'''
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
import pandas as pd
from pm4py.algo.discovery.dfg.adapters.pandas.df_statistics import get_partial_order_dataframe
from pm4py.util import exec_utils, constants, xes_constants
from pm4py.util import typing
[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
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
ZETA = "zeta"
BUSINESS_HOURS = "business_hours"
WORKTIMING = "worktiming"
WEEKENDS = "weekends"
WORKCALENDAR = "workcalendar"
[docs]def apply(df: pd.DataFrame, temporal_profile: typing.TemporalProfile,
parameters: Optional[Dict[Any, Any]] = None) -> typing.TemporalProfileConformanceResults:
"""
Checks the conformance of the dataframe 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
---------------
df
Pandas dataframe
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.CASE_ID_KEY => column to use as case identifier
Returns
---------------
list_dev
A list containing, for each case, 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 = {}
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, None)
case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME)
zeta = exec_utils.get_param_value(Parameters.ZETA, parameters, 6.0)
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)
temporal_profile = pd.DataFrame([{activity_key: x[0], activity_key + "_2": x[1], "@@min": y[0] - zeta * y[1],
"@@max": y[0] + zeta * y[1], "@@mean": y[0], "@@std": y[1]} for x, y in
temporal_profile.items()])
cases = list(df[case_id_key].unique())
ret = [[] for c in cases]
efg = get_partial_order_dataframe(df, activity_key=activity_key, timestamp_key=timestamp_key,
start_timestamp_key=start_timestamp_key, case_id_glue=case_id_key,
keep_first_following=False, business_hours=business_hours, worktiming=worktiming,
weekends=weekends, workcalendar=workcalendar)
efg = efg[[case_id_key, activity_key, activity_key + "_2", "@@flow_time"]]
efg = efg.merge(temporal_profile, on=[activity_key, activity_key + "_2"])
efg = efg[(efg["@@flow_time"] < efg["@@min"]) | (efg["@@flow_time"] > efg["@@max"])][
[case_id_key, activity_key, activity_key + "_2", "@@flow_time", "@@mean", "@@std"]].to_dict("records")
for el in efg:
this_zeta = abs(el["@@flow_time"] - el["@@mean"]) / el["@@std"] if el["@@std"] > 0 else sys.maxsize
ret[cases.index(el[case_id_key])].append(
(el[activity_key], el[activity_key + "_2"], el["@@flow_time"], this_zeta))
return ret