def experiment_department():
input = LogFile("../Data/bpic2018.csv",
",",
0,
None,
"startTime",
"case",
convert=False)
input.remove_attributes([
"eventid", "identity_id", "event_identity_id", "year", "penalty_",
"amount_applied", "payment_actual", "penalty_amount", "risk_factor",
"cross_compliance", "selected_random", "selected_risk",
"selected_manually", "rejected"
])
input.convert2int()
data = input.filter_copy("self.data.department == 1")
model = cd.create_model(data, data)
print("Starting writing model to file")
with open("model_department", "wb") as fout:
pickle.dump(model, fout)
print("Done")
with open("model_department", "rb") as fin:
model = pickle.load(fin)
for dept in [1, 2, 3, 4]:
data = input.filter_copy("self.data.department == " + str(dept))
scores = cd.get_event_detailed_scores(data, model)
cd.plot_attribute_graph(scores, model.current_variables)
def learn_and_dump_model():
train = LogFile("../Data/bpic2018.csv", ",", 0, 30000, "startTime", "case", activity_attr=None, integer_input=False, convert=False)
train.remove_attributes(["eventid", "identity_id", "event_identity_id", "year", "penalty_", "amount_applied", "payment_actual", "penalty_amount", "risk_factor", "cross_compliance", "selected_random", "selected_risk", "selected_manually", "rejected"])
train.convert2int()
model = cd.create_model(train, train)
with open("model_30000b", "wb") as fout:
pickle.dump(model, fout)
def experiment_standard():
with open("model_30000b", "rb") as fin:
model = pickle.load(fin)
print("Get Scores")
train = LogFile("../Data/bpic2018.csv", ",", 0, 30000, "startTime", "case", activity_attr=None, integer_input=False, convert=False)
train.remove_attributes(["eventid", "identity_id", "event_identity_id", "year", "penalty_", "amount_applied", "payment_actual", "penalty_amount", "risk_factor", "cross_compliance", "selected_random", "selected_risk", "selected_manually", "rejected"])
train.convert2int()
data = LogFile("../Data/bpic2018.csv", ",", 0, None, "startTime", "case", convert=False, values=train.values, integer_input=False)
data.remove_attributes(["eventid", "identity_id", "event_identity_id", "year", "penalty_", "amount_applied", "payment_actual", "penalty_amount", "risk_factor", "cross_compliance", "selected_random", "selected_risk", "selected_manually", "rejected"])
data.convert2int()
scores = cd.get_event_scores(data, model)
cd.plot_single_scores(scores)
cd.plot_pvalues(scores, 400)
def experiment_outliers():
with open("model_30000", "rb") as fin:
model = pickle.load(fin)
train = LogFile("../Data/bpic2018.csv",
",",
0,
30000,
"startTime",
"case",
activity_attr=None,
integer_input=False,
convert=False)
train.remove_attributes([
"eventid", "identity_id", "event_identity_id", "year", "penalty_",
"amount_applied", "payment_actual", "penalty_amount", "risk_factor",
"cross_compliance", "selected_random", "selected_risk",
"selected_manually", "rejected"
])
train.convert2int()
attr_dicts = []
data = LogFile("../Data/bpic2018.csv",
",",
0,
None,
"startTime",
"case",
convert=False,
values=train.values)
data.filter("self.data.year == 1")
data.remove_attributes([
"event_identity_id", "year", "penalty_", "amount_applied",
"payment_actual", "penalty_amount", "risk_factor", "cross_compliance",
"selected_random", "selected_risk", "selected_manually", "rejected"
])
scores = cd.get_event_scores(data, model)
for s in scores:
if sum(scores[s]) != 0:
score = math.log10(sum(scores[s]) / len(scores[s]))
if score < -12:
for case in attr_dicts[0]:
if attr_dicts[0][case] == s:
print(s, case, score)
def experiment_clusters():
with open("model_30000", "rb") as fin:
model = pickle.load(fin)
train = LogFile("../Data/bpic2018.csv", ",", 0, 30000, "startTime", "case", activity_attr=None, integer_input=False, convert=False)
train.remove_attributes(["eventid", "identity_id", "event_identity_id", "year", "penalty_", "amount_applied", "payment_actual", "penalty_amount", "risk_factor", "cross_compliance", "selected_random", "selected_risk", "selected_manually", "rejected"])
train.convert2int()
data = LogFile("../Data/bpic2018.csv", ",", 0, None, "startTime", "case", convert=False, values=train.values)
data.remove_attributes(["event_identity_id", "year", "penalty_", "amount_applied", "payment_actual",
"penalty_amount", "risk_factor", "cross_compliance", "selected_random",
"selected_risk", "selected_manually", "rejected"])
data.convert2int()
data.filter("self.data.year == 1")
scores = cd.get_event_detailed_scores(data, model)
# First calculate score per trace
attributes = list(scores.keys())
num_traces = len(scores[attributes[0]])
upper = {}
lower = {}
for a in attributes:
upper[a] = []
lower[a] = []
for trace_ix in range(num_traces):
score = 1
for a in scores:
a_score = scores[a][trace_ix]
if a_score == -5:
score = 0
break
score *= a_score
if -8 < score < -10:
for a in scores:
upper[a].append(scores[a][trace_ix])
elif -10 < score < -12:
for a in scores:
lower[a].append(scores[a][trace_ix])
print(attributes)
print(upper)
cd.plot_attribute_graph(upper, attributes)
cd.plot_attribute_graph(lower, attributes)
def experiment_attributes_standard():
with open("model_30000b", "rb") as fin:
model = pickle.load(fin)
train = LogFile("../Data/bpic2018.csv",
",",
0,
30000,
"startTime",
"case",
activity_attr=None,
integer_input=False,
convert=False)
train.remove_attributes([
"eventid", "identity_id", "event_identity_id", "year", "penalty_",
"amount_applied", "payment_actual", "penalty_amount", "risk_factor",
"cross_compliance", "selected_random", "selected_risk",
"selected_manually", "rejected"
])
train.convert2int()
input = LogFile("../Data/bpic2018.csv",
",",
0,
10000000,
"startTime",
"case",
convert=False,
values=train.values)
input.remove_attributes([
"eventid", "identity_id", "event_identity_id", "penalty_",
"amount_applied", "payment_actual", "penalty_amount", "risk_factor",
"cross_compliance", "selected_random", "selected_risk",
"selected_manually", "rejected"
])
input.convert2int()
data = input.filter_copy("self.data.year == 1")
scores_year1 = cd.get_event_detailed_scores(data, model)
cd.plot_attribute_graph(scores_year1, model.current_variables)
data = input.filter_copy("self.data.year == 2")
scores_year2 = cd.get_event_detailed_scores(data, model)
cd.plot_attribute_graph(scores_year2, model.current_variables)
data = input.filter_copy("self.data.year == 3")
scores_year3 = cd.get_event_detailed_scores(data, model)
cd.plot_attribute_graph(scores_year3, model.current_variables)
p_vals_year1_2 = []
p_vals_year2_3 = []
p_vals_year1_3 = []
for key in sorted(scores_year1.keys()):
p_vals_year1_2.append(
stats.ks_2samp(scores_year1[key], scores_year2[key]).pvalue)
p_vals_year2_3.append(
stats.ks_2samp(scores_year2[key], scores_year3[key]).pvalue)
p_vals_year1_3.append(
stats.ks_2samp(scores_year1[key], scores_year3[key]).pvalue)
def tmp(x):
if x == 0:
return x
else:
return 1
p_vals_year1_2 = [tmp(x) for x in p_vals_year1_2]
plt.plot(sorted(scores_year1.keys()), p_vals_year1_2, "o")
plt.xticks(rotation='vertical')
plt.show()
p_vals_year2_3 = [tmp(x) for x in p_vals_year2_3]
plt.plot(sorted(scores_year1.keys()), p_vals_year2_3, "o")
plt.xticks(rotation='vertical')
plt.show()
p_vals_year1_3 = [tmp(x) for x in p_vals_year1_3]
plt.plot(sorted(scores_year1.keys()), p_vals_year1_3, "o")
plt.xticks(rotation='vertical')
plt.show()
x = []
y_1 = []
y_2 = []
y_3 = []
for key in sorted(scores_year1.keys()):
x.append(key)
y_1.append(np.median(scores_year1[key]))
y_2.append(np.median(scores_year2[key]))
y_3.append(np.median(scores_year3[key]))
plt.plot(x, y_1, "o")
plt.plot(x, y_2, "o")
plt.plot(x, y_3, "o")
plt.xticks(rotation='vertical')
plt.xlabel("Attributes")
plt.ylabel("Median Score")
plt.legend(["2015", "2016", "2017"])
plt.show()
p_vals_year1_2 = []
p_vals_year2_3 = []
p_vals_year1_3 = []
for key in sorted(scores_year1.keys()):
p_vals_year1_2.append(
stats.ks_2samp(scores_year1[key],
[np.median(scores_year1[key])]).pvalue)
p_vals_year2_3.append(
stats.ks_2samp(scores_year2[key],
[np.median(scores_year2[key])]).pvalue)
p_vals_year1_3.append(
stats.ks_2samp(scores_year1[key],
[np.median(scores_year3[key])]).pvalue)
def tmp(x):
if x == 0:
return x
else:
return 1
p_vals_year1_2 = [tmp(x) for x in p_vals_year1_2]
plt.plot(sorted(scores_year1.keys()), p_vals_year1_2, "o")
plt.xticks(rotation='vertical')
plt.show()
p_vals_year2_3 = [tmp(x) for x in p_vals_year2_3]
plt.plot(sorted(scores_year1.keys()), p_vals_year2_3, "o")
plt.xticks(rotation='vertical')
plt.show()
p_vals_year1_3 = [tmp(x) for x in p_vals_year1_3]
plt.plot(sorted(scores_year1.keys()), p_vals_year1_3, "o")
plt.xticks(rotation='vertical')
plt.show()
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)
