def test_alphadoc1(self):
from pm4py.log.importer import xes as xes_importer
log = xes_importer.import_from_file_xes(
"inputData\\running-example.xes")
from pm4py.algo.alpha import factory as alpha_miner
net, initial_marking, final_marking = alpha_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_alphaMinerVisualizationFromXES(self):
log, net, marking, fmarking = self.obtainPetriNetThroughAlphaMiner(
os.path.join(INPUT_DATA_DIR, "running-example.xes"))
log.sort()
log = log.sample()
log.insert_trace_index_as_event_attribute()
petri_exporter.export_petri_to_pnml(
net, marking, os.path.join(OUTPUT_DATA_DIR,
"running-example.pnml"))
os.remove(os.path.join(OUTPUT_DATA_DIR, "running-example.pnml"))
gviz = pn_viz.graphviz_visualization(net)
final_marking = petri.petrinet.Marking()
for p in net.places:
if not p.out_arcs:
final_marking[p] = 1
[
traceIsFit, traceFitnessValue, activatedTransitions, placeFitness,
reachedMarkings, enabledTransitionsInMarkings
] = token_replay.apply_log(log,
net,
marking,
final_marking,
enable_placeFitness=True)
time3 = time.time()
print("time3 - time2: "+str(time3-time2))
#dataframe = df_filtering.filter_df_on_ncases(dataframe, case_id_glue="case:concept:name", max_no_cases=1000)
time4 = time.time()
print("time4 - time3: "+str(time4-time3))
#dataframe = df_filtering.filter_df_on_case_length(dataframe, case_id_glue="case:concept:name", min_trace_length=3, max_trace_length=50)
print(dataframe)
time5 = time.time()
print("time5 - time4: "+str(time5-time4))
dataframe = csv_importer.convert_timestamp_columns_in_df(dataframe)
time6 = time.time()
print("time6 - time5: "+str(time6-time5))
#dataframe = dataframe.sort_values('time:timestamp')
time7 = time.time()
print("time7 - time6: "+str(time7-time6))
# show the filtered dataframe on the screen
activities_count = df_statistics.get_activities_count(dataframe)
[dfg_frequency, dfg_performance] = df_statistics.get_dfg_graph(dataframe, measure="both")
#activities_count = df_statistics.get_activities_count(dataframe, activity_key="event")
#[dfg_frequency, dfg_performance] = df_statistics.get_dfg_graph(dataframe, measure="both", case_id_glue="case", activity_key="event", timestamp_key="startTime")
time8 = time.time()
print("time8 - time7: "+str(time8-time7))
gviz = dfg_vis_factory.apply(dfg_frequency, activities_count=activities_count)
gviz.view()
net, initial_marking, final_marking = inductive_factory.apply_dfg(dfg_frequency)
gviz = pn_viz.graphviz_visualization(net, initial_marking=initial_marking, final_marking=final_marking, debug=True)
gviz.view()
time9 = time.time()
print("time9 - time8: "+str(time9-time8))
print("time9 - time1: "+str(time9-time1))
import time
log = xes_importer.import_from_file_xes(
'..\\tests\\inputData\\running-example.xes')
#log = xes_importer.import_from_path_xes('a32f0n00.xes')
net, marking, final_marking = alpha_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)
gviz.view()
log = log[0:min(100, len(log))]
time0 = time.time()
print("started token replay")
[
traceIsFit, traceFitnessValue, activatedTransitions, placeFitness,
reachedMarkings, enabledTransitionsInMarkings
] = token_replay.apply_log(log,
net,
marking,
final_marking,
enable_placeFitness=True,
consider_remaining_in_fitness=False)
for place in placeFitness:
#log = log_transform.transform_trace_log_to_event_log(log)
#print('time to transform the log: %s' % (time.time() - start))
#print('new first event %s' % log[0])
#start = time.time()
#log = log_instance.EventLog(sorted(log, key=lambda e: e['time:timestamp'], reverse=True), attributes=log.attributes, classifiers=log.classifiers, omni_present=log.omni_present, extensions=log.extensions)
#print('time to sort the log on time stamp: %s' % (time.time() - start))
#print(log[0])
#start = time.time()
#log = log_transform.transform_event_log_to_trace_log(log)
#print('time to transform the log: %s' % (time.time() - start))
start = time.time()
dfg = dfg_instance.apply(log)
print('time to compute dfg: %s' % (time.time()-start))
start = time.time()
cag = causal_instance.compute_causal_relations(dfg, causal_instance.CAUSAL_ALPHA)
print('time to compute causal graph: %s' % (time.time() - start))
start = time.time()
'''
net, initial_marking, final_marking = alpha_classic.apply(
trace_log, 'Activity')
print('time to compute alpha: %s' % (time.time() - start))
gviz = pn_viz.graphviz_visualization(net)
gviz.view()