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 algorithm as simplicity
simp = simplicity.apply(net)
from pm4py.algo.evaluation import algorithm as evaluation_method
eval = evaluation_method.apply(log, net, im, fm)
def test_evaluation_pm2(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)
metrics = evaluation_alg.apply(log, net, marking, final_marking)
del metrics
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 execute_script():
log = xes_importer.apply(
os.path.join("..", "tests", "input_data", "reviewing.xes"))
net, marking, final_marking = inductive_miner.apply(log)
metrics = general_evaluation.apply(log, net, marking, final_marking)
print("metrics=", metrics)
