'''
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.conformance.tokenreplay.variants import token_replay, backwards
from pm4py.objects.conversion.log import converter as log_converter
from enum import Enum
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.util import typing
[docs]class Variants(Enum):
TOKEN_REPLAY = token_replay
BACKWARDS = backwards
VERSIONS = {Variants.TOKEN_REPLAY, Variants.BACKWARDS}
DEFAULT_VARIANT = Variants.TOKEN_REPLAY
[docs]def apply(log: Union[EventLog, EventStream, pd.DataFrame], net: PetriNet, initial_marking: Marking, final_marking: Marking, parameters: Optional[Dict[Any, Any]] = None, variant=DEFAULT_VARIANT) -> typing.ListAlignments:
"""
Method to apply token-based replay
Parameters
-----------
log
Log
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
parameters
Parameters of the algorithm, including:
Parameters.ACTIVITY_KEY -> Activity key
variant
Variant of the algorithm to use:
- Variants.TOKEN_REPLAY
- Variants.BACKWARDS
"""
if parameters is None:
parameters = {}
return exec_utils.get_variant(variant).apply(log_converter.apply(log, variant=log_converter.TO_EVENT_LOG, parameters=parameters), net, initial_marking,
final_marking, parameters=parameters)
[docs]def get_diagnostics_dataframe(log: Union[EventLog, EventStream, pd.DataFrame], tbr_output: typing.ListAlignments, variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> pd.DataFrame:
"""
Gets the results of token-based replay in a dataframe
Parameters
--------------
log
Event log
tbr_output
Output of the token-based replay technique
variant
Variant of the algorithm to use:
- Variants.TOKEN_REPLAY
- Variants.BACKWARDS
Returns
--------------
dataframe
Diagnostics dataframe
"""
if parameters is None:
parameters = {}
return exec_utils.get_variant(variant).get_diagnostics_dataframe(log, tbr_output, parameters=parameters)