Source code for pm4py.evaluation.evaluator

    This file is part of PM4Py (More Info:

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <>.
from pm4py import util as pmutil
from pm4py.algo.conformance.tokenreplay.variants import token_replay
from pm4py.evaluation.generalization.variants import token_based as generalization_token_based
from pm4py.evaluation.precision.variants import etconformance_token as precision_token_based
from pm4py.evaluation.replay_fitness.variants import token_replay as fitness_token_based
from pm4py.evaluation.simplicity.variants import arc_degree as simplicity_arc_degree
from pm4py.objects import log as log_lib
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.util import xes_constants as xes_util
from pm4py.util import constants
from enum import Enum
from pm4py.util import exec_utils
import deprecation
from pm4py.meta import VERSION
import warnings

[docs]class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY PARAM_FITNESS_WEIGHT = 'fitness_weight' PARAM_PRECISION_WEIGHT = 'precision_weight' PARAM_SIMPLICITY_WEIGHT = 'simplicity_weight' PARAM_GENERALIZATION_WEIGHT = 'generalization_weight'
[docs]@deprecation.deprecated(deprecated_in="2.2.5", removed_in="3.0", current_version=VERSION, details="Use the pm4py.algo.evaluation.evaluator class") 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 """ warnings.warn("Use the pm4py.algo.evaluation.evaluator class") 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