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.export_net(
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_log(log1, net1, marking1,
final_marking)
self.assertEqual(aligned_traces, aligned_traces)
def test_importExportCSVtoCSV(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"
event_log = csv_importer.import_event_stream(
os.path.join(INPUT_DATA_DIR, "running-example.csv"))
event_log = sorting.sort_timestamp(event_log)
event_log = sampling.sample(event_log)
event_log = index_attribute.insert_event_index_as_event_attribute(
event_log)
log = log_conv_fact.apply(event_log)
log = sorting.sort_timestamp(log)
log = sampling.sample(log)
log = index_attribute.insert_trace_index_as_event_attribute(log)
event_log_transformed = log_conv_fact.apply(
log, variant=log_conv_fact.TO_EVENT_STREAM)
csv_exporter.export(
event_log_transformed,
os.path.join(OUTPUT_DATA_DIR, "running-example-exported.csv"))
event_log_imported_after_export = csv_importer.import_event_stream(
os.path.join(OUTPUT_DATA_DIR, "running-example-exported.csv"))
log_imported_after_export = log_conv_fact.apply(
event_log_imported_after_export)
self.assertEqual(len(log), len(log_imported_after_export))
os.remove(os.path.join(OUTPUT_DATA_DIR,
"running-example-exported.csv"))
def test_importExportCSVtoCSV(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"
df = pd.read_csv(os.path.join(INPUT_DATA_DIR, "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
event_log = log_conversion.apply(
df, variant=log_conversion.TO_EVENT_STREAM)
event_log = sorting.sort_timestamp(event_log)
event_log = sampling.sample(event_log)
event_log = index_attribute.insert_event_index_as_event_attribute(
event_log)
log = log_conversion.apply(event_log)
log = sorting.sort_timestamp(log)
log = sampling.sample(log)
log = index_attribute.insert_trace_index_as_event_attribute(log)
event_log_transformed = log_conversion.apply(
log, variant=log_conversion.TO_EVENT_STREAM)
df = log_conversion.apply(event_log_transformed,
variant=log_conversion.TO_DATA_FRAME)
df.to_csv(os.path.join(OUTPUT_DATA_DIR,
"running-example-exported.csv"))
df = pd.read_csv(
os.path.join(OUTPUT_DATA_DIR, "running-example-exported.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
event_log_imported_after_export = log_conversion.apply(
df, variant=log_conversion.TO_EVENT_STREAM)
log_imported_after_export = log_conversion.apply(
event_log_imported_after_export)
self.assertEqual(len(log), len(log_imported_after_export))
os.remove(os.path.join(OUTPUT_DATA_DIR,
"running-example-exported.csv"))
def test_importExportCSVtoXES(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"
event_log = csv_importer.import_event_stream(os.path.join(INPUT_DATA_DIR, "running-example.csv"))
event_log = sorting.sort_timestamp(event_log)
event_log = sampling.sample(event_log)
event_log = index_attribute.insert_event_index_as_event_attribute(event_log)
log = log_transform.transform_event_stream_to_event_log(event_log)
log = sorting.sort_timestamp(log)
log = sampling.sample(log)
log = index_attribute.insert_trace_index_as_event_attribute(log)
xes_exporter.export_log(log, os.path.join(OUTPUT_DATA_DIR, "running-example-exported.xes"))
log_imported_after_export = xes_importer.import_log(
os.path.join(OUTPUT_DATA_DIR, "running-example-exported.xes"))
self.assertEqual(len(log), len(log_imported_after_export))
os.remove(os.path.join(OUTPUT_DATA_DIR, "running-example-exported.xes"))
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)
