Source code for pm4py.algo.discovery.performance_spectrum.variants.log_disconnected
'''
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 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, List
from pm4py.objects.log.obj import EventLog
[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
