Source code for pm4py.algo.discovery.minimum_self_distance.utils

'''
    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
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but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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Contact: info@processintelligence.solutions
'''
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


[docs] class Parameters(Enum): ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
[docs] 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 <a> is infinity, of a in <a,a> is 0, in <a,b,a> 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 <a,b,c,a> 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