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_xes))
self.test_event_names = unique_events(self.test_log)
self.test_add_col = get_additional_columns(self.test_log)
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_xes))
self.encoding = create_test_encoding(
value_encoding=ValueEncodings.BOOLEAN.value,
prefix_length=2,
add_elapsed_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value)
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_can_find_log_file(self):
log = Log.objects.get(name="general_example.xes",
path=general_example_filepath)
log_file = get_log(log)
self.assertEqual(6, len(log_file))
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):
test_log = get_log(create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath_xes))
training_log = get_log(create_test_log(log_name=general_example_train_filename,
log_path=general_example_train_filepath))
self.training_df, self.test_df = encode_label_logs(training_log,
test_log,
create_test_job(
encoding=create_test_encoding(
value_encoding=ValueEncodings.BOOLEAN.value,
add_elapsed_time=True
),
predictive_model=create_test_predictive_model(
predictive_model=PredictiveModels.CLASSIFICATION.value
)
))
def test_can_find_split_original_file(self):
log = Log.objects.get(name="general_example.xes",
path=general_example_filepath)
split = Split.objects.get(original_log=log)
log_file = get_log(split.original_log)
self.assertEqual(6, len(log_file))
def setUp(self):
test_log = get_log(
create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath_xes))
training_log = get_log(
create_test_log(log_name=general_example_train_filename,
log_path=general_example_train_filepath))
self.encoding = create_test_encoding(
value_encoding=ValueEncodings.FREQUENCY.value,
add_elapsed_time=True,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
prefix_length=1)
self.labelling = create_test_labelling(
label_type=LabelTypes.REMAINING_TIME.value)
self.training_df, self.test_df = encode_label_logs(
training_log, test_log,
create_test_job(encoding=self.encoding, labelling=self.labelling))
def setUp(self):
self.log = get_log(create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath_xes))
self.labelling = create_test_labelling(label_type=LabelTypes.REMAINING_TIME.value)
self.encoding = create_test_encoding(
value_encoding=ValueEncodings.SIMPLE_INDEX.value,
task_generation_type=TaskGenerationTypes.ONLY_THIS.value,
add_elapsed_time=True,
prefix_length=1)
def test_trace_attributes(self):
self.log = get_log(create_test_log(log_name=financial_log_filename,
log_path=financial_log_filepath))
result = trace_attributes(self.log)
self.assertEqual(2, len(result))
self.assertDictEqual({'name': 'AMOUNT_REQ', 'type': 'number', 'example': '20000'},
result[0])
self.assertDictEqual({'name': 'REG_DATE', 'type': 'string', 'example': '2011-10-01 00:38:44.546000+02:00'},
result[1])
def get_log_traces_attributes(request, pk):
log = Log.objects.get(pk=pk)
try:
log_file = get_log(log)
except FileNotFoundError:
logger.error("Log id: %s, path %s not found", log.id, log.path)
return Response({'error': 'log file not found'}, status=status.HTTP_404_NOT_FOUND)
value = get_log_trace_attributes(log_file)
return Response(value, status=status.HTTP_200_OK)
def test_replay_prediction(self):
job = create_test_job(create_models=True)
runtime_log = create_test_log(
log_name='runtime_example.xes',
log_path='cache/log_cache/test_logs/runtime_test.xes')
log = get_log(runtime_log)
prediction_task(job.id)
job.refresh_from_db()
replay_prediction_task(job, job, log)
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_xes))
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 setUp(self):
self.log = get_log(create_test_log(log_name=general_example_test_filename,
log_path=general_example_test_filepath_xes))
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 _split_single_log(split: Split):
log = get_log(split.original_log)
logger.info("\t\tExecute single split ID {}, split_type {}, test_size {}".format(split.id, split.type, split.test_size))
if split.splitting_method == SplitOrderingMethods.SPLIT_TEMPORAL.value:
return _temporal_split(log, split.test_size)
elif split.splitting_method == SplitOrderingMethods.SPLIT_STRICT_TEMPORAL.value:
return _temporal_split_strict(log, split.test_size)
elif split.splitting_method == SplitOrderingMethods.SPLIT_SEQUENTIAL.value:
return _split_log(log, split.test_size, shuffle=False)
elif split.splitting_method == SplitOrderingMethods.SPLIT_RANDOM.value:
return _split_log(log, split.test_size, random_state=None)
else:
raise ValueError('splitting method {} not recognized'.format(split.splitting_method))
def test_eval(self):
encoding = create_test_encoding(
value_encoding=ValueEncodings.FREQUENCY.value,
task_generation_type=TaskGenerationTypes.ALL_IN_ONE.value,
add_elapsed_time=True,
prefix_length=12,
padding=True)
df = simple_index(
get_log(create_test_log(log_path=general_example_filepath, log_name=general_example_filename)),
create_test_labelling(label_type=LabelTypes.REMAINING_TIME.value), encoding)
self.assertEqual(df.shape, (41, 15))
row1 = df[df.trace_id == '4'].iloc[4]
self.assertListEqual(
['4', 'register request', 'check ticket', 'examine thoroughly', 'decide', 'reject request', 0, 0, 0,
0, 0, 0, 0, 520920.0, 0.0], row1.values.tolist())
self.assertFalse(df.isnull().values.any())
def replay_prediction(replay_job: Job, training_initial_job: Job,
trace_id) -> list:
"""The function create a set with timestamps of events, then create a list of requests
simulating the log in the time passing
:param trace_id:
:param replay_job: job dictionary
:param training_initial_job: job dictionary
:return: List of requests
"""
split = replay_job.split
log = get_log(split.train_log)
requests_list = list()
eventlog = EventLog()
trace = log[int(trace_id)]
for key in log.attributes.keys():
eventlog.attributes[key] = log.attributes[key]
for index in range(len(trace)):
new_trace = Trace(trace[0:index])
for key in trace.attributes:
new_trace.attributes[key] = trace.attributes[key]
eventlog.append(new_trace)
replay_job.case_id = trace_id
replay_job.event_number = len(trace)
replay_job.save()
try:
logger.error("Sending request for replay_prediction task.")
r = requests.post(
url="http://127.0.0.1:8000/runtime/replay_prediction/",
data=export_log_as_string(eventlog),
params={
'jobId': replay_job.id,
'training_job': training_initial_job.id
},
headers={
'Content-Type': 'text/plain',
'charset': 'UTF-8'
})
requests_list.append(str(r))
except Exception as e:
requests_list.append(str(e))
logger.warning(str(e))
return requests_list
def replay_core(replay_job: Job, training_initial_job: Job) -> list:
"""The function create a set with timestamps of events, then create a list of requests
simulating the log in the time passing
:param replay_job: job dictionary
:param training_initial_job: job dictionary
:return: List of requests
"""
split = replay_job.split
log = get_log(split.train_log)
requests_list = list()
eventlog = EventLog()
for key in log.attributes.keys():
eventlog.attributes[key] = log.attributes[key]
for trace in log:
new_trace = Trace(trace)
for key in trace.attributes:
new_trace.attributes[key] = trace.attributes[key]
eventlog.append(new_trace)
times = sorted(
set([event['time:timestamp'] for trace in eventlog
for event in trace]))
for t in times[2::int((len(times) - 2) / 5)]:
filtered_eventlog = timestamp_filter.apply_events(
eventlog, times[0].replace(tzinfo=None), t.replace(tzinfo=None))
trace_list = list()
event_number = dict()
for trace in filtered_eventlog:
trace_list.append(trace.attributes['concept:name'])
event_number[trace.attributes['concept:name']] = len(trace)
replay_job.case_id = trace_list
replay_job.event_number = event_number
replay_job.save()
try: #TODO check logger usage
logger.info("Sending request for replay_prediction task.")
r = requests.post(
url="http://server:8000/runtime/replay_prediction/",
data=export_log_as_string(filtered_eventlog),
params={
'jobId': replay_job.id,
'training_job': training_initial_job.id
},
headers={
'Content-Type': 'text/plain',
'charset': 'UTF-8'
})
requests_list.append(str(r))
except Exception as e:
requests_list.append(str(e))
logger.warning(str(e))
training_log, test_log, additional_columns = get_train_test_log(
replay_job.split)
training_df, _ = encode_label_logs(training_log,
test_log,
replay_job,
additional_columns=additional_columns)
gold_values = dict(zip(training_df['trace_id'], training_df['label']))
parent_id = replay_job.id
# final_job = duplicate_orm_row(replay_job) #todo: replace with simple CREATE
final_job = Job.objects.create(
created_date=replay_job.created_date,
modified_date=replay_job.modified_date,
error=replay_job.error,
status=replay_job.status,
type=replay_job.type,
create_models=replay_job.create_models,
case_id=replay_job.case_id,
event_number=replay_job.event_number,
gold_value=replay_job.gold_value,
results=replay_job.results,
parent_job=replay_job.parent_job,
split=replay_job.split,
encoding=replay_job.encoding,
labelling=replay_job.labelling,
clustering=replay_job.clustering,
predictive_model=replay_job.predictive_model,
evaluation=replay_job.evaluation,
hyperparameter_optimizer=replay_job.hyperparameter_optimizer,
incremental_train=replay_job.incremental_train)
final_job.parent_job = Job.objects.filter(pk=parent_id)[0]
final_job.gold_value = gold_values
final_job.type = JobTypes.REPLAY_PREDICT.value
final_job.save()
return requests_list
def setUp(self):
self.log = get_log(create_test_log(log_name=general_example_filename,
log_path=general_example_filepath))
