Source code for pm4py.statistics.ocel.edge_metrics

'''
    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.objects.ocel.obj import OCEL
from typing import Optional, Dict, Any, Tuple, Collection, Set, List
from enum import Enum
from pm4py.util import exec_utils, constants
from pm4py.objects.ocel import constants as ocel_constants
from pm4py.util.business_hours import BusinessHours


[docs]class Parameters(Enum): EVENT_ID = ocel_constants.PARAM_EVENT_ID OBJECT_ID = ocel_constants.PARAM_OBJECT_ID OBJECT_TYPE = ocel_constants.PARAM_OBJECT_TYPE EVENT_ACTIVITY = ocel_constants.PARAM_EVENT_ACTIVITY EVENT_TIMESTAMP = ocel_constants.PARAM_EVENT_TIMESTAMP BUSINESS_HOURS = "business_hours" WORKTIMING = "worktiming" WEEKENDS = "weekends" WORKCALENDAR = "workcalendar"
[docs]def performance_calculation_ocel_aggregation(ocel: OCEL, aggregation: Dict[str, Dict[Tuple[str, str], Set[Any]]], parameters: Optional[Dict[Any, Any]] = None) -> Dict[ str, Dict[Tuple[str, str], List[float]]]: """ Calculates the performance based on one of the following aggregations: - aggregate_ev_couples - aggregate_total_objects Parameters ---------------- ocel Object-centric event log aggregation Aggregation calculated using one of the aforementioned methods parameters Parameters of the algorithm, including: - Parameters.EVENT_ID => the event identifier - Parameters.EVENT_TIMESTAMP => the timestamp - Parameters.BUSINESS_HOURS => enables/disables the business hours - Parameters.WORKTIMING => the work timing (default: [7, 17]) - Parameters.WEEKENDS => the weekends (default: [6, 7]) Returns ---------------- edges_performance For each object type, associate a dictionary where to each activity couple all the times between the activities are recorded. """ if parameters is None: parameters = {} event_id = exec_utils.get_param_value(Parameters.EVENT_ID, parameters, ocel.event_id_column) timestamp_key = exec_utils.get_param_value(Parameters.EVENT_TIMESTAMP, parameters, ocel.event_timestamp) timestamps = ocel.events.groupby(event_id)[timestamp_key].apply(list).to_dict() timestamps = {x: y[0] for x, y in timestamps.items()} business_hours = exec_utils.get_param_value(Parameters.BUSINESS_HOURS, parameters, False) worktiming = exec_utils.get_param_value(Parameters.WORKTIMING, parameters, [7, 17]) weekends = exec_utils.get_param_value(Parameters.WEEKENDS, parameters, [6, 7]) workcalendar = exec_utils.get_param_value(Parameters.WORKCALENDAR, parameters, constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR) ret = {} for ot in aggregation: ret[ot] = {} for act in aggregation[ot]: ret[ot][act] = [] for el in aggregation[ot][act]: if business_hours: bh = BusinessHours(timestamps[el[0]], timestamps[el[1]], worktiming=worktiming, weekends=weekends, workcalendar=workcalendar) diff = bh.getseconds() else: diff = timestamps[el[1]].timestamp() - timestamps[el[0]].timestamp() ret[ot][act].append(diff) ret[ot][act] = sorted(ret[ot][act]) return ret
[docs]def aggregate_ev_couples(edges: Dict[str, Dict[Tuple[str, str], Collection[Any]]]) -> Dict[ str, Dict[Tuple[str, str], Set[Any]]]: """ Performs an aggregation of the occurrences of a given edge on the couple of events (source event, target event). Parameters ------------------- edges Edges calculated using the find_associations_per_edge function Returns ------------------- aggregation A dictionary associating to each object type another dictionary where to each edge (activity couple) all the couples of related events are associated. """ ret = {} for ot in edges: ret[ot] = {} for act in edges[ot]: ret[ot][act] = set((x[0], x[1]) for x in edges[ot][act]) return ret
[docs]def aggregate_unique_objects(edges: Dict[str, Dict[Tuple[str, str], Collection[Any]]]) -> Dict[ str, Dict[Tuple[str, str], Set[Any]]]: """ Performs an aggregation of the occurrences of a given edge in the involved object. Parameters ------------------- edges Edges calculated using the find_associations_per_edge function Returns ------------------- aggregation A dictionary associating to each object type another dictionary where to each edge (activity couple) all the involved objects are associated. """ ret = {} for ot in edges: ret[ot] = {} for act in edges[ot]: ret[ot][act] = set(x[2] for x in edges[ot][act]) return ret
[docs]def aggregate_total_objects(edges: Dict[str, Dict[Tuple[str, str], Collection[Any]]]) -> Dict[ str, Dict[Tuple[str, str], Set[Any]]]: """ Performs an aggregation of the occurrences of a given edge on the triple (source event, target event, object). Parameters ------------------- edges Edges calculated using the find_associations_per_edge function Returns ------------------- aggregation A dictionary associating to each object type another dictionary where to each edge (activity couple) all the triples (source event, target event, object) are associated. """ ret = {} for ot in edges: ret[ot] = {} for act in edges[ot]: ret[ot][act] = set(edges[ot][act]) return ret
[docs]def find_associations_per_edge(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None) -> Dict[ str, Dict[Tuple[str, str], Collection[Any]]]: """ Finds all the occurrences of a given edge (activity couple), expressed as triples (source event, target event, object ID). Parameters ------------------- ocel Object-centric event log parameters Parameters of the algorithm, including: - Parameters.EVENT_ACTIVITY => the activity - Parameters.EVENT_ID => the event identifier - Parameters.OBJECT_ID => the object identifier - Parameters.OBJECT_TYPE => the object type Returns ------------------ edges A dictionary associating to each object type a dictionary where to each edge (activity couple) the list of triples (source event, target event, object ID) is associated. """ if parameters is None: parameters = {} event_activity = exec_utils.get_param_value(Parameters.EVENT_ACTIVITY, parameters, ocel.event_activity) 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) object_type = exec_utils.get_param_value(Parameters.OBJECT_TYPE, parameters, ocel.object_type_column) identifiers = list(ocel.events[event_id]) activities = ocel.events.groupby(event_id)[event_activity].apply(list).to_dict() activities = {x: y[0] for x, y in activities.items()} omap = ocel.relations.groupby(event_id)[object_id].apply(list).to_dict() objtypes = ocel.objects.groupby(object_id)[object_type].apply(list).to_dict() objtypes = {x: y[0] for x, y in objtypes.items()} history = {} edges = {} for evid in identifiers: if evid in omap: for obj in omap[evid]: if obj in history: objtype = objtypes[obj] if objtype not in edges: edges[objtype] = {} previd = history[obj] acttup = (activities[previd], activities[evid]) if acttup not in edges[objtype]: edges[objtype][acttup] = list() edges[objtype][acttup].append((previd, evid, obj)) history[obj] = evid return edges