''' 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 . ''' from enum import Enum from typing import Any, Dict, Optional, Set, Union import pm4py from pm4py.objects.conversion.log import converter from pm4py.algo.discovery.minimum_self_distance import algorithm as msd_algo from pm4py.objects.log.obj import EventLog from pm4py.util import constants, exec_utils, xes_constants class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY def derive_msd_witnesses(log: EventLog, msd: Optional[Dict[Any, int]] = None, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Set[str]]: ''' This function derives the minimum self distance witnesses. The self distance of a in is infinity, of a in is 0, in is 1, etc. The minimum self distance is the minimal observed self distance value in the event log. A 'witness' is an activity that witnesses the minimum self distance. For example, if the minimum self distance of activity a in some log L is 2, then, if trace is in log L, b and c are a witness of a. Parameters ---------- log Event Log to use msd Optional minimum self distance dictionary parameters Optional parameters dictionary Returns ------- Dictionary mapping each activity to a set of witnesses. ''' log = converter.apply(log, variant=converter.Variants.TO_EVENT_LOG, parameters=parameters) act_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY) alphabet = pm4py.get_event_attribute_values(log, act_key) msd = msd if msd is not None else msd_algo.apply(log, parameters) log = list(map(lambda t: list(map(lambda e: e[act_key], t)), log)) witnesses = dict() for a in alphabet: if a in msd and msd[a] > 0: witnesses[a] = set() else: continue for t in log: if len(list(filter(lambda e: e == a, t))) > 1: indices = [i for i, x in enumerate(t) if x == a] for i in range(len(indices) - 1): if indices[i + 1] - indices[i] - 1 == msd[a]: for b in t[indices[i] + 1:indices[i + 1]]: witnesses[a].add(b) return witnesses