def prepare_logs(split: Split):
"""Returns training_log and test_log"""
if split.type == SplitTypes.SPLIT_SINGLE.value:
additional_columns = get_additional_columns(get_log(
split.original_log))
training_log, test_log = _split_single_log(split)
logger.info("\t\tLoaded single log from {}".format(
split.original_log.path))
else:
# Have to use sklearn to convert some internal data types
training_log = get_log(split.train_log)
additional_columns = get_additional_columns(training_log)
if split.additional_columns is None:
split.additional_columns = split.train_log.name + split.test_log.name + '_ac.xes'
split.save()
training_log, _ = train_test_split(training_log,
test_size=0,
shuffle=False)
test_log, _ = train_test_split(get_log(split.test_log),
test_size=0,
shuffle=False)
logger.info("\t\tLoaded double logs from {} and {}.".format(
split.train_log.path, split.test_log.path))
if len(training_log) == 0:
raise TypeError(
"Training log is empty. Create a new Split with better parameters")
return training_log, test_log, additional_columns
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 get_train_test_log(split: Split):
"""Returns training_log and test_log"""
if split.type == SplitTypes.SPLIT_SINGLE.value and Split.objects.filter(
type=SplitTypes.SPLIT_DOUBLE.value,
original_log=split.original_log,
test_size=split.test_size,
splitting_method=split.splitting_method
).exists() and split.splitting_method != SplitOrderingMethods.SPLIT_RANDOM.value:
return get_train_test_log(Split.objects.filter(
type=SplitTypes.SPLIT_DOUBLE.value,
original_log=split.original_log,
test_size=split.test_size,
splitting_method=split.splitting_method
)[0])
elif split.original_log is not None and (not Split.objects.filter(
type=SplitTypes.SPLIT_DOUBLE.value,
original_log=split.original_log,
test_size=split.test_size,
splitting_method=split.splitting_method
).exists() or split.splitting_method == SplitOrderingMethods.SPLIT_RANDOM.value):
training_log, test_log = _split_single_log(split)
additional_columns = get_additional_columns(get_log(split.original_log))
if split.splitting_method != SplitOrderingMethods.SPLIT_RANDOM.value:
_ = Split.objects.get_or_create(
type=SplitTypes.SPLIT_DOUBLE.value,
original_log=split.original_log,
test_size=split.test_size,
splitting_method=split.splitting_method,
train_log=create_log(EventLog(training_log), '0-' + str(100 - int(split.test_size * 100)) + '.xes'),
test_log=create_log(EventLog(test_log), str(100 - int(split.test_size * 100)) + '-100.xes'),
additional_columns=split.additional_columns
)[0]
logger.info("\t\tLoaded single log from {}".format(split.original_log.path))
else:
# Have to use sklearn to convert some internal data types
training_log = get_log(split.train_log)
additional_columns = get_additional_columns(training_log)
if split.additional_columns is None:
split.additional_columns = split.train_log.name + split.test_log.name + '_ac.xes'
split.save()
training_log, train_log_to_append = train_test_split(training_log, test_size=0, shuffle=False)
test_log, test_log_to_append = train_test_split(get_log(split.test_log), test_size=0, shuffle=False)
logger.info("\t\tLoaded double logs from {} and {}.".format(split.train_log.path, split.test_log.path))
if len(training_log) == 0:
raise TypeError("Training log is empty. Create a new Split with better parameters")
return training_log, test_log, additional_columns
def test_global_event_attributes(self):
log = get_log(
create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath_xes))
attributes = get_additional_columns(log)
self.assertListEqual(attributes['event_attributes'],
['Activity', 'Costs', 'Resource', 'org:resource'])
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 test_no_label(self):
labelling = create_test_labelling(label_type=LabelTypes.NO_LABEL.value)
_, df = encode_label_logs(
self.train_log, self.test_log,
create_test_job(
encoding=self.encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.train_log))
self.assertEqual((2, 12), df.shape)
def test_next_activity(self):
labelling = create_test_labelling(
label_type=LabelTypes.NEXT_ACTIVITY.value)
_, df = encode_label_logs(
self.train_log, self.test_log,
create_test_job(
encoding=self.encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.train_log))
self.assertEqual(df.shape, (2, 14))
def test_remaining_time(self):
labelling = create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value)
_, df = encode_label_logs(
self.train_log, self.test_log,
create_test_job(
encoding=self.encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.train_log))
self.assertEqual(df.shape, (2, 14))
def replay_prediction_calculate(job: Job, log) -> dict:
"""calculate the prediction for the log coming from replayers
:param job: job idctionary
:param log: log model
:return: runtime results
"""
additional_columns = get_additional_columns(log)
data_df, _ = train_test_split(log, test_size=0, shuffle=False)
data_df, _ = encode_label_logs(data_df, EventLog(), job, additional_columns)
results = MODEL[job.predictive_model.predictive_model][ModelActions.PREDICT.value](job, data_df)
logger.info("End {} job {}, {} . Results {}".format('runtime', job.predictive_model.predictive_model, get_run(job), results))
return results
def test_attribute_number(self):
labelling = create_test_labelling(
label_type=LabelTypes.ATTRIBUTE_NUMBER.value,
attribute_name='AMOUNT')
_, df = encode_label_logs(
self.test_log, self.test_log,
create_test_job(
encoding=self.encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.test_log))
self.assertEqual(df.shape, (2, 15))
def setUp(self):
self.train_log = get_log(
create_test_log(log_name=general_example_train_filename,
log_path=general_example_train_filepath))
self.test_log = get_log(
create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath))
self.add_col = get_additional_columns(self.train_log)
self.encoding = create_test_encoding(
value_encoding=ValueEncodings.COMPLEX.value,
add_elapsed_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
prefix_length=2)
self.encodingPadding = create_test_encoding(
value_encoding=ValueEncodings.COMPLEX.value,
add_elapsed_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
prefix_length=10,
padding=True)
def test_no_label_zero_padding(self):
# add things have no effect
labelling = create_test_labelling(label_type=LabelTypes.NO_LABEL.value)
encoding = create_test_encoding(
value_encoding=ValueEncodings.COMPLEX.value,
add_elapsed_time=True,
add_remaining_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
prefix_length=10,
padding=True)
_, df = encode_label_logs(
self.train_log, self.test_log,
create_test_job(
encoding=encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.train_log))
self.assertEqual(df.shape, (2, 52))
def test_add_new_traces(self):
labelling = create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value)
encoding = create_test_encoding(
value_encoding=ValueEncodings.COMPLEX.value,
prefix_length=2,
add_new_traces=True,
add_elapsed_time=True)
_, df = encode_label_logs(
self.train_log, self.test_log,
create_test_job(
encoding=encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.train_log))
self.assertEqual(df.shape, (2, 15))
self.assertTrue('new_traces' in df.columns.values.tolist())
self.assertListEqual(df['new_traces'].tolist(), [0, 0])
def test_next_activity_zero_padding_elapsed_time(self):
labelling = create_test_labelling(
label_type=LabelTypes.NEXT_ACTIVITY.value)
encoding = create_test_encoding(
value_encoding=ValueEncodings.COMPLEX.value,
add_elapsed_time=True,
add_remaining_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
prefix_length=10,
padding=True)
_, df = encode_label_logs(
self.train_log, self.test_log,
create_test_job(
encoding=encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.train_log))
self.assertEqual(df.shape, (2, 55))
self.assertTrue('elapsed_time' in df.columns.values.tolist())
def test_label_remaining_time_with_elapsed_time_custom_threshold(self):
labelling = create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value,
threshold_type=ThresholdTypes.THRESHOLD_CUSTOM.value,
threshold=40000)
encoding = create_test_encoding(
value_encoding=ValueEncodings.COMPLEX.value,
add_elapsed_time=True,
add_remaining_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
prefix_length=10,
padding=True)
_, df = encode_label_logs(
self.train_log, self.test_log,
create_test_job(
encoding=encoding,
labelling=labelling,
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value)),
get_additional_columns(self.test_log))
self.assertEqual(df.shape, (2, 55))
def setUp(self):
self.log = get_log("cache/log_cache/repairExample.xes")
# self.log = get_logs("log_cache/BPI Challenge 2017.xes.gz")[0]
self.label = LabelContainer(LabelTypes.NO_LABEL.value)
self.add_col = get_additional_columns(self.log)
