'''
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
'''
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
BUSINESS_HOURS = "business_hours"
BUSINESS_HOUR_SLOTS = "business_hour_slots"
WORKCALENDAR = "workcalendar"
[docs]
def apply(
df: pd.DataFrame, parameters: Optional[Dict[Any, Any]] = None
) -> typing.TemporalProfile:
"""
Gets the temporal profile from a dataframe.
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
Dataframe
parameters
Parameters, including:
- Parameters.ACTIVITY_KEY => the column to use as activity
- Parameters.START_TIMESTAMP_KEY => the column to use as start timestamp
- Parameters.TIMESTAMP_KEY => the column to use as timestamp
- Parameters.CASE_ID_KEY => the column to use as case ID
Returns
-------
temporal_profile
Temporal profile of the dataframe
"""
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
)
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,
)
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[[activity_key, activity_key + "_2", "@@flow_time"]]
temporal_profile = (
efg.groupby([activity_key, activity_key + "_2"])
.agg(["mean", "std"])
.reset_index()
.fillna(0)
.to_dict("records")
)
temporal_profile = {
(x[(activity_key, "")], x[(activity_key + "_2", "")]): (
x[("@@flow_time", "mean")],
x[("@@flow_time", "std")],
)
for x in temporal_profile
}
return temporal_profile
