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 train_vars_and_test(model, alias, filename, event_emit_obj):
file = UPLOAD_FOLDER + "/" + alias + "/" + filename
folder = UPLOAD_FOLDER + "/" + alias + "/"
train_file = get_constructed_file(file)
test_file = get_constructed_file(file, type="test")
train_data = LogFile(train_file, ",", 0, 500000, None, "Case")
train_data.remove_attributes(["Anomaly", "time"])
event_emit_obj('score_resp', {'step': 2, "msg": "Data loaded."})
train_data.create_k_context()
event_emit_obj('score_resp', {
'step': 3,
"msg": "Build K-Context for data."
})
model_trained_on_data = edbn.train_seperate(train_data, model)
event_emit_obj('score_resp', {'step': 4, "msg": "Finished training data."})
test_data = LogFile(test_file,
",",
header=0,
rows=500000,
time_attr=None,
trace_attr="Case",
values=train_data.values)
edbn.test(test_data,
folder + "output.csv",
model_trained_on_data,
label="Anomaly",
normal_val="0")
event_emit_obj('score_resp', {'step': 5, "msg": "Finished testing"})
# # Plot the ROC curve based on the results
# plot.plot_single_roc_curve(experiment_folder + "output.csv")
event_emit_obj('score_resp', {'step': 6, "msg": "Preparing to score."})
scores = get_event_scores(test_data.data, model_trained_on_data)
r = list(scores.keys())
one = np.random.randint(0, len(r))
random_key = r[one]
print(random_key)
print(test_data.convert_int2string('Case', int(random_key)))
# results = plottable(scores)
event_emit_obj('score_resp', {'step': 7, "msg": "Finished scoring!"})
print("Finished scoring...")
# plot_single_scores(scores)
# r, ps = plot_pvalues(scores, 20)
return scores
def test_file(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", "event")
#train_data.remove_attributes(["event_position"])
model = edbn.train(train_data)
test_data = LogFile(file + "_test.csv", ",", 0, 1000000, None, "case_id", "event", values=train_data.values)
edbn.test(test_data, file + "_output.csv", model, "label", "0")
plot.plot_single_roc_curve(file + "_output.csv", file, save_file="../Data/Nolle_Graphs/" + file.split("/")[-1] + "_roc.png")
plot.plot_single_prec_recall_curve(file + "_output.csv", file, save_file="../Data/Nolle_Graphs/" + file.split("/")[-1] + "_precrec.png")
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")
def compare_bpics(path):
for i in range(1, 6):
# Input Files
train = path + "BPIC15_train_%i.csv" % (i)
test = path + "BPIC15_test_%i.csv" % (i)
output = path + "Output/BPIC15_output_%i.csv" % (i)
output_edbn = path + "Output/BPIC15_edbn_output_%i.csv" % (i)
prec_recall = path + "Output/prec_recall_%i.png" % (i)
roc = path + "Output/roc_%i.png" % (i)
train_data = LogFile(train,
",",
0,
500000,
"Time",
"Case",
activity_attr="Activity",
convert=False)
train_data.remove_attributes(["Anomaly", "Type", "Time"])
test_data = LogFile(test,
",",
0,
500000,
"Time",
"Case",
activity_attr="Activity",
values=train_data.values,
convert=False)
#bohmer_model = bmr.train(train_data)
#bmr.test(test_data, output, bohmer_model, label = "Anomaly", normal_val = "0")
train_data.convert2int()
test_data.convert2int()
edbn_model = edbn.train(train_data)
edbn.test(test_data,
output_edbn,
edbn_model,
label="Anomaly",
normal_val="0")
plt.plot_compare_prec_recall_curve([output, output_edbn],
["Likelihood Graph", "eDBN"],
save_file=prec_recall)
plt.plot_compare_roc_curve([output, output_edbn],
["Likelihood Graph", "eDBN"], roc)
def run(default_dataset="edbn/Data/BPIC15_1_sorted.csv", default_alias="run/"):
# 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
# which_dataset = "edbn/Data/BPIC15_1_sorted.csv"
# which_dataset = "edbn/Data/BPIC15_1_sorted.csv"
# preprocess_folder = "run/"
which_dataset = default_dataset
preprocess_folder = default_alias
train_file, test_file, experiment_folder = preProcessFile(
which_dataset, preprocess_folder)
# Indicate which are the training and test files
# train_file = "../Data/{}BPIC15_train_1.csv".format(preprocess_folder)
# test_file = "../Data/{}BPIC15_test_1.csv".format(preprocess_folder)
# Load logfile to use as training data
train_data = LogFile(train_file, ",", 0, 500000, None, "Case")
train_data.remove_attributes(["Anomaly", "time"])
# 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,
experiment_folder + "output.csv",
model,
label="Anomaly",
normal_val="0")
# # Plot the ROC curve based on the results
# plot.plot_single_roc_curve(experiment_folder + "output.csv")
scores = get_event_scores(test_data.data, model)
print("Finished scoring...")
# plot_single_scores(scores)
r, ps = plot_pvalues(scores, 20)
return scores, (r, ps), model
def compare_bpic_total(path):
train = path + "BPIC15_train_total.csv"
test = path + "BPIC15_test_total.csv"
output = path + "Output/BPIC_15_output_total.csv"
output_edbn = path + "Output/BPIC15_edbn_output_total.csv"
prec_recall = path + "Output/prec_recall_total.png"
roc = path + "Output/roc_total.png"
#bohmer_model = bmr.train(train, header = 0, length = 5000000)
#bmr.test(train, test, output, bohmer_model, ",", 5000000, skip=0)
train_data = LogFile(train, ",", 0, 500000, None, "Case")
train_data.remove_attributes(["Anomaly"])
test_data = LogFile(test, ",", 0, 500000, None, "Case", train_data.string_2_int, train_data.int_2_string)
edbn_model = edbn.train(train_data)
edbn.test(test_data, output_edbn, edbn_model, "Anomaly", "0")
plt.plot_compare_prec_recall_curve([output, output_edbn], ["Likelihood Graph", "eDBN"], save_file=prec_recall)
plt.plot_compare_roc_curve([output, output_edbn], ["Likelihood Graph", "eDBN"], roc)
def compare_bpics(path):
for i in range(1,6):
# Input Files
train = path + "BPIC15_train_%i.csv" % (i)
test = path + "BPIC15_test_%i.csv" % (i)
output = path + "Output/BPIC15_output_%i.csv" % (i)
output_edbn = path + "Output/BPIC15_edbn_output_%i.csv" % (i)
prec_recall = path + "Output/prec_recall_%i.png" % (i)
roc = path + "Output/roc_%i.png" % (i)
#bohmer_model = bmr.train(train + "_ints", header = 0, length = 500000)
#bmr.test(train + "_ints", test + "_ints", output, bohmer_model, ",", 500000, skip=0)
train_data = LogFile(train, ",", 0, 500000, None, "Case")
train_data.remove_attributes(["Anomaly"])
test_data = LogFile(test, ",", 0, 500000, None, "Case", train_data.string_2_int, train_data.int_2_string)
edbn_model = edbn.train(train_data)
edbn.test(test_data, output_edbn, edbn_model, "Anomaly", "0")
plt.plot_compare_prec_recall_curve([output, output_edbn], ["Likelihood Graph", "eDBN"], save_file=prec_recall)
plt.plot_compare_roc_curve([output, output_edbn], ["Likelihood Graph", "eDBN"], roc)
generator.create_shipment_data(10000, 10000, anoms_rates[i][0],
anoms_rates[i][1], train_file,
test_file)
train_date = LogFile(train_file, ",", 0, 1000000, None, "Case")
train_date.remove_attributes(["Anomaly"])
test_date = LogFile(test_file,
",",
0,
1000000,
None,
"Case",
string_2_int=train_date.string_2_int,
int_2_string=train_date.int_2_string)
model = edbn.train(train_date)
edbn.test(test_date, path + "Output_%i_%i.csv" % anoms_rates[i],
model, "Anomaly", "0")
output_file = path + "Output_%i_%i.csv" % anoms_rates[i]
output_roc = path + "roc_%i_%i.png" % anoms_rates[i]
output_prec = path + "prec_recall_%i_%i.png" % anoms_rates[i]
score = plt.get_roc_auc(output_file)
scores.append(plt.get_roc_auc(output_file))
print("Score = %f" % score)
with open(path + "results.txt", "a") as fout:
fout.write("Testing:\ntrain rate: %i\ntest rate: %i\n" %
(anoms_rates[i][0], anoms_rates[i][1]))
fout.write("Result: " + str(scores) + "\n")
fout.write("Mean: %f Median: %f\n" %
(np.mean(scores), np.median(scores)))
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")
utils.convert2ints(train_file, train_file + "_ints", True,
dict_dict)
test_length = utils.convert2ints(test_file, test_file + "_ints",
True, dict_dict)
model = edbn.train(train_file + "_ints",
"Case",
"Anomaly",
1,
0,
1000000,
ignore=["Anomaly"])
edbn.test(test_file + "_ints",
path + "Output_%i_%i.csv" % anoms_rates[i],
model,
"Anomaly",
1,
",",
test_length,
skip=0)
output_file = path + "Output_%i_%i.csv" % anoms_rates[i]
output_roc = path + "roc_%i_%i.png" % anoms_rates[i]
output_prec = path + "prec_recall_%i_%i.png" % anoms_rates[i]
score = plt.get_roc_auc(output_file)
scores.append(plt.get_roc_auc(output_file))
print("Score = %f" % score)
with open(path + "results.txt", "a") as fout:
fout.write("Testing:\ntrain rate: %i\ntest rate: %i\n" %
(anoms_rates[i][0], anoms_rates[i][1]))