.PERFORMANCE_DECORATION)
vis_save(inductive_vis, os.path.join(pngFolder, logNamePrefix + "_inductive.png"))
print(str(time.time()) + " inductive visualization for " + logName + " succeeded!")
except:
print(str(time.time()) + " inductive visualization for " + logName + " failed!")
traceback.print_exc()
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