def test_docmeasures11(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" from pm4py.objects.log.importer.xes import factory as xes_importer log = xes_importer.import_log(os.path.join("input_data", "receipt.xes")) from pm4py.algo.discovery.alpha import factory as alpha_miner from pm4py.algo.discovery.inductive import factory as inductive_miner alpha_petri, alpha_initial_marking, alpha_final_marking = alpha_miner.apply( log) inductive_petri, inductive_initial_marking, inductive_final_marking = inductive_miner.apply( log) from pm4py.evaluation.replay_fitness import factory as replay_factory fitness_alpha = replay_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking) fitness_inductive = replay_factory.apply(log, inductive_petri, inductive_initial_marking, inductive_final_marking) del fitness_alpha del fitness_inductive from pm4py.evaluation.precision import factory as precision_factory precision_alpha = precision_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking) precision_inductive = precision_factory.apply( log, inductive_petri, inductive_initial_marking, inductive_final_marking) del precision_alpha del precision_inductive from pm4py.evaluation.generalization import factory as generalization_factory generalization_alpha = generalization_factory.apply( log, alpha_petri, alpha_initial_marking, alpha_final_marking) generalization_inductive = generalization_factory.apply( log, inductive_petri, inductive_initial_marking, inductive_final_marking) del generalization_alpha del generalization_inductive from pm4py.evaluation.simplicity import factory as simplicity_factory simplicity_alpha = simplicity_factory.apply(alpha_petri) simplicity_inductive = simplicity_factory.apply(inductive_petri) del simplicity_alpha del simplicity_inductive from pm4py.evaluation import factory as evaluation_factory alpha_evaluation_result = evaluation_factory.apply( log, alpha_petri, alpha_initial_marking, alpha_final_marking) inductive_evaluation_result = evaluation_factory.apply( log, inductive_petri, inductive_initial_marking, inductive_final_marking) del alpha_evaluation_result del inductive_evaluation_result
def test_docmeasures11(self): from pm4py.log.importer import xes as xes_importer log = xes_importer.import_from_file_xes('inputData\\receipt.xes') from pm4py.algo.alpha import factory as alpha_miner from pm4py.algo.inductive import factory as inductive_miner alpha_petri, alpha_initial_marking, alpha_final_marking = alpha_miner.apply(log) inductive_petri, inductive_initial_marking, inductive_final_marking = inductive_miner.apply(log) from pm4py.evaluation.replay_fitness import factory as replay_factory fitness_alpha = replay_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking) fitness_inductive = replay_factory.apply(log, inductive_petri, inductive_initial_marking, inductive_final_marking) # print("fitness_alpha=",fitness_alpha) # print("fitness_inductive=",fitness_inductive) from pm4py.evaluation.precision import factory as precision_factory precision_alpha = precision_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking) precision_inductive = precision_factory.apply(log, inductive_petri, inductive_initial_marking, inductive_final_marking) # print("precision_alpha=",precision_alpha) # print("precision_inductive=",precision_inductive) from pm4py.evaluation.generalization import factory as generalization_factory generalization_alpha = generalization_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking) generalization_inductive = generalization_factory.apply(log, inductive_petri, inductive_initial_marking, inductive_final_marking) # print("generalization_alpha=",generalization_alpha) # print("generalization_inductive=",generalization_inductive) from pm4py.evaluation.simplicity import factory as simplicity_factory simplicity_alpha = simplicity_factory.apply(alpha_petri) simplicity_inductive = simplicity_factory.apply(inductive_petri) # print("simplicity_alpha=",simplicity_alpha) # print("simplicity_inductive=",simplicity_inductive) from pm4py.evaluation import factory as evaluation_factory alpha_evaluation_result = evaluation_factory.apply(log, alpha_petri, alpha_initial_marking, alpha_final_marking) # print("alpha_evaluation_result=",alpha_evaluation_result) inductive_evaluation_result = evaluation_factory.apply(log, inductive_petri, inductive_initial_marking, inductive_final_marking)
def test_evaluation_pm1(self): log = xes_importer.import_from_file_xes( os.path.join(INPUT_DATA_DIR, "running-example.xes")) net, marking, final_marking = dfg_only.apply(log, None) fitness = fitness_factory.apply(log, net, marking, final_marking) precision = precision_factory.apply(log, net, marking, final_marking) generalization = generalization_factory.apply(log, net, marking, final_marking) simplicity = simplicity_factory.apply(net)
def test_evaluation(self): log = xes_importer.apply( os.path.join("input_data", "running-example.xes")) from pm4py.algo.discovery.alpha import factory as alpha_miner net, im, fm = alpha_miner.apply(log) from pm4py.evaluation.simplicity import factory as simplicity simp = simplicity.apply(net) from pm4py.evaluation import factory as evaluation_method eval = evaluation_method.apply(log, net, im, fm)
def test_heu_log(self): log = xes_importer.apply( os.path.join("input_data", "running-example.xes")) net, im, fm = heuristics_miner.apply(log) aligned_traces_tr = tr_factory.apply(log, net, im, fm) aligned_traces_alignments = align_factory.apply(log, net, im, fm) evaluation = eval_factory.apply(log, net, im, fm) fitness = rp_fit_factory.apply(log, net, im, fm) precision = precision_factory.apply(log, net, im, fm) generalization = generalization_factory.apply(log, net, im, fm) simplicity = simplicity_factory.apply(net)
def test_inductiveminer_df(self): log = csv_import_adapter.import_dataframe_from_path( os.path.join("input_data", "running-example.csv")) net, im, fm = inductive_miner.apply(log) aligned_traces_tr = tr_factory.apply(log, net, im, fm) aligned_traces_alignments = align_factory.apply(log, net, im, fm) evaluation = eval_factory.apply(log, net, im, fm) fitness = rp_fit_factory.apply(log, net, im, fm) precision = precision_factory.apply(log, net, im, fm) generalization = generalization_factory.apply(log, net, im, fm) simplicity = simplicity_factory.apply(net)
def test_inductiveminer_stream(self): stream = csv_importer.apply( os.path.join("input_data", "running-example.csv")) net, im, fm = inductive_miner.apply(stream) aligned_traces_tr = tr_factory.apply(stream, net, im, fm) aligned_traces_alignments = align_factory.apply(stream, net, im, fm) evaluation = eval_factory.apply(stream, net, im, fm) fitness = rp_fit_factory.apply(stream, net, im, fm) precision = precision_factory.apply(stream, net, im, fm) generalization = generalization_factory.apply(stream, net, im, fm) simplicity = simplicity_factory.apply(net)
def test_evaluation_pm1(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 = xes_importer.import_log( os.path.join(INPUT_DATA_DIR, "running-example.xes")) net, marking, final_marking = inductive_miner.apply(log) fitness = fitness_factory.apply(log, net, marking, final_marking) precision = precision_factory.apply(log, net, marking, final_marking) generalization = generalization_factory.apply(log, net, marking, final_marking) simplicity = simplicity_factory.apply(net) del fitness del precision del generalization del simplicity
t2 = time.time() times_alignments_imdf[logName] = t2 - t1 precision_alpha[logName] = precision_factory.apply( log, alpha_model, alpha_initial_marking, alpha_final_marking, parameters=parameters) generalization_alpha[logName] = generalization_factory.apply( log, alpha_model, alpha_initial_marking, alpha_final_marking, parameters=parameters) simplicity_alpha[logName] = simplicity_factory.apply( alpha_model, parameters=parameters) precision_imdf[logName] = precision_factory.apply( log, inductive_model, inductive_im, inductive_fm, parameters=parameters) generalization_imdf[logName] = generalization_factory.apply( log, inductive_model, inductive_im, inductive_fm, parameters=parameters) simplicity_imdf[logName] = simplicity_factory.apply( inductive_model, parameters=parameters)