'''
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 constants, xes_constants, exec_utils
[docs]class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
INT_CASE_ACT_SIZE = "@@int_case_act_size"
[docs]def apply(df: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, int]:
"""
Associates to each activity (with at least one rework) the number of cases in the log for which
the rework happened.
Parameters
------------------
df
Dataframe
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY => the attribute to be used as activity
- Parameters.CASE_ID_KEY => the attribute to be used as case ID
Returns
------------------
dict
Dictionary associating to each activity the number of cases for which the rework happened
"""
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)
df = df.copy()
df = df[{activity_key, case_id_key}]
df[INT_CASE_ACT_SIZE] = df.groupby([activity_key, case_id_key]).cumcount()
df = df[df[INT_CASE_ACT_SIZE] > 0]
df = df.groupby([activity_key, case_id_key]).last()
ret = df.groupby(activity_key).size().to_dict()
return ret