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)
