def view_petri_net(petri_net, initial_marking, final_marking, format="png"):
"""
Views a (composite) Petri net
Parameters
-------------
petri_net
Petri net
initial_marking
Initial marking
final marking
Final marking
format
Format of the output picture (default: png)
"""
from pm4py.visualization.petrinet import visualizer as pn_visualizer
gviz = pn_visualizer.apply(
petri_net,
initial_marking,
final_marking,
parameters={
pn_visualizer.Variants.WO_DECORATION.value.Parameters.FORMAT:
format
})
pn_visualizer.view(gviz)
def execute_script():
log_path = os.path.join("..", "tests", "input_data",
"interval_event_log.xes")
log = xes_importer.apply(log_path)
parameters = {}
parameters[
constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY] = "start_timestamp"
parameters[constants.PARAMETER_CONSTANT_TIMESTAMP_KEY] = "time:timestamp"
parameters[constants.PARAMETER_CONSTANT_ACTIVITY_KEY] = "concept:name"
parameters["format"] = "svg"
start_activities = sa_get.get_start_activities(log, parameters=parameters)
end_activities = ea_get.get_end_activities(log, parameters=parameters)
parameters["start_activities"] = start_activities
parameters["end_activities"] = end_activities
dfg_freq = dfg_algorithm.apply(log,
parameters=parameters,
variant=dfg_algorithm.Variants.FREQUENCY)
dfg_perf = dfg_algorithm.apply(log,
parameters=parameters,
variant=dfg_algorithm.Variants.PERFORMANCE)
dfg_gv_freq = dfg_vis_fact.apply(dfg_freq,
log=log,
variant=dfg_vis_fact.Variants.FREQUENCY,
parameters=parameters)
dfg_vis_fact.view(dfg_gv_freq)
dfg_gv_perf = dfg_vis_fact.apply(dfg_perf,
log=log,
variant=dfg_vis_fact.Variants.PERFORMANCE,
parameters=parameters)
dfg_vis_fact.view(dfg_gv_perf)
net, im, fm = dfg_conv.apply(dfg_freq)
gviz = pn_vis.apply(net, im, fm, parameters=parameters)
pn_vis.view(gviz)
def execute_script():
# import the log
log_path = os.path.join("..", "tests", "input_data", "receipt.xes")
log = xes_importer.apply(log_path)
# apply Inductive Miner
net, initial_marking, final_marking = inductive_miner.apply(log)
# get visualization
variant = pn_vis.Variants.PERFORMANCE
parameters_viz = {
pn_vis.Variants.PERFORMANCE.value.Parameters.AGGREGATION_MEASURE:
"mean",
pn_vis.Variants.PERFORMANCE.value.Parameters.FORMAT: "svg"
}
gviz = pn_vis.apply(net,
initial_marking,
final_marking,
log=log,
variant=variant,
parameters=parameters_viz)
pn_vis.view(gviz)
# do another visualization with frequency
variant = pn_vis.Variants.FREQUENCY
parameters_viz = {pn_vis.Variants.FREQUENCY.value.Parameters.FORMAT: "svg"}
gviz = pn_vis.apply(net,
initial_marking,
final_marking,
log=log,
variant=variant,
parameters=parameters_viz)
pn_vis.view(gviz)
def model_metrics(model_log_path, metric_log_path, gexp_name):
start_time = time()
model_log_csv = pd.read_csv(model_log_path, ',')
metric_log_csv = pd.read_csv(metric_log_path, ',')
parameters = {log_converter.Variants.TO_EVENT_LOG.value.Parameters.CASE_ID_KEY: 'number'}
model_log = log_converter.apply(model_log_csv, parameters=parameters, variant=log_converter.Variants.TO_EVENT_LOG)
metric_log = log_converter.apply(metric_log_csv, parameters=parameters, variant=log_converter.Variants.TO_EVENT_LOG)
parameters = {inductive_miner.Variants.DFG_BASED.value.Parameters.CASE_ID_KEY: 'number',
inductive_miner.Variants.DFG_BASED.value.Parameters.ACTIVITY_KEY: 'incident_state',
inductive_miner.Variants.DFG_BASED.value.Parameters.TIMESTAMP_KEY: 'sys_updated_at',
alignments.Variants.VERSION_STATE_EQUATION_A_STAR.value.Parameters.ACTIVITY_KEY: 'incident_state'}
petrinet, initial_marking, final_marking = inductive_miner.apply(model_log, parameters=parameters)
gviz = pn_visualizer.apply(petrinet, initial_marking, final_marking)
#gviz.render('petrinets\\'+gexp_name+'\\petri_' + model_base + '.png')
gviz.render('test_time\\test.png')
pn_visualizer.view(gviz)
alignments_res = alignments.apply_log(metric_log, petrinet, initial_marking, final_marking, parameters=parameters)
fitness = replay_fitness.evaluate(alignments_res, variant=replay_fitness.Variants.ALIGNMENT_BASED,
parameters=parameters)
precision = calc_precision.apply(metric_log, petrinet, initial_marking, final_marking, parameters=parameters)
generaliz = calc_generaliz.apply(metric_log, petrinet, initial_marking, final_marking, parameters=parameters)
#generaliz = 0
simplic = calc_simplic.apply(petrinet)
f_score = 2 * ((fitness['averageFitness'] * precision) / (fitness['averageFitness'] + precision))
end_time = time()
m, s = divmod(end_time - start_time, 60)
h, m = divmod(m, 60)
print('Fin %02d:%02d:%02d' % (h, m, s))
print(' F:', '%.10f' % fitness['averageFitness'], ' P:', '%.10f' % precision,
' FS:', '%.10f' % f_score, ' G:', '%.10f' % generaliz, ' S:', '%.10f' % simplic, ' T:',
'%02d:%02d:%02d' % (h, m, s))
#metrics = pd.Series([model_base, metric_base, '%.10f' % fitness['averageFitness'],
# '%.10f' % precision, '%.10f' % f_score, '%.10f' % generaliz, '%.10f' % simplic,
# '%02d:%02d:%02d' % (h, m, s)])
return model_base
def apply(net, im, fm, parameters=None):
"""
Transforms a WF-net to a process tree
Parameters
-------------
net
Petri net
im
Initial marking
fm
Final marking
Returns
-------------
tree
Process tree
"""
if parameters is None:
parameters = {}
debug = exec_utils.get_param_value(Parameters.DEBUG, parameters, False)
fold = exec_utils.get_param_value(Parameters.FOLD, parameters, True)
grouped_net = group_blocks_in_net(net, parameters=parameters)
if len(grouped_net.transitions) == 1:
pt_str = list(grouped_net.transitions)[0].label
pt = pt_util.parse(pt_str)
return pt_util.fold(pt) if fold else pt
else:
if debug:
from pm4py.visualization.petrinet import visualizer as pn_viz
pn_viz.view(pn_viz.apply(grouped_net, parameters={"format": "svg"}))
raise ValueError('Parsing of WF-net Failed')
def execute_script():
log = xes_importer.apply(os.path.join("..", "tests", "compressed_input_data", "09_a32f0n00.xes.gz"))
heu_net = heuristics_miner.apply_heu(log, parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH: 0.99})
gviz = hn_vis.apply(heu_net, parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz)
net, im, fm = heuristics_miner.apply(log, parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH: 0.99})
gviz2 = petri_vis.apply(net, im, fm, parameters={petri_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"})
petri_vis.view(gviz2)
def petrinet_visualizer(net,
initial_marking,
final_marking,
parameters=
{pn_visualizer.Variants.WO_DECORATION.\
value.Parameters.FORMAT:"png"}):
gviz = pn_visualizer.apply(net,
initial_marking,
final_marking,
parameters=parameters)
pn_visualizer.view(gviz)
def visualization(log, C, petrinet=True, heu_net=False):
if petrinet:
# net, im, fm = inductive_miner.apply(variants_filter.apply(log, C))
net, im, fm = heuristics_miner.apply(variants_filter.apply(log, C))
gviz = pn_visualizer.apply(net, im, fm)
pn_visualizer.view(gviz)
if heu_net:
heu_net = inductive_miner.apply_heu(variants_filter.apply(log, C))
gviz = hn_vis_factory.apply(heu_net)
hn_vis_factory.view(gviz)
def execute_script():
log_path = os.path.join("..", "tests", "input_data",
"interval_event_log.csv")
dataframe = pm4py.read_csv(log_path)
log_path = os.path.join("..", "tests", "input_data", "reviewing.xes")
log = pm4py.read_xes(log_path)
dataframe = pm4py.convert_to_dataframe(log)
parameters = {}
#parameters[constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY] = "start_timestamp"
parameters[constants.PARAMETER_CONSTANT_TIMESTAMP_KEY] = "time:timestamp"
parameters[constants.PARAMETER_CONSTANT_ACTIVITY_KEY] = "concept:name"
parameters[constants.PARAMETER_CONSTANT_CASEID_KEY] = "case:concept:name"
parameters["strict"] = True
parameters["format"] = "svg"
start_activities = sa_get.get_start_activities(dataframe,
parameters=parameters)
end_activities = ea_get.get_end_activities(dataframe,
parameters=parameters)
att_count = att_get.get_attribute_values(dataframe,
"concept:name",
parameters=parameters)
parameters["start_activities"] = start_activities
parameters["end_activities"] = end_activities
soj_time = soj_time_get.apply(dataframe, parameters=parameters)
print("soj_time")
print(soj_time)
conc_act = conc_act_get.apply(dataframe, parameters=parameters)
print("conc_act")
print(conc_act)
efg = efg_get.apply(dataframe, parameters=parameters)
print("efg")
print(efg)
dfg_freq, dfg_perf = df_statistics.get_dfg_graph(
dataframe, measure="both", start_timestamp_key="start_timestamp")
dfg_gv_freq = dfg_vis_fact.apply(dfg_freq,
activities_count=att_count,
variant=dfg_vis_fact.Variants.FREQUENCY,
soj_time=soj_time,
parameters=parameters)
dfg_vis_fact.view(dfg_gv_freq)
dfg_gv_perf = dfg_vis_fact.apply(dfg_perf,
activities_count=att_count,
variant=dfg_vis_fact.Variants.PERFORMANCE,
soj_time=soj_time,
parameters=parameters)
dfg_vis_fact.view(dfg_gv_perf)
net, im, fm = dfg_conv.apply(dfg_freq)
gviz = pn_vis.apply(net, im, fm, parameters=parameters)
pn_vis.view(gviz)
def execute_script():
df = pm4py.read_csv("../tests/input_data/interval_event_log.csv")
log = pm4py.read_xes("../tests/input_data/interval_event_log.xes")
heu_net = plusplus.apply_heu(log, parameters={"heu_net_decoration": "performance"})
heu_net_2 = plusplus.apply_heu_pandas(df, parameters={"heu_net_decoration": "performance"})
gviz = visualizer.apply(heu_net, parameters={"format": "svg"})
visualizer.view(gviz)
gviz2 = visualizer.apply(heu_net_2, parameters={"format": "svg"})
visualizer.view(gviz2)
net1, im1, fm1 = plusplus.apply(log)
net2, im2, fm2 = plusplus.apply(log)
gviz3 = pn_visualizer.apply(net1, im1, fm1, parameters={"format": "svg"})
pn_visualizer.view(gviz3)
gviz4 = pn_visualizer.apply(net2, im2, fm2, parameters={"format": "svg"})
pn_visualizer.view(gviz4)
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
log = xes_import.apply(log_path)
net, i_m, f_m = alpha_miner.apply(log)
gviz = pn_vis.apply(
net,
i_m,
f_m,
parameters={
pn_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg",
pn_vis.Variants.WO_DECORATION.value.Parameters.DEBUG: False
})
pn_vis.view(gviz)
def execute_script():
# import csv & create log
dataframe = csv_import_adapter.import_dataframe_from_path(
datasourceMockdata(), sep=";")
dataframe = dataframe.rename(columns={
'coID': 'case:concept:name',
'Activity': 'concept:name'
})
log = conversion_factory.apply(dataframe)
# option 1: Directly-Follows Graph, represent frequency or performance
parameters = {constants.PARAMETER_CONSTANT_ACTIVITY_KEY: "concept:name"}
variant = 'frequency'
dfg = dfg_factory.apply(log, variant=variant, parameters=parameters)
gviz1 = dfg_vis_factory.apply(dfg,
log=log,
variant=variant,
parameters=parameters)
dfg_vis_factory.view(gviz1)
# option 2: Heuristics Miner, acts on the Directly-Follows Graph, find common structures, output: Heuristic Net (.svg)
heu_net = heuristics_miner.apply_heu(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.00
})
gviz2 = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz2)
# option 3: Petri Net based on Heuristic Miner (.png)
net, im, fm = heuristics_miner.apply(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.00
})
gviz3 = petri_vis.apply(
net,
im,
fm,
parameters={
petri_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "png"
})
petri_vis.view(gviz3)
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
log = xes_importer.apply(log_path)
net, marking, final_marking = inductive.apply(
log, variant=inductive.Variants.IM)
for place in marking:
print("initial marking " + place.name)
for place in final_marking:
print("final marking " + place.name)
gviz = pn_vis.apply(
net,
marking,
final_marking,
parameters={
pn_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg",
pn_vis.Variants.WO_DECORATION.value.Parameters.DEBUG: True
})
pn_vis.view(gviz)
if True:
fit_traces = []
for i in range(0, len(log)):
try:
print("\n", i, [x["concept:name"] for x in log[i]])
cf_result = pm4py.algo.conformance.alignments.variants.state_equation_a_star.apply(
log[i], net, marking, final_marking)['alignment']
if cf_result is None:
print("alignment is none!")
else:
is_fit = True
for couple in cf_result:
print(couple)
if not (couple[0] == couple[1]
or couple[0] == ">>" and couple[1] is None):
is_fit = False
print("isFit = " + str(is_fit))
if is_fit:
fit_traces.append(log[i])
except TypeError:
print("EXCEPTION ", i)
traceback.print_exc()
print(fit_traces)
print(len(fit_traces))
def execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "receipt.xes"))
log = sorting.sort_timestamp(log)
net, im, fm = inductive_miner.apply(log)
log1 = EventLog(log[:500])
log2 = EventLog(log[len(log) - 500:])
statistics = element_usage_comparison.compare_element_usage_two_logs(
net, im, fm, log1, log2)
gviz = pn_vis.apply(
net,
im,
fm,
variant=pn_vis.Variants.FREQUENCY,
aggregated_statistics=statistics,
parameters={pn_vis.Variants.FREQUENCY.value.Parameters.FORMAT: "svg"})
pn_vis.view(gviz)
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
log = xes_importer.apply(log_path)
net, marking, final_marking = alpha_miner.apply(log)
for place in marking:
print("initial marking " + place.name)
for place in final_marking:
print("final marking " + place.name)
gviz = pn_vis.apply(net, marking, final_marking,
parameters={pn_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"})
pn_vis.view(gviz)
print("started token replay")
aligned_traces = token_replay.apply(log, net, marking, final_marking)
fit_traces = [x for x in aligned_traces if x['trace_is_fit']]
perc_fitness = 0.00
if len(aligned_traces) > 0:
perc_fitness = len(fit_traces) / len(aligned_traces)
print("perc_fitness=", perc_fitness)
def execute_script():
log_path = os.path.join("..", "tests", "input_data", "running-example.xes")
log = xes_importer.apply(log_path)
dfg = dfg_algorithm.apply(log)
dfg_gv = dfg_vis_fact.apply(
dfg,
log,
parameters={
dfg_vis_fact.Variants.FREQUENCY.value.Parameters.FORMAT: "svg"
})
dfg_vis_fact.view(dfg_gv)
net, im, fm = dfg_conv.apply(dfg)
gviz = pn_vis.apply(
net,
im,
fm,
parameters={
pn_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"
})
pn_vis.view(gviz)
def execute_script():
dataframe2 = csv_import_adapter.import_dataframe_from_path(
datasourceMockdata(), sep=",")
dataframe2 = dataframe2.rename(
columns={
'timestamp': 'time:timestamp',
'source': 'case:concept:name',
'message': 'concept:name'
})
log2 = conversion_factory.apply(dataframe2)
# option 1: Heuristics Miner, acts on the Directly-Follows Graph, find common structures, output: Heuristic Net (.svg)
heu_net = heuristics_miner.apply_heu(
log2,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
})
gviz2 = hn_vis.apply(
heu_net,
parameters={hn_vis.Variants.PYDOTPLUS.value.Parameters.FORMAT: "svg"})
hn_vis.view(gviz2)
# option 2: Petri Net based on Heuristic Miner (.png)
net, im, fm = heuristics_miner.apply(
log2,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
})
gviz3 = petri_vis.apply(
net,
im,
fm,
parameters={
petri_vis.Variants.WO_DECORATION.value.Parameters.FORMAT: "png"
})
petri_vis.view(gviz3)
actrescount = getactivityresourcecount(log, list0, "concept:name", "org:group")
#print(actrescount,"actrescount")
roles = roles_discovery.apply(
log, variant=None, parameters={rpd.Parameters.RESOURCE_KEY: "org:group"})
#rescluster = getresoucecluster(log,roles,"concept:name","org:resource")
print(roles, "roles")
resourcesimulation = simulateresource(log, actrescount, roles, "concept:name",
"org:group")
print(resourcesimulation, "resourcesimulation")
#join activity but nothing different to the last segment.
#ja_values = sna.apply(log, variant=sna.Variants.JOINTACTIVITIES_LOG)
#gviz_ja_py = sna_visualizer.apply(ja_values, variant=sna_visualizer.Variants.PYVIS)
#sna_visualizer.view(gviz_ja_py, variant=sna_visualizer.Variants.PYVIS)
#print(hw_values,'~~', roles,'~~~',ja_values,"hw_values,roles,ja_values")
'''
net, im, fm = inductive_miner.apply(log)
gviz = visualizer.apply(net, im, fm, parameters={visualizer.Variants.WO_DECORATION.value.Parameters.DEBUG: True})
visualizer.view(gviz)
clf, feature_names, classes = decision_mining.get_decision_tree(log, net, im, fm, decision_point="p_6")
gviz1 = tree_visualizer.apply(clf, feature_names, classes)
visualizer.view(gviz1)
X, y, class_names = decision_mining.apply(log, net, im, fm, decision_point="p_6")
print(y,class_names)
pt_visualizer.save(gviz,"/Users/jiao.shuai.1998.12.01outlook.com/code/07.01.2021/DES1/testfile/decisionnet.png")
pt_visualizer.save(gviz1,"/Users/jiao.shuai.1998.12.01outlook.com/code/07.01.2021/DES1/testfile/decisiontree.png")
'''
'''
np.set_printoptions(threshold=1000)
np.set_printoptions(linewidth=1000)
import os
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.visualization.petrinet import visualizer
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
# Algoritimo Alpha
log = xes_importer.apply('sample_data/running-example.xes')
net, initial_marking, final_marking = alpha_miner.apply(log)
# Visualização
alpha_miner.apply(log)
gviz = visualizer.apply(net, initial_marking, final_marking)
visualizer.view(gviz)
def execute_script():
net = PetriNet("")
start = PetriNet.Place("start")
end = PetriNet.Place("end")
c1 = PetriNet.Place("c1")
c2 = PetriNet.Place("c2")
c3 = PetriNet.Place("c3")
c4 = PetriNet.Place("c4")
c5 = PetriNet.Place("c5")
c6 = PetriNet.Place("c6")
c7 = PetriNet.Place("c7")
c8 = PetriNet.Place("c8")
c9 = PetriNet.Place("c9")
net.places.add(c1)
net.places.add(c2)
net.places.add(c3)
net.places.add(c4)
net.places.add(c5)
net.places.add(c6)
net.places.add(c7)
net.places.add(c8)
net.places.add(c9)
net.places.add(start)
net.places.add(end)
t1 = PetriNet.Transition("t1", "a")
t2 = PetriNet.Transition("t2", None)
t3 = PetriNet.Transition("t3", "b")
t4 = PetriNet.Transition("t4", "c")
t5 = PetriNet.Transition("t5", "d")
t6 = PetriNet.Transition("t6", "e")
t7 = PetriNet.Transition("t7", None)
t8 = PetriNet.Transition("t8", "f")
t9 = PetriNet.Transition("t9", "g")
t10 = PetriNet.Transition("t10", "h")
t11 = PetriNet.Transition("t11", None)
net.transitions.add(t1)
net.transitions.add(t2)
net.transitions.add(t3)
net.transitions.add(t4)
net.transitions.add(t5)
net.transitions.add(t6)
net.transitions.add(t7)
net.transitions.add(t8)
net.transitions.add(t9)
net.transitions.add(t10)
net.transitions.add(t11)
add_arc_from_to(start, t1, net)
add_arc_from_to(t1, c1, net)
add_arc_from_to(t1, c2, net)
add_arc_from_to(c1, t2, net)
add_arc_from_to(c1, t3, net)
add_arc_from_to(c2, t4, net)
add_arc_from_to(t2, c3, net)
add_arc_from_to(t3, c3, net)
add_arc_from_to(t4, c4, net)
add_arc_from_to(c3, t5, net)
add_arc_from_to(c4, t5, net)
add_arc_from_to(t5, c5, net)
add_arc_from_to(c5, t6, net)
add_arc_from_to(t6, c1, net)
add_arc_from_to(t6, c2, net)
add_arc_from_to(c5, t7, net)
add_arc_from_to(t7, c7, net)
add_arc_from_to(t7, c6, net)
add_arc_from_to(c7, t8, net)
add_arc_from_to(c6, t9, net)
add_arc_from_to(t8, c8, net)
add_arc_from_to(t9, c9, net)
add_arc_from_to(c8, t11, net)
add_arc_from_to(c9, t11, net)
add_arc_from_to(t11, end, net)
add_arc_from_to(c5, t10, net)
add_arc_from_to(t10, end, net)
im = Marking()
im[start] = 1
fm = Marking()
fm[end] = 1
gvizs = []
gvizs.append(
visualizer.apply(net,
im,
final_marking=fm,
parameters={"format": "svg"}))
visualizer.view(gvizs[len(gvizs) - 1])
decomposed_net = decomposition.decompose(net, im, fm)
for snet, sim, sfm in decomposed_net:
gvizs.append(
visualizer.apply(snet,
sim,
final_marking=sfm,
parameters={"format": "svg"}))
visualizer.view(gvizs[len(gvizs) - 1])
import os from pm4py.objects.log.importer.xes import importer as xes_importer logfile = "D://process mining//HMM//pnml//reference//reference7.xes" log = xes_importer.apply(os.path.join(logfile)) from pm4py.algo.discovery.alpha import algorithm as alpha_miner net, initial_marking, final_marking = alpha_miner.apply(log) from pm4py.visualization.petrinet import visualizer as pn_visualizer gviz = pn_visualizer.apply(net, initial_marking, final_marking) pn_visualizer.view(gviz) from pm4py.objects.petri.exporter import exporter as pnml_exporter filename = "Alpha Miner.pnml" pnml_exporter.apply(net, initial_marking, filename)
def sample():
log = importer.apply('../tests/input_data/running-example.xes')
net, initial_marking, final_marking = alpha_miner.apply(log)
gviz = visualizer.apply(net, initial_marking, final_marking)
visualizer.view(gviz)
