def apply(log, petri_net, initial_marking, final_marking, parameters=None, variant=None):
"""
Apply fitness evaluation starting from an event log and a marked Petri net,
by using one of the replay techniques provided by PM4Py
Parameters
-----------
log
Trace log object
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
parameters
Parameters related to the replay algorithm
variant
Chosen variant:
- Variants.ALIGNMENT_BASED
- Variants.TOKEN_BASED
Returns
----------
fitness_eval
Fitness evaluation
"""
if parameters is None:
parameters = {}
# execute the following part of code when the variant is not specified by the user
if variant is None:
if not (
check_easy_soundness_net_in_fin_marking(petri_net, initial_marking,
final_marking)):
# in the case the net is not a easy sound workflow net, we must apply token-based replay
variant = TOKEN_BASED
else:
# otherwise, use the align-etconformance approach (safer, in the case the model contains duplicates)
variant = ALIGNMENT_BASED
if variant == TOKEN_BASED:
# execute the token-based replay variant
return exec_utils.get_variant(variant).apply(log_conversion.apply(log, parameters, log_conversion.TO_EVENT_LOG),
petri_net,
initial_marking, final_marking, parameters=parameters)
else:
# execute the alignments based variant, with the specification of the alignments variant
align_variant = exec_utils.get_param_value(Parameters.ALIGN_VARIANT, parameters,
alignments.algorithm.DEFAULT_VARIANT)
return exec_utils.get_variant(variant).apply(log_conversion.apply(log, parameters, log_conversion.TO_EVENT_LOG),
petri_net,
initial_marking, final_marking, align_variant=align_variant,
parameters=parameters)
def apply(log, net, marking, final_marking, parameters=None, variant=None):
"""
Method to apply ET Conformance
Parameters
-----------
log
Trace log
net
Petri net
marking
Initial marking
final_marking
Final marking
parameters
Parameters of the algorithm, including:
pm4py.util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> Activity key
variant
Variant of the algorithm that should be applied:
- Variants.ETCONFORMANCE_TOKEN
- Variants.ALIGN_ETCONFORMANCE
"""
warnings.warn("Use the pm4py.algo.evaluation.precision package")
if parameters is None:
parameters = {}
log = log_conversion.apply(log, parameters, log_conversion.TO_EVENT_LOG)
# execute the following part of code when the variant is not specified by the user
if variant is None:
if not (check_easy_soundness_net_in_fin_marking(
net,
marking,
final_marking)):
# in the case the net is not a easy sound workflow net, we must apply token-based replay
variant = ETCONFORMANCE_TOKEN
else:
# otherwise, use the align-etconformance approach (safer, in the case the model contains duplicates)
variant = ALIGN_ETCONFORMANCE
return exec_utils.get_variant(variant).apply(log, net, marking,
final_marking, parameters=parameters)
def apply_log(log,
petri_net,
initial_marking,
final_marking,
parameters=None,
variant=DEFAULT_VARIANT):
"""
apply alignments to a log
Parameters
-----------
log
object of the form :class:`pm4py.log.log.EventLog` event log
petri_net
:class:`pm4py.objects.petri.petrinet.PetriNet` the model to use for the alignment
initial_marking
:class:`pm4py.objects.petri.petrinet.Marking` initial marking of the net
final_marking
:class:`pm4py.objects.petri.petrinet.Marking` final marking of the net
variant
selected variant of the algorithm, possible values: {\'Variants.VERSION_STATE_EQUATION_A_STAR, Variants.VERSION_DIJKSTRA_NO_HEURISTICS \'}
parameters
:class:`dict` parameters of the algorithm,
Returns
-----------
alignment
:class:`list` of :class:`dict` with keys **alignment**, **cost**, **visited_states**, **queued_states** and
**traversed_arcs**
The alignment is a sequence of labels of the form (a,t), (a,>>), or (>>,t)
representing synchronous/log/model-moves.
"""
if parameters is None:
parameters = dict()
if not check_soundness.check_easy_soundness_net_in_fin_marking(
petri_net, initial_marking, final_marking):
raise Exception(
"trying to apply alignments on a Petri net that is not a easy sound net!!"
)
start_time = time.time()
max_align_time = exec_utils.get_param_value(
Parameters.PARAM_MAX_ALIGN_TIME, parameters, sys.maxsize)
max_align_time_case = exec_utils.get_param_value(
Parameters.PARAM_MAX_ALIGN_TIME_TRACE, parameters, sys.maxsize)
parameters_best_worst = copy(parameters)
best_worst_cost = exec_utils.get_variant(variant).get_best_worst_cost(
petri_net,
initial_marking,
final_marking,
parameters=parameters_best_worst)
variants_idxs = exec_utils.get_param_value(Parameters.VARIANTS_IDX,
parameters, None)
if variants_idxs is None:
variants_idxs = variants_module.get_variants_from_log_trace_idx(
log, parameters=parameters)
one_tr_per_var = []
variants_list = []
for index_variant, var in enumerate(variants_idxs):
variants_list.append(var)
for var in variants_list:
one_tr_per_var.append(log[variants_idxs[var][0]])
all_alignments = []
for trace in one_tr_per_var:
this_max_align_time = min(max_align_time_case,
(max_align_time -
(time.time() - start_time)) * 0.5)
parameters[Parameters.PARAM_MAX_ALIGN_TIME_TRACE] = this_max_align_time
all_alignments.append(
apply_trace(trace,
petri_net,
initial_marking,
final_marking,
parameters=copy(parameters),
variant=variant))
al_idx = {}
for index_variant, variant in enumerate(variants_idxs):
for trace_idx in variants_idxs[variant]:
al_idx[trace_idx] = all_alignments[index_variant]
alignments = []
for i in range(len(log)):
alignments.append(al_idx[i])
# assign fitness to traces
for index, align in enumerate(alignments):
if align is not None:
unfitness_upper_part = align[
'cost'] // align_utils.STD_MODEL_LOG_MOVE_COST
if unfitness_upper_part == 0:
align['fitness'] = 1
elif (len(log[index]) + best_worst_cost) > 0:
align['fitness'] = 1 - (
(align['cost'] // align_utils.STD_MODEL_LOG_MOVE_COST) /
(len(log[index]) + best_worst_cost))
else:
align['fitness'] = 0
return alignments
def apply_log_multiprocessing(log, petri_net, initial_marking, final_marking, parameters=None, version=DEFAULT_VARIANT):
warnings.warn('factory methods are deprecated, use algorithm entrypoint instead', DeprecationWarning)
if parameters is None:
parameters = dict()
if not check_soundness.check_easy_soundness_net_in_fin_marking(petri_net, initial_marking, final_marking):
raise Exception("trying to apply alignments on a Petri net that is not a relaxed sound net!!")
activity_key = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY] if PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else DEFAULT_NAME_KEY
model_cost_function = parameters[
PARAM_MODEL_COST_FUNCTION] if PARAM_MODEL_COST_FUNCTION in parameters else None
sync_cost_function = parameters[
PARAM_SYNC_COST_FUNCTION] if PARAM_SYNC_COST_FUNCTION in parameters else None
if model_cost_function is None or sync_cost_function is None:
# reset variables value
model_cost_function = dict()
sync_cost_function = dict()
for t in petri_net.transitions:
if t.label is not None:
model_cost_function[t] = align_utils.STD_MODEL_LOG_MOVE_COST
sync_cost_function[t] = 0
else:
model_cost_function[t] = 1
parameters[pm4py.util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY] = activity_key
parameters[
PARAM_MODEL_COST_FUNCTION] = model_cost_function
parameters[
PARAM_SYNC_COST_FUNCTION] = sync_cost_function
parameters_best_worst = copy(parameters)
if PARAM_MAX_ALIGN_TIME_TRACE in parameters_best_worst:
del parameters_best_worst[PARAM_MAX_ALIGN_TIME_TRACE]
best_worst_cost = VERSIONS_COST[version](petri_net, initial_marking, final_marking,
parameters=parameters_best_worst)
variants_idxs = parameters[VARIANTS_IDX] if VARIANTS_IDX in parameters else None
if variants_idxs is None:
variants_idxs = variants_module.get_variants_from_log_trace_idx(log, parameters=parameters)
variants_list = [[x, len(y)] for x, y in variants_idxs.items()]
no_cores = mp.cpu_count()
petri_net_string = petri_exporter.export_petri_as_string(petri_net, initial_marking, final_marking)
n = math.ceil(len(variants_list) / no_cores)
variants_list_split = list(chunks(variants_list, n))
# Define an output queue
output = mp.Queue()
processes = [mp.Process(
target=VERSIONS_VARIANTS_LIST_MPROCESSING[version](output, x, petri_net_string, parameters=parameters)) for x in
variants_list_split]
# Run processes
for p in processes:
p.start()
results = []
for p in processes:
result = output.get()
results.append(result)
al_idx = {}
for index, el in enumerate(variants_list_split):
for index2, var_item in enumerate(el):
variant = var_item[0]
for trace_idx in variants_idxs[variant]:
al_idx[trace_idx] = results[index][variant]
alignments = []
for i in range(len(log)):
alignments.append(al_idx[i])
# assign fitness to traces
for index, align in enumerate(alignments):
if align is not None:
unfitness_upper_part = align['cost'] // align_utils.STD_MODEL_LOG_MOVE_COST
if unfitness_upper_part == 0:
align['fitness'] = 1
elif (len(log[index]) + best_worst_cost) > 0:
align['fitness'] = 1 - (
(align['cost'] // align_utils.STD_MODEL_LOG_MOVE_COST) / (len(log[index]) + best_worst_cost))
else:
align['fitness'] = 0
return alignments
def apply_log(log, petri_net, initial_marking, final_marking, parameters=None, version=DEFAULT_VARIANT):
"""
apply alignments to a log
Parameters
-----------
log
object of the form :class:`pm4py.log.log.EventLog` event log
petri_net
:class:`pm4py.objects.petri.petrinet.PetriNet` the model to use for the alignment
initial_marking
:class:`pm4py.objects.petri.petrinet.Marking` initial marking of the net
final_marking
:class:`pm4py.objects.petri.petrinet.Marking` final marking of the net
version
:class:`str` selected variant of the algorithm, possible values: {\'state_equation_a_star\'}
parameters
:class:`dict` parameters of the algorithm,
for key \'state_equation_a_star\':
pm4py.util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> Attribute in the log that contains the activity
pm4py.algo.conformance.alignments.variants.state_equation_a_star.PARAM_MODEL_COST_FUNCTION ->
mapping of each transition in the model to corresponding synchronous costs
pm4py.algo.conformance.alignments.variants.state_equation_a_star.PARAM_SYNC_COST_FUNCTION ->
mapping of each transition in the model to corresponding model cost
pm4py.algo.conformance.alignments.variants.state_equation_a_star.PARAM_TRACE_COST_FUNCTION ->
mapping of each index of the trace to a positive cost value
Returns
-----------
alignment
:class:`list` of :class:`dict` with keys **alignment**, **cost**, **visited_states**, **queued_states** and
**traversed_arcs**
The alignment is a sequence of labels of the form (a,t), (a,>>), or (>>,t)
representing synchronous/log/model-moves.
"""
warnings.warn('factory methods are deprecated, use algorithm entrypoint instead', DeprecationWarning)
if parameters is None:
parameters = dict()
if not check_soundness.check_easy_soundness_net_in_fin_marking(petri_net, initial_marking, final_marking):
raise Exception("trying to apply alignments on a Petri net that is not a easy sound net!!")
start_time = time.time()
activity_key = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY] if PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else DEFAULT_NAME_KEY
model_cost_function = parameters[
PARAM_MODEL_COST_FUNCTION] if PARAM_MODEL_COST_FUNCTION in parameters else None
sync_cost_function = parameters[
PARAM_SYNC_COST_FUNCTION] if PARAM_SYNC_COST_FUNCTION in parameters else None
max_align_time = parameters[PARAM_MAX_ALIGN_TIME] if PARAM_MAX_ALIGN_TIME in parameters else DEFAULT_MAX_ALIGN_TIME
max_align_time_case = parameters[
PARAM_MAX_ALIGN_TIME_TRACE] if PARAM_MAX_ALIGN_TIME_TRACE in parameters else DEFAULT_MAX_ALIGN_TIME_TRACE
if model_cost_function is None or sync_cost_function is None:
# reset variables value
model_cost_function = dict()
sync_cost_function = dict()
for t in petri_net.transitions:
if t.label is not None:
model_cost_function[t] = align_utils.STD_MODEL_LOG_MOVE_COST
sync_cost_function[t] = 0
else:
model_cost_function[t] = 1
parameters[pm4py.util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY] = activity_key
parameters[
PARAM_MODEL_COST_FUNCTION] = model_cost_function
parameters[
PARAM_SYNC_COST_FUNCTION] = sync_cost_function
parameters_best_worst = copy(parameters)
if PARAM_MAX_ALIGN_TIME_TRACE in parameters_best_worst:
del parameters_best_worst[PARAM_MAX_ALIGN_TIME_TRACE]
best_worst_cost = VERSIONS_COST[version](petri_net, initial_marking, final_marking,
parameters=parameters_best_worst)
variants_idxs = parameters[VARIANTS_IDX] if VARIANTS_IDX in parameters else None
if variants_idxs is None:
variants_idxs = variants_module.get_variants_from_log_trace_idx(log, parameters=parameters)
one_tr_per_var = []
variants_list = []
for index_variant, variant in enumerate(variants_idxs):
variants_list.append(variant)
for variant in variants_list:
one_tr_per_var.append(log[variants_idxs[variant][0]])
all_alignments = []
for trace in one_tr_per_var:
this_max_align_time = min(max_align_time_case, (max_align_time - (time.time() - start_time)) * 0.5)
parameters[PARAM_MAX_ALIGN_TIME_TRACE] = this_max_align_time
all_alignments.append(apply_trace(trace, petri_net, initial_marking, final_marking, parameters=copy(parameters),
version=version))
al_idx = {}
for index_variant, variant in enumerate(variants_idxs):
for trace_idx in variants_idxs[variant]:
al_idx[trace_idx] = all_alignments[index_variant]
alignments = []
for i in range(len(log)):
alignments.append(al_idx[i])
# assign fitness to traces
for index, align in enumerate(alignments):
if align is not None:
unfitness_upper_part = align['cost'] // align_utils.STD_MODEL_LOG_MOVE_COST
if unfitness_upper_part == 0:
align['fitness'] = 1
elif (len(log[index]) + best_worst_cost) > 0:
align['fitness'] = 1 - (
(align['cost'] // align_utils.STD_MODEL_LOG_MOVE_COST) / (len(log[index]) + best_worst_cost))
else:
align['fitness'] = 0
return alignments
def apply(log, net, marking, final_marking, parameters=None):
"""
Get Align-ET Conformance precision
Parameters
----------
log
Trace log
net
Petri net
marking
Initial marking
final_marking
Final marking
parameters
Parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> Activity key
"""
if parameters is None:
parameters = {}
debug_level = parameters[
"debug_level"] if "debug_level" in parameters else 0
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, log_lib.util.xes.DEFAULT_NAME_KEY)
# default value for precision, when no activated transitions (not even by looking at the initial marking) are found
precision = 1.0
sum_ee = 0
sum_at = 0
unfit = 0
if not check_soundness.check_easy_soundness_net_in_fin_marking(
net, marking, final_marking):
raise Exception(
"trying to apply Align-ETConformance on a Petri net that is not a easy sound net!!"
)
prefixes, prefix_count = precision_utils.get_log_prefixes(
log, activity_key=activity_key)
prefixes_keys = list(prefixes.keys())
fake_log = precision_utils.form_fake_log(prefixes_keys,
activity_key=activity_key)
align_stop_marking = align_fake_log_stop_marking(fake_log,
net,
marking,
final_marking,
parameters=parameters)
all_markings = transform_markings_from_sync_to_original_net(
align_stop_marking, net, parameters=parameters)
for i in range(len(prefixes)):
markings = all_markings[i]
if markings is not None:
log_transitions = set(prefixes[prefixes_keys[i]])
activated_transitions_labels = set()
for m in markings:
# add to the set of activated transitions in the model the activated transitions
# for each prefix
activated_transitions_labels = activated_transitions_labels.union(
x.label for x in utils.
get_visible_transitions_eventually_enabled_by_marking(
net, m) if x.label is not None)
escaping_edges = activated_transitions_labels.difference(
log_transitions)
sum_at += len(activated_transitions_labels) * prefix_count[
prefixes_keys[i]]
sum_ee += len(escaping_edges) * prefix_count[prefixes_keys[i]]
if debug_level > 1:
print("")
print("prefix=", prefixes_keys[i])
print("log_transitions=", log_transitions)
print("activated_transitions=", activated_transitions_labels)
print("escaping_edges=", escaping_edges)
else:
unfit += prefix_count[prefixes_keys[i]]
if debug_level > 0:
print("\n")
print("overall unfit", unfit)
print("overall activated transitions", sum_at)
print("overall escaping edges", sum_ee)
# fix: also the empty prefix should be counted!
start_activities = set(get_start_activities(log, parameters=parameters))
trans_en_ini_marking = set([
x.label for x in get_visible_transitions_eventually_enabled_by_marking(
net, marking)
])
diff = trans_en_ini_marking.difference(start_activities)
sum_at += len(log) * len(trans_en_ini_marking)
sum_ee += len(log) * len(diff)
# end fix
if sum_at > 0:
precision = 1 - float(sum_ee) / float(sum_at)
return precision
def run_verifier(file):
net, initial_marking, final_marking = petri_importer.apply(file)
cycles = utils.get_cycles_petri_net_places(net)
soundness = check_easy_soundness_net_in_fin_marking(
net, initial_marking, final_marking)
return {"soundness": soundness, "cycles": len(cycles)}
