def test_applyAlphaMinerToCSV(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
# calculate and compare Petri nets obtained on the same log to verify that instances
# are working correctly
log1, net1, marking1, fmarking1 = self.obtainPetriNetThroughAlphaMiner(
os.path.join(INPUT_DATA_DIR, "running-example.csv"))
log2, net2, marking2, fmarking2 = self.obtainPetriNetThroughAlphaMiner(
os.path.join(INPUT_DATA_DIR, "running-example.csv"))
log1 = sorting.sort_timestamp(log1)
log1 = sampling.sample(log1)
log1 = index_attribute.insert_trace_index_as_event_attribute(log1)
log2 = sorting.sort_timestamp(log2)
log2 = sampling.sample(log2)
log2 = index_attribute.insert_trace_index_as_event_attribute(log2)
petri_exporter.apply(net1, marking1, os.path.join(OUTPUT_DATA_DIR, "running-example.pnml"))
os.remove(os.path.join(OUTPUT_DATA_DIR, "running-example.pnml"))
self.assertEqual(len(net1.places), len(net2.places))
self.assertEqual(len(net1.transitions), len(net2.transitions))
self.assertEqual(len(net1.arcs), len(net2.arcs))
final_marking = petri.petrinet.Marking()
for p in net1.places:
if not p.out_arcs:
final_marking[p] = 1
aligned_traces = token_replay.apply(log1, net1, marking1, final_marking)
self.assertEqual(aligned_traces, aligned_traces)
def test_importingExportingPetri(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
imported_petri1, marking1, fmarking1 = petri_importer.apply(
os.path.join(INPUT_DATA_DIR, "running-example.pnml"))
soundness = check_soundness.check_petri_wfnet_and_soundness(
imported_petri1)
del soundness
petri_exporter.apply(
imported_petri1, marking1,
os.path.join(OUTPUT_DATA_DIR, "running-example.pnml"))
imported_petri2, marking2, fmarking2 = petri_importer.apply(
os.path.join(OUTPUT_DATA_DIR, "running-example.pnml"))
soundness = check_soundness.check_petri_wfnet_and_soundness(
imported_petri2)
del soundness
self.assertEqual(sorted([x.name for x in imported_petri1.places]),
sorted([x.name for x in imported_petri2.places]))
self.assertEqual(sorted([x.name for x in imported_petri1.transitions]),
sorted([x.name for x in imported_petri2.transitions]))
self.assertEqual(
sorted(
[x.source.name + x.target.name for x in imported_petri1.arcs]),
sorted(
[x.source.name + x.target.name for x in imported_petri2.arcs]))
self.assertEqual([x.name for x in marking1],
[x.name for x in marking2])
os.remove(os.path.join(OUTPUT_DATA_DIR, "running-example.pnml"))
def test_51(self):
import os
from pm4py.objects.petri.importer import importer as pnml_importer
net, initial_marking, final_marking = pnml_importer.apply(
os.path.join("input_data", "running-example.pnml"))
from pm4py.visualization.petrinet import visualizer as pn_visualizer
gviz = pn_visualizer.apply(net, initial_marking, final_marking)
from pm4py.objects.petri.exporter import exporter as pnml_exporter
pnml_exporter.apply(net, initial_marking, "petri.pnml")
pnml_exporter.apply(net, initial_marking, "petri_final.pnml", final_marking=final_marking)
os.remove("petri.pnml")
os.remove("petri_final.pnml")
from pm4py.objects.petri import semantics
transitions = semantics.enabled_transitions(net, initial_marking)
places = net.places
transitions = net.transitions
arcs = net.arcs
for place in places:
stru = "\nPLACE: " + place.name
for arc in place.in_arcs:
stru = str(arc.source.name) + " " + str(arc.source.label)
def write_petri_net(petri_net: PetriNet, initial_marking: Marking,
final_marking: Marking, file_path: str) -> None:
warnings.warn('write_petri_net is deprecated, please use write_pnml',
DeprecationWarning)
"""
Exports a (composite) Petri net object
Parameters
------------
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
file_path
Destination path
Returns
------------
void
"""
from pm4py.objects.petri.exporter import exporter as petri_exporter
petri_exporter.apply(petri_net,
initial_marking,
file_path,
final_marking=final_marking)
def write_pnml(petri_net: PetriNet, initial_marking: Marking,
final_marking: Marking, file_path: str) -> None:
"""
Exports a (composite) Petri net object
Parameters
------------
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
file_path
Destination path
Returns
------------
void
"""
from pm4py.objects.petri.exporter import exporter as petri_exporter
petri_exporter.apply(petri_net,
initial_marking,
file_path,
final_marking=final_marking)
def test_52(self):
# creating an empty Petri net
from pm4py.objects.petri.petrinet import PetriNet, Marking
net = PetriNet("new_petri_net")
# creating source, p_1 and sink place
source = PetriNet.Place("source")
sink = PetriNet.Place("sink")
p_1 = PetriNet.Place("p_1")
# add the places to the Petri Net
net.places.add(source)
net.places.add(sink)
net.places.add(p_1)
# Create transitions
t_1 = PetriNet.Transition("name_1", "label_1")
t_2 = PetriNet.Transition("name_2", "label_2")
# Add the transitions to the Petri Net
net.transitions.add(t_1)
net.transitions.add(t_2)
# Add arcs
from pm4py.objects.petri import utils
utils.add_arc_from_to(source, t_1, net)
utils.add_arc_from_to(t_1, p_1, net)
utils.add_arc_from_to(p_1, t_2, net)
utils.add_arc_from_to(t_2, sink, net)
# Adding tokens
initial_marking = Marking()
initial_marking[source] = 1
final_marking = Marking()
final_marking[sink] = 1
from pm4py.objects.petri.exporter import exporter as pnml_exporter
pnml_exporter.apply(net, initial_marking, "createdPetriNet1.pnml", final_marking=final_marking)
from pm4py.visualization.petrinet import visualizer as pn_visualizer
gviz = pn_visualizer.apply(net, initial_marking, final_marking)
from pm4py.visualization.petrinet import visualizer as pn_visualizer
parameters = {pn_visualizer.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"}
gviz = pn_visualizer.apply(net, initial_marking, final_marking, parameters=parameters)
from pm4py.visualization.petrinet import visualizer as pn_visualizer
parameters = {pn_visualizer.Variants.WO_DECORATION.value.Parameters.FORMAT: "svg"}
gviz = pn_visualizer.apply(net, initial_marking, final_marking, parameters=parameters)
pn_visualizer.save(gviz, "alpha.svg")
os.remove("createdPetriNet1.pnml")
os.remove("alpha.svg")
def test_alphaMinerVisualizationFromXES(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
log, net, marking, fmarking = self.obtainPetriNetThroughAlphaMiner(
os.path.join(INPUT_DATA_DIR, "running-example.xes"))
log = sorting.sort_timestamp(log)
log = sampling.sample(log)
log = index_attribute.insert_trace_index_as_event_attribute(log)
petri_exporter.apply(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)
self.assertEqual(gviz, gviz)
final_marking = petri.petrinet.Marking()
for p in net.places:
if not p.out_arcs:
final_marking[p] = 1
aligned_traces = token_replay.apply(log, net, marking, fmarking)
self.assertEqual(aligned_traces, aligned_traces)
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)
utils.add_arc_from_to(t_4, p_3, net) utils.add_arc_from_to(p_3, t_6, net) utils.add_arc_from_to(t_6, p_4, net) utils.add_arc_from_to(p_4, t_5, net) utils.add_arc_from_to(p_4, t_7, net) utils.add_arc_from_to(t_5, p_2, net) utils.add_arc_from_to(t_7, p_5, net) utils.add_arc_from_to(t_7, p_6, net) utils.add_arc_from_to(p_5, t_9, net) utils.add_arc_from_to(p_6, t_10, net) utils.add_arc_from_to(t_9, p_7, net) utils.add_arc_from_to(t_10, p_7, net) utils.add_arc_from_to(p_7, t_8, net) utils.add_arc_from_to(t_8, p_3, net) initial_marking = Marking() initial_marking[start] = 1 final_marking = Marking() final_marking[end] = 1 from pm4py.objects.petri.exporter import exporter as pnml_exporter pnml_exporter.apply(net, initial_marking, "createdPetriNet1.pnml", final_marking=final_marking) 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="petri.pnml" pnml_exporter.apply(net, initial_marking, filename)
import sys, os
import pandas as pd
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
from pm4py.objects.conversion.process_tree import converter as pt_converter
from pm4py.objects.petri.exporter import exporter as pnml_exporter
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
dirname = sys.argv[1]
# dirname = "C:/Users/User/Desktop/DIPLOM/SampleHelloWorldProject/_behavioral_model_data/logs"
for filename in os.listdir(dirname):
if (filename.endswith(".csv")):
log_csv = pd.read_csv(dirname + "/" + filename, sep=',')
log_csv.rename(columns={'activity name': 'concept:name'}, inplace=True)
log_csv.rename(columns={'case ID': 'case:concept:name'}, inplace=True)
event_log = log_converter.apply(
log_csv, variant=log_converter.Variants.TO_EVENT_LOG)
net, initial_marking, final_marking = alpha_miner.apply(event_log)
for transition in net.transitions:
transition.name = transition.name.replace(
" ", "_") + "_" + filename.split('.')[0]
transition.label = transition.label.replace(" ", "_")
for place in net.places:
place.name = place.name.replace(" ",
"_") + "_" + filename.split('.')[0]
# tree = inductive_miner.apply_tree(event_log)
# net, initial_marking, final_marking = pt_converter.apply(tree, variant=pt_converter.Variants.TO_PETRI_NET)
pnml_exporter.apply(net, initial_marking,
dirname + "/" + filename.split('.')[0] + ".pnml")
import os
from pm4py.objects.log.importer.xes import importer as xes_importer
logfile = "D://process mining//HMM//pnml//reference//reference5.xes"
log = xes_importer.apply(os.path.join(logfile))
from pm4py.algo.discovery.heuristics import algorithm as heuristics_miner
net, im, fm = heuristics_miner.apply(
log,
parameters={
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
})
from pm4py.visualization.petrinet import visualizer as pn_visualizer
gviz = pn_visualizer.apply(net, im, fm)
pn_visualizer.view(gviz)
from pm4py.objects.petri.exporter import exporter as pnml_exporter
filename = "Heuristic Miner.pnml"
pnml_exporter.apply(net, im, filename)
def discover_process_models(log_path, log_name):
custom_print('Importando log')
log_complete = xes_importer.apply(log_path)
log = variants_filter.filter_log_variants_percentage(log_complete, 0.9)
#A_ACTIVATED, A_DECLINED, A_CANCELLED
#log = attributes_filter.apply(log_complete, ["A_ACTIVATED"], parameters={attributes_filter.Parameters.ATTRIBUTE_KEY: "concept:name", attributes_filter.Parameters.POSITIVE: True})
custom_print('Log importado')
if (1 == 2):
#Inductive Miner
custom_print('Iniciando Inductive Miner')
parameters = {
inductive_miner.Variants.IM.value.Parameters.CASE_ID_KEY:
'case:concept:name',
inductive_miner.Variants.IM.value.Parameters.TIMESTAMP_KEY:
'time:timestamp'
}
variant = inductive_miner.Variants.IM
petrinet = inductive_miner.apply(log,
parameters=parameters,
variant=variant)
print_statistics(petrinet[0], 'IM')
custom_print('Inductive Miner finalizado\n')
if (1 == 2):
#Inductive Miner Infrequent 0.2
custom_print('Iniciando Inductive Miner Infrequent 0.2')
parameters = {
inductive_miner.Variants.IMf.value.Parameters.NOISE_THRESHOLD:
0.2,
inductive_miner.Variants.IMf.value.Parameters.CASE_ID_KEY:
'case:concept:name',
inductive_miner.Variants.IMf.value.Parameters.TIMESTAMP_KEY:
'time:timestamp'
}
variant = inductive_miner.Variants.IMf
petrinet = inductive_miner.apply(log,
parameters=parameters,
variant=variant)
print_statistics(petrinet[0], 'IMf0.2')
custom_print('Inductive Miner Infrequent 0.2 finalizado\n')
if (1 == 1):
#Inductive Miner Infrequent 0.5
custom_print('Iniciando Inductive Miner Infrequent 0.5')
parameters = {
inductive_miner.Variants.IMf.value.Parameters.NOISE_THRESHOLD:
0.5,
inductive_miner.Variants.IMf.value.Parameters.CASE_ID_KEY:
'case:concept:name',
inductive_miner.Variants.IMf.value.Parameters.TIMESTAMP_KEY:
'time:timestamp'
}
variant = inductive_miner.Variants.IMf
petrinet, initial_marking, final_marking = inductive_miner.apply(
log, parameters=parameters, variant=variant)
print_statistics(petrinet, 'IMf0.5')
custom_print('Inductive Miner Infrequent 0.5 finalizado\n')
ts = reachability_graph.construct_reachability_graph(
petrinet, initial_marking)
gviz = ts_visualizer.apply(
ts,
parameters={
ts_visualizer.Variants.VIEW_BASED.value.Parameters.FORMAT:
"png"
})
gviz.render('petrinets/simple-reach', cleanup=True)
pnml_exporter.apply(petrinet, initial_marking,
"petrinets/simple-petri.pnml")
if (1 == 2):
#Inductive Miner Infrequent 0.8
custom_print('Iniciando Inductive Miner Infrequent 0.8')
parameters = {
inductive_miner.Variants.IMf.value.Parameters.NOISE_THRESHOLD:
0.8,
inductive_miner.Variants.IMf.value.Parameters.CASE_ID_KEY:
'case:concept:name',
inductive_miner.Variants.IMf.value.Parameters.TIMESTAMP_KEY:
'time:timestamp'
}
variant = inductive_miner.Variants.IMf
petrinet = inductive_miner.apply(log,
parameters=parameters,
variant=variant)
print_statistics(petrinet[0], 'IMf0.8')
custom_print('Inductive Miner Infrequent 0.8 finalizado\n')
if (1 == 2):
#Inductive Miner Directly-Follows
custom_print('Iniciando Inductive Miner Directly-Follows')
parameters = {
inductive_miner.Variants.IMd.value.Parameters.CASE_ID_KEY:
'case:concept:name',
inductive_miner.Variants.IMd.value.Parameters.TIMESTAMP_KEY:
'time:timestamp'
}
variant = inductive_miner.Variants.IMd
petrinet = inductive_miner.apply(log,
parameters=parameters,
variant=variant)
print_statistics(petrinet[0], 'IMd')
custom_print('Inductive Miner Infrequent Directly-Follows\n')
if (1 == 2):
#Alpha Miner
custom_print('Iniciando Alpha Miner')
parameters = {}
variant = alpha_miner.Variants.ALPHA_VERSION_CLASSIC
petrinet = alpha_miner.apply(log,
parameters=parameters,
variant=variant)
print_statistics(petrinet[0], 'Alpha')
custom_print('Alpha Miner finalizado\n')
if (1 == 2):
#Heuristic Miner 0.5
custom_print('Iniciando Heuristic Miner 0.5')
parameters = {
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.5
}
petrinet = heuristics_miner.apply(log, parameters=parameters)
print_statistics(petrinet[0], 'HM0.5')
custom_print('Heuristic Miner 0.5 finalizado\n')
if (1 == 2):
#Heuristic Miner 0.99
custom_print('Iniciando Heuristic Miner 0.99')
parameters = {
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.99
}
petrinet = heuristics_miner.apply(log, parameters=parameters)
print_statistics(petrinet[0], 'HM0.99')
custom_print('Heuristic Miner 0.99 finalizado\n')
if (1 == 2):
#Heuristic Miner 0.1
custom_print('Iniciando Heuristic Miner 0.1')
parameters = {
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
0.1
}
petrinet = heuristics_miner.apply(log, parameters=parameters)
print_statistics(petrinet[0], 'HM0.1')
custom_print('Heuristic Miner 0.1 finalizado\n')
if (1 == 2):
#Heuristic Miner 1.0
custom_print('Iniciando Heuristic Miner 1.0')
parameters = {
heuristics_miner.Variants.CLASSIC.value.Parameters.DEPENDENCY_THRESH:
1.0
}
petrinet = heuristics_miner.apply(log, parameters=parameters)
print_statistics(petrinet[0], 'HM1.0')
custom_print('Heuristic Miner 1.0 finalizado\n')
if (1 == 2):
#DFG
custom_print('Iniciando DFG')
dfg = dfg_discovery.apply(log)
parameters = {
dfg_visualization.Variants.FREQUENCY.value.Parameters.FORMAT: 'png'
}
gviz = dfg_visualization.apply(
dfg,
log=log,
variant=dfg_visualization.Variants.FREQUENCY,
parameters=parameters)
dfg_visualization.save(gviz, 'petrinets/simple-DFG.png')
custom_print('DFG finalizado\n')
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
parameters = {alpha_miner.Variants.ALPHA_CLASSIC.value.Parameters.ACTIVITY_KEY: "customClassifier"}
net, initial_marking, final_marking = alpha_miner.apply(log, parameters=parameters)
#Petrinet Management
import os
from pm4py.objects.petri.importer import importer as pnml_importer
net, initial_marking, final_marking = pnml_importer.apply(os.path.join("tests","input_data","running-example.pnml"))
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
pnml_exporter.apply(net, initial_marking, "petri.pnml")
pnml_exporter.apply(net, initial_marking, "petri_final.pnml", final_marking=final_marking)
#tree generation
from pm4py.simulation.tree_generator import simulator as tree_gen
parameters = {}
tree = tree_gen.apply(parameters=parameters)
from pm4py.objects.process_tree import semantics
log = semantics.generate_log(tree, no_traces=100)
from pm4py.objects.conversion.process_tree import converter as pt_converter
net, im, fm = pt_converter.apply(tree)
from pm4py.objects.log.importer.xes import importer as xes_importer
from pm4py.objects.petri.exporter import exporter as pnml_exporter
from pm4py.objects.petri.importer import importer as pnml_importer
from pm4py.algo.discovery.inductive import algorithm as inductive_miner
from pydream.LogWrapper import LogWrapper
from pydream.EnhancedPN import EnhancedPN
from pydream.predictive.nap.NAP import NAP
if __name__ == "__main__":
log = xes_importer.apply('sample_data\\toy.xes')
net, im, fm = inductive_miner.apply(log)
pnml_exporter.apply(net, im, "sample_data\\discovered_pn.pnml", fm)
net, initial_marking, final_marking = pnml_importer.apply(
"sample_data\\discovered_pn.pnml")
log_wrapper = LogWrapper(log)
enhanced_pn = EnhancedPN(net, initial_marking)
enhanced_pn.enhance(log_wrapper)
enhanced_pn.saveToFile("sample_data\\enhanced_discovered_pn.json")
enhanced_pn = EnhancedPN(
net,
initial_marking,
decay_function_file="sample_data\\enhanced_discovered_pn.json")
tss_json, tss_objs = enhanced_pn.decay_replay(log_wrapper=log_wrapper)
