Source code for pm4py.algo.evaluation.precision.dfg.algorithm
'''
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 collections import Counter
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.obj import EventLog, EventStream
from pm4py.util import exec_utils, xes_constants, constants
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
def __is_allowed_prefix(exiting_activities, sa, prefix):
if not prefix:
return True
if prefix[0] not in sa:
return False
prev_act = prefix[0]
for i in range(1, len(prefix)):
curr_act = prefix[i]
if (
prev_act not in exiting_activities
or curr_act not in exiting_activities[prev_act]
):
return False
prev_act = curr_act
if not prefix[-1] in exiting_activities:
return False
return True
[docs]
def apply(
log: Union[EventLog, EventStream],
dfg: Dict[Tuple[str, str], int],
start_activities: Dict[str, int],
end_activities: Dict[str, int],
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> float:
"""
Computes the precision of a directly-follows graph using the ETConformance approach
Parameters
---------------
log
Event log
dfg
Directly-follows graph
start_activities
Start activities
end_activities
End activities
parameters
Parameters of the algorithm:
- Parameters.ACTIVITY_KEY: the key to use
Returns
----------------
precision
Precision value
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
)
log = log_converter.apply(
log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters
)
precision = 1.0
sum_ee = 0
sum_at = 0
exiting_activities = {}
for act_couple in dfg:
if not act_couple[0] in exiting_activities:
exiting_activities[act_couple[0]] = set()
exiting_activities[act_couple[0]].add(act_couple[1])
prefixes = {}
prefixes_count = Counter()
for trace in log:
prefix_act = []
for i in range(len(trace)):
prefix_act_tuple = tuple(prefix_act)
if prefix_act_tuple not in prefixes:
prefixes[prefix_act_tuple] = set()
prefixes_count[prefix_act_tuple] += 1
prefixes[prefix_act_tuple].add(trace[i][activity_key])
prefix_act.append(trace[i][activity_key])
for prefix in prefixes:
if __is_allowed_prefix(exiting_activities, start_activities, prefix):
log_transitions = prefixes[prefix]
activated_transitions = (
set(start_activities.keys())
if not prefix
else exiting_activities[prefix[-1]]
)
escaping_edges = activated_transitions.difference(log_transitions)
sum_ee += len(escaping_edges) * prefixes_count[prefix]
sum_at += len(activated_transitions) * prefixes_count[prefix]
if sum_at > 0:
precision = 1 - float(sum_ee) / float(sum_at)
return precision
