def test_file_bohmer(file):
split_dataset(file + "_data.csv", file + "_labels.csv",
file + "_train.csv", file + "_test.csv", 10000)
train_data = LogFile(file + "_train.csv",
",",
0,
1000000,
None,
"case_id",
"name",
convert=False)
train_data.remove_attributes(["label"])
model = bohmer.train(train_data, 3, 4, 1)
test_data = LogFile(file + "_test.csv",
",",
0,
1000000,
None,
"case_id",
"name",
convert=False,
values=train_data.values)
bohmer.test(test_data, file + "_output_bohmer.csv", model, "label", 0)
plot.plot_single_roc_curve(file + "_output_bohmer.csv",
file,
save_file="../Data/Nolle_Graphs/" +
file.split("/")[-1] + "_roc_bohmer.png")
plot.plot_single_prec_recall_curve(file + "_output_bohmer.csv",
file,
save_file="../Data/Nolle_Graphs/" +
file.split("/")[-1] +
"_precrec_bohmer.png")
def score_continuous_net(model,
test,
label_attr,
output_file=None,
title=None):
import Utils.PlotResults as plot
ranking = model.test_parallel(test)
ranking.sort(key=lambda l: l[0].get_total_score())
scores = []
y = []
for r in ranking:
scores.append((getattr(r[1], "Index"), r[0].get_total_score(),
getattr(r[1], label_attr) != 0))
y.append(r[0].get_total_score())
print(len(scores))
if output_file is None:
output_file = "../output.csv"
with open(output_file, "w") as fout:
for s in scores:
fout.write(",".join([str(i) for i in s]))
fout.write("\n")
plot.plot_single_roc_curve(output_file, title)
plot.plot_single_prec_recall_curve(output_file, title)
def test_file_full(file):
split_dataset(file + "_data.csv", file + "_labels.csv",
file + "_train.csv", file + "_test.csv", None)
train_data = LogFile(file + "_train.csv", ",", 0, 1000000, None, "case_id",
"name")
train_data.remove_attributes(["label"])
model = edbn.train(train_data)
test_data = LogFile(file + "_test.csv",
",",
0,
1000000,
None,
"case_id",
"name",
values=train_data.values)
edbn.test(test_data, file + "_output_full.csv", model, "label", "0",
train_data)
plot.plot_single_roc_curve(file + "_output_full.csv",
file,
save_file="../Data/Nolle_Graphs/" +
file.split("/")[-1] + "_roc.png")
plot.plot_single_prec_recall_curve(file + "_output_full.csv",
file,
save_file="../Data/Nolle_Graphs/" +
file.split("/")[-1] + "_precrec.png")
def stephenRun():
# Use the BPIC15_x_sorted.csv to generate new training and test datafiles with anomalies introduced
# After running this once you can comment this line out
# preProcessData("../Data/")
# Indicate which are the training and test files
train_file = "../Data/BPIC15_train_1.csv"
test_file = "../Data/BPIC15_test_1.csv"
# Load logfile to use as training data
train_data = LogFile(train_file, ",", 0, 500000, None, "Case")
train_data.remove_attributes(["Anomaly"])
# Train the model
model = edbn.train(train_data)
# Test the model and save the scores in ../Data/output.csv
test_data = LogFile(test_file,
",",
header=0,
rows=500000,
time_attr=None,
trace_attr="Case",
values=train_data.values)
edbn.test(test_data,
"../Data/output.csv",
model,
label="Anomaly",
normal_val="0")
# Plot the ROC curve based on the results
plot.plot_single_roc_curve("../Data/output.csv")
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")
for edge in net.edges():
relations.append((edge[0], edge[1]))
for relation in relations:
# if relation not in mappings:
edbn.get_variable(relation[1]).add_parent(edbn.get_variable(relation[0]))
print(relation[0], "->", relation[1])
edbn.train(train, single=True)
ranking = edbn.test(test)
ranking.sort(key=lambda l: l[0].get_total_score())
scores = []
y = []
for r in ranking:
scores.append((getattr(r[1], "Index"), r[0].get_total_score(), getattr(r[1], "Class") != 1))
y.append(r[0].get_total_score())
print(len(scores))
with open("../output.csv", "w") as fout:
for s in scores:
fout.write(",".join([str(i) for i in s]))
fout.write("\n")
plot.plot_single_roc_curve("../output.csv")
plot.plot_single_prec_recall_curve("../output.csv")
plt.plot(list(range(len(y))), y)
plt.show()
def run_full():
# Use the BPIC15_x_sorted.csv to generate new training and test datafiles with anomalies introduced
# After running this once you can comment this line out
#preProcessData("../Data/")
for i in range(1, 2):
# Indicate which are the training and test files
train_file = "../Data/bpic15_%i_train.csv" % (i)
test_file = "../Data/bpic15_%i_test.csv" % (i)
# Load logfile to use as training data
train_data = LogFile(train_file,
",",
0,
500000,
time_attr="Complete_Timestamp",
trace_attr="Case_ID",
activity_attr="Activity")
train_data.remove_attributes(["Anomaly"])
# train_data.keep_attributes(["Case_ID", "Complete_Timestamp", "Activity", "Resource", "case_termName"])
train_data.remove_attributes(["planned"])
train_data.remove_attributes(["dueDate"])
train_data.remove_attributes(["dateFinished"])
# train_data.keep_attributes(["Case_ID", "Complete_Timestamp", "Activity", "Resource", "Weekday"])
# train_data.create_k_context()
# train_data.add_duration_to_k_context()
# Train the model
model = edbn.train(train_data)
# Test the model and save the scores in ../Data/output.csv
test_data = LogFile(test_file,
",",
header=0,
rows=500000,
time_attr="Complete_Timestamp",
trace_attr="Case_ID",
values=train_data.values)
# test_data.create_k_context()
# test_data.add_duration_to_k_context()
edbn.test(test_data,
"../Data/output2_%i.csv" % (i),
model,
label="Anomaly",
normal_val="0",
train_data=train_data)
# Plot the ROC curve based on the results
plot.plot_single_roc_curve("../Data/output2_%i.csv" % (i),
title="BPIC15_%i" % (i))
plot.plot_single_prec_recall_curve("../Data/output2_%i.csv" % (i),
title="BPIC15_%i" % (i))
out_files = []
labels = []
for i in range(1, 6):
out_files.append("../Data/output2_%i.csv" % (i))
labels.append("MUNIS_%i" % (i))
plot.plot_compare_roc_curve(out_files, labels, "BPIC15 Comparison")
plot.plot_compare_prec_recall_curve(out_files, labels, "BPIC15 Comparison")
