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)