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'''
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 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
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
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
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