'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
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
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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 <https://www.gnu.org/licenses/>.
'''
from pm4py.algo.evaluation.replay_fitness.variants import alignment_based, token_replay
from pm4py.algo.conformance import alignments
from pm4py.objects.conversion.log import converter as log_conversion
from pm4py.util import exec_utils
from pm4py.objects.petri_net.utils.check_soundness import check_easy_soundness_net_in_fin_marking
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
from pm4py.objects.petri_net.obj import PetriNet, Marking
import pandas as pd
[docs]class Variants(Enum):
ALIGNMENT_BASED = alignment_based
TOKEN_BASED = token_replay
[docs]class Parameters(Enum):
ALIGN_VARIANT = "align_variant"
ALIGNMENT_BASED = Variants.ALIGNMENT_BASED
TOKEN_BASED = Variants.TOKEN_BASED
VERSIONS = {ALIGNMENT_BASED, TOKEN_BASED}
[docs]def apply(log: Union[EventLog, pd.DataFrame], petri_net: PetriNet, initial_marking: Marking, final_marking: Marking, parameters: Optional[Dict[Union[str, Parameters], Any]] = None, variant=None) -> Dict[str, Any]:
"""
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.petri_net.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)
[docs]def evaluate(results, parameters=None, variant=TOKEN_BASED):
"""
Evaluate replay results when the replay algorithm has already been applied
Parameters
-----------
results
Results of the replay algorithm
parameters
Possible parameters passed to the evaluation
variant
Indicates which evaluator is called
Returns
-----------
fitness_eval
Fitness evaluation
"""
return exec_utils.get_variant(variant).evaluate(results, parameters=parameters)