'''
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 enum import Enum
from typing import Optional, Dict, Any, Tuple, Union
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import exec_utils, constants, xes_constants
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
CASE_ATTRIBUTES = "case_attributes"
RETURN_NODES_ATTRIBUTES = "return_nodes_attributes"
[docs]
def apply(
log: EventLog,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Union[
Tuple[
Dict[Tuple[str, str], Dict[str, Dict[str, Any]]],
Dict[str, Dict[str, Dict[str, Any]]],
],
Dict[Tuple[str, str], Dict[str, Dict[str, Any]]],
]:
"""
Discovers a directly-follows graph from an event log, with the edges that are annotated with the different values
for the given case attributes.
Parameters
-----------------
log
Event log
parameters
Parameters of the variant, including:
- Parameters.ACTIVITY_KEY => the attribute to use as activity
- Parameters.CASE_ATTRIBUTES => the case attributes that are used to annotate the edges (default: the case ID)
- Parameters.RETURN_NODES_ATTRIBUTES => (optional) returns also a dictionary with the values of the
attributes for each activity of the graph (default: False)
Returns
-----------------
dfg
Directly-follows graph (with the edges annotated with the specified case attributes), e.g.:
{('register request', 'examine casually'): {'creator': {'Fluxicon Nitro': 3}, 'concept:name':
{'3': 1, '6': 1, '5': 1}} ...
nodes
(Optional) dictionary of activities (annotated with the specified case attributes), e.g.:
{'register request': {'creator': {'Fluxicon Nitro': 6}, 'concept:name':
{'3': 1, '2': 1, '1': 1, '6': 1, '5': 1, '4': 1}} ...
"""
if parameters is None:
parameters = {}
log = log_converter.apply(
log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters
)
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
)
case_attributes = exec_utils.get_param_value(
Parameters.CASE_ATTRIBUTES,
parameters,
list([xes_constants.DEFAULT_TRACEID_KEY]),
)
return_nodes_attributes = exec_utils.get_param_value(
Parameters.RETURN_NODES_ATTRIBUTES, parameters, False
)
dfg = {}
for trace in log:
for attr in set(case_attributes).intersection(set(trace.attributes)):
attr_value = trace.attributes[attr]
for i in range(len(trace) - 1):
ev_couple = (
trace[i][activity_key],
trace[i + 1][activity_key],
)
if ev_couple not in dfg:
dfg[ev_couple] = {}
if attr not in dfg[ev_couple]:
dfg[ev_couple][attr] = {}
if attr_value not in dfg[ev_couple][attr]:
dfg[ev_couple][attr][attr_value] = 0
dfg[ev_couple][attr][attr_value] += 1
if return_nodes_attributes:
nodes = {}
for trace in log:
for attr in set(case_attributes).intersection(
set(trace.attributes)
):
attr_value = trace.attributes[attr]
for i in range(len(trace)):
act = trace[i][activity_key]
if act not in nodes:
nodes[act] = {}
if attr not in nodes[act]:
nodes[act][attr] = {}
if attr_value not in nodes[act][attr]:
nodes[act][attr][attr_value] = 0
nodes[act][attr][attr_value] += 1
return dfg, nodes
return dfg
