'''
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
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, pandas_utils
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"
BUSINESS_HOUR_SLOTS = "business_hour_slots"
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
)
business_hours_slots = exec_utils.get_param_value(
Parameters.BUSINESS_HOUR_SLOTS,
parameters,
constants.DEFAULT_BUSINESS_HOUR_SLOTS,
)
workcalendar = exec_utils.get_param_value(
Parameters.WORKCALENDAR,
parameters,
constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR,
)
temporal_profile = pandas_utils.instantiate_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 = pandas_utils.format_unique(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,
business_hours_slot=business_hours_slots,
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
