'''
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 pm4py.algo.discovery.dfg.adapters.pandas.df_statistics import get_partial_order_dataframe
from pm4py.util import exec_utils, constants, xes_constants
from typing import Optional, Dict, Any, Union, Tuple, List, Set
import pandas as pd
[docs]class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
KEEP_FIRST_FOLLOWING = "keep_first_following"
[docs]def apply(dataframe: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[Tuple[str, str], int]:
if parameters is None:
parameters = {}
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY)
case_id_glue = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME)
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)
keep_first_following = exec_utils.get_param_value(Parameters.KEEP_FIRST_FOLLOWING, parameters, False)
partial_order_dataframe = get_partial_order_dataframe(dataframe, start_timestamp_key=start_timestamp_key,
timestamp_key=timestamp_key, case_id_glue=case_id_glue,
activity_key=activity_key,
keep_first_following=keep_first_following)
ret_dict = partial_order_dataframe.groupby([activity_key, activity_key + '_2']).size().to_dict()
# assure to avoid problems with np.float64, by using the Python float type
for el in ret_dict:
ret_dict[el] = int(ret_dict[el])
return ret_dict