def apply(log, net, initial_marking, final_marking, parameters=None):
"""
Calculates all metrics based on token-based replay and returns a unified dictionary
Parameters
-----------
log
Log
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
parameters
Parameters
Returns
-----------
dictionary
Dictionary containing fitness, precision, generalization and simplicity; along with the average weight of
these metrics
"""
if parameters is None:
parameters = {}
log = log_conversion.apply(log, parameters, log_conversion.TO_EVENT_LOG)
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, log_lib.util.xes.DEFAULT_NAME_KEY)
fitness_weight = exec_utils.get_param_value(
Parameters.PARAM_FITNESS_WEIGHT, parameters, 0.25)
precision_weight = exec_utils.get_param_value(
Parameters.PARAM_PRECISION_WEIGHT, parameters, 0.25)
simplicity_weight = exec_utils.get_param_value(
Parameters.PARAM_SIMPLICITY_WEIGHT, parameters, 0.25)
generalization_weight = exec_utils.get_param_value(
Parameters.PARAM_GENERALIZATION_WEIGHT, parameters, 0.25)
sum_of_weights = (fitness_weight + precision_weight + simplicity_weight +
generalization_weight)
fitness_weight = fitness_weight / sum_of_weights
precision_weight = precision_weight / sum_of_weights
simplicity_weight = simplicity_weight / sum_of_weights
generalization_weight = generalization_weight / sum_of_weights
parameters_tr = {token_replay.Parameters.ACTIVITY_KEY: activity_key}
aligned_traces = token_replay.apply(log,
net,
initial_marking,
final_marking,
parameters=parameters_tr)
parameters = {token_replay.Parameters.ACTIVITY_KEY: activity_key}
fitness = fitness_token_based.evaluate(aligned_traces)
precision = precision_token_based.apply(log,
net,
initial_marking,
final_marking,
parameters=parameters)
generalization = generalization_token_based.get_generalization(
net, aligned_traces)
simplicity = simplicity_arc_degree.apply(net)
metrics_average_weight = fitness_weight * fitness["log_fitness"] + precision_weight * precision \
+ generalization_weight * generalization + simplicity_weight * simplicity
fscore = 0.0
if (fitness['log_fitness'] + precision) > 0:
fscore = (2 * fitness['log_fitness'] *
precision) / (fitness['log_fitness'] + precision)
dictionary = {
"fitness": fitness,
"precision": precision,
"generalization": generalization,
"simplicity": simplicity,
"metricsAverageWeight": metrics_average_weight,
"fscore": fscore
}
return dictionary
def apply_token_replay(log, net, initial_marking, final_marking, parameters=None):
"""
Calculates all metrics based on token-based replay and returns a unified dictionary
Parameters
-----------
log
Trace log
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
parameters
Parameters
Returns
-----------
dictionary
Dictionary containing fitness, precision, generalization and simplicity; along with the average weight of
these metrics
"""
if parameters is None:
parameters = {}
activity_key = parameters[
PARAM_ACTIVITY_KEY] if PARAM_ACTIVITY_KEY in parameters else log_lib.util.xes.DEFAULT_NAME_KEY
fitness_weight = parameters[PARAM_FITNESS_WEIGHT] if PARAM_FITNESS_WEIGHT in parameters else 0.25
precision_weight = parameters[PARAM_PRECISION_WEIGHT] if PARAM_PRECISION_WEIGHT in parameters else 0.25
simplicity_weight = parameters[PARAM_SIMPLICITY_WEIGHT] if PARAM_SIMPLICITY_WEIGHT in parameters else 0.25
generalization_weight = parameters[
PARAM_GENERALIZATION_WEIGHT] if PARAM_GENERALIZATION_WEIGHT in parameters else 0.25
sum_of_weights = (fitness_weight + precision_weight + simplicity_weight + generalization_weight)
fitness_weight = fitness_weight / sum_of_weights
precision_weight = precision_weight / sum_of_weights
simplicity_weight = simplicity_weight / sum_of_weights
generalization_weight = generalization_weight / sum_of_weights
parameters_tr = {pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY: activity_key}
aligned_traces = token_replay.apply(log, net, initial_marking, final_marking, parameters=parameters_tr)
parameters = {
"activity_key": activity_key
}
fitness = fitness_token_based.evaluate(aligned_traces)
precision = precision_token_based.apply(log, net, initial_marking, final_marking, parameters=parameters)
generalization = generalization_token_based.get_generalization(net, aligned_traces)
simplicity = simplicity_arc_degree.apply(net)
metrics_average_weight = fitness_weight * fitness["averageFitness"] + precision_weight * precision \
+ generalization_weight * generalization + simplicity_weight * simplicity
dictionary = {
"fitness": fitness,
"precision": precision,
"generalization": generalization,
"simplicity": simplicity,
"metricsAverageWeight": metrics_average_weight
}
return dictionary
def apply_token_replay(log,
net,
initial_marking,
final_marking,
parameters=None):
"""
Calculates all metrics based on token-based replay and returns a unified dictionary
Parameters
-----------
log
Trace log
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
parameters
Parameters
Returns
-----------
dictionary
Dictionary containing fitness, precision, generalization and simplicity; along with the average weight of these metrics
"""
if parameters is None:
parameters = {}
activity_key = parameters[
PARAM_ACTIVITY_KEY] if PARAM_ACTIVITY_KEY in parameters else log_lib.util.xes.DEFAULT_NAME_KEY
fitness_weight = parameters[
PARAM_FITNESS_WEIGHT] if PARAM_FITNESS_WEIGHT in parameters else 0.25
precision_weight = parameters[
PARAM_PRECISION_WEIGHT] if PARAM_PRECISION_WEIGHT in parameters else 0.25
simplicity_weight = parameters[
PARAM_SIMPLICITY_WEIGHT] if PARAM_SIMPLICITY_WEIGHT in parameters else 0.25
generalization_weight = parameters[
PARAM_GENERALIZATION_WEIGHT] if PARAM_GENERALIZATION_WEIGHT in parameters else 0.25
sum_of_weights = (fitness_weight + precision_weight + simplicity_weight +
generalization_weight)
fitness_weight = fitness_weight / sum_of_weights
precision_weight = precision_weight / sum_of_weights
simplicity_weight = simplicity_weight / sum_of_weights
generalization_weight = generalization_weight / sum_of_weights
[traceIsFit, traceFitnessValue, activatedTransitions, placeFitness, reachedMarkings, enabledTransitionsInMarkings] =\
token_replay.apply(log, net, initial_marking, final_marking, activity_key=activity_key)
parameters = {}
parameters["activity_key"] = activity_key
fitness = fitness_token_based.get_fitness(traceIsFit, traceFitnessValue)
precision = precision_token_based.apply(log,
net,
initial_marking,
final_marking,
parameters=parameters)
generalization = generalization_token_based.get_generalization(
net, activatedTransitions)
simplicity = simplicity_arc_degree.apply(net)
dictionary = {}
dictionary["fitness"] = fitness
dictionary["precision"] = precision
dictionary["generalization"] = generalization
dictionary["simplicity"] = simplicity
metricsAverageWeight = fitness_weight * fitness["averageFitness"] + precision_weight * precision\
+ generalization_weight * generalization + simplicity_weight * simplicity
dictionary["metricsAverageWeight"] = metricsAverageWeight
return dictionary
