Source code for pm4py.ocel

'''
    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 List, Dict, Collection

import pandas as pd

from pm4py.objects.ocel.obj import OCEL


[docs]def ocel_get_object_types(ocel: OCEL) -> List[str]: """ Gets the list of object types contained in the object-centric event log (e.g., ["order", "item", "delivery"]). Parameters ----------------- ocel Object-centric event log Returns ---------------- object_types_list List of object types contained in the event log (e.g., ["order", "item", "delivery"]) """ return list(ocel.objects[ocel.object_type_column].unique())
[docs]def ocel_get_attribute_names(ocel: OCEL) -> List[str]: """ Gets the list of attributes at the event and the object level of an object-centric event log (e.g. ["cost", "amount", "name"]) Parameters ------------------- ocel Object-centric event log Returns ------------------- attributes_list List of attributes at the event and object level (e.g. ["cost", "amount", "name"]) """ from pm4py.objects.ocel.util import attributes_names return attributes_names.get_attribute_names(ocel)
[docs]def ocel_flattening(ocel: OCEL, object_type: str) -> pd.DataFrame: """ Flattens the object-centric event log to a traditional event log with the choice of an object type. In the flattened log, the objects of a given object type are the cases, and each case contains the set of events related to the object. Parameters ------------------- ocel Object-centric event log object_type Object type Returns ------------------ dataframe Flattened log in the form of a Pandas dataframe """ from pm4py.objects.ocel.util import flattening return flattening.flatten(ocel, object_type)
[docs]def ocel_object_type_activities(ocel: OCEL) -> Dict[str, Collection[str]]: """ Gets the set of activities performed for each object type Parameters ---------------- ocel Object-centric event log Returns ---------------- dict A dictionary having as key the object types and as values the activities performed for that object type """ from pm4py.statistics.ocel import ot_activities return ot_activities.get_object_type_activities(ocel)
[docs]def ocel_objects_ot_count(ocel: OCEL) -> Dict[str, Dict[str, int]]: """ Counts for each event the number of related objects per type Parameters ------------------- ocel Object-centric Event log parameters Parameters of the algorithm, including: - Parameters.EVENT_ID => the event identifier to be used - Parameters.OBJECT_ID => the object identifier to be used - Parameters.OBJECT_TYPE => the object type to be used Returns ------------------- dict_ot Dictionary associating to each event identifier a dictionary with the number of related objects """ from pm4py.statistics.ocel import objects_ot_count return objects_ot_count.get_objects_ot_count(ocel)
[docs]def ocel_temporal_summary(ocel: OCEL) -> pd.DataFrame: """ Returns the ``temporal summary'' from an object-centric event log. The temporal summary aggregates all the events performed in the same timestamp, and reports the set of activities and the involved objects. :param ocel: object-centric event log :rtype: ``pd.DataFrame`` .. code-block:: python3 import pm4py temporal_summary = pm4py.ocel_temporal_summary(ocel) """ gdf = ocel.relations.groupby(ocel.event_timestamp) act_comb = gdf[ocel.event_activity].agg(set).to_frame() obj_comb = gdf[ocel.object_id_column].agg(set).to_frame() temporal_summary = act_comb.join(obj_comb).reset_index() return temporal_summary
[docs]def ocel_objects_summary(ocel: OCEL) -> pd.DataFrame: """ Gets the objects summary of an object-centric event log :param ocel: object-centric event log :rtype: ``pd.DataFrame`` .. code-block:: python3 import pm4py objects_summary = pm4py.ocel_objects_summary(ocel) """ gdf = ocel.relations.groupby(ocel.object_id_column) act_comb = gdf[ocel.event_activity].agg(list).to_frame().rename(columns={ocel.event_activity: "activities_lifecycle"}) lif_start_tim = gdf[ocel.event_timestamp].min().to_frame().rename(columns={ocel.event_timestamp: "lifecycle_start"}) lif_end_tim = gdf[ocel.event_timestamp].max().to_frame().rename(columns={ocel.event_timestamp: "lifecycle_end"}) objects_summary = act_comb.join(lif_start_tim) objects_summary = objects_summary.join(lif_end_tim) objects_summary = objects_summary.reset_index() objects_summary["lifecycle_duration"] = (objects_summary["lifecycle_end"] - objects_summary["lifecycle_start"]).astype('timedelta64[s]') ev_rel_obj = ocel.relations.groupby(ocel.event_id_column)[ocel.object_id_column].apply(list).to_dict() objects_ids = set(ocel.objects[ocel.object_id_column].unique()) graph = {o: set() for o in objects_ids} for ev in ev_rel_obj: rel_obj = ev_rel_obj[ev] for o1 in rel_obj: for o2 in rel_obj: if o1 != o2: graph[o1].add(o2) objects_summary["interacting_objects"] = objects_summary[ocel.object_id_column].map(graph) return objects_summary