'''
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, Union
import pandas as pd
from pm4py.util import exec_utils, constants, xes_constants
[docs]class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
[docs]def apply(df: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Dict[str, int]]:
"""
Computes for each trace of the event log how much rework occurs.
The rework is computed as the difference between the total number of activities of a trace and the
number of unique activities.
Parameters
----------------
df
Pandas dataframe
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY => the activity key
- Parameters.CASE_ID_KEY => the case identifier attribute
Returns
-----------------
dict
Dictionary associating to each case ID:
- The number of total activities of the case (number of events)
- The rework (difference between the total number of activities of a trace and the number of unique activities)
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY)
case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME)
grouped_df = df.groupby(case_id_key)[activity_key].agg(["count", "nunique"]).reset_index().to_dict("records")
rework_cases = {}
for el in grouped_df:
rework_cases[el["case:concept:name"]] = {"number_activities": el["count"], "rework": el["count"] - el["nunique"]}
return rework_cases