def breast_discrete_exec():
data = "../Data/breast_data.csv"
labels = "../Data/breast_labels.csv"
log = pd.read_csv(data, header=None)
labels = pd.read_csv(labels, header=None)
log["Label"] = labels[0]
cols = []
for c in log.columns:
cols.append("V" + str(c))
log.columns = cols
log['ID'] = log.reset_index().index
print(log)
train = log[:100]
test = log[100:]
train = train[train.VLabel == 0].drop(columns=["VLabel"])
train.to_csv("../Data/breast_train.csv", index=False)
test.to_csv("../Data/breast_test.csv", index=False)
train_data = LogFile("../Data/breast_train.csv",
",",
0,
500000,
None,
"ID",
activity_attr="Activity")
train_data.k = 0
model = edbn.train(train_data)
test_data = LogFile("../Data/breast_test.csv",
",",
0,
500000,
None,
"ID",
activity_attr="Activity")
test_data.k = 0
print(test_data.data)
edbn.test(test_data, "../Data/breast_discrete_output.csv", model, "VLabel",
"0")
plot.plot_single_roc_curve("../Data/breast_discrete_output.csv",
"breast_discrete")
plot.plot_single_prec_recall_curve("../Data/breast_discrete_output.csv",
"breast_discrete")
case_attr = "case"
act_attr = "event"
logfile = LogFile(data,
",",
0,
None,
None,
case_attr,
activity_attr=act_attr,
convert=False,
k=5)
prefix_size = max(logfile.data.groupby(logfile.trace).size())
if prefix_size > 40:
prefix_size = 40
logfile.k = prefix_size
logfile.add_end_events()
logfile.keep_attributes(["case", "event", "role"])
logfile.convert2int()
logfile.create_k_context()
train_log, test_log = logfile.splitTrainTest(66,
case=False,
method="train-test")
model = train(train_log, 100, 10)
acc = test(test_log, model)
print(acc)
