'''
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 pm4py.algo.discovery.dfg.variants import native, performance
from typing import Optional, Dict, Any, Union, Tuple, List, Set
from pm4py.objects.log.obj import EventLog
from pm4py.objects.conversion.log import converter as log_converter
[docs]def apply(log: EventLog, activity: str, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]:
"""
Gets the time passed from each preceding activity and to each succeeding activity
Parameters
-------------
log
Log
activity
Activity that we are considering
parameters
Possible parameters of the algorithm
Returns
-------------
dictio
Dictionary containing a 'pre' key with the
list of aggregated times from each preceding activity to the given activity
and a 'post' key with the list of aggregates times from the given activity
to each succeeding activity
"""
if parameters is None:
parameters = {}
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters)
dfg_frequency = native.native(log, parameters=parameters)
dfg_performance = performance.performance(log, parameters=parameters)
pre = []
sum_perf_post = 0.0
sum_acti_post = 0.0
post = []
sum_perf_pre = 0.0
sum_acti_pre = 0.0
for entry in dfg_performance.keys():
if entry[1] == activity:
pre.append([entry[0], float(dfg_performance[entry]), int(dfg_frequency[entry])])
sum_perf_pre = sum_perf_pre + float(dfg_performance[entry]) * float(dfg_frequency[entry])
sum_acti_pre = sum_acti_pre + float(dfg_frequency[entry])
if entry[0] == activity:
post.append([entry[1], float(dfg_performance[entry]), int(dfg_frequency[entry])])
sum_perf_post = sum_perf_post + float(dfg_performance[entry]) * float(dfg_frequency[entry])
sum_acti_post = sum_acti_post + float(dfg_frequency[entry])
perf_acti_pre = 0.0
if sum_acti_pre > 0:
perf_acti_pre = sum_perf_pre / sum_acti_pre
perf_acti_post = 0.0
if sum_acti_post > 0:
perf_acti_post = sum_perf_post / sum_acti_post
return {"pre": pre, "post": post, "post_avg_perf": perf_acti_post, "pre_avg_perf": perf_acti_pre}