def test_evaluation(self):
log = xes_importer.apply(
os.path.join("input_data", "running-example.xes"))
from pm4py.algo.discovery.alpha import algorithm as alpha_miner
net, im, fm = alpha_miner.apply(log)
from pm4py.algo.evaluation.simplicity import evaluator as simplicity
simp = simplicity.apply(net)
from pm4py.algo.evaluation import evaluator as evaluation_method
eval = evaluation_method.apply(log, net, im, fm)
def test_inductiveminer_log(self):
log = xes_importer.apply(
os.path.join("input_data", "running-example.xes"))
net, im, fm = inductive_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_alphaminer_df(self):
log = pd.read_csv(os.path.join("input_data", "running-example.csv"))
log = dataframe_utils.convert_timestamp_columns_in_df(log)
net, im, fm = alpha_miner.apply(log)
aligned_traces_tr = tr_alg.apply(log, net, im, fm)
aligned_traces_alignments = align_alg.apply(log, net, im, fm)
evaluation = eval_alg.apply(log, net, im, fm)
fitness = rp_fit.apply(log, net, im, fm)
precision = precision_evaluator.apply(log, net, im, fm)
gen = generalization.apply(log, net, im, fm)
sim = simplicity.apply(net)
def test_inductiveminer_stream(self):
df = pd.read_csv(os.path.join("input_data", "running-example.csv"))
df = dataframe_utils.convert_timestamp_columns_in_df(df)
stream = log_conversion.apply(df,
variant=log_conversion.TO_EVENT_STREAM)
net, im, fm = inductive_miner.apply(stream)
aligned_traces_tr = tr_alg.apply(stream, net, im, fm)
aligned_traces_alignments = align_alg.apply(stream, net, im, fm)
evaluation = eval_alg.apply(stream, net, im, fm)
fitness = rp_fit.apply(stream, net, im, fm)
precision = precision_evaluator.apply(stream, net, im, fm)
gen = generalization.apply(stream, net, im, fm)
sim = simplicity.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.apply(os.path.join(INPUT_DATA_DIR, "running-example.xes"))
net, marking, final_marking = inductive_miner.apply(log)
fitness = fitness_alg.apply(log, net, marking, final_marking)
precision = precision_alg.apply(log, net, marking, final_marking)
generalization = generalization_alg.apply(log, net, marking, final_marking)
simplicity = simplicity_alg.apply(net)
del fitness
del precision
del generalization
del simplicity
else:
precision_alpha[logName] = 0.0
print(
str(time.time()) + " precision_alpha for " + logName +
" succeeded! " + str(precision_alpha[logName]))
generalization_alpha[logName] = generalization_evaluator.apply(
log,
alpha_model,
alpha_initial_marking,
alpha_final_marking,
parameters=parameters)
print(
str(time.time()) + " generalization_alpha for " + logName +
" succeeded! " + str(generalization_alpha[logName]))
simplicity_alpha[logName] = simplicity_evaluator.apply(
alpha_model, parameters=parameters)
print(
str(time.time()) + " simplicity_alpha for " + logName +
" succeeded! " + str(simplicity_alpha[logName]))
if ENABLE_PRECISION:
precision_imdf[logName] = precision_evaluator.apply(
log,
inductive_model,
inductive_im,
inductive_fm,
variant=precision_evaluator.Variants.ETCONFORMANCE_TOKEN,
parameters=parameters)
else:
precision_imdf[logName] = 0.0
print(
