'''
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/>.
'''
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"
WORKTIMING = "worktiming"
WEEKENDS = "weekends"
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.WORKTIMING => work schedule of the company (provided as a list where the first number is the start
of the work time, and the second number is the end of the work time), if business hours are enabled
Default: [7, 17] (work shift from 07:00 to 17:00)
- Parameters.WEEKENDS => indexes of the days of the week that are weekend
Default: [6, 7] (weekends are Saturday and Sunday)
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)
worktiming = exec_utils.get_param_value(Parameters.WORKTIMING, parameters, [7, 17])
weekends = exec_utils.get_param_value(Parameters.WEEKENDS, parameters, [6, 7])
workcalendar = exec_utils.get_param_value(Parameters.WORKCALENDAR, parameters, constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR)
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].replace(tzinfo=None),
trace[j][start_timestamp_key].replace(tzinfo=None),
worktiming=worktiming,
weekends=weekends, workcalendar=workcalendar)
this_diff = bh.getseconds()
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