'''
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 import algorithm as executor
from pm4py.algo.conformance.tokenreplay.variants import token_replay
from pm4py.util import exec_utils
from pm4py.util.xes_constants import DEFAULT_NAME_KEY
from enum import Enum
from pm4py.util import constants
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.util import typing
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
TOKEN_REPLAY_VARIANT = "token_replay_variant"
CLEANING_TOKEN_FLOOD = "cleaning_token_flood"
MULTIPROCESSING = "multiprocessing"
SHOW_PROGRESS_BAR = "show_progress_bar"
[docs]
def evaluate(
aligned_traces: typing.ListAlignments,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, float]:
"""
Gets a dictionary expressing fitness in a synthetic way from the list of boolean values
saying if a trace in the log is fit, and the float values of fitness associated to each trace
Parameters
------------
aligned_traces
Result of the token-based replayer
parameters
Possible parameters of the evaluation
Returns
-----------
dictionary
Containing two keys (percFitTraces and averageFitness)
"""
if parameters is None:
parameters = {}
no_traces = len(aligned_traces)
fit_traces = len([x for x in aligned_traces if x["trace_is_fit"]])
sum_of_fitness = sum([x["trace_fitness"] for x in aligned_traces])
perc_fit_traces = 0.0
average_fitness = 0.0
log_fitness = 0
total_m = sum([x["missing_tokens"] for x in aligned_traces])
total_c = sum([x["consumed_tokens"] for x in aligned_traces])
total_r = sum([x["remaining_tokens"] for x in aligned_traces])
total_p = sum([x["produced_tokens"] for x in aligned_traces])
if no_traces > 0 and total_c > 0 and total_p > 0:
perc_fit_traces = float(100.0 * fit_traces) / float(no_traces)
average_fitness = float(sum_of_fitness) / float(no_traces)
log_fitness = 0.5 * (1 - total_m / total_c) + 0.5 * (
1 - total_r / total_p
)
return {
"perc_fit_traces": perc_fit_traces,
"average_trace_fitness": average_fitness,
"log_fitness": log_fitness,
"percentage_of_fitting_traces": perc_fit_traces,
}
[docs]
def apply(
log: EventLog,
petri_net: PetriNet,
initial_marking: Marking,
final_marking: Marking,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, float]:
"""
Apply token replay fitness evaluation
Parameters
-----------
log
Trace log
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
parameters
Parameters
Returns
-----------
dictionary
Containing two keys (percFitTraces and averageFitness)
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, DEFAULT_NAME_KEY
)
token_replay_variant = exec_utils.get_param_value(
Parameters.TOKEN_REPLAY_VARIANT,
parameters,
executor.Variants.TOKEN_REPLAY,
)
cleaning_token_flood = exec_utils.get_param_value(
Parameters.CLEANING_TOKEN_FLOOD, parameters, False
)
show_progress_bar = exec_utils.get_param_value(
Parameters.SHOW_PROGRESS_BAR, parameters, constants.SHOW_PROGRESS_BAR
)
case_id_key = exec_utils.get_param_value(
Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
)
parameters_tr = {
token_replay.Parameters.ACTIVITY_KEY: activity_key,
token_replay.Parameters.CONSIDER_REMAINING_IN_FITNESS: True,
token_replay.Parameters.CLEANING_TOKEN_FLOOD: cleaning_token_flood,
token_replay.Parameters.SHOW_PROGRESS_BAR: show_progress_bar,
token_replay.Parameters.CASE_ID_KEY: case_id_key,
}
aligned_traces = executor.apply(
log,
petri_net,
initial_marking,
final_marking,
variant=token_replay_variant,
parameters=parameters_tr,
)
return evaluate(aligned_traces)
