def run_sdl():
from Methods.SDL.sdl import train, test
labeled_logfile = "../Data/Outcome_Prediction/BPIC15_1_f2.csv"
log = LogFile(labeled_logfile,
";",
0,
None,
"time_timestamp",
"Case_ID",
activity_attr="label",
convert=True,
k=10)
columns = [
"label", "Case_ID", "Activity", "monitoringResource", "question",
"org_resource", "Responsible_actor", "SUMleges"
]
log.keep_attributes(columns)
log.create_k_context()
train_log, test_log = log.splitTrainTest(80, True, "train-test")
train_log.ignoreHistoryAttributes.add("label")
test_log.ignoreHistoryAttributes.add("label")
model = train(train_log, 200, 42)
print(test(test_log, model))
results1 = []
results2 = []
for case in test_log.get_cases():
pass
def train_edbn(data_folder, model_folder, k=None, next_event=True):
from EDBN.Execute import train
from Predictions.eDBN_Prediction import learn_duplicated_events, predict_next_event, predict_suffix
if k is None:
best_model = {}
for k in range(1, 6):
train_log = LogFile(data_folder + "train_log.csv",
",",
0,
None,
None,
"case",
activity_attr="event",
convert=False,
k=k)
train_train_log, train_test_log = train_log.splitTrainTest(80)
train_train_log.add_end_events()
train_train_log.convert2int()
train_train_log.create_k_context()
train_test_log.values = train_train_log.values
train_test_log.add_end_events()
train_test_log.convert2int()
train_test_log.create_k_context()
model = train(train_train_log)
# Train average number of duplicated events
model.duplicate_events = learn_duplicated_events(train_train_log)
if next_event:
acc = predict_next_event(model, train_test_log)
else:
acc = predict_suffix(model, train_test_log)
print("Testing k=", k, " | Validation acc:", acc)
if "Acc" not in best_model or best_model["Acc"] < acc:
best_model["Acc"] = acc
best_model["Model"] = model
best_model["k"] = k
print("Best k value:", best_model["k"], " | Validation acc of",
best_model["Acc"])
k = best_model["k"]
train_log = LogFile(data_folder + "train_log.csv",
",",
0,
None,
None,
"case",
activity_attr="event",
convert=False,
k=k)
train_log.add_end_events()
train_log.convert2int()
train_log.create_k_context()
model = train(train_log)
# Train average number of duplicated events
model.duplicate_events = learn_duplicated_events(train_log)
with open(os.path.join(model_folder, "model"), "wb") as pickle_file:
pickle.dump(model, pickle_file)
with open(os.path.join(model_folder, "k"), "w") as outfile:
outfile.write(str(k))
def run_edbn():
from eDBN_Prediction import get_probabilities
from Methods.EDBN.Train import train
labeled_logfile = "../Data/Outcome_Prediction/BPIC15_1_f2.csv"
log = LogFile(labeled_logfile,
";",
0,
None,
"time_timestamp",
"Case_ID",
activity_attr="label",
convert=True,
k=1)
columns = [
"label", "Case_ID", "time_timestamp", "Activity", "monitoringResource",
"question", "org_resource", "Responsible_actor", "SUMleges"
]
log.keep_attributes(columns)
log.create_k_context()
train_log, test_log = log.splitTrainTest(80, True, "train-test")
train_log.ignoreHistoryAttributes.add("label")
model = train(train_log)
results1 = []
results2 = []
for case in test_log.get_cases():
case_df = case[1]
case_probs = {1: 1, 2: 1}
ground = 0
for row in case_df.iterrows():
ground = getattr(row[1], "label")
parents = model.variables["label"].conditional_table.parents
value = []
for parent in parents:
value.append(getattr(row[1], parent.attr_name))
tuple_val = tuple(value)
activity_var = model.variables["label"]
probs, unknown = get_probabilities(activity_var, tuple_val,
parents)
case_probs[1] += probs.get(1, 0)
case_probs[2] += probs.get(2, 0)
# correct_prob = sum(case_probs) / len(case_probs)
if ground == 1:
if case_probs[1] > case_probs[2]:
results1.append(1)
else:
results1.append(0)
if ground == 2:
if case_probs[2] > case_probs[1]:
results2.append(1)
else:
results2.append(0)
print(len(results1), sum(results1) / len(results1))
print(len(results2), sum(results2) / len(results2))