Source code for pm4py.algo.conformance.temporal_profile.variants.log

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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]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, J├╝rgen 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