Source code for pm4py.algo.transformation.ocel.features.objects.object_lifecycle_paths
'''
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
License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from pm4py.objects.ocel.obj import OCEL
from typing import Optional, Dict, Any
[docs]
def apply(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None):
"""
Adds for each object an one-hot-encoding of the paths performed in its lifecycle
Parameters
-----------------
ocel
OCEL
parameters
Parameters of the algorithm
Returns
-----------------
data
Values of the added features
feature_names
Names of the added features
"""
if parameters is None:
parameters = {}
ordered_objects = (
parameters["ordered_objects"]
if "ordered_objects" in parameters
else ocel.objects[ocel.object_id_column].to_numpy()
)
lifecycle = (
ocel.relations.groupby(ocel.object_id_column)[ocel.event_activity]
.agg(list)
.to_dict()
)
data = []
paths = {}
all_paths = set()
for obj in lifecycle:
paths[obj] = []
lobj = lifecycle[obj]
for i in range(len(lobj) - 1):
path = lobj[i] + "##" + lobj[i + 1]
paths[obj].append(path)
all_paths.add(path)
all_paths = sorted(list(all_paths))
feature_names = ["@@ocel_lif_path_" + str(x) for x in all_paths]
for obj in ordered_objects:
lif = paths[obj] if obj in paths else []
data.append([])
for p in all_paths:
data[-1].append(float(len(list(x for x in lif if x == p))))
return data, feature_names