'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from pm4py.algo.conformance.tokenreplay.variants import token_replay
from pm4py.algo.evaluation.generalization.variants import (
token_based as generalization_token_based,
)
from pm4py.algo.evaluation.precision.variants import (
etconformance_token as precision_token_based,
)
from pm4py.algo.evaluation.replay_fitness.variants import (
token_replay as fitness_token_based,
)
from pm4py.algo.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 constants
from enum import Enum
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
import pandas as pd
[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]
def apply(
log: Union[EventLog, pd.DataFrame],
net: PetriNet,
initial_marking: Marking,
final_marking: Marking,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, float]:
"""
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
