Source code for pm4py.statistics.ocel.act_utils

'''
    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 typing import Optional, Dict, Any, Tuple, Set
from enum import Enum
from pm4py.util import exec_utils
from pm4py.objects.ocel import constants as ocel_constants
import pandas as pd
from pm4py.objects.ocel.obj import OCEL
from copy import copy


[docs]class Parameters(Enum): EVENT_ID = ocel_constants.PARAM_EVENT_ID OBJECT_ID = ocel_constants.PARAM_OBJECT_ID EVENT_ACTIVITY = ocel_constants.PARAM_EVENT_ACTIVITY PREFILTERING = "prefiltering"
[docs]def aggregate_events(associations: Dict[str, Set[Tuple[str, str]]]) -> Dict[str, Set[str]]: """ Utility method to calculate the "events" metric from the associations. """ ret = {} for act in associations: ret[act] = set() for el in associations[act]: ret[act].add(el[0]) return ret
[docs]def aggregate_unique_objects(associations: Dict[str, Set[Tuple[str, str]]]) -> Dict[str, Set[str]]: """ Utility method to calculate the "unique objects" metric from the associations. """ ret = {} for act in associations: ret[act] = set() for el in associations[act]: ret[act].add(el[1]) return ret
[docs]def aggregate_total_objects(associations: Dict[str, Set[Tuple[str, str]]]) -> Dict[str, Set[Tuple[str, str]]]: """ Utility method to calculate the "total objects" metric from the associations. """ return associations
[docs]def find_associations_from_relations_df(relations_df: pd.DataFrame, parameters: Optional[Dict[Any, Any]] = None) -> \ Dict[ str, Set[Tuple[str, str]]]: """ Associates each activity in the relationship dataframe with the combinations of event identifiers and objects that are associated to the activity. Parameters ------------------ rel_df Relations dataframe parameters Parameters of the method, including: - Parameters.EVENT_ID => the attribute to use as event identifier - Parameters.OBJECT_ID => the attribute to use as object identifier - Parameters.EVENT_ACTIVITY => the attribute to use as activity Returns ----------------- dict_associations Dictionary that associates each activity to its (ev. id, obj id.) combinations. """ if parameters is None: parameters = {} event_id = exec_utils.get_param_value(Parameters.EVENT_ID, parameters, ocel_constants.DEFAULT_EVENT_ID) object_id = exec_utils.get_param_value(Parameters.OBJECT_ID, parameters, ocel_constants.DEFAULT_OBJECT_ID) event_activity = exec_utils.get_param_value(Parameters.EVENT_ACTIVITY, parameters, ocel_constants.DEFAULT_EVENT_ACTIVITY) prefiltering = exec_utils.get_param_value(Parameters.PREFILTERING, parameters, "none") if prefiltering == "start": relations_df = relations_df.groupby(object_id).first().reset_index() elif prefiltering == "end": relations_df = relations_df.groupby(object_id).last().reset_index() ids = list(relations_df[event_id]) oids = list(relations_df[object_id]) acts = list(relations_df[event_activity]) associations = {} i = 0 while i < len(acts): if acts[i] not in associations: associations[acts[i]] = set() associations[acts[i]].add((ids[i], oids[i])) i = i + 1 return associations
[docs]def find_associations_from_ocel(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Set[Any]]: """ Associates each activity in the OCEL with the combinations of event identifiers and objects that are associated to the activity. Parameters ------------------ ocel Object-centric event log parameters Parameters of the method, including: - Parameters.EVENT_ID => the attribute to use as event identifier - Parameters.OBJECT_ID => the attribute to use as object identifier - Parameters.EVENT_ACTIVITY => the attribute to use as activity Returns ----------------- dict_associations Dictionary that associates each activity to its (ev. id, obj id.) combinations. """ if parameters is None: parameters = {} event_id = exec_utils.get_param_value(Parameters.EVENT_ID, parameters, ocel.event_id_column) object_id = exec_utils.get_param_value(Parameters.OBJECT_ID, parameters, ocel.object_id_column) event_activity = exec_utils.get_param_value(Parameters.EVENT_ACTIVITY, parameters, ocel.event_activity) new_parameters = copy(parameters) new_parameters[Parameters.EVENT_ID] = event_id new_parameters[Parameters.OBJECT_ID] = object_id new_parameters[Parameters.EVENT_ACTIVITY] = event_activity return find_associations_from_relations_df(ocel.relations, parameters=new_parameters)