def detect_cut(self, second_iteration=False, parameters=None):
if pkgutil.find_loader("networkx"):
import networkx as nx
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters,
pmutil.xes_constants.DEFAULT_NAME_KEY)
# check base cases:
empty_log = base_case.empty_log(self.log)
single_activity = base_case.single_activity(self.log, activity_key)
if empty_log:
self.detected_cut = 'empty_log'
elif single_activity:
self.detected_cut = 'single_activity'
# if no base cases are found, search for a cut:
else:
conn_components = detection_utils.get_connected_components(
self.ingoing, self.outgoing, self.activities)
this_nx_graph = transform_dfg_to_directed_nx_graph(
self.dfg, activities=self.activities)
strongly_connected_components = [
list(x)
for x in nx.strongly_connected_components(this_nx_graph)
]
xor_cut = self.detect_xor(conn_components)
# the following part searches for a cut in the current log_skeleton
# if a cut is found, the log_skeleton is split according to the cut, the resulting logs are saved in new_logs
# recursion is used on all the logs in new_logs
if xor_cut[0]:
logging.debug("xor_cut")
self.detected_cut = 'concurrent'
new_logs = split.split_xor(xor_cut[1], self.log,
activity_key)
for i in range(len(new_logs)):
new_logs[
i] = filtering_utils.keep_one_trace_per_variant(
new_logs[i], parameters=parameters)
for l in new_logs:
new_dfg = [(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreePlain(l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
noise_threshold=self.noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
else:
sequence_cut = cut_detection.detect_sequential_cut(
self, self.dfg, strongly_connected_components)
if sequence_cut[0]:
logging.debug("sequence_cut")
new_logs = split.split_sequence(
sequence_cut[1], self.log, activity_key)
for i in range(len(new_logs)):
new_logs[
i] = filtering_utils.keep_one_trace_per_variant(
new_logs[i], parameters=parameters)
self.detected_cut = "sequential"
for l in new_logs:
new_dfg = [(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreePlain(
l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
noise_threshold=self.noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
else:
parallel_cut = self.detect_concurrent()
if parallel_cut[0]:
logging.debug("parallel_cut")
new_logs = split.split_parallel(
parallel_cut[1], self.log, activity_key)
for i in range(len(new_logs)):
new_logs[
i] = filtering_utils.keep_one_trace_per_variant(
new_logs[i], parameters=parameters)
self.detected_cut = "parallel"
for l in new_logs:
new_dfg = [(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.
get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreePlain(
l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
noise_threshold=self.noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
else:
loop_cut = self.detect_loop()
if loop_cut[0]:
logging.debug("loop_cut")
new_logs = split.split_loop(
loop_cut[1], self.log, activity_key)
for i in range(len(new_logs)):
new_logs[
i] = filtering_utils.keep_one_trace_per_variant(
new_logs[i], parameters=parameters)
self.detected_cut = "loopCut"
for l in new_logs:
new_dfg = [
(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items()
if v > 0
]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.
get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.
get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreePlain(
l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
noise_threshold=self.
noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
# if the code gets to this point, there is no base_case and no cut found in the log_skeleton
# therefore, we now apply fall through:
else:
self.apply_fall_through(parameters)
else:
msg = "networkx is not available. inductive miner cannot be used!"
logging.error(msg)
raise Exception(msg)
def detect_cut_if(self, second_iteration=False, parameters=None):
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters,
pmutil.xes_constants.DEFAULT_NAME_KEY)
# check base cases:
empty_log = base_case.empty_log(self.log)
single_activity = base_case.single_activity(self.log, activity_key)
if empty_log:
self.detected_cut = 'empty_log'
elif single_activity:
self.detected_cut = 'single_activity'
# if no base cases are found, search for a cut:
# use the cutting and splitting functions of im_plain:
else:
found_plain_cut, type_of_cut, cut = self.check_cut_im_plain()
if found_plain_cut:
self.apply_cut_im_plain(type_of_cut, cut, activity_key)
# if im_plain does not find a cut, we filter on our threshold and then again apply the im_cut detection
# but this time, we have to use different splitting functions:
else:
self.filter_dfg_on_threshold()
found_plain_cut, type_of_cut, cut = self.check_cut_im_plain()
if found_plain_cut:
if type_of_cut == 'concurrent':
logging.debug("concurrent_cut_if")
self.detected_cut = 'concurrent'
new_logs = splitting_infrequent.split_xor_infrequent(
cut[1], self.log, activity_key)
for l in new_logs:
new_dfg = [(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreeInfrequent(
l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
self.f,
noise_threshold=self.noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
elif type_of_cut == 'sequential':
logging.debug("sequential_if")
new_logs = splitting_infrequent.split_sequence_infrequent(
cut[1], self.log, activity_key)
self.detected_cut = "sequential"
for l in new_logs:
new_dfg = [(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreeInfrequent(
l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
self.f,
noise_threshold=self.noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
elif type_of_cut == 'parallel':
logging.debug("parallel_if")
new_logs = split.split_parallel(
cut[1], self.log, activity_key)
self.detected_cut = "parallel"
for l in new_logs:
new_dfg = [(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreeInfrequent(
l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
self.f,
noise_threshold=self.noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
elif type_of_cut == 'loopCut':
logging.debug("loopCut_if")
new_logs = splitting_infrequent.split_loop_infrequent(
cut[1], self.log, activity_key)
self.detected_cut = "loopCut"
for l in new_logs:
new_dfg = [(k, v) for k, v in dfg_inst.apply(
l, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
l, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
l, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
l, parameters=parameters).keys())
self.children.append(
SubtreeInfrequent(
l,
new_dfg,
self.master_dfg,
self.initial_dfg,
activities,
self.counts,
self.rec_depth + 1,
self.f,
noise_threshold=self.noise_threshold,
start_activities=start_activities,
end_activities=end_activities,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.
initial_end_activities,
parameters=parameters))
else:
self.apply_fall_through_infrequent(parameters)