'''
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/>.
'''
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"
WORKTIMING = "worktiming"
WEEKENDS = "weekends"
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)
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)
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[[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