'''
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
'''
import sys
from enum import Enum
from typing import Optional, Dict, Any
from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import exec_utils, constants, xes_constants
from pm4py.util import typing
from pm4py.util.business_hours import BusinessHours
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
ZETA = "zeta"
BUSINESS_HOURS = "business_hours"
BUSINESS_HOUR_SLOTS = "business_hour_slots"
WORKCALENDAR = "workcalendar"
[docs]
def apply(
log: EventLog,
temporal_profile: typing.TemporalProfile,
parameters: Optional[Dict[Any, Any]] = None,
) -> typing.TemporalProfileConformanceResults:
"""
Checks the conformance of the log using the provided temporal profile.
Implements the approach described in:
Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. "Temporal Conformance Checking at Runtime based on Time-infused Process Models." arXiv preprint arXiv:2008.07262 (2020).
Parameters
---------------
log
Event log
temporal_profile
Temporal profile
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY => the attribute to use as activity
- Parameters.START_TIMESTAMP_KEY => the attribute to use as start timestamp
- Parameters.TIMESTAMP_KEY => the attribute to use as timestamp
- Parameters.ZETA => multiplier for the standard deviation
- Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time.
Default: False
- Parameters.BUSINESS_HOURS_SLOTS =>
work schedule of the company, provided as a list of tuples where each tuple represents one time slot of business
hours. One slot i.e. one tuple consists of one start and one end time given in seconds since week start, e.g.
[
(7 * 60 * 60, 17 * 60 * 60),
((24 + 7) * 60 * 60, (24 + 12) * 60 * 60),
((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),
]
meaning that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00
Returns
---------------
list_dev
A list containing, for each trace, all the deviations.
Each deviation is a tuple with four elements:
- 1) The source activity of the recorded deviation
- 2) The target activity of the recorded deviation
- 3) The time passed between the occurrence of the source activity and the target activity
- 4) The value of (time passed - mean)/std for this occurrence (zeta).
"""
if parameters is None:
parameters = {}
log = log_converter.apply(
log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters
)
business_hours = exec_utils.get_param_value(
Parameters.BUSINESS_HOURS, parameters, False
)
business_hours_slots = exec_utils.get_param_value(
Parameters.BUSINESS_HOUR_SLOTS,
parameters,
constants.DEFAULT_BUSINESS_HOUR_SLOTS,
)
activity_key = exec_utils.get_param_value(
Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
start_timestamp_key = exec_utils.get_param_value(
Parameters.START_TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
zeta = exec_utils.get_param_value(Parameters.ZETA, parameters, 6.0)
ret = []
for trace in log:
deviations = []
for i in range(len(trace) - 1):
act_i = trace[i][activity_key]
time_i = trace[i][timestamp_key].timestamp()
for j in range(i + 1, len(trace)):
time_j = trace[j][start_timestamp_key].timestamp()
if time_j >= time_i:
act_j = trace[j][activity_key]
if (act_i, act_j) in temporal_profile:
if business_hours:
bh = BusinessHours(
trace[i][timestamp_key],
trace[j][start_timestamp_key],
business_hour_slots=business_hours_slots,
)
this_diff = bh.get_seconds()
else:
this_diff = time_j - time_i
mean = temporal_profile[(act_i, act_j)][0]
std = temporal_profile[(act_i, act_j)][1]
if (
this_diff < mean - zeta * std
or this_diff > mean + zeta * std
):
this_zeta = (
abs(this_diff - mean) / std
if std > 0
else sys.maxsize
)
deviations.append(
(act_i, act_j, this_diff, this_zeta)
)
ret.append(deviations)
return ret
