'''
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 enum import Enum
from pm4py.objects.log.util import sorting
from pm4py.util import constants, exec_utils
from pm4py.util import points_subset
from pm4py.util import xes_constants as xes
from pm4py.objects.log.util import basic_filter
from typing import Optional, Dict, Any, Union, Tuple, List
from pm4py.objects.log.obj import EventLog, EventStream
[docs]class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY
PARAMETER_SAMPLE_SIZE = "sample_size"
SORT_LOG_REQUIRED = "sort_log_required"
[docs]def apply(log: EventLog, list_activities: List[str], sample_size: int, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]:
"""
Finds the disconnected performance spectrum provided a log
and a list of activities
Parameters
-------------
log
Log
list_activities
List of activities interesting for the performance spectrum (at least two)
sample_size
Size of the sample
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY
- Parameters.TIMESTAMP_KEY
Returns
-------------
points
Points of the performance spectrum
"""
if parameters is None:
parameters = {}
sort_log_required = exec_utils.get_param_value(Parameters.SORT_LOG_REQUIRED, parameters, True)
all_acti_combs = set(tuple(list_activities[j:j + i]) for i in range(2, len(list_activities) + 1) for j in
range(0, len(list_activities) - i + 1))
two_acti_combs = set((list_activities[i], list_activities[i + 1]) for i in range(len(list_activities) - 1))
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY)
case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, xes.DEFAULT_TRACEID_KEY)
parameters[Parameters.ATTRIBUTE_KEY] = activity_key
log = basic_filter.filter_log_events_attr(log, list_activities, parameters=parameters)
if sort_log_required:
log = sorting.sort_timestamp_log(log, timestamp_key=timestamp_key)
points = []
for trace in log:
matches = [(i, i + 1) for i in range(len(trace) - 1) if
(trace[i][activity_key], trace[i + 1][activity_key]) in two_acti_combs]
i = 0
while i < len(matches) - 1:
matchAct = (trace[mi][activity_key] for mi in (matches[i] + matches[i + 1][1:]))
if matches[i][-1] == matches[i + 1][0] and matchAct in all_acti_combs:
matches[i] = matches[i] + matches[i + 1][1:]
del matches[i + 1]
i = 0
else:
i += 1
if matches:
matches = set(matches)
timest_comb = [{'points': [(trace[i][activity_key], trace[i][timestamp_key].timestamp()) for i in match]}
for match in matches]
for p in timest_comb:
p['case_id'] = trace.attributes[case_id_key]
points += timest_comb
points = sorted(points, key=lambda x: min(x['points'], key=lambda x: x[1])[1])
if len(points) > sample_size:
points = points_subset.pick_chosen_points_list(sample_size, points)
return points