def apply_fall_through(self, parameters=None):
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters,
pmutil.xes_constants.DEFAULT_NAME_KEY)
# set flags for fall_throughs, base case is True (enabled)
use_empty_trace = (Parameters.EMPTY_TRACE_KEY not in parameters
) or parameters[Parameters.EMPTY_TRACE_KEY]
use_act_once_per_trace = (
Parameters.ONCE_PER_TRACE_KEY
not in parameters) or parameters[Parameters.ONCE_PER_TRACE_KEY]
use_act_concurrent = (Parameters.CONCURRENT_KEY not in parameters
) or parameters[Parameters.CONCURRENT_KEY]
use_strict_tau_loop = (Parameters.STRICT_TAU_LOOP_KEY not in parameters
) or parameters[Parameters.STRICT_TAU_LOOP_KEY]
use_tau_loop = (Parameters.TAU_LOOP_KEY not in parameters
) or parameters[Parameters.TAU_LOOP_KEY]
if use_empty_trace:
empty_trace, new_log = fall_through.empty_trace(self.log)
# if an empty trace is found, the empty trace fallthrough applies
#
else:
empty_trace = False
if empty_trace:
logging.debug("empty_trace")
activites_left = []
for trace in new_log:
for act in trace:
if act[activity_key] not in activites_left:
activites_left.append(act[activity_key])
self.detected_cut = 'empty_trace'
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=self.parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=self.parameters).keys())
self.children.append(
SubtreePlain(
new_log,
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:
if use_act_once_per_trace:
activity_once, new_log, small_log = fall_through.act_once_per_trace(
self.log, self.activities, activity_key)
small_log = filtering_utils.keep_one_trace_per_variant(
small_log, parameters=parameters)
else:
activity_once = False
if use_act_once_per_trace and activity_once:
self.detected_cut = 'parallel'
# create two new dfgs as we need them to append to self.children later
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
small_dfg = [(k, v) for k, v in dfg_inst.apply(
small_log, parameters=parameters).items() if v > 0]
small_activities = attributes_filter.get_attribute_values(
small_log, activity_key)
self.children.append(
SubtreePlain(
small_log,
small_dfg,
self.master_dfg,
self.initial_dfg,
small_activities,
self.counts,
self.rec_depth + 1,
noise_threshold=self.noise_threshold,
initial_start_activities=self.initial_start_activities,
initial_end_activities=self.initial_end_activities,
parameters=parameters))
# continue with the recursion on the new log_skeleton
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=self.parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=self.parameters).keys())
self.children.append(
SubtreePlain(
new_log,
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:
if use_act_concurrent:
activity_concurrent, new_log, small_log, activity_left_out = fall_through.activity_concurrent(
self,
self.log,
self.activities,
activity_key,
parameters=parameters)
small_log = filtering_utils.keep_one_trace_per_variant(
small_log, parameters=parameters)
else:
activity_concurrent = False
if use_act_concurrent and activity_concurrent:
self.detected_cut = 'parallel'
# create two new dfgs on to append later
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
small_dfg = [(k, v) for k, v in dfg_inst.apply(
small_log, parameters=parameters).items() if v > 0]
small_activities = attributes_filter.get_attribute_values(
small_log, activity_key)
# append the concurrent activity as leaf:
self.children.append(
SubtreePlain(
small_log,
small_dfg,
self.master_dfg,
self.initial_dfg,
small_activities,
self.counts,
self.rec_depth + 1,
noise_threshold=self.noise_threshold,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.initial_end_activities,
parameters=parameters))
# continue with the recursion on the new log_skeleton:
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=self.parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=self.parameters).keys())
self.children.append(
SubtreePlain(
new_log,
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:
if use_strict_tau_loop:
strict_tau_loop, new_log = fall_through.strict_tau_loop(
self.log, self.start_activities,
self.end_activities, activity_key)
new_log = filtering_utils.keep_one_trace_per_variant(
new_log, parameters=parameters)
else:
strict_tau_loop = False
if use_strict_tau_loop and strict_tau_loop:
activites_left = []
for trace in new_log:
for act in trace:
if act[activity_key] not in activites_left:
activites_left.append(act[activity_key])
self.detected_cut = 'strict_tau_loop'
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=self.parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=self.parameters).keys())
self.children.append(
SubtreePlain(new_log,
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:
if use_tau_loop:
tau_loop, new_log = fall_through.tau_loop(
self.log, self.start_activities, activity_key)
new_log = filtering_utils.keep_one_trace_per_variant(
new_log, parameters=parameters)
else:
tau_loop = False
if use_tau_loop and tau_loop:
activites_left = []
for trace in new_log:
for act in trace:
if act[activity_key] not in activites_left:
activites_left.append(
act[activity_key])
self.detected_cut = 'tau_loop'
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items()
if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log,
parameters=self.parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log,
parameters=self.parameters).keys())
self.children.append(
SubtreePlain(
new_log,
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:
logging.debug("flower model")
activites_left = []
for trace in self.log:
for act in trace:
if act[activity_key] not in activites_left:
activites_left.append(
act[activity_key])
self.detected_cut = 'flower'
def apply_fall_through_infrequent(self, parameters=None):
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, self.parameters,
pmutil.xes_constants.DEFAULT_NAME_KEY)
# set flags for fall_throughs, base case is True (enabled)
use_empty_trace = (Parameters.EMPTY_TRACE_KEY not in parameters
) or parameters[Parameters.EMPTY_TRACE_KEY]
use_act_once_per_trace = (
Parameters.ONCE_PER_TRACE_KEY
not in parameters) or parameters[Parameters.ONCE_PER_TRACE_KEY]
use_act_concurrent = (Parameters.CONCURRENT_KEY not in parameters
) or parameters[Parameters.CONCURRENT_KEY]
use_strict_tau_loop = (Parameters.STRICT_TAU_LOOP_KEY not in parameters
) or parameters[Parameters.STRICT_TAU_LOOP_KEY]
use_tau_loop = (Parameters.TAU_LOOP_KEY not in parameters
) or parameters[Parameters.TAU_LOOP_KEY]
if use_empty_trace:
empty_traces_present, enough_traces, new_log = fall_through_infrequent.empty_trace_filtering(
self.log, self.f)
self.log = new_log
else:
empty_traces_present = False
enough_traces = False
# if an empty trace is found, the empty trace fallthrough applies
if empty_traces_present and enough_traces:
logging.debug("empty_trace_if")
self.detected_cut = 'empty_trace'
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=self.parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=parameters).keys())
self.children.append(
SubtreeInfrequent(
new_log,
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 empty_traces_present and not enough_traces:
# no node is added to the PT, instead we just use recursion on the log without the empty traces
self.detect_cut_if(parameters=parameters)
else:
if use_act_once_per_trace:
activity_once, new_log, small_log = fall_through.act_once_per_trace(
self.log, self.activities, activity_key)
else:
activity_once = False
if activity_once:
self.detected_cut = 'parallel'
# create two new dfgs as we need them to append to self.children later
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
small_dfg = [(k, v) for k, v in dfg_inst.apply(
small_log, parameters=parameters).items() if v > 0]
small_activities = attributes_filter.get_attribute_values(
small_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=parameters).keys())
# append the chosen activity as leaf:
self.children.append(
SubtreeInfrequent(
small_log,
small_dfg,
self.master_dfg,
self.initial_dfg,
small_activities,
self.counts,
self.rec_depth + 1,
self.f,
noise_threshold=self.noise_threshold,
initial_start_activities=self.initial_start_activities,
initial_end_activities=self.initial_end_activities,
parameters=parameters))
# continue with the recursion on the new log
self.children.append(
SubtreeInfrequent(
new_log,
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:
if use_act_concurrent:
activity_concurrent, new_log, small_log, key = fall_through.activity_concurrent(
self,
self.log,
self.activities,
activity_key,
parameters=parameters)
else:
activity_concurrent = False
if activity_concurrent:
self.detected_cut = 'parallel'
# create two new dfgs on to append later
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
small_dfg = [(k, v) for k, v in dfg_inst.apply(
small_log, parameters=parameters).items() if v > 0]
small_activities = attributes_filter.get_attribute_values(
small_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=parameters).keys())
# append the concurrent activity as leaf:
self.children.append(
SubtreeInfrequent(
small_log,
small_dfg,
self.master_dfg,
self.initial_dfg,
small_activities,
self.counts,
self.rec_depth + 1,
self.f,
noise_threshold=self.noise_threshold,
initial_start_activities=self.
initial_start_activities,
initial_end_activities=self.initial_end_activities,
parameters=parameters))
# continue with the recursion on the new log:
self.children.append(
SubtreeInfrequent(
new_log,
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:
if use_strict_tau_loop:
strict_tau_loop, new_log = fall_through.strict_tau_loop(
self.log, self.start_activities,
self.end_activities, activity_key)
else:
strict_tau_loop = False
if strict_tau_loop:
self.detected_cut = 'strict_tau_loop'
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items() if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=parameters).keys())
self.children.append(
SubtreeInfrequent(
new_log,
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:
if use_tau_loop:
tau_loop, new_log = fall_through.tau_loop(
self.log, self.start_activities, activity_key)
else:
tau_loop = False
if tau_loop:
self.detected_cut = 'tau_loop'
new_dfg = [(k, v) for k, v in dfg_inst.apply(
new_log, parameters=parameters).items()
if v > 0]
activities = attributes_filter.get_attribute_values(
new_log, activity_key)
start_activities = list(
start_activities_filter.get_start_activities(
new_log, parameters=parameters).keys())
end_activities = list(
end_activities_filter.get_end_activities(
new_log, parameters=parameters).keys())
self.children.append(
SubtreeInfrequent(
new_log,
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:
logging.debug("flower_if")
self.detected_cut = 'flower'
