def test_inductivedoc1(self):
from pm4py.log.importer import xes as xes_importer
log = xes_importer.import_from_file_xes(
"inputData\\running-example.xes")
from pm4py.algo.inductive import factory as inductive_miner
net, initial_marking, final_marking = inductive_miner.apply(log)
from pm4py.models.petri import visualize as pn_viz
gviz = pn_viz.graphviz_visualization(net,
initial_marking=initial_marking,
final_marking=final_marking)
def test_docmeasures11(self):
from pm4py.log.importer import xes as xes_importer
log = xes_importer.import_from_file_xes('inputData\\receipt.xes')
from pm4py.algo.alpha import factory as alpha_miner
from pm4py.algo.inductive import factory as inductive_miner
alpha_petri, alpha_initial_marking, alpha_final_marking = alpha_miner.apply(log)
inductive_petri, inductive_initial_marking, inductive_final_marking = inductive_miner.apply(log)
from pm4py.evaluation.replay_fitness import factory as replay_factory
fitness_alpha = replay_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking)
fitness_inductive = replay_factory.apply(log, inductive_petri, inductive_initial_marking, inductive_final_marking)
# print("fitness_alpha=",fitness_alpha)
# print("fitness_inductive=",fitness_inductive)
from pm4py.evaluation.precision import factory as precision_factory
precision_alpha = precision_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking)
precision_inductive = precision_factory.apply(log, inductive_petri, inductive_initial_marking, inductive_final_marking)
# print("precision_alpha=",precision_alpha)
# print("precision_inductive=",precision_inductive)
from pm4py.evaluation.generalization import factory as generalization_factory
generalization_alpha = generalization_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking)
generalization_inductive = generalization_factory.apply(log, inductive_petri, inductive_initial_marking,
inductive_final_marking)
# print("generalization_alpha=",generalization_alpha)
# print("generalization_inductive=",generalization_inductive)
from pm4py.evaluation.simplicity import factory as simplicity_factory
simplicity_alpha = simplicity_factory.apply(alpha_petri)
simplicity_inductive = simplicity_factory.apply(inductive_petri)
# print("simplicity_alpha=",simplicity_alpha)
# print("simplicity_inductive=",simplicity_inductive)
from pm4py.evaluation import factory as evaluation_factory
alpha_evaluation_result = evaluation_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking)
# print("alpha_evaluation_result=",alpha_evaluation_result)
inductive_evaluation_result = evaluation_factory.apply(log, inductive_petri, inductive_initial_marking,
inductive_final_marking)
sys.path.insert(0, parentdir)
#from pm4py.algo.inductive.versions import dfg_only
from pm4py.algo.inductive import factory as inductive_factory
from pm4py.log.importer import xes as xes_importer
from pm4py.models.petri import visualize as pn_viz
import pm4py.algo.alignments as align
from pm4py.algo.tokenreplay.versions import token_replay
import traceback
from pm4py.models.petri.exporter import pnml as petri_exporter
if __name__ == "__main__":
logPath = "..\\tests\\inputData\\running-example.xes"
log = xes_importer.import_from_file_xes(logPath)
net, marking, final_marking = inductive_factory.apply(log)
for place in marking:
print("initial marking " + place.name)
"""final_marking = petri.petrinet.Marking()
for p in net.places:
if not p.out_arcs:
final_marking[p] = 1"""
for place in final_marking:
print("final marking " + place.name)
gviz = pn_viz.graphviz_visualization(net,
initial_marking=marking,
final_marking=final_marking,
debug=True)
gviz.view()
if True:
def get_process_schema():
"""
Gets the process model in the specified format (e.g. SVG)
Argument parameters:
process -> (MANDATORY) Name of the process to consider
decreasingfactor -> Filtering factor that is passed to the algorithms
format -> Format of the diagram that is returned
activitykey -> Activity key (if not specified, then concept:name)
timestampkey -> Timestamp key (if not specified, then time:timestamp)
decreasingfactor -> Decreasing factor for the filtering algorithm
discoveryalgorithm -> Applied discovery algorithm (Alpha, Inductive)
replayenabled -> Is replay enabled?
replaymeasure -> Measure to show in the replay (frequency/performance)
:return:
"""
# read the requested process name
process = request.args.get('process', type=str)
# read the activity key
activity_key = request.args.get('activitykey', default=None, type=str)
# read the timestamp key
timestamp_key = request.args.get('timestampkey',
default="time:timestamp",
type=str)
# read the decreasing factor
decreasingFactor = request.args.get('decreasingfactor',
default=0.6,
type=float)
# read the image format
imageFormat = request.args.get('format', default='svg', type=str)
# specification of process discovery algorithm
discoveryAlgorithm = request.args.get('discoveryalgorithm',
default='inductive',
type=str)
# replay enabled
replayEnabled = request.args.get('replayenabled', default=True, type=bool)
# replay measure
replayMeasure = request.args.get('replaymeasure',
default="frequency",
type=str)
# acquire the semaphore as we want to access the logs
# without desturbing
shared.sem.acquire()
try:
# if the specified process is in memory, then proceed
if process in shared.trace_logs:
# retrieve the log
original_log = shared.trace_logs[process]
original_log, classifier_key = insert_classifier.search_and_insert_event_classifier_attribute(
original_log)
if activity_key is None:
activity_key = classifier_key
if activity_key is None:
activity_key = "concept:name"
# release the semaphore
shared.sem.release()
# apply automatically a filter
log = auto_filter.apply_auto_filter(
copy(original_log),
decreasingFactor=decreasingFactor,
activity_key=activity_key)
# apply a process discovery algorithm
if discoveryAlgorithm == "dfg":
# gets the number of occurrences of the single attributes in the filtered log
filtered_log_activities_count = activities_module.get_activities_from_log(
log)
# gets an intermediate log that is the original log restricted to the list
# of attributes that appears in the filtered log
intermediate_log = activities_module.filter_log_by_specified_attributes(
original_log, filtered_log_activities_count)
# gets the number of occurrences of the single attributes in the intermediate log
activities_count = activities_module.get_activities_from_log(
intermediate_log)
# calculate DFG of the filtered log and of the intermediate log
dfg_filtered_log = dfg_factory.apply(log,
variant=replayMeasure)
dfg_intermediate_log = dfg_factory.apply(intermediate_log,
variant=replayMeasure)
# replace edges values in the filtered DFG from the one found in the intermediate log
dfg_filtered_log = dfg_replacement.replace_values(
dfg_filtered_log, dfg_intermediate_log)
# retrieve the diagram in base64
diagram = dfg_visualize.return_diagram_as_base64(
activities_count,
dfg_filtered_log,
format=imageFormat,
measure=replayMeasure)
else:
if discoveryAlgorithm == "inductive":
net, initial_marking, final_marking = inductive_factory.apply(
log, activity_key=activity_key)
elif discoveryAlgorithm == "alpha":
net, initial_marking, final_marking = alpha_factory.apply(
log, activity_key=activity_key)
if replayEnabled:
# do the replay
[traceIsFit, traceFitnessValue, activatedTransitions, placeFitness, reachedMarkings, enabledTransitionsInMarkings] =\
token_replay.apply_log(original_log, net, initial_marking, final_marking, activity_key=activity_key)
element_statistics = performance_map.single_element_statistics(
original_log,
net,
initial_marking,
activatedTransitions,
activity_key=activity_key,
timestamp_key=timestamp_key)
aggregated_statistics = performance_map.aggregate_statistics(
element_statistics, measure=replayMeasure)
# return the diagram in base64
diagram = return_diagram_as_base64(
net,
format=imageFormat,
initial_marking=initial_marking,
final_marking=final_marking,
decorations=aggregated_statistics)
else:
# return the diagram in base64
diagram = return_diagram_as_base64(
net,
format=imageFormat,
initial_marking=initial_marking,
final_marking=final_marking)
return diagram
else:
# release the semaphore
shared.sem.release()
except Exception as e:
# manage exception
logging.error("exception calculating process schema: " + str(e))
logging.error("traceback: " + traceback.format_exc())
return ""
activity_key = "concept:name"
if not classifier_key is None:
activity_key = classifier_key
t1 = time.time()
alpha_model, alpha_initial_marking, alpha_final_marking = alpha.apply(
log, activity_key=activity_key)
pnml_exporter.export_petri_to_pnml(
alpha_model, alpha_initial_marking,
os.path.join(pnmlFolder, logNamePrefix + "_alpha.pnml"))
t2 = time.time()
print("time interlapsed for calculating Alpha Model", (t2 - t1))
t1 = time.time()
inductive_model, inductive_initial_marking, inductive_final_marking = inductive.apply(
log, activity_key=activity_key)
pnml_exporter.export_petri_to_pnml(
inductive_model, inductive_initial_marking,
os.path.join(pnmlFolder, logNamePrefix + "_inductive.pnml"))
t2 = time.time()
print("time interlapsed for calculating Inductive Model", (t2 - t1))
parameters = {"activity_key": activity_key, "format": "png"}
alpha_vis = petri_vis_factory.apply(alpha_model,
alpha_initial_marking,
alpha_final_marking,
log=log,
parameters=parameters,
variant="frequency")
vis_save(alpha_vis,