def _eventlog_to_dataframe(log: EventLog,
encoding: Encoding,
labelling: Labelling,
additional_columns=None,
cols=None):
if encoding.prefix_length < 1:
raise ValueError("Prefix length must be greater than 1")
if encoding.value_encoding == ValueEncodings.SIMPLE_INDEX.value:
run_df = simple_index(log, labelling, encoding)
elif encoding.value_encoding == ValueEncodings.BOOLEAN.value:
if cols is None:
cols = unique_events(log)
run_df = boolean(log, cols, labelling, encoding)
elif encoding.value_encoding == ValueEncodings.FREQUENCY.value:
if cols is None:
cols = unique_events(log)
run_df = frequency(log, cols, labelling, encoding)
elif encoding.value_encoding == ValueEncodings.COMPLEX.value:
run_df = complex(log, labelling, encoding, additional_columns)
elif encoding.value_encoding == ValueEncodings.LAST_PAYLOAD.value:
run_df = last_payload(log, labelling, encoding, additional_columns)
# elif encoding.value_encoding == ValueEncodings.SEQUENCES.value: #TODO JONAS
# run_df = sequences(log, labelling, encoding, additional_columns)
elif encoding.value_encoding == ValueEncodings.DECLARE.value:
run_df = declare_encoding(log,
labelling,
encoding,
additional_columns,
cols=cols)
if cols is None:
cols = list(run_df.columns)
else:
raise ValueError("Unknown value encoding method {}".format(
encoding.value_encoding))
return run_df, cols
def setUp(self):
self.train_log = get_log(create_test_log(log_name=general_example_train_filename,
log_path=general_example_train_filepath))
self.train_event_names = unique_events(self.train_log)
self.train_add_col = get_additional_columns(self.train_log)
self.test_log = get_log(create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath))
self.test_event_names = unique_events(self.test_log)
self.test_add_col = get_additional_columns(self.test_log)
def method_self(self, encoding):
start_time = time.time()
# log = get_logs("log_cache/repairExample.xes")[0]
event_names = unique_events(self.log)
encode_label_log(self.log, encoding, PredictiveModels.REGRESSION.value, self.label, event_names=event_names,
additional_columns=self.add_col)
print("Total for %s %s seconds" % (encoding, time.time() - start_time))
def setUp(self):
self.log = get_log(create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath))
self.event_names = unique_events(self.log)
self.labelling = create_test_labelling(label_type=LabelTypes.REMAINING_TIME.value)
self.add_col = get_additional_columns(self.log)
self.encoding = create_test_encoding(
value_encoding=ValueEncodings.LAST_PAYLOAD.value,
add_elapsed_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
prefix_length=1)
def do_test(self, encoding, log):
start_time = time.time()
# log = get_logs(log_path)[0]
add_col = get_additional_columns(log)
event_names = unique_events(log)
encoding = EncodingContainer(encoding, prefix_length=20, padding=ZERO_PADDING)
log = encode_label_log(log, encoding, PredictiveModels.REGRESSION.value, self.label,
event_names=event_names,
additional_columns=add_col)
print(log.shape)
print("Total for %s %s seconds" % (encoding.method, time.time() - start_time))
def do_test(encoding):
start_time = time.time()
# log = get_logs("log_cache/general_example.xes")[0]
log = get_log("cache/log_cache/Sepsis Cases - Event Log.xes")
label = LabelContainer(LabelTypes.REMAINING_TIME.value, add_elapsed_time=True)
encoding = EncodingContainer(encoding, prefix_length=185, generation_type=ALL_IN_ONE,
padding=ZERO_PADDING)
event_names = unique_events(log)
log = encode_label_log(log, encoding, PredictiveModels.REGRESSION.value, label, event_names=event_names)
print(log.shape)
print("Total for %s %s seconds" % (encoding, time.time() - start_time))
def test_unique_events(self):
events = unique_events(self.log)
self.assertEqual(7, len(events))
